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
0%
Research Effort Categories
Basic
60%
Applied
30%
Developmental
10%
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).