Midseason fertilizer N recommendations in corn (Zea mays L.) and wheat (Triticum aestivum L.) are not consistent from one region to the next. Preplant soil testing, yield goals, economic optimums, chlorophyll meters, and optical sensor‐based yield prediction models are limited regionally. The objective of this paper is to introduce an applied approach for applying preplant N fertilizer in automated gradients used for determining midseason N rates based on plant response. This approach assumes that midseason biomass estimated using normalized difference vegetation index (NDVI) sensor readings is directly related to corn and wheat grain yield, and that delaying applied N until midseason (eight‐leaf stage in corn and Feekes 5 in winter wheat) can result in near‐maximum yields. The ramped calibration strip (RCS) applicator applies 16 different incremental N rates (3‐to 6‐m intervals), over 45 to 90 m (number of rates, intervals, and distances can be adjusted depending on the crop). Because the RCS is superimposed on the farmer practice, producers can examine plant responsiveness over the range of rates to determine the optimum topdress N rate. The point where midseason growth differences no longer exist is the topdress N rate. Recording distance is required as you walk the RCS since distance is associated with an incremental N rate. Where adequate but not excessive preplant N is available, the ramp interpolated rate provides an applied method to determine how much midseason N should be applied to achieve the maximum yields based on growth response evidenced within the RCS.

Improving N management for corn (Zea mays L.) production with precision agriculture technologies requires that spatial N recommendations adequately represent in‐field variability in N availability. Our objective was to evaluate corn response to increasing N rates in several in‐field locations that represented the range of soil organic matter (OM) content in the field. In a 2‐yr study, three center pivot–irrigated fields were selected in south‐central Kansas and south‐central Nebraska. Four or five locations were selected within each field. At each location, five or six N treatments (0–336 kg N ha−1) were surface‐applied early in the growing season. The minimum N rate to achieve maximum yield varied by as much as 130 kg N ha−1 among in‐field locations at three site‐years. The least amount of N to achieve maximum yield did not coincide with locations representing greater soil OM. Yield response at two site‐years was the same among in‐field locations; however, mean yield among in‐field locations varied by as much as 4.2 Mg ha−1, representing potential for improvement in N use efficiency. Leaf tissue N was below the critical threshold for 60 to 100% of observations at three different in‐field locations but below the critical threshold for <35% of the observations at all other in‐field locations. The reason for the discrepancy in N availability among in‐field locations was not conclusively identified but was not only related to soil OM content. Variable N recommendations based only on soil OM is too simplistic to reflect variability in N availability within a field.

If a nutrient is deficient, that deficiency should be expressed both as a function of intensity (severity of the deficiency) and capacity (total demand). Liebig's law of the minimum states that the nutrient present in the least relative amount is the limiting nutrient (Bray, 1954). Bray (1954) interpreted Liebig's law of the minimum to mean that yield would be directly proportional to the amount of deficient nutrient present and the crop content of that nutrient. Stanford (1973) reported that optimum use of N included the N requirement of the crop at an expected level of yield, the amount of N mineralized during the season, the amount of residual N present early in the season, and the expected efficiency of the N to be applied. Stanford (1973) concluded that the validity of N fertilizer predictions depend on realistic estimates of yield, efficiency, and residual mineral N supply. Importance of Grain Yield Potential for Making Nitrogen RecommendationsUnless otherwise indicated, "yield" used in this paper is in reference to grain yield for predominantly maize and wheat data that are included in this paper. Research in the Netherlands by Spiertz and De Vos (1983) indicated that winter wheat N rate recommendations should be based on the amount of residual soil N and the crop requirement in a given environment, where both components were expected to vary considerably due to environmental conditions. They further reported that an accurate assessment of the potential yield for different growing conditions would improve N fertilizer recommendations. Ying et al. (1998) showed that N requirements increased with increasing yield for high-yield rice (Oryza sativa L.) in tropical and subtropical environments. Similar work by Mohammed et al. (2013) reported the need to make N recommendations by year since yield levels at the same N rate changed radically over time. Results from Mullen et al. (2003) were consistent noting the importance of first recognizing yield potential, and that ensuing fertilizer N requirements would depend on the likelihood of obtaining a response. Fowler (2003) noted that N fertilization rates increased when grain protein concentration targets increased for high yield potential wheat varieties. Schepers et al. (1992) suggested that SPAD 502 chlorophyll meter readings may provide a better estimate of potential yield than leaf N concentration. They were the first to compare chlorophyll meter readings from well fertilized rows to those from the test area (precursor to using N-rich strips). This method encouraged having an N reference for local growing conditions (Schepers et al., 1992). Findings by Lory and Scharf (2003) showed that fertilizer recommendations that ignore yield entirely are limited to explaining <50% of the variation in the economic optimum N rate for maize.Work by Raun et al. (2001) focused on predicting actual wheat grain yield using mid-season spectral measurements. They reported that the normalized difference vegetation index (NDVI) collected from winter wheat at the Feekes 5 growth ...

N ha Ϫ1 (corresponding to maximum yield), NO 3 leaching increased exponentially. Reducing N application rates Improving N management for corn (Zea mays L.) production with by 5% less than required to achieve maximum corn yield precision agriculture technologies requires that spatial N recommendations adequately represent in-field variability in N availability. Our reduced NO 3 leaching by 40 to 45% (Sexton et al., 1996). objective was to evaluate corn response to increasing N rates in severalApplying an economically optimal N rate minimizes in-field locations that represented the range of soil organic matter (OM)

Conservation tillage had initial roots in the Great Plains, but the current adoption of conservation tillage, especially no-till, lags behind in the rest of the United States. This paper documents the results of a recent survey of Oklahoma producers, which was conducted to assess the current status of conservation tillage in the state. Based on responses from 1,703 producers, econometric analysis was conducted to identify factors explaining the observed use of conservation tillage practices in Oklahoma. The survey found that conventional tillage remains the most common tillage practice among Oklahoma producers. According to the survey, conventional tillage is used on 43.2% of the state's total acreage, conservation tillage on 26.7% of the total acreage, and reduced tillage on the remaining 30.1% of the crop acreage. A Tobit model was developed to explain patterns of tillage use based on producer characteristics and their perceptions on how conservation tillage performs relative to conventional tillage according to various economic and agronomic factors. The Tobit model identified operator age, farm size, crop rotation, knowledge, and erosion control as highly significant factors explaining the observed use of conservation tillage. The model results also identified potential constraints to conservation tillage adoption and use in the Southern Plains, suggesting that the unique needs of mixed crop-livestock farming systems, and the dominant winter wheat (Triticum aestivum L.) monoculture, hinder further diffusion of conservation tillage. Future policy should consider addressing the needs of Oklahoma producers, particularly crop producers heavily engaged in livestock activities, as well as finding viable rotation crops to provide alternatives for the winter wheat monoculture.