Quantifying the Impacts of an Integrated Crop-Livestock System on Plant Nutrient Accumulation, Crop Yield, and Economic Performance

Integrated crop-livestock systems (ICLs), if managed properly, have the potential for enhancing crop production and economics. Cover crops in ICL can be grazed to provide feed for livestock and improve nutrient recycling. This dissertation focused on assessing the impacts of ICL on crop yield and economics using field and modeling studies. Specific objectives of this dissertation are: (i) evaluating the impacts of the ICL on crop yield, and economic performance in a 3-yr oat (Avena sativa L.) – maize (Zea mays L.) – soybean (Glycine max (L.) Merr.) rotation, (ii) assessing the impacts of the ICL on maize biomass accumulation, harvest index (HI) and uptake of N, P, K, S, Ca, and Mg nutrients during the reproductive phase, (iii) evaluating the Cropping System Model (CSM)- CERES-Maize and CSM-CROPGRO-Soybean models for the prevalent no-till (NT) and conventional-till (CT) systems, and comparing the long-term impacts of NT and CT on crop yield and soil organic carbon (SOC), and (iv) developing a simple simulation methodology for crop-livestock interaction using Decision Support System for Agrotechnology Transfer (DSSAT), and evaluating the performance of cover crops grazing on maize production. The field study was established in 2016 and included treatments comprising of (i) oat – maize – soybean (CNT), (ii) oat – cover crops – maize – soybean (CC), and (iii) oat– cover crop + grazing – maize + residue grazing – soybean + residue grazing (ICL). The crop yield was unaffected by the cover crops or livestock grazing over the study period. A 17.7% reduction in maize yield under CC as compared to the CNT was observed for 2017, however, the differences were non-significant (P = 0.06). Despite no significant differences in crop performance, the economic analysis showed ICL to be significantly more profitable (P = 0.003) than the CNT (64 % higher returns) and CC (91% higher returns). To determine whether ICLs are better in nutrient recycling, nutrient uptake for N, P, K, S, Ca, and Mg was estimated in the above ground maize biomass at R1 and R6 growth stages, along with HI. Treatments did not impact biomass yield and HI. However, N, K, S, and Ca contents of maize plants, averaged across years and growth stages, for the CNT was similar to the ICL treatment, and significantly greater than the CC treatment. Magnesium content in maize biomass was significantly greater under ICL than CNT and CC treatments. The treatments did not have any impact on P content in the above ground biomass of maize, however, the trend of CNT \u3e ICL \u3e CC was still observed. Field trials that involve livestock under croplands are often expensive and laborious to maintain for longer duration. Therefore, process-based cropping system models (CSM) can play a vital role in addressing some of the issues associated with longterm research. Therefore, the DSSAT program was used to develop a simulation methodology for ICL, after calibrating and evaluating the CSM-CERES-Maize and CSM-CROPGRO-Soybean using long-term crop yield data from a 2-yr maize – soybean rotation grown under prevalent CT and NT systems. A satisfactory coefficient of determination (R2) for evaluation of CSM-CERES-Maize (R2 = 1.00) and CSMCROPGRO- Soybean (R2 = 0.65) confirmed that the trends in the field data were captured well by the simulations. For simulating crop-livestock interaction using DSSAT, the difference in pregrazing and post-grazing dry biomass of the cover crops, averaged over the grazing period was used to determine the daily biomass consumption by the livestock. The invitro dry matter digestibility (IVDMD) of the cover crops was used to determine the amount of manure that is being returned to the soil during the grazing period. The data generated from the field experiments was used to calibrate and evaluate the CSMCERES- Maize of the Decision Support System for Agrotechnology Transfer (DSSAT). The index of agreement (d) values for calibration and evaluation of maize yield were 0.99 and 0.95, respectively. The trends in the field data were, therefore, well represented by the simulated data. Results of the study suggest that livestock grazing did not incur any yield penalties on the cash crop and made the system more profitable. The nutrient recycling in above ground maize biomass, although insignificant, was improved in case of ICL as compared to cover crops without grazing. These results suggest that incorporating cover crops in ICLs can enhance nutrient recycling and improve farm profitability. The simulation methodology developed in DSSAT using field data can be applied further for various crop-livestock interactions and scenario analysis by scientists and policy-makers alike. However, extensive testing and further improvements in the methodology may be expected

Linking Remote Sensing with APSIM through Emulation and Bayesian Optimization to Improve Yield Prediction

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 Sentinel-2 and Landsat-8 for constraining the APSIM-Maize model parameters. We leveraged leaf area index (LAI) retrieved from Sentinel-2 and Landsat-8 NDVI (Normalized Difference Vegetation Index) to constrain a series of APSIM-Maize model parameters in three different Bayesian multi-criteria optimization frameworks across 13 different calibration sites in the U.S. Midwest. The novelty of the current study lies in its approach in providing a mathematical framework to directly integrate EOs into process-based models for improved parameter estimation and system representation. Thus, a time variant sensitivity analysis was performed to identify the most influential parameters driving the LAI (Leaf Area Index) estimates in APSIM-Maize model. Then surrogate models were developed 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. The lowest RMSE 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 for 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

Probabilistic Assessment of Cereal Rye Cover Crop Impacts on Regional Crop Yield and Soil Carbon

Field research for exploring the impact of winter cover crops (WCCs) integration into cropping systems is resource intensive, time-consuming and offers limited application beyond the study area. To bridge this gap, we used the APSIM model, to simulate corn (Zea mays L.)-rye (Secale cereale L.)-corn-rye and corn-rye-soybean (Glycine max L.)-rye rotations in comparison with corn-corn and corn-soybean rotations across the state of Illinois at a spatial resolution of 5 km × 5 km from 2000 to 2020 to study the impact of WCCs on soil organic carbon (SOC) dynamics and crop production. By propagating the uncertainty in model simulations associated with initial conditions, weather, soil, and management practices, we estimated the probability and the expected value of change in crop yield and SOC following WCC integration. Our results suggest that integrating cereal rye into the crop rotations imparted greater yield stability for corn across the state. It was found that the areas with low probability of increase in SOC (p < 0.75) responded equally well for soil carbon sequestration through long term adoption of WCCs. This study presents the most complete uncertainty accounting of WCC benefits across a broad region and provides greater insights into the spatiotemporal variability of WCCs benefits for increasing WCC adoption rate

Linking Remote Sensing with APSIM through Emulation and Bayesian Optimization to Improve Yield Prediction

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 Sentinel-2 and Landsat-8 for constraining the APSIM-Maize model parameters. We leveraged leaf area index (LAI) retrieved from Sentinel-2 and Landsat-8 NDVI (Normalized Difference Vegetation Index) to constrain a series of APSIM-Maize model parameters in three different Bayesian multi-criteria optimization frameworks across 13 different calibration sites in the U.S. Midwest. The novelty of the current study lies in its approach in providing a mathematical framework to directly integrate EOs into process-based models for improved parameter estimation and system representation. Thus, a time variant sensitivity analysis was performed to identify the most influential parameters driving the LAI (Leaf Area Index) estimates in APSIM-Maize model. Then surrogate models were developed 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. The lowest RMSE 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 for 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