DSSAT-based evaluation of optimal planting windows for rainfed sorghum under climate uncertainty

Introduction

In most years, a two-week shift in planting date barely matters for irrigated crops but for rainfed sorghum in the southeastern United States, that same shift can mean the difference between catching the right rainfall window or missing it entirely [1] . Sorghum is relatively droughttolerant, yet its yield still depends on adequate moisture during panicle initiation and grain fill [2,3] . Climate projections for the US Southeast indicate higher temperatures, more erratic rainfall, and longer dry spells by mid-century [4] . These changes will likely shift the optimal planting window, but by how much and in which direction isn't clear without simulation analysis [5] . DSSAT's CERES-Sorghum module has been calibrated for a range of environments and can run long-term seasonal analyses using historical and projected weather [6,7] . This research used DSSAT to simulate sorghum yield across seven planting dates under baseline and two RCP climate scenarios at Gainesville, Florida, quantifying both mean yield and year-toyear variability to identify the optimal window under current and future conditions [8,9,10] .

Simulation Parameters and Model Configuration

DSSAT v4.7.5 with CERES-Sorghum was used. Cultivar coefficients for grain sorghum hybrid DKS 53-67 were calibrated against three years of University of Florida trial data (2017-2019) [6,11] . Baseline weather comprised 31 years (1990-2020) of daily records from Gainesville. Future climate scenarios were generated using the delta method applied to five CMIP5 GCM outputs under RCP 4.5 and RCP 8.5 for the 2040-2070 period [4,12] . Soil profile for Arredondo fine sand was parameterized from USDA-NRCS survey data to 180 cm depth. Simulations assumed no irrigation, 80 kg N/ha split application, and automatic planting triggered on each target date when soil moisture exceeded 50% of available water [7,13] .

Seasonal Variations

Under the baseline period, late planting dates (May 15, June 1) coincided with the onset of Florida's summer afternoon convective storms, which provided rainfall but also increased the risk of waterlogging and foliar disease. Early dates (March 1, March 15) often faced cool soil temperatures that slowed emergence. Under RCP 8.5, the rainy season onset shifted roughly 10 days later in the GCM ensemble median, which penalized late plantings more severely. The April 1 window remained robust because it fell between the cold risk and the shifted rain belt.

Material and Methods Material

The research was conducted at the Department of Agronomy, University of Florida, Gainesville, USA (29.63°N, 82.37°W, 23 m elevation). Historical daily weather data (solar radiation, maximum and minimum temperature, rainfall) were obtained from the Florida Automated Weather Network station on campus [14] . Soil data for the Arredondo fine sand series (Grossarenic Paleudult, 94% sand, pH 5.8, organic carbon 0.87%) were taken from the ISRIC World Soil Information database and verified against local profile measurements [15] . Methods: Seven planting dates at biweekly intervals from March to June 1 were simulated across 31 baseline years and 150 future-scenario years (30 years x 5 GCMs). Each simulation assumed rainfed conditions with 80 kg N/ha applied as urea in two equal splits at planting and 35 days after emergence [16] . Model output included grain yield, days to anthesis, days to maturity, and seasonal water use. Yield variability was expressed as coefficient of variation (CV) across years. An optimal window was defined as the planting date range producing mean yield within 90% of the maximum and CV below 25% [17] . ANOVA and Tukey's test at 5% were applied to compare planting date means within each climate scenario. Protocol AGR-2021-UF-039 was approved January 2021.

Results

Limitations

The delta method for generating future weather preserves historical variability patterns, which may not capture novel extremes under climate change. Simulations assumed constant management (N rate, cultivar) across scenarios; in practice, farmers would adapt. Soil degradation over time was not modelled. The single-site analysis limits extrapolation; a multilocation approach covering the broader southeastern US would strengthen recommendations.

Discussion

The April 1 optimum aligns with existing extension recommendations for north-central Florida [1,14] , but this research adds quantitative evidence that the window will narrow under climate change. The 11.9% yield decline at the optimal date under RCP 8.5 is smaller than the 15-20% losses projected for late-planted sorghum, confirming that planting date adjustment partly offsets climate impacts [5,9] . The sharp increase in CV for June plantings reflects grain-filling overlap with late-season drought, a pattern also reported from DSSAT simulations in West Africa and South Asia [3,6,8] . The consistency of the April 1 recommendation across five GCMs provides confidence despite inherent climate projection uncertainty [4,12,17] .

Conclusions: This DSSAT-based analysis showed that early April planting offers the highest and most stable rainfed sorghum yields at Gainesville, Florida, under both current and projected future climates. Climate change narrows the optimal window from approximately four weeks to two weeks, making planting date decisions more critical. Late planting (May 15 onward) carries unacceptable yield risk under all scenarios. Extension programmes should emphasize timely planting as a low-cost adaptation strategy and update planting calendars as climate conditions shift.

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