Optical sensor-based algorithm for crop nitrogen fertilization

• Published in: Communications in soil science and plant analysis Vol. 36; no. 19-20; pp. 2759 - 2781
• Main Authors: Raun, W.R, Solie, J.B, Stone, M.L, Martin, K.L, Freeman, K.W, Mullen, R.W, Zhang, H, Schepers, J.S, Johnson, G.V
• Format: Journal Article
• Language: English
• Published: Philadelphia, PA Taylor & Francis Group 01.10.2005; Taylor & Francis
• Subjects: Agronomy. Soil science and plant productions; algorithms; Biological and medical sciences; corn; equations; fertilizer application; fertilizer rate calculator; fertilizer rates; Fundamental and applied biological sciences. Psychology; General agronomy. Plant production; grain crops; grain yield; nitrogen fertilizers; Nitrogen rate calculator; Nitrogen, phosphorus, potassium fertilizations; normalized difference vegetative index; prediction; Soil-plant relationships. Soil fertility. Fertilization. Amendments; spatial variation; temporal variation; topdress nitrogen; Triticum aestivum; wheat; yield based nitrogen fertilization; Zea mays; United States; North America; Oklahoma; America; yield based nitrogen fertilization; Nitrogen fertilization; Monocotyledones; corn; Optical sensor; Nitrogen rate calculator; Nitrogen; Algorithm; Cereal crop; Topdressing; topdress nitrogen; Gramineae; Angiospermae; Spermatophyta; Yield; wheat; Triticum aestivum
• ISSN: 0010-3624; 1532-2416
• DOI: 10.1080/00103620500303988

Nitrogen (N) 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. This 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 0.4 m 2 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 0.4 m 2 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 (0.4 m 2 resolution) can increase yields and decrease environmental contamination due to excessive N fertilization. *Contribution from the Oklahoma Agricultural Experiment Station.