USE OF SELECTED PRECISION AGRICULTURAL PRACTICES ON A COMMERCIAL ROW CROP FARM
Genetics and Precision Agriculture Research
2010 Annual Report
1a.Objectives (from AD-416)
The objective of this cooperative research project: (1) determine if a topologically based general linear mixed model statistical approach is applicable for the analysis of the effects of site-specific and conventional management practices which occur at similar or different times and locations on commercial farms; (2) develop a cooperative project between a commercial farm and ARS research to determine if the proposed statistical/geographical information system (GIS) analysis process is applicable for commercial farms and if it can be formulated into a farmer/consultant friendly approach when crops are grown on a rotational basis; and (3) determine the utility of aplications of wireless technology to capture, share, and transfer spatial information about farm operations so that it strengthens the ability of farms to complete analyses of their farm operation.
1b.Approach (from AD-416)
Work will be done in fields on a cooperator farm that rotates crops. Define experimental units in a unique way so that independence among control and management treatments is achieved. The analysis process uses the spatial demographics modeled in the GIS to create zones of conventional agricultural practice within commercial production fields. Experimental precision agricultural practices are evaluated against conventional practices by use of smaller floating plots imbedded within the larger zones by spatially including the necessary information into a prescription file used by the VR controller. The various agricultural practices are evaluated using analysis of covariance to obtain regression effects describing the plot demographics. Using geospatial information the spatial correlation structure describing the relationships among plot residuals can be modeled. Wireless systems networks are developed to communicate commands and feedback information among personnel, computers, and field variable rate equipment.
Several crop years of yield monitor data for three commercial fields of the Dee River Ranch were obtained. Corn and soybean crops were rotated on these fields. The Reagan 11A field consisting of 66 ha was chosen to begin research in the use of Real Time Kinetics (RTK) – based yield monitor data. In 2006 corn was grown in the field and in 2007–2009 soybean was grown. The procedure developed on the Good Longview Farm using Light Detection and Ranging (LIDAR) data to generate a crop yield stability map was used; however, RTK elevation data was used on Dee River Ranch in lieu of LIDAR elevation data. Results show that RTK elevation data can be used to create statistically reliable crop yield stability maps. LIDAR maps are not widely available for producer fields; however, many growers are now using RTK for equipment auto-steer capability. Our research showed that RTK elevation data can be used to develop crop yield stability maps for the growers' fields that seem to be comparable to maps using LIDAR. This project was monitored by frequent visits by the principal investigator to the farm to apply treatments and to collect data.