Basis For Site-specific Management Research Articles

Assessing wheat spatial variation based on proximal and remote spectral vegetation indices and soil properties

Assessing the spatial variation of soil and crop properties is the basis for site specific management of crop practices in precision agriculture applications. To this aim, proximal and remote spectral vegetation indices are increasingly replacing soil analysis. In this study the spatial variation of soil properties, proximal and remote spectral vegetation indices were compared in a winter wheat (Triticum aestivum L.) crop grown in a 4.15 ha field in northern Italy. Soil analysis (particle size distribution, pH, carbonates, C, total N, available P, exchangeable cations and electrical conductivity) was geo-referentially carried out; the proximal indices chlorophyll content by N-Tester and normalised difference vegetation index through GreenSeeker were determined in three dates during stem elongation; the remote indices PurePixelTM chlorophyll index and PurePixelTM vegetation index were determined through the Landsat 8 satellite in three dates during the same wheat stage. Dry biomass yield (DBY), grain yield (GY) and yield components were determined at harvest. Soil, proximal and remote data were submitted to principal component analysis (PCA), and the retained PCs were clustered to delineate areas at low, intermediate and high yield potential, based on soil parameters (CLUsp), proximal (CLUpi), and remote vegetation indices (CLUri). DBY and GY were significantly correlated with several soil parameters and vegetation indices. Spatial distribution of soil and crop data consistently depicted a low performing area (GY<3 Mg ha–1) and a high performing one (GY>8 Mg ha–1). CLUsp determined a lower GY difference between low and high performing area (+60%), compared to CLUpi and CLUri (almost +100%). In CLUsp and CLUpi the low and high performing area were of similar size (25 and 29% for the two respective areas in CLUsp; 25 and 33% in CLUpi), whereas in CLUri they were quite different (16 and 46%). Lastly, yield potential levels determined by vegetation indices (CLUpi and CLUri) exhibited a better degree of agreement with DBY and GY levels, than soil parameters (CLUsp). In exchange for this, the above referred soil parameters are quite consistent in time, allowing soil data to be used for more years. On concluding, PCA followed by clustering resulted in a robust delineation of field areas at different yield potential. This is the premise for developing research driven strategies of practical use.

Evaluation of site-specific management zones on a farm with 124 contiguous small paddy fields in a multiple-cropping system

The identification of homogeneous management zones (MZs) within a field is a basis for site-specific management (SSM). We assessed the method of defining MZs based on the spatio-temporal homogeneity of six soil properties and above-ground biomass data from paddy rice, winter wheat and soybean over 3 years on a farm with 124 contiguous small paddy fields. The soil data were recorded at 372 soil sampling sites on a rectangular grid over the farm. A non-hierarchical cluster analysis was applied to the soil data and the algorithm grouped the sites into three clusters with similar soil properties. These clusters represent soil fertility and soil drainage. The three clusters were not randomly distributed across the fields, but formed contiguous areas associated with landscape position. This was due to the spatial variation of the soil in the study area. We delineated five MZs based on the spatial structure of the soil heterogeneity of the study area. The validity of the MZs was evaluated using the biomass data from paddy rice, winter wheat and soybean in each MZ; this depended mainly on soil fertility when conditions were dry. When the growing season precipitation was greater than the 10-year average, the biomass of winter wheat and soybean depended on soil drainage. This suggested that the delineation of MZs for site-specific management in fields under a paddy-upland crop rotation system should be based on several soil properties. The biomass data from the three crops over 3 years was not effective for delimiting MZs.

Use of Wavelets to Compare Simulated Yield Patterns for Precision Agriculture at the Field Scale

In this paper spatial patterns of simulated water limited potato production for a farm field in the Netherlands are analyzed using wavelets. The simulated yield patterns are decomposed using wavelets into crystals with a varying resolution. These crystals are used to relate the simulated spatial patterns to weather conditions. We compare simulated yield patterns from 10 successive years to arrive at a prototype patterns that can be used as a basis for site-specific management. Typical patterns for dry and wet years are quantified using wavelets. Wavelets provide a mechanism to distinguish between spatial patterns and allow a quantitative approach for classification of these spatial patterns.