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ARS Home » Plains Area » Lincoln, Nebraska » Agroecosystem Management Research » Research » Publications at this Location » Publication #199743
Title: Optical Sensor Based Algorithm for Crop Nitrogen Fertilization

Author
item RAUN, WILLIAM - OKLA STATE UNIV
item SOLIE, J - OKLA STATE UNIV
item STONE, K - OKLA STATE UNIV
item MARTIN, K - KANSAS STATE UNIV
item FREEMAN, K - OKLA STATE UNIV
item MULLEN, R - OHIO STATE UNIV
item ZHANG, H - OKLA STATE UNIV
item Schepers, James
item JOHNSON, G - PRIVATE CITIZEN

Submitted to: Communications in Soil Science and Plant Analysis
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/5/2005
Publication Date: 9/9/2005
Citation: Raun, W., Solie, J.B., Stone, K.L., Martin, K.L., Freeman, K.W., Mullen, R.W., Zhang, H., Schepers, J.S., Johnson, G.V. 2005. Optical Sensor Based Algorithm for Crop Nitrogen Fertilization. Communications in Soil Science and Plant Analysis 36:2759-2781.

Interpretive Summary: Supplying the entire nitrogen needs for a wheat crop at planting in the fall is not environmentally sound and even if some nitrogen fertilizer is applied the following spring it is difficult for producers to determine the proper amount. A new technology involving active crop canopy sensors that generate their own light using diodes now makes it possible for producers to measure the relative amount of living vegetation in the crop and it greenness to estimate its yield potential. This measurement takes into account any losses in plants over the winter and assesses early-season vigor that is dependent on the weather. An algorithm was developed that incorporates sensor readings that are transformed into a normalized difference vegetation index (NDVI) and a time/temperature component since planting to estimate yield potential. The heart of this concept is to establish an area or strip in the field that has adequate nitrogen fertility as a reference. The yield potential is then used to back-calculate how much additional nitrogen fertilizer to apply to the rest of the field. A fertilizer use efficiency factor is incorporated into the algorithm to make the recommendation more robust and reflect the realities of nitrogen utilization by the crop. This monitoring and in-season approach to nitrogen management has increased nitrogen use efficiency by 15% in initial studies by accounting for missing plants, winter-kill, and small-scale differences in plant vigor.

Technical Abstract: Nitrogen fertilization for cereal crop production does not follow any kind of generalized methodology that guarantees maximum nitrogen use efficiency (NUE). The objective of this work was to amalgamate some of the current concepts for N management in cereal production into an applied algorithm. Our work at Oklahoma State University from 1992 to present has focused primarily on the use of optical sensors in red and near infrared bands for predicting yield, and using that information in an algorithm to estimate fertilizer requirements. The current algorithm, “WheatN.1.0”, may be separated into several discreet components: 1) mid-season prediction of grain yield, determined by dividing the normalized difference vegetative index (NDVI) by the number of days from planting to sensing (estimate of biomass produced per day on the specific date when sensor readings are collected); 2) estimating temporally dependent responsiveness to applied N by placing non-N-limiting strips in production fields each year, and comparing these to the farmer practice (response index); and 3) determining the spatial variability within each 0.4m2 area using the coefficient of variation (CV) from NDVI readings. These components are then integrated into a functional algorithm to estimate application rate whereby N removal is estimated based on the predicted yield potential for each 0.4m2 area and adjusted for the seasonally dependent responsiveness to applied N. This work shows that yield potential prediction equations for winter wheat can be reliably established with only 2-years of field data. Furthermore, basing mid-season N fertilizer rates on predicted yield potential and a response index can increase NUE by over 15% in winter wheat when compared to conventional methods. Using our optical sensor based algorithm that employs yield prediction and N responsiveness by location (0.4m2 resolution) can increase yields and decrease environmental contamination due to excessive N fertilization.