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.

As research intensifies on developing precision agricultural practices for corn (Zea mays L.) production, an important component will be to identify the scale at which these practices should be implemented. We hypothesized that optical sensing can be used to measure individual corn plant biomass and N uptake. A 3-yr study was conducted at three locations in Oklahoma. Optical sensor readings of normalized difference vegetation index (NDVI) and plant height measurements were collected on individual corn plants at various growth stages ranging from V8 (collar of eighth leaf unfolded) to VT (last branch of the tassel is completely visible) and correlated with individual plant biomass, forage yield per unit area occupied by the plant, and N uptake of that plant. Individual plant height measurement, collected before reproductive growth, was a good predictor of plant biomass across the six site years of the study (r 2 5 0.81). The index of NDVI 3 plant height provided the highest correlation with by-plant forage yield on an area basis. Optical sensor and plant height measurements collected at the V8 to V10 (collar of 10th leaf unfolded) growth stage can distinguish individual plants and provide information as to their biomass accumulation and N uptake. This research demonstrates that by-plant information can be collected and used to direct high resolution N applications. The index, NDVI 3 plant height, may be used to refine midseason fertilizer N rates based on expected N removal and by-plant measurements at or before V10.

Improving crop management inputs with remote sensing devices is an emerging technology. This study documented the progression of the normalized difference vegetative index (NDVI) during the life cycle of corn (Zea mays L.), evaluated the spatial variability of corn growth in terms of the CV (calculated from NDVI readings), and documented the relationships between NDVI, CV (calculated from NDVI), and grain and biomass yields and plant density. Four rows, 30 m in length, from two locations during 2 yr were randomly selected for this study. An optical sensor was used to collect NDVI readings at multiple growth stages during the life cycle of corn. The NDVI increased with progression of vegetative growth stages until V10, where a plateau was encountered, followed by a decline in NDVI after the VT growth stage. Coefficient of variation data from the NDVI readings revealed two dominant peaks during the life cycle of corn, one between the V6 and V8 growth stages and the second during the late reproductive growth stages. The CV data illustrated that the greatest variation expressed by corn during the vegetative growth stages was between the V6 and V8 growth stages. The highest correlation of NDVI with corn grain yield was found at the V7 to V9 growth stages; likewise, CV and plant density were also more highly correlated from V7 to V9. The CV from NDVI readings was highly correlated with grain and biomass yields at all growth stages.

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resulting in an estimated 1066 kg ha Ϫ1 (17 bu ac Ϫ1 ) yield loss over 354 commercial fields. Lauer and Rankin Corn (Zea mays L.) grain yields are known to vary from plant to (2004) and Liu et al. (2004) had differing results, noting plant, but the extent of this variability across a range of environments has not been evaluated. This study was initiated to evaluate by-plant that PSV did not significantly alter grain yields in Wiscorn grain yield variability over a range of production environments consin and Ontario, Canada, respectively. Nafziger et and to establish the relationships among mean grain yield, standard al. (1991) noted that uneven emergence of corn can deviation, coefficient of variation, and yield range. A total of fortyoccur when soils are dry at the time of planting and six 8-to 30-m corn transects were harvested by plant in Argentina, could lead to decreased grain yields. It is generally ac-Mexico, Iowa, Nebraska, Ohio, Virginia, and Oklahoma from 2002 cepted that when adjacent plants differ by more than to 2004. By-plant corn grain yields were determined, and the average two leaf stages, the younger plant may not develop to individual plant yields were calculated. Over all sites in all countries its fullest potential. A two leaf stage difference can result and states, plant-to-plant variation in corn grain yield averaged 2765 from delayed emergence ranging from 5 to 10 d, which kg ha Ϫ1 (44.1 bu ac Ϫ1 ). At the sites with the highest average corn can cause a 1% yield loss for each 1-d delay (Robert grain yield (11 478 and 14 383 kg ha Ϫ1 , Parana Argentina, and Phillips, NE), average plant-to-plant variation in yield was 4211 kg ha Ϫ1 (67 bu L. Nielsen, Purdue University, personal communication, ac Ϫ1 ) and 2926 kg ha Ϫ1 (47 bu ac Ϫ1 ), respectively. As average grain 2004). Tollenaar and Wu (1999) found increased stress yields increased, so did the standard deviation of the yields obtained tolerance in corn when plant-to-plant variability was within each row. Furthermore, the yield range (maximum corn grain lower. In general, these statistics identify a twofold probyield minus the minimum corn grain yield per row) was found to lem: first, the need to homogenize plant spacing and increase with increasing yield level. Regardless of yield level, plantemergence and second, the need to recognize differto-plant variability in corn grain yield can be expected and averaged ences in yield potential that clearly exist by plant. more than 2765 kg ha Ϫ1 over sites and years. Averaging yield over Some technologies in precision agriculture have been distances Ͼ0.5 m removed the extreme by-plant variability, and thus, driven commercially. The most notable has been comthe scale for treating other factors affecting yield should be less than bine yield monitors. Depending on combine speed, 0.5 m. Methods that homogenize corn plant stands and emergence may decrease plant-to-plant variation and could lead to increased Published in Agron.