Abstract
Process-based crop simulation models are useful for simulating the impacts of climate change on crop yields. Currently, estimation of spatially calibrated soil parameters for crop models can be challenging, as it requires the availability of long-term and detailed input data from several sentinel sites. The use of aggregated regional data for model calibrations has been proposed but not been employed in regional climate change studies. The study: 1) employed the use of county-level data to estimate spatial soil parameters for the calibration of CROPGRO-Soybean model and 2) used the calibrated model, assimilated with future climate data, in assessing the impacts of climate change on soybean yields. The CROPGRO-Soybean model was calibrated using major agricultural soil types, crop yield and current climate data at county level, for selected counties in Alabama for the period 1981-2010. The calibrated model simulations were acceptable with performance indicators showing Root Mean Square Error percent of between 27 - 43 and Index of Agreement ranging from 0.51 to 0.76. Projected soybean yield decreased by an average of 29% and 23% in 2045, and 19% and 43% in 2075, under Representative Concentration Pathways 4.5 and 8.5, respectively. Results showed that late-maturing soybean cultivars were most resilient to heat, while late-maturing cultivators needed optimized irrigation to maintain appropriate soil moisture to sustain soybean yields. The CROPGRO-Soybean phenological and yield simulations suggested that the negative effects of increasing temperatures could be counterbalanced by increasing rainfall, optimized irrigation, and cultivating late-maturing soybean cultivars.
Highlights
Global climate change and variability is expected to affect crop phenological processes and yield
The final saturation point/volumetric soil water content (SAT) values ranged between 0.449 cm3/cm3 and 0.690 cm3/cm3 (Table 5). These increases in delta (DUL-LL) and SAT were done with reduction in SLPF that led to low Root Mean Square Error (RMSE) percentages and greater than 0.5 d-statistic
Observations from field experiments, showed that this temperature-induced lengthening of the life cycle leads to an improvement in sources (Kumagai & Sameshima, 2014), i.e. increase in leaf area and leaf photosynthesis which contributes to an increase in photosynthesis and assimilate partitioning which results in increased yields
Summary
Global climate change and variability is expected to affect crop phenological processes and yield. One commonly used process-based crop model is the Crop Growth (CROPGRO) (Jones et al, 2003). Numerous calibration and validation studies on CROPGRO-soybean have established the model’s ability to simulate crop development and observed seed yield under different climate conditions and regions (Batchelor et al, 1993; Tsuji et al, 1998; Lal et al, 1999; Southworth et al, 2002; Wang et al, 2003; Mera et al, 2006; Res et al, 2007). There is difficulty in obtaining long-term (30 or so years) data of detailed and complete regional crop growth and development information, crop yields and crop management records for use in conventional model calibration (Jiang et al, 2014). Soil survey reports fall short in their descriptions of soil characteristics, especially and with respect to details on physical and chemical parameters needed to approximate soil components (water, air, nutrients) and soil structure which affects root distribution (Mavromatis et al, 2001; Irmak et al, 2001)
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