N to surface water is primarily by subsurface flow of nitrate (Schilling, 2002; Steinheimer et al., 1998), partic-Applying only as much N fertilizer as is needed by a crop has ecoularly when N fertilizer has been applied at rates exnomic and environmental benefits. Understanding variability in need for N fertilizer within individual fields is necessary to guide approaches ceeding crop needs (Burwell et al., 1976).to meeting crop needs while minimizing N inputs and losses. Our ob-Small-plot research has shown that experiments in jective was to characterize the spatial variability of corn (Zea mays L.) different production corn fields can differ substantially N need in production corn fields. Eight experiments were conducted in their need for N fertilizer (Bundy and Andraski, 1995; in three major soil areas (Mississippi Delta alluvial, deep loess, clay- Schmitt and Randall, 1994). Need for N fertilizer may pan) over 3 yr. Treatments were field-length strips of discrete N rates also vary widely over large fields (Malzer et al., 1996; from 0 to 280 kg N ha Ϫ1 . Yield data were partitioned into 20-m Mamo et al., 2003) though very little research has been increments, and a quadratic-plateau function was used to describe published addressing this issue. Attempts to predict the yield response to N rate for each 20-m section. Economically optimal amount of N fertilizer needed have met with limited N fertilizer rate (EONR) was very different between fields and was success in humid regions (Kitchen and Goulding, 2001). also highly variable within fields. Median EONR for individual fields ranged from 63 to 208 kg N ha Ϫ1 , indicating a need to manage NThe dominant practice for agricultural producers is to fertilizer differently for different fields. In seven of the eight fields, apply the same rate of N fertilizer over whole fields and a uniform N application at the median EONR would cause more than even whole farms. In fields with spatially variable N needs, half of the field to be over-or underfertilized by at least 34 kg N this practice leads to frequent mismatches between N ha Ϫ1 . Coarse patterns of spatial variability in EONR were observed fertilizer rate and crop N need. Overapplication is more in some fields, but fine and complex patterns were also observed in frequent since producers have an economic incentive most fields. This suggests that the use of a few appropriate manageto err more frequently in that direction: The cost of ment zones per field might produce some benefits but that N manageunneeded N fertilizer in areas of overapplication is less ment systems using spatially dense information have potential for than the cost of lost yield potential in areas of undergreater benefits. Our results suggest that further attempts to develop application. systems for predicting and addressing spatially variable N needs are justified in these production environments.
Nitrogen fertilizer is typically applied to corn (Zea mays L.) shortly before planting, but there are several reasons why later N applications may be of interest: to spread work away from the busy planting season, to avoid the frequent wet field conditions in spring, to reduce or remedy in‐season N loss in wet years, or to allow use of in‐season diagnostic tools. One of the obstacles to the use of later N applications is the fear that irreversible yield loss will occur due to N stress. Our objective was to evaluate the yield impact of delaying N applications until the late vegetative growth stages and as far as silking. We conducted a total of 28 experiments with timing of a single N application as the experimental treatment. We found little or no evidence of irreversible yield loss when N applications were delayed as late as stage V11, even when N stress was highly visible. There was weak evidence of minor yield loss (about 3%) when N applications were delayed until stage V12 to V16. Only 3 of the 28 experiments had N applications later than V16—all were at silking and relative yields were 0.71, 0.89, and 0.95. Though full yield was not achieved when N applications were delayed until silking, yield was still highly responsive to N application at this stage—yield response exceeded 2.2 Mg ha−1 in all three experiments.
Fertilizer N needs of corn (Zea mays L.) vary widely both among and within fields. Many states use yield goal to help determine differences in fertilizer N need, but some states have questioned yield goal–derived recommendations because of the poor correlation of yield with fertilizer N need. In this study, data from 298 previously reported experiments in five states (Illinois, Minnesota, Missouri, Pennsylvania, and Wisconsin) were combined to evaluate fertilizer N response of corn. Corn grain yield at the economically optimum N rate (EONR) was positively but poorly correlated with EONR (r2 = 0.02). This was consistent with others who have observed that maximum or optimum economic yield is a poor predictor of EONR. Our analysis indicated N supplied by the soil and previous management reduced N need from that predicted by yield alone at most locations. Delta yield (grain yield at optimum N rate minus grain yield of the control) was a much better predictor of EONR at these same locations (r2 = 0.47). A theoretically derived equation based on the delta yield concept was similarly capable of predicting EONR for corn. These results indicate that fertilizer recommendation systems that rely solely on yield or ignore yield entirely are limited to explaining less than 50% of the variation in EONR for corn. Farmers should be encouraged to monitor delta yield as a more effective indicator of EONR than actual yield. Greater understanding of the delta yield concept is needed before relying on it to predict fertilizer N requirements.
Many states have invested significant resources to identify components of their Phosphorus (P) Index that reliably estimate the relative risk of P loss and incentivize conservation management. However, differences in management recommendations and manure application guidelines for similar field conditions among state P Indices, coupled with minimal reductions in the extent of P-impaired surface waters and soil test P (STP) levels, led the U.S. Natural Resources Conservation Service (NRCS) to revise the 590 Nutrient Management Standard. In preparation for this revision, NRCS requested that a review of the scientific underpinnings and accuracy of current P Indices be undertaken. They also sought to standardize the interpretation and management implications of P Indices, including establishment of ratings above which P applications should be curtailed. Although some states have initiated STP thresholds above which no application of P is allowed, STP alone cannot define a site's risk of P loss. Phosphorus Indices are intended to account for all of the major factors leading to P loss. A rigorous evaluation of P Indices is needed to determine if they are directionally and magnitudinally correct. Although use of observed P loss data under various management scenarios is ideal, such data are spatially and temporally limited. Alternatively, the use of a locally validated water quality model that has been shown to provide accurate estimates of P loss may be the most expedient option to conduct Index assessments in the short time required by the newly revised 590 Standard.
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