|Malone, Robert - Rob|
Submitted to: Geoderma
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/4/2006
Publication Date: 7/19/2007
Citation: Kanwar, R.S., Bakhsh, A., Malone, R.W. 2007. Role of landscape and hydrologic attributes in developing and interpreting yield clusters. Geoderma. 140:235-246. Interpretive Summary: Developing techniques to identify hydrologic and topographic factors that significantly affect crop yield patterns may help optimize agricultural management. Therefore, we investigated landscape and hydrologic attributes that affect corn/soybean yields. Soil, topographic curvature, and topographic aspect contributed significantly to yield for four of the six years. The results suggest that the analysis used in this research (cluster and discriminant analysis) can be useful for identification of soil and topographic attributes affecting corn and soybean yield patterns, which should help in delineation of management zones for site-specific management practices. This work will initially help scientists develop better tools to precisely manage fields to optimize crop production. This work will eventually help decision-makers and farmers optimize agricultural management to reduce nitrate leaching to shallow groundwater and tile drains while maintaining crop production goals.
Technical Abstract: Management of agricultural fields based on yield patterns may help watershed stakeholders adopt the environment friendly farming practices. Our objective was to investigate landscape and hydrologic attributes that affect spatial clusters of corn (Zea mays L.) – soybean (Glycine max 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 R2, pseudo F-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 dry year of 1994. The annual normalized subsurface drainage volume, nitrate leaching losses, and the soil and topographic attributes of slope, aspect, and curvature were used in stepwise discriminant analysis to identify the significant variables forming the clusters. Soil and topographic attributes of curvature and aspect contributed significantly in cluster formations for four of the six years at P<0.10. 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 should help in delineation of management zones for site specific management practices.