No abstract

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.

Crop models can be evaluated based on accuracy in simulating several years' yields for one location or on accuracy in simulating long‐term mean yields for several locations. Our objective was to see how the ALMANAC (Agricultural Land Management Alternatives with Numerical Assessment Criteria) model and a new version of CERES‐Maize (Crop‐Environment Resource Synthesis) simulate grain yield of rainfed maize (Zea mays L.). We tested the models at one county in each of nine states: Minnesota, New York, Iowa, Illinois, Nebraska, Missouri, Kansas, Louisiana, and Texas (MN, NY, IA, IL, NE, MO, KS, LA, and TX). Simulated grain yields were compared with grain yields reported by the National Agricultural Statistical Service (NASS) for 1983 to 1992. In each county we chose a soil commonly used in maize production, and we used measured weather data. Mean simulated grain yield for each county was always within 5% of the mean measured grain yield for the location. Within locations, measured grain yield was regressed on simulated grain yields and tested to see if the slope was significantly different from 1.0 and if the y‐intercept was significantly different from 0.0, both at the 95% confidence level. Only at MN, NY, and NE for ALMANAC and at MN, NY, and TX for CERES was slope significantly different from 1.0 or intercept significantly different from 0.0. The CVs of simulated grain yields were similar to the those of measured yields at most sites. Also, both models were appropriate for predicting an individual year's yield for most counties. Values for plant parameters, such as heat units for development and the harvest index, and values for soil parameters describing soil water‐holding capacity offer users reasonable inputs for simulating maize grain yield over a wide range of locations.

et al. (1989) modeled EC a as a function of soil water content (both the mobile and immobile fractions), the Apparent profile soil electrical conductivity (EC a) can be an indielectrical conductivity (EC) of the soil water, soil bulk rect indicator of a number of soil physical and chemical properties. Commercially available EC a sensors can be used to efficiently and density, and the EC of the soil solid phase. inexpensively develop the spatially dense data sets desirable for de-Measurements of EC a can be used to provide indirect scribing within-field spatial soil variability in precision agriculture. measures of the soil properties listed above if the contri-The objective of this research was to compare EC a measurements butions of the other soil properties affecting the EC a from a noncontact, electromagnetic induction-based sensor (Geonics measurement are known or can be estimated. If the EC a EM38) 1 to those obtained with a coulter-based sensor (Veris 3100) changes due to one soil property are much larger than and to relate EC a data to soil physical properties. Data were collected those attributable to other factors, then EC a can be on two fields in Illinois (Argiudoll and Endoaquoll soils) and two in calibrated as a direct measurement of that dominant Missouri (Aqualfs). At 12 to 21 sampling sites in each field, 120-cmfactor. Lesch et al. (1995a, 1995b) used this direct-calideep soil cores were obtained for soil property determination. Depth bration approach to quantify variations in soil salinity response curves for each EC a sensor were derived or obtained from the literature. Within a single field and measurement date, EM38 data within a field where water content, bulk density, and and Veris deep (0-100 cm depth) data were most highly correlated other soil properties were "reasonably homogeneous." (r ϭ 0.74-0.88). Differences between EC a sensors were more pro-Research in Missouri has established direct, within-field nounced on the more layered Missouri soils due to differences in calibrations between EC a and the depth of topsoil above depth-weighted response curves. Correlations of EC a with response a subsoil claypan horizon (Doolittle et al., 1994; Sudduth curve-weighted clay content and cation exchange capacity were gener