Optimal Scale Derivation for Relating Topographical Attributes and Cover Crop Plant Biomass.

The use of cover crops generates a number of agro-ecological benefits for sustainable row-crop agriculture. However, their performance across agricultural fields is often highly spatially variable and there is insufficient information on factors affecting this variability and on tools to manage it. Topography is one of the main factors affecting spatial patterns of plant growth in Midwest. Digital elevation models are readily available for deriving topographical attributes; also sensor digital data can be used to indirectly assess cover crop biomass. However, processing procedures for identifying the proper scale of topographical and biomass representations are not well defined. The objectives of this study are to examine how relationships between cover crop biomass, assessed using NDVI, and topography depend on the neighborhood size used for topographical attribute derivation and on the neighborhood size used to create NDVI maps; and to identify the optimal neighborhood size for correlation and regression analyses. Slope, solar radiation and relative elevation were the variables that contributed the most to explaining variability in NDVI for raw data. However, other topographical attributes became significant predictors of NDVI at larger neighborhood sizes. We demonstrated that neighborhood size greatly affects some topographical attributes, i.e. curvature, flow accumulation, flow length and wetness index; and changing the neighborhood size in both topography and NDVI considerably changes the strength of the predictive power in multiple regression models. On average across all studied fields the best performance of multiple regression, as determined by adjusted R², was obtained at neighborhood sizes 20 and 40 m. Parameters of semivariogram models, such as spatial autocorrelation range and nugget:sill ratio, for terrain slope were found to be good indicators of predictive power and optimum neighborhood size for filtering out raw data. The results demonstrate that topographical effects on growth and biomass production of cover crops are most pronounced at certain spatial scales and topographical model predictions will be most accurate when used at the optimal scales.