Performing Department
(N/A)
Non Technical Summary
Adverse weather conditions associated with a changing climate pose serious challenges to productivity of simplified agrifood systems. We will use a database of legacy data from 21 long-term cropping-systems experiments in North America to address knowledge gaps about rotational diversity as a climate adaptation strategy. This unique database, built by the DRIVES Project (Diverse Rotations Improve Valuable Ecosystem Services), provides a robust resource for exploring rotational diversity effects on cropping system performance over a wide geographic and temporal range. We will quantify the benefits and trade-offs of crop rotational diversity under adverse weather conditions by examining key aspects of vulnerability--i.e., exposure, sensitivity, potential impacts, and adaptive capacity--separately and together. We will use multilevel Bayesian statistical modeling to evaluate if increasing crop rotation diversity: (1) reduces negative impacts of adverse weather because crops differ in sensitivity and exposure to weather events; (2) improves producer economic stability and adaptive capacity due to diffused risk of volatility in yields, crop prices, and input costs; and (3) improves nutritive output for people due to differing nutrient profiles among crops. Our Bayesian approach allows us to incorporate prior scientific knowledge and provides important information about parameter uncertainty, allowing us to evaluate the reliability of statistical inferences from varying experimental designs. Our project addresses the Foundational Knowledge program areas "Conduct syntheses of existing data to derive general principles about performance of agricultural production systems", and "Investigate how diversification of cropping systems affect outcomes beneficial to system resilience."
Animal Health Component
30%
Research Effort Categories
Basic
60%
Applied
30%
Developmental
10%
Goals / Objectives
Our long-term goal is to reduce barriers to adoption of crop rotational diversity by defining positive and negative effects over a range of locations, soil types, and production systems for multiple stakeholders in the agrifood system. Building on our previous work, we will model risks from adverse weather explicitly and develop new metrics for evaluating system performance. We will quantify the impacts of increased rotational diversity on agronomic, economic, and nutritional performance of cropping systems in a changing climate by testing three hypotheses: Hypothesis 1: Negative impacts of adverse weather on crop productivity are reduced with increasing crop rotation diversity because crops differ in their sensitivity and exposure to weather events, thereby spreading risk in diverse rotations (Objs. 1.1, 1.2, 1.3). Hypothesis 2: Farmer economic stability improves with increasing crop rotation diversity due to diffused risk of yearly volatility in yields, crop prices, and input costs (Obj. 2). Hypothesis 3: Nutritive output for people improves with increasing crop rotation diversity--at the cost of reduced caloric output--due to different crops having different nutrient profiles (Obj. 3).Objective 1.1 Quantify the inherent vulnerability of crop rotations to adverse weatherTo achieve this objective we will create a vulnerability index for a rotation based on occurrence of adverse weather (e.g., drought, extreme heat, and excessive precipitation) during critical growth periods of crops in four steps: 1) define weather features for each crop and site, 2) quantify the sensitivity of each crop to each weather feature, 3) combine weather features and sensitivity scores from each crop to create rotation-level vulnerability scores, and 4) compare how dispersion of vulnerability scores changes with increasing rotational diversity.Objective 1.2: Quantify the degree crop rotations can buffer against severe weatherRotation-level vulnerability to adverse weather may be reduced because crops grown in more diverse rotations are less affected, possibly due to changes in soil or other agroecosystem components that help buffer crops against stress. To address this objective, we will build on our previous work describing how crop- and rotation-level yields respond to varying rotational diversity and growing conditions (Bybee-Finley et al., 2024). While our previous work modeled growing conditions as site-year-average crop yields, the current work will incorporate crop responses to adverse weather directly, based on historical yield and weather data. We will evaluate how rotations buffer yield responses to weather first by comparing how simple and more diverse rotations perform in statistical model simulations under more- and less-stressful conditions, defined based on crop-specific responses to multiple weather variables.Objective 1.3: Evaluate effects of increasing crop rotation diversity under future climate scenarios. Obj. 1.2 will provide statistical models describing how yield outcomes are influenced by rotational diversity. To examine how rotational diversity may buffer yield responses to future weather, we will use these models to simulate yield outcomes under a set of future weather scenarios.Objective 2: Stabilize producer net returns. We will evaluate how volatility in yields, crop prices, and input costs contribute to the stability of net returns in different crop rotations. Our analysis will use coefficients of variation (CV), which measure the amount of volatility per unit of return and are the inverse of the Sharpe Ratio, commonly used to assess financial stock portfolios. The CV represents a risk-reward ratio, allowing us to compare risk from different rotations after normalizing by net returns. We will use CVs to examine, 1) how the stability of different sources of volatility (yields, crop prices, and input costs) differ among rotations, and 2) how the separate sources of volatility contribute to differences in economic stability among rotations.Objective 3: Nutritive valueWe will evaluate the hypothesis that more diverse crop rotations provide a broader range of consumable macronutrients (protein, fat, carbohydrate) and micronutrients (vitamins and minerals) at the cost of reduced caloric output compared to simpler crop rotations. This analysis will combine DRIVES yield data with nutritional information from the USDA National Nutrient Database for Standard Reference. Nutritional outcomes for humans will be compared across alternate end-uses for each crop, based on their model production systems.
Project Methods
This project addresses critical knowledge gaps limiting understanding of the risk-reward ratio of rotational diversity. We assess rotational diversity as a climate adaptation strategy under present and future scenarios. Our approach goes beyond the conventional focus on individual crop responses by analyzing outcomes for whole rotations, thus identifying potential trade-offs comprehensively. In analyzing outcomes, we consider multiple metrics besides yield, including profitability and economic stability for producers (Obj. 2) and nutritional quality for consumers (Obj. 3) to provide a more complete understanding of the value of rotational diversity and provide insights that are currently lacking in the literature connecting agricultural practices to producer and consumer outcomes.This project expands on work completed under a 2019 USDA-AFRI grant (2019-67019-29468). The DRIVES (Diverse Rotations Improve Valuable Ecosystem Services) Project has created a database of 21 long-term experiments (LTEs) across the US, Mexico, and Canada with 467 site-years of legacy data. Data from this network of LTEs enables quantifying the impact of rotational diversity across environmental or geographical gradients, allowing us to evaluate how crop rotations-usually selected to match social-ecological production systems-deliver ecosystem services. Long-term experiments capture outcomes from events that occur infrequently. For example, experiments covering many years are likely to capture rare weather events (e.g., drought) that can be used to analyze responses to adverse weather and extrapolate to future weather scenarios. Objectives 1 and 3 rely on datasets that are already organized in the DRIVES database--i.e., crop yields, weather station data, and experimental metadata. For the economic analysis in Obj. 2, we will expand the database to include agronomic management data that can be used to estimate input costs. We are also adding additional LTEs to widen the range of regions and production systems represented in the database.Our analytical approach relies on Bayesian multilevel modeling to accommodate varying experimental designs within the network and gaps in individual LTE legacy data. Linear multilevel, or mixed-effects, models are powerful and flexible statistical tools for analyzing data with a hierarchical structure (e.g., treatments and replicates within LTEs). The Bayesian approach allows assimilation of non-data-based expert knowledge about parameter uncertainty through the specification of model priors. Also, Bayesian methods provide straightforward measurements for parameter uncertainty, which allows us to evaluate the reliability of statistical inferences. Because Bayesian models do not rely on asymptotic theory, they can accommodate low and uneven sample sizes that prevent model convergence in a frequentist framework. This is especially valuable for combining LTEs with varying experimental designs and year ranges.Results from our statistical modeling will provide a comprehensive framework for quantifying risk-reduction benefits from crop portfolio diversification, known as the portfolio effect.Objective 1quantifies the portfolio effect attributable to crop-level differences in sensitivity and exposure to adverse weather (Obj. 1.1) and buffering at the rotation level (Obj. 1.2). We will use statistical models from historical data to explore the consequences of crop rotation diversity under future climate scenarios (Obj. 1.3). Future climate scenarios will be explored by sampling historical weather (increasing the probability of adverse events) and by applying weather predictions from global climate models (CMIP6). Although our approach is primarily statistical, we will use the process-based DSSAT Cropping System Model for a subset of sites as a check for statistical models as these take into account the actual biological and physical processes occurring and thus are not limited to the correlative relationships between historical weather and yields.While crop yields provide outcomes for agroecosystem performance, outcomes for farmers require additional economic considerations. Therefore, our work underObjective 2will address how volatility in yields, crop prices, and input costs contribute to the portfolio effect of diverse rotations. The coefficient of variation (CV) measures the amount of volatility per unit of return, and is the inverse of the Sharpe Ratio, commonly used to assess financial stock portfolios. We will calculate CVs for yield, crop prices, and input costs separately. The DRIVES database contains historical yield data and management records that can be used in calculating input costs. Crop prices and other data needed to calculate input costs (e.g., fertilizer and seed prices) will be gleaned from public data sources, such as the National Agricultural Statistics Service (NASS). Because different experimental lengths will affect variability in expenditures and returns, we will restrict our analysis to a common time period of 1996 to 2016. This period coincides with periods of high volatility of agricultural profits due to expansion of the federal crop insurance program and food price spikes in 2008. We will use CVs for yields, crop prices, and input costs as response variables for multilevel regression models comparing each source of volatility among rotations. We will also investigate how the separate sources of volatility contribute to volatility in net returns calculated from yield, crop prices, and input costs.Nutrition is a key outcome for agricultural systems at a societal level. Our work forObjective 3evaluates the hypothesis that more diverse crop rotations provide a broader range of consumable macronutrients (protein, fat, carbohydrate) and micronutrients (vitamins and minerals) at the cost of reduced caloric output compared to simpler crop rotations. The consumable nutrient content and caloric output will be determined separately for each crop and then combined by rotations to evaluate potential tradeoffs associated with increased rotational diversity. To generate a nutrient profile for a crop, the primary end uses and downstream food products for each crop (i.e., human consumable grain, meat, eggs, and dairy products) will be combined with nutritional information from the USDA National Nutrient Database for Standard Reference. We will explore different end-use scenarios based on the production systems represented in the DRIVES database. To compare nutritional yield among rotations, separate mixed linear regression models will be constructed for calories, carbohydrates, proteins, fats, vitamins, and minerals. We will also model nutritional yields in response to environmental conditions and rotational diversity, using the statistical framework developed for Obj. 1.2. In addition to comparing separate nutritive yield among rotations, we will perform a multivariate analysis to investigate how combined nutrient yields differ among rotations and to determine how the composition of a rotation affects trade-offs among nutritive components (e.g., calories v. micronutrients).Evaluation:We have developed a timeline and set of milestones for attaining project goals. The milestones include benchmarks for data curation, analysis, and manuscript preparation corresponding to each project objective. All public-facing research products, including presentations and publications, will be reported internally within the USDA (e.g., ARS-115 forms for presentations and manuscripts and annual reports and final reports to NIFA) and externally on our website.The core team will meet monthlyto evaluate progress on these benchmarks and adjust our timeline as needed. Ouradvisory team of collaborators and site representatives will meet once every two monthsto discuss our progress and provide feedback. If we cannot meet our benchmarks within the 36-month grant period, we will apply for no-cost extensions.