ABSTRACTand Ͼ10% in corn (Hilton et al., 1994). Fertilizer N losses due to surface runoff range between 1 and 13%In 2001, N fertilizer prices nearly doubled as a result of increased (Blevins et al., 1996; Chichester and Richardson, 1992 23% of the total N applied (Drury et al., 1996). In

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 2759 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 sensorbased 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.

ABSTRACTand Ͼ10% in corn (Hilton et al., 1994). Fertilizer N losses due to surface runoff range between 1 and 13%In 2001, N fertilizer prices nearly doubled as a result of increased (Blevins et al., 1996; Chichester and Richardson, 1992 23% of the total N applied (Drury et al., 1996). In

Drastic increases in the cost of N fertilizer and increased public scrutiny have encouraged development and implementation of improved N management practices. This study evaluated the relationship between corn (Zea mays L.) grain yield and early season normalized difference vegetation index (NDVI) sensor readings using the Green-Seeker sensor. The relationships between grain yield and several predictor variables were determined using linear and nonlinear regression analysis. Categorizing NDVI measurement by leaf stage indicated that growth stage was critical for predicting grain yield potential. Poor exponential relationships existed between NDVI from early sensor measurements (V6-V7 leaf stage) and grain yield. By the V8 stage, a strong relationship (R 2 5 0.77) was achieved between NDVI and grain yield. Later sensor measurements (V9 and later) failed to distinguish variation in green biomass as a result of canopy closure. Normalizing the NDVI with GDD (growing degree days) did not significantly improve yield potential prediction (R 2 5 0.73), but broadened the yield potential prediction equation to include temperature and allowed for adaptation into various climates. Sensor measurements at the range of 800 to 1000 GDD resulted in a significant exponential relationship between grain yield and NDVI (R 2 5 0.76) similar to the V8 leaf stage categorization. Categorizing NDVI by GDD (800-1000 GDD) extended the sensing time by two additional leaf stages (V7-V9) to allow a practical window of opportunity for sidedress N applications. This study showed that yield potential in corn could be accurately predicted in season with NDVI measured with the GreenSeeker sensor.

season, while potentially costly, could significantly increase NUE. Current nitrogen use efficiency (NUE) of cereal crop productionRecently, methods for estimating winter wheat N reis estimated to be near 33%, indicating that much of the applied quirements based on early season estimates of N uptake fertilizer N is not utilized by the plant and is susceptible to loss from and potential yield were developed (Lukina et al., 2001; the soil-plant system. Supplying fertilizer N only when a crop response is expected may improve use efficiency and profitability. A response Raun et al., 2002). Remote sensing collected by a modiindex using harvest data was recently proposed that indicates the fied daytime-lighting reflectance-sensor was used to esactual crop response to additional N within a given year. This response timate early season plant N uptake. The estimate was index, RI Harvest , is calculated by dividing the average grain yield of the based on a relationship between NDVI and plant N uphighest yielding treatment receiving N by the average yield of a check take between Feekes physiological stage 4 (leaf sheaths treatment (0 N). Although theoretically useful, RI Harvest does not allow lengthen) and 6 (first node of stem visible) (Large, 1954; for in-season adjustment of N application. The objective of this work Stone et al., 1996; Solie et al., 1996). The NDVI was was to determine the relationship between RI Harvest and the response calculated using the following equation:index measured in-season (RI NDVI ) using the normalized difference vegetative index (NDVI). Research was conducted in 23 existing field NDVI ϭ [(NIR ref /NIR inc ) experiments in Oklahoma. Each field experiment evaluated crop re-Ϫ (Red ref /Red inc )]/[(NIR ref /NIR inc )sponse to varying levels of preplant N. At Feekes growth stages 5, 9, and 10.5, RI Harvest was accurately predicted using RI NDVI (r 2 Ͼ 0.56).