Split fertilizer nitrogen application with a cereal rye cover crop reduces tile nitrate loads in a corn-soybean rotation.

Splitting fertilizer nitrogen (N) applications and using cover crops are management strategies to reduce nitrate in tile drainage water. We investigated split fertilizer N applications to corn (Zea mays L.) on crop yields and tile nitrate loss in both corn and soybean (Glycine max L.) in rotation from 2016 through 2019. We evaluated the inclusion of cover crops in a split-N treatment. Fertilizer N treatments included 100% in the fall; 50% in the fall + 25% at planting + 25% at side-dress; 100% as spring preplant; 75% as spring preplant (reduced N rate); 50% as spring preplant + 50% at side-dress; and 50% as spring preplant + 50% at side-dress with a cover crop. We did not find significant differences between split and single full rate N application treatments for corn yields or tile nitrate loss; however, the reduced N rate treatment significantly decreased corn yield by 10%. Cumulative tile nitrate losses (over four seasons) ranged from 115 kg ha-1 for all of the N in the fall to 65 kg ha-1 for 50% as spring preplant + 50% at side-dress with a cover crop, a decrease of 43%. Tile nitrate loss responded similarly to (corn) N treatments under both corn and soybean, with 64% of the loss under corn and 36% under soybean. Our results suggest that decreasing the fertilizer N rate may impact corn yield more than nitrate loss, while split fertilizer N application with a cover crop has potential to reduce tile nitrate loss without decreasing crop yield.

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 (R 2 = 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.

Simulating nitrogen management impacts on maize production in the U.S. Midwest.

Nutrient loss reduction strategies have recently been developed in the U.S. Midwest to decrease the environmental footprint associated with nitrogen (N) fertilizer use. Although these strategies generally suggest decreasing N rates and shifting the timing of N application from fall to spring, the spatiotemporal impacts of these practices on maize yield and fertilizer N use efficiency (NUE, kg grain yield increase per kg N applied) have not been assessed at the watershed scale using crop simulation models. We simulated the effects of N fertilizer rate (0, 168, 190, 224 kg N ha-1) and application timing [fall-applied N (FN): 100% N applied on 1 December; spring-applied N (SN): 100% N applied 10 days before planting; split N: 66% N applied on 1 December + 34% N applied 10 days before planting] on maize grain yield (GY) across 3042 points in Illinois during 2011-2015 using the DSSAT-CERES-Maize model. When simulations were scaled up to the watershed level, results suggest that increases in average maize GY for SN compared to FN occurred in years with higher than average winter rainfall (2011, 2013), whereas yields were similar (+/- 4%) in 2012, 2014, and 2015. Accordingly, differences in NUE for SN compared to FN were small (0.0-1.4 kg GY/kg N) when cumulative winter rainfall was < 300 mm, but increased to 0.1-9.2 kg GY/kg N when winter rainfall was > 500 mm at both 168 kg N ha-1 and 224 kg N ha-1. The combined practice of reducing N fertilizer amounts from 224 kg N ha-1 to 190 kg N ha-1 and shifting from FN to SN resulted in a wide range of yield responses during 2011-2015, with the probability of increasing yields varying from <10% to >70% of simulation points within a watershed. Positive impacts on both GY and NUE occurred in only 60% of simulations for this scenario, highlighting the challenge of simultaneously improving yield and NUE with a 15% N rate reduction in this region.

Tile Drainage Nitrate Losses and Corn Yield Response to Fall and Spring Nitrogen Management.

Nitrogen (N) management strategies that maintain high crop productivity with reduced water quality impacts are needed for tile-drained landscapes of the US Midwest. The objectives of this study were to determine the effect of N application rate, timing, and fall nitrapyrin addition on tile drainage nitrate losses, corn ( L.) yield, N recovery efficiency, and postharvest soil nitrate content over 3 yr in a corn-soybean [ (L.) Merr.] rotation. In addition to an unfertilized control, the following eight N treatments were applied as anhydrous ammonia in a replicated, field-scale experiment with both corn and soybean phases present each year in Illinois: fall and spring applications of 78, 156, and 234 kg N ha, fall application of 156 kg N ha + nitrapyrin, and sidedress (V5-V6) application of 156 kg N ha. Across the 3-yr study period, increases in flow-weighted NO concentrations were found with increasing N rate for fall and spring N applications, whereas N load results were variable. At the same N rate, spring vs. fall N applications reduced flow-weighted NO concentrations only in the corn-soybean-corn rotation. Fall nitrapyrin and sidedress N treatments did not decrease flo8w-weighted NO concentrations in either rotation compared with fall and spring N applications, respectively, or increase corn yield, crop N uptake, or N recovery efficiency in any year. This study indicates that compared with fall N application, spring and sidedress N applications (for corn-soybean-corn) and sidedress N applications (for soybean-corn-soybean) reduced 3-yr mean flow-weighted NO concentrations while maintaining yields.

A Vision for Incorporating Environmental Effects into Nitrogen Management Decision Support Tools for U.S. Maize Production.

Meeting crop nitrogen (N) demand while minimizing N losses to the environment has proven difficult despite significant field research and modeling efforts. To improve N management, several real-time N management tools have been developed with a primary focus on enhancing crop production. However, no coordinated effort exists to simultaneously address sustainability concerns related to N losses at field- and regional-scales. In this perspective, we highlight the opportunity for incorporating environmental effects into N management decision support tools for United States maize production systems by integrating publicly available crop models with grower-entered management information and gridded soil and climate data in a geospatial framework specifically designed to quantify environmental and crop production tradeoffs. To facilitate advances in this area, we assess the capability of existing crop models to provide in-season N recommendations while estimating N leaching and nitrous oxide emissions, discuss several considerations for initial framework development, and highlight important challenges related to improving the accuracy of crop model predictions. Such a framework would benefit the development of regional sustainable intensification strategies by enabling the identification of N loss hotspots which could be used to implement spatially explicit mitigation efforts in relation to current environmental quality goals and real-time weather conditions. Nevertheless, we argue that this long-term vision can only be realized by leveraging a variety of existing research efforts to overcome challenges related to improving model structure, accessing field data to enhance model performance, and addressing the numerous social difficulties in delivery and adoption of such tool by stakeholders.

Climate-induced reduction in US-wide soybean yields underpinned by region- and in-season-specific responses.

The United States is one of the largest soybean exporters in the world. Production is concentrated in the upper Midwest(1). Much of this region is not irrigated, rendering soybean production systems in the area highly sensitive to in-season variations in weather. Although the influence of in-season weather trends on the yields of crops such as soybean, wheat and maize has been explored in several countries(2-6), the potentially confounding influence of genetic improvements on yields has been overlooked. Here we assess the effect of in-season weather trends on soybean yields in the United States between 1994 and 2013, using field trial data, meteorological data and information on crop management practices, including the adoption of new cultivars. We show that in-season temperature trends had a greater impact on soybean yields than in-season precipitation trends over the measurement period. Averaging across the United States, we show that soybean yields fell by around 2.4% for every 1 °C rise in growing season temperature. However, the response varied significantly among individual states, ranging from -22% to +9%, and also with the month of the year in which the warming occurred. We estimate that year-to-year changes in precipitation and temperature combined suppressed the US average yield gain by around 30% over the measurement period, leading to a loss of US$11 billion. Our data highlight the importance of developing location-specific adaptation strategies for climate change based on early-, mid- and late-growing season climate trends.