Title: Relationships between soil-based management zones and canopy sensing for corn nitrogen management Authors
|Roberts, Darrin -|
|Ferguson, Richard -|
|Adamchuk, Viacheslav -|
|Shanahan, John -|
Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: September 14, 2011
Publication Date: December 1, 2011
Citation: Roberts, D.F., Ferguson, R.B., Kitchen, N.R., Adamchuk, V.I., Shanahan, J.F. 2011. Relationships between soil-based management zones and canopy sensing for corn nitrogen management. Agronomy Journal. 104(1):119-129. Interpretive Summary: Nitrogen (N) management in cereal crops has been the subject of considerable research for decades because soils usually don’t naturally provide enough N to achieve optimal crop growth. Thus N fertilizers are heavily used. Further, concerns have amplified regarding the impact of N movement off fields on the atmosphere and water resources. Unfortunately, current crop management practices typically give low N fertilizer use efficiency (NUE), estimated to be as low as 30-40 percent for some crops like corn. This means that over half of the N applied may be lost to the environment. Canopy reflectance sensing, a plant-based method recently developed for determining how much N fertilizer to apply, shows promise for increasing NUE. Another approach independently developed for improving N fertilizer management has focused on using soil properties to create field management zones (MZ). The objective of this study was to integrate these two approaches (active canopy reflectance sensing and MZ) for potential improvement in N fertilizer management. We found in three of six study fields soil properties could be used to delineate MZ that could be used to identify spatial variability in response to canopy sensor-based N rate. Sandy fields and fields with substantial elevation changes were best suited for this integrated plant sensing and soil approach. Soil electrical conductivity and soil reflectance were the properties found to be most important in creating MZ. While the results of this study showed improved NUE and economic benefits when combining canopy sensing with MZ, additional research in needed to know how to adjust N fertilizer rates between MZ. Farmers will benefit from this research because they can reduce excess N applications, which with increasing N fertilizer cost, should save them money. If fertilizer can be better matched with crop need, N fertilizer loss to the environment will be reduced, thus helping to protect soil, water, and air resources.
Technical Abstract: Integrating soil-based management zones (MZ) with crop-based active canopy sensors to direct spatially variable nitrogen (N) applications has been proposed for improving N fertilizer management of corn (Zea mays L.). Analyses are needed to evaluate relationships between canopy sensing and soil-based MZ, and their combined potential to improve N management. The objectives of this study were to: (1) identify soil variables related to in-season crop canopy reflectance and yield, and use these variables to delineate MZ for N fertilizer management; and (2) evaluate the MZ obtained in terms of corn response to different N fertilizer treatments. Eight N rates (0 to 274 kg N/ha in 39 kg/ha increments) were applied in replicated small plots across six irrigated fields in 2007-08 throughout central Nebraska. Soil variables evaluated for MZ delineation included: apparent soil electrical conductivity (ECa), soil optical reflectance, and landscape topography. Crop response to N was determined via active sensor assessments of in-season canopy reflectance (chlorophyll index; CI590) and grain yield. Relationships between soil and topography data and crop performance were evaluated, with selected soil variables used to delineate two MZ within four of the six fields. Economic benefits to N application according to soil-based MZ were observed in fields with silty clay loam and silt loam soils with substantial relief and eroded slopes. Further benefit may be attained by integrating soil-based MZ and in-season canopy sensing, however, sensor-based algorithms may need to be adjusted according to MZ to account for differences in crop N response.