Fertilizing maize at an optimum nitrogen rate is imperative to maximize productivity and sustainability. Using a combination of long-term (n = 379) and short-term (n = 176) experiments, we show that the economic optimum nitrogen rate for US maize production has increased by 2.7 kg N ha−1 yr−1 from 1991 to 2021 (1.2% per year) simultaneously with grain yields and nitrogen losses. By accounting for societal cost estimates for nitrogen losses, we estimate an environmental optimum rate, which has also increased over time but at a lower rate than the economic optimum nitrogen rate. Furthermore, we provide evidence that reducing rates from the economic to environmental optimum nitrogen rate could reduce US maize productivity by 6% while slightly reducing nitrogen losses. We call for enhanced assessments and predictability of the economic and environmental optimum nitrogen rate to meet rising maize production while avoiding unnecessary nitrogen losses. Maize production is dependent on Nitrogen fertilizer input. Here, the authors use long-term and short-term experiments to demonstrate that economic and environmental optimum nitrogen fertilization rates have increased between 1991 and 2021.

Context: Quantifying historical changes from plant breeding and increasing plant density on maize biomass production and allocation to organs is crucial for understanding historical grain yield increase and its implications for soil health, and carbon sequestration. Yet, such information is scarce. Objective: To quantify and partially distinguish the effects of maize breeding and increasing plant density on maize biomass production, biomass allocation to different plant organs, and biomass re-allocation during grainfilling period. Methods: We studied 18 commercial hybrids (111-day relative maturity) released between 1983 and 2017 across seven environments in the US Corn Belt. Hybrids were grown at current plant density (8.1 pl m-2) and historically increasing plant density (4.7, 5.9, 7.0, and 8.1 plants m-2 for hybrids released in decadal eras 1985, 1995, 2005, and 2015, respectively). Biomass and its distribution to stems, ears (including cobs and kernels), and leaves (including green and senesced) were assessed at the beginning and end of the effective grain-filling period through destructive plant sampling. Results: New hybrids planted at 8.1 pl m-2 produced 6.2 Mg ha-1 more biomass than old hybrids at 4.7 pl m-2. Maize biomass production linearly increased by 107 kg ha-1 year-1 (0.4% year-1) and by 185 kg ha-1 year-1 (0.8 % year-1) under current and historically increasing plant density, respectively. Breeding accounted for 58 % and plant density for 42 % of the total biomass increase at physiological maturity. Plant density did not influence the biomass increase that occurred during the reproductive phase (3 Mg ha-1). Breeding caused a significant shift in biomass allocation, favoring the ear over stems with little impact on leaves. New hybrids remobilized less stem dry matter (1.1 % year-1) and had 15% more green leaf biomass at physiological maturity than older hybrids. Conclusions: Breeding and plant density effects on biomass production and partitioning differed between crop stages. Maize breeding increased reproductive biomass production while plant density increased vegetative biomass production. Breeding and plant density together increased biomass production by 30% from 1983 to 2017. Maize breeding had a greater influence on biomass allocation than plant density. Modern hybrids allocate more dry matter to the ear, have more green leaves at physiological maturity, and remobilize less stem dry matter compared to the old hybrids. Significance: Our results can help explain historical grain yield increase in the US Corn Belt and accurately estimate residue carbon inputs for sustainability assessments and inform crop model calibration tasks. Our findings provide valuable new insights into understanding changes in the maize plant over the years and breeding and plant density interactions.

The interaction between nitrogen (N) rate, plant density, and hybrid on net return to seed and fertilizer cost remains unknown but important to be addressed given the multi-input decision process corn growers make every year. We collected grain yield from a factorial experiment with five N rates (0-291 kg N ha-1), five plant densities (3.7-11.4 plants m-2), and four hybrids (old vs. new) over 2 years in Iowa. Data were sufficiently modeled with a quadratic plateau model with continuous covariate terms to include N x plant density interactions (R2 = 0.925, n = 387). We found a wide range of plant densities (6.7-8.4 plants m-2) and N rate (150-212 kg N ha-1) combinations to achieve 99% of the maximum net revenue. The economic optimum N rate increased with increasing plant density from 4 to 7 plants m-2. The fundamental relationships between grain yield response to N fertilizer and plant density were similar across old and new hybrids; however, the absolute values were different, with newer hybrids having 20% higher economic optimum N rate and 23% higher yield than the older hybrids. We concluded that by accounting for two inputs into the revenue equation, a larger range of combinations was found to achieve 99% of the maximum net revenue. Our study offers new insights into N rate by plant density by hybrid interaction to assist future research. The greatest economic risk occurs at high plant density and low N rates. A wide range of optimum N rate (150-210 kg N ha-1) and plant density (6.7-8.4 plants m-2) reached 99% of max revenue. New hybrids had 10% higher optimum plant density, 20% higher optimum N rate, and 23% higher yield than old hybrids. The optimum N rate varied the most and the optimum plant density the least across years and hybrids. The relationship between optimum yield and optimum N rate depends on the choice of the fitted regression model.

The interaction between nitrogen (N) rate, plant density, and hybrid on net return to seed and fertilizer cost remains unknown but important to be addressed given the multi‐input decision process corn growers make every year. We collected grain yield from a factorial experiment with five N rates (0–291 kg N ha −1 ), five plant densities (3.7–11.4 plants m −2 ), and four hybrids (old vs. new) over 2 years in Iowa. Data were sufficiently modeled with a quadratic plateau model with continuous covariate terms to include N × plant density interactions ( R 2 = 0.925, n = 387). We found a wide range of plant densities (6.7–8.4 plants m −2 ) and N rate (150–212 kg N ha −1 ) combinations to achieve 99% of the maximum net revenue. The economic optimum N rate increased with increasing plant density from 4 to 7 plants m −2 . The fundamental relationships between grain yield response to N fertilizer and plant density were similar across old and new hybrids; however, the absolute values were different, with newer hybrids having 20% higher economic optimum N rate and 23% higher yield than the older hybrids. We concluded that by accounting for two inputs into the revenue equation, a larger range of combinations was found to achieve 99% of the maximum net revenue. Our study offers new insights into N rate by plant density by hybrid interaction to assist future research.

Ranking the contribution of genotype, environment, and management (G x E x M) on maize's economic optimum nitrogen fertilizer rate (EONR) variability could improve understanding and predictability of EONR. We performed a simulation experiment using the Agricultural Production Systems sIMulator model with the objectives to (1) rank the effects of 24 individual G x E x M factors on the magnitude and interannual variability of the EONR across the US Midwest and (2) investigate the impact of G x M factors on the EONR variability under present and future climate scenarios. Results indicate that genetics (27%), management (31%), and environmental conditions (41%) each influence the EONR variability. Within these broad categories, the top three individual factors impacting the EONR were interannual weather variability, crop radiation use efficiency, and the soil inorganic N carryover from the previous year. The G x E x M factors influenced the yield response to N fertilizer in different ways. Soil-related factors (e.g., organic matter and residual nitrate) influenced grain yields at the low N rates, while management factors (e.g., planting date and density) influenced yield at all N rates. Combining increases in plant density and changes in genetics synergistically increased the EONR by 15% from baseline. Future climate scenarios without adaptation decreased the EONR and yield loss, but crop adaptation was buffered against the negative climate change impacts. We concluded that 59% of the annual EONR variability is manageable (due to genetics and management) and that G x M factors could buffer climate change's negative effects on crop production. Present results can inform experimental research on N fertilizer and N rate decisions. Genotype, management, and environment explained 27%, 31%, and 41% of the economic optimum N rate (EONR) variability. Weather variability, crop radiation use efficiency, and inorganic nitrogen were the most influential factors on the EONR. Soil nitrogen-related factors mostly affected the grain yield at low N-fertilizer rates. Increases in plant density and changes in genetics synergistically increased EONR.

The agricultural sector is responsible for substantial amounts of greenhouse gas emissions that exacerbate climate change. Such greenhouse gas emissions from upland crops are difficult to abate because they are dominated by nitrous oxide (N2O) production from soil processes. Strategies to reduce these emissions focus on N fertilizer management, and there is a widespread assumption that legume crops, which do not receive N fertilizer, emit little N2O. Here we show that this assumption is incorrect; approximately 40% of N2O emissions from the most extensive cropping system in North America-the maize-soybean rotation-occur during the soybean phase. Yet, due to the lack of N fertilizer input, opportunities for emissions abatement from the soybean phase are unclear. Using models of cropping systems, we developed a strategy that combines cover-crop management and earlier planting of extended growth soybean varieties to reduce emissions from soybean production by 33%. These practices, which complement N fertilizer management in maize, are widely accessible and represent an immediate, climate-smart strategy to reduce nitrous oxide emissions from soybean production, thus not only contributing to climate-change mitigation but also maintaining productivity while adapting to changing weather patterns. Soil processes involved in agricultural practices emit considerable levels of nitrous oxide, which detrimentally contribute to climate change. This study explores strategies to reduce nitrous oxide emissions while maintaining crop productivity in the US maize-soybean rotational cropping system.

CONTEXT: Process-based models are increasingly used to explain and predict crop yields and long-term changes in soil organic matter (SOM) and hence should be regularly evaluated for their accuracy. Currently, there is a knowledge gap of how well process-based models can estimate the economic optimum nitrogen rate (EONR) across environments and years.OBJECTIVES: We evaluated the Agricultural Production Systems sIMulator (APSIM) software ability to simulate corn yield response to nitrogen (N) input and crop rotation in long term experiments. Furthermore, we explored causes for over/under prediction of the EONR. METHODS: Measurements included crop yields from 14 long-term (N) fertilizer rate experiments representing major production regions in the U.S. Corn Belt and SOM distribution from seven long-term experiments. Corn yield response to N rate was analyzed with statistical models to estimate the EONR (386 N rate trials).RESULTS And CONCLUSIONS: The model successfully captured spatiotemporal patterns in observed crop yields across N rates in the soybean-corn (SC) system, but overpredicted crop yield by 5-25% at high N rates in the corn-corn (CC) system. This overprediction was partially resolved by adding algorithms in APSIM to account for the well-known continuous corn yield penalty. The improved model simulated yield response to N and the EONR with a model agreement of 0.93 and 0.66, respectively, across rotations. The lower accuracy in predicting EONR compared to crop yields was attributed to 1) inherent model error in simulating yields: for example, a 10% error in yield simulation of a single point can result in a 34% error in EONR; and 2) the inability of the APSIM model to fully generate the quadratic nature of corn yield response to N rate, which resulted in the linear-plateau model being selected in most cases. Forcing use of only a quadratic plateau or quadratic regression model to fit the simulated yields, which is the expected biophysical yield response to N, improved EONR prediction by 23% while the accuracy of the statistical model fit was minimally decreased (<1%). The APSIM model simulated SOM distributions after 20 years of cropping with an agreement index of 0.93. We believe that APSIM is well suited to supplement N research in the U.S. Corn Belt, recognizing identified limitations between yield estimation and N response determination. SIGNIFICANCE: This research provided a solution for process-based models to cope with the continuous corn yield penalty, thoroughly evaluated APSIM, and identified research priorities towards increasing EONR prediction.

Maize breeding programs have indirectly altered many plant traits; however, our knowledge of some important phenological traits remains unexplored. One such trait is leaf appearance rate, which is crucial for predicting maize development. We studied 40 short-season (103-day) and 38 long-season (111-day) hybrids released from 1980 to 2020 by Bayer Crop Science. Measurements included weekly counting of collared leaves across 13 experiments in the US Corn Belt. The progression of leaf number was expressed as a function of thermal time and described with a trilinear model. Results indicated that new 111-day hybrids produce leaves faster than old hybrids throughout the vegetative phase (7.4% and 3.1% faster before and after the ninth leaf stage, respectively), whereas new 103-day hybrids produce leaves faster only after the ninth leaf stage (9.4%). Thermal time to silking and anthesis decreased by about 1 and 0.56 & DEG;C day year(-1), respectively. Our data revealed that silking and anthesis can precede the final collared leaf by 96 & DEG;C day (3.3 days under optimal conditions), which indicates an overlap between vegetative and reproductive phases. We concluded that maize breeding has indirectly altered the rate of vegetative development of maize hybrids without affecting the final leaf number. Present results expand our knowledge base on the genotypic variability in maize development traits, which can improve empirical and process-based models used for crop stage and yield prediction.

Context: Quantifying historical changes in maize harvest index (HI), the fraction of above-ground biomass allocated to grain yield, can enhance our ability to explain grain yield trends and estimate stover carbon inputs for sustainability assessments. However, the HI genetic gain has not been the primary focus of previous era studies. Objective: The aim of this study is to enhance our knowledge of maize HI genetic gain. Our first objective is to quantify HI genetic gain in Bayer Crop Science Legacy hybrids and investigate the contribution of breeding and agronomic management. Our second objective is to develop a general-use model to describe the temporal evo-lution of maize HI.Methods: We studied 54 commercial hybrids (103-day and 111-day relative maturities) released from 1983 to 2020 across 13 environments, including plant density (current and historical increasing rate) and N-fertilizer (low and sufficient N rates) treatments. The HI was estimated at physiological maturity by destructively sampling plants. Then we synthesize new experimental data with literature findings (n = 16) to provide a robust HI genetic gain estimate.Results: Results showed that HI has increased over the years from 0.516 to 0.571 in 103-day hybrids and from 0.537 to 0.584 in 111-day hybrids. The genetic gains were similar across environments and management treatments within the studied range, indicating that this increase is attributed to maize breeding. The N-fertilizer treatments affected the magnitude of the HI, but plant density did not. Our results, combined with 16 literature datasets, revealed a 0.26% year  1 relative increase in HI since 1964. We estimated that the increase in HI ac-counts for ca. 15% of the historical maize yield increase in the US Corn Belt over the past 50 years.Conclusions: The maize HI has increased over the last 50 years, and this increase was attributed to breeding, not to management. Significance: Our findings enhance our knowledge of maize HI, will support robust estimations of carbon inputs in sustainability studies, and inform crop models to better capture historical yield increases.

There is a strong link between nitrate (NO 3 -N) leaching from fertilized annual crops and the rate of nitrogen (N) fertilizer input. However, this leaching-fertilizer relationship is poorly understood and the degree to which soil type, weather, and cropping system influence it is largely unknown. We calibrated the Agricultural Production Systems sIMulator process-based cropping system model using 56 site-years of data sourced from eight field studies across six states in the U.S. Midwest that monitored NO 3 -N leaching from artificial subsurface drainage in two cropping systems: continuous maize and two-year rotation of maize followed by unfertilized soybean (maize-soybean rotation). We then ran a factorial simulation experiment and fit statistical models to the leaching-fertilizer response. A bi-linear model provided the best fit to the relationship between N fertilizer rate (kg ha −1 ) and NO 3 -N leaching load (kg ha −1 ) (from one year of continuous maize or summed over the two-year maize-soybean rotation). We found that the cropping system dictated the slopes and breakpoint (the point at which the leaching rate changes) of the model, but the site and year determined the intercept i.e. the magnitude of the leaching. In both cropping systems, the rate of NO 3 -N leaching increased at an N fertilizer rate higher than the N rate needed to optimize the leaching load per kg grain produced. Above the model breakpoint, the rate of NO 3 -N leaching per kg N fertilizer input was 300% greater than the rate below the breakpoint in the two-year maize-soybean rotation and 650% greater in continuous maize. Moreover, the model breakpoint occurred at only 16% above the average agronomic optimum N rate (AONR) in continuous maize, but 66% above the AONR in the maize-soybean rotation. Rotating maize with soybean, therefore, allows for a greater environmental buffer than continuous maize with regard to the impact of overfertilization on NO 3 -N leaching.

Planting date and cultivar selection are major factors in determining the yield potential of any crop and in any region. However, there is a knowledge gap in how climate scenarios affect these choices. To explore this gap, we performed a regional scale analysis (11 planting dates x 8 cultivars x 281 fields x 36 weather years x 6 climate scenarios) using the APSIM model and pSIMS software for Iowa, the leading US maize (Zea mays L.) producing state. Our objectives were to determine how the optimum planting date (optPD)changes with weather scenarios and cultivars and the potential economic implications of planting outside the optimum windows. Results indicated that the mean optPD corresponds to the US Department of Agriculture, National Agriculture Statistics Service (USDA-NASS) 18.4% planting progress (April 28th) in Iowa. The opt optPD was found to be advancing by -0.13 d yr(-1) from 1980 to 2015. A 1 degrees C increase in mean temperature increased the length of the growing season by 10 days while the opt PD changed by -2 to + 6 days, depending on cultivar. Under a more realistic scenario of increasing the minimum temperature by 0.5 degrees C, decreasing the maximum temperature by 0.5 degrees C, increasing spring rainfall by 10% and decreasing summer rainfall by 10%, the optPD only changed by -2 days compared to current trends, however, yield increased by 6.6%. Analysis of historical USDA-NASS planting durations indicated that on average, the planting duration (1-99% statewide reported planting progress) is 44 days, while it can be as low as 21 days in years with favorable weather. A simple economic analysis illustrated a potential revenue loss up to $340 million per year by planting maize outside the optimum window. We conclude that future investments in planting technologies to accelerate planting, especially in challenging weather years, as well as improved optPD x cultivar recommendations to farmers, will provide economic benefits and buffer climate variability.

We used the Agricultural Production Systems sIMulator (APSIM) to predict and explain maize and soybean yields, phenology, and soil water and nitrogen (N) dynamics during the growing season in Iowa, USA. Historical, current and forecasted weather data were used to drive simulations, which were released in public four weeks after planting. In this paper, we 1) describe the methodology used to perform forecasts; 2) evaluate model prediction accuracy against data collected from 10 locations over 4-years; and 3) identify inputs that are key in forecasting yields and soil N dynamics. We found that the predicted median yield at planting was a very good indicator of end-of-season yields (relative root mean square error, RRMSE ~ 20%). For reference, the prediction at maturity, when all the weather was known, had a RRMSE of 14%. The good prediction at planting time was explained by the existence of shallow water tables, which decreased model sensitivity to unknown summer precipitation by 50 to 64%. Model initial conditions and management information accounted for 1⁄4 of the variation in maize yield. End of season model evaluations indicated that the model simulated well crop phenology (R2=0.88), root depth (R2=0.83), biomass production (R2=0.93), grain yield (R2=0.90), plant N uptake (R2=0.87), soil moisture (R2=0.42), soil temperature (R2=0.93), soil nitrate (R2=0.77), and water table depth (R2=0.41). We concluded that model set-up by the user (e.g. inclusion of water table), initial conditions, and early season measurements are very important for accurate predictions of soil water, N and crop yields in this environment. Disciplines Agriculture | Agronomy and Crop Sciences | Bioresource and Agricultural Engineering | Soil Science | Statistical Models Comments This is a manuscript of an article published as Archontoulis, Sotirios V., Michael J. Castellano, Mark A. Licht, Virginia Nichols, Mitch Baum, Isaiah Huber, Rafael Martinez‐Feria et al. "Predicting crop yields and soil‐plant nitrogen dynamics in the US Corn Belt." Crop Science (2020). doi: 10.1002/csc2.20039. Authors Sotirios V. Archontoulis, Michael J. Castellano, Mark A. Licht, Virginia Nichols, Mitch Baum, Isaiah Huber, Rafael Martinez-Feria, Laila Puntel, Raziel A. Ordonez, Javed Iqbal, Emily E. Wright, Ranae N. Dietzel, Matthew Helmers, Andy VanLoocke, Matt Liebman, Jerry L. Hatfield, Daryl Herzmann, S. Carolina Córdova, Patrick Edmonds, Kaitlin Togliatti, Ashlyn Kessler, Gerasimos Danalatos, Heather Pasley, Carl Pederson, and Kendall R. Lamkey This article is available at Iowa State University Digital Repository: https://lib.dr.iastate.edu/agron_pubs/623 This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi:

Core Ideas Planting in mid‐May can significantly diminish Iowa maize grain yields. Grain yield variability is explained mostly by planting date with minor effect from relative maturity. Silking date is a good indicator of grain yield; silking beyond 25 July was detrimental. Unfavorable weather conditions frequently cause farmers to plant maize ( Zea mays L.) outside the optimum planting timeframe. We analyzed maize yield and phenology from a multi‐location, year, hybrid relative maturity, and planting date experiment performed in Iowa, USA. Our objectives were to determine the optimum combination of planting date and relative maturity to maximize maize grain yield per environment and to elucidate the risk associated with the use of “full‐season hybrids” when planting occurs beyond the optimum planting date. Analysis of variance (ANOVA) attributed 70% of the variability in grain yield to planting date and only 10% to relative maturity indicating that short and full‐season hybrid relative maturities produced similar grain yields regardless of when they were planted as long as the crops reached maturity before harvesting. Our analysis indicated time to silking is a good indication of expected yield potential with a critical time (beyond which yield is reduced) to be 23 July for Iowa. Furthermore, we found that a minimum growing degree accumulation of 648°C‐day during the grain‐filling period maximized maize yield. Overall, this study brings new results to assist decision making regarding planting date by hybrid relative maturity across Iowa.

Current maize (Zea mays L.) planting date recommendations have not been updated in the state of Iowa since 2001. A state that produced 68.8 million tons of maize on 5.5 million hectares in 2016. It is imperative that this information be regularly updated as both climate and maize hybrid selection are constantly changing. We analyzed maize yield and phenology, from a multi-location, year, relative maturity (RM), and planting date (PD) experiment carried out in Iowa, US. The dataset was used to calibrate a sitespecific model (Agricultural Production System sIMulation, APSIM) and extrapolate APSIM results across Iowa, using a region scale model (parallel System for Integrating Impact Models and Sectors, pSIMS). Our objectives were to determine the combination of PD and RM to maximize maize grain yield by environment and to explain the risk associated with the use of full season RM when planting dates are delayed beyond the optimum PD. Additionally, the impact of climate change effects on optimum PD and RM selection by location were examined. Field scale analysis found slight grain yield differences between full and short season RM on a given PD with yield maximized when planting occurred at or before May 5. However, running a regional scale model over 36 years, we determined that a static recommendation of optimum PD is not suitable as large variation exists between locations within the state and between years. The coefficient of variation (CV) was 20% and 68% for the optimum PD within and between years respectively. Furthermore, the PD window, or time frame around the optimum PD to achieve 98% of maximum yield, across years was heavily influenced by latitude and RM selection. Overall, this study brings new results to assist decision making regarding PD and RM across Iowa.

Corn planting began a couple of weeks ago and according to the May 5 USDA-NASS Crop Progress and Condition report only 36 percent of the corn crop is planted; 15 percent behind the 5-year average. The greatest progress has been in central and west central Iowa at 56 percent and 57 percent, respectively. Since May 5 there has been limited opportunity for planting to occur. Current weather forecasts for May 8 to 14 indicate two inches of rain and 20 to 30 lower than normal GDD accumulation across Iowa, which may cause additional planting delays. Disciplines Agricultural Science | Agriculture This article is available at Iowa State University Digital Repository: https://lib.dr.iastate.edu/cropnews/2538 6/26/2019 Late Corn Planting Options | Integrated Crop Management https://crops.extension.iastate.edu/cropnews/2019/05/late-corn-planting-options 1/5 Integrated Crop Management Late Corn Planting Options