Sustainability of cover cropping practice with changing climate in Illinois.

Climate change could adversely impact the best management practices (BMPs) designed to build a sustainable agro-ecological environment. Cover cropping is a conservation practice capable of reducing nitrate-nitrogen (NO3-N) loadings by consuming water and nitrate from the soil. The objective of this study was to investigate how climate change would impact the proven water quality benefits of cereal rye as a winter cover crop (CC) over the climate divisions of Illinois using the DSSAT model. Moreover, this study explores the sustainability of the CC with the changing climate conditions by using five regional climate models (RCMs) projections of two warming scenarios-rcp45 (a medium emission scenario - radiative forcing of 4.5 W/m2) and rcp85 (a high emission scenario - radiative forcing of 8.5 W/m2)). The CC impact simulated in the warming scenarios for the near-term (2021-2040) and the far-term future (2041-2060) were compared with the baseline scenario (2001-2020). Our results conclude that the climate change may negatively impact [average of CC and no CC (NCC)] maize yield (-6.6%) while positively affecting soybean yield (17.6%) and CC biomass (73.0%) by the mid-century. Increased mineralization caused by rising temperature could increase the nitrate loss via tile flow (NLoss) and nitrate leached (NLeached) up to 26.3% and 7.6% on average by the mid-century in Illinois. Increasing CC biomass could reduce the NLoss more considerably in all the scenarios compared to the baselines. Nevertheless, the NLoss level in the CC treatment can increase from the near-term to far-term future and could get closer to the baseline levels in the NCC treatment. These results suggest that CC alone may not address nitrate loss goals via subsurface drainage (caused by increasing N mineralization) in future. Therefore, more robust and cost-effective BMPs are needed to aid the CC benefits in preventing nutrient loss from the agricultural fields.

Evaluation of long-term impact of cereal rye as a winter cover crop in Illinois.

Extensive tile drainage usage combined with excess nitrogen fertilization has triggered nutrient loss and water quality issues in Illinois, which over time endorsed the hypoxia formation in the Gulf of Mexico. Past research reported that the use of cereal rye as a winter cover crop (CC) could be beneficial in reducing nutrient loss and improving water quality. The extensive use of CC may aid in reducing the hypoxic zone in the Gulf of Mexico. The objective of this study is to analyze the long-term impact of cereal rye on soil water­nitrogen (N) dynamics and cash crops growth in the maize-soybean agroecosystem in the state of Illinois. A gridded simulation approach was developed using the DSSAT model for the CC impact analysis. The CC impacts were estimated for the last two decades (2001-2020) for two fertilization scheduling (FA-SD = Fall and side-dress N and SP-SD = Spring pre-plant and side-dress N) comparing between CC scenario (FA-SD-C/SP-SD-C) with no CC (NCC) scenario (FA-SD-N/SP-SD-N). Our results suggest that the nitrate-N loss (via tile flow) and leaching reduced by 30.6 % and 29.4 %, assuming extensive adaptation of cover crop. The tile flow and deep percolation decreased by 20.8 % and 5.3 %, respectively, due to cereal rye inclusion. The model performance was relatively poor in simulating the CC impact on soil water dynamics in the hilly topography of southern Illinois. Generalizing changes in the soil properties (due to cereal rye inclusion) from the field scale to whole state (regardless of soil type) could be one of the possible limitations in this research. Overall, these findings substantiated the long-term benefits of cereal rye as a winter cover crop and found the spring N fertilizer application reduced nitrate-N loss compared to fall N application. These results could be helpful in promoting the practice in the Upper Mississippi River basin.

Development of a data-assimilation system to forecast agricultural systems: A case study of constraining soil water and soil nitrogen dynamics in the APSIM model.

As we face today's large-scale agricultural issues, the need for robust methods of agricultural forecasting has never been clearer. Yet, the accuracy and precision of our forecasts remains limited by current tools and methods. To overcome the limitations of process-based models and observed data, we iteratively designed and tested a generalizable and robust data-assimilation system that systematically constrains state variables in the APSIM model to improve forecast accuracy and precision. Our final novel system utilizes the Ensemble Kalman Filter to constrain model states and update model parameters at observed time steps and incorporates an algorithm that improves system performance through the joint estimation of system error matrices. We tested this system at the Energy Farm, a well-monitored research site in central Illinois, where we assimilated observed in situ soil moisture at daily time steps for two years and evaluated how assimilation impacted model forecasts of soil moisture, yield, leaf area index, tile flow, and nitrate leaching by comparing estimates with in situ observations. The system improved the accuracy and precision of soil moisture estimates for the assimilation layers by an average of 42% and 48%, respectively, when compared to the free model. Such improvements led to changes in the model's soil water and nitrogen processes and, on average, increased accuracy in forecasts of annual tile flow by 43% and annual nitrate loads by 10%. Forecasts of aboveground measures did not dramatically change with assimilation, a fact which highlights the limited potential of soil moisture as a constraint for a site with no water stress. Extending the scope of previous work, our results demonstrate the power of data assimilation to constrain important model estimates beyond the assimilated state variable, such as nitrate leaching. Replication of this study is necessary to further define the limitations and opportunities of the developed system.