Context No-tillage (NT) has been gaining popularity over the conventional tillage (CT) for agricultural sustainability. Field experiments conducted worldwide to compare crop production under NT vs CT systems are generally site specific and expensive to maintain over longer duration. To overcome this gap, process-based models have been used to simulate the potential impact and benefits of various management practices on crop yield and soil properties under different environmental conditions. Aims (1) We evaluated the Cropping System Model (CSM)-CERES-Maize and CSM-CROPGRO-Soybean model for NT and CT systems; and (2) compared the long-term impacts of NT and CT on crop yield and soil organic carbon (SOC). Methods Two crop models, available in the Decision Support System for Agrotechnology Transfer (DSSAT), were calibrated and evaluated using maize (Zea mays L.) and soybean (Glycine max L.) yield data from 2006 through 2011 under CT and NT treatments. Key results For crop yield, we showed that the coefficient of determination (R2) for the calibration and evaluation phases of CERES-Maize model were 0.94 and 0.94, respectively, while the index of agreement (d) for these phases were 0.93 and 0.86. Similarly, the R2 values for the calibration and evaluation phases of CROPGRO-Soybean model were 1.00 and 0.65, respectively, with d-values of 0.99 and 0.85. Conclusions The results from these long-term (30-year) simulations suggest that compared to CT, the NT system enhanced SOC over time and, hence, crop yield and biomass production. Implications Application of NT can be beneficial for enhancing the soils and crop production in the long-term as compared to the CT system.
Integrating cover crops in the rotation did not reduce the yields of succeeding crops. Fall grazing of cover crops did not have any negative impact on crop productivity. Maize was sensitive to soil water depletion due to cover crops. Grazing cover crops and crop stubble enhanced net returns.
The enormous increase in the volume of Earth Observations (EOs) has provided the scientific community with unprecedented temporal, spatial, and spectral information. However, this increase in the volume of EOs has not yet resulted in proportional progress with our ability to forecast agricultural systems.This study examines the applicability of EOs obtained from Sentinel2 and Landsat8 for constraining the APSIM-Maize model parameters. We leveraged leaf area index (LAI) retrieved from Sentinel2 and Landsat8 NDVI to constrain a series of APSIM-Maize model parameters in three different Bayesian multi-criteria optimization frameworks across 13 different sites across the U.S Midwest. A time variant sensitivity analysis was performed to identify the most influential parameters driving the LAI estimates in APSIM-Maize model. Then surrogate models were develop using random samples taken from the parameter space using Latin hypercube sampling to emulate APSIM’s behavior in simulating NDVI and LAI at all sites. Site-level, global and hierarchical Bayesian optimization models were then developed using the site-level emulators to simultaneously constrain all parameters and estimate the site to site variability in crop parameters. For within sample predictions, site-level optimization showed the largest predictive uncertainty around LAI and crop yield, whereas the global optimization showed the most constraint predictions for these variables. Lowest RMSE for within sample yield prediction was found for hierarchical optimization scheme (1423 Kg ha−1) while the largest RMSE was found for site-level (1494 Kg ha−1). In out-of-sample predictions within the spatio-temporal extent of the training sites, global optimization showed lower RMSE (1627 Kg ha−1) compared to the hierarchical approach (1822 Kg ha−1) across 90 independent sites in the U.S Midwest. On comparison between these two optimization schemes across another 242 independent sites outside the spatio-temporal extent of the training sites, global optimization also showed substantially lower RMSE (1554 Kg ha−1) as compared to the hierarchical approach (2532 Kg ha−1). Overall, EOs demonstrated their real use case for constraining process-based crop models and showed comparable results to model calibration exercises using only field measurements.
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