The use of cover crops is reported to enhance agro-ecological services in rotational crop systems, however their adoption by farmers has remained limited. A challenge to farmer uptake is high spatial and temporal variability in cover crop establishment and growth. Since the benefits of cover crop use are a function of the amount of cover crop biomass that enters the soil, it is important to quantify cover crop biomass production across the field. The ability to easily and inexpensively quantify the spatial variability of cover crop biomass is needed to better understand and predict its potential as an input to agricultural systems. My study demonstrated that hierarchical nonlinear models can adequately predict biomass of the cover crop from the easily measured Normalized Difference Vegetation Index (NDVI) data thus providing a relatively inexpensive method of obtaining dense cover crop biomass measurements. Topography plays an important role in spatial processes that ultimately affect plant performance, it could be used to quantify and predict cover crop spatial variability and cover crop contribution to a subsequent cash crop. However, my results show that the utility of topographical information in plant performance predictions depends on the analysis scale. I explored the relationships between cover crop biomass and topography at different scales of derivation, and demonstrated that neighborhood size greatly affects the strength of the prediction performance in multiple regression models. Equipped with resulting information on the optimal analyses scales I then studied the effects of topography and cover crop biomass on corn yields. Topographic attributes were found to contribute significantly to explaining the variability in both red clover biomass and corn yields and red clover biomass positively influenced corn yield, however, the magnitude of that effect varied both temporally and spatially. Resulting better understanding of how variations in topography affect cover crops and row-crops will contribute to increased cover crop adoption, allowing producers to tailor management to site-specific features of their fields. The combined effect of topography and cover crops in row-crops can be implemented in a process-based crop simulation model (SALUS) to predict the performance of conventional and cover crop-enhanced managements under future climate scenarios. Projections of crop performance under 100 years of future climate scenarios showed a significant decline in corn yields, in particular for the organic-based treatment. On the other hand, soybean and wheat yields showed a slight increment. These simulation outcomes then can be used to identify potential strategies for climate change mitigation.
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