Title: Spatial analyses to evaluate multi-crop yield stability for a field Authors
Submitted to: Computers and Electronics in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: September 28, 2009
Publication Date: January 1, 2010
Citation: McKinion, J.M., Willers, J.L., Jenkins, J.N. 2010. Spatial Analyses to Evaluate Multi-crop Yield Stability for a Field. Computers and Electronics in Agriculture. 70:187-198. Interpretive Summary: Five crop years of yield data, three years of cotton and two years of corn, together with a highly accurate field map using data from a laser radar device flown via aircraft were considered in an analysis. The analysis focused on answering the hypothesis that areas of crop stability can be determined for a field which can then be used by growers using precision agriculture technology to make better decisions on optimizing crop yields. Both statistical tools and image analysis software tools were used in this study. The conclusion of the study found that using the above techniques areas of a field could be identified, regardless of whether cotton or corn was being grown, that showed yield stability attributes that could be used advantageously by growers using precision agriculture.
Technical Abstract: This paper proposes that yield stability patterns exist for multiple crops planted on the same land area over a period of years that growers can use to their advantage in planning crop management strategies using precision agriculture technologies. This initial study examines the relationship of soil elevation, slope, aspect and curvature to crop yield stability using a digital elevation model of the study area derived from a precise light detection and ranging (LIDAR) image of the farming area and surroundings. Three crop years of cotton and two crop years of corn yields were used to evaluate this hypothesis. The interpolation methods of Inverse Distance Weighted (IDW), simple Kriging and Natural Neighbor found in ESRI’s ARCGIS were used to produce crop yield maps. These methods were also compared in the analysis. Simple Kriging gave the best R2 estimates.