Deriving the optimal scale for relating topographic 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 the American Midwest. Digital elevation models are readily available for deriving topographic attributes; also sensor digital data can be used to indirectly assess cover crop biomass. However, processing procedures for identifying the proper scale of topographic and biomass representations are not well defined. The objectives of this study are to examine how relationships between cover crop biomass, assessed using the normalized difference vegetation index (NDVI), and topography depend on the neighborhood size used for deriving topographic attributes and creating NDVI maps; and identify the optimal neighborhood size for correlation and regression analyses. Slope, relative elevation and the potential solar radiation index were the variables that contributed the most to explaining variability in NDVI for raw data. However, other topographic attributes became significant predictors of NDVI at larger neighborhood sizes. We demonstrated that neighborhood size greatly affects some topographic attributes, i.e. curvature, flow accumulation, flow length and the wetness index; and changing the neighborhood size in both topography and NDVI considerably changes the strength of the prediction performance in multiple regression models. We studied six neighborhood sizes from 1 to 40 m and the original raw data. On average, across all studied fields the best performance of multiple regression, as determined by the adjusted-R2, was obtained at neighborhood sizes 20 and 40 m. Parameters of semivariogram models for terrain slope, such as the spatial autocorrelation range and the nugget/sill ratio, were found to be good indicators of prediction performance and optimum neighborhood size for filtering the raw data. The results demonstrate that topographic effects on growth and biomass production of cover crops are most pronounced at certain spatial scales, and topographic model predictions will be most accurate when used at the optimal scales.

Selection of the optimal scale for filtering topographic and NDVI data prior to analyzing the relationship between them can be aided by examining topographic “complexity” within a field. Here the first principal component (PC1), an indicator of field's “complexity” is correlated to the mean adjusted-R2 values from multiple regression analyses between topographic attributes and NDVI (Black squares). Fields with relatively flat topography had positive values of PC1, while fields with more diverse topography had negative values of PC1. PC1 is a good indicator of those fields in which filtering raw sensor-based data will improve the adjusted-R2 values (Red circles). Adjusted-R2 values from multiple regressions using raw data were plotted as a reference with their respective increase (White squares).Figure optionsDownload as PowerPoint slideHighlights
► Scale of derivation affects the relationship between topography and plant biomass.
► Filtering sensor-based data improved topography-plant biomass associations.
► Optimal scale of derivation varies with landscape condition.
► Slope, elevation and solar radiation were the most relevant predictors of biomass.
► Semivariogram parameters in slope and NDVI maps were indicators of optimal scale.