Submitted to: Journal of Soil and Water Conservation
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/20/2005
Publication Date: 11/7/2006
Citation: Delgado, J.A., Bausch, W.C. 2006. Potential Use of Precision Conservation Techniques to Reduce Nitrate Leaching in Irrigated Crops. Journal of Soil and Water Conservation. 6-:379-387.
Interpretive Summary: The combination of NLEAP and GIS was a powerful tool to evaluate the spatial RSoN variability in a commercial irrigated corn field. This spatial variability was correlated with soil texture and productivity zones and agreed with results previously presented by Delgado (1999), Delgado (2001) and Delgado et al. (2001a) for barley, canola, lettuce (Lactuca sativa L)and potato. The areas with higher coarser texture had a lower yield, lower RSoN content and a higher NO3-N leaching potential. The NRI method as applied by Bausch and Delgado (2003) for irrigated commercial corn systems can maximize the synchronization of “in season” N applications with corn N uptake needs, significantly increasing NUE while reducing NO3-N leaching losses by 85% when compared to farmer practices. This study shows that there is potential to use RSe, GPS, GIS and models for to evaluate the effect of productivity zones on RSoN and NO3-N leaching potential losses in center-pivot irrigated corn field. These technologies can then be applied to precision conservation by identifying the hot spots in the field that are sensitive to off-site transport of NO3-N out of the system. Precision conservation technologies can be used to evaluate the effects of best management practices and minimize NO3-N leaching potential.
Technical Abstract: There is a continuing need to develop advanced nitrogen (N) management practices that increase N use efficiencies (NUE) and reduce nitrate-nitrogen (NO3-N) leaching. Our objective was to use geographic information systems (GIS), global position systems (GPS), modeling and remote sensing (RSe) as precision conservation technologies to evaluate the effect of productivity zones on residual soil NO3-N (RSoN) and NO3-N leaching potential in center-pivot irrigated corn (Zea mays L) field. We wanted to determine if productivity zones delineated using precision agriculture technologies could correctly identify unique areas within corn production areas that differed in RSoN and NO3-N leaching potential. Additionally, we conducted a modeling evaluation on the potential to use remote sensing (RSe) to reduce NO3-N leaching losses. This study was conducted in northeastern Colorado during the 2000 and 2001 growing seasons in a 70 ha center-pivot irrigated commercial corn field. Initial and final soil samples after harvesting were collected at known locations in high, medium and low productivity areas across this field and in a low productivity area where “in season” N management was conducted with RSe. Crop yields and total N were determined on plant samples located at the soil samples coordinates. Nitrogen Reflectance Index (NRI) was used to determine the “in season” N application. Remote-sensing-based N fertilization treatment occurred whenever the mean NRI was lower than 0.95 and/or more than 50 % of the area had an NRI < 0.95. The modeling evaluation of the system was conducted with the Nitrate Leaching Economic Analysis Package (NLEAP) and GIS on the potential to use RSe to reduce NO3-N leaching losses. We found that GIS, GPS, and modeling technologies can be used as precision conservation technologies to identify and simulating the spatial RSoN variability. Productivity zones delineated using precision agriculture technologies identify areas within corn production fields that differed in RSoN and NO3-N leaching potential. This spatial variability was negatively correlated with the soil texture (P<0.001), having lower RSoN on the lower productivity sandier coarser areas, which also had a higher NO3-N leaching potential. The NRI method can maximize the synchronization of “in season” N applications with corn N uptake needs to increase NUE and reduce NO3-N leaching losses by 85% when compared to farmer traditional practices (P<0.0001)