Performing Department
(N/A)
Non Technical Summary
Soil health management is crucial for climate change resilience in cotton farming, enhancing soil-water-plant dynamics by boosting available water for plants, improving water infiltration, and optimizing water distribution. Traditional field experiments, while valuable, are expensive and slow. Modeling offers a faster, resource-efficient alternative to explore soil health and crop management strategies. However, current models lack accurate representations of soil health impacts, leaving uncertainties about its role in climate adaptation for cotton. Our aim is to identify drought conditions where soil health practices can counteract climate change's impact on cotton yields. We hypothesize that integrating realistic soil health effects into the Decision Support System for Agrotechnology Transfer (DSSAT) will reveal specific drought indices at which soil health management offsets the adverse effects of climate change on yields. We'll assess how soil health practices and increases in soil organic carbon (SOC) influence DSSAT's soil water balance, using new formulas that link SOC gains to higher plant-available water. Our research will pinpoint the conditions under which soil health management and SOC improvements enhance cotton's climate resilience. By simulating different scenarios across five key cotton states, we'll illustrate the potential yield benefits of higher SOC under two climate change projections. This work aims to forecast the climate resilience of cotton cultivation that promotes soil health and carbon sequestration.
Animal Health Component
0%
Research Effort Categories
Basic
(N/A)
Applied
(N/A)
Developmental
100%
Goals / Objectives
Our overall project goal is to determine drought thresholds for which soil health management and associated SOC increases mitigate the effects of climate change on cotton yield. Our central hypothesis is that by incorporating realistic soil health effects in the Decision Support System for Agrotechnology Transfer (DSSAT) Cropping System Model (CSM) (Jones et al. 2003; Hoogenboom et al. 2019) we will identify drought index values for which soil health management negates the effects of climate change on cotton yield. For this one-year proposal, we have two main objectives:Objective 1: Quantify the impact of soil health management and associated SOC increases on the components of the soil water balance in DSSAT We have developed new pedotransfer functions (predictive empirical relationships) from the North American Project to Evaluation Soil Health Measurements (NAPESHM), with 124 sites across Canada, Mexico, and the U.S.A, relating increases in SOC, driven by management changes, to increases in plant available water. Based on those findings, our working hypothesis is that incorporating the new pedotransfer functions into DSSAT will improve the overall soil water holding characteristics and thus, simulate root water uptake and plant transpiration (H1.1). Our approach to testing this will be to compare modeled transpiration in the improved DSSAT version to the current version. Based on previous work outlined in the Background section, we expect that changes in permanent wilting point, field capacity, plant stress coefficients (based on water holding capacity), and soil evaporation will likely change the ratio of transpiration to evaporation and affect modeled yield (H1.2). This will be easily tested by comparing the ratio of transpiration to evaporation between the current and improved versions of DSSAT. Based on our prior work, we anticipate that iterative and holistic updates are needed to bring DSSAT in line with soil health knowledge. We expect that including additional effects of soil health management (e.g., residue effects on evaporation) and revising management selections to represent soil health systems (e.g., surface roughness and residue reflect real soil health management rather than untilled conventional fields) will result in greater transpiration (H1.3) which has the potential to increase cotton yield.Objective 2: Identify in what soil, management, and climatic scenarios soil health management and an increase in SOC will improve cotton resilience to climate change We hypothesize that there are upper and lower threshold levels of drought for which soil health management and associated SOC increases provide climate resilience to cotton production systems (H2.1). Using 30-year simulations to generate distributions of cotton yield, H2.1 will be tested using the Standardized Precipitation-Evapotranspiration Index (SPEI) to quantify drought severity and simulate cotton growth and response in conventional and soil health management systems. The relationship between drought severity and simulated cotton production will be analyzed across four U.S. cotton-producing states and two climate change scenarios. We also hypothesize that soil health management systems and associated SOC increases will show a greater positive effect on cotton production in a lower emissions scenario (H2.2) because higher emissions scenarios will have effects that overwhelm the benefits of soil health management. The two climate scenarios will provide model outputs for a direct test of this hypothesis.These objectives will allow simulation of realistic effects of soil health management systems for climate resilience in cotton that can elucidate under what conditions soil health management is effective at climate change mitigation. The novel empirical relationships from NAPESHM, along with our holistic approach to updating the water balance and management selections, will bring DSSAT in line with current soil health knowledge by correctly representing soil health management and soil properties. We will test and publish the effects of changes in SOC due to soil health management on climate resilience in cotton systems with specific attention to cotton yield and plant available soil water storage for crops. We will distill the relevant farmer-facing material from our peer-reviewed publication into a free factsheet that will be hosted and promoted by the Soil Health Institute. We will also deliver a guidance document that will outline data and algorithms needed for future model improvements. The guidance document will support the establishment of new soil health experiments targeted to improve models further and the improved DSSAT version will inform cotton producers and the cotton industry about the true benefits of soil health in climate-disrupted growing conditions.
Project Methods
Methods for Objective 1 The Postdoctoral Research Associate (University of Florida) and Research Soil Scientist (SHI) will both familiarize themselves with the DSSAT CSM with special attention to the CROPGRO-Cotton and soil dynamics components using literature and example DSSAT datasets provided by the Co-PI. They will also attend the DSSAT CSM international training scheduled for May 2024. New DSSAT code will be written in Fortran. Model development meetings will occur weekly between the postdoctoral model developer and the research soil scientist. It is anticipated that both will also participate in the DSSAT Mini-Sprint sessions that are organized weekly to focus on crop model improvement. PI Bagnall and Co-PI Hoogenboom will review monthly oral progress reports in Zoom meetings or as needed to ensure progress. Data analysis and scenario development will be done by the Research Soil Scientist in R studio. Elements to be included in the guidance document will be continuously identified in weekly project check-ins and recorded by the research soil scientist who will be responsible for identifying literature that can be used to recommend data acquisition approaches. The document will be edited by PI Bagnall and Co-PI Hoogenboom, evaluated as described in the project evaluation plan, and published by SHI's agency of record, the River's Agency, before being listed on both the SHI and DSSAT website as a PDF.Methods for Objective 2 The research soil scientist will be responsible for organizing all input data for the simulations run in Objective 3. Data analysis will be done in R Studio. Files will be stored, and data shared in accordance with our data management plan. Simulations for Objective 3 will be run by the postdoctoral Model Developer. For each combination of site, climate scenario, and soil health treatment, we will compute the log response ratio of yield and soil water storage for the three levels of SOC. We will use the rma.mv function in the metafor package in R (Viechbaur, 2010) to fit a meta-analytic model to predict log response ratios controlling for site and year and weighting by the number of replications of treatment pairs at each site. Differences in yield or stored plant available water will be considered statistically different if the 95% confidence interval calculated by the meta-analytic model, does not contain zero. Because some differences that are not statistical may be of practical importance for growers or the industry, we will report the magnitude of differences in yield, and the Research Soil Scientist and Postdoctoral Researcher will jointly draft one or more manuscripts describing the outcomes of Objective 3. We will include estimates of model uncertainty. The SPEI will be calculated using precipitation as the input data. The SPEI uses the monthly difference between precipitation and potential evapotranspiration, which will be calculated by using the Priestley-Taylor method. We will translate the most salient findings from our peer-reviewed publication into a farmer-facing factsheet that will be hosted and promoted by SHI. PI Bagnall has experience developing factsheets for lay audiences. Co-PI Hoogenboom will have access to UF's Cooperative Extension Service for dissemination to local extension clientele.