Nitrogen management for corn

As pressure continues to reduce production costs, to manage risks, and to reduce loss of N to the environment, managing nitrogen remains a major challenge to Corn Belt corn producers. Major considerations include N rate, form of nitrogen, use of stabilizers, and method and timing of application

Agronomics of high-yielding corn

Corn yields in the Corn Belt have been increasing steadily, with the state average yield in Illinois rising at a rate of 1.9 bushels per acre per year from 1970 to 2009, but 3.6 bushels per acre per year since 1996. With few exceptions, the weather during the past 15 years has been very favorable for corn. But improvements in hybrids and in management have played an important part in these yield increases as well

Continuous corn response to residue removal, tillage, and nitrogen

There has been a great deal of recent interest in “bioenergy” crops that could be burned to generate electricity or heat or used as a feedstock in the manufacture of liquid fuels. Cornstalks represent one of the major “biomass” sources that currently exist. Because today’s healthy, high-yielding hybrids leave behind stalks that present a management challenge, some people are wondering why we don’t help solve both problems—the need for biomass and the difficulty of managing residue—by harvesting cornstalks to use as fuel

Regional Approach to Making Nitrogen Fertilizer Rate Decisions for Corn

Nitrogen fertilizer is one of the largest input costs for growing corn. Across the Corn Belt, N is typically the most yield-limiting nutrient. Facing record high N fertilizer prices and potential supply problems, producers are concerned about N fertilization rates. Soil fertility researchers and extension specialists from seven states across the Corn Belt (see list in acknowledgements section) have been discussing com N fertilization needs and evaluating N rate recommendation systems for approximately the past two years. These discussions could not have been timelier considering the current N fertilizer issues. In recent years N recommendation systems have become more diverse across states in the Com Belt. Of particular significance has been the movement away from yield goal as a basis of N rate decisions in some states to other methods such as cropping system (Iowa) or soil specific yield potential (Wisconsin). Research from across the Com Belt has also been indicating that economic optimum N rate (EONR) does not vary according to yield level. At the same time, corn yields have been at historic high levels, leading to increases in yield goal. This has added to concerns that increasing yield-based N rates are often found to be substantially greater than EONR observed in N rate trials. Also, watersheds being targeted to receive incentive and cost­ share funds for N rate management sometimes cross state boundaries, which causes problems if suggested rates are not consistent. These issues have increased uncertainty regarding current N rate recommendations. An outcome of the multi-state discussions has been development of a consistent approach for N rate guideline development that can be utilized on a regional basis. This does not necessarily mean that fertilizer N rates will be the same across states. Rather, there is a common approach to guideline development. Depending upon the research database, rates could be the same or quite different Another outcome of this approach has been an improved ability to evaluate the economic returns to N, and the ability to estimate the most profitable fertilizer N rates. This has become very valuable information for dealing with today\u27s high N fertilizer prices and water quality issues

Uneven emergence in corn

1 online resource (PDF, 6 pages)This archival publication may not reflect current scientific knowledge or recommendations. Current information available from the University of Minnesota Extension: https://www.extension.umn.edu

Agronomic responses of corn to stand reducation at vegetative growth stages

Yield loss charts for hail associated with stand reduction assume that remaining plants lose the ability to compensate for lost plants by mid-vegetative growth. Yield losses and stand losses after V8 – leaf collar system – and throughout the remaining vegetative stages are 1:1 according to the current standards. We conducted field experiments from 2006 to 2009 at twelve site-years in Illinois, Iowa, and Ohio to determine responses of corn to stand reduction at the fifth, eighth, eleventh, and fifteenth leaf collar stages (V5, V8, V11, and V15, respectively). We also wanted to know whether these responses varied between uniform and random patterns of stand reduction with differences in within-row interplant spacing. When compared to a control of 36,000 plants per acre, grain yield decreased linearly as stand reduction increased from 16.7 to 50% (Table 3), but was not affected by the pattern of stand reduction. This rate of yield loss was greatest when stand reduction occurred at V11 or V15, and least when it occurred at V5. With 50% stand loss, yield was 83 and 69% of the control when stand loss occurred at V5 and V15, respectively. With 16.7% stand loss at V5, V8, or V11, yield averaged 96% of the control. Per-plant grain yield increased when stand loss occurred earlier and was more severe. With 50% stand loss at V11 or V15, per-plant grain yield increased by 37 to 46% compared to the control. Corn retains the ability to compensate for lost plants through the late vegetative stages, indicating that current standards for assessing the effect of stand loss in corn should be reevaluated

Maize Leaf Appearance Rates: A Synthesis From the United States Corn Belt

The relationship between collared leaf number and growing degree days (GDD) is crucial for predicting maize phenology. Biophysical crop models convert GDD accumulation to leaf numbers by using a constant parameter termed phyllochron (°C-day leaf−1) or leaf appearance rate (LAR; leaf oC-day−1). However, such important parameter values are rarely estimated for modern maize hybrids. To fill this gap, we sourced and analyzed experimental datasets from the United States Corn Belt with the objective to (i) determine phyllochron values for two types of models: linear (1-parameter) and bilinear (3-parameters; phase I and II phyllochron, and transition point) and (ii) explore whether environmental factors such as photoperiod and radiation, and physiological variables such as plant growth rate can explain variability in phyllochron and improve predictability of maize phenology. The datasets included different locations (latitudes between 48° N and 41° N), years (2009–2019), hybrids, and management settings. Results indicated that the bilinear model represented the leaf number vs. GDD relationship more accurately than the linear model (R2 = 0.99 vs. 0.95, n = 4,694). Across datasets, first phase phyllochron, transition leaf number, and second phase phyllochron averaged 57.9 ± 7.5°C-day, 9.8 ± 1.2 leaves, and 30.9 ± 5.7°C-day, respectively. Correlation analysis revealed that radiation from the V3 to the V9 developmental stages had a positive relationship with phyllochron (r = 0.69), while photoperiod was positively related to days to flowering or total leaf number (r = 0.89). Additionally, a positive nonlinear relationship between maize LAR and plant growth rate was found. Present findings provide important parameter values for calibration and optimization of maize crop models in the United States Corn Belt, as well as new insights to enhance mechanisms in crop models

Evaluation of the Haney Soil Health Tool for corn nitrogen recommendations across eight Midwest states

Use and development of soil biological tests for estimating soil nitrogen (N) availability and subsequently corn (Zea mays L.) fertilizer N recommendations is garnering considerable interest. The objective of this research was to evaluate relationships between the Haney Soil Health Test (HSHT), also known as the Soil Health Tool or Haney test, and the economically optimum N rate (EONR) for corn grain yield at 17 sites in eight Midwest US states in 2016. Trials were conducted with a standard set of protocols that included a nonfertilized control plus six N rates applied at planting or as a split between planting and sidedress, soil samples for the HSHT prior to planting, and grain harvest at physiological maturity, and determination of EONR for each N application timing. Results indicated that HSHT recommendations with expected yield accounted for ≤28% of the variation in EONR among sites and N timings. Two components of the HSHT not directly used in the HSHT N recommendation for corn, the soil health calculation, or soil health score, and the Solvita carbon dioxide (CO2)-Burst lab test, accounted for the most variation in EONR. These two components were moderately related (R2 = 0.29 to 0.39) to soil organic matter (OM), highly related (R2 = 0.98) with each other, and subsequently both accounted for over one-half (R2 = 0.55) of the variation in EONR for N applied at planting or as a split. With additional research, these two components may help improve N recommendations for corn in the Midwest, especially Solvita CO2-Burst because it costs less to determine than the soil health calculation

Active-Optical Reflectance Sensing Evaluated for Red and Red-Edge Waveband Sensitivity

Uncertainty exists with corn (Zea mays L.) N management due to year-to-year variation in crop N need, soil N supply, and N loss from leaching, volatilization, and denitrification. Active-optical reflectance sensing (AORS) has proven effective in some fields for generating N fertilizer recommendations that improve N use efficiency. However, various sensors utilize different wavebands of light to calculate N fertilizer recommendations making it difficult to know which waveband is most sensitive to plant health. The objective of this research was to evaluate across the US Midwest Corn Belt the performance and sensitivity of the red (R) and red-edge (RE) wavebands. Forty-nine N response trials were conducted across eight states and three growing seasons. Reflectance measurements were collected and topdress N rates (40 to 240 lbs N ac-1 on 40 lbs ac-1 increments) applied at approximately V9 corn development stage. Both R and RE wavebands were compared to the at-planting N fertilizer rate, V5 soil nitrate-N, and end-of-season calculated relative yield. In every comparison the RE waveband demonstrated higher coefficient of determination values over the R waveband. These findings suggest the RE waveband is most sensitive to variations in N management and would work best for in-season AORS management over a geographically-diverse soil and weather region

Statistical and machine learning methods evaluated for incorporating soil and weather into corn nitrogen recommendations

Nitrogen (N) fertilizer recommendation tools could be improved for estimating corn (Zea mays L.) N needs by incorporating site-specific soil and weather information. However, an evaluation of analytical methods is needed to determine the success of incorporating this information. The objectives of this research were to evaluate statistical and machine learning (ML) algorithms for utilizing soil and weather information for improving corn N recommendation tools. Eight algorithms [stepwise, ridge regression, least absolute shrinkage and selection operator (Lasso), elastic net regression, principal component regression (PCR), partial least squares regression (PLSR), decision tree, and random forest] were evaluated using a dataset containing measured soil and weather variables from a regional database. The performance was evaluated based on how well these algorithms predicted corn economically optimal N rates (EONR) from 49 sites in the U.S. Midwest. Multiple algorithm modeling scenarios were examined with and without adjustment for multicollinearity and inclusion of two-way interaction terms to identify the soil and weather variables that could improve three dissimilar N recommendation tools. Results showed the out-of-sample root-mean-square error (RMSE) for the decision tree and some random forest modeling scenarios were better than the stepwise or ridge regression, but not significantly different than any other algorithm. The best ML algorithm for adjusting N recommendation tools was the random forest approach (r2 increased between 0.72 and 0.84 and the RMSE decreased between 41 and 94 kg N ha−1). However, the ML algorithm that best adjusted tools while using a minimal amount of variables was the decision tree. This method was simple, needing only one or two variables (regardless of modeling scenario) and provided moderate improvement as r2 values increased between 0.15 and 0.51 and RMSE decreased between 16 and 66 kg N ha−1. Using ML algorithms to adjust N recommendation tools with soil and weather information shows promising results for better N management in the U.S. Midwest