Role of landscape and hydrologic attributes in developing and interpreting yield clusters

Abstract

<p>Management of agricultural fields based on yield patterns may help farmers adopt environmentally friendly farming practices. Our objective was to investigate landscape and hydrologic attributes that affect spatial clusters of corn (<em>Zea mays</em> L.)–soybean (<em>Glycine max</em> L.) yields. The study was conducted at Iowa State University's northeastern research center near Nashua, Iowa, from 1993 to 1998. The yield data, normalized for annual climatic variability, were used in cluster and discriminant analysis, and the landscape and hydrologic data were overlain using ArcGIS software. Three clusters of low, medium and high categories were formed using 10 iterations with zero convergence options and satisfying the <em>R</em>², pseudo <em>F</em>-statistic and cubic clustering criteria. The spatial clusters, however, varied greatly over space and time domain for the study period. The map overlay analysis using ArcGIS showed that high yield clusters were affected by soil and lower elevation levels in the below average precipitation year of 1994. The annual normalized subsurface drainage volume, nitrate leaching losses, soil type and topographic attributes of slope, aspect, and curvature were used in stepwise discriminant analysis to identify the variables significantly related to the clusters. Soil and topographic attributes of curvature and aspect contributed significantly in cluster formations for four of the six years at <em>P</em> ≤ 0.15. The results suggest that cluster and discriminant analysis can be useful for identification of soil and topographic attributes affecting corn and soybean yield patterns, which can help in delineation of management zones for site specific management practices.</p>