PARTNERSHIP: A BETTER BET FOR THE FARM: BUILDING A PREDICTIVE MODEL TO QUANTIFY HOW DIVERSIFIED CROPPING SYSTEMS AFFECT PRODUCTION AND ECONOMIC RISKS ACROSS A MILLION FIELDS IN THE MIDWEST

• Sponsoring Institution: National Institute of Food and Agriculture
• Project Status: ACTIVE
• Funding Source: AFRI COMPETITIVE GRANT
• Reporting Frequency: Annual
• Accession No.: 1030387
• Grant No.: 2023-67019-39724
• Cumulative Award Amt.: $800,000.00
• Proposal No.: 2022-09810
• Project Start Date: Jun 1, 2023
• Project End Date: May 31, 2026
• Grant Year: 2023
• Program Code: [A1451]- Renewable Energy, Natural Resources, and Environment: Agroecosystem Management

Recipient Organization
UNIVERSITY OF CALIFORNIA, BERKELEY
(N/A)
BERKELEY,CA 94720

Performing Department
(N/A)

Non Technical Summary
Farming has always been risky, with droughts, floods, heat waves and other hazards harming crop production and farmers' livelihoods for millenia. Climate change increases the severity of these hazards, including heavier spring rainfall and drier summers. For example, the 2012 drought reduced maize yields by ~25% in the U.S. Midwest, causing the U.S. government's most expensive year for crop insurance payouts to date, at $18.6 billion. In 2019, historic spring flooding coupled with summer drought combined to cause a 24% spike in farm bankruptcies over the prior year. At the same time, regional specialization in just two crops, maize and soybeans, make the Midwest increasingly vulnerable to stressful weather events. While safety nets like crop insurance help mitigate farmers' exposure to negative outcomes, they do not protect food supplies from being disrupted, with concomitant price spikes. Moreover, economic incentives in the current federal crop insurance system encourage simplified crop rotations that may be more vulnerable to stressful weather, and thus can increase risk for the farmer and insurer. Here, we propose to assess how more diversified cropping systems impact maize and soybean yields during stressful weather across ~1M fields in the Midwest, identifying hot spots where additional cash crops and cover crops would lead to greater resilience. Further, we will translate these impacts into actuarial and economic information that can inform agricultural insurance and lending industries to appropriately value benefits of risk mitigation. If diversifying cropping systems does indeed reduce risk - and we identify when, where, and to what extent - then appropriate valuation will be a critical component of a policy and economic environment that enables farmers to shift toward these practices.Diversifying cropping systems includes adding additional cash crops into a rotation and/or adding cover crops that increase the presence and duration of ground cover. Syntheses of long-term experiments have recently shown how systems with more complex crop rotations can reduce the vulnerability of grain yields to stressful weather, including to droughts and heat waves. At the farm level and beyond, more diverse crop rotations help stabilize food production through the so-called "portfolio effect", based on how distinct crops respond to environmental stressors differently. Several studies have also shown how cover crops best support maize yields in dry years and drier landscape positions, though syntheses of cover crops' effects on crop yields during stress have not yet been conducted. Diversified crop rotations and cover cropping also come with well-recognized environmental benefits, such as reduced need for fertilizer and pesticide inputs and their associated greenhouse gas (GHG) emissionsand increases in soil carbon that can help offset GHG emissions. Thus, more complex crop rotations have potential not only to help adapt agriculture to climate change hazards by reducing production risks, but also mitigate climate change. How diversified cropping systems reduce risk has recently been identified as a critical "research priority for global food security under extreme events" .Despite their benefits, diverse cash crop rotations and cover crops are both rare in the Midwest. Cash crop rotation has continued on a century-long decline in the US Midwest, with corn-soy rotations increasingly dominating over historical cash crop sequences that included small grains and perennials, and with corn monocultures on the rise in the past decade. In contrast to cash crop rotations, cover crop adoption has been increasing in recent years yet remains low, <5% of cropland area nationally. While there are distinct technical challenges and barriers to the adoption of these practices, there may be a significant opportunity to create an incentive for their adoption that does not rely on new funding sources, but rather leverages the value of risk reduction.Crop insurance and agricultural lending -- the two main industries that value risk in agriculture -- do not typically reward farmers for changing their production systems to reduce vulnerability to weather and climate hazards. If diversified cropping systems do reduce production and/or profitability risks, then the dollar value of this risk reduction could be applied to insurance and lending policies and passed onto farmers. But achieving these savings will require actuarially sound models that can guide stakeholders in determining the risk reductions associated with these practices in farm-specific contexts.Our overarching, long-term goals are to: 1) Help farmers capture more of the value of diversifying cash crop rotations and adopting cover crops; and 2) Increase the adoption of these practices by informing better public and private policies.To work toward these long-term research goals, this proposed project will leverage advances in remote sensing of agricultural practices, modeling of crop yields, and applied data science. We will use a longitudinal dataset of historical corn and soybean yield maps spanning ~1M fields across 9 states and two decades in the U.S. Corn Belt, coupled with remotely sensed field-scale information on cash crop rotation, cover cropping, and environmental variables.

Animal Health Component

30%

Research Effort Categories

Basic

60%

Applied

30%

Developmental

10%

Classification

Knowledge Area (KA)	Subject of Investigation (SOI)	Field of Science (FOS)	Percent
601	2410	2090	100%

Knowledge Area
601 - Economics of Agricultural Production and Farm Management;

Subject Of Investigation
2410 - Cross-commodity research--multiple crops;

Field Of Science
2090 - Statistics, econometrics, and biometrics;

Keywords

cover crops

crop rotation

diversified cropping systems

risk

stressful weather

yields

Goals / Objectives
Our overarching, long-term goals are to: 1) Help farmers capture more of the value of diversifying cash crop rotations and adopting cover crops; and 2) Increase the adoption of these practices by informing better public and private policies.To work toward these long-term research goals, this proposed project will leverage advances in remote sensing of agricultural practices, modeling of crop yields, and applied data science. We will use a longitudinal dataset of historical corn and soybean yield maps spanning ~1M fields across 9 states and two decades in the U.S. Corn Belt, coupled with remotely sensed field-scale information on cash crop rotation, cover cropping, and environmental variables. Our four objectives include:Objective 1: Build datasets. Compile and process datasets for yield, management, weather, and soils at the field level across 9 states, and conduct quality control analyses.Objective 2: Quantify risk reduction effect. Determine the spatial patterns and magnitudes of cropping system diversification's impacts on rainfed corn and soybean yields during stressful weather in the U.S. Corn Belt.Objective 3: Create actuarially sound information on present and future risk. Conduct Bayesian analyses to quantify uncertainties regarding risk reduction of cropping system diversification. Objective 4: Identify farm economic impacts. Quantify farm-level economic impacts of cropping system diversification, particularly net returns and variance of net returns, using current and projected climate information.

Project Methods
We will first compile a dataset including field-scale information on crop rotation, cover cropping, corn and soybean yields, weather, and soils, using several well-validated data sources (Obj. 1). This dataset spans nine states and nearly two decades. Next, we will build statistical models to estimate the impact of diversified cropping systems (crop rotation and cover cropping) on corn and soybean yields across different weather and soil conditions, taking into account interactions and spatial autocorrelation (Obj. 2a). These models will also be used to ask how these yields' risk profiles would change under different weather scenarios (Obj. 2b) and to identify regions and rotations for which risk reduction, relative to a conventional cropping system baseline, is particularly strong ("hot spots" and "hot rotations", respectively) (Obj. 2c). The models will then be adapted to propagate statistical uncertainties to create actuarially sound, frequency-severity risk profiles under different diversification practices and weather risks (Obj. 3) -- geared toward both agricultural insurers (Obj. 3a) and lenders (Obj. 3b) -- as well as how such profiles could evolve under several realistic climate change scenarios (Obj. 3c). Simultaneously, we will combine our models with financial and market data sources to assess farm-level economic outcomes (Obj. 4). This includes net revenue for model farms adopting or not adopting diversified cropping systems and the spatial distribution of those returns based on "hot spots" and "hot rotations" (Obj. 4a). Then, using methods similar to one used in current crop insurance programs, we will develop crop insurance rates implied for adopting farms (Obj. 4b). Finally, using implied rates, a policy selection game will help us assess the value to insurers of the risk reduction implied by a portfolio of farms who adopt diversified practices, compared to the risk before adoption, in those farms' current crop insurance coverage (Obj. 4c).

Progress 06/01/23 to 05/31/24

Outputs
Target Audience:The key target audiences during this reporting period included undergraduate and graduate students and postdocs, academic researchers, as well as agricultural lenders and U.S. federal policymakers. Engagement in applied statistical research provided experiential learning opportunities for undergraduate and graduate students and postdocs. These trainees are a key target audience because it is important they gain domain knowledge of agriculture and diversified cropping systems as well as experience communicating their findings to knowledgeable laypeople. Academic researchers were another key audiences via academic audiences including the Conference on Applied Statistics in Agriculture and Natural Resources (attended by students, professors, and contractors working in fields related to agriculture and natural resources) and the American Geophysical Union Conference (attended by a wide array of academic disciplines as well as representatives from large agriculture / natural resources NGOs). Academic professionals are a key audience in order to receive feedback on our research methods and spark potential for collaboration. During this period, we also focused on engaging agricultural lenders, primarily in the private sector. Since this research can play an important role in helping the institutions who are in the business of assessing agricultural risk understand the risk-mitigating impacts of diversified cropping systems, early engagement is important so they might consider how to develop pricing or incentives that reflect the impact on production risk and on-farm profitability. In turn, the lenders we engaged also provided their expertise on loan pricing, underwriting, appraisal, crop insurance, and legal requirements to help inform the development of our beta risk model tool and the underlying research. To a lesser degree during this period, we also engaged agricultural insurers, both private and public/federal, to share our project's approach, and understand their current considerations, constraints, availability of data, etc. when assessing yield risk in agriculture, developing actuarial tables and writing or updating policies of insurance. ?We have also engaged in educational outreach with Congressional offices, particularly those within the nine state region of the US Midwest where the Risk Model focuses, as well as with members on the House and Senate Agriculture Committees, and USDA Risk Management Agency staff. Our outreach to policymakers allows us to share our research approach, partnerships, and preliminary findings on our risk-reduction potential of diverse crop rotations, and discuss potential key takeaways for federal policymakers. It also allows us to better understand informational needs for developing new FCIP policies that reflect risk profiles of diversified cropping systems within current statutory limits. Changes/Problems:A delay that occurred early in the award period came from difficulty in acquiring ground truth data for developing a dataset on the presence and absence of cover crops. We had to pivot to creating our own cover-cropping data due to a challenge with the partner we had identified in the grant proposal. While we have largely resolved the technical challenges, especially through a partnership with the Eric and Wendy Schmidt Center for Data Science for the Environment at UC Berkeley, we are still looking for ways to obtain more ground truth data that is required to develop detection models for cover crops from remotely sensed data. The change that may result from this challenge is narrowing the geographic scope at which we are able to assess the risk mitigating effects of cover crops, from nine states to possibly three or four. What opportunities for training and professional development has the project provided?This project has supported multiple PhD students and a postdoc in gaining skills in applied statistics in agricultural systems. They have received both 1:1 mentorship from all PIs from both angles of 1) advancing their applied statistical methods in cutting-edge ways (e.g. causal inference, spatial statistics of large datasets) and 2) knowledge of diversified agricultural systems and their interactions with climate. They have also received training and experience in communicating their work with a transdisciplinary group that includes non-profits, policymakers and lenders. They have also presented their research at academic conferences. How have the results been disseminated to communities of interest?Our team has given multiple conference presentations, including at the Conference on Applied Statistics in Agriculture and Natural Resources and American Geophysical Union. Co-PI Aria McLauchlan and other members of her non-profit, Land Core, have led efforts to communicate early findings with agricultural lenders and insurers, as well as members of Congress and their staffers. For example, we created a tool that allows for examining outputs from our statistical model of yields, crop rotation and climate in IL, showing patterns of risk mitigation and its magnitude. We provided in-depth demos of the tool's functionality to our flagship banking partner, Compeer Financial, and to pioneering lender, Fractal Ag, and we presented the tool and preliminary findings to audiences of agricultural lenders, investors, insurers & policymakers. These demos and conversations have been instrumental in providing user feedback and allow us to refine aspects of the tool. Co-PI McLauchlan also met with over 20 Congressional offices in October 2023 to share the project research, preliminary findings and the beta tool. These offices expressed great interest in the tool, and were curious to learn more about how the tool could be used to scale adoption of conservation practices in their districts and inform crop insurance. PI Bowles met multiple times with staff from the Risk Management Agency to share results and obtain feedback on the research. What do you plan to do during the next reporting period to accomplish the goals?Our main effort in the next reporting period will be to expand the work assessing how diversifed crop rotations and cover cropping affect maize and soybean yields across the full nine state region. We are first going to complete a comparison of causal inference methods to see which performs best with the less computational intensity. This is an important step to dealing with the confounding factors present in our observational data. We will also continue mapping cover crops, using remotely sensed data based on machine learning methods. This will include improving the accuracy of our current map (which only covers Indiana due to the presence of a lot of ground truth data) and expanding the geographic scope of the maps (to the best of our ability as more ground truth data is obtained). We will also continue the initial work on farm revenue prediction, including a full analysis of the correlation structure between various types of corn price (country elevator prices, grain terminal prices, national-level futures contracts), and how field yield predictions are correlated on a given farm. We will also begin to assess the uncertainty due to the prices of inputs and other farm operation costs such as grain storage, and how farmers can optimize their cropping and marketing decisions based on all these uncertainty assessments.

Impacts
What was accomplished under these goals? We have made excellent progress toward several objectives. For objective 1, we have assembled a large geospatial dataset of high resolution (30m for most datasets) on crop rotational complexity, maize and soybean yields, soil characteristics, and climate information, which we have aggregated to the field level across five of our nine focus states in the Corn Belt. The remaining four states are currently in progress. One major effort in parallel was creating a field-level dataset on the presence and absence of cover cropping. A geospatial data scientist, Kangogo Sogomo, working with co-PI Land Core, developed custom pipelines to rapidly process Harmonized Landsat and Sentinel imagery to 1) generate cloud-free gap filled daily annual data at 30m resolution; 2) identify "green up" dates for cash and putative cover crops; and 3) efficiently extract candidate features from these NDVI time series that could be used to distinguish cover crops leveraging UC Berkeley's High Power Computing resources. This approach is based on a novel strategy combining methods from Zhou et al (2022) and Gao et al (2020) which distinguishes presence or absence of cover in fields. In tandem, we have collected >36,000 field points ground-truth cover crop observations from 2014 to 2019 for model training and validation, though greater geographic scope is ideally needed. The next steps are to determine the dynamic thresholds that define cover cropped and non cover cropped fields and develop a model that can accurately predict fields in the entire state of Indiana and then the additional 8 midwestern project states. Regarding Objective 2, we have also nearly completed a study of how crop rotational diversity affects corn yields during stressful weather in two states, IL and MN. Specifically, we used Bayesian modeling to show spatial patterns of yield benefits that result from increasing rotational diversity in a range of weather conditions. Data from over 2.2 million field-years revealed that diverse rotations decreased the risk of corn yield losses in dry years in areas that experience these conditions more frequently, while simultaneously increasing yields under favorable conditions across the region. This resilience effect was particularly clear across Illinois, where increasing rotational complexity typically does not negatively impact cash crop yields, and helps limit losses in drier years. There is also some indication that, in certain counties in Illinois, these more complex rotations can increase the chance of a bumper crop in good years (years with average rainfall). The potential for yield risk mitigation underscores the critical need for crop rotation adoption as changing climates threaten yield stability in the US and across the globe. We highlight areas where diverse rotations are most likely to improve yields and mitigate risk, amidst spatial heterogeneity in the magnitude of these benefits. For Objective 3, we have completed and submitted a paper examining future risk. In this first study for this project, we were interested in understanding how climate change might affect crop insurance indemnity claims for corn, and in subsequent studies we will focus on the role of diversified systems in mitigating these claims. This research area recognizes that climate change not only threatens agricultural producers but also strains financial institutions. We use an artificial neural network to predict future maize yields in the U.S. Corn Belt, finding alarming changes to institutional risk exposure within the Federal Crop Insurance Program. Specifically,our machine learning method anticipates more frequent and more severe yield losses that would result in the annual probability of Yield Protection (YP) claims to more than double at mid-century relative to simulations without continued climate change. Furthermore, our dual finding of relatively unchanged average yields paired with decreasing yield stability reveals targeted opportunities to adjust coverage formulas to include variability. This important structural shift may help regulators support grower adaptation to continued climate change by recognizing the value of risk-reducing strategies such as regenerative agriculture. Altogether, paired with open source interactive tools for deeper investigation, our risk profile simulations fill an actionable gap in current understanding, bridging granular historic yield estimation and climate-informed prediction of future insurer-relevant loss. Finally, we have begun research under Objective 4. Initial results focus on predicting uncertainty in farm revenue from cash crops. Our preliminary results show (1) cost uncertainty is a significantly larger share of variability than weather uncertainty or idiosyncratic (unexplained) variability at the field level; (2) revenue uncertainty is significantly smaller at farm level than field level; and (3) there is evidence of revenue risk reduction at the field level when going from 2 crops to 3 crops in rotation. This is work in progress since the analysis still needs to incorporate the fact that when crop yields are expected to go down, crop prices tend to start rising

Publications

• Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Cross, H. (2023). Quantifying and Reducing Production Risk in Agriculture: Key Takeaways from a New Soil Health Risk Model, Extension Risk Management Education National Conference (ERME), Chicago, IL.
• Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: McLauchlan, A. (2023). How Policy is Shaping Finance and Investment in Regeneration. Regenerative Food Systems Investment Forum, 2023, Denver, CO.
• Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Cross, H. (2023) Rethinking Risk in Agricultural Portfolios: Adapting to Climate Volatility in Farmland Management and Underwriting. CREO Global Meeting, Montreal, QC, Canada.
• Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Sogomo, K., Kang, Y., Bowles, T.M., Gao, F. (2023). Developing a Remote Sensed Cover Crop Dataset for the U.S. Midwest using Harmonized Landsat Sentinel (HLS) poster presentation, American Geophysical Union Conference, San Francisco, CA.
• Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Bowles, T.M., Vendig, I., Socolar, Y., Manski, S., Cross, H., Fettes, K., Goldstein, B., McLauchlan, A., Pizzo, G., Viens, F. (2023) Diversifying cropping systems to build soil carbon and adapt to climate risks. Invited oral presentation, American Geophysical Union Conference, December, 2024, San Francisco, CA.
• Type: Conference Papers and Presentations Status: Published Year Published: 2024 Citation: Gina Pizzo, Lawson Connor, Eunchun Park, Zirong Liu, and Frederi Viens. (2024) Farm Revenue Uncertainty: a Bayesian study for Corn in the US Midwest", oral presentation, Conference on Applied Statistics in Agriculture and Natural Resources, Iowa State University.
• Type: Conference Papers and Presentations Status: Published Year Published: 2024 Citation: Tyler Bagwell, Gina Pizzo, Sam Manski, and Frederi Viens. (2024) Constructing interpretable statistical crop yield prediction models using field-level data, oral presentation, Conference on Applied Statistics in Agriculture and Natural Resources, Iowa State University.