Corn Belt States Research Articles

Corn Acreage Intensification Levels in U.S. Corn Belt States

Abstract Crop rotations in the United States increasingly involve few crops dominated by corn frequently combined with soybeans. We assess factors tied to corn acreage intensification over the past two decades. Using state-level data of 11 U.S. Corn Belt states from 2000 to 2021, we applied a panel fixed effects instrumental variable modeling approach to investigate these linkages. Findings suggest Conservation Reserve Program acreage releases, crop prices, ethanol demand, farm size, productivity, and genetically modified varieties positively impact corn acreage intensity. These results imply crop planting decisions are complex and are not uniquely attributed to biofuel considerations.

Agricultural Drainage, Water Pollution, and Biotic Health in the USA Cornbelt

A stable landscape balances geologic fragility, climate change and land use. In the USA Cornbelt, land improvement contractors have moved earth to fill wetlands, build ditches and plow in varying diameters of subsurface pipe to alter the hydrology and natural watershed plumbing. Nonpoint source (NPS) pollution from rural sediment and nutrients remains the most difficult challenge to meeting water quality standards in the USA. Cornbelt states have invested in nutrient management plans to reduce the nitrogen and phosphorus loads to the Gulf of Mexico, and most local levels of government are engaged in water quality planning to reduce sediment, bacteria, and nutrient impacts on local lakes and streams. Rural drainage systems constructed decades ago need upgrading because of changes in cropping technology and climate. However, drainage engineers/contractors need 21st century technology to upgrade water management. Downstream flooding, infrastructure damage and loss of aquatic habitat have been observed systemically in the Midwestern USA. States have set goals to reduce NPS pollution, yet a proposed drainage improvement could degrade ecosystem services. The drainage authority/engineer needs a protocol/model to better assess water management by examining the fluvial processes within, at and below the outlet of a proposed drainage project.

Modeling days suitable for fieldwork using machine learning, process-based, and rule-based models

ContextPrediction of days suitable for fieldwork is important for understanding the potential effects of climate change and for selecting machinery systems to improve efficiency in field operations and avoid soil damage. Yet, we lack predictive models to inform decision-making at scale. ObjectiveWe filled this knowledge gap by developing and testing five new workability models. MethodsOne model follows soil moisture-based methods (APSIM), one uses simple rain and temperature thresholds, and three follow machine learning techniques (Random Forest, Decision Table, Neural Network). We parameterized the models using USDA survey data from Iowa, USA and evaluated their temporal and spatial prediction over twelve US Corn Belt States and different time periods using multiple statistical indexes and sensitivity analysis. The models operate at a 5-arcminute resolution. Results and conclusionsResults indicated that the simple rule model, the Decision Table model, and the process-based model predicted field workable days with an agreement index of 0.88, 0.86, and 0.84, respectively for the testing datasets (n = 22,671), and hence were deemed sufficient for future use. The selected models are better suited for large timespan evaluations of workability (monthly to annual, normalized root mean square error, nRMSE = 8 to 15%)) than weekly predictions (nRMSE = 21%). The machine learning models tended to cluster their predictions around a mean value and were about 50% less responsive to precipitation than the process-based or rule-based models. We concluded that simple approaches are more robust to be applied at scale than complex approaches with many data input requirements. SignificanceThe developed models enhance our capacity to predict climate change impacts on workability, a valuable indicator for decision-making and overall sustainability.

County-scale crop yield prediction by integrating crop simulation with machine learning models.

Crop yield prediction is of great importance for decision making, yet it remains an ongoing scientific challenge. Interactions among different genetic, environmental, and management factors and uncertainty in input values are making crop yield prediction complex. Building upon a previous work in which we coupled crop modeling with machine learning (ML) models to predict maize yields for three US Corn Belt states, here, we expand the concept to the entire US Corn Belt (12 states). More specifically, we built five new ML models and their ensemble models, considering the scenarios with and without crop modeling variables. Additional input values in our models are soil, weather, management, and historical yield data. A unique aspect of our work is the spatial analysis to investigate causes for low or high model prediction errors. Our results indicated that the prediction accuracy increases by coupling crop modeling with machine learning. The ensemble model overperformed the individual ML models, having a relative root mean square error (RRMSE) of about 9% for the test years (2018, 2019, and 2020), which is comparable to previous studies. In addition, analysis of the sources of error revealed that counties and crop reporting districts with low cropland ratios have high RRMSE. Furthermore, we found that soil input data and extreme weather events were responsible for high errors in some regions. The proposed models can be deployed for large-scale prediction at the county level and, contingent upon data availability, can be utilized for field level prediction.

Climate change will increase aflatoxin presence in US Corn

The impacts of climate change on agricultural production are a global concern and have already begun to occur (Kawasaki 2018 Am. J. Agric. Econ. 101 172–92; Ortiz-Bobea et al 2021 Nat. Clim. Change 11 306–12), with major drivers including warmer temperatures and the occurrence of extreme weather events (Lobell and Field 2007 Environ. Res. Lett. 2 014002; Challinor et al 2014 Nat. Clim. Change 4 287; Rosenzweig et al 2001 Glob. Change Hum. Health 2 90–104; Schlenker and Roberts 2009 Proc. Natl Acad. Sci. USA 106 15594–8; Lobell et al 2014 Science 344 516–9; Ortiz-Bobea et al 2019 Environ. Res. Lett. 14 064003). An important dimension of the climate change-crop yield relationship that has often been overlooked in the empirical literature is the influence that warming temperatures can have on plant damage arriving through biotic channels, such as pest infestation or fungal infection (Rosenzweig et al 2001 Glob. Change Hum. Health 2 90–104). Aflatoxins are carcinogenic chemicals produced by the fungi Aspergillus flavus and A. parasiticus, which commonly infect food crops. Currently, in the United States, aflatoxin is a perennial contaminant in corn grown in the South, but rare in the Corn Belt and northern states. Climate change may expand aflatoxin’s geographical prevalence, however; because hot, dry summers promote aflatoxin accumulation. Here we model aflatoxin risk as a function of corn plant growth stages and weather to predict US regions with high aflatoxin risk in 2031–2040, based on 16 climate change models. Our results suggest that over 89.5% of corn-growing counties in 15 states, including the Corn Belt, will experience increased aflatoxin contamination in 2031–2040 compared to 2011–2020. Interestingly, the results are spatially heterogeneous and include several southern counties expected to have lower aflatoxin risk, because the causative fungi become inactivated at very high temperatures.

The MRTN Approach to Making Nitrogen Rate Recommendations: Background and Implementation

Based on a seminal paper published by Stanford in 1973, many land grant universities adopted N rate guidelines that used expected yield times a factor (such as 1.2 lb N/bu), with adjustments for previous crop, to formulate N rate recommendations for corn. However, discrepancies between yield‐based N rate recommendations and recent N response data led to the development of an empirical approach, using N response data to generate optimum N rates. The idea of combining crop N response data, dubbed the Maximum Return to Nitrogen (MRTN) approach, was initiated in the mid‐2000s and is today used in seven Corn Belt states. Earn 1.5 CEUs in Nutrient Management by taking the quiz for the article at https://bit.ly/35Fy683. View all CEUs at https://web.sciencesocieties.org/Learning‐Center/Courses.

Analyzing Effects of Crops on SMAP Satellite-Based Soil Moisture Using a Rainfall–Runoff Model in the U.S. Corn Belt

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> -band microwave satellite missions provide soil moisture information potentially useful for streamflow and, hence, flood predictions. However, these observations are also sensitive to the presence of vegetation that makes satellite soil moisture estimations prone to errors. In this study, the authors evaluate satellite soil moisture estimations from Soil Moisture Active Passive (SMAP) and Soil Moisture Ocean Salinity and two distributed hydrologic models with measurements from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> sensors in the Corn Belt state of Iowa, a region dominated by annual row crops of corn and soybean. First, the authors compare model and satellite soil moisture products across Iowa using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> data for more than 30 stations. Then, they compare satellite soil moisture products with state-wide model-based fields to identify regions of low and high agreement. Finally, the authors analyze and explain the resulting spatial patterns with Moderate Resolution Imaging Spectroradiometer vegetation indices and SMAP vegetation optical depth. The results indicate that satellite soil moisture estimations are drier than those provided by the hydrologic model, and the spatial bias depends on the intensity of row-crop agriculture. The work highlights the importance of developing a revised SMAP algorithm for regions of intensive row-crop agriculture to increase SMAP utility in the real-time streamflowpredictions.

Towards Routine Mapping of Crop Emergence within the Season Using the Harmonized Landsat and Sentinel-2 Dataset

Crop emergence is a critical stage for crop development modeling, crop condition monitoring, and biomass accumulation estimation. Green-up dates (or the start of the season) detected from remote sensing time series are related to, but generally lag, crop emergence dates. In this paper, we refine the within-season emergence (WISE) algorithm and extend application to five Corn Belt states (Iowa, Illinois, Indiana, Minnesota, and Nebraska) using routine harmonized Landsat and Sentinel-2 (HLS) data from 2018 to 2020. Green-up dates detected from the HLS time series were assessed using field observations and near-surface measurements from PhenoCams. Statistical descriptions of green-up dates for corn and soybeans were generated and compared to county-level planting dates and district- to state-level crop emergence dates reported by the National Agricultural Statistics Service (NASS). Results show that emergence dates for corn and soybean can be reliably detected within the season using the HLS time series acquired during the early growing season. Compared to observed crop emergence dates, green-up dates from HLS using WISE were ~3 days later at the field scale (30-m). The mean absolute difference (MAD) was ~7 days and the root mean square error (RMSE) was ~9 days. At the state level, the mean differences between median HLS green-up date and median crop emergence date were within 2 days for 2018–2020. At this scale, MAD was within 4 days, and RMSE was less than 5 days for both corn and soybeans. The R-squares were 0.73 and 0.87 for corn and soybean, respectively. The 2019 late emergence of crops in Corn Belt states (1–4 weeks to five-year average) was captured by HLS green-up date retrievals. This study demonstrates that routine within-season mapping of crop emergence/green-up at the field scale is practicable over large regions using operational satellite data. The green-up map derived from HLS during the growing season provides valuable information on spatial and temporal variability in crop emergence that can be used for crop monitoring and refining agricultural statistics used in broad-scale modeling efforts.

Corn Yield Prediction With Ensemble CNN-DNN.

We investigate the predictive performance of two novel CNN-DNN machine learning ensemble models in predicting county-level corn yields across the US Corn Belt (12 states). The developed data set is a combination of management, environment, and historical corn yields from 1980 to 2019. Two scenarios for ensemble creation are considered: homogenous and heterogenous ensembles. In homogenous ensembles, the base CNN-DNN models are all the same, but they are generated with a bagging procedure to ensure they exhibit a certain level of diversity. Heterogenous ensembles are created from different base CNN-DNN models which share the same architecture but have different hyperparameters. Three types of ensemble creation methods were used to create several ensembles for either of the scenarios: Basic Ensemble Method (BEM), Generalized Ensemble Method (GEM), and stacked generalized ensembles. Results indicated that both designed ensemble types (heterogenous and homogenous) outperform the ensembles created from five individual ML models (linear regression, LASSO, random forest, XGBoost, and LightGBM). Furthermore, by introducing improvements over the heterogenous ensembles, the homogenous ensembles provide the most accurate yield predictions across US Corn Belt states. This model could make 2019 yield predictions with a root mean square error of 866 kg/ha, equivalent to 8.5% relative root mean square and could successfully explain about 77% of the spatio-temporal variation in the corn grain yields. The significant predictive power of this model can be leveraged for designing a reliable tool for corn yield prediction which will in turn assist agronomic decision makers.

Conservation tillage mitigates drought-induced soybean yield losses in the US Corn Belt

Abstract Conservation tillage has been widely recommended for implementation in the U.S. for its environmental benefits. The effect of conservation tillage on crop yield is a subject of continued concern amongst farmers who have not adopted the practice. Previous empirical research on the yield performance of conservation tillage is largely limited to field trials, while observational studies remain scant. This article estimates the effects of conservation tillage on county average corn and soybean yields using remotely-sensed tillage practice adoption data in 646 counties across 12 Corn Belt states from 2005 to 2018. Exploiting deviations from county-specific means in the data, we find no evidence that conservation tillage negatively affects corn or soybean yields. We also find that it can mitigate the impact of drought on soybean yields. We explore how wider use of conservation tillage might offset the increase in drought-induced downside risk to soybean yields under climate change projections from five global climate models.

Understanding the non-stationary relationships between corn yields and meteorology via a spatiotemporally varying coefficient model

The relationships between crop yields and meteorology are naturally non-stationary because of spatiotemporal heterogeneity. Many studies have examined spatial heterogeneity in the regression model, but only limited research has attempted to account for both spatial autocorrelation and temporal variation. In this article, we develop a novel spatiotemporally varying coefficient (STVC) model to understand non-stationary relationships between crop yields and meteorological variables. We compare the proposed model with variant models specialized for time or spatial, namely spatial varying coefficient (SVC) model and temporal varying coefficient (TVC) model. This study was conducted using the county-level corn yield and meteorological data, including seasonal Growing Degree Days (GDD), Killing Degree Days (KDD), Vapor Pressure Deficit (VPD), and precipitation (PCPN), from 1981 to 2018 in three Corn Belt states, including Illinois, Indiana, and Iowa. Allowing model coefficients varying in both temporal and spatial dimensions gives the best performance of STVC in simulating the corn yield responses toward various meteorological conditions. The STVC reduced the root-mean-square error to 10.64 Bu/Ac (0.72 Mg/ha) from 15.68 Bu/Ac (1.06 Mg/ha) for TVC and 16.48 Bu/Ac (1.11 Mg/ha) for SVC. Meanwhile, the STVC resulted in a higher R2 of 0.81 compared to 0.56 for SVC and 0.64 for TVC. The STVC showed better performance in handling spatial dependence of corn production, which tends to cluster estimation residuals when counties are close, with the lowest Moran's I of 0.10. Considering the spatiotemporal non-stationarity, the proposed model significantly improves the power of the meteorological data in explaining the variations of corn yields.

Quantitative Assessment of Satellite L-Band Vegetation Optical Depth in the U.S. Corn Belt

Satellite L-band vegetation optical depth (L-VOD) contains new information about terrestrial ecosystems. However, it has not been evaluated against the geophysical variable that it represents, plant water, the mass of liquid water contained within vegetation tissue per ground area. We quantitatively assess the seasonal variation of three L-VOD products at the South Fork Core Validation Site in the Corn Belt state of Iowa where L-VOD is directly proportional to crop plant water. We use three satellite-scale crop plant water estimates: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measurements; a normalized difference water index (NDWI) calibrated with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measurements; and a crop model. We find that overall the L-VOD satellite products are 0.02–0.09 Np (0.4– <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${1.7} \,\,\text {kg} \cdot \text {m}^{-2}$ </tex-math></inline-formula> ) lower than the three estimates. We show that overestimation of L-VOD can be attributed to dynamic soil surface roughness, and hypothesize that crop plant water observations will require the incorporation of this effect into retrieval algorithms.

Market microstructure and the historical relationship between the US farm credit system, farm service agency and commercial bank lending

PurposeThe purpose of this paper is to investigate the market microstructure related to the Farm Credit System (FCS), Commercial Banks (CB) and Farm Services Administration (FSA). The commercial banks frequently call out the FCS as having an unfair advantage in the agricultural finance market place due to tax exempt bonds, and an implied guarantee of those bonds. This paper addresses the issue by examining the interrelationships since 1939, while addressing the historically distinctive roles that the FCS, CB and FSA have played in the US agricultural credit market.Design/methodology/approachThere are two components to our model. The first is the estimation of short and long run credit demand elasticities, as well as land elasticities. These are estimated from a dynamic duality model using seemingly unrelated regression. The point elasticity measures are then used as independent variables in least square regressions, combined with farm specific and related macro variables, for the Cornbelt states. The dependent variable is the year-over-year changes in paired FCS, CB and FSA loans.FindingsThe genesis of the FCS was to provide credit to farmers in good and bad years. Therefore, we expected to see a countercyclical relationship between FCS and CB. This is found for the farm crisis years in the 1980s but is not a continuous characteristic of FCS lending. In good times the FCS and CB appear to compete, albeit with differentiated market segmentation into short- and long-term credit. The FSA, which was established to provide tertiary support to both the FCS and CB, appears to be responding as designed, with greater activity in bad years. The authors find the elasticity measures to be economically significant.Research limitations/implicationsThe authors conclude that the market microstructure of the agricultural credit market in the US is important. Our analysis applies a broader definition of market microstructure for institutions and intermediaries and reveals that further research examining the economic frictions caused by comparative bond vs deposit funding of agricultural credit is important.Originality/valueThe authors believe that this is the first paper to examine agricultural finance through the market microstructure lens. In addition our long-term data measures allow us to examine the economics through various sub-periods. Finally, we believe that our introduction of credit and land demand elasticities into a comparative credit model is also a first.

Forecasting Corn Yield With Machine Learning Ensembles.

The emergence of new technologies to synthesize and analyze big data with high-performance computing has increased our capacity to more accurately predict crop yields. Recent research has shown that machine learning (ML) can provide reasonable predictions faster and with higher flexibility compared to simulation crop modeling. However, a single machine learning model can be outperformed by a “committee” of models (machine learning ensembles) that can reduce prediction bias, variance, or both and is able to better capture the underlying distribution of the data. Yet, there are many aspects to be investigated with regard to prediction accuracy, time of the prediction, and scale. The earlier the prediction during the growing season the better, but this has not been thoroughly investigated as previous studies considered all data available to predict yields. This paper provides a machine leaning based framework to forecast corn yields in three US Corn Belt states (Illinois, Indiana, and Iowa) considering complete and partial in-season weather knowledge. Several ensemble models are designed using blocked sequential procedure to generate out-of-bag predictions. The forecasts are made in county-level scale and aggregated for agricultural district and state level scales. Results show that the proposed optimized weighted ensemble and the average ensemble are the most precise models with RRMSE of 9.5%. Stacked LASSO makes the least biased predictions (MBE of 53 kg/ha), while other ensemble models also outperformed the base learners in terms of bias. On the contrary, although random k-fold cross-validation is replaced by blocked sequential procedure, it is shown that stacked ensembles perform not as good as weighted ensemble models for time series data sets as they require the data to be non-IID to perform favorably. Comparing our proposed model forecasts with the literature demonstrates the acceptable performance of forecasts made by our proposed ensemble model. Results from the scenario of having partial in-season weather knowledge reveals that decent yield forecasts with RRMSE of 9.2% can be made as early as June 1st. Moreover, it was shown that the proposed model performed better than individual models and benchmark ensembles at agricultural district and state-level scales as well as county-level scale. To find the marginal effect of each input feature on the forecasts made by the proposed ensemble model, a methodology is suggested that is the basis for finding feature importance for the ensemble model. The findings suggest that weather features corresponding to weather in weeks 18–24 (May 1st to June 1st) are the most important input features.

Climate smart agriculture opportunities for mitigating soil greenhouse gas emissions across the U.S. Corn-Belt

Widespread adoption of climate smart agriculture (CSA) has the potential to greatly mitigate agricultural greenhouse gas (GHG) emissions by increasing soil organic carbon (SOC) stocks and decreasing nitrous oxide (N2O) emissions. However, quantifying the impact of land management practices on soil GHG fluxes including CO2 and N2O is difficult due temporal and spatial variability of localized weather conditions and subfield soil properties. A process-based biogeochemistry modeling framework coupled with public soils, weather, and yield data layers was used to estimate regionally specific soil GHG reductions associated with the adoption of one or more CSA practices in maize (Zea mays L.) and soybean (Glycine max (L.) Merr.) crop production fields. Site-specific, multi-year DeNintrification-DeComposition model simulations were performed on agricultural fields within 11 U.S. Corn-Belt states that were identified to be in maize or soybean production from 2013 to 2016. Differences between modeled changes in SOC stocks to a depth of 50 cm, N2O emissions, and CH4 fluxes corresponding with alternative land management scenarios simulated across field sites were converted to a CO2 equivalent basis and spatially weighted to a county-scale to highlight regional variability. County-scale GHG reductions corresponding with a conversion from conventional tillage to no-tillage practices are estimated to be have a mean reduction potential of 1477 kg CO2e ha−1 yr−1 (SOC, N2O, and CH4 flux reductions of 945, 549, −17 kg CO2e ha−1yr−1, respectively, where a negative reduction indicates an increase in emissions.) with a standard deviation of 605 kg CO2e ha−1 yr−1. Additionally, the adoption of cover crops is predicted to provide a mean reduction of 678 kg CO2e ha−1 yr−1 (SOC, N2O, and CH4 flux reductions of 824, -173, 26.7 kg CO2e ha−1yr−1, respectively), and improved N-fertilizer timing is estimated to mitigate 413 kg CO2e ha−1 yr−1 (SOC, N2O, and CH4 flux reductions of 75, 337, 1 kg CO2e ha−1yr−1, respectively). The adoption of multiple CSA practices is estimated to have the greatest mean reduction potential of 2861 kg CO2e ha−1 yr−1 (SOC, N2O, and CH4 flux reductions of 2210, 611, 39 kg CO2e ha−1yr−1, respectively). Use of the spatially explicit subfield modeling approach based on public data provides a relatively low-cost approach for strategically targeting CSA practices to agricultural regions where adoption is most impactful.

Determinants of land value volatility in the U.S. Corn Belt

ABSTRACT Understanding land value volatility and its reaction to exogenous shocks helps land owners, investors, and lenders assess risk. Land value volatility, the variance of the unpredictable component of land value growth rates, is modelled for each of the Corn Belt states in the U.S. using EGARCH. A pooled VAR system is then estimated to capture the interactions between land value determinants and land value volatility. The variables of the pooled VAR are split into negative and positive vectors to allow for asymmetric impacts. Impulse response functions are mapped. All states exhibit land value volatility clustering. Inflation, cash rent and population growth rates granger cause land value volatility. Land value volatility responses to negative shocks are greater than those to positive shocks. Lenders and investors should expect greater swings in land values after negative shocks to land value growth rates, but not an overreaction of land values from shocks to cash rent growth rates. Positive shocks to changes in interest rates increases land value volatility, but unexpected shocks to population growth rates do not have statistically significant impact on land value volatility.

Multilevel Deep Learning Network for County-Level Corn Yield Estimation in the U.S. Corn Belt

Accurate and timely estimation of crop yield at a small scale is of great significance to food security and harvest management. Recent studies have proven remote sensing is an efficient method for yield estimation and machine learning, especially deep learning, can infer a good prediction by integrating multisource datasets such as satellite data, climate data, soil data, and so on. However, there are some bottleneck challenges to improve accuracy. First, the popular remote sensing data used for yield prediction fall into two major groups—time-series data and constant data. Surprisingly little attention has been devoted to deep learning networks which can integrate the two kinds of data effectively; second, both temporal and spatial features play a role in affecting the yields. But most of the existing approaches employed either convolutional neural network (CNN) or recurrent neural network (RNN). CNN cannot learn temporal patterns, while RNN barely can learn spatial characteristics. This work proposed a novel multilevel deep learning model coupling RNN and CNN to extract both spatial and temporal features. The inputs include both time-series remote sensing data, soil property data, and the model outputs yield. We experimented with the model in U.S. Corn Belt states, and used it to predict corn yield from 2013 to 2016 at the county-level. The results approve the effectiveness and advantages of the proposed approach over the other methods. In the future, the model will be used on other crops such as soybean and winter wheat to assist agricultural decision-making.

Corn nitrogen rate recommendation tools’ performance across eight US midwest corn belt states

AbstractDetermining which corn (Zea mays L.) N fertilizer rate recommendation tools best predict crop N need would be valuable for maximizing profits and minimizing environmental consequences. Simultaneous comparisons of multiple tools across various environmental conditions have been limited. The objectives of this research were to evaluate the performance of publicly‐available N fertilizer recommendation tools across diverse soil and weather conditions for: (i) prescribing N rates for planting and split‐fertilizer applications, and (ii) economic and environmental effects. Corn N‐response trials using standardized methods were conducted at 49 sites, spanning eight US Midwest states and three growing seasons. Nitrogen applications included eight rates in 45 kg N ha−1 increments all at‐planting and matching rates with 45 kg N ha−1 at‐planting plus at the V9 development stage. Tool performances were compared to the economically optimal N rate (EONR). Over this large geographic region, only 10 of 31 recommendation tools (mainly soil nitrate tests) produced N rate recommendations that weakly correlated to EONR (P ≤ .10; r2 ≤ .20). With other metrics of performance, the Maximum Return to N (MRTN) soil nitrate tests, and canopy reflectance sensing came close to matching EONR. Economically, all tools but the Maize‐N crop growth model had similar returns compared to EONR. Environmentally, yield goal based tools resulted in the highest environmental costs. Results show that no tool was universally reliable over this study's diverse growing environments, suggesting that additional tool development is needed to better represent N inputs and crop utilization at a larger regional level.

Satellite L–band vegetation optical depth is directly proportional to crop water in the US Corn Belt

NASA's Soil Moisture Active Passive (SMAP) and ESA's Soil Moisture Ocean Salinity (SMOS) carry satellite L-band radiometers whose primary missions are to measure soil moisture. However, they also allow retrieving of vegetation optical depth (VOD), the degree to which vegetation attenuates microwave radiation. Because attenuation is primarily a function of the total amount of liquid water in a vegetation canopy that is contained within vegetation tissue, VOD could be used to monitor seasonal changes in this quantity, which we call crop water, in major agricultural regions such as the US Corn Belt. There are two main advantages of L-band VOD: it observes the entire canopy volume because soil moisture sensitivity is maintained throughout the growing season; and since it is unaffected by cloud cover there are close to daily measurements. To show its value, we compare SMAP and SMOS VOD to satellite-scale estimates of crop productivity created using the Agricultural Integrated BIosphere Simulator (Agro-IBIS) and observed weather at the South Fork SMAP Core Validation Site in the Corn Belt state of Iowa. We find that SMAP and SMOS VOD are directly proportional to crop water. New empirical models that relate crop water to crop dry mass were required to make this finding. We created these models with in situ data spanning multiple years and stages of crop development. The value of the proportionality constant (or “b-parameter”) relating VOD to crop water at the satellite scale is about half as large as previous estimates. Because L-band VOD is directly proportional to crop water at the satellite scale, and because we understand the relationship between crop water and crop dry mass, SMAP and SMOS have the potential to evaluate the large-scale performance of crop models in the Corn Belt on a near daily basis.

Towards applying N balance as a sustainability indicator for the US Corn Belt: realistic achievable targets, spatio-temporal variability and policy implications

Gains in nitrogen use efficiency in the production of corn (Zea mays L.) remain low due to management constraints and difficulties in accurately predicting the optimum fertilizer application rate. Retailers and consumers are looking for robust sustainability indicators to help drive the industry towards more sustainable food production, including the simple input-output based ‘N balance’ metric. Seven-year simulations for 25 locations across five US Corn Belt States (NE, IA, MN, IL, IN) were conducted using the biogeochemical Adapt-N® model to determine (i) realistically achievable N balance values when N rates are optimized, (ii) the effects of climate and soil type on achievable N balance values, and (iii) the relative importance of N application timing (fall, spring, split in-season) and formulation (+/− nitrapyrin) in reducing N balance. Split in-season applications reduced N rates by 39% and 22% over fall and spring applications and N balance by 36% and 22%, respectively. Adding nitrapyrin to fall or spring preplant applications modestly reduced N inputs by 9% and 4% and N balance by 18% and 12%. Split N management reduced N losses by 52% and 31% of total area-scaled N losses compared to fall and spring N applications and adding nitrapyrin by 13% and 10%, respectively. Benefits from improved timing and formulation were greater in the more humid eastern part of the region. Split in-season N management allows farmers to reach sustainable N balance levels in 88% of cases, with the remainder mostly affected by mid-season droughts. Economic assessment found partial profit to be enhanced with lower N balance, suggesting that N balance reductions may be achieved through voluntary approaches. The model simulations offered ranges of realistic N balance values that can be used to inform policy discussions. It appears that N balance is best applied when averaged over multiple seasons and threshold levels should be guided by characteristics of the production environment, including soil type and climate.