agriculture research essay

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187 Agriculture Essay Topics & Research Questions + Examples

Are you looking for the best agriculture topics to write about? You’re at the right place! StudyCorgi has prepared a list of important agriculture research topics. On this page, any student can find essay questions and project ideas on various agricultural issues, such as food safety, genetically engineered crops, and sustainable farming practices.

👨‍🌾 TOP 7 Agriculture Research Topics – 2024

🏆 best essay topics on agriculture, 🎓 most interesting agriculture topics for college students, 👍 good agriculture research topics & essay examples, 💡 cool agricultural research topics for high school students, ❓ research questions about agriculture, 🔎 current agriculture research paper topics, 📝 agriculture argumentative essay topics, 🗣️ agriculture topics for speech.

Commercial Agriculture, Its Role and Definition
Agriculture and Its Role in Economic Development
Agriculture: Personal Field Visit
Soil: The Essential Aspect of Agriculture
Globalization Impact on Sustainable Agriculture
Food Safety Issues in Modern Agriculture
Agricultural Biotechnology and Its Pros and Cons
In Support of Robotics Use in Agriculture Robotic technologies have vast potential to be used in the agricultural sector due to the multi-dimensional nature of their applications and the possibilities for ongoing improvement.
Pedagogical Content Knowledge in Secondary Level Agricultural Science Apart from internal student factors, such as the ability to generalize and absorb new knowledge, the learning process is significantly affected by the teacher.
History of Agricultural Technology Development Agricultural technologies were majorly developed during the Medieval period to ensure sufficient product yields for growing populations around the world.
Population Growth and Agriculture in the Future The current industrial agriculture needs to be advanced and developed in combination with sustainable agricultural practices.
Industry and Agriculture: Use of Technology Industry and agriculture are among the areas that have experienced a vast rise in effectiveness and performance quality due to the integration of new types of technology into them.
Climate Changes Impact on Agriculture and Livestock The project evaluates the influences of climate changes on agriculture and livestock in different areas in the Kingdom of Saudi Arabia.
Sustainable Agriculture Against Food Insecurity The paper argues sustainable agriculture is one way to reduce food insecurity without harming the planet because the number of resources is currently decreasing.
The Agriculture Industry’s Digital Transformation This study seeks to explore the dynamics of digital technology in agriculture over the past two decades, focusing on the perspectives and perceptions of the farmers.
Improving Stress Resistance in Agricultural Crops The essay suggests that stress-resistant crops are needed to ensure yield stability under stress conditions and to minimize the environmental impacts of crop production.
Climate Change and Its Potential Impact on Agriculture and Food Supply The global food supply chain has been greatly affected by the impact of global climate change. There are, however, benefits as well as drawbacks to crop production.
Repeasantization: Impact on Agriculture The repeasantization led to fundamental changes that created a new system of agriculture that is still relevant today.
Agriculture and Food in Ancient Greece The paper states that agricultural practices and goods from Greece extended to neighboring countries in the Mediterranean as the dominance increased.
The Big History of Civilizations – Origins of Agriculture: Video Analysis This paper aims to analyze the origins of agriculture – what was a foraging economy and way of life like, as well as compare foragers and farmers.
Hunting and Gathering Versus Agricultural Society The hunting and gathering society is considered the most equitable of all seven types, while the agricultural community gives rise to the development of civilization.
Agriculture and Mayan Society Resilience The Yucatan peninsula had a vast landscape which was good for agriculture thus making agriculture to be the main economic base for the Mayans.
Agriculture and Food Safety in the United States Agriculture in the United States has grown progressively centralized. The shortcomings in the 2018 U.S. farm legislation resulted in multiple challenges in the food system.
Colonialism and Economic Development of Africa Through Agriculture The colonial period is characterized by the exploitation of the agricultural sector in Africa to make a profit and provide Western countries with raw materials.
The Neolithic Era: Architecture and Agriculture The improvements to agriculture, society, architecture, and culture made during the Neolithic period had an undeniable impact on aspects of the world.
Agriculture: Application of Information Technology IT application in agriculture has contributed to food security in most modern communities. Farming has become easier than before as new inventions are made.
Agriculture the Backbone of Ancient Egypt’s Economy In pre-industrial societies, agriculture was the backbone of most economies. This is true in ancient times and very much evident in ancient Egypt.
Agriculture in Honduras: Existing Challenges and Possible Solutions This paper tackles the issue of existing challenges and possible solutions to the problems of agriculture in Honduras.
Freedom in American Countryside and Agriculture This paper portrays how freedom has been eliminated in the countryside by the state agriculture department, and whether the farmer has a moral right to do his farming practices.
Market Revolution: Agriculture and Global Trade In the era of traders, the vast land area and rich natural resources created many economic opportunities. Most people lived in rural areas and were engaged in agriculture.
Agriculture, Water, and Food Security in Tanzania This paper evaluates the strategies applicable to the development and further maintenance of agriculture, water, and food security in Tanzania.
The Australian Agriculture Company’s Financial Analysis The Australian Agriculture Company shows a positive sign for investment due to its financial analysis indicating company resilience and strong prospects of growth.
Governmental Price Control in Agricultural Sector The consequences of real-life governmental price control are the evolutionary nature of transformations in the agricultural sector.
Aspects of Pesticide Use in Agriculture This paper investigates socio-environmental factors connected with pesticide use in agriculture and food production. It has a destructive impact on the environment
Agriculture-Led Food Crops and Cash Crops in Tanzania This paper aims to explore the contributions of the agriculture sector in Tanzania to the country’s industrialization process by using recent data about its food and cash crops.
Agriculture and Food Production in the Old Kingdom
Agriculture and the Transition to the Market in Asia
Agrarian Reform and Subsistence Agriculture in Russia
Agriculture, Nutrition, and the Green Revolution in Bangladesh
Agriculture Business and Management
Agriculture, Horticulture, and Ancient Egypt
Agriculture and Food Production in the Old Kingdom of Egypt
Administrative and Transaction-Related Costs of Subsidising Agriculture
Agriculture and Economic Growth in Argentina, 1913-84
Agriculture and Economic Development in Brazil, 1960-1995
Agriculture and Greenhouse Gas Cap-And-Trade
Croatian Agriculture Towards World Market Liberalization
Adapting Credit Risk Models to Agriculture
Agriculture and European Union Enlargement
Agriculture and Food Security in Pakistan
Cash Flows and Financing in Texas Agriculture
Current Problems With Indian Agriculture
Agriculture and Its Drain on California
Agriculture and the Economic Life of India
Agriculture and Global Climate Stabilization
Achieving Regional Growth Dynamics in African Agriculture
Agriculture and Non-agricultural Liberalization in the Millennium Round
Corporate Agriculture and Modern Times
Agriculture and Rural Employment Agricultural in Bolivia
Climatic Fluctuations and the DI¤Usion of Agriculture
Agriculture Global Market Briefing
Agriculture and the Industrial Revolution of the Late 1700s
Agriculture and Animal Husbandry in Ecuador
Biofuels, Agriculture, and Climate Change
Aggregate Technical Efficiency and Water Use in U.S. Agriculture
The Impact of Pesticides’ Use on Agriculture Pesticides are mostly known for their adverse effects and, therefore, have a mostly negative connotation when discussed among general audiences.
Cuisine and Agriculture of Ancient Greece There are many reasons for modern students to investigate the development of cuisine and agriculture in Ancient Greece.
Agricultural Influences on the Developing Civil Society Agriculture had a significant influence on developing societies, ranging from creating trade to bringing industrialization, education, and social classes.
Sustainable Agriculture and Future Perspectives Sustainable agriculture is essential to the earth’s environment. When farmers take care of their land and crops, they are taking care of environmental sustainability.
Agricultural Adaptation to Changing Environments The paper discusses the impact of climate change on agriculture in Canada. This phenomenon is real and has affected the industry over at least the last three decades.
Food and Agriculture of Ancient Greece The concepts of agriculture and cuisine both have a deep connection to Greek history, culture, development, and social trends.
Trade Peculiarities in Food and Agriculture Food trading is a peculiar area, as food is the basis for surviving the population. The one who controls food production and trading routes, also controls all populations.
Multinational Agricultural Manufacturing Companies’ Standardization & Adaptation The most popular approaches that multinational companies use to serve their customers from various countries are standardization and adaptation.
Agricultural Technology Implementation by Medieval Europeans and West Africans The paper examines how West Africans and Medieval Europeans were affected by their corresponding climates and why their methods were unique to their respective locations.
Impacts of Climate Change on Agriculture and Food This paper will examine four aspects of climate change: variation in the rainfall pattern, water levels, drought, temperature, and heatwaves.
Canadian Laws Regarding Agricultural Sector The unions in Canada are the concept over which there has been an excessive dispute involving court proceedings and questioning the constitutional rights of citizens.
Agriculture Development and Related Theories There are two main domestication models used to describe the development of agriculture: unconscious and conscious.
Agricultural Traditions of Canadians In Canada there is a very good agricultural education, so young people can get higher education in agriculture and use it on their own farms.
Sharecropping. History of Racial Agriculture Sharecropping became a variation of racialized agriculture, that which has negative impact on the capabilities of the black population to generate and pass down wealth.
Food Additives Use in Agriculture in the United States Food additives in agriculture become a debatable issue because their benefits do not always prevail over such shortages like health issues and environmental concerns.
Radio-Frequency Identification in Healthcare and Agriculture Specifically, radio-frequency identification (RFID) has gained traction due to its ability to transmit data over distance.
Mechanism of US Agricultural Market The fact that lower interest rates increased the number of potential customers for real estate in the 2000s shows that housing prices should have increased.
A Biological Terror Attack in Agriculture The United States is highly vulnerable to terror attacks of biological nature in agriculture yet such an occurrence can cripple the economy.
The Economics of Race, Agriculture and Environment This research paper is going to answer the question; do public policies reduce or enhance racial inequality in agricultural and environmental affairs?
Impact of Bioterrorism on the U.S Agriculture System The paper describes that the term bioterrorism has several definitions depending upon the origin of the attack but in general terms, it refers to any form of terrorist attack.
Impacts of Genetic Engineering of Agricultural Crops In present days the importance of genetic engineering grew due to the innovations in biotechnologies and Sciences.
The Effects of Genetic Modification of Agricultural Products Discussion of the threat to the health of the global population of genetically modified food in the works of Such authors as Jane Brody and David Ehrenfeld.
Homeland Security in Agriculture and Health Sectors Lack of attention to the security and protection of the agricultural sector in the U.S. economy can create a serious threat to the health and safety of the population.
Water Savings and Virtual Trade in Agriculture Water trade in agriculture is not a practice that is unique to the modern generation. The practice was common long before the emergence of the Egyptian Empire.
Virtual Water Trade and Savings in Agriculture This essay discusses the savings associated with virtual water trade in agriculture and touches on the effects of a shift to local agricultural production on global water savings.
Virtual Water Trade of Agricultural Products Virtual water trade is a concept associated with globalization and the global economy. Its rise was motivated by growing water scarcity in arid areas around the world.
Agricultural Role in African Development Diao et al. attempt to determine the role of agriculture in overcoming the challenge of poverty in rural areas of Africa compared to alternative theories of economic growth.
Virtual Water Savings and Trade in Agriculture The idea of virtual water was initially created as a method for assessing how water-rare nations could offer food, clothing, and other water-intensive products to their residents.
European Invasion and Agriculture in the Caribbean The early invasion of the Europeans in the Caribbean did not prompt the employment of the slave trade in the agricultural activities until the development of the sugar plantations.
Agricultural Problems in Venezuela Agriculture has been greatly underdeveloped in Venezuela, yet it is a country that has vital minerals and resources required for the global economy.
America’s Agriculture in the Period of 1865-1938 This paper analyzes America’s contribution in prevention of natural calamities, decline of soil quality, promotion of production outlay and provision of sufficient food.
Capital Taxes and Agriculture
Canadian Trade With the Chinese Agriculture Market
Agriculture and Its Impact on Economic Development
Bacteriocins From the Rhizosphere Microbiome From an Agriculture Perspective
Agriculture and Its Impact on Financial Institutions
Agriculture, Fisheries, and Food in the Irish Economy
Adoption and Economic Impact of Site-Specific Technologies in U.S. Agriculture
Cash Rents and Land Values in U.S. Agriculture
Crises and Structural Change in Australian Agriculture
Biotechnology and Its Application in Agriculture
Alternative Policies for Agriculture in Europe
Agriculture and Food Security in Asia by 2030
Agriculture and Coping Climate Change in Nepal
Agriculture and Ethiopia’s Economic Transformation
Culture: Agriculture and Egalitarian Social
Adaptation, Climate Change, Agriculture, and Water
Agriculture and the Literati in Colonial Bengal, 1870 to 1940
Agriculture and Barley Farming Taro
Agriculture and Agricultural Inputs Markets
Agriculture and Environmental Challenges
Challenges for Sustainable Agriculture in India
Agriculture and German Reunification
Agriculture and Tourism Relationship in Malaysia Tourism
21st Century Rural America: New Horizons for U.S. Agriculture
Canadian Agriculture and the Canadian Agricultural Industry
California Agriculture Dimensions and Issues
Advancements and the Development of Agriculture in Ancient Greece and Rome
Agriculture and Early Industrial Revolution
Aztec: Agriculture and Habersham County
Agriculture and Current Deforestation Practices
How Has Agriculture Changed From Early Egypt, Greece, and Rome to the Present?
What Are the Advantages of Using Pesticides on Agriculture?
Are Digital Technologies for the Future of Agriculture?
How Did Agriculture Change Our Society?
Does Agriculture Help Poverty and Inequality Reduction?
Can Agriculture Prosper Without Increased Social Capital?
Are Mega-Farms the Future of Global Agriculture?
How Can African Agriculture Adapt to Climate Change?
Does Agriculture Really Matter for Economic Growth in Developing Countries?
Can Conservation Agriculture Save Tropical Forests?
How Can Sustainable Agriculture Be Better for Americans?
Are U.S. and European Union Agriculture Policies Becoming More Similar?
Should Pollution Reductions Count as Productivity Gains for Agriculture?
Can Market Access Help African Agriculture?
How Does Genetic Engineering Affect Agriculture?
Does Individualization Help Productivity of Transition Agriculture?
Can Spot and Contract Markets Co-Exist in Agriculture?
How Has Biotechnology Changed Agriculture Throughout the Years?
Does Trade Policy Impact Food and Agriculture Global Value Chain Participation of Sub-Saharan African Countries?
Can Sustainable Agriculture Feed Africa?
How Can Multifunctional Agriculture Support a Transition to a Green Economy in Africa?
Does Urban Agriculture Enhance Dietary Diversity?
How Did Government Policy, Technology, and Economic Conditions Affect Agriculture?
Can the Small Dairy Farm Remain Competitive in US Agriculture?
What Are the Main Changes in French Agriculture Since 1945 and What Challenges Does It Face Today?
How Can Marketing Theory Be Applied to Policy Design to Deliver Sustainable Agriculture in England?
Will African Agriculture Survive Climate Change?
How Has Agriculture Changed Civilizations?
Does Urban Agriculture Improve Food Security?
Can US and Great Plains Agriculture Compete in the World Market?
The effect of climate change on crop yields and food security.
Sustainable agricultural practices for soil health.
Precision agriculture techniques and applications.
The impact of genetically engineered organisms on crop yields and safety.
The benefits of agroforestry systems for the environment.
Current challenges in water management in agriculture.
The environmental impact of organic farming.
The potential of urban agriculture to address food insecurity.
Food waste in the agricultural supply chain.
Comparing the effectiveness of aquaponic and hydroponic systems.
Organic vs. conventional farming.
Can regenerative agriculture combat climate change?
Agricultural subsidies: pros and cons.
Should harmful pesticides be banned to protect pollinators?
Should arable land be used for biofuels or food production?
Do patent protections of seeds hinder agricultural innovation?
Agricultural robots: increased efficiency or displaced rural labor?
Should GMO labeling be mandatory?
Do the benefits of pesticides outweigh their potential health harms?
Is it unsustainable to grow water-intensive crops in arid regions?
The economics of organic farming.
The need for climate-adaptive crops.
The role of bees in agriculture and threats to their survival.
Smart agriculture: transforming farming with data and connectivity.
The journey of food in modern agricultural supply chains.
The role of agri-tech startups in agricultural innovation.
Youth in agriculture: inspiring the next generation of farmers.
Why should we shift to plant-based meat alternatives?
The importance of preserving indigenous agricultural practices.
Smart irrigation systems: optimizing water use in agriculture.

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StudyCorgi . 2022. "187 Agriculture Essay Topics & Research Questions + Examples." March 1, 2022. https://studycorgi.com/ideas/agriculture-essay-topics/.

These essay examples and topics on Agriculture were carefully selected by the StudyCorgi editorial team. They meet our highest standards in terms of grammar, punctuation, style, and fact accuracy. Please ensure you properly reference the materials if you’re using them to write your assignment.

This essay topic collection was updated on June 20, 2024 .

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114 Agriculture Essay Topic Ideas & Examples

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Agriculture plays a vital role in the development and sustainability of societies around the world. From crop cultivation to animal husbandry, agriculture encompasses a wide range of practices that affect our food production, environment, and economy. If you're looking for essay topics related to agriculture, we've compiled a comprehensive list of 114 ideas and examples to inspire your writing.

The impact of climate change on agriculture: challenges and adaptation strategies.
The role of genetically modified organisms (GMOs) in modern agriculture.
Organic farming: benefits, challenges, and future prospects.
The use of pesticides in agriculture: balancing productivity and environmental concerns.
Agricultural subsidies: their impact on farmers and the economy.
The importance of soil health for sustainable agriculture.
Precision farming: the integration of technology in agricultural practices.
The role of women in agriculture: empowerment and gender equality.
Urban agriculture: promoting food security in cities.
The impact of globalization on agriculture: opportunities and threats.
The role of agricultural education in shaping the future of farming.
Food waste in agriculture: causes, consequences, and solutions.
Sustainable livestock production: balancing meat consumption and environmental impact.
The role of small-scale farmers in global food production.
The ethics of animal welfare in modern farming practices.
Agricultural trade policies: implications for developing countries.
The impact of deforestation on agricultural practices.
The role of agricultural biotechnology in feeding a growing population.
The challenges and benefits of aquaculture in meeting global seafood demand.
The impact of agricultural practices on water resources.
The role of agricultural cooperatives in supporting small-scale farmers.
The future of vertical farming: opportunities and limitations.
The impact of agricultural pollution on human health.
Agroforestry: integrating trees into agricultural landscapes.
The role of agricultural extension services in rural development.
The potential of hydroponics in urban agriculture.
The impact of industrial agriculture on biodiversity.
The role of agricultural research and development in innovation.
The influence of social media on consumer perceptions of agriculture.
The challenges and opportunities of agricultural mechanization in developing countries.
The role of agricultural insurance in mitigating risks for farmers.
The impact of land tenure systems on agricultural productivity.
The role of agricultural cooperatives in sustainable development.
The potential of vertical farming to reduce food miles and carbon footprint.
The impact of agricultural subsidies on food prices for consumers.
The role of urban agriculture in community development.
The importance of seed banks in preserving agricultural biodiversity.
The impact of agricultural practices on pollinators and ecosystem services.
The role of agricultural drones in precision farming.
The challenges and benefits of transitioning to regenerative agriculture.
The impact of agricultural practices on soil erosion.
The role of agricultural education in fostering entrepreneurship.
The potential of agricultural waste management in bioenergy production.
The impact of agricultural practices on rural livelihoods.
The role of agricultural cooperatives in improving market access for small-scale farmers.
The challenges and benefits of transitioning to organic dairy farming.
The impact of climate-smart agriculture on resilience and adaptation.
The role of agricultural biotechnology in improving crop yields.
The potential of agroecology in sustainable farming.
The impact of agricultural practices on air quality.
The role of agricultural research in addressing food security challenges.
The challenges and benefits of transitioning to sustainable palm oil production.
The impact of agricultural practices on wildlife conservation.
The role of agricultural cooperatives in promoting fair trade.
The potential of precision livestock farming in improving animal welfare.
The impact of agricultural practices on rural migration patterns.
The challenges and benefits of transitioning to organic vegetable farming.
The role of agricultural biotechnology in addressing malnutrition.
The potential of urban rooftop gardens in enhancing food security.
The impact of agricultural practices on groundwater contamination.
The role of agricultural entrepreneurship in rural development.
The challenges and benefits of transitioning to agroforestry systems.
The impact of agricultural practices on food safety.
The role of agricultural cooperatives in empowering marginalized communities.
The potential of hydroponics in space agriculture.
The impact of agricultural practices on indigenous food systems.
The challenges and benefits of transitioning to sustainable cotton production.
The role of agricultural biotechnology in reducing post-harvest losses.
The potential of vertical farming in food deserts.
The impact of agricultural practices on rural poverty alleviation.
The role of agricultural cooperatives in promoting climate-smart agriculture.
The challenges and benefits of transitioning to organic wine production.
The impact of agricultural practices on soil degradation.
The role of agricultural education in promoting sustainable farming practices.
The potential of aquaponics in sustainable food production.
The impact of agricultural practices on food sovereignty.
The challenges and benefits of transitioning to sustainable coffee farming.
The role of agricultural biotechnology in reducing pesticide use.
The potential of urban agriculture in reducing food waste.
The impact of agricultural practices on indigenous land rights.
The role of agricultural cooperatives in promoting gender equality.
The challenges and benefits of transitioning to organic beekeeping.
The impact of agricultural practices on rural resilience.
The role of agricultural extension services in promoting climate resilience.
The potential of rooftop farming in urban sustainability.
The impact of agricultural practices on food culture.
The challenges and benefits of transitioning to sustainable cocoa production.
The role of agricultural biotechnology in improving nutritional quality.
The potential of vertical farming in disaster-prone areas.
The impact of agricultural practices on food sovereignty in indigenous communities.
The role of agricultural cooperatives in promoting sustainable seafood.
The challenges and benefits of transitioning to organic tea production.
The impact of agricultural practices on rural social capital.
The role of agricultural extension services in promoting sustainable water management.
The potential of hydroponics in space exploration.
The impact of agricultural practices on food justice.
The challenges and benefits of transitioning to sustainable sugar production.
The role of agricultural biotechnology in reducing food waste.
The potential of urban agriculture in promoting social cohesion.
The impact of agricultural practices on land rights in developing countries.
The role of agricultural cooperatives in promoting sustainable palm oil.
The challenges and benefits of transitioning to organic cotton farming.
The impact of agricultural practices on rural cultural heritage.
The role of agricultural extension services in promoting sustainable energy use.
The potential of aquaponics in sustainable urban development.
The impact of agricultural practices on food sovereignty in marginalized communities.
The challenges and benefits of transitioning to sustainable chocolate production.
The role of agricultural biotechnology in improving drought tolerance.
The potential of vertical farming in post-disaster recovery.
The impact of agricultural practices on food security in conflict zones.
The role of agricultural cooperatives in promoting sustainable timber production.
The challenges and benefits of transitioning to organic coffee farming.
The impact of agricultural practices on rural cultural landscapes.
The role of agricultural extension services in promoting sustainable waste management.

These essay topic ideas cover a wide range of aspects related to agriculture, providing a plethora of opportunities for research and critical analysis. Whether you're interested in environmental sustainability, social justice, or technological innovation, there is a topic here that will inspire your writing and contribute to the ongoing dialogue about the future of agriculture.

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176 Agriculture Essay Topic Ideas & Examples

🏆 best agriculture topic ideas & essay examples, 💡 most interesting agriculture topics to write about, 📌 simple & easy agriculture essay titles, 👍 good essay topics on agriculture, ❓ agriculture essay questions.

Sustainable Agriculture It is believed that the increase in the demand for food due to the increase in global population and change in dietary habit of the population.
The Impact of Groundwater Pollution on Agriculture and Its Prevention People have to be aware about the impact of their activities on groundwater and be able to improve the conditions, they live under, and this piece of writing will inform each reader about each detail […]
The Difference Between Agricultural Societies and Hunter-Gathers Societies in the Past In the course of time, people have been searching for techniques and approaches to adjust to geographical, social, and cultural environment in the past and in the modern contexts.
Environmental Degradation and the Use of Technology in the Agricultural Sector According to the United Nations Environmental Program, environmental degradation is the term used to refer to the destruction of the environment through the exhaustion f natural resources such as air water, and soil along with […]
Culture and Agriculture: Nature and Significance Understanding Seeing that agriculture shapes the society and defines the course of its further development, promoting the ideas of environmentalism and sustainability, it will be reasonable to assume that agriculture belongs to the domain of cultures.
Smart Farming and Sustainable Agriculture Smart farming allows for a wide range of options, from robotization and satellite imagery to the Internet of Things and the blockchain technology that increases the efficiency of crop cultivation by optimizing the use of […]
Agriculture Effects on Wild Animals An increase in agricultural activities has subjected a majority of the wild animals to the danger of extinction. Prior to the introduction of the mongoose in Hawaii, it was easy to find a Nene goose […]
Growing Pumpkins: Here’s What You Need to Know One way of keeping the leaves dry is by ensuring that the pumpkins are watered early in the morning to give them sufficient time to dry during the day. Microbes found in the soil contribute […]
Urban Agriculture Effects on Economy The preparation of the journals involved conducting interviews with the urban farmers and surveys on the certainty of the farming practices.
Effects of Industrialized Agriculture Finally, the corporations that are involved in the process of food production are responsible for the creation of new markets for consumption and the global trade of agricultural products.
Agricultural Sector: The Use of Drones Thompson states that the application of drones in agriculture, specifically in the United Kingdom, can promote the enhancement of the crops and reduce the usage of pesticides.
Agriculture Versus Forestry Sequentially, in the endeavor to determine what type of an activity to be dedicated to a land, it is proper to comprehend how the activity would work towards maintaining an excellent ecosystem’s functionality.
The Olmec and the Inca Civilizations Agriculture Practices The aim of this paper is to compare the lifestyles and achievements of the Olmec and the Inca civilizations. The creation of the civilization was instigated by the fact that local alluvial soil was well […]
Agricultural Modernization in Third World Countries Due to underdevelopment in third world countries, the state considered the need for integrated rural development to reduce poverty in rural areas.
Improving Stress Resistance in Agricultural Crops The biotechnology involved in producing such crops faces many difficulties and there are a lot of considerations of the methods used to improve the crop’s resistance that need to be assessed.
Hydroponics in Agriculture These different setups have the same idea of hydroponics growing but the difference comes in the type of medium used in the growing and the state of the nutrient solution.
Application of Geography (GIS) in Biotechnology in Field of Agriculture and Environment According to Wyland, “the ability of GIS to analyze and visualize agricultural environments and work flows has proved to be very beneficial to those involved in the farming industry”.
Sprinkler Drones in the Agricultural Sector The introduction of drones in agriculture is expected to solve the problem of the shifting structure of the workforce in agriculture.
Internet of Things in Agriculture According to Chalimov, farmers can control such indicators as soil contamination, the proportion of harmful substances in the air, the level of water pollution, and many other characteristics that are crucial to address timely.
Agricultural Geography and the Production and Consumption of Food in British Columbia The impact of the disparity in the natural environment which causes variable conditions in different geographical areas is reflected in the productivity, production cost and efficiency of production.
The Indian Agriculture Sector Given the significance of the agricultural sector to the economy, the government introduced the 11th five-year plan to provide support and incentives to farmers and other stakeholders in order to enhance production of food.
Agricultural Revolution and Changes to Ancient Societies in Terms of the State, Urbanization, and Labor This made the climate and soil more adaptable to plant growth and farming as some of the wild variants of barley and fruit began to grow in the region on their own.
Agriculture and Regulations in African Countries This work is aimed at determining the significance of agriculture in African countries, the main features of the regulation of this field, as well as the causes leading to a failure in a traditional developmental […]
Global Warming and Agriculture The first and the most obvious result of the global warming is the decrease of the harvest in the majority of regions all over the world.
Agriculture Development in Economic Development This they attribute to the division of labour, where the workers that perform the basic, manual jobs that demand a lot of strength are the least paid, while those that perform the lightest and sophisticated […]
The Agricultural Revolution: From the Neolithic Age to a New Era of Agricultural Growth The discovery of tools is recognized to be one of the most important events of human development, as it is a well-known fact that “The development of tools such as flint points, axes, weapons such […]
Application of Biotechnology in Agriculture and Health Care The more I studied this issue, the more I became interested in biotechnologies and the possibilities of their use for people.
The Main Objective of DNA Fingerprinting in Agriculture Therefore, the main objective of DNA fingerprinting in agriculture is to overcome the limitation of insufficient dissimilarity among prior genotypes and come up with the best ideas to discover new molecular markers and collect data […]
Advices to the French Minister of Agriculture, the Head of the French Wine Industry Association and the Owners of Vineyards One of the major problems of the French wine industry is the incapacity to produce the cheap wine due to the climate characteristics of the region, luck of commercial interest and the low support of […]
Recycling of Wastewater for Agricultural Use in Arid Areas Given that in these arid areas water is a rare commodity, recycling of wastewaters has been considered as one of the ways that can be used to increase the amount of water for irrigation for […]
Zimbabwe’s Agriculture Sectors: Role in the Economic Development This report is dedicated to exploring the agriculture sectors of Zimbabwe and their role in the economic development of the country.
Use of Pesticides in Agriculture The general narrative on pesticide use in agriculture is the assertion that it saves labor and ensures higher crop yields. These adversities show just how greater danger than the usefulness of pesticide use is in […]
History of Agriculture in the American West The introduction of electric and gasoline-powered machinery, the use of chemical fertilizers and pesticides made agriculture one of the main sources of income for West America.
Changes in Agriculture in the Next 25 Years The most dramatic change will be the lives and lifestyles of the farmers that will in the next 25 years be the envy of urban folks.
Soil Degradation as an Issue Facing Agriculture The most informative indicators of purely hydrological degradation of soils are a decrease in the total moisture capacity of the soil and a reduction in the lowest moisture capacity of the soil, which characterizes the […]
Agriculture and Farming in Abu Dhabi Many researches have been done on soil taxonomy in the UAE, with the invention of a non-absorbent type of soil that was one of the breakthroughs that have greatly influenced agriculture in Abu Dhabi.
Intensification of Agriculture Industries in Canada and the USA Therefore, one should not suppose that the growth of production can be explained by the increase in the number of people who wanted to work in this industry.
Lucretius’s View About the Roman Agriculture This was not a mere rhetoric considering that writers on the Roman agriculture also highlighted the decline in land productivity either due to the land being old or because of humans’ failure to preserve the […]
Agricultural Pesticide Negative Impacts The presence of pesticide residues in water, air, and the food is considered the main consequence of the neglectful use of pesticides in agriculture as it puts a serious risk to the safety of people […]
History of Mexican Agriculture and Land Tenure The topics covered in the article are related to the history of land tenure in Mexico. Furthermore, it is vital to adapt to the emerging situation in terms of protecting the farmers and land from […]
Organic Agriculture Funding: Regenerative Organic Agriculture In turn, organic farming will persist in enriching the soil and the products, Additionally, products that are certified organic continue to be in high demand due to consumer preference.
Industrial Revolution in Agriculture On the other hand, the industrial revolution in agriculture has led to the introduction of new safety challenges. In conclusion, as a result of the industrial revolution in agriculture, automation has become increasingly relied upon […]
Environmental Ethics of Pesticide Usage in Agriculture For example, pesticides are responsible for the destruction of the soil and harm to the overall ecosystem. The soil, water, and air resources are at a high risk of contamination from the toxins that are […]
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Financial Profile of Oman Agriculture Development Company Although the year 2008 has been the most beneficial for the company, yet in comparison to the year 2009, the company has managed to improve the figures in 2010.
Agriculture and Environment: Organic Foods Nitrogen has various effects on the food supply, and it’s present in the soil in the form of nitrates and nitrites.
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Common Agricultural Policy in Italy One of the latter is the so called Common Agricultural Policy implemented by the EU officials in 2003 to develop for the coming decades and ensure the equal development of the agricultural spheres of all […]
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Should Common Agricultural Policy Be Reformed? So with the CAP policy, it is sending a strong message to the world in that it is through the CAP policy that our farmers will be in a position to strongly compete with world […]
Libyan Agricultural Infrastructure Analysis The telecommunications network in Libya is in the process of being modernized. The development of agricultural infrastructure has played a big role in alleviating poverty in this nation.
World Trade as the Adjustment Mechanism of Agriculture to Climate Change by Julia & Duchin The significant value of the article under consideration consists in the authors’ presenting a new methodological framework for the evaluation of a trade as the stated mechanism and its use for analysis of changes in […]
Social Capital in Agriculture and Rural Development The first usage of this term is traced back to 1899 when John Dewey made the first direct mainstream use of the term social capital in the book, “The school and the society”.
Weather Tracking and Effects on Agriculture The success of weather forecasting to meet the needs of different stakeholders depends on the tools and technologies put in place.
A Technique for Controlling Plant Characteristics: Genetic Engineering in the Agriculture A cautious investigation of genetic engineering is required to make sure it is safe for humans and the environment. The benefit credited to genetic manipulation is influenced through the utilization of herbicide-tolerant and pest-safe traits.
Pesticide Ban and Its Effects on US Agriculture The findings of the research also challenged the notion that a ban on insecticides would help the environment. Sam is whether to protect the lives that can be lost through the harmful effects of the […]
Agriculture: “Yield Prospects by Land and Air” by Schafer The crop tour allows farmers to participate in learning a lot during their visits. The editors of the article, however, failed to share what the farmers learned at the tour despite the delayed corn.
Common Agricultural Policy in the EU The number of funds that were being used for the payments was proposed to be used in developing the countryside through the establishment of a budget for rural development.
Agricultural Policies in the EU vs. the US It is the position of this paper concerning the European Union, and the United States, particularly in the light of the political implications on policymaking in the Agricultural Sectors, that both the EU’s Common Agricultural […]
“The Political Economy of Agricultural Transition” by Rozelle and Swinnen Other important highlights of the article include the motivations behind the actor’s push for economic reform in China and the Soviet Union.
Agricultural Issues in the Global South The latter has ensured that food is produced in plenty and that the citizens do not starve at the expense of cash crops.
The Agricultural Revolutions: Timeline, Causes, Inventions This revolution prevented food emergencies in Latin America and Asia during the 1970s and 1980s. However, the revolution was not a successful tactic in ending global food shortage and hunger.
Agricultural Issues in the “Food Inc.” Documentary One of the reasons is that large corporations can launch a mass-scale production of food, and therefore, they can dictate pricing policies to the small farmers, who, in their turn, have to work with these […]
Agricultural Products vs. Animal Rights Dilemma A while back I was looking for a summer job and I was able to get one in the farms that rear chicken for their eggs and meat.
Agricultural Policies in African and Asian Countries Agriculture is the largest contributor to the GDP in most countries accounting for 32% of the GDP. Agriculture is the main source of income for the majority of the population.
Farmers and Their Role in the American Agriculture The recent changes in the world’s largest countries’ economies can be a good illustration of the exclusive role of agriculture which can enable a state to play an important role in the world.
Big Data and Agriculture Big Data is expected to feed the world in the future by analyzing large volumes of data associated with predicting the weather, finding appropriate regions for farming and agriculture, and eliminating possible adverse outcomes.
Poverty and Global Food Crisis: Food and Agriculture Model Her innovative approach to the issue was to measure food shortages in calories as opposed to the traditional method of measuring in pounds and stones.
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Australian Economy: Agriculture, Industry and Services Most of the responsibility for the upsurge lies on the technological advancement of the industry that drives the growth and productivity.
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Urban Agriculture in Chicago: Pros and Cons The climatic changes that have adversely affected the ability of farmers in the rural areas to generate high yields in their farms have led to a reduction in the number of fresh products reaching the […]
Agriculture in the Pacific Northwest The large variety of marine and terrestrial resources made agriculture the secondary food source and allowed for the development of storage-based subsistence economy in the Pacific Northwest, especially in Oregon.
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Agricultural Nutrient Pollution and Its Reduction The solutions that have been proposed for the issue are varied: there is the possibility of upgrading farms with the help of better technologies, controlling the use of fertilizers and waste discharge with the help […]
Native Americans’ History, Farming, Agriculture Nowadays, the task of primary importance is to educate the society and convey the idea that the rich past of the American Indians should be remembered.
Urban Agricultural Impact on Human Life One major characteristic of urban agriculture that differentiates it from rural agriculture is the integration of agriculture in the urban economic and ecological system.
Canadian Small Agricultural Business and Its Trends Some of the misconceptions are illustrated in the report are that the sector is shrinking with no modernization and innovation. In reality, the study showed that over 95% of the farmers in Canada take measures […]
Management Accounting in Agriculture The farming industry of the nature of John and Mary falls in this category however with such a management accounting system like the one portrayed, then the management is likely to be more easy and […]
Exchange Rates Impact on the Australian Agriculture The random trend in the foreign exchange market is a macroeconomic issue that has significant implications on the export market prices and the appreciation of the Australian dollar.
Energy Problems in the Agriculture Sector From the start, I recognized that using the diesel generator was not the most effective way to solve the power needs of the farm.
Agriculture Improvement: The US Farm Bill Nadine Lehrer, who has been studying the bill, asserts, “The bill was developed in the wake of 1930’s farm crisis to bring farm incomes up to the par with the required minimum incomes”.
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Vicious Cycle: The Flipside of Brazil’s Agricultural Expansionist Policies But more importantly, environmental policymakers in Brazil should realize that another vicious cycle between economic development and income distribution will set in the near future as long as farmers in North-East regions of the country […]
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Essays on Agriculture

The importance of writing an essay on agriculture cannot be overstated. Agriculture is the backbone of our society, providing us with the food and resources we need to survive. By writing an essay on agriculture, you can help raise awareness about the importance of sustainable farming practices, the impact of climate change on food production, and the need for technological advancements in the agricultural industry.

When writing an essay on agriculture, it's important to first do thorough research on the topic. This may involve reading academic journals, interviewing experts in the field, and gathering data on current agricultural practices and trends. This research will provide you with the necessary information to make strong arguments and support your claims.

Additionally, it's important to consider the audience for your essay. Are you writing for a general audience or for a specific group of people, such as policymakers or farmers? Tailoring your writing to your audience will help ensure that your essay is relevant and impactful.

Another important tip for writing an essay on agriculture is to use clear and concise language. Avoid jargon and technical terms that may be difficult for the average reader to understand. Instead, focus on communicating your ideas in a straightforward manner that is accessible to a wide range of readers.

Finally, don't forget to include evidence to support your arguments. This may include statistics, case studies, and expert opinions. Providing evidence will help strengthen your essay and make it more persuasive.

The Impact of Climate Change on Agriculture Climate change is one of the biggest challenges facing agriculture today. Write an essay exploring the various ways in which climate change is affecting agriculture, including changes in temperature and precipitation patterns, increased frequency of extreme weather events, and the spread of pests and diseases. Discuss potential strategies for adapting to and mitigating the effects of climate change on agriculture.

The Role of Technology in Modern Agriculture Advances in technology have revolutionized the way we produce food. In this essay, discuss the impact of technology on agriculture, including the use of precision farming techniques, drones and other aerial technologies, and the development of genetically modified organisms. Explore the potential benefits and drawbacks of these technological advancements on the agricultural industry.

Sustainable Agriculture Practices Sustainability is a growing concern in agriculture, as farmers and policymakers seek to minimize the environmental impact of food production. Write an essay discussing sustainable agriculture practices, such as organic farming, crop rotation, and integrated pest management. Explore the potential benefits of these practices for both the environment and the long-term viability of the agricultural industry.

The Importance of Soil Health in Agriculture Healthy soil is essential for productive and sustainable agriculture. In this essay, explore the role of soil health in agriculture, including the importance of soil conservation, the impact of soil degradation on crop yields, and the potential benefits of regenerative agriculture practices. Discuss potential strategies for improving and maintaining soil health on farms.

The Future of Agriculture: Urban Farming and Vertical Agriculture As the global population continues to grow, the demand for food is increasing, leading to new innovations in agricultural practices. Write an essay discussing the potential of urban farming and vertical agriculture to address food security and sustainability challenges. Explore the benefits and drawbacks of these alternative farming methods and their potential impact on the agricultural industry.

The Economics of Agriculture: Farm Subsidies and Trade Policies The agricultural industry is heavily influenced by government policies and international trade agreements. In this essay, explore the economic factors that shape agriculture, including the role of farm subsidies, tariffs, and trade barriers. Discuss the potential impact of these policies on farmers, consumers, and the global food supply.

The Ethical Considerations of Animal Agriculture The treatment of animals in the agricultural industry is a topic of growing concern. Write an essay exploring the ethical considerations of animal agriculture, including the use of factory farming practices, the treatment of livestock, and the impact of animal agriculture on the environment and public health. Discuss potential strategies for promoting ethical and sustainable practices in animal agriculture.

The Impact of Biotechnology on Agriculture Biotechnology has the potential to revolutionize the agricultural industry, from the development of genetically modified crops to the use of biotechnology in food processing and preservation. In this essay, explore the potential benefits and drawbacks of biotechnology in agriculture, including its impact on food security, environmental sustainability, and public health.

The Role of Women in Agriculture Women play a crucial role in agriculture, from farm labor and management to entrepreneurship and leadership. Write an essay discussing the contributions of women to the agricultural industry, including the challenges and opportunities they face. Explore potential strategies for promoting gender equality and empowering women in agriculture.

The Future of Agriculture: Sustainable Food Systems As the world grapples with the challenges of climate change, food security, and environmental degradation, there is growing interest in developing sustainable food systems. In this essay, discuss the potential of sustainable food systems to address these challenges, including the role of regenerative agriculture, local food movements, and alternative food distribution models. Explore the potential benefits of sustainable food systems for both the environment and human health.

In , agriculture is a complex and multifaceted industry that intersects with numerous social, economic, and environmental issues. These essay topics provide a starting point for exploring the many dimensions of agriculture, from the impact of climate change and technological advancements to the ethical considerations of food production and the potential of sustainable food systems. By delving into these topics, students and researchers can gain a deeper understanding of the challenges and opportunities facing the agricultural industry and contribute to the development of innovative solutions for a more sustainable and equitable food system.

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Published: 27 April 2017

Technology: The Future of Agriculture

Anthony King

Nature volume 544 , pages S21–S23 ( 2017 ) Cite this article

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Agriculture

A technological revolution in farming led by advances in robotics and sensing technologies looks set to disrupt modern practice.

Over the centuries, as farmers have adopted more technology in their pursuit of greater yields, the belief that 'bigger is better' has come to dominate farming, rendering small-scale operations impractical. But advances in robotics and sensing technologies are threatening to disrupt today's agribusiness model. “There is the potential for intelligent robots to change the economic model of farming so that it becomes feasible to be a small producer again,” says robotics engineer George Kantor at Carnegie Mellon University in Pittsburgh, Pennsylvania.

Twenty-first century robotics and sensing technologies have the potential to solve problems as old as farming itself. “I believe, by moving to a robotic agricultural system, we can make crop production significantly more efficient and more sustainable,” says Simon Blackmore, an engineer at Harper Adams University in Newport, UK. In greenhouses devoted to fruit and vegetable production, engineers are exploring automation as a way to reduce costs and boost quality (see ‘ Ripe for the picking ’). Devices to monitor vegetable growth, as well as robotic pickers, are currently being tested. For livestock farmers, sensing technologies can help to manage the health and welfare of their animals (‘ Animal trackers ’). And work is underway to improve monitoring and maintenance of soil quality (‘ Silicon soil saviours ’), and to eliminate pests and disease without resorting to indiscriminate use of agrichemicals (‘ Eliminating enemies ’).

Although some of these technologies are already available, most are at the research stage in labs and spin-off companies. “Big-machinery manufacturers are not putting their money into manufacturing agricultural robots because it goes against their current business models,” says Blackmore. Researchers such as Blackmore and Kantor are part of a growing body of scientists with plans to revolutionize agricultural practice. If they succeed, they'll change how we produce food forever. “We can use technology to double food production,” says Richard Green, agricultural engineer at Harper Adams.

Ripe for the picking

The Netherlands is famed for the efficiency of its fruit- and vegetable-growing greenhouses, but these operations rely on people to pick the produce. “Humans are still better than robots, but there is a lot of effort going into automatic harvesting,” says Eldert van Henten, an agricultural engineer at Wageningen University in the Netherlands, who is working on a sweet-pepper harvester. The challenge is to quickly and precisely identify the pepper and avoid cutting the main stem of the plant. The key lies in fast, precise software. “We are performing deep learning with the machine so it can interpret all the data from a colour camera fast,” says van Henten. “We even feed data from regular street scenes into the neural network to better train it.”

In the United Kingdom, Green has developed a strawberry harvester that he says can pick the fruit faster than humans. It relies on stereoscopic vision with RGB cameras to capture depth, but it is its powerful algorithms that allow it to pick a strawberry every two seconds. People can pick 15 to 20 a minute, Green estimates. “Our partners at the National Physical Laboratory worked on the problem for two years, but had a brainstorm one day and finally cracked it,” says Green, adding that the solution is too commercially sensitive to share. He thinks that supervised groups of robots can step into the shoes of strawberry pickers in around five years. Harper Adams University is considering setting up a spin-off company to commercialize the technology. The big hurdle to commercialization, however, is that food producers demand robots that can pick all kinds of vegetables, says van Henten. The variety of shapes, sizes and colours of tomatoes, for instance, makes picking them a tough challenge, although there is already a robot available to remove unwanted leaves from the plants.

Another key place to look for efficiencies is timing. Picking too early is wasteful because you miss out on growth, but picking too late slashes weeks off the storage time. Precision-farming engineer Manuela Zude-Sasse at the Leibniz Institute for Agricultural Engineering and Bioeconomy in Potsdam, Germany, is attaching sensors to apples to detect their size, and levels of the pigments chlorophyll and anthocyanin. The data are fed into an algorithm to calculate developmental stage, and, when the time is ripe for picking, growers are alerted by smartphone.

So far, Zude-Sasse has put sensors on pears, citrus fruits, peaches, bananas and apples ( pictured ). She is set to start field trials later this year in a commercial tomato greenhouse and an apple orchard. She is also developing a smartphone app for cherry growers. The app will use photographs of cherries taken by growers to calculate growth rate and a quality score.

Growing fresh fruit and vegetables is all about keeping the quality high while minimizing costs. “If you can schedule harvest to optimum fruit development, then you can reap an economic benefit and a quality one,” says Zude-Sasse.

Eliminating enemies

The Food and Agriculture Organization of the United Nations estimates that 20–40% of global crop yields are lost each year to pests and diseases, despite the application of around two-million tonnes of pesticide. Intelligent devices, such as robots and drones, could allow farmers to slash agrichemical use by spotting crop enemies earlier to allow precise chemical application or pest removal, for example. “The market is demanding foods with less herbicide and pesticide, and with greater quality,” says Red Whittaker, a robotics engineer at Carnegie Mellon who designed and patented an automated guidance system for tractors in 1997. “That challenge can be met by robots.”

“We predict drones, mounted with RGB or multispectral cameras, will take off every morning before the farmer gets up, and identify where within the field there is a pest or a problem,” says Green. As well as visible light, these cameras would be able to collect data from the invisible parts of the electromagnetic spectrum that could allow farmers to pinpoint a fungal disease, for example, before it becomes established. Scientists from Carnegie Mellon have begun to test the theory in sorghum ( Sorghum bicolor ), a staple in many parts of Africa and a potential biofuel crop in the United States.

Agribotix, an agriculture data-analysis company in Boulder, Colorado, supplies drones and software that use near-infrared images to map patches of unhealthy vegetation in large fields. Images can also reveal potential causes, such as pests or problems with irrigation. The company processes drone data from crop fields in more than 50 countries. It is now using machine learning to train its systems to differentiate between crops and weeds, and hopes to have this capability ready for the 2017 growing season. “We will be able to ping growers with an alert saying you have weeds growing in your field, here and here,” says crop scientist Jason Barton, an executive at Agribotix.

Modern technology that can autonomously eliminate pests and target agrichemicals better will reduce collateral damage to wildlife, lower resistance and cut costs. “We are working with a pesticide company keen to apply from the air using a drone,” says Green. Rather than spraying a whole field, the pesticide could be delivered to the right spot in the quantity needed, he says. The potential reductions in pesticide use are impressive. According to researchers at the University of Sydney's Australian Centre for Field Robotics, targeted spraying of vegetables used 0.1% of the volume of herbicide used in conventional blanket spraying. Their prototype robot is called RIPPA (Robot for Intelligent Perception and Precision Application) and shoots weeds with a directed micro-dose of liquid. Scientists at Harper Adams are going even further, testing a robot that does away with chemicals altogether by blasting weeds close to crops with a laser. “Cameras identify the growing point of the weed and our laser, which is no more than a concentrated heat source, heats it up to 95 °C, so the weed either dies or goes dormant,” says Blackmore.

Animal trackers

Smart collars — a bit like the wearable devices designed to track human health and fitness — have been used to monitor cows in Scotland since 2010. Developed by Glasgow start-up Silent Herdsman, the collar monitors fertility by tracking activity — cows move around more when they are fertile — and uses this to alert farmers to when a cow is ready to mate, sending a message to his or her laptop or smartphone. The collars ( pictured ), which are now being developed by Israeli dairy-farm-technology company Afimilk after they acquired Silent Herdsman last year, also detect early signs of illness by monitoring the average time each cow spends eating and ruminating, and warning the farmer via a smartphone if either declines.

“We are now looking at more subtle behavioural changes and how they might be related to animal health, such as lameness or acidosis,” says Richard Dewhurst, an animal nutritionist at Scotland's Rural College (SRUC) in Edinburgh, who is involved in research to expand the capabilities of the collar. Scientists are developing algorithms to interrogate data collected by the collars.

In a separate project, Dewhurst is analysing levels of exhaled ketones and sulfides in cow breath to reveal underfeeding and tissue breakdown or excess protein in their diet. “We have used selected-ionflow-tube mass spectrometry, but there are commercial sensors available,” says Dewhurst.

Cameras are also improving the detection of threats to cow health. The inflammatory condition mastitis — often the result of a bacterial infection — is one of the biggest costs to the dairy industry, causing declines in milk production or even death. Thermal-imaging cameras installed in cow sheds can spot hot, inflamed udders, allowing animals to be treated early.

Carol-Anne Duthie, an animal scientist at SRUC, is using 3D cameras to film cattle at water troughs to estimate the carcass grade (an assessment of the quality of a culled cow) and animal weight. These criteria determine the price producers are paid. Knowing the optimum time to sell would maximize profit and provide abattoirs with more-consistent animals. “This has knock on effects in terms of overall efficiency of the entire supply chain, reducing the animals which are out of specification reaching the abattoir,” Duthie explains.

And researchers in Belgium have developed a camera system to monitor broiler chickens in sheds. Three cameras continually track the movements of thousands of individual birds to spot problems quickly. “Analysing the behaviour of broilers can give an early warning for over 90% of problems,” says bioengineer Daniel Berckmans at the University of Leuven. The behaviour-monitoring system is being sold by Fancom, a livestock-husbandry firm in Panningen, the Netherlands. The Leuven researchers have also launched a cough monitor to flag respiratory problems in pigs, through a spin-off company called SoundTalks. This can give a warning 12 days earlier than farmers or vets would normally be able to detect a problem, says Berckmans. The microphone, which is positioned above animals in their pen, identifies sick individuals so that treatment can be targeted. “The idea was to reduce the use of antibiotics,” says Berckmans.

Berckmans is now working on downsizing a stress monitor designed for people so that it will attach to a cow's ear tag. “The more you stress an animal, the less energy is available from food for growth,” he says. The monitor takes 200 physiological measurements a second, alerting farmers through a smartphone when there is a problem.

Silicon soil saviours

The richest resource for arable farmers is soil. But large harvesters damage and compact soil, and overuse of agrichemicals such as nitrogen fertilizer are bad for both the environment and a farmer's bottom line. Robotics and autonomous machines could help.

Data from drones are being used for smarter application of nitrogen fertilizer. “Healthy vegetation reflects more near-infrared light than unhealthy vegetation,” explains Barton. The ratio of red to near-infrared bands on a multispectral image can be used to estimate chlorophyll concentration and, therefore, to map biomass and see where interventions such as fertilization are needed after weather or pest damage, for example. When French agricultural technology company Airinov, which offers this type of drone survey, partnered with a French farming cooperative, they found that over a period of 3 years, in 627 fields of oilseed rape ( Brassica napus ), farmers used on average 34 kilograms less nitrogen fertilizer per hectare than they would without the survey data. This saved on average €107 (US$115) per hectare per year.

Bonirob ( pictured ) — a car-sized robot originally developed by a team of scientists including those at Osnabrück University of Applied Sciences in Germany — can measure other indicators of soil quality using various sensors and modules, including a moisture sensor and a penetrometer, which is used to assess soil compaction. According to Arno Ruckelshausen, an agricultural technologist at Osnabrück, Bonirob can take a sample of soil, liquidize it and analyse it to precisely map in real time characteristics such as pH and phosphorous levels. The University of Sydney's smaller RIPPA robot can also detect soil characteristics that affect crop production, by measuring soil conductivity.

Soil mapping opens the door to sowing different crop varieties in one field to better match shifting soil properties such as water availability. “You could differentially seed a field, for example, planting deep-rooting barley or wheat varieties in more sandy parts,” says Maurice Moloney, chief executive of the Global Institute for Food Security in Saskatoon, Canada. Growing multiple crops together could also lead to smarter use of agrichemicals. “Nature is strongly against monoculture, which is one reason we have to use massive amounts of herbicide and pesticides,” says van Henten. “It is about making the best use of resources.”

Mixed sowing would challenge an accepted pillar of agricultural wisdom: that economies of scale and the bulkiness of farm machinery mean vast fields of a single crop is the most-efficient way to farm, and the bigger the machine, the more-efficient the process. Some of the heaviest harvesters weigh 60 tonnes, cost more than a top-end sports car and leave a trail of soil compaction in their wake that can last for years.

But if there is no need for the farmer to drive the machine, then one large vehicle that covers as much area as possible is no longer needed. “As soon as you remove the human component, size is irrelevant,” says van Henten. Small, autonomous robots make mixed planting feasible and would not crush the soil.

In April, researchers at Harpers Adams began a proof-of-concept experiment with a hectare of barley. “We plan to grow and harvest the entire crop from start to finish with no humans entering the field,” says Green. The experiment will use existing machinery, such as tractors, that have been made autonomous, rather than new robots, but their goal is to use the software developed during this trial as the brains of purpose-built robots in the future. “Robots can facilitate a new way of doing agriculture,” says van Henten. Many of these disruptive technologies may not be ready for the prime time just yet, but the revolution is coming.

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King, A. Technology: The Future of Agriculture. Nature 544 , S21–S23 (2017). https://doi.org/10.1038/544S21a

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Published : 27 April 2017

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Article Contents

1. introduction, 2. analytical framework, 3. literature search, 5. discussion, 6. conclusion, acknowledgement.

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Research impact assessment in agriculture—A review of approaches and impact areas

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Peter Weißhuhn, Katharina Helming, Johanna Ferretti, Research impact assessment in agriculture—A review of approaches and impact areas, Research Evaluation , Volume 27, Issue 1, January 2018, Pages 36–42, https://doi.org/10.1093/reseval/rvx034

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Research has a role to play in society’s endeavour for sustainable development. This is particularly true for agricultural research, since agriculture is at the nexus between numerous sustainable development goals. Yet, generally accepted methods for linking research outcomes to sustainability impacts are missing. We conducted a review of scientific literature to analyse how impacts of agricultural research were assessed and what types of impacts were covered. A total of 171 papers published between 2008 and 2016 were reviewed. Our analytical framework covered three categories: (1) the assessment level of research (policy, programme, organization, project, technology, or other); (2) the type of assessment method (conceptual, qualitative, or quantitative); and (3) the impact areas (economic, social, environmental, or sustainability). The analysis revealed that most papers (56%) addressed economic impacts, such as cost-effectiveness of research funding or macroeconomic effects. In total, 42% analysed social impacts, like food security or aspects of equity. Very few papers (2%) examined environmental impacts, such as climate effects or ecosystem change. Only one paper considered all three sustainability dimensions. We found a majority of papers assessing research impacts at the level of technologies, particularly for economic impacts. There was a tendency of preferring quantitative methods for economic impacts, and qualitative methods for social impacts. The most striking finding was the ‘blind eye’ towards environmental and sustainability implications in research impact assessments. Efforts have to be made to close this gap and to develop integrated research assessment approaches, such as those available for policy impact assessments.

Research has multiple impacts on society. In the light of the international discourse on grand societal challenges and sustainable development, the debate is reinforced about the role of research on economic growth, societal well-being, and environmental integrity ( 1 ). Research impact assessment (RIA) is a key instrument to exploring this role ( 2 ).

A number of countries have begun using RIA to base decisions for allocation of funding on it, and to justify the value of investments in research to taxpayers ( 3 ). The so-called scientometric assessments with a focus on bibliometric and exploitable results such as patents are the main basis for current RIA practices ( 4–6 ). However, neither academic values of science, based on the assumption of ‘knowledge as progress’, nor market values frameworks (‘profit as progress’) seem adequate for achieving and assessing broader public values ( 7 ). Those approaches do not explicitly acknowledge the contribution of research to solving societal challenges, although they are sufficient to measure scientific excellence ( 8 ) or academic impact.

RIA may however represent a vital element for designing socially responsible research processes with orientation towards responsibility for a sustainable development ( 9 , 10 ). In the past, RIAs occurred to focus on output indicators and on links between science and productivity while hardly exploring the wider societal impacts of science ( 11 ). RIA should entail the consideration of intended and non-intended, positive and negative, and long- and short-term impacts of research ( 12 ). Indeed, there has been a broadening of impact assessments to include, for example, cultural and social returns to society ( 13 ). RIA is conceptually and methodologically not yet sufficiently equipped to capture wider societal implications, though ( 14 ). This is due to the specific challenges associated with RIA, including inter alia unknown time lags between research processes and their impacts ( 15–17 ). Independent from their orientation, RIAs are likely to influence research policies for years to come ( 18 ).

Research on RIA and its potential to cover wider societal impacts has examined assessment methods and approaches in specific fields of research, and in specific research organizations. The European Science Foundation ( 19 ) and Guthrie et al. ( 20 ) provided overviews of a range of methods usable in assessment exercises. They discuss generic methods (e.g. economic analyses, surveys, and case studies) with view to their selection for RIAs. Methods need to fit the objectives of the assessment and the characteristics of the disciplines examined. Econometric methods consider the rate of return over investment ( 21 ), indicators for ‘productive interactions’ between the stakeholders try to capture the social impact of research ( 22 ), and case study-based approaches map the ‘public values’ of research programmes ( 8 , 23 ). No approach is generally favourable over another, while challenges exist in understanding which impact areas are relevant in what contexts. Penfield et al. ( 6 ) looked at the different methods and frameworks employed in assessment approaches worldwide, with a focus on the UK Research Excellence Framework. They argue that there is a need for RIA approaches based on types of impact rather than research discipline. They point to the need for tools and systems to assist in RIAs and highlight different types of information needed along the output-outcome-impact-chain to provide for a comprehensive assessment. In the field of public health research, a minority of RIAs exhibit a wider scope on impacts, and these studies highlight the relevance of case studies ( 24 ). However, case studies often rely on principal investigator interviews and/or peer review, not taking into account the views of end users. Evaluation practices in environment-related research organizations tend to focus on research uptake and management processes, but partially show a broader scope and longer-term outcomes. Establishing attribution of environmental research to different types of impacts was identified to be a key challenge ( 25 ). Other authors tested impact frameworks or impact patterns in disciplinary public research organizations. For example, Gaunand et al. ( 26 ) analysed an internal database of the French Agricultural research organization INRA with 1,048 entries to identify seven impact areas, with five going beyond traditional types of impacts (e.g. conservation of natural resources or scientific advice). Besides, for the case of agricultural research, no systematic review of RIA methods exists in the academic literature that would allow for an overview of available approaches covering different impact areas of research.

Against this background, the objective of this study was to review in how far RIAs of agricultural research capture wider societal implications. We understand agricultural research as being a prime example for the consideration of wider research impacts. This is because agriculture is a sector which has direct and severe implications for a range of the UN Sustainable Development Goals. It has a strong practice orientation and is just beginning to develop a common understanding of innovation processes ( 27 ).

The analysis of the identified literature on agricultural RIA (for details, see next section ‘Literature search’) built on a framework from a preliminary study presented at the ImpAR Conference 2015 ( 28 ). It was based on three categories to explore the impact areas that were addressed and the design of RIA. In particular, the analytical framework consisted of: ( 1 ) the assessment level of research; ( 2 ) the type of assessment method; and ( 3 ) the impact areas covered. On the side, we additionally explored the time dimension of RIA, i.e. whether the assessment was done ex ante or ex post (see Fig. 1 ).

Analytical framework for the review of non-scientometric impact assessment literature of agricultural research.

Agricultural research and the ramifications following from that refer to different levels of assessment (or levels of evaluation, ( 29 )). We defined six assessment levels that can be the subject of a RIA: policy, programme, organization, project, technology, and other. The assessment level of the RIA is a relevant category, since it shapes the approach to the RIA (e.g. the impact chain of a research project differs to that at policy level). The assessment level was clearly stated in all of the analysed papers and in no case more than one assessment level was addressed. Articles were assigned to the policy level, if a certain public technology policy ( 30 ) or science policy, implemented by governments to directly or indirectly affect the conduct of science, was considered. Exemplary topics are research funding, transfer of research results to application, or contribution to economic development. Research programmes were understood as instruments that are adopted by government departments, or other organizational entities to implement research policies and fund research activities in a specific research field (e.g. programmes to promote research on a certain crop or cultivation technique). Articles dealing with the organizational level assess the impact of research activities of a specific research organization. The term research organization comprises public or private research institutes, associations, networks, or partnerships (e.g. the Consultative Group on International Agricultural Research (CGIAR) and its research centres). A research project is the level at which research is actually carried out, e.g. as part of a research programme. The assessment of a research project would consider the impacts of the whole project, from planning through implementation to evaluation instead of focusing on a specific project output, like a certain agricultural innovation. The technology level was considered to be complementary to the other assessment levels of research and comprises studies with a strong focus on specific agricultural machinery or other agricultural innovation such as new crops or crop rotations, fertilizer applications, pest control, or tillage practices, irrespective of the agricultural system (e.g. smallholder or high-technology farming, or organic, integrated, or conventional farming). The category ‘other’ included one article addressing RIA at the level of individual researchers (see ( 31 )).

We categorized the impact areas along the three dimensions of sustainable development by drawing upon the European Commission’s impact assessment guidelines (cf. ( 32 )). The guidelines entail a list of 7 environmental impacts, such as natural resource use, climate change, or aspects of nature conservation; 12 social impacts, such as employment and working conditions, security, education, or aspects of equity; and 10 economic impacts, including business competitiveness, increased trade, and several macroeconomic aspects. The European Commission’s impact assessment guidelines were used as a classification framework because it is one of the most advanced impact assessment frameworks established until to date ( 33 ). In addition, we opened a separate category for those articles exploring joint impacts on the three sustainability dimensions. Few articles addressed impacts in two sustainability dimensions which we assigned to the dominating impact area.

To categorize the type of RIA method, we distinguished between conceptual, qualitative, and quantitative. Conceptual analyses include the development of frameworks or concepts for measuring impacts of agricultural research (e.g. tracking of innovation pathways or the identification of barriers and supporting factors for impact generation). Qualitative and quantitative methods were identified by the use of qualitative data or quantitative data, respectively (cf. ( 34–36 )). Qualitative data can be scaled nominally or ordinally. It is generated by interviews, questionnaires, surveys or choice experiments to gauge stakeholder attitudes to new technologies, their willingness to pay, and their preference for adoption measures. The generation of quantitative data involves a numeric measurement in a standardized way. Such data are on a metric scale and are often used for modelling. The used categorization is rather simple. We assigned approaches which employed mixed-method approaches according to their dominant method. We preferred this over more sophisticated typologies to achieve a high level of abstraction and because the focus of our analysis was on impact areas rather than methods. However, to show consistencies with existing typologies of impact assessment methods ( 19 , 37 ), we provide an overview of the categorization chosen and give examples of the most relevant types of methods.

To additionally explore the approach of the assessment ( 38 ), the dimensions ex ante and ex post were identified. The two approaches are complementary: whereas ex ante impact assessments are usually conducted for strategic and planning purposes to set priorities, ex post impact assessments serve as accountability validation and control against a baseline. The studies in our sample that employed an ex ante approach to RIA usually made this explicit, while in the majority of ex post impact assessments, this was indicated rather implicitly.

This study was performed as a literature review based on Thomson Reuters Web of Science TM Core Collection, indexed in the Science Citation Index Expanded (SCI-Exp) and the Social Sciences Citation Index (SSCI). The motivation for restricting the analysis to articles from ISI-listed journals was to stay within the boundaries of internationally accepted scientific quality management and worldwide access. The advantages of a search based on Elsevier’s Scopus ® (more journals and alternative publications, and more articles from social and health science covered) would not apply for this literature review, with regard to the drawbacks of an index system based on abstracts instead of citation indexes, which is not as transparent as the Core Collection regarding the database definable by the user. We selected the years of 2008 to mid-2016 for the analysis (numbers last updated on 2 June 2016) . First, because most performance-based funding systems have been introduced since 2000, allowing sufficient time for the RIA approaches to evolve and literature to be published. Secondly, in 2008 two key publications on RIA of agricultural research triggered the topic: Kelley, et al. ( 38 ) published the lessons learned from the Standing Panel on Impact Assessment of CGIAR; Watts, et al. ( 39 ) summarized several central pitfalls of impact assessment concerning agricultural research. We took these publications as a starting point for the literature search. We searched in TOPIC and therefore, the terms had to appear in the title, abstract, author keywords, or keywords plus ® . The search query 1 filtered for agricultural research in relation to research impact. To cover similar expressions, we used science, ‘R&D’, and innovation interchangeably with research, and we searched for assessment, evaluation, criteria, benefit, adoption, or adaptation of research.

We combined the TOPIC search with a less strict search query 2 in TITLE using the same groups of terms, as these searches contained approximately two-thirds non-overlapping papers. Together they consisted of 315 papers. Of these, we reviewed 282 after excluding all document types other than articles and reviews (19 papers were not peer-reviewed journal articles) and all papers not written in English language (14 papers). After going through them, 171 proved to be topic-relevant and were included in the analysis.

Analysis matrix showing the number of reviewed articles, each categorized to an assessment level and an impact area (social, economic, environmental, or all three (sustainability)). Additionally, the type of analytical method (conceptual, quantitative, and qualitative) is itemized

Assessment level .	Policy .	Programme .	Organization .	Project .	Technology .	Others .	Sum .
Impact area .	Policy .	Programme .	Organization .	Project .	Technology .	Others .	Sum .
Social issues
Conceptual	7	3	1	4	6	0	21
Qualitative	6	4	4	10	11	0	35
Quantitative	5	2	5	2	2	0	16

Economy
Conceptual	3	4	6	5	13	1	32
Qualitative	2	2	1	3	19	0	27
Quantitative	8	6	4	4	14	0	36

Environment
Conceptual	0	0	0	0	1	0	1
Qualitative	0	0	0	1	0	0	1
Quantitative	0	0	0	0	1	0	1

Sustainability
Conceptual	0	0	0	0	1	0	1
Qualitative	0	0	0	0	0	0	0
Quantitative	0	0	0	0	0	0	0

Total

Assessment level .	Policy .	Programme .	Organization .	Project .	Technology .	Others .	Sum .
Impact area .	Policy .	Programme .	Organization .	Project .	Technology .	Others .	Sum .
Social issues
Conceptual	7	3	1	4	6	0	21
Qualitative	6	4	4	10	11	0	35
Quantitative	5	2	5	2	2	0	16

Economy
Conceptual	3	4	6	5	13	1	32
Qualitative	2	2	1	3	19	0	27
Quantitative	8	6	4	4	14	0	36

Environment
Conceptual	0	0	0	0	1	0	1
Qualitative	0	0	0	1	0	0	1
Quantitative	0	0	0	0	1	0	1

Sustainability
Conceptual	0	0	0	0	1	0	1
Qualitative	0	0	0	0	0	0	0
Quantitative	0	0	0	0	0	0	0

Total

In the agricultural RIA, the core assessment level of the reviewed articles was technology (39%), while the other levels were almost equally represented (with the exception of ‘other’). Generally, most papers (56%) addressed economic research impacts, closely followed by social research impacts (42%); however, only three papers (2%) addressed environmental research impacts and only 1 of 171 papers addressed all three dimensions of sustainable development. Assessments at the level of research policy slightly emphasized social impacts over economic impacts (18 papers, or 58%), whereas assessments at the level of technology clearly focused primarily on economic impacts (46 papers, or 68%).

The methods used for agricultural RIA showed no preference for one method type (see Table 1 ). Approximately 31% of the papers assessed research impacts quantitatively, whereas 37% used qualitative methods. Conceptual considerations on research impact were applied by 32% of the studies. A noticeable high number of qualitative studies were conducted to assess social impacts. At the evaluation level of research policy and research programmes, we found a focus on quantitative methods, if economic impacts were assessed.

Overview on type of methods used for agricultural RIA

Method Type I .	Method Type II .	Example .
Conceptual	Review	Document analysis, literature review, argumentation, anecdotes
Conceptual	Framework development	Conceptual innovation
Qualitative	Survey	Questionnaire, interview, expert surveys, etc.
Quantitative	Stochastic method	Regression analysis, Bayesian probabilistic method
Quantitative	Economic valuation	Econometric analysis, cost–benefit analysis, cost-effectiveness
Mixed	Participatory evaluation	Individual rating, group voting, actor mapping, evaluation of assessment tools
Mixed	Case studies	Detailed analysis of individual research projects, programmes, etc.

Method Type I .	Method Type II .	Example .
Conceptual	Review	Document analysis, literature review, argumentation, anecdotes
Conceptual	Framework development	Conceptual innovation
Qualitative	Survey	Questionnaire, interview, expert surveys, etc.
Quantitative	Stochastic method	Regression analysis, Bayesian probabilistic method
Quantitative	Economic valuation	Econometric analysis, cost–benefit analysis, cost-effectiveness
Mixed	Participatory evaluation	Individual rating, group voting, actor mapping, evaluation of assessment tools
Mixed	Case studies	Detailed analysis of individual research projects, programmes, etc.

a Mix of conceptual and qualitative methods.

b Mix of conceptual, qualitative, and quantitative methods.

Additionally, 37 ex ante studies, compared to 134 ex post studies, revealed that the latter clearly dominated, but no robust relation to any other investigated characteristic was found. Of the three environmental impact studies, none assessed ex ante , while the one study exploring sustainability impacts did. The share of ex ante assessments regarding social impacts was very similar to those regarding economic impacts. Within the assessment levels of research (excluding ‘others’ with only one paper), no notable difference between the shares of ex ante assessments occurred as they ranged between 13 and 28%.

The most relevant outcome of the review analysis was that only 3 of the 171 papers focus on the environmental impacts of agricultural research. This seems surprising because agriculture is dependent on an intact environment. However, this finding is supported by two recent reviews: one from Bennett, et al. ( 40 ) and one from Maredia and Raitzer ( 41 ). Both note that not only international agricultural research in general but also research on natural resource management shows a lack regarding large-scale assessments of environmental impacts. The CGIAR also recognized the necessity to deepen the understanding of the environmental impacts of its work because RIAs had largely ignored environmental benefits ( 42 ).

A few papers explicitly include environmental impacts of research in addition to their main focus. Raitzer and Maredia ( 43 ) address water depletion, greenhouse gas emissions, and landscape effects; however, their overall focus is on poverty reduction. Ajayi et al. ( 44 ) report the improvement of soil physical properties and soil biodiversity from introducing fertilizer trees but predominantly measure economic and social effects. Cavallo, et al. ( 45 ) investigate users’ attitudes towards the environmental impact of agricultural tractors (considered as technological innovation) but do not measure the environmental impact. Briones, et al. ( 46 ) configure an environmental ‘modification’ of economic surplus analysis, but they do not prioritize environmental impacts.

Of course, the environmental impacts of agricultural practices were the topic of many studies in recent decades, such as Kyllmar, et al. ( 47 ), Skinner, et al. ( 48 ), Van der Werf and Petit ( 49 ), among many others. However, we found very little evidence for the impact of agricultural research on the environment. A study on environmental management systems that examined technology adoption rates though not the environmental impacts is exemplarily for this ( 50 ). One possible explanation is based on the observation made by Morris, et al. ( 51 ) and Watts, et al. ( 39 ). They see impact assessments tending to accentuate the success stories because studies are often commissioned strategically as to demonstrate a certain outcome. This would mean to avoid carving out negative environmental impacts that conflict with, when indicated, the positive economic or societal impacts of the assessed research activity. In analogy to policy impact assessments, this points to the need of incentives to equally explore intended and unintended, expected and non-expected impacts from scratch ( 52 ). From those tasked with an RIA, this again requires an open attitude in ‘doing RIA’ and towards the findings of their RIA.

Another possible explanation was given by Bennett, et al. ( 40 ): a lack of skills in ecology or environmental economics to cope with the technically complex and data-intensive integration of environmental impacts. Although such a lack of skills or data could also apply to social and economic impacts, continuous monitoring of environmental data related to agricultural practices is particularly scarce. A third possible explanation is a conceptual oversight, as environmental impacts may be thought to be covered by the plenty of environmental impact assessments of agricultural activities itself.

The impression of a ‘blind eye’ on the environment in agricultural RIA may change when publications beyond Web of Science TM Core Collection are considered ( 53 ) or sources other than peer-reviewed journal articles are analysed (e.g. reports; conference proceedings). See, for example, Kelley, et al. ( 38 ), Maredia and Pingali ( 54 ), or FAO ( 55 ). Additionally, scientific publications of the highest quality standard (indicated by reviews and articles being listed in the Web of Science TM Core Collection) seem to not yet reflect experiences and advancements from assessment applications on research and innovation policy that usually include the environmental impact ( 56 ).

Since their beginnings, RIAs have begun to move away from narrow exercises concerned with economic impacts ( 11 ) and expanded their scope to social impacts. However, we only found one sustainability approach in our review that would cover all three impact areas of agricultural research (see ( 57 )). In contrast, progressive approaches to policy impact assessment largely attempt to cover the full range of environmental, social, and economic impacts of policy ( 33 , 58 ). RIAs may learn from them.

Additionally, the focus of agricultural research on technological innovation seems evident. Although the word innovation is sometimes still used for new technology (as in ‘diffusion of innovations’), it is increasingly used for the process of technical and institutional change at the farm level and higher levels of impact. Technology production increasingly is embedded in innovation systems ( 59 ).

The review revealed a diversity of methods (see Table 2 ) applied in impact assessments of agricultural research. In the early phases of RIA, the methods drawn from agricultural economics were considered as good standard for an impact assessment of international agricultural research ( 39 ). However, quantitative methods most often address economic impacts. In addition, the reliability of assessments based on econometric models is often disputed because of strong relationships between modelling assumptions and respective results.

Regarding environmental (or sustainability) impacts of agricultural research, the portfolio of assessment methods could be extended by learning from RIAs in other impact areas. In our literature sample, only review, framework development (e.g. key barrier typologies, environmental costing, or payments for ecosystem services), life-cycle assessment, and semi-structured interviews were used for environmental impacts of agricultural research.

In total, 42 of the 171 analysed papers assessed the impact of participatory research. A co-management of public research acknowledges the influence of the surrounding ecological, social, and political system and allows different types of stakeholder knowledge to shape innovation ( 60 ). Schut, et al. ( 36 ) conceptualize an agricultural innovation support system, which considers multi-stakeholder dynamics next to multilevel interactions within the agricultural system and multiple dimensions of the agricultural problem. Another type of participation in RIAs is the involvement of stakeholders to the evaluation process. A comparatively low number of six papers considered participatory evaluation of research impact, of them three in combination with impact assessment of participatory research.

Approximately 22% of the articles in our sample on agricultural research reported that they conducted their assessments ex ante , but most studies were ex post assessments. Watts, et al. ( 39 ) considered ex ante impact assessment to be more instructive than ex post assessment because it can directly guide the design of research towards maximizing beneficial impacts. This is particularly true when an ex ante assessment is conducted as a comparative assessment comprising a set of alternative options ( 61 ).

Many authors of the studies analysed were not explicit about the time frames considered in their ex post studies. The potential latency of impacts from research points to the need for ex post (and ex ante) studies to account for and analyse longer time periods, either considering ‘decades’ ( 62 , 63 ) or a lag distribution covering up to 50 years, with a peak approximately in the middle of the impact period ( 64 ). This finding is in line with the perspective of impact assessments as an ongoing process throughout a project’s life cycle and not as a one-off process at the end ( 51 ). Nevertheless, ex post assessments are an important component of a comprehensive evaluation package, which includes ex ante impact assessment, impact pathway analysis, programme peer reviews, performance monitoring and evaluation, and process evaluations, among others ( 38 ).

RIA is conceptually and methodologically not yet sufficiently equipped to capture wider societal implications, though ( 14 ). This is due to the specific challenges associated with RIA, including inter alia unknown time lags between research processes and their impacts ( 15–17 ). Independent from their orientation, RIAs are likely to influence research policies for years to come ( 18 ).

However, in the cases in which a RIA is carried out, an increase in the positive impacts (or avoidance of negative impacts) of agricultural research does not follow automatically. Lilja and Dixon ( 65 ) state the following methodological reasons for the missing impact of impact studies: no accountability with internal learning, no developed scaling out, the overlap of monitoring and evaluation and impact assessment, the intrinsic nature of functional and empowering farmer participation, the persistent lack of widespread attention to gender, and the operational and political complexity of multi-stakeholder impact assessment. In contrast, a desired impact of research could be reached or boosted by specific measures without making an impact assessment at all. Kristjanson, et al. ( 66 ), for example, proposed seven framework conditions for agricultural research to bridge the gap between scientific knowledge and action towards sustainable development. RIA should develop into process-oriented evaluations, in contrast to outcome-oriented evaluation ( 67 ), for addressing the intended kind of impacts, the scope of assessment, and for choosing the appropriate assessment method ( 19 ).

This review aimed at providing an overview of impact assessment activities reported in academic agricultural literature with regard to their coverage of impact areas and type of assessment method used. We found a remarkable body of non-scientometric RIA at all evaluation levels of agricultural research but a major interest in economic impacts of new agricultural technologies. These are closely followed by an interest in social impacts at multiple assessments levels that usually focus on food security and poverty reduction and rely slightly more on qualitative assessment methods. In contrast, the assessment of the environmental impacts of agricultural research or comprehensive sustainability assessments was exceptionally limited. They may have been systematically overlooked in the past, for the reason of expected negative results, thought to be covered by other impact studies or methodological challenges. RIA could learn from user-oriented policy impact assessments that usually include environmental impacts. Frameworks for RIA should avoid narrowing the assessment focus and instead considering intended and unintended impacts in several impact areas equally. It seems fruitful to invest in assessment teams’ environmental analytic skills and to expand several of the already developed methods for economic or social impact to the environmental impacts. Only then, the complex and comprehensive contribution of agricultural research to sustainable development can be revealed.

The authors would like to thank Jana Rumler and Claus Dalchow for their support in the Web of Science analysis and Melanie Gutschker for her support in the quantitative literature analysis.

This work was supported by the project LIAISE (Linking Impact Assessment to Sustainability Expertise, www.liaisenoe.eu ), which was funded by Framework Programme 7 of the European Commission and co-funded by the Leibniz-Centre for Agricultural Landscape Research. The research was further inspired and supported by funding from the ‘Guidelines for Sustainability Management’ project for non-university research institutes in Germany (‘Leitfaden Nachhaltigkeitsmanagement’, BMBF grant 311 number 13NKE003A).

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The exact TITLE query was: agricult* AND (research* OR *scien* OR "R&D" OR innovati*) AND (impact* OR assess* OR evaluat* OR criteria* OR benefit* OR adoption* OR adaptation*)

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130 Agriculture Research Topics To Write An Excellent Paper

The preparation of an agriculture research paper involves several nuances and complexities. The first aspect is technical requirements, such as text formatting, structure, and source list. It's also important to choose those agriculture topics that you can analyze and find expert material. Any research paper is based on theses and statements, which are supported by evidence and factual information.

This is especially important when you tend to choose agricultural controversial topics. Then you need to find studies with verified information and prepare arguments for your paper. The whole process of work requires meticulous data collection and analysis of alternative sources. Then choosing any agricultural essay topics won't seem like a heady decision.

Your academic paper may relate to environmental factors, the economic feasibility of starting a farm, or the nuances of breeding. The main plus is that you can choose any of the agricultural related topics for research preparation. Here are 130 options for you.

Fisheries And Aquaculture

Such agricultural research paper topics allow revealing the topic of fishery and agricultural procurement. Students can concentrate on many aspects of the payback of farms and fisheries. The topics are quite extensive, and you can find a lot of research on the Internet for choosing trust sources.

Trout breeding in freshwaters.
Effect of algae on oxygen levels in fish rates.
Seasonal spawning of oceanic fish.
Prohibited fishing waters in the United States.
Exploration of the Pacific Ocean.
The impact of cyclones on fishing.
Poisonous fish and the reasons for their breeding in North America.
Seasonal diseases of trout.
Sea horse: A case study.
Risk analysis of water quality in aquaculture.

Plant Science And Crop Production

Crop Production agricultural research topics and plant science are not the easiest, but they contain a ton of information on the Internet. It is not a problem to find research by leading scientists and create your own research paper based on their statistics. The plus is that you don't have to start from scratch.

Innovative plant breeding.
Reclamation as a method of increasing yields.
Hybrid plants of Montana.
Citrus growing methods.
Technical cannabis and plantations in the USA.
Analysis of the yield of leguminous crops.
Method for creating genetically modified plants.
Field analysis of wheat for pesticides.
New plants and methods of growing them.
Hybrids and cold-resistant plants.

Topics in Agricultural Science

Agriculture essay topics like this allow you to select a specific aspect to research. You can concentrate on vegetation breeding or high tech greenhouse methodology. A large amount of research is a definite plus because you can build your theses on the basis of available data, criticizing or supporting research by scientists.

Harvesting robots.
Methodology for improving agricultural performance.
The influence of technology on the growth of grain crops.
How important is the timely irrigation of fields?
Climatic changes and impact on yield.
Breeding earthworms.
Hydroponic gardening.
Genetically modified organisms and their distribution.
Starting a garden.
How can we make medicine from plants?

Topics in Agronomy

Agronomy agriculture projects for students allow you to consider the aspects of growing crops in conditions with a specific soil type and natural characteristics. You can base your claims on statistics with the ability to draw on facts from other research. For example, this is relevant for papers examining the fertility of the topsoil.

Choosing the type of soil for the cornfield.
Innovative land reclamation.
New branches in agronomy.
Phosphate-free fertilizers.
Hydroponics and greenhouses.
Hybrid yield analysis.
Methodology for assessing agronomic losses.
Stages of preparing a field for harvesting.
The role of GMOs in the fight against insect pests.
Cultivation of technical hemp and soil fertilization methods.

Topics in Animal Breeding And Genetics

Agriculture related topics are interesting because you can touch on aspects of genetics and breeding. Students can concentrate on specific aspects of species modification and animal rearing. The research paper will look more convincing when there are references to real scientific papers with statistics and experimental results.

Breeding new types of sheep.
Breeding bulls and genetic engineering.
The influence of selection on the growth of the animal population.
Proper nutrition for livestock in winter.
Vitamin complexes for animals.
Genetic changes in chickens for resistance to cold.
Nuances of animal genetic modifications.
Stages of caring for newborn kittens.
What is a negative selection?
Basic methods of genetic experiments on animals.

Topics in Animal Production And Health

Such agriculture research paper topics are especially interesting because you can write about farming aspects in the context of raising animals, vegetables, and various crops. It is broad enough, so you will not be limited by narrow boundaries and will be able to consider many aspects of your research paper.

Environmental threats to the oversupply of the sheep population.
The role of livestock in marginal areas.
Livestock digitalization.
Animal selection for meat preparation.
Analysis of livestock farms.
Animal production evaluation technique.
Cow health during calving.
The importance of animal vaccination.
Technical aspects of the medical treatment of animals.
Environmental aspects of animal husbandry.

Topics in Ecotourism And Wildlife

Ecotourism is gaining momentum all over the world. The new trend is aimed at bringing people closer to nature and exploring the beauty of different countries. This issue will be of interest to those who want to talk about wildlife and nature reserves. The topic is quite extensive, so students will not have problems with preparing a research paper.

Minnesota and Eco-Tourism.
The influence of wolves on the formation of the local ecosystem.
Recreational tourism in the USA.
Methods for preparing resorts for eco-tourism.
Lakes and environmental factors.
A technique for preserving wildlife in its original form.
Classic models of eco-tourism.
Stages of creating ecological reserves.
The role of tourism in the restoration of the ecological environment.
The main factors of wildlife conservation.
The legislative framework for wildlife protection.
The nuances of creating a farm in reserve.
Consolidation of resources for the development of a livestock farm.

Topics in Farm Management

Managing a farm can be a complex and multifaceted process. Many students may choose this topic to talk about aspects of breeding and breeding pets or crops. The topic is quite extensive and allows you to touch on any aspect of the farmer's activities related to the production and sale of products.

Farm methods to improve performance.
Stages of creating a livestock farm.
Farm success analysis forms.
Management of the process of planting crops.
The role of modern equipment in cow milking.
Farm reporting and profitability analysis.
Breeding exotic animals.
Rabbit population management.
Statistical methodology for farm control.
Stages of the animal population control on the farm.

Topics in Fisheries And Aquaculture

A similar topic is associated with fish farming, introductory aquaculture, and general aquaculture. Quite a few students can prepare a good research paper if they turn to other people's research and use it as a basis to prove or disprove their own claims and theories. It is also a good opportunity to select food related research topics as you can touch upon the aspect of fish farming and marketing.

Creation and management of a fish rate.
Sturgeon breeding and distribution.
Methods for improving the ecological state of water bodies.
Planting plants in reservoirs for liquid purification.
Fish spawning control.
The aquaculture aspect and social trends.
Methods for increasing fish resources.
Breeding in the fishing industry.
Methods for creating a fish farm.
River resource monitoring and digitalization.

Topics in Agric Business And Financial Management

Control of a livestock or vegetable enterprise depends on many factors, so such a topic's choice will be extremely relevant. The student's most important task is to bring only proven facts and arguments of his own judgments. These agriculture topics for students include an overview of many business processes and farm management.

The farm cost reduction methodology.
US agricultural financing sector.
Agricultural business practices.
Data analysis and farming development.
Financial management of small livestock farms.
Impact of drought on yield.
Cost and payback of farms.
Selecting a region for creating a farm.
A method for analyzing animal resources on a farm.
Management of automated farming enterprises.
Local farming business.
Key factors of farm management.
Farm reports and breeding work.

Topics in Agric Meteorology And Water Management

Meteorological aspects are very important for the management of a company or agricultural enterprises. Another aspect of this topic is water management, which may also be interesting for those who are going to reveal the nuances of fish farming in local waters. The topic will be especially interesting for those who want to connect their lives with agronomy and a similar field.

Cattle breeding methodology.
Pig breeding methods.
Water management to maximize profits.
The choice of a reservoir for growing fish.
Analysis of the ecological situation in water bodies.
Farm equipment management techniques.
Water supply for farm households.
Analysis and selection of a farm development methodology.
Finding the right methods for creating protected reservoirs.
Stages of development of a water farm.

How to Write a Good Agriculture Research Paper?

One of the main life hacks for getting a high mark is choosing controversial agricultural topics. Choosing this option allows students to consider an interesting statement and back it up with real facts. A paper-based on real statistics with proof of student work is valued above all else.

But even when choosing a good topic, you still need to prepare the right outline for writing your research paper. The introduction should be of the highest quality as well as the final paragraph since these are the main parts that affect the assessment. Real facts and statistics must support all the statements above if you are talking about specific figures. Many colleges and universities have their own paper requirements as well as the nuances of the design of research work. You must consider each parameter in order to get the best result.

If it is difficult to find controversial topics in agriculture and write a high-quality research paper, we can help you with this issue. Our best essay writing service has been in operation for many years and provides writing assistance for many types of essays, research papers, and theses. We will help you synchronize your preparation process and create an expert paper that gets high marks. You can switch to other tasks and get the opportunity to free up some time to study other disciplines.

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Sustainable Agriculture Research and Education in the Field: A Proceedings (1991)

Chapter: introduction, introduction.

Charles M. Benbrook

These proceedings are based on a workshop that brought together scientists, farmer-innovators, policymakers, and interested members of the public for a progress report on sustainable agriculture research and education efforts across the United States. The workshop, which was held on April 3 and 4, 1990, in Washington, D.C., was sponsored by the Office of Science and Education of the U.S. Department of Agriculture and the Board on Agriculture of the National Research Council. The encouraging new science discussed there should convince nearly everyone of two facts.

First, the natural resource, economic, and food safety problems facing U.S. agriculture are diverse, dynamic, and often complex. Second, a common set of biological and ecological principles—when systematically embodied in cropping and livestock management systems—can bring improved economic and environmental performance within the reach of innovative farmers. Some people contend that this result is not a realistic expectation for U.S. agriculture. The evidence presented here does not support such a pessimistic assessment.

The report of the Board on Agriculture entitled Alternative Agriculture (National Research Council, 1989a) challenged everyone to rethink key components of conventional wisdom and contemporary scientific dogma. That report has provided encouragement and direction to those individuals and organizations striving toward more sustainable production systems, and it has provoked skeptics to articulate why they feel U.S. agriculture cannot—some even say should not—seriously contemplate the need for such change. The debate has been spirited and generally constructive.

Scholars, activists, professional critics, and analysts have participated in

this debate by writing papers and books, conducting research, and offering opinions about alternative and sustainable agriculture for over 10 years. Over the past decade, many terms and concepts have come and gone. Most people—and unfortunately, many farmers—have not gone very far beyond the confusion, frustration, and occasional demagoguery that swirls around the different definitions of alternative, low-input, organic, and sustainable agriculture.

Fortunately, though, beginning in late 1989, a broad cross-section of people has grown comfortable with the term sustainable agriculture. The May 21, 1990, issue of Time magazine, in an article on sustainable agriculture entitled “It's Ugly, But It Works” includes the following passage:

[A] growing corps of experts [are] urging farmers to adopt a new approach called sustainable agriculture. Once the term was synonymous with the dreaded O word—a farm-belt euphemism for trendy organic farming that uses no synthetic chemicals. But sustainable agriculture has blossomed into an effort to curb erosion by modifying plowing techniques and to protect water supplies by minimizing, if not eliminating, artificial fertilizers and pest controls.

Concern and ridicule in farm publications and during agribusiness meetings over the philosophical roots of low-input, sustainable, or organic farming have given way to more thoughtful appraisals of the ecological and biological foundations of practical, profitable, and sustainable farming systems. While consensus clearly does not yet exist on how to “fix” agriculture's contemporary problems, a constructive dialogue is now under way among a broad cross-section of individuals, both practitioners and technicians involved in a wide variety of specialties.

This new dialogue is powerful because of the people and ideas it is connecting. Change will come slowly, however. Critical comments in some farm magazines will persist, and research and on-farm experimentation will not always lead to the hoped for insights or breakthroughs. Some systems that now appear to be sustainable will encounter unexpected production problems. Nonetheless, progress will be made.

The Board on Agriculture believes that over the next several decades significant progress can and will be made toward more profitable, resource-conserving, and environmentally prudent farming systems. Rural areas of the United States could become safer, more diverse, and aesthetically pleasing places to live. Farming could, as a result, become a more rewarding profession, both economically and through stewardship of the nation's soil and water resources. Change will be made possible; and it will be driven by new scientific knowledge, novel on-farm management tools and approaches, and economic necessity. The policy reforms adopted in the 1990 farm bill, and ongoing efforts to incorporate environmental objectives

into farm policy, may also in time make a significant difference in reshaping the economic environment in which on-farm management decisions are made.

This volume presents an array of new knowledge and insight about the functioning of agricultural systems that will provide the managerial and technological foundations for improved farming practices and systems. Examples of the research projects under way around the country are described. Through exploration of the practical experiences, recent findings, and insights of these researchers, the papers and discussions presented in this volume should demonstrate the value of field- and farm-level systems-based research that is designed and conducted with ongoing input from farmer-innovators.

Some discussion of the basic concepts that guide sustainable agriculture research and education activities may be useful. Definitions of key terms, such as sustainable agriculture, alternative agriculture, and low-input sustainable agriculture, are drawn from Alternative Agriculture and a recent paper (Benbrook and Cook, 1990).

BASIC CONCEPTS AND OPERATIONAL DEFINITIONS

Basic concepts.

Sustainable agriculture, which is a goal rather than a distinct set of practices, is a system of food and fiber production that

improves the underlying productivity of natural resources and cropping systems so that farmers can meet increasing levels of demand in concert with population and economic growth;

produces food that is safe, wholesome, and nutritious and that promotes human well-being;

ensures an adequate net farm income to support an acceptable standard of living for farmers while also underwriting the annual investments needed to improve progressively the productivity of soil, water, and other resources; and

complies with community norms and meets social expectations.

Other similar definitions could be cited, but there is now a general consensus regarding the essential elements of sustainable agriculture. Various definitions place differing degrees of emphasis on certain aspects, but a common set of core features is now found in nearly all definitions.

While sustainable agriculture is an inherently dynamic concept, alternative agriculture is the process of on-farm innovation that strives toward the goal of sustainable agriculture. Alternative agriculture encompasses efforts by farmers to develop more efficient production systems, as well as

efforts by researchers to explore the biological and ecological foundations of agricultural productivity.

The challenges inherent in striving toward sustainability are clearly dynamic. The production of adequate food on a sustainable basis will become more difficult if demographers are correct in their estimates that the global population will not stabilize before it reaches 11 billion or 12 billion in the middle of the twenty-first century. The sustainability challenge and what must be done to meet it range in nature from a single farm field, to the scale of an individual farm as an enterprise, to the food and fiber needs of a region or country, and finally to the world as a whole.

A comprehensive definition of sustainability must include physical, biological, and socioeconomic components. The continued viability of a farming system can be threatened by problems that arise within any one of these components. Farmers are often confronted with choices and sacrifices because of seemingly unavoidable trade-offs—an investment in a conservation system may improve soil and water quality but may sacrifice near-term economic performance. Diversification may increase the efficiency of resource use and bring within reach certain biological benefits, yet it may require additional machinery and a more stable and versatile labor supply. Indeed, agricultural researchers and those who design and administer farm policy must seek ways to alleviate seemingly unwelcome trade-offs by developing new knowledge and technology and, when warranted, new policies.

Operational Definitions

Sustainable agriculture is the production of food and fiber using a system that increases the inherent productive capacity of natural and biological resources in step with demand. At the same time, it must allow farmers to earn adequate profits, provide consumers with wholesome, safe food, and minimize adverse impacts on the environment.

As defined in our report, alternative agriculture is any system of food or fiber production that systematically pursues the following goals (National Research Council, 1989a):

more thorough incorporation of natural processes such as nutrient cycling, nitrogen fixation, and beneficial pest-predator relationships into the agricultural production process;

reduction in the use of off-farm inputs with the greatest potential to harm the environment or the health of farmers and consumers;

productive use of the biological and genetic potential of plant and animal species;

improvement in the match between cropping patterns and the productive potential and physical limitations of agricultural lands; and

profitable and efficient production with emphasis on improved farm management, prevention of animal disease, optimal integration of livestock and cropping enterprises, and conservation of soil, water, energy, and biological resources.

Conventional agriculture is the predominant farming practices, methods, and systems used in a region. Conventional agriculture varies over time and according to soil, climatic, and other environmental factors. Moreover, many conventional practices and methods are fully sustainable when pursued or applied properly and will continue to play integral roles in future farming systems.

Low-input sustainable agriculture (LISA) systems strive to achieve sustainability by incorporating biologically based practices that indirectly result in lessened reliance on purchased agrichemical inputs. The goal of LISA systems is improved profitability and environmental performance through systems that reduce pest pressure, efficiently manage nutrients, and comprehensively conserve resources.

Successful LISA systems are founded on practices that enhance the efficiency of resource use and limit pest pressures in a sustainable way. The operational goal of LISA should not, as a matter of first principles, be viewed as a reduction in the use of pesticides and fertilizers. Higher yields, lower per unit production costs, and lessened reliance on agrichemicals in intensive agricultural systems are, however, often among the positive outcomes of the successful adoption of LISA systems. But in much of the Third World an increased level of certain agrichemical and fertilizer inputs will be very helpful if not essential to achieve sustainability. For example, the phosphorous-starved pastures in the humid tropics will continue to suffer severe erosion and degradation in soil physical properties until soil fertility levels are restored and more vigorous plant growth provides protection from rain and sun.

Farmers are continuously modifying farming systems whenever opportunities arise for increasing productivity or profits. Management decisions are not made just in the context of one goal or concern but in the context of the overall performance of the farm and take into account many variables: prices, policy, available resources, climatic conditions, and implications for risk and uncertainty.

A necessary step in carrying out comparative assessments of conventional and alternative farming systems is to understand the differences between farming practices, farming methods, and farming systems. It is somewhat easier, then, to determine what a conventional practice, method, or system is and how an alternative or sustainable practice, method, or system might or should differ from a conventional one. The following definitions are drawn from the Glossary of Alternative Agriculture (National Research Council, 1989a).

A farming practice is a way of carrying out a discrete farming task such as a tillage operation, particular pesticide application technology, or single conservation practice. Most important farming operations—preparing a seedbed, controlling weeds and erosion, or maintaining soil fertility, for example—require a combination of practices, or a method. Most farming operations can be carried out by different methods, each of which can be accomplished by several unique combinations of different practices. The manner in which a practice is carried out—the speed and depth of a tillage operation, for example—can markedly alter its consequences.

A farming method is a systematic way to accomplish a specific farming objective by integrating a number of practices. A discrete method is needed for each essential farming task, such as preparing a seedbed and planting a crop, sustaining soil fertility, managing irrigation, collecting and disposing of manure, controlling pests, and preventing animal diseases.

A farming system is the overall approach used in crop or livestock production, often derived from a farmer's goals, values, knowledge, available technologies, and economic opportunities. A farming system influences, and is in turn defined by, the choice of methods and practices used to produce a crop or care for animals.

In practice, farmers are constantly adjusting cropping systems in an effort to improve a farm's performance. Changes in management practices generally lead to a complex set of results—some positive, others negative—all of which occur over different time scales.

The transition to more sustainable agriculture systems may, for many farmers, require some short-term sacrifices in economic performance in order to prepare the physical resource and biological ecosystem base needed for long-term improvement in both economic and environmental performance. As a result, some say that practices essential to progress toward sustainable agriculture are not economically viable and are unlikely to take hold on the farm (Marten, 1989). Their contention may prove correct, given current farm policies and the contemporary inclination to accept contemporary, short-term economic challenges as inviolate. Nonetheless, one question lingers: What is the alternative to sustainable agriculture?

PUBLIC POLICY AND RESEARCH IN SUSTAINABLE AGRICULTURE

Farmers, conservationists, consumers, and political leaders share an intense interest in the sustainability of agricultural production systems. This interest is heightened by growing recognition of the successes achieved by innovative farmers across the country who are discovering alternative agriculture practices and methods that improve a farm's economic and environmental performance. Ongoing experimental efforts on the farm, by no

means universally successful, are being subjected to rigorous scientific investigation. New insights should help farmers become even more effective stewards of natural resources and produce food that is consistently free of man-made or natural contaminants that may pose health risks.

The major challenge for U.S. agriculture in the 1990s will be to strike a balance between near-term economic performance and long-term ecological and food safety imperatives. As recommended in Alternative Agriculture (National Research Council, 1989a), public policies in the 1990s should, at a minimum, no longer penalize farmers who are committed to resource protection or those who are trying to make progress toward sustainability. Sustainability will always remain a goal to strive toward, and alternative agriculture systems will continuously evolve as a means to this end. Policy can and must play an integral role in this process.

If sustainability emerges as a principal farm and environmental policy goal, the design and assessment of agricultural policies will become more complex. Trade-offs, and hence choices, will become more explicit between near-term economic performance and enhancement of the long-term biological and physical factors that can contribute to soil and water resource productivity.

Drawing on expertise in several disciplines, policy analysts will be compelled to assess more insightfully the complex interactions that link a farm's economic, ecological, and environmental performance. It is hoped that political leaders will, as a result, recognize the importance of unraveling conflicts among policy goals and more aggressively seizing opportunities to advance the productivity and sustainability of U.S. agriculture.

A few examples may help clarify how adopting the concept of sustainability as a policy goal complicates the identification of cause-and-effect relationships and, hence, the design of remedial policies.

When a farmer is pushed toward bankruptcy by falling crop prices, a farm operation can become financially unsustainable. When crop losses mount because of pest pressure or a lack of soil nutrients, however, the farming system still becomes unsustainable financially, but for a different reason. In the former example, economic forces beyond any individual farmer's control are the clear cause; in the latter case the underlying cause is rooted in the biological management and performance of the farming system.

The biological and economic performance of a farming system can, in turn, unravel for several different reasons. Consider an example involving a particular farm that is enrolled each year in the U.S. Department of Agriculture's commodity price support programs. To maintain eligibility for government subsidies on a continuing basis, the farmer understands the importance of growing a certain minimum (base) acreage of the same crop each year. Hence, the cropping pattern on this farm is likely to lead to a

buildup in soilborne pathogens that attack plant roots and reduce yields. As a result, the farmer might resort to the use of a fumigant to control the pathogens, but the pesticide might become ineffective because of steadily worsening microbial degradation of the fumigant, or a pesticide-resistant pathogen may emerge.

A solution to these new problems might be to speed up the registration of another pesticide that could be used, or relax regulatory standards so more new products can get registered, or both. Consider another possibility. A regulatory agency may cancel use of a fumigant a farmer has been relying upon because of food safety, water quality, or concerns about it effect on wildlife. The farmer might then seek a change in grading standards or an increase in commodity prices or program benefits if alternative pesticides are more costly.

Each of these problems is distinctive when viewed in isolation and could be attacked through a number of changes in policy. The most cost-effective solution, however, will prove elusive unless the biology of the whole system is perceptively evaluated. For this reason, in the policy arena, just as on the farm, it is critical to know what the problem is that warrants intervention and what the root causes of the problem really are.

Research Challenges

In thinking through agricultural research priorities, it should be acknowledged that the crossroads where the sciences of agriculture and ecology meet remain largely undefined, yet clearly promising. There is too little information to specify in detail the features of a truly sustainable agriculture system, yet there is enough information to recognize the merit in striving toward sustainability in a more systematic way.

The capacity of current research programs and institutions to carry out such work is suspect (see Investing in Research [National Research Council, 1989b]). It also remains uncertain whether current policies and programs that were designed in the 1930s or earlier to serve a different set of farmer needs can effectively bring about the types of changes needed to improve ecological management on the modern farm.

In the 1980s, the research community reached consensus on the diagnosis of many of agriculture's contemporary ills; it may take most of the 1990s to agree on cures, and it will take at least another decade to get them into place. Those who are eager for a quick fix or who are just impatient are bound to be chronically frustrated by the slow rate of change.

Another important caution deserves emphasis. The “silver bullet” approach to solving agricultural production problems offers little promise for providing an understanding of the ecological and biological bases of sustainable agriculture. The one-on-one syndrome seeks to discover a new

pesticide for each pest, a new plant variety when a new strain of rust evolves, or a new nitrogen management method when nitrate contamination of drinking water becomes a pressing social concern. This reductionist approach reflects the inclination in the past to focus scientific and technological attention on products and outcomes rather than processes and on overcoming symptoms rather than eliminating causes. This must be changed if research aimed at making agriculture more sustainable is to move ahead at the rate possible given the new tools available to agricultural scientists.

One area of research in particular—biotechnology—will benefit from a shift in focus toward understanding the biology and ecology underlying agricultural systems. Biotechnology research tools make possible powerful new approaches in unraveling biological interactions and other natural processes at the molecular and cellular levels, thus shedding vital new light on ecological interactions with a degree of precision previously unimagined in the biological sciences. However, rather than using these new tools to advance knowledge about the functioning of systems as a first order of priority, emphasis is increasingly placed on discovering products to solve specific production problems or elucidating the mode of action of specific products.

This is regrettable for several reasons. A chance to decipher the physiological basis of sustainable agriculture systems is being put off. The payoff from focusing on products is also likely to be disappointing. The current widespread pattern of failure and consolidation within the agricultural biotechnology industry suggests that biotechnology is not yet mature enough as a science to reliably discover, refine, and commercialize product-based technologies. Products from biotechnology are inevitable, but a necessary first step must be to generate more in-depth understanding of biological processes, cycles, and interactions.

Perhaps the greatest potential of biotechnology lies in the design and on-farm application of more efficient, stable, and profitable cropping and livestock management systems. For farmers to use such systems successfully, they will need access to a range of new information and diagnostic and analytical techniques that can be used on a real-time basis to make agronomic and animal husbandry judgments about how to optimize the efficiencies of the processes and interactions that underlie plant and animal growth.

Knowledge, in combination with both conventional and novel inputs, will be deployed much more systematically to avoid soil nutrient or animal nutrition-related limits on growth; to ensure that diseases and pests do not become serious enough to warrant the excessive use of costly or hazardous pesticides; to increase the realistically attainable annual level of energy flows independent of purchased inputs within agroecosystems; and to maximize a range of functional symbiotic relationships between soil micro-

and macrofauna, plants, and animals. Discrete goals will include pathogen-suppressive soils, enhanced rotation effects, pest suppression by populations of plant-associated microorganisms, nutrient cycling and renewal, the optimization of general resistance mechanisms in plants by cultural practices, and much more effective soil and water conservation systems that benefit from changes in the stability of soil aggregates and the capacity of soils to absorb and hold moisture.

Because of the profound changes needed to create and instill this new knowledge and skills on the farm, the recommendations in Alternative Agriculture (National Research Council, 1989a) emphasize the need to expand systems-based applied research, on-farm experimentation utilizing farmers as research collaborators, and novel extension education strategies—the very goals of the U.S. Department of Agriculture's LISA program.

Future research efforts—and not just those funded through LISA—should place a premium on the application of ecological principles in the multidisciplinary study of farming system performance. A diversity of approaches in researching and designing innovative farming systems will ensure broad-based progress, particularly if farmers are actively engaged in the research enterprise.

Benbrook, C., and J. Cook. 1990. Striving toward sustainability: A framework to guide on-farm innovation, research, and policy analysis. Speech presented at the 1990 Pacific Northwest Symposium on Sustainable Agriculture, March 2.

Marten, J. 1989. Commentary: Will low-input rotations sustain your income? Farm Journal, Dec. 6.

National Research Council. 1989a. Alternative Agriculture. Washington, D.C.: National Academy Press.

National Research Council. 1989b. Investing in Research: A Proposal to Strengthen the Agricultural, Food, and Environmental System. Washington, D.C.: National Academy Press.

Interest is growing in sustainable agriculture, which involves the use of productive and profitable farming practices that take advantage of natural biological processes to conserve resources, reduce inputs, protect the environment, and enhance public health. Continuing research is helping to demonstrate the ways that many factors—economics, biology, policy, and tradition—interact in sustainable agriculture systems.

This book contains the proceedings of a workshop on the findings of a broad range of research projects funded by the U.S. Department of Agriculture. The areas of study, such as integrated pest management, alternative cropping and tillage systems, and comparisons with more conventional approaches, are essential to developing and adopting profitable and sustainable farming systems.

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127 Exceptional Agriculture Essay Topics For Students

What is agriculture? Also known as farming, agriculture is the practice of cultivating and harvesting a wide variety of crops and raising livestock. Nowadays, agriculture is an essential part of our economy and our lives. Without it, there would be widespread famine. And this is just one of the reasons why you should search for the most interesting essay topics on agriculture.

You can write your academic paper on just about anything, as long as it pertains to agriculture. Remember, you can also write about livestock and farm animals. You can write the paper on agricultural technology or even the latest fertilizers and pesticides. You can even research agriculture in remote parts of the world and write an extraordinary paper about it. However, you need a good topic to get you started. To help you out, we’ve created a list of 127 original agriculture essay topics that you can use for free. Check it out below:

Sustainable Agriculture Essay Topics

Our experienced writers and editors have managed to put together a list of sustainable agriculture essay topics that will surely impress any professor. Pick one and start writing today:

What are cover crops and why are they important?
Talk about biofuels
An in-depth look at agritourism
Agroforestry in the 21st century
The importance of environmental health
Social equity in sustainable agriculture
Humane methods used for pest management
Water management in sustainable agriculture
The importance of crop rotation and diversity
Reducing erosion through sustainable agriculture

Easy Agriculture Topics To Write About

You probably don’t want to spend too much time writing your paper. After all, you have other things to do. No problem, just take a look at this list of easy agriculture topics to write about:

How much does raising a pig cost?
Would you work on a farm? Why?
Agriculture in India
Talk about the world’s population and agriculture
Discuss the use of water in agriculture
Discuss agriculture in China
The latest agricultural technology you’ve heard about
Organic agriculture: pros and cons
Talk about agriculture in Latin America
Talk about genetic engineering in agriculture
Agriculture in Eastern Europe

Interesting Agriculture Topics

In this list, we have collected all of the most interesting agriculture topics (in our opinion, of course). You can pick any one of these topics and use it for free. Yes, you can even reword them.

The relation between agriculture and culture
Challenges in livestock production in 2023
How has the Covid-19 pandemic affected agriculture?
Improving agricultural productivity using sustainable methods
An in-depth research of the global food system
Grain and corn from Ukrainian farms affected by the war
Renewable energy in agriculture
Fish hatcheries: pros and cons

Agricultural Research Paper Topics

Our seasoned agriculture experts have just finished putting together a list of unique agricultural research paper topics. Take a look at these ideas and choose the one you like the most:

An in-depth research of agriculture in Taiwan
Talk about seed pathology in agriculture
Discuss agricultural issues in North Korea
The use of banned GMOs in Europe
A closer look at Turkey’s agriculture
Research the topic of water management in agriculture
Food chain risks posed by the war in Ukraine
Natural farming versus organic farming

Technology In Agriculture Topics

Technology plays a major role in today’s agriculture, as you can imagine. So, why now write your paper about one of these interesting technology in agriculture topics:

Soil data sensing technology
The Internet of Things in agriculture
Talk about satellite imaging in agriculture
Discuss weather tracking and its benefits
Research pervasive automation in agriculture
The use of RFID tech in agriculture
What is vertical farming and how is it done?

Agriculture Persuasive Speech Topics

If you are struggling to write a persuasive speech about agriculture and don’t know what to talk about, we can help you out. Here are some original agriculture persuasive speech topics for you:

Problems with soil degradation in the United States
Talk about employment in the agricultural sector
How is the genetic improvement of seeds done?
The importance of the potato for our world
Talk about sustainable grazing methods
The importance of home gardening in 2023
Managing plant weeds without using glyphosate

Food And Agriculture Essay Ideas

All of our food comes from agriculture, so it’s a great idea to talk about this link. We have a long list of unique food and agriculture essay ideas for high school and college students right here:

Vegans and animal husbandry
Where does KFC get all its meat from?
The quality of meat coming from intensive farming
Animal husbandry in the Middle Ages
Dangerous nitrate concentrations in vegetables
Talk about minerals in vitamins in vegetables
Using chemicals in agriculture: a danger to our health

Importance Of Agriculture Essay Topics

There is much to talk about when it comes to the importance of agriculture. Here are some importance of agriculture essay topics that should get you started right away:

The importance of good sheepdogs
Talk about the importance of agriculture in India
Discuss the importance of subsidence farming
Agriculture in ancient times
Talk about the importance of agriculture for Mayans
The most interesting agricultural tools ever discovered
Supply chain problems for KFC

Complex Agriculture Topics

If you want to impress your professor, you can give a more difficult topic a try. You can get some bonus points for it. Check out our latest list of complex agriculture topics:

Discuss GMO corn in North America
Talk about the use of solar power in agriculture
Pumping water efficiently with minimal costs
The latest global economic issues affecting farmers
Greenhouse gas emissions caused by agriculture
Talk about the controversies surrounding chemical fertilizers
Challenges for modern agriculture in the United Kingdom

Livestock Topic Ideas

Yes, raising livestock is a significant part of agriculture today. So, why now write your essay or research paper on one of our interesting livestock topic ideas:

How important are bees for our world?
The dangers of raising yaks on your farm
Research cattle farming in North America
Discuss pig farming in European countries
Talk about intensive animal farming (chickens)
Talk about raising animals humanely
Negative effects of cattle farms

Best Topics For Discussion Agriculture

Did your professor ask you to prepare for a discussion or debate on a topic in agriculture? Don’t worry, we’ve got your back! Here are the best topics for discussion:

What animal do you think is the best for a farm?
Do we really need farm subsidies?
Talk about food processing tech
Discuss the use of drones in agriculture
Automation in agriculture
Talk about the benefits of vertical farming

Agricultural Essay Topics For High School

Are you a high school student? Do you need to write a paper on agriculture? Perfect! Here are the absolute best agricultural essay topics for high school students:

Hunter gatherer versus agricultural societies
Talk about the negative effects of industrial agriculture
Talk about the agricultural policy in Europe
How has the rise of global temperature affected agriculture?
Talk about how drought can completely destroy the global food system in less than 10 years
The effects of pesticides on the population of bees in the US

Agriculture Paper Topics For College

College students should choose topics that are more complex in nature if they want to get a top grade. Check out this list of agriculture paper topics for college and choose one right now:

The economics behind a sheep farm in the UK
How important is the price of energy for local farms in Germany?
An in-depth look at agricultural subsidies in North America
Differences between the agricultural policies of North America and Europe
An effective business model for an organic farm in 2023
The impact of a 1 degree Celsius (33.8 Fahrenheit) increase in global temperature on grain crops in the UK

Controversial Agriculture Topics For Essays

Our experienced staff has worked hard to find the most controversial agriculture topics for essays. You won’t need to buy cheap essays with these topics! All of these topics are original, so you are already on your way to getting bonus points from your professor:

The use of pesticides in North America
Talk about genetically modified organisms
Discuss the local food controversy
Talk about climate change and its effect on agriculture
The rise in demand for high quality food
Organic food in 2023
Discuss the wages of people working in agriculture
Destroying the soil through intensive agriculture

History Of Agriculture Topics

Talking about the history of agriculture can be both fun and educative. After all, agriculture has suffered many major transformations over time. Here are some great topics to write about:

Agriculture during the Roman Empire
Talk about agriculture in ancient Egypt
Agriculture in South Asia
Agricultural tools in Mesopotamia
Ancient Greek agriculture
Discuss the evolution of organic agriculture
Discuss the British agricultural revolution
What is the Green revolution?
Agriculture in Mesoamerica
Research agriculture in the 20th century
How has the war in Ukraine changed agriculture in Europe?
Early development of agricultural tools

Other Agriculture Research Paper Ideas

This list contains all the agricultural topics that didn’t quite fit anywhere else. It’s a collection of other agriculture research paper ideas that professors may find interesting:

Negative effects of modern pesticides
The dangers of over-using fertilizer
The most profitable livestock in 2023
Raising myotonic goats
The strange eating habits of geese
Research the farmers of Gambia
Raising Mangalitza pigs
Talk about intensive animal farming in China
The peculiarities of a yak farm
Dangerous farm animals you should never raise

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Explore 118+ Agriculture Research Topics for Innovation and Possibilities.

Dec 20, 2023 | 0 comments

Dec 20, 2023 | Topics | 0 comments

Embarking on a journey through agriculture research topics unveils a realm of possibilities and innovation that underpin our food systems and farming practices. Have you ever wondered what areas researchers delve into to improve our agricultural landscape? Well, get ready for a glimpse into intriguing research topics in agriculture that scientists are currently exploring. From sustainable development to genetic improvement of crops and livestock, the field is diverse and crucial for tackling our planet’s challenges. So, what exactly are these agriculture research topics, and how do they contribute to making our food systems more resilient and sustainable? Let’s uncover the answers to these questions as we explore the exciting world of agricultural research.

Table of Contents

How do you select the best Agriculture Research Paper topic?

Choosing the right agriculture research paper topic is like picking the perfect crop to plant – it requires careful consideration and a bit of know-how. So, how do you select the best agriculture research paper topic that stands out in the field? Let’s break it down:

Passion Points: Ask yourself, “What aspects of agriculture am I most passionate about?” Whether it’s sustainable farming, animal breeding, or soil health, picking a topic you’re genuinely interested in makes the research journey more exciting.
Identify Gaps: Consider the gaps in current knowledge. Where does agriculture need more insight? Think about the questions that intrigue you – those uncharted territories where you can contribute valuable information.
Feasibility: Assess the feasibility of your chosen topic. Are there enough resources and data available to support your research? Avoid topics that might be too complex or lack the necessary information.
Relevance: Ask yourself, “How relevant is my chosen topic to the current issues in agriculture?” Staying up-to-date with industry trends ensures your research contributes to solving real-world problems.
Impactful Research: Consider the potential impact of your research. Will it bring about positive changes in farming practices or contribute to sustainable agriculture? Aim for topics that have practical implications and can make a difference.
Consult Experts: Don’t hesitate to consult with teachers, experts, or researchers in the field. They can provide valuable insights, helping refine your topic and ensure it aligns with current research priorities.
Stay Flexible: Be open to adjusting your topic as you delve deeper into the research. The initial idea might sometimes evolve, leading to a more refined and focused research question.
Ask for Feedback: Seek feedback from peers or mentors. Present your chosen topic and gather input – a fresh perspective can help you fine-tune your focus.

Best Agriculture Research Topics

Integrating Sustainable Agriculture Practices for Enhanced Food Security
Assessing the Impact of Climate Change on Livestock Production Systems
Innovations in Irrigation Techniques to Promote Sustainable Food Systems
Enhancing Food Safety Protocols in Agricultural Supply Chains
Conservation Strategies for Biodiversity in Agroecosystems
Pest Management Approaches for Sustainable Crop Yield Improvement
The Role of Ecological Practices in Mitigating Agricultural Waste
Sustainable Livestock Farming: Balancing Productivity and Environmental Impact
Evaluating the Ecological Impact of Pesticide Use in Modern Agriculture
Promoting Sustainable Food Systems through Community-Based Agriculture Initiatives

Agricultural Economics Research Topics

Market Dynamics and Price Volatility in Agricultural Commodities
Economic Impacts of Climate Change on Agricultural Production Systems
Policy Interventions for Promoting Sustainable Agriculture and Rural Development
Assessing the Role of Technology Adoption in Agricultural Productivity
Economic Analysis of Precision Farming Technologies and Practices
Income Inequality in Agricultural Communities: Causes and Remedies
The Role of Agricultural Trade in Global Economic Development
Economic Evaluation of Ecosystem Services in Agricultural Landscapes

Agricultural Engineering Research Topics

Innovative Engineering Approaches for Sustainable Water Management in Agriculture
Precision Agriculture Technologies: Advancements and Implementation Challenges
Automated Systems for Crop Monitoring and Yield Prediction
Energy-Efficient Solutions in Agricultural Machinery and Equipment
Sensor Technologies for Real-time Monitoring of Soil Health and Crop Conditions
Robotics and Automation in Agricultural Practices: Opportunities and Limitations
Waste-to-Energy Technologies for Sustainable Agricultural Operations
Engineering Solutions for Mitigating the Impact of Climate Change on Farming Systems

Interesting Agriculture Research Topics For Students

Microbial Applications for Enhancing Nutrient Cycling in Agricultural Systems
Sustainable Agricultural Practices in Rural Areas: A Case Study Analysis
Greenhouse Gas Emissions in Urban Agriculture: Assessing Sustainability
Innovative Agricultural Water Management Techniques for Increased Productivity
Fertility Management Strategies for Sustainable Crop Production Systems
Exploring the Role of Nutrient-Rich Food Products in Improving Human Health
Assessing the Environmental Impact of Agricultural Waste in Production Systems
Integrating NIFA Initiatives for Advancing Food and Agriculture Research
Enhancing Agricultural Productivity through Technology-driven Production Systems
The Intersection of Food Security and Sustainability in Modern Agricultural Practices

Agriculture-Related Research Paper Topics

Analytical Approaches to Assessing the Environmental Sustainability of Local Food Systems
The Impact of Bioenergy Production on Biodiversity in Agricultural Landscapes
Intervention Strategies for Addressing Depletion of Crop Varieties in Modern Agriculture
Exploring the Role of Agricultural Enterprises in Rural Development
Assessing the Ecological Consequences of Invasive Species in Food Production Systems
Local Food Initiatives and Their Influence on the Global Food Supply Chain
Investigating the Analytical Methods for Monitoring and Improving Food Supply Chain Efficiency
Biodiversity Conservation in Agricultural Landscapes: A Focus on Crop Varieties
The Intersection of Rural Development and Environmental Sustainability in Agriculture
Examining the Impact of Intervention Programs on Sustainable Food Production Practices

List of Agriculture Research Paper Topics

Sustainable Development Strategies in High-Yielding Agriculture
Integrated Pest Management Approaches for Crop Improvement
Organic Farming and Its Impact on Soil Health and Fertility
Assessing the Ecological and Economic Dimensions of Soil Degradation
National and International Perspectives on Water Management Practices in Agriculture
USDA Initiatives for Promoting Sustainable Agriculture in Rural Communities
Balancing High-Yielding Crop Practices with Ecological Considerations
Exploring the Relationship Between Soil Fertility and Agricultural Productivity

Expanded Agriculture Research Paper Topics

Enhancing Crop Productivity through Innovative Input Strategies
The Role of Forestry Practices in Sustainable Agriculture
Microorganism Diversity and its Impact on Soil Health and Crop Yield
Advancements in Horticulture Techniques for Improved Crop Management
Wastewater Reuse in Agriculture: Challenges and Opportunities
Physiological Mechanisms Underlying Crop Responses to Environmental Stress
Engineering Approaches for Efficient Water Management in Agriculture
Genomic Applications for Crop Improvement and Biotic Stress Resistance

Agricultural Research Topics in Animal Breeding And Genetics

Genomic Selection and its Application in Animal Breeding Programs
Genetic Improvement of Livestock for Enhanced Productivity and Disease Resistance
Molecular Markers and their Role in Characterizing Genetic Diversity in Animal Populations
Selective Breeding for Improved Reproductive Performance in Farm Animals
Genomic Tools for Identifying and Managing Genetic Disorders in Livestock
Application of Quantitative Genetics in Improving Feed Efficiency in Farm Animals
Genetic and Genomic Approaches to Enhance Heat Tolerance in Livestock
Advances in Marker-Assisted Selection for Traits of Economic Importance in Animal Agriculture

Agriculture Related Research Topics in Plant Science And Crop Production

Innovative Approaches to Enhance Crop Productivity in Sustainable Agriculture
Genetic Modification for Crop Resistance to Biotic and Abiotic Stresses
Precision Farming Technologies for Optimal Resource Utilization in Crop Production
Investigating the Impact of Climate Change on Crop Physiology and Yield
Sustainable Management of Soil Health for Improved Crop Production
Functional Genomics in Understanding Plant Responses to Environmental Challenges
Development and Deployment of High-Yielding Crop Varieties with Desired Traits
Exploring Novel Strategies for Integrated Pest Management in Crop Agriculture

Agriculture Research Project Topics in Fisheries And Aquaculture

Sustainable Aquaculture Practices: Balancing Production and Environmental Conservation
Genetic Improvement of Aquatic Species for Enhanced Aquaculture Productivity
Aquatic Ecosystem Health and Its Impact on Fisheries Sustainability
Innovative Technologies for Water Quality Monitoring in Aquaculture Systems
Socio-economic Impacts of Aquaculture on Local Communities
Development and Optimization of Feed Formulations for Aquaculture Species
Disease Management Strategies in Aquatic Organisms: A Focus on Probiotics and Immunostimulants
Assessing the Ecological Impact of Aquaculture Practices on Coastal and Inland Water Bodies

Topics in Agricultural Science

Understanding the Physiology of Insect Species in Agricultural Ecosystems
Sensitive Information Handling in Agricultural Science Research
Addressing Water Scarcity Challenges in Agricultural Practices
Livelihood Impact of Agricultural Practices on Local Communities
Manure Management Strategies for Sustainable Agriculture
Energy Production from Agricultural Waste: Biochemical Approaches
Exploring Nutrient Composition in Plants for Improved Crop Yield
Cover Crops and Medicinal Herbs: Contributions to Sustainable Agriculture in a Growing World Population

Agricultural Economics Research Topics in Farm Management

Economic Analysis of Disease Management Strategies for Plant Pathogens in Crop Production
Cost-Benefit Analysis of Precision Farming Technologies in Livestock Rearing
Financial Viability of Integrated Pest Management Practices in Farm Management
Evaluating the Economic Impact of Climate Change on Crop Rearing Systems
Adoption and Economic Implications of Sustainable Agriculture Practices in Livestock Farms
Farm-Level Decision-Making for Efficient Resource Allocation in Rearing Operations
Economic Evaluation of Technology Adoption for Disease Control in Plant Pathogen Management
Assessing the Profitability and Sustainability of Diversification Strategies in Farm Enterprises

Topics in Agric Meteorology And Water Management

Climate Variability and its Impact on Agricultural Water Management
Precision Irrigation Technologies for Efficient Water Use in Agriculture
Modeling and Simulation of Meteorological Factors in Crop Growth
Weather Forecasting for Optimal Decision-Making in Agriculture
Integrated Water Resource Management for Sustainable Agriculture
Evaluating the Impact of Climate Change on Water Availability for Agriculture
Meteorological Approaches to Assessing Drought Risk in Agricultural Regions
Remote Sensing Applications in Monitoring and Managing Agricultural Water Resources

Agriculture Research Paper Topics in Agronomy

Optimizing Crop Rotation Systems for Sustainable Agronomic Practices
Soil Health Assessment Techniques for Precision Agriculture
Evaluating the Impact of Cover Crops on Weed Management in Agronomic Systems
Enhancing Nitrogen Use Efficiency in Crop Production through Agronomic Practices
Investigating the Role of Plant-Microbe Interactions in Crop Health and Yield
Sustainable Management of Agricultural Residues for Improved Soil Quality
Precision Farming Technologies for Efficient Resource Utilization in Agronomy
Agronomic Approaches to Mitigate the Effects of Climate Change on Crop Production

Get Help With Your Agriculture Research Paper

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Which topic is best for research in agriculture?

Determining the best research topic in agriculture depends on your interests and the current needs of the industry, ranging from sustainable practices to genetic improvements in crops and livestock.

What are the research paper topics on organic farming?

Research paper topics on organic farming can include soil health in organic systems, the impact of organic practices on crop yield, and the economic viability of organic farming compared to conventional methods.

What are some of the projects in agriculture?

Projects in agriculture cover a broad spectrum, such as precision farming using technology, sustainable water management practices, genetic improvement of crops, and innovative approaches to pest management.

What is a research topic example?

An example of a research topic could be “Assessing the Impact of Climate Change on Crop Productivity” or “Exploring Sustainable Livestock Farming Practices for Environmental Conservation.”

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USDA Employees

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Selvaprakash Ramalingam

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Amisha Kaushik
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Maria Musarat

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Aline Cavalcanti Dantas
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Ma. del Carmen Orozco-Mosqueda
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Gopal Singh
Uday Sharma

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Haitham S. Mohammed

Amylase Activity and Soluble Sugars Content of Durum Wheat Seeds During Germination Under Water Stress

Kamel Zemour
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Assessment of Lung Damage via Mitochondrial ROS Production Upon Chronic Exposure to Fipronil and Imidacloprid

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Biochar and AMF Improve Growth, Physiological Traits, Nutrients of Turmeric and Soil Biochemical Properties in Drought Stress

Dilfuza Jabborova
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Nucleoredoxin Vis-à-Vis a Novel Thioredoxin in Regulating Oxidative Stress in Plants: A Review

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Luis Lopez-Bellido

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Kayode Paul Baiyeri

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Chang-Hyok Kim
Song-Hyok Pak

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Agricultural Sector: Status, Challenges and it's Role in Indian Economy

January 2016
Journal of Commerce and Management Thought 7(2):209

Sinhgad Technical Education Society

North Maharashtra University

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REVIEW article

Digitalization of agriculture for sustainable crop production: a use-case review.

1 Leibniz Institute for Agricultural Engineering and Bioeconomy, Potsdam, Germany
2 Technische Universitat Berlin, Chair of Agromechatronics, Berlin, Germany
3 Albrecht Daniel Thaer-Institute of Agricultural and Horticultural Sciences, Humboldt Universität zu Berlin, Berlin, Germany
4 Department of Food Agricultural and Biological Engineering, Ohio State University, Columbus, OH, United States
5 Department of Agronomy, Kansas State University, Manhattan, KS, United States
6 Department of Farm Machinery and Power, Faculty of Agricultural Engineering and Technology, University of Agriculture, Faisalabad, Pakistan
7 Department of ICT and Natural Sciences, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU), Trondheim, Norway

The digitalization of agriculture is rapidly changing the way farmers do business. With the integration of advanced technology, farmers are now able to increase efficiency, productivity, and precision in their operations. Digitalization allows for real-time monitoring and management of crops, leading to improved yields and reduced waste. This paper presents a review of some of the use cases that digitalization has made an impact in the automation of open-field and closed-field cultivations by means of collecting data about soils, crop growth, and microclimate, or by contributing to more accurate decisions about water usage and fertilizer application. The objective was to address some of the most recent technological advances that are leading to increased efficiency and sustainability of crop production, reduction in the use of inputs and environmental impacts, and releasing manual workforces from repetitive field tasks. The short discussions included at the end of each case study attempt to highlight the limitations and technological challenges toward successful implementations, as well as to introduce alternative solutions and methods that are rapidly evolving to offer a vast array of benefits for farmers by influencing cost-saving measures. This review concludes that despite the many benefits of digitalization, there are still a number of challenges that need to be overcome, including high costs, reliability, and scalability. Most of the available setups that are currently used for this purpose have been custom designed for specific tasks and are still too expensive to be implemented on commercial scales, while others are still in their early stages of development, making them not reliable or scalable for widespread acceptance and adoption by farmers. By providing a comprehensive understanding of the current state of digitalization in agriculture and its impact on sustainable crop production and food security, this review provides insights for policy-makers, industry stakeholders, and researchers working in this field.

1 Introduction

Digital Agriculture (DA) deals with the practice of advanced technological solutions such as sensors, robotics, and data analysis for improving the ecological and economic viability of agricultural operations, and simultaneously elevating crop output and quality. Conventional farming methods have faced significant challenges in the past three decades to respond to the increasing demand for food, rising labor costs, reducing carbon footprint, and climate change ( Abbas et al., 2022a ; Abbas et al., 2022b ; Elahi et al., 2022 ; Elahi et al., 2024 ). On the other hand, improving long-term efficiency and maintaining the viability of crop production requires adaptations of digital technologies to reduce input costs and increase profit margins. Digitalization of agriculture benefits from a wide range of automation software and hardware platforms to contribute to replacing tedious manual operations with continuous automated processes with the ultimate objective of securing food production for the increasing world population. In modern farms, multiple ground-based sensors combined with maps and drone-generated images, as well as artificial intelligence (AI) and prediction models are delivering detailed agronomic data on crop conditions to support farmers with short-term and long-term decision-making. With the advances in wireless communication and high-performance data processing hardware, the farms of the future are expected to be entirely connected ( Figure 1 ). In this regard, digital agriculture is offering significant potential for innovative solutions toward automation and robotics, which in return frees human force from fieldwork, providing farmers with time to focus on developing scientific cultivation methods and agribusiness. In addition, the demands for the use of robots in agriculture to eliminate repetitive and dull tasks have introduced an exciting high-tech market that is constantly gaining attention from startup companies and investors. Some of the technology and methods involved in these practices, such as Geographic Information System (GIS), yield monitoring platforms, and variable-rate applications have been studied and covered in numerous published materials under the title of Precision Agriculture (PA) ( Cisternas et al., 2020 ; Pathak et al., 2019 ). However, their impact on the entire agri-food value chain, as well as the relatively newer concepts such as the Internet-of-Things (IoT), mobile apps, robotics, Artificial Intelligence (AI), Unmanned Aerial Vehicles (UAV), big data analysis, digital twins, and Blockchain fall under the umbrella of digital agriculture ( Fielke et al., 2020 ).

Figure 1 . A conceptual view of data-sharing in connected farms for the implementation of digital agriculture.

Digital agriculture is being practiced in many regions, either on commercial scales or in pilot plants. The fundamentals for DA however began to shape after 2010, with the popularity of some of the core technologies such as low-power wide area network (LPWAN) for IoT applications ( Klaina et al., 2022 ), open-source software for robotics ( Mier et al., 2023 ), and machine learning tools for data processing ( Sharma et al., 2020 ; Sharma et al., 2021 ), which redefined the existing concepts of precision agriculture and smart farming. In the past 10 years, low-resolution satellite-based maps have been frequently replaced with affordable UAVs that are equipped with sophisticated sensors for generating high-spatial and temporal resolution maps that can better support site-specific applications such as early disease detection and variable rate sprayers. In addition to the improvement in the hardware, DA is taking advantage of the advances in data processing and data management tools, deep learning, and cloud-based computing resources. A review of the published literature in the last decade shows that DA has employed a wide range of available technology to enable automation and create added value for sustainable food production. The inputs and outputs of DA have evolved based on data streams, reliable data-sharing services, and flexible data analysis tools that lead toward optimum automation processes and decision-makings. From an agronomic point of view, the digitalization of agriculture has widely supported farmers and researchers with solutions for a better understanding of different crop varieties and species that are more resistant to specific climate conditions or can be adapted to different soil conditions. For example, automated classification of seeds and plants was nearly impossible before the existence of software and high-performance computers to accomplish massive computations and to recommend which genome is suitable for breeding. In some cases such as plant health assessment or early disease detection, it is required to compare and categorize samples according to their colors, leaf morphology, shapes, or invisible symptoms using Support Vector Machine (SVM) classifiers or wavelet analysis methods. Digital agriculture is a growing research field that offers a wide variety of techniques to eliminate uncertainties involved with farming and food production.

The objective of this review is to present some of the use cases of digital technologies in agriculture and their impact on sustainable crop production and food security. The primary focus of each section of this review is to assess the benefits and challenges of digitalization in agriculture and how it can be leveraged to enhance the efficiency and productivity of crop production. The sections of the paper are organized to highlight and examine some of the core technological solutions in this field, including variable rate application, computer vision, UAV imagery, agricultural robotics, wireless sensing, and IoT monitoring. In the last section, the use of 5G network, Digital Twins, and Blockchain are covered as future scenarios. The objective is to provide a comprehensive understanding of the current state of digitalization in agriculture, its potential to address the challenges faced by the industry, and the role it can play in ensuring food security for the future.

2 Transition from precision agriculture to digital agriculture

The integration of automation and control systems alongside data processing software, web-based applications, and mobile tools has significantly influenced farming practices over the past three decades, largely aiming to enhance efficiency in land and resource utilization. Prior to 2010, farmers relied on technologies such as the Global Positioning System (GPS) ( Shamshiri et al., 2013 ; Shamshiri and Ismail, 2013 ), ground-based sensing platforms, satellite maps, and local sensing devices like data loggers to monitor fields and identify deficiencies. However, the introduction of more compact technological solutions, such as autonomous drones, LiDAR sensors, high-resolution cameras, small-scale robots, and long-range wireless transmitters, has led to a shift in precision agriculture and smart farming methods towards digitization. These advancements have played a crucial role in fostering economic growth and promoting sustainability in food production. In its workflow, precision agriculture utilizes data from different resources, such as satellite images, in-situ sensors, and mobile sensing platforms, to identify deficiencies and enhance crop yield through improved resource management, including the application of variable rate technology ( Shamshiri et al., 2018a ). In contrast, digital agriculture encompasses a broader spectrum of technological innovations aimed at the ongoing monitoring, assessment, and management of soil conditions, water resources, and fluctuations in weather patterns across farmlands ( Balasundram et al., 2023 ). These solutions are geared towards boosting field productivity while simultaneously minimizing operational expenses ( Basso and Antle, 2020 ; Sparrow and Howard, 2021 ), mitigating climate change, and ensuring food security ( Balasundram et al., 2023 ). Example cases include leveraging satellites and high-resolution UAV imagery to monitor crop water levels and quality, assessment of soil moisture and salinity, generating NDVI and yield maps, conducting health assessments for early disease detection, and identifying crop stress. In terms of automation, the integration of wireless sensors and IoT devices has facilitated the implementation of smart irrigation systems, management of water loss, and continuous assessment of soil nutrient levels in remote regions. Additionally, DA offers a diverse array of software applications accessible via smartphones and tablets. These tools empower farmers to benefit from live monitoring of field variabilities ( Shamshiri and Weltzien, 2021 ), implement remote automation ( Ahamed et al., 2023 ), and employ systematic management techniques.

3 Digitalization in farming data collection, analysis, and sharing

3.1 uav-based remote sensing for estimation of plant’s height and leaf area index.

Fixed-wing and multi-rotor drones that are equipped with high-resolution imaging sensors provide farmers with more accurate datasets when compared to satellite-based images. UAV-based remote sensing platforms are mainly used to monitor soil properties and crop stress, creating valuable information for developing decision support systems in pest control applications, smart fertilization, and irrigation management ( Lajoie-OMalley et al., 2020 ). Although satellite images can also provide information about the existing of such variability in the fields in a shorter period of time, however the quality of their images depends on a cloud-free view, which limits their applications at any time and location. In addition, they do not offer a flexible and affordable platform for experimenting with multiple sensors. On the other hand, UAVs offer higher spatial and temporal resolution data which makes them a versatile remote sensing platform in different season and growth stages for supporting a wide variety of applications such as plant phenotyping ( Shamshiri et al., 2018c ; Comba et al., 2020 ), Leaf Area Density (LAD) estimation ( Garcerá et al., 2021 ; Bates et al., 2021 ), determination of Leaf Chlorophyll Content (LCC) ( Vergara-Díaz et al., 2016 ), and plant breeding ( Guo W. et al., 2021 ). A conceptual illustration of a UAV-based image acquisition system with different sensors that are used in digital agriculture for estimation of crop parameters along with other in situ sensors and manual measurements is shown in Figure 2 .

Figure 2 . UAV-based remote sensing with different sensors and mapping software for digital agriculture ( Li et al., 2021 ).

Utilizing UAV imagery to estimate the height and density of plant canopies offers valuable insights into the growth status of field plants. This method can be outlined in three main steps as (i) generating a digital surface model (DSM), (ii) creating a digital terrain model (DTM), and (iii) determining plant height by subtracting the DTM from the DSM. This approach holds particular significance in crop management decisions reliant on site-specific canopy characterization. The information generated through this method find applications across various domains of DA and PA, including leaf area index evaluation ( Comba et al., 2020 ), precision crop protection ( Garcerá et al., 2021 ), site-specific irrigation ( Jiménez-Brenes et al., 2017 ), nutrient management ( Tee et al., 2023 ), yield prediction ( Gené-Mola et al., 2020 ), autonomous navigation ( Pathak et al., 2019 ; Fielke et al., 2020 ), and early disease detection ( Jurado et al., 2020 ). Moreover, detailed and reliable canopy information aids farmers in making timely and site-specific management decisions, underscoring the potential of 3D point cloud datasets for economic and environmental conservation strategies. Leaf area index estimation is crucial for enhancing crop growth models and addressing field uncertainties such as terrain erosion ( Rodrigo-Comino, 2018 ), soil organic carbon problems ( Chen et al., 2021 ), and climate change impacts ( Balasundram et al., 2023 ). Collecting LAI data traditionally involves manual measurements using in-field portable instruments ( Mourad et al., 2020 ) such as LI-3000C (LI-COR Biosciences GmbH, Homburg, Germany) or AccuPAR LP-80 (Metergroup, Pullman, WA, United States). However, UAVs equipped with high-resolution imaging sensors, LiDAR, multi-spectral, and hyperspectral cameras ( Zhang et al., 2009 ; Hardin and Jensen, 2011 ; Wallace et al., 2012 ; Knoth et al., 2013 ; Shahbazi et al., 2014 ; Whitehead et al., 2014 ; Linchant et al., 2015 ) have proven successful in estimating LAI for various crops, including maize ( Han et al., 2018 ), berries ( Herrero-Huerta et al., 2015 ), almonds ( Torres-Sánchez et al., 2018 ), olives ( Jiménez-Brenes et al., 2017 ), grapes ( Mathews and Jensen, 2013 ), apples ( Hobart et al., 2020 ), and pears ( Guo Y. et al., 2021 ). UAV remote sensing also shows promise in estimating LAI and canopy coverage ratio at the plant and canopy levels ( Lei et al., 2019 ), essential components for estimating evapotranspiration, surface energy, and water balance ( Mourad et al., 2020 ).

Several studies have demonstrated the effectiveness of UAV-based LAI estimation methods across different crop types and environmental conditions. For instance, Córcoles et al. ( Córcoles et al., 2013 ) employed a UAV-based automated infrared imaging system to estimate LAI for onion crops, showing a linear correlation between canopy cover and LAI. Lendzioch et al. ( Lendzioch et al., 2019 ) successfully estimated winter LAI and snow depth in a spruce forest using UAV-based imagery, while Sha et al. ( Sha et al., 2018 ) compared UAV-based LAI estimation with field measurements for grassland pastures in China, revealing inconsistencies in near-infrared spectrum measurements. Additionally, Roosjen et al. ( Roosjen et al., 2018 ) estimated LAI and leaf chlorophyll content of potatoes using UAV imagery, noting the impact of multi-angular angles and zenith angle on LAI estimation accuracy. Figure 3 provides a schematic overview of the key steps involved in estimating plant height and LAI using UAV imagery.

Figure 3 . Schematic illustration of the three main steps involved in estimation of canopy height and LAI from UAV images ( Li et al., 2022 ).

3.2 UAV-based hyperspectral imaging for crop disease management

Identifying plant diseases using RGB images or visual inspection is often only feasible once visible symptoms manifest, often too late for effective intervention by farmers. For instance, Ganoderma disease, a significant threat to oil palm plantations, typically presents noticeable symptoms like foliar chlorosis, frond breakage, decayed tissues at the palm base, and fruiting body production at an advanced stage. This disease, causing both basal and upper stem rot, remains a severe issue in Southeast Asia, leading to stand loss, reduced yields, and the need for premature replanting. Young palms exhibiting symptoms may perish within 6–24 months, while mature palms can survive up to 3 years, although basal stem rot can destroy up to 80% of the total standing palms. Studies suggest a strong correlation between oil palm yields and nutrient levels. Hyperspectral analysis of images in agriculture offers promising opportunities for early Ganoderma disease detection in oil palms, with preliminary data indicating distinct spectral characteristics of infected leaves. Developing a rapid and effective field-level detection and mapping method for Ganoderma would aid growers in disease management and potentially enhance financial outcomes.

The methodology outlined in Figure 4 proposes a customizable solution, adaptable and scalable with various multi-spectral and hyperspectral cameras for disease detection. The procedure involves systematic steps involving (i) analyzing disease spectral characteristics in controlled lab settings, (ii) developing a classification method to differentiate the disease from other stresses and similar diseases, (iii) exploring the use of low-cost spectral radiometers for rapid screening, (iv) creating an instrumented platform for hyperspectral image collection and georeferencing on farms, and (v) conducting field trials to assess hyperspectral imagery effectiveness in diverse conditions. Adapting a UAV remote sensing platform for early disease detection entails addressing key questions: (i) the disease’s detectability at different infection stages, (ii) unique spectral characteristics of Ganoderma reflectance data, (iii) optimal statistical or mathematical methods for analyzing Ganoderma spectral data, and (iv) the effectiveness of low-cost multiband radiometers in aiding scouting crews to identify suspiciously infected trees.

Figure 4 . Feasibility of using autonomous UAV-based hyperspectral imaging for the detection of Ganoderma Boninense disease in oil palms, showing (A) scanning the palm, (B) hyperspectral data collected at different wavelenghts, and (C) sample of one image generated at a specific wavelength ( Shamshiri et al., 2018c ).

3.3 Computer vision and line scanning for plant phenotyping

Computer vision is a rapidly growing field within digital agriculture that aims to automate the process of phenotyping, which is the measurement and analysis of the physical characteristics of plants. This technology uses cameras and image processing algorithms to capture and analyze data on plant growth, development, and health. One of the main advantages of using computer vision for phenotyping is that it allows for the rapid and efficient collection of large amounts of data. Traditional methods of phenotyping, such as manual measurement and visual inspection, can be time-consuming and labor-intensive. With computer vision, data can be collected at a much faster rate, allowing for more frequent and detailed monitoring of plant growth and development. In addition, computer vision can provide more accurate and consistent data than traditional methods. Human error and subjectivity can affect the accuracy and consistency of manual measurements and visual inspections. Computer vision algorithms, on the other hand, are able to provide a more objective and consistent assessment of plant characteristics, providing a cutting-edge solution to analyze plant stress and disease identification. This is done by capturing images of the plant and then using image processing algorithms to analyze the images for signs of stress or infection. Various studies have highlighted the contributions of computer vision to improving yields and reducing costs. The technology has been also used to automate the process of seedling counting and selection, using image processing algorithms to accurately count and identify seedlings, which can help to improve the efficiency and accuracy of seedling selection. The following sub-sections provide summary reports on some of the projects in digital agriculture that incorporated computer vision.

Hyperspectral imaging and line scanning are two advanced non-destructive and non-invasive techniques that are being used in digital agriculture to collect data on the crop, even during the growing season and without affecting crop yields, with the objective of improving crop monitoring and management. Hyperspectral imaging captures images of crop plants and leaves using a wide range of wavelengths of light, from the visible to the infrared, and uses these images to identify different plant species, detect signs of stress or disease, or measure the amount of moisture, chlorophyll, and other important plant characteristics. This technology can provide farmers with detailed information about the health and growth of their crops, and provide knowledge-based decisions about irrigation, fertilization, and pest control. In recent years, hyperspectral imaging has gathered a large amount of interest in the field of non-destructive techniques. Originally developed for remote sensing applications, hyperspectral imaging is now being widely used in a multitude of fields including the food and agricultural sector. In the food industry, the commonly used standard methods are destructive and invasive in nature. Thus, they are not only time-consuming but also resource and energy intensive. With varying quality parameters across different products, the food industry continuously seeks in/on-line processing techniques that meet the quality demands as well as provide rapid, accurate, and reliable results. This approach combines the salient features of machine vision and near-infrared spectroscopy ( Yu et al., 2020 ). Through the spectral and spatial information obtained from hyperspectral imaging, detailed information on the product has now become possible. Of the different acquisition techniques, line scanning is one of the most commonly used methods within the food industry ( Ma et al., 2019 ). A typical hyperspectral imaging line scanner is shown in Figure 5 .

Figure 5 . Principle of hyperspectral imaging using line scanning method.

Line scanning is a technique that captures images of crops by scanning a line of light across the field. This allows farmers to rapidly and efficiently collect detailed information about the crop canopy, including the height, width, and density of the plants. This information can be used to optimize planting density and monitor crop growth. Moreover, line scanning allows for continuous scanning of the product line-by-line, thus acquiring extensive spectral information on the product. This technique is being applied to predict moisture content and the distribution within fruits and vegetables such as apples, and purple-speckled cocoyam ( Crichton et al., 2018 ; Ndisya et al., 2021 ). In addition, moisture content hyperspectral imaging has also shown the ability to predict several quality parameters such as total phenols and antioxidants properties in cocoa beans ( Caporaso et al., 2018 ), chromaticity in apples’ slices ( Crichton et al., 2017 ), and total carotenoids content in carrots ( Md Saleh et al., 2022 ). Crichton et al., 2017 also implemented HSI to classify the freshness in beef. The results from this investigation present successful classification between the different storage conditions (i.e., fresh, matured, fresh-frozen thawed and matured-frozen thawed) through the varying color changes among the beef slices. With the view of moving towards in-line monitoring using hyperspectral imaging, Sturm et al., 2020 ( Sturm et al., 2020 ) integrated a Vis-NIR camera within a pilot-scale hop dryer to investigate the dynamic changes within the hop cones. The study shows a proof of concept of integration of method within semi-industrial scale drying systems to the dynamic changes occurring within the product. In conjunction with this study ( Sturm et al., 2020 ; von Gersdorff et al., 2021 ; Shrestha et al., 2020 ), also compared hyperspectral imaging and standard laboratory methods to assess its applicability for continuous monitoring. Through the application of methods comparison, these studies proved the feasibility of hyperspectral imaging for replacing standard laboratory methods and thus paving the way for real-time monitoring and quality assessment within the product. It should be noted that hyperspectral imaging and line scanning can be integrated with other technologies such as drones and robots, enabling farmers to scan large areas in a short time and collect a large amount of data, which can be used to optimize crop yields.

3.4 Wireless sensors and IoT monitoring

Implementation of digital agriculture requires wireless communication between sensors and controllers for remote monitoring and sending warning messages in open-field and closed-field farming via a flexible and modular automation solution that is compact in size, cost-effective, and easy to install and maintain. Studies show that smart irrigation and fertilization management systems ( Giannoccaro et al., 2020 ; Lin et al., 2020 ) are capable of maintaining optimum level of pH and nutrient contents for plants with minimum inputs. The success of such an optimization relies on the integration and adaptation of the sensors and controllers with wireless communication and the IoT concepts for incorporating real-time data transfer and live monitoring. Wireless sensor network (WSN) was adopted in agriculture in the early 2000s, and has served as the backbone of IoT-driven automation systems, comprising various sensor nodes, repeaters, and receivers interconnected and meshed across fields to sustain DA. This network aids in comprehending the interplay among soil, crop, and weather, thereby enhancing productivity and profitability. In a wireless monitoring setup, data storage occurs locally on the receiver node, limiting accessibility. Conversely, in IoT-based monitoring systems, data from the receiver node are uploaded to a cloud web server, enabling access from any internet-connected client device ( Shamshiri and Weltzien, 2021 ; Shamshiri and Hameed, 2021 ). In large-scale farming operations, data gathered from multiple wireless sensors are integrated into conventional decision support systems, AI algorithms, or crop growth models via internet connections and cloud-based streaming platforms, aiming to optimize production efficiency and financial returns. An example of such application is the works of ( Sanjeevi et al., 2020 ; Popescu et al., 2020 ) that involved a scalable and collaborative UAV-WSN architecture for IoT monitoring and controlling in remote areas. IoT devices have been shown to be effective solutions to improve agricultural resource management and contribute to the sustainability of production. Farmers need mobile applications offering real-time data monitoring and dynamic decision support systems that leverage wireless automation and control. This technology significantly reduces wiring costs and maintenance efforts, especially in remote agricultural regions. However, studies have highlighted challenges in radio wave propagation inside dense plant populations ( Shamshiri and Hameed, 2021 ), and therefore necessitating the use of low-powered long-range wireless communication protocols. In large-scale farming operations, factors like sensor node quantity, repeater placement, power usage, operating frequencies, and transmitter-receiver distances require careful consideration for seamless data collection.

In recent years, LoRa technology has emerged as a solution, enabling long-range communication between sensor nodes and receivers for field parameter monitoring. LoRaWAN, its networking protocol layer, is a leading LPWAN technology renowned for ultra-long-range wireless data transmission with minimal power consumption, ideal for digital agriculture applications ( Shamshiri and Weltzien, 2021 ). LoRa bridges the gap between power efficiency and transmission range in remote areas lacking mobile coverage, utilizing reserved ISM radio bands like 433 MHz (Asia), 868 MHz (Europe), and 915 MHz (Australia and North America). Depending on network architecture and repeater node density, LoRa can cover distances of 2–10 km in rural areas, extendable to 100 km with repeaters. Figure 6 illustrates the primary components of this technology, comprising a smart sender node, communication module, and IoT platform.

Figure 6 . Main components of an IoT monitoring system in agriculture using LoRa transmitters showing (A) data flow from the sensor node to the cloud, (B) elements of the smart sensor node with LoRa and CANBUS communication for in-field measurements, and (C) back-end software for storing sensor measurements in the database. (Source: Adaptive AgroTech) ( Shamshiri and Hameed, 2021 ).

This setup allows live data viewing and sharing with unlimited users and applications from any location. The design or selection of compact sender nodes ( Figure 6B ) with efficient battery management and durability in harsh indoor environments is crucial. These sensing modules typically feature a programmable microcontroller interfaced with diverse sensor probes, onboard memory storage, a LoRa module (e.g., SX1276 or E220-900M22S LoRa breakout board), and battery management units with solar charging circuits. For control actuator nodes (e.g., smart fertigation or model-predictive microclimate control), sensor data is received by a LoRa controller node via peer-to-peer communication or first transmitted to secure cloud-based applications using a LoRaWAN gateway before reaching IoT-based controllers. The architecture presented in Figure 6C encompasses multiple hardware and software layers connected either via wires or wirelessly through standard communication protocols like WiFi and CANBUS. Previous studies have highlighted numerous successful applications of this technology for real-time monitoring and control in both closed-field and open-field crop production, particularly in remote areas lacking mobile network coverage. These applications have been extensively discussed in ( Shamshiri and Weltzien, 2021 ; Shamshiri and Hameed, 2021 ; Singh et al., 2020 ; Shamshiri et al., 2020 ). It should be noted that while LoRa is the main physical layer of the LoRaWAN network, but the LoRaWAN protocol can also use other physical layer protocols such as GFSK in specific bands. In addition, LoRa can be used as the physical layer for other networking technologies. LoRaWAN topology is star, or star-to-star, which is capable of increasing communication range and maintaining low battery consumption. This is demonstrated in Figure 7 where each sensor node can transmits data to multiple gateways, and the network sends messages to a central server by means of these gateways. Sensor data that are successfully forwarded to the web-server are checked for redundancy and security. The LoRaWAN gateway typically comprises three primary components: a concentrator board linked to an antenna, a Raspberry Pi onboard computer facilitating connections between the concentrator and the LoRaWAN backend, and custom-written C++ codes managing the entire process. This gateway utilizes the existing LAN or WiFi network within the farm office to establish a connection with the web server. For deployment in farming applications, all devices must be waterproof, housed in IP68-rated cases, and equipped with appropriate connectors. The LoRa sensor node depicted in Figure 7 can be connected to various plug-and-sense probes, including the DS1820 for soil temperature sensing, BlueDot BME280 + TSL2591 for microclimate and light condition monitoring, ADP-LWS2020 for leaf wetness, and SKU capacitive sensor for soil moisture measurement.

Figure 7 . Schematic demonstration of remote monitoring and control in digital agriculture using LoRaWAN technology. (Source: Adaptive AgroTech).

3.5 LoRa connectivity for wireless monitoring of field machine index

By tracking of agricultural machinery using LoRa GPS tracker it is possible to determine their timeliness in large scale operations. This is of interest for growers from a management perspective, providing them with an overview of the efficient time that the machine has spent on the field, and the number of hours that has been spent on stops and row-end turning. For this purpose, information such as time, latitude, and longitude from standard NMEA GPS strings are stored and transmitted using one or multiple LoRaWAN GPS tracker modules. The messages are received by one or more LoRaWAN gateways that can be located up to 10 km or more from the machine. The gateways might benefit from preprocessing software before uploading the data to a cloud-based mobile management app, for live monitoring of the total operation time, total stops and row-end turning time (ineffective operation time), total covered area, and average travel speed. An overview of the steps involved in data collection and processing of this approach together with sample results are shown in Figure 8 . The outputs of the software is directly used to calculate field efficiency and machine index ( Shamshiri et al., 2013 ). One of the main difficulties in processing raw GPS data is that they usually contain empty lines or broken strings. The application software that was used to produce results demonstrated in Figure 8 has built-in features that can detect different interruptions and outliers before processing the data via a simple user interface. For offline data processing, the entire calculation is carried out via three simple steps: “ Open data ”, “ extract data ”, and “ process data ”. As a result, the software generates an output table in Excel containing detailed information about the operation time and location of the machine in the field.

Figure 8 . Overview of a tracking software for determining field machine index of agricultural machinery ( Shamshiri et al., 2013 ).

3.6 Hybrid data loggers for IoT-based monitoring of microclimate parameters

Conventional data loggers that have been integrated with wireless modules and IoT patches have demonstrated to be a promising solution for improving the reliability of data collection for digital agriculture applications. These redundant devices minimize the disruptive effect of outdoor environment on field monitoring. A multi-channel hybrid data logger, illustrated in Figure 9A , features an IP66 enclosure, WiFi and LoRa antennas, an external power supply, and aviation plug connectors specifically designed for seamless integration with various sensor probes in both closed-field and open-field crop production systems. Each node is equipped with two separate circuit boards: one for transmitting sensor and GPS data via LoRa 868 MHz ( Figure 9B ) and another for LoRa/WiFi communication and data storage on an SD card ( Figure 9C ). This design facilitates the addition of new sensing capabilities to existing wireless networks and allows for easy replacement of defective sensor probes, minimizing network maintenance costs. The three connectivity boards demonstrated in Figure 9 include all necessary electronics and sockets for connecting typical sensors used in wireless monitoring of the indoor environment. These sensors include the BME280 (for air temperature, humidity, and atmospheric pressure), DS18B20 (for soil temperature), LDR Photoresistor (for light sensing), SX239 (for soil moisture), and NEO-7 GNSS modules. To ensure robust and efficient processing, the sensor node utilizes powerful ESP32 and Atmega328P microcontrollers integrated with customized codes for high efficiency and ultra-low power consumption. For example, the logger board shown in Figure 9C supports Bluetooth and WiFi communication and can save data on an onboard SD card via SPI data transfer. This board can also be interfaced with other microcontrollers using the onboard CANBUS modules. All sensor boards have been optimized for low-power consumption (deep sleep mode) and utilize MOSFET transistors in switch mode for sensor probes and memory cards in a way that when the board wakes up from a deep sleep mode, its controller triggers the MOSFET transistor to activate all power lines. The sensor node has a DS1337 IC for real-time logging clock and can access dates and times from an available world clock server in the presence of a WiFi network. The final log file is saved on a cloud server or the onboard SD card with GPS and time stamps and may include hundreds or thousands of data lines, depending on the data collection frequency and growing season. Several sensor nodes has been deployed and tested successfully in multiple farming applications and has measured, recorded, and transferred data without interruptions.

Figure 9 . Components of a hybrid data logger including (A) an IP66 enclosure with an LCD, (B) a multi-channel sensor controller with Ethernet ports, and (C) a LoRa/WiFi wireless connectivity board with an onboard clock and a memory card, (D, E) desktop software for downloading and monitoring sensor data from data logger and cloud storage ( Shamshiri and Weltzien, 2021 ).

The hybrid data logger system presented in Figure 9C is used for dynamic assessment of controlled environments, particularly regarding microclimate parameters and soil temperature (ST) set-points prior to cultivation. Understanding the reference values for air temperature, relative humidity (RH), vapor pressure deficit (VPD), and ST across various growth stages of fodder production ( Ahamed et al., 2023 ), allows for real-time visualization of collected data on a mobile app, offering insights into deviations from ideal conditions. This approach is vital for decision-making in large-scale productions, where a controlled environment model is initially constructed and tested. To facilitate the monitoring and download of data from multiple sensors and cloud storage, the two desktop software applications shown in Figures 9D, E were developed. These applications can interface with sensor controllers via multiple serial COM ports, allowing users to execute commands and configure custom settings. Additionally, the software enables users to download log files containing sensor performance data (e.g., battery status, clock status, and historical parameters) and upload stored data to a cloud server. Users can also assign labels to each node for simultaneous reading and writing of log files from multiple devices, storing the data on local memory cards. These applications were created using the C# programming language and Microsoft.Net Core technology, ensuring compatibility with Microsoft Windows, Apple macOS, and Linux operating systems. It should be noted that Microsoft.Net Core is free, open-source software supporting various programming languages such as C#, C++, and VB.NET. These features offer a cost-effective and adaptable solution for future enhancements of the Port Logger and the IoT monitoring software. To optimize performance, both applications utilize multi-threading technology to execute parallel routines, enabling simultaneous listening to multiple ports and channels, and concurrent execution of multiple tasks. Each thread defines a unique flow of control, automatically setting different execution paths for complex and time-consuming parallel operations.

The workflow of an IoT-based monitoring system that has been realized by means of distributed nodes and modular hardware in a digital agriculture project for berry orchards ( Shamshiri and Weltzien, 2021 ) is shown in Figure 10 . In this scheme, each platform is custom-designed for specific applications in open-field cultivations based on a powerful microcontroller (32-bit, dual-core, 240 MHz) with LoRa modulation at 868 MHz. The nodes’ controllers were installed on long wood supports at an average height of 2 m from ground to overcome the issues with signal connectivity near high-density bushes and plants. For large-scale farms, the number of the sensor nodes, locations of the repeaters, power consumption, operating frequencies, and the distance between transmitters and receivers should be considered for continuous data collection. The set of hardware that is used in IoT-based automation of farming processes includes controllers that collect data from sensor probes (or send command signals to control actuators), connectivity nodes (ex., LoRaWAN), gateways, and protocol servers. Some of the radio protocols that are most widely used in IoT applications include WiFi, BLE, LoRaWAN, SigFox, NB-IoT, and LTE. It is sometimes necessary to have protocol conversion in the network. Various studies have discussed the limitations of radio wave propagation in the presence of high-density plants ( Shamshiri and Hameed, 2021 ; Rezvani et al., 2020 ) and therefore suggested redundant nodes with onboard memory to ensure data transmission. Experimenting with different combinations of hardware and software setup in real farming conditions have shown that a typical challenge with IoT deployment is that most of the gateways use single-board computers with ARM processors, while many software applications are designed for x86 processors, causing compatibility issues and strange behaviors. Other challenges include battery failures due to low or high air temperature, solar-panel failure due to lightning, high winds, and birds droppings, and sensor probes failure due to corrosion.

Figure 10 . Workflow of an IoT-based distribution automation system with solar-powered modular hardware for farms located in remote, hard-to-access areas.

Figure 11 presents plots generated from a dataset obtained using the hybrid data logger system in an Agricube prototype model, which includes an electrical heater and thermoelectric cooling device (utilizing Peltier elements and fans) to manipulate the environment. The experiment aimed to investigate heat exchange between air and soil bed, hypothesizing a potential linear correlation between air and soil temperature during substantial environmental temperature fluctuations. Further details of this experiment can be found in Shamshiri et al. (2021) ( Shrestha et al., 2020 ). The proposed data acquisition system, along with sensor probes and modular battery packs, was deployed inside the cube for a 7-day period. Data were collected at 10-minute intervals and stored on a private cloud via WiFi connection for IoT monitoring. Simultaneously, the same data were logged on an onboard SD card and could be retrieved using a standard USB port as needed. The plots displayed in Figure 11 validate the reliability and resilience of the low-cost data logger system in operating on battery power under varying temperatures, crucial for long-term evaluations of controlled environment crop production systems, particularly in remote regions. This monitoring system offers seamless integration into modular shipping containers, ensuring efficient and precise operations for controlled environment fodder production facilities.

Figure 11 . Testing connectivity and robustness of an IoT data logger in evaluating an experimental Agricube with large temperature gradients.

3.7 AI-based data analysis for identification of plants and weeds

Traditional methods of plant disease identification, such as visual inspections and manual measurements, are time-consuming and labor-intensive. Plant disease symptoms, weed plants, or pest insects are normally tiny constituents in a plant canopy and are usually hard to detect with conventional remote sensing applications specially in the early stages of the outbreak or growth of the pest. The plant disease yellow rust ( Puccinia striiformis West . F . sp. tritici ), for example, develops small but distinctive features as symptoms that resemble long and narrow yellow to orange stripes. They usually occur on the plant leaves between the veins and consist of Urediniospores pustules with a dimension of 0.4–0.7 mm accompanied by chlorosis and necrosis ( Chen et al., 2015 ). An expert that assesses crop diseases in the field can easily distinguish yellow rust from other crop diseases and score its severity at that location. This is possible because the visual symptoms of most diseases have unique features that are quite different from each other. This is true for many weed plants and other pests as well. A monitoring system for crop protection that can exploit this information in a timely, site-specific and selective manner would help to improve control strategies for crop protection and reduce pesticides by applying measures more precisely and sustainably in the field. However, those systems would be in dire need of very high-resolution data about the crop canopy. The unique and decisive features cannot be found at the canopy or field scale but rather at the plant or leaf scale. Thus, even drones operating at altitudes 20–100 m typically used for photogrammetric orthophoto production cannot resolve the features accurately enough to detect and distinguish pests in the field successfully. It is therefore required to have a closer canopy view, not more than 2 m away, which is the area where proximal sensing is operating and sensor systems are installed on platforms that are very close or even in contact with the object of interest ( Adamchuk et al., 2018 ; Viscarra Rossel et al., 2010 ). In proximal sensing, even low-cost imaging systems, such as RGB cameras, would deliver highly detailed data that includes vital information for crop protection. The problem, however, is the sheer amount of unstructured image data that needs to be evaluated to extract the important crop information.

With the advent of more sophisticated neural network architectures including hundreds of layers and millions of adaptable weights and the possibility of training them with the currently available computing infrastructure, specialized deep learning models for image classification and image object detection (Convolution neural networks) became practically available ( He et al., 2016 ). Deep learning is a type of machine learning that uses neural networks to analyze data and make predictions. With deep learning, the neural network can learn from thousands of images of different plant species, allowing for accurate and efficient identification. The main advantage of this technique is its ability to identify and classify plants based on subtle differences in their characteristics. This can be particularly useful for identifying different varieties or cultivars of the same species. Deep learning is a powerful tool for digital agriculture in solving object detection problems as an alternative to traditional vision methods in which feature descriptors (such as a scale-invariant feature transform (SIFT)) are used for recognizing objects. In the SIFT approach, all hand-crafted key features are used to form a definition for an interested object class from a set of reference images and these definitions are searched in new images to detect objects ( OMahony et al., 2020 ). The major drawback of these feature descriptors is that they require a priori expert knowledge, time-consuming hand-tuning and poor reproducibility ( Lu and Young, 2020 ). On the other hand, the deep learning method uses a series of hidden layers to discover the most descriptive and salient features of an object class on a given dataset of images, annotated with interested classes of objects, during model training. A typical deep convolutional neural network (CNN) architecture such as the one shown in Figure 12 consists of a multilayer stack of simple modules that learn to map a fixed-size input (an image of dimensions 224 × 224 × 3) to a fixed-size output (probability for each of predicted classes 1 × 1 × 60). Within a convolutional layer, a collection of adaptable filters (or kernels) traverses the width and height of the input volume, generating feature maps through the dot product computation between the filters and the input region where they overlap. The outcomes of this locally weighted summation subsequently undergo a non-linear transformation, often employing functions like the rectified linear unit (ReLU). The Pooling layer facilitates feature map down-sampling by consolidating semantically similar features. Finally, a fully connected layer is employed to flatten the preceding layer volume into a feature vector, followed by the application of a regression function such as Softmax to normalize the feature vector into a probability distribution representing predicted classes ( OMahony et al., 2020 ; LeCun et al., 2015 ). Initial findings suggest significant promise in utilizing this approach for grasslands monitoring, offering advantages over conventional monitoring techniques ( Basavegowda et al., 2022 ).

Figure 12 . A deep-CNN architecture used in identifying indicator plant species from grasslands.

3.8 Deep learning for detection of yellow rust

Today, convolutional neural networks outperform humans in the accuracy of detecting the information of interest in images ( Zhou et al., 2021 ). One important leap forward with CNNs is that the filters that recognize interesting features for the model are now trained within the network itself and not deterministically chosen such as for example in the case of the bag of visual words classifiers ( Pflanz et al., 2018 ). This makes CNNs much more versatile and adaptable for automatic image evaluation. In Schirrmann et al. (2021) ( Schirrmann et al., 2021 ), a deep learning model was trained to detect yellow rust from very high-resolution RGB images at different stages of the disease outbreak. A deep residual neural network (ResNet-18) was used as deep learning architecture. ResNets are CNNs that include shortcut connections in the network architecture based on residual functions that enable skipping specific layers in the network, which increases the training performance of the deeper layers ( He et al., 2016 ). Input for training and for testing included thousands of images taken at 2 m in nadir perspective from an RGB camera. Image acquisition was performed unselectively to mimic sensor data collection from a moving platform. For supervised training, snippets of images with and without yellow rust symptoms were used, which had been annotated earlier by experts on the screen. Although images could resolve sub-leaf features easily, there were cases in which even an expert was unable to identify the symptoms of stripe rust conclusively. Reasons were manifold and included occlusion of symptoms by other leaves, contrast issues, or highly similar features to yellow rust such as symptoms due to sunburn, water deficits, or damages caused by feeding. For example, the cereal leaf beetle produced symptoms on the leaves with a remarkable resemblance to yellow rust because they feed between the leaf veins. One distinguishing feature of the damages is their rough edges, whereas stripe rust symptoms show a smoother transition from invested to healthy leaf areas ( Figure 13 ). Thus, it is not an easy task for the model to decide if yellow rust infection occurs, especially at the early infection stages.

Figure 13 . Winter wheat canopy with and without yellow rust (YR) symptoms (A) . Deep learning architecture used for classifying yellow rust symptoms in the images (B) . Receiver operating characteristics (ROC) curves for the classification of images taken at different days after inoculation (DAI) ( Schirrmann et al., 2021 ).

The trained ResNet model showed high accuracy for estimating the yellow rust symptoms after the disease has spread into the canopy to about 2%–4%, which was after 40 days of inoculation (DAI). During this time, symptoms showed the orange uredospore pustules aligned along stripes on the leaves. For these images, the model had a high area under the curve in the corresponding ROC plot ( Figure 13 ) and the estimation accuracy was greater than 80%. With drone imagery from 20 m above the canopy, the disease spreading was still not recognizable because no apparent disease loci had developed. One week before, however, when symptoms were sparser and underdeveloped, estimation accuracy was worse with an overall accuracy of about 57%. These first symptoms were mostly chlorotic patches with a stripe-like appearance on the leaves that occurred randomly in the lower leaf layers and only in a few areas of the infected plots. These first symptoms were quite hard to detect by the model.

3.9 Optimized deep learning model for weed detection

Weed detection is one of the most important aspects of digital agriculture that has received significant attention in recent years, with the goal of applying computer vision and machine learning algorithms to analyze images of crops in real time for rapid identification and removal of weeds. Some companies have developed sensor-based weed detection systems, which use a combination of sensors, such as cameras, infrared, and LiDAR, to detect weeds. These systems can be mounted on UAVs, field robots, tractors, or other ground vehicles to scan a field while the vehicle is in motion. In addition, some studies have reported on the development of weed detection systems that can scan a large area in a short time and are trained to recognize specific weeds by analyzing large amounts of image data in order to improve the accuracy and efficiency of weed classification based on their characteristics. An example includes the work of de Camargo et al. (2021) ( de Camargo et al., 2021 ), in which the optimization of a ResNet-18 model for the classification of weed and crop plants in UAV imagery was considered. This study is part of a larger project that aims to develop an intelligent real-time monitoring and mapping system for the detection of weed distribution in cereal crops. The idea is to capture low-altitude imagery from UAVs with sufficient details to differentiate individual weed plants and evaluate the images directly on the drone using optimized onboard AI image recognition during flying. The planned system will not only differentiate between crop plants and weeds but will also enable the identification of learned plant species in a cultivated area. Based on this, better application maps for site-specific and selective herbicide management can be derived, which increases environmental and consumer protection. In the referenced study, the prediction pipeline of the ResNet-18 model underwent optimization to eliminate redundant computations within a classification model applied to overlapping tiles in a larger input image, such as a full camera image captured from the UAV, as described in Figure 14 .

Figure 14 . An intelligent online mapping system for monitoring and mapping weed plants with UAVs in crop fields based on optimized deep learning models that can be implemented in embedded systems for fast, automatic image evaluation ( de Camargo et al., 2021 ).

Convolution and max pooling layers, along with residual blocks, span a broader spatial dimension due to the enlarged input size resulting from the full camera image. To circumvent redundant pooling operations across overlapping tiles, a custom cumulative local average pooling layer was integrated into the network. Annotated tiles of the UAV images were used for training the ResNet model, which depicted weed, wheat, or soil background. The optimized model was implemented on an NVIDIA Jetson AGX Xavier embedded system with TensorRT (NVIDIA CORPORATE, Santa Clara, CA, United States). In 16-bit mode, a full-image evaluation with the optimized model was about 2.2 frames per second. No memory issues occurred during training and testing. Using images from a test field, the image classifier had an overall accuracy of 94%. Even in more challenging parts of the images where plants overlapped, the model quite accurately identified the weed species. Both exemplary research studies show that combining low-cost imaging technologies, e.g., RGB imaging, with artificial intelligence enables the extraction of more specific field information for crop protection. This would allow autonomous monitoring of crops using imaging platforms in near proximity to the plants, e.g., with low-flying drones or tractors passing through the field because the images can be evaluated by specialized deep learning models automatically while the sensor platform operates over the field.

4 Digitalization in automation and remote operation

4.1 internet of robotic things for robot teleoperation.

The integration of robotics and wireless connectivity that are integrated with virtual reality, digital twin concepts, and IoT platforms, is often denoted as the Internet of Robotic Things (IoRT) ( Vermesan et al., 2020 ) and has emerged in the last few years for collaborative control and teleoperation ( Su, 2020 ) to optimize the use of autonomous agricultural machinery in unstructured farms. The main justifications for the deployment of IoRT infrastructure in agriculture can be summarized as (i) to provide real-time monitoring and control of the robot’s states and functionality (i.e., location, orientation, speed, distance to obstacles, and battery status), (ii) to feed these data to simulation models, digital shadows, and cloud-based decision support systems, and (iii) to send instant responses to the robot for assisting the autonomous navigation. An effective IoRT-based solution should incorporate the use of long-range wireless communication, simulation environment, and web-based applications to constantly monitor the robot in the field, and transmit human-in-the-loop control commands for robot teleoperation. A conceptual illustration of the proposed IoRT solution using a local LoRa network for exchanging messages between the actual mobile robot in the field and the digital shadow of that robot inside a virtual environment is shown in Figure 15 . This approach assists the navigation of the robot in complex situations without the need for high-end network infrastructure.

Figure 15 . Overview of the IoRT concept for exchanging float32 messages with a field robot and assisting autonomous navigation in complex situations using LoRa transceivers without the need for high-end network infrastructure, highlighting the architecture of the LoRa connectivity for exchanging messages between the actual robot and its digital shadow.

An overview of the message exchange between the actual robot and its digital shadow is presented in Figure 15 . The input of the digital shadow inside the simulation is a new target position (x, y), and the outputs are a ROS topic called SimIrus , and a CSV file that includes the corresponding latitude and longitude coordinates of the simulated robot. The LoRa transceiver board that was interfaced with the digital shadow of the robot using ROS serial subscribes to this SimIrus topic and extracts the latitude and longitude coordinates. This node also receives LoRa messages that include the current position of the actual robot. When the digital shadow receives a new target position, it autonomously drives to this position using the Pure-Pursuit controller. The current positions of the digital shadow ( x c , y c ) are then translated to real-world coordinates (lat, long) every 50 milliseconds to generate a path. This path is first visualized and validated on Google Maps using a custom-designed web GUI (front end). The key waypoints of this path are sent as LoRa messages to the actual robot in the field via the LoRa transmitter connected to the simulation software and multiple repeater nodes placed in different locations in the field to ensure reliable connectivity. As the actual robot receives the latitude and longitude coordinates of the path, they are published as ROS messages so the path-tracking controller software of the actual robot can subscribe to this message and perform autonomous navigation. At the same time, the new position of the actual robot is also transmitted as a LoRa message to the digital shadow (that is running inside the simulation environment) and is used to update the scene when the software switch shown in Figure 15 is activated.

4.2 Digital automation in variable rate applications

In precision agriculture, variable rate applications such as spraying or fertilizing were either realized by means of georeferenced prescription maps that were usually generated based on satellite remote sensing techniques, or by using on-the-go sensors. To this aim, tractors and other large machinery were required, and the availability of accurate GPS signals was crucial for the success of the operation. In digital agriculture however, drones ( Shamshiri et al., 2018c ) and swarms of small-scale robots that benefit from sensor fusion can operate in GPS denial environments and can deliver more precise VR applications by targeting individual plants ( Shamshiri et al., 2018a ). This is possible due to the availability of low-cost sensors, high-performance microcontrollers, and onboard computers that can process big data, support complex models, and simulate parallel decision-making scenarios for converting precise data into actions, which in return provides farmers with local-specific information on-the-go. Figure 16 showcases a novel design of a variable rate liquid fertilizer applicator, featuring a distinctive flow control and spray system capable of administering NPK (Nitrogen, Phosphorus, and Potassium) simultaneously at variable rates around oil palm trees in a single pass. This system, developed following the spot application method, is capable of evaluating the NPK status of a 25 m 2 soil area and applying N, P, and K nutrients at different variable rates using aqueous solutions of straight fertilizers ( Yamin et al., 2020a ; Yamin et al., 2020b ). Based on simulation analysis, six 8006 flat fan nozzles were meticulously chosen to ensure optimal swath coverage of fertilizer spray. Nozzles 1–3 were affixed vertically on the horizontal boom to apply spray on the machine side of oil palm trees, while nozzles 4–6 were positioned at −22°, −21°, and −20° angles to the horizontal plane on a 45° inclined boom to administer spray across the tree, employing the trajectory approach as depicted in Figure 16 . Employing this approach, an average simulated liquid velocity of 14.05 m/s per nozzle was determined, allowing for spraying at a distance of 2.5 m across the oil palm tree. Consequently, this technique achieves enhanced fertilizer distribution around the tree within a 25 m 2 region encompassing the most effective roots. Finite element analysis was utilized to devise the mechanical structure of the applicator, responsible for housing all equipment and fertilizer tanks. The mechanical structure and fertilizer tank assembly exhibited minimum safety factors of 3.13 and 11.34, respectively, ensuring their ability to withstand the requisite weights during field operation.

Figure 16 . An automatic variable-rate liquid fertilizer with adjustable spray coverage, developed for practicing digital agriculture in oil palm plantations ( Yamin et al., 2022 ).

4.3 Agro-food robotics

Agro-food robotics represents a fast advancing domain that is transforming farm production capacities, leveraging the advantages of robots over human labor, including heightened accuracy and efficiency, enhanced consistency and reliability, and reduced operational costs. In digital agriculture, farmers are eager to identify deficiencies and variations in large-scale cultivations, employing precise technology and accurate management solutions to address them effectively. Furthermore, optimizing input utilization is a promising approach to boost farm profitability. Comprehensive research and development in agricultural robotics have been documented in a wide range of review papers ( Shamshiri et al., 2018a ; Bergerman et al., 2016 ; Duong et al., 2020 ; Kootstra et al., 2020 ; Oliveira et al., 2021a ) covering specific tasks such as phenotyping ( Atefi et al., 2021 ; Yao et al., 2021 ; Xu and Li, 2022 ), arable farming ( Emmi and Gonzalez-de-Santos, 2017 ), livestock farming ( Ren et al., 2020 ), greenhouse horticulture ( Barth et al., 2016 ), orchard management ( Zhang et al., 2019 ), forestry ( Oliveira et al., 2021b ), and food processing ( Duong et al., 2020 ). Review papers also cover specific technologies used in agricultural robotics, such as computer vision ( Lu and Young, 2020 ; Tian et al., 2020 ; Fountas et al., 2022 ; Wang et al., 2022 ), active perception ( Magalhães et al., 2022 ), path planning ( Santos et al., 2020 ), and grasping and soft grasping ( Elfferich et al., 2022 ; Navas et al., 2024 ). The majority of these studies have emphasized that in order for agricultural robots to operate efficiently in harsh and unpredictable environments (i.e., including extreme weather conditions), they must be equipped with redundant sensing solutions to effectively perceive their surroundings and be able to communicate and interact seamlessly with other robots and machinery in the field. Some studied have proposed robotic platforms with flexible designs that can be integrated with custom-built attachments to perform specific tasks such as mowing ( Verne, 2020 ), weeding ( Gerhards et al., 2022 ), and spraying ( Meshram et al., 2022 ). Robots that are equipped with several data acquisition devices such as multi-spectral ( Karpyshev et al., 2021 ), hyperspectral ( Zhang et al., 2012 ), NDVI ( Tiozzo Fasiolo et al., 2022 ), thermal ( da Silva et al., 2021 ), or NIR cameras ( Milella et al., 2019 ) provide a great opportunity for field scouting ( Yamasaki et al., 2022 ), early disease detection ( Mishra et al., 2020 ), and yield estimation ( Kurtser et al., 2020 ; Massah et al., 2021 ). Employing robots for these tasks have a high potential for saving costs, however this is justified if additional cultivation steps or a higher repetition rate leads to a qualitative or quantitative increase in yield. A recent study on the acceptance level of agricultural robots in Germany clearly indicates that the majority of the farmers surveyed are keen to immediately use this technology on their farms specially for tasks such as weeding due to the potential benefits of saving labor and practicing more sustainable farming methods ( Rübcke von Veltheim and Heise, 2021 ). A survey in the published studies also shows that small robots are particularly of interest for small and irregularly shaped fields where large machinery are unable to operate efficiently ( Shamshiri et al., 2018a ). The scalability of field robots and suitability for small field sizes besides their lower ownership costs create opportunities for smaller farms to become economically viable.

4.4 Autonomous navigation with collision avoidance

The development of robust collision avoidance systems for mobile robots that operate inside unstructured agricultural fields proposes serious challenges due to the extreme variations in high-density bushes and disturbances of the outdoor environment. Data fusion and multiple perception solutions are usually employed to assist the existing GPS-based navigation and to improve the reliability of the operation. Figure 17 shows the hardware layer of a control system that benefits from a set of ROS-based multi-channel infrared sensors for providing feedback, and a Jetson Nano onboard computer for performing the computation. The system is expected to maintain an agricultural tractor between the plants’ rows with an accuracy of 5–10 cm from the side with an ideal speed of 5–8 km/h ( Weltzien and Shamshiri, 2019 ). In the software layer, different controllers including PID, machine learning, and fuzzy knowledge-based algorithms ( Shamshiri et al., 2024 ) can be implemented and compared. However successful development of such systems requires a proof-of-concept via extensive validation tests with the digital representation of the sensors, a dynamic model of the robot platform, and a virtual replica of the orchard. The effectiveness and throughput of agricultural mobile robots are propelled by the utilization of machine learning (ML) and deep learning (DL) techniques, which empower robots to learn from and analyze data autonomously, without explicit programming. ML/DL has emerged concurrently with the discipline of Big Data, facilitating the detection of relationships, analysis of patterns, and generation of predictions within farming activities. An illustrative example of the application of supervised machine learning algorithms, coupled with multiple distance detection sensors, is demonstrated by the SunBot project, as depicted in Figure 17 , which proposes the use of a field agent robot for autonomous navigation within berry orchards, conducting health assessments and gathering data to support digital agriculture initiatives. Given the limitations of traditional farming approaches in enhancing productivity, modern farms increasingly rely on IoT systems for data collection, alongside ML/DL techniques for data analysis and decision-making. This integration enables farms to automate partially or fully, thereby optimizing operations and driving productivity growth.

Figure 17 . A proposed control system for collision avoidance in a four-wheel steering field robot agent for berry orchards, utilizing multiple programmable distance sensors to implement machine learning and knowledge-based algorithms for assisted navigation ( Shamshiri et al., 2024 ).

To verify the effectiveness of the proposed collision avoidance system depicted in Figure 17 , initial field visits were conducted to collect preliminary data using high-precision RTK GPS. These data served as the foundation for creating a virtual orchard within CoppeliaSim ( Shamshiri et al., 2018b ), which was interfaced with the ROS ( Quigley et al., 2009 ). This setup facilitated the testing of different sensors, hardware in the loop, and control algorithms on a full-scale simulated tractor and orchard model, as depicted in Figure 18 . The simulation methodology involved translating raw data streams from various sensor inputs (such as GNSS, LiDAR, laser, radar, and RGB camera) into actionable information within the command and control system. This allowed for experimentation with autonomous navigation, enabling the tractor to avoid both moving and stationary obstacles within the orchard environment. Through this simulation-based approach, the proposed collision avoidance system could be thoroughly evaluated and refined before implementation in real-world settings. The result provided a safe, fast, and low-cost experiment platform for the development, testing, and validating of the sensing and control strategies with different algorithms. The simulation scene shown in Figure 18A enabled human-aware navigation by finding the best positions for each sensor on different tractors and provided a flexible solution for attaching other implements and determining the optimum row-end turning patterns in presence of random obstacles. It also accelerated complicated analysis with the weight distribution of the attached implements and to understand the behavior of the tractor on uneven terrains. The main elements of the simulation scenes in this project were (i) mesh files representing plants, tractors, and obstacles, (ii) API and codes that created interfaces between different software environments, and (iii) algorithms and dynamic models including image processing for human detection, inverse kinematics for the hydraulic arms, minimum distance calculation, steering system, path following, and obstacle avoidance algorithms. A prototype of the final proposed solution that benefits from various sensors for autonomous navigation, obstacle avoidance, and safety is shown in Figure 18B . It should be noted that although electrical tractors and mobile robots are contributing to the digital transformation of agriculture by replacing drivers and human operators with artificial intelligence, they are still functioning in experimental phases and require supervision, which makes them far from being deployed on commercial and large operational scales.

Figure 18 . Demonstration of a proof-of-concept for assisted autonomous navigation showing (A) a dynamic simulation for experimenting with multiple sensing solutions, and (B) an articulated steering electric tractor manufactured by Weidemann, equipped with a custom-built electric mower (courtesy of the SunBot project).

4.5 IoT-based control of irrigation pumps

Maintaining precise control of environmental variables within both open-field and closed-field production systems has significant potential for enhancing operational sustainability. By minimizing water, chemical, and energy demands while simultaneously mitigating disease spread, and increasing yield, such control measures can result higher profits. In controlled environments like aeroponic or hydroponic indoor farming, automation systems encounter various uncertainties and disturbances that elude complete modeling or implementation via conventional control algorithms. Consequently, adaptive solutions are necessary to effectively limit production costs and enhance efficiency. To achieve this, data collected from multiple wireless sensors distributed across the growth environment should be leveraged in conjunction with knowledge-based software and dynamic models. For instance, the IoT-based fertigation control system, shown in Figure 19 , can monitor various aspects of a hydroponic production, including flow rate, electrical conductivity (EC), and pH of the fertigation solution, alongside external variables such as solar radiation and climatic conditions. Utilizing the collected data, the system integrates them into models, rule-based algorithms, or adaptive control laws to ensure that specific control commands, such as triggering particular pumps or initiating other processes, are executed at the right moments to effect environmental adjustments. This approach optimizes resource utilization, and enhance sustainability and overall system performance.

Figure 19 . Realization of IoT-based control for multiple actuators using LoRa sensors and a WiFi receiver for precision irrigation, showing (A) the main components and connections between modules, (B) a wireless sensor node with onboard GPS, and (C) a wireless controller with onboard relays. (Source: Adaptive AgroTech).

Since control of some actuators require separate driver boards that can only receive specific type of messages, a separate custom-designed IoT-based controller was designed that communicates with wireless sensor nodes, end-users, and actuators drivers, and can send and receive command signals via CANBUS as shown in Figure 20 . This controller board benefits from a STM32 32-bit ARM processor, and an ESP8266 microcontroller, an onboard RTC clock, two CANBUS ports for industrial communication, and an SD card for data logging. The board can also be interfaced simultaneously with multiple controller driver boards such as relay modules via wired communication ports such as I2C, USART, and SPI, or by means of WiFi wireless signals. The control commands can be generated by the crop growth models that have been implemented in the processor as codes or Simulink blocks. Furthermore, the controller is capable of receiving command signals from cloud-based applications. Concurrently, environmental sensors are attached to collect measurements, storing data on an SD card, and transmitting data either directly to a web server or through wireless communication to a gateway utilizing LoRa modulation. An in-depth elucidation of this framework pertaining to greenhouse tomatoes is provided in ( Shamshiri et al., 2020 ; Rezvani et al., 2020 ). The board presented in Figure 20 was utilized to collect air temperature data within an experimental Agricube. Programmed to read and transmit measurements every 10 s, the board conveyed this information to an open-source secure cloud database via WiFi connection. These data points, each assigned a unique ID representing the collection time and location, were stored on a private cloud database accessible via a secured API key address. Subsequently, they were utilized by the IoT controller as feedback for the control algorithm.

Figure 20 . Realization of IoT-based control for multiple actuators with separate drivers via WiFi, showing the connectivity board that functions as the receiver and main controller, and sample data collected from an experimental Agricube, demonstrating the closed-loop response of a simple ON/OFF control system for a heater to maintain the air temperature between 25°C and 27°C with a feedback frequency of 0.16 Hz (Source: Adaptive AgroTech).

To ensure the reliability of IoT control, numerous data collection samples were conducted and analyzed. The results indicated that no data points were lost during the tests as long as the WiFi network remained available. Enhancing the system’s reliability can be achieved by augmenting the number of WiFi access points, enabling the controller to seamlessly switch between networks. The response of the controller, as depicted in Figure 20 , illustrates the robust performance of IoT-based automation, characterized by high spatiotemporal resolution and excellent stability in data transfer, with 10 readings per minute achievable within a 1.0 km distance from the wireless controller. This level of performance underscores the reliability of this approach in adjusting growth parameters for controlled environment crop production systems.

4.6 IoT-based monitoring in remote locations

The majority of agricultural fields are situated in remote regions with restricted mobile coverage and network accessibility. Consequently, it is commonplace to utilize wireless transmitters and WiFi repeaters to extend coverage over broader areas. However, the energy consumption of these devices, coupled with their reliance on limited energy sources, poses a significant challenge that necessitates design of power management boards. Figure 21 illustrates multiple solar-powered LoRaWAN sensors deployed across various berry orchards in the state of Brandenburg, Germany ( Shamshiri and Weltzien, 2021 ). These sensors are employed for IoT monitoring of various agricultural parameters, including air and soil temperature, relative humidity, soil moisture, leaf wetness, light conditions, and dew-point temperature. Using solar power, these sensors offer a sustainable solution for remote monitoring, ensuring battery charging for continuous data collection and transmission without relying on frequently battery replacement. Some of the main difficulties experienced with the implementation and use of these IoT sensors in orchards can be mentioned as: lack of infrastructure for mobile network coverage, data management and concerns regarding security and privacy, issues related to cost and maintenance, scalability limitations, tolerance to faults, and the need for skilled professionals to implement and manage the system. In addition, inflexibility of the available IoT solutions to operate in harsh environmental conditions, as well as regulatory compliance and standards adherence can be mentioned as the main factors that prevents farmers from adopting these devices. Although LPWAN, point-to-point LoRa, and LoRaWAN sensors are low-energy solutions with long communication range, but they can also face connectivity limitations such as signal interruption and wireless signal quality. For example, in remote areas characterized by geographical constraints and diverse land topologies, wireless signals may encounter attenuation issues due to environmental obstacles or electromagnetic interference from other devices. Such challenges can delay the propagation of wireless signals, impacting the reliability of communication networks. To address these issues, various solutions such as installing signal repeaters to amplify and extend the reach of wireless signals can be used. Additionally, designing more efficient network topologies, such as mesh networks, can help optimize signal propagation by establishing multiple communication pathways and enabling data to circumvent obstacles more effectively. These measures contribute to improving the robustness and reliability of wireless communication systems in remote and challenging environments.

Figure 21 . Implementation of an IoT-based monitoring system in a berry orchard to overcome uncertainties and connectivity issues in remote locations, showing (A) multiple solar-powered WiFi/LoRa sensors with modular accessories, (B,C) wireless data logger and transmitter for wireless monitoring of microclimate parameters, and (D) connectivity boards with WiFi/LoRa modules (Source: SunBot.de).

In small-scale fields, the costs associated with maintenance and ownership may not be justifiable for farmers, particularly when concerns arise regarding the potential sharing of sensitive field information and the associated risks to their production reputation due to inadequate IoT security protocols. The differences between hardware and software from different manufacturers imply heterogeneity in wireless communication protocols and connectivity standards, making it difficult to integrate and standardize the IoT automation process. Additionally, there is currently a lack of standardization and regulation in the IoT industry, which can lead to confusion and complexity when implementing IoT devices in agriculture. Therefore implementation and maintenance of IoT in commercial farms can be expensive and require significant investment in hardware, software, and network infrastructure. Moreover, the reliability of IoT-based automation systems in agriculture is significantly influenced by the harsh environmental conditions and varying climatic characteristics, such as high temperatures, wind speeds, heavy rain, and dusty environments, which can damage sensors or disrupt their performance. Consequently, selecting robust hardware setups capable of withstanding these conditions is paramount. An illustrative example of such a robust hardware setup is presented in Figure 21 , showcasing a modular IoT solution featuring multiple LoRaWAN sensors and gateways custom-built for live field monitoring projects ( Shamshiri and Weltzien, 2021 ; Weltzien and Shamshiri, 2019 ). These devices are designed to withstand harsh field conditions and address challenges associated with WiFi instability. Each sensor benefits from multiple transmitters to mitigate the risk of signal loss, while multiple gateways ensure data uploads to private clouds, enhancing system reliability and resilience.

5 Perspectives of agriculture digitalization in near future

5.1 the 5g network.

The introduction of the fifth generation mobile network (5G) is reshaping and redefining digital agriculture, initiating new possibilities for farming mechanization. A noticeable trend in this context involves the deployment of distributed automation systems, including collaborative robots and swarms of small-scale unmanned machinery. These systems can autonomously perform a range of site-specific operations such as weeding and spraying, leveraging IoT-based cloud computing services. Although similar solutions have been piloted or implemented on a commercial scale, ensuring stable and secure connections between nodes remains a persistent concern. The dynamic nature of agricultural environments, coupled with the reliance on wireless communication, underscores the importance of addressing connectivity stability and security to maximize the effectiveness of these automation systems. Efforts to enhance connection stability and security between nodes are essential for realizing the full potential of distributed automation systems in digital agriculture. The 5G network will provide a reliable and secure communication infrastructure with low latency capabilities for the realization of automated farms ( Ma et al., 2017 ; Khanna and Kaur, 2019 ; Valecce et al., 2019 ; Tang et al., 2021 ) with AI-robotics. Compared with 4G networks, 5G has a faster information transmission rate with higher quality of dissemination, which can effectively be used in developing smart systems with high-speed data transfer, up to 20 Gbps, and can connect more devices per square kilometer ( Li and Li, 2020 ; Said Mohamed et al., 2021 ). This is crucial to enable robotization and digital agriculture processes. Simultaneous use of local mesh and cellular networks can effectively address the problems with poor communications, allowing growers to have uninterrupted stream of data ( Franchi et al., 2021 ), including crop yield, soil, fertilization, smart monitoring, irrigation management, pesticide applications, disease management, autonomous navigation, fruits harvesting ( Navas et al., 2024 ), and supply chain management ( Khujamatov et al., 2021 ; Friha et al., 2021 ). An example lies in the work of Xue et al. (2021) ( Xue et al., 2022 ), in which a frame structure for a drip irrigation remote control system (DIRCS) utilizing 5G-IoT technology alongside a mobile application was introduced. Additionally, Tang et al. (2021) ( Tang et al., 2021 ) demonstrated significant benefits achieved through the implementation of IoT, including a 20% reduction in labor force, a corresponding 20% decrease in pesticide usage, and optimized utilization of water resources and fertilizers ( Yu et al., 2021 ). Figure 22 visually depicts various applications of the 5G network in digital agriculture, illustrating the connectivity links between different sections. These applications showcase the potential of 5G technology to revolutionize agricultural practices, enabling efficient remote control and monitoring systems that enhance productivity while promoting sustainable resource management.

Figure 22 . Selected applications of 5G technology in digital agriculture for improving crop production and increasing efficiency.

The deployment of the 5G mobile network is currently underway in some developed countries, including the United States, the United Kingdom, Germany, South Korea, Japan, and China. However, the initiation of 5G network deployment in many least developed countries is anticipated to require a significantly longer timeframe ( Rahman et al., 2021 ). While the 5G network offers advantages in wireless communication, ensuring uninterrupted connectivity, there remain substantial challenges such as reducing interference, minimizing latency, optimizing power consumption, and enhancing data rates ( Sah et al., 2022 ). Despite assurances from 5G service providers regarding data integrity, confidentiality, and availability, security remains a critical concern that necessitates attention ( Humayun et al., 2021 ). Moreover, the limited battery capacity of sensor nodes poses a challenge for achieving sustainable digital agriculture within a 5G framework. When sensor nodes exhaust their energy reserves, the data center becomes unable to capture environmental information, leading to potential disruptions in decision-making and action implementation ( Chien et al., 2022 ). However, nevertheless these challenges, IoT-enabled precision smallholder farming holds significant promise for enhancing livelihoods and expediting the journey to self-reliance for low- and middle-income countries ( Antony et al., 2020 ). By leveraging advancements in connectivity and data analytics, digital agriculture powered by 5G technology has the potential to revolutionize existing farming practices, and contributing to a more sustainable agriculture.

5.2 Digital twin concept in greenhouse crop production

Digital twin (DT) is one of the trending solutions toward real-time evaluation, optimization, and predictive control of complex systemic process, which has been successfully implemented in various industrial fields including manufacturing ( Kritzinger et al., 2018 ), construction ( Korenhof et al., 2021 ), automotive ( Vachálek et al., 2017 ), energy ( Howard et al., 2020 ). Originated back in 2003 by Michael Grieves ( Jones et al., 2020 ), digital twin is commonly described as consisting of real-world entity (i.e., a physical product, a process, or a machine component) that is interfaced with a virtual replication of that entity (i.e., a simulation model) via bi-directional data connections for feeding data and exchanging information between the two ( Grieves and Vickers, 2017 ). In this concept, the physical system interacts with the digital counterpart within a centralized or cloud-based architecture in order to optimize the process, update control parameters, and generate predictive solutions for what-if scenarios. It should be noted that a system without a connection from the virtual object to the physical object is different from digital twin, and is called digital shadow ( Elahi et al., 2022 ).

Digital Twins have been used successfully in agriculture for developing autonomous farming robots ( Foldager et al., 2020 ), identification of plant pests and diseases in crop production ( Pylianidis et al., 2021 ), stock monitoring of feed silos of livestock farms ( Raba et al., 2021 ), and energy management in commercial greenhouses ( Ashraf et al., 2021 ; Chaux et al., 2021 ; Howard et al., 2021 ). Compared to the industrial application, the agricultural use of DT is still limited, but has a high potential to be expanded in the near future. The main use cases of digital twin in agriculture are focused on predictive analytics, remote monitoring, resource optimization, and risk mitigation ( Purcell et al., 2023 ). Examples includes studies on predictive models that simulate crop growths and soil conditions in order to improve fertilizing and irrigation ( Skobelev et al., 2021 ), or IoT monitoring of plants health and environmental conditions and simulate difference scenarios such as disease outbreaks to mitigate potential losses ( Tekinerdogan and Verdouw, 2020 ). Recently, digital twins have been adopted and employed as a framework in the automation process of commercial greenhouses and hydroponic farms with the objective of reducing energy cost and improving sustainability of the production. To run the digital twin, a connection between the virtual and the physical object is necessary, which includes a various range of sensors at and around the physical object, so the digital twin can realise and react in real-time to all internal and external impacts that have an effect on the behaviour of the physical object. These sensor data are then processed by the digital twin and compared with the physical object from the real world, often carried out with artificial intelligence and machine learning. In this way, the digital twin is always learning from its physical counterpart. Using this approach, it is possible to have the DT trained in a way that it can predict the physical reactions solely from the external impacts, which result in great opportunities such as perceiving failures before they occur or simulating different scenarios to optimize processes without any physical effort. The connections between the physical and virtual part can also exist the other way round, so the gathered data and the resulting predictions from the virtual part can be used to control and correct the actions of the physical part. An overview of the data architecture and concepts of applying DT for greenhouse crop production is shown in Figure 23 that involves three main tasks as energy control, microclimate control, and production changes. The goal is to monitor optimality degrees of microclimate parameters ( Shamshiri, 2017 ), reduce energy inputs ( Ahamed et al., 2019 ; Jain and Tiwari, 2002 ), and enhance all processes from the start of the incoming plants until the delivery.

Figure 23 . Workflow and architecture of a digital twin approach for optimizing environmental control in greenhouse crop production under tropical lowland climate conditions (Source: Adaptive AgroTech).

Modern commercial indoor growing systems have embraced predictive models and model-reference adaptive controllers within their automation systems, leveraging feedback from wireless sensing or IoT monitoring platforms to overcome the challenges posed by conventional timer-based control methods. Through the utilization of digital twin concepts, real-time monitoring of parameters facilitates the training of AI-based algorithms such as machine learning controllers or the development of self-optimizing dynamic models ( Shamshiri and Hameed, 2021 ; Shamshiri et al., 2020 ; Ashraf et al., 2021 ; Shamshiri H. C. M. et al., 2017 ; Shamshiri R. et al., 2017 ; Asfahan et al., 2021 ; Rezvani, 2021 ; Sultan et al., 2021 ), aimed at minimizing energy inputs while maximizing profitability. Figure 23 also illustrates the architecture of a digital twin designed for optimizing greenhouse environmental control, that continuously updates parameters based on various objective functions, including maximizing yield, profit, waste reduction, and minimizing energy consumption. In this framework, the control objective revolves around maintaining internal parameters close to predefined set-points by minimizing a cost function that drives the error between reference values and model values to zero. For microclimate parameters, the controller engages ventilation fans, shading covers, heating, or cooling systems to achieve optimal air temperature and relative humidity, corresponding to an optimal vapor pressure deficit. It should be noted that the dynamics of such systems are highly nonlinear, subject to variations in solar radiation, crop growth stages, covering materials, external conditions, and other disturbances, necessitating the self-tuning of control parameters to mitigate nonlinearities. To address these challenges, multiple wireless sensor nodes are deployed both inside and outside the greenhouse, transmitting measurements to a receiver board. A control algorithm is then employed to adjust the growth environment by activating pumps and other actuators, thereby ensuring optimal conditions for plant growth and productivity while minimizing resource consumption and waste.

5.3 Blockchain

Blockchain is an emerging digital technology that has the potential to revolutionize the way farming and food production is conducted by creating a decentralized and secure network, contributing to better transparency, traceability, and efficiency in the agricultural supply chain. Blockchain can be used to create a digital ledger that records all of the data generated by sensors and controllers. This data can then be used to make more informed decisions about planting, fertilizing, and harvesting crops. For example, growers can use blockchain-based smart contracts to automatically adjust the amount of fertilizer used in their fields by taking into account the soil’s nutrient content in order to reduce the amount needed and minimize environmental impact. A key application of this technology in digital agriculture is supply chain traceability, which means creating a digital ledger that records the entire history of a product, from farm to consumer. This can help to improve food safety, reduce the risk of fraud by tracking the origin of products, and ensure that they meet certain quality standards. Such information are required to improve the efficiency of supply chains, as it allows for better tracking of inventory and logistics. Additionally, by providing consumers with a transparent view of the entire supply chain, Blockchain leads to higher consumers’ trust in the products they are buying. In terms of farmers’ compensation, Blockchain can be used to create decentralized platforms where farmers can sell their products directly to consumers, bypassing intermediaries and increasing their profit margins. This also helps to ensure that farmers are paid a fair price for their products, rather than relying on intermediaries who may take a significant cut of the profits. For example, from farmers and seed seed to consumers’ shelf, a blockchain-based supply chain solution can track each process with unique identifiers as shown in Figure 24 and create a digitally traceable end-to-end journey.

Figure 24 . Schematic description of Blockchain application in agriculture.

Another potential application of blockchain technology in agricultural robotics is the use of autonomous drones and other robots. Blockchain can be used to create a secure and decentralized network that allows drones and robots to communicate and share data in real-time. This can help to improve efficiency, reduce costs, and minimize human error in the agricultural supply chain. For example, drones can be used to survey crops and identify areas that require attention, while robots can be used to perform tasks such as planting, harvesting, and maintaining equipment. In addition to these applications, blockchain technology can be used to improve the way that agricultural land is managed. For example, it can be used to create an immutable record of land ownership, reducing disputes and increasing transparency in land transactions. Additionally, blockchain can be used to create a digital record of land use, making it easier for farmers to access government subsidies and other benefits. In addition, it can help to reduce environmental impact, optimize crop yields, and increase revenue potential for farmers. This is particularly important in countries where land ownership records are often poorly maintained or subject to corruption. It should be noted that while blockchain is considered a promising technology towards a transparent supply chain of food, there are still many barriers and challenges that hinder its wider acceptance among farmers and producers. These challenges are mainly due to technical aspects and limitations in the existing infrastructure, as well as education, policies, and regulations. Nevertheless, it is widely discussed that when successfully implemented, blockchain has the potential to create a more efficient, transparent, and secure supply chain. This reduces environmental impact and optimize crop yields, making agriculture more sustainable and profitable for farmers.

6 Economic, social, and technical considerations

While the highlighted technological solutions play a significant role in the digitalization of agriculture, there exists several limitations and barriers such as high costs that farmers, especially those operating on tighter budgets, must address to ensure broad acceptance, adoption, and utilization. For example, farmers should consider the return on investment (ROI) ( Griffin et al., 2018 ) associated with deploying expensive 5G infrastructure ( van Hilten and Wolfert, 2022 ), autonomous electric tractors and robots ( Rose et al., 2021 ), and IoT devices ( Liu and Wu, 2021 ), alongside exploring potential subsidies or financial support mechanisms. For ROI calculations, factors such as reduced labor costs, optimized resource utilization (such as water and fertilizers) ( Sandor et al., 2022 ), minimized waste, and enhanced decision-making should be taken into account. Robotics, wireless automation, and live monitoring systems can provide excellent insights into crop health to prevent losses, as well as targeted application of inputs for cost savings and yield improvements. Therefore calculating the ROI should involve evaluating not only the initial investment but also the long-term savings and increased productivity they offer. In addition, challenges related to the reliability and scalability of current technologies pose significant concerns to their widespread adoption.

Looking to the future, potential breakthroughs in digital agriculture involve advancements in AI and machine learning algorithms for predictive modeling and decision support ( Aworka et al., 2022 ), the integration of Blockchain technology for transparent and traceable supply chains ( Kamilaris et al., 2019 ), and the development of biotechnology solutions for crop improvement and pest management ( Steinwand and Ronald, 2020 ). Additionally, the continued expansion of rural connectivity and the adoption of 5G technology is expected to further accelerate the digital transformation, enabling real-time data exchange even in remote areas. Although this transition to highly digitalized farming practices, particularly for those in developing countries, is still a significant challenge, but it can be facilitated by the availability of affordable hardware and infrastructure, customized solutions, training centers, and feedback mechanisms. It should be noted that this transition carries social and cultural implications, especially for rural communities deeply rooted in traditional methods, leading to a restructuring of their economies, potentially changing existing employment patterns. For instance, as fieldworks become more automated and data-driven, there may be a shift away from labor-intensive tasks, hence impacting the roles of seasonal workforce and their integration within local communities. This results in both opportunities and challenges, such as the creation of new skilled jobs in technology-related fields, as well as the displacement of workers who lack the necessary digital literacy or access to training opportunities. While some farmers may benefit from the increased productivity and efficiency enabled by digital tools, others struggle to afford or access the necessary technology and training. This can divide and widen socioeconomic inequalities within rural communities, reinforcing disparities between large commercial farms and small-scale or subsistence farmers. Preserving and honoring these cultural legacies while simultaneously embracing innovation pose a delicate balancing act for rural communities undergoing digital transformation. To this aim, developing robots, sensors, mobile apps, and software that are compatible with low-resource settings and support multiple languages, or organizing community-based hands-on workshops, peer-to-peer learning networks, and collaboration between research institutions for enhancing digital literacy will accelerate the accessibility of technology to a broader range of farmers irrespective of their geographic location or socioeconomic status.

The digitalization of agriculture is revolutionizing the way crops are produced and food is secured. The use of cutting-edge technologies such as robotics, computer vision, IoT, 5G, digital twin, and blockchain has allowed farmers to make more informed decisions, optimize crop yields, and reduce costs. This has led to more sustainable and efficient agriculture, which is crucial for ensuring food security in an increasingly populated world. The use of robotics in agriculture has increased efficiency and reduced labor costs, while computer vision and IoT have allowed for real-time monitoring and data collection. Whether it is through the use of drones for crop scouting, autonomous tractors for tilling and planting, or robot manipulators for harvesting, agricultural robots are changing the way farming activities have been conducted for decades. The integration of 5G networks has improved connectivity and data transfer speeds, making it easier for farmers to access information and make decisions. Future trends in this field shows that new concepts such as digital twin allows for virtual testing and simulations, providing a cost-effective way for farmers to make informed decisions. In addition, blockchain technology has the potential to improve traceability and food safety by providing a secure and transparent way to track the movement of crops from the farm to the consumer. However, the widespread adoption of these technologies in agriculture is not without its challenges and limitations. Network coverage and connectivity, data management and storage, security and privacy, cost, interoperability and integration, and regulation and standards are just some of the challenges that were highlighted in this paper that need to be overcome. To address these challenges and promote the acceptance of digital technologies in agriculture, it is important for all stakeholders, including governments, industry, and the research community, to collaborate and work together. Governments can play a key role by providing funding and support for the development and implementation of these technologies. Industry can help by investing in research and development and providing solutions to the challenges faced by farmers. The research community can contribute by conducting studies to better understand the limitations and challenges of these technologies and exploring new and innovative solutions. In conclusion, with the right support and investments, digital agriculture has the potential to make a significant contribution to transform crop production into a more sustainable and efficient system that can ensure food security for generations to come. Future studies may involve analyzing of the socio-economic impacts of digital technologies in agriculture, such as the impacts of digitalization on farmers and rural communities, the accessibility and affordability of the existing solutions, and the policies and regulations that support or hinder the adoption of future developments.

Author contributions

RS: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing–original draft, Writing–review and editing. BS: Conceptualization, Supervision, Validation, Writing–review and editing. CW: Conceptualization, Funding acquisition, Supervision, Writing–review and editing. JF: Conceptualization, Supervision, Validation, Writing–review and editing. RK: Conceptualization, Methodology, Supervision, Writing–review and editing. MS: Conceptualization, Writing–review and editing. SR: Writing–review and editing. DB: Writing–review and editing. MY: Conceptualization, Writing–review and editing. IH: Conceptualization, Supervision, Writing–review and editing.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This study is based on research funded by: the European Innovation Partnership Program for Agricultural Productivity and Sustainability (EIP-AGRI); European Agricultural Fund for Rural Development (EFRE); Project SunBot; GAN/FKZ: 204018000001/80173118 the Federal Ministry for Digital and Traffic (BMDV), Federal Agency for Administrative Services (BAV); Funding measure: 5G implementation funding as part of the 5G innovation program; Project foodChain; GAN/FKZ: 45FGU119_E the Federal Ministry of Food and Agriculture (BMEL); Federal Agency for Agriculture and Food (BLE); Funding program Big Data in agriculture - Innovation funding; Project FungiDetect; GAN/FKZ 2815705615 the Federal Ministry of Education and Research (BMBF); Funding agency Project Management Jülich (PtJ); Funding framework Agricultural systems for the future; Project DAKIS; GAN/ FKZ 031B0513A.

Acknowledgments

The technical support from Maryam Behjati and the laboratory facilities from Adaptive AgroTech are duly acknowledged.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: digital agriculture, artificial intelligence, robotics, digital twins, internet of things, wireless, 5G, block chain

Citation: Shamshiri RR, Sturm B, Weltzien C, Fulton J, Khosla R, Schirrmann M, Raut S, Basavegowda DH, Yamin M and Hameed IA (2024) Digitalization of agriculture for sustainable crop production: a use-case review. Front. Environ. Sci. 12:1375193. doi: 10.3389/fenvs.2024.1375193

Received: 23 January 2024; Accepted: 02 July 2024; Published: 25 July 2024.

Reviewed by:

Copyright © 2024 Shamshiri, Sturm, Weltzien, Fulton, Khosla, Schirrmann, Raut, Basavegowda, Yamin and Hameed. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Redmond R. Shamshiri, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

DOI: 10.11648/j.jbed.20240903.11
Corpus ID: 271436621

Strategic Foresight and Corporate Efficiency of Agricultural Research Institutions in Kenya: Mediating Influence of Leadership Commitment

Enock Warinda , Domeniter Kathula , Michael Ngala
Published in Journal of Business and… 23 July 2024
Agricultural and Food Sciences, Business

27 References

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