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

Characterizing Genotype X Management Interactions on Soybean Seed Yield

Increased soybean [Glycine max (L.) Merr.] commodity prices in recent years have generated interest in high-input systems to increase yield. The objective of this study was to evaluate the effects of current, high-yielding cultivars under high- and low-input systems on soybean yield and yield components. Research trials were conducted at 19 locations spanning nine states from 2012 to 2014. At each location, six high-yielding cultivars were grown under three input systems: (i) standard practice (SP, current recommended practices), (ii) high-input treatment consisting of a seed treatment fungicide, insecticide, nematistat, inoculant, and lipo-chitooligosaccharide (LCO); soil-applied N fertilizer; foliar LCO, fertilizer, antioxidant, fungicide and insecticide (SOYA), and (iii) SOYA minus foliar fungicide (SOYA-FF). An individual site-year yield analysis found only 3 of 53 (5.7%) site-years examined had a significant cultivar × input system interaction, suggesting cultivar selection and input system decisions can remain independent. Across all site-years, the SOYA and SOYA-FF treatments yielded 231 (5.5%) and 147 kg ha–1 (3.5%) more than the SP, and input system differences were found among maturity groups. Yield component measurements (seeds m–2, seed mass, early-season and final plant stand, pods plant–1, and seeds pod–1) indicated positive yield responses were due to increased seeds m–2 and seed mass. While both high-input systems increased yield on average, grower return on investment (ROI) would be negative given today’s commodity prices. These results further support the use of integrated pest management principles for making input decisions instead of using prophylactic applications to maximize soybean yield and profitability

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

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

High-Input Management Systems Effect on Soybean Seed Yield, Yield Components, and Economic Break-Even Probabilities

Elevated soybean [Glycine max (L.) Merr.] prices have spurred interest in maximizing soybean seed yield and has led growers to increase the number of inputs in their production systems. However, little information exists about the effects of high-input management on soybean yield and profitability. The purpose of this study was to investigate the effects of individual inputs, as well as combinations of inputs marketed to protect or increase soybean seed yield, yield components, and economic break-even probabilities. Studies were established in nine states and three soybean growing regions (North, Central, and South) between 2012 and 2014. In each site-year both individual inputs and combination high-input (SOYA) management systems were tested. When averaged between 2012 and 2014, regional results showed no seed yield responses in the South region, but multiple inputs affected seed yield in the North region. In general, the combination SOYA inputs resulted in the greatest yield increases (up to 12%) compared to standard management, but Bayesian economic analysis indicated SOYA had low break-even probabilities. Foliar insecticide had the greatest break-even probabilities across all environments, although insect pressure was generally low across all site-years. Soybean producers in North region are likely to realize a greater response from increased inputs, but producers across all regions should carefully evaluate adding inputs to their soybean management systems and ensure that they continue to follow the principles of integrated pest management