Geostatistical Analysis of Yield Monitor Data for Precision Agriculture

Download or read book Geostatistical Analysis of Yield Monitor Data for Precision Agriculture written by Moshood A. Bakare and published by . This book was released on 2015 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: It is long known that yield and other crop and soil characteristics vary across a farm field with measurements in the neighborhood being more similar than those far apart. However, such in-field spatial variability has been generally ignored because uniformity is required as a convenient means of operating modern farm equipment for most farming practices such as crop inputs and harvest. Moreover, until recently, the ability to detect and assess the in-field spatial variability has been limited. The situation is now changing with the recent advent of geomatic technologies such as yield monitors equipped with GPS on combine harvesters. The objective of this research was the geostatistical analysis of data from one such technology (yield monitor data). The focus was investigating the utility of multi-year yield monitor data from the same farm field located in southern Alberta for identifying patterns and stability of spatial variability. In this 125 ha field, three crops were grown in four years: wheat (Triticum aestivum L.) in 2008, canola (Brassica napus L.) in 2009, wheat in 2010 and barley (Hordeum vulgare L.) in 2011. Yield readings were cleaned using Yield Editor version 2.0 and normalized to remove scaling effect over different crops and years. The cleaned and normalized data were analyzed to fit three variogram models (exponential, Gaussian, and spherical) that are commonly used in geostatistical applications. The model fitting indicated that the similarity between yield readings were best described by an exponential function of the distance separating the readings, but with the similarity disappearing at different distances in all four crop years, ranging from 39.6 m (2008) to 99.6 m (2009). The spatial stability of yield patterns over the years was measured by Pearson's correlations using interpolated yields mapped to a common grid. The apparent lack of spatial stability over the years suggests that recommended inputs or farm-level decisions such as variable rate applications cannot be based just on 'eyeballing' yield/soil maps from raw data at one farm in one year. Instead, these recommendations or decisions should be based on the maps or information derived from predicted data at multiple farms/locations over multiple years under tested, statistically sound spatial models for precise and profitable management of farm fields.