What does technology have to do with sustainable agriculture?

Agricultural production today faces two key challenges: meeting the demands of a growing population and mitigating the environmental impacts of agriculture. Nutrient run-off and greenhouse gas emissions are two of the major categories of agriculturally-related environmental impacts in the U.S. Nutrient run-off is a major source of water pollution and greenhouse gas emissions from agriculture are a major contributor to climate change. Will the growing popularity of technology-based farm management systems mitigate these challenges of agricultural production? What will be the nature of this impact?
 
In a recent study published in the journal Precision Agriculture, researchers from the University of Kentucky attempt to answer these questions by designing a model to explore the impact of "precision agriculture" adoption at the farm level, on the policy level, and how these impacts depend on variables such as crop prices and rotations. Precision agriculture has many definitions. The U.S. Department of Agriculture defines it as a management system that is information- and technology-based, is site specific, and uses one or more of the following sources of data: soils, crops, nutrients, pests, moisture, or yield for optimum profitability, sustainability, and protection of the environment. 
 
Focusing on large corn and soybean farms, the researchers use a method called the whole-farm model that simulates a typical farm system, the biophysical characteristics of the farm, management decisions, agricultural yields and historical weather patterns. A 1,052 hectare farm in Henderson County, Kentucky was chosen as the site for simulation as a representative of large farms in the Ohio Valley region. Large farms tend to adopt precision agriculture more readily than smaller operations, which is why the researchers have based their simulation on a larger operation.
 
The researchers use three precision agriculture based technologies and a baseline of no-technology. Policies tested included no-policy (baseline), nitrogen limit, nitrogen tax, carbon limit, and carbon tax. It turns out that, in terms of a farm's overall net returns and environmental impacts, precision agriculture led to mixed results and the impact greatly varied depending on the type of technology or combination of technology as well as the market situation face by the farm. In terms of policy, the adoption of precision agriculture altered the effectiveness of policies geared towards environmental objectives and was very dependent on the type of technology or combination of technology once again. The combination of all three technologies fared much better in environmental performance while it lowered the effectiveness of tax policies. The effectiveness of limit policies was also decreased when a combination of technologies was used. Overall, the researchers suggest that quantity-based policies like quotas and limits may be more effective to technological change or uncertainty in environmental targets.
 
The researchers conclude that with the increase in the popularity of data-driven agriculture, it is imperative that policy makers understand how it impacts the farm, the farmer and the environment. Environmental policies that worked in the absence of technology might not have the same impact when new technology is adopted. New technology to increase efficiency in agriculture can have huge benefits to the environment — but when the policies are not aligned, it might do more harm than good.