Title: Assessing Intra and Inter-Field Variability of Corn Nitrogen Fertilizer Need Using Ground-Based Reflectance Sensors Authors
|Scharf, Peter - UNIVERSITY OF MISSOURI|
|Palm, Harlan - UNIVERSITY OF MISSOURI|
|Shannon, Donald - UNIVERSITY OF MISSOURI|
Submitted to: International Conference on Precision Agriculture Abstracts & Proceedings
Publication Type: Proceedings
Publication Acceptance Date: May 2, 2008
Publication Date: July 21, 2008
Citation: Kitchen, N.R., Sudduth, K.A., Drummond, S.T., Scharf, P.C., Palm, H.L., Shannon, D.K. 2008. Assessing intra- and inter-field variability of corn nitrogen fertilizer need using ground-based reflectance sensors. In. Proceedings of the 9th International Conference on Precision Agriculture. 9th International Conference on Precision Agriculture, July 20-23, 2008, Denver, Colorado. 2008 CDROM. Interpretive Summary: With increasing costs for crop inputs, corn farmers are interested in better methods to help them precisely apply the rate of nitrogen (N) fertilizer that will give them optimal profit. Additionally, environmental concerns continue because a large amount of N from agricultural fields moves into streams, rivers, and the ocean. Since more N fertilizer in the U.S. is applied to corn than any other crop, there is much interest in exploring new technologies for improved corn N management. One such technology that can be used to control and vary N application rates on growing corn is crop reflectance sensing, which assesses the color and darkness of the corn as an indicator of N need. The objective of this research was to assess the utility of these reflectance sensors for determining the most profitable N rates in corn. Over a four year period and on three different soil types, we found optimal N rate and canopy sensor readings to be poorly related. When we used these results to determine the potential profit from sensor-based variable-N, we found modest returns, ranging from $1 to $12 per acre. However, as fertilizer cost increased relative to the price of corn grain, the value of using canopy sensors for N management improved. Also, a different story emerged when profit was examined by different soil types. Our findings suggest canopy sensing for N applications is better suited for deep loess soils, with profits ranging from $10 to $50 per acre. The results of this study will be used to develop methods and decision rules for how much N to apply in corn. Farmers will benefit because they can reduce excess N applications, saving money. If fertilizer can be better matched with crop need, N loss to lakes and streams will be reduced and the environment will be improved.
Technical Abstract: Since soil types within and between corn (Zea mays L.) fields can be highly variable, the amount of nitrogen (N) provided by those different soil types to support production can also be highly variable. Ideally, the amount of N fertilizer added during a given growing season should be both climate-sensitive and site-specific. In recent years, active canopy sensing for in-season assessment of crop N health has been proposed as a technology on which to base side-dress variable-rate N applications. The objective of this research is to assess the utility of active crop-canopy reflectance sensors for determining intra- and inter-field corn N need on production fields. A total of 16 field-scale (400 to 800 m in length) experiments were conducted over four growing seasons (2004-2007) in three major soil areas of Missouri: river alluvium, deep loess, and claypan. Multiple blocks of N rate response plots were arranged in a randomized complete block design traversing the length of the field. Each block consisted of 8 N treatments from 0 to 235 kg N ha-1 on 34 kg N ha-1 increments top-dressed between vegetative growth stage V7 and V12. Crop canopy reflectance sensor measurements were obtained from N response blocks and adjacent N-rich reference strips at the time of N application. Yield response to N rate was modeled using quadratic plateau functions then compared to canopy sensor readings. While this relationship was generally poor when fields were combined, a profit optimization of these results showed sensing could improve profit and save N inputs when fertilizer costs were high relative to grain price and/or when the analysis was restricted to deep loess soils.