How EO Data can be a Key Enabler for Uncovering the Cost Component of Sustainable Agriculture Transitions
County level maps for percent of cover crop acres, per acre Environmental Quality Incentives Program (EQIP) payments, and per acre Conservation Stewardship Program (CSP) payments (for latest year available in data). (a) Percent of cropland acres planted with cover crops in 2015. (b) Per acre EQIP payments, 2015. (c) Per acre CSP payments, 2014
Project Summary
EO data in agriculture can generate economic value and economic insight in four primary ways, which Co-I Rejesus (NCSU), a leading quantitative agricultural economist, will evaluate:
1) EO data as large scale analysis enabler: The availability of EO data has enabled economics researchers to investigate the impact of agricultural policies on CSA practice adoption at scales not previously possible (Connor et al., 2022; Park et al., 2022). Co-I Rejesus will utilize product outputs from Section II. B&C (practice adoption, yield, field size) to demonstrate how EO data can be a key “enabler” for uncovering the cost component of sustainable agriculture transitions. Specifically, he will collaborate with Hively et al. to merge the longitudinal MD and DE policy implementation data about when farmers chose to receive particular payments with the EO-derived products and RUSLE-R outcomes to undertake a “difference-in-differences” econometric framework (Wooldridge, 2001) to quantitatively estimate the causal impact of varying incentive payment structures on cover crop performance and reductions in soil loss (cost per kg N preserved and per unit of soil loss avoided) and determine the cost optimized incentive structure. This DID framework can be adjusted to allow for more complexity and examine other practices, policies, and outcomes that arise in the ACRES timeframe.
2) EO data as input to increase on-farm profit: Utilizing the cases of variable rate N-application tools (e.g. Guan; PSA Network; Bandaru) and water management tools (e.g. Yang; Ghezzehei), Rejesus will analyze whether farmers who use EO-based-tools to manage N and water inputs have higher yields and profits relative to those who do not use these tools.
3) EO data for early warning to reduce risk & losses: Rejesus will work with Gold, Jiang, and their users to assess the economic impact of EO-based disease detection tools by comparing cost and profit differentials between growers who used these tools versus those who did not.
4) EO data as a provider of information security: Spatially-explicit agriculture information, increasingly provided by “IoT” approaches to digital agriculture, is essential to effective farmer decision making and farm profitability. Once adopted, sudden lack of access to this information can be a “choke point” in the success of farm operations. EO data may mitigate this risk by serving as a redundancy measure. Based on Jiang’s disease sensing IoT approach and Guan’s cross-scale N-tool, Rejesus with AI/ML/Robotics lead Kerner will quantitatively evaluate the error and choke-point risk reduction enabled by satellite EO as supplements to complete IoT systems, and the quality of simulated satellite-only information as a redundant back-up source.
Study Area:
North Carolina
Lead Institution
Project Lead
Rod Rejesus
North Carolina State University
Other Collaborators:
Kaiyu Guan UIUC
Hannah Kerner ASU
Katie Gold Cornell
Yu Jiang Cornell
Teamrat Ghezzehei UC Merced
Yun Yang Mississippi State
W. D. Hively USGS
Serkan Aglasan U. of Arizona
Le Chen U. of Tennessee
Yuyuan Che Texas Tech
Lawson Connor U. of Arkansas
Alison Thieme U. of Maryland