Digital tools can transform agriculture to be more sustainable

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URBANA, IL. — Agricultural producers face a double challenge: to increase production for a growing world population while reducing the negative effects on the environment. Digital technologies and artificial intelligence can facilitate sustainable production, but farmers must weigh the opportunities and risks when deciding whether or not to adopt such tools.

In a new paper on agricultural economics, scientists from the University of Illinois offer a research methodology to measure the willingness of producers to adopt new technologies related to digital agriculture.

The paper outlines some of the sustainability challenges for U.S. agriculture and why it’s difficult to meet those challenges with conventional technologies, says Madhu Khanna, Distinguished Professor of Agricultural and Consumer Economics (ACE) and Director of the Institute for sustainability, energy and the environment (iSEE) at the U of I.

“Digital and artificial intelligence technologies can play a role in our move towards a more sustainable future, but there are barriers to their use,” says Khanna. “Farmers are generally cautious about adopting new technologies until the benefits have been well demonstrated and the uncertainties have been reduced, and they see their neighbors and other peers adopting.”

The authors of the document include agricultural economists, engineers, computer scientists and environmental specialists. All are part of the Center for Digital Agriculture (CDA) and the AIFARMS Institute at the USDA National Institute of Food and Agriculture/National Science Foundation at the U of I. These projects are intended to promote the application of the artificial intelligence towards a sustainable future. agriculture.

Digital technologies can compile large amounts of information and offer site-specific management guidelines, helping to increase production efficiency and reduce environmental damage. For example, precision irrigation systems can monitor crop and soil conditions to provide site-specific irrigation. Artificial intelligence can provide information on crop health and soil fertility to help adjust input application rates and reduce nitrogen runoff.

Digital technologies can also help address agricultural labor shortages. Small robots that move under the canopy can enable site-specific fertilization, seeding, diagnostics and mechanical weeding to reduce pesticide use. Canopy robots can also sow cover crops between rows, helping to improve soil health.

While these innovative tools can provide benefits such as lower costs and better yield, they also require upfront investment and farmers need to learn new skills and knowledge to use them. Many digital technologies are still in the early stages of development and immediate results may not be obvious. There are limited programs available to reward farmers for the ecosystem services they provide and often these are not enough to cover the cost of adoption, the researchers say.

“Existing research suggests that in addition to economic factors, behavioral factors play an important role in technology adoption. Even though something may seem profitable, there are often hidden costs or barriers such as concerns about risk or how long it will take to get the return on investment. It’s important to consider all of these behavioral factors as we think about implementing these new technologies,” says Khanna.

While other studies focus on technology adoption that has already occurred, Khanna and his co-authors suggest a novel approach that allows researchers to predict willingness to adopt based on dynamic analysis. .

“We recommend combining survey-based methods with spatial and temporal computer simulation methods where we can model the effect of adoption decisions on the ecosystem. ‘today and tomorrow’s environmental outcomes, which then affects subsequent decisions,’ says Shadi Atallah, associate professor at ACE and co-author of the paper.

“For example, the long-term management of herbicide resistance using robots for non-chemical weed control illustrates the dynamics of costs and benefits. The results are influenced by the decisions farmers make and also by the decisions their neighbors make,” adds Atallah.

For the survey, farmers are presented with choice cards that depict various scenarios which are what neighbors are doing, weed level, technology cost and other factors. Each participant receives a subset of cards featuring different suits. The survey data is then combined with agent-based modeling, which captures individual differences at the farmer level, rather than the population level. Computer simulations then model the dynamic effects of farmers’ decisions in these different scenarios over time.

“In a nutshell, we advocate moving from a static to a more spatially dynamic analysis of adoption and we conduct computational experiments on how policy will affect the adoption of technologies for more sustainable agriculture,” concludes Atallah.

Researchers are currently conducting a survey of the adoption of new cover crop technologies among a random sample of farmers.

The article “Digital Transformation for Sustainable Agriculture in the United States: Opportunities and Challenges” is published in Agricultural Economics. (https://doi.org/10.1111/agec.12733). The authors are Madhu Khanna, Shady Atallah, Saurajyoti Kar, Bijay Sharma, Linghui Wu, Chengzheng Yu, Girish Chowdhary, Chinmay Soman and Kaiyu Guan.

This work is supported by Agriculture and Food Research Initiative (AFRI) Grant No. 2020-67021-32799/project accession no.1024178 from the USDA National Institute of Food and Agriculture.

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