USA Corn Belt Research Articles

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.

Agronomic and Economic Evaluations of N Fertilization in Maize under Recent Market Dynamics

An online calculator is available to determine economic optimum nitrogen rate (EONR) for maize (Zea mays L.) production in the USA Corn Belt. For Minnesota, this calculator considers nitrogen (N) fertilizer cost, maize grain price and crop history, and produces a statewide N rate based on maximum economic return to N (MRTN). However, a clear precipitation and temperature gradient, and soil heterogeneity across the state, and recent changes in fertilizer cost and maize grain price require the comparison of EONR from this calculator with results from field study. The objectives of this research were to determine the agronomic and economic benefits of basal and split application of N fertilizer on maize grain yield and yield components and compare EONR from field study with N recommendation from the online calculator. The nitrogen fertilizer rate for the field study ranged from 0 to 224 kg N ha−1, either split or all applied at planting. The results showed that there were no interaction effects of N rate by time or N rate by year on maize grain yield. Maize grain yield had a quadratic response to N rate, and agronomic maximum grain yield peaked at 205 kg N ha−1. The EONR from the field study was 168 kg N ha−1 and it remained stable under a wide range of economic analysis scenarios with net benefit reaching up to 2474 USD ha−1. The N rate from the online calculator at MRTN was 151 kg N ha−1 and this rate may cause N deficiency in maize resulting in a yield penalty compared with the field study results. The field study was performed under specific soil and climatic conditions. Therefore, extensive research under various soil types, agronomic management practices and climatic conditions is warranted to evaluate the online calculator performance and its reliability as a precision tool for N fertilizer management in maize production.

Prediction of Corn Yield in the USA Corn Belt Using Satellite Data and Machine Learning: From an Evapotranspiration Perspective

The reliable prediction of corn yield for the United States of America is essential for effective food and energy management of the world. Three satellite-derived variables were selected, namely enhanced vegetation index (EVI), leaf area index (LAI) and land surface temperature (LST). The least absolute shrinkage and selection operator (LASSO) was used for regression, while random forest (RF), support vector regression (SVR) and long short-term memory (LSTM) methods were selected for machine learning. The three variables serve as inputs to these methods, and their efficacy in predicting corn yield was assessed in relation to evapotranspiration (ET). The results confirmed that a high level of performance can be achieved for yield prediction (mean predicted R2 = 0.63) by combining EVI + LAI + LST with the four methods. Among them, the best results were obtained by using LSTM (mean predicted R2 = 0.67). EVI and LST provided extra and unique information in peak and early growth stages for corn yield, respectively, and the usefulness of including LAI was not readily apparent across the whole season, which was consistent with the field growing conditions affecting the ET of corn. The satellite-derived data and the methods used in this study could be used for predicting the yields of other crops in different regions.

Can we harness digital technologies and physiology to hasten genetic gain in US maize breeding?

Plant physiology can offer invaluable insights to accelerate genetic gain. However, translating physiological understanding into breeding decisions has been an ongoing and complex endeavor. Here we demonstrate an approach to leverage physiology and genomics to hasten crop improvement. A half-diallel maize (Zea mays) experiment resulting from crossing 9 elite inbreds was conducted at 17 locations in the USA corn belt and 6 locations at managed stress environments between 2017 and 2019 covering a range of water environments from 377 to 760 mm of evapotranspiration and family mean yields from 542 to 1,874 g m-2. Results from analyses of 35 families and 2,367 hybrids using crop growth models linked to whole-genome prediction (CGM-WGP) demonstrated that CGM-WGP offered a predictive accuracy advantage compared to BayesA for untested genotypes evaluated in untested environments (r = 0.43 versus r = 0.27). In contrast to WGP, CGMs can deal effectively with time-dependent interactions between a physiological process and the environment. To facilitate the selection/identification of traits for modeling yield, an algorithmic approach was introduced. The method was able to identify 4 out of 12 candidate traits known to explain yield variation in maize. The estimation of allelic and physiological values for each genotype using the CGM created in silico phenotypes (e.g. root elongation) and physiological hypotheses that could be tested within the breeding program in an iterative manner. Overall, the approach and results suggest a promising future to fully harness digital technologies, gap analysis, and physiological knowledge to hasten genetic gain by improving predictive skill and definition of breeding goals.

Where is the USA Corn Belt, and how is it changing?

The “Corn Belt” is a commonly used term, but often referenced as a vaguely defined region in the Midwest USA. A few key studies have delineated synoptic maps of the Corn Belt boundaries going back to the early 20th century, but a modern flexible and accessible framework for mapping the Corn Belt in space and time is needed. New tools provide reference maps for the Corn Belt in the 21st century and the ability to quantify space-time changes in corn cropping patterns. The Landuse and Agricultural Management Practices web-Service (LAMPS) was used to estimate the average corn (maize, Zea mays L.) area in each county of the contiguous 48 USA states for the years 2010–2016. LAMPS provides a modified areal Fraction of corn (Fc) used to map the Corn Belt at three intensity levels, for example. The resulting patterns illustrate a mostly contiguous Midwest Corn Belt surrounded by more scattered regions, including southern and eastern regions. We also mapped irrigated areas and temporal changes in Fc. Mapped patterns have the potential to help researchers study issues related to food, feed, biofuel, and water security.

Agricultural drought monitoring using MODIS-based drought indices over the USA Corn Belt

In 2012, the USA Corn Belt, an intensive agricultural region of the USA, was hit by a widespread severe drought, affecting states such as Illinois, Iowa, Nebraska, and Indiana. In this study, time series (2000–2012) of Moderate Resolution Imaging Spectroradiometer (MODIS) measurements were investigated to assess the 2012 drought conditions during the corn-growing season. Seven MODIS indices generated based on eight day MODIS reflectance and land surface temperature (LST) products were examined with standardized precipitation index (SPI) and Palmer-Z across the Corn Belt to evaluate the relative performance of each MODIS index to detect agricultural drought. The normalized difference infrared index (NDII6) anomaly shows the highest correlation coefficient (r) with SPI at three time scales and correlates best with Palmer-Z, which suggests good sensitivity of the NDII6 anomaly to precipitation and moisture deficiency in agricultural areas. The temporal and spatial features of drought provided by MODIS indices were compared with maps of the USA Drought Monitor (USDM), the current advanced tool for drought monitoring. The rapid intensification of drought across the Corn Belt in 2012 summer captured by MODIS index anomalies agreed with the changes of USDM maps quite well, especially in August and September when extreme drought occurred. Through comparison with the USDM drought map, the NDII6 anomaly demonstrated an advantage in monitoring drought condition over irrigated land and showed the potential to advance fine-scale agricultural drought monitoring by providing more detailed spatial characterization.

Simulation of hydrological effects on subsurface drainage of substituting perennial grass for corn/soybean rotation in Corn-belt of USA

Simulation of hydrological effects on subsurface drainage of substituting perennial grass for corn/soybean rotation in Corn-belt of USA

Modeling the effect of a heat wave on maize production in the USA and its implications on food security in the developing world

This study uses geo-spatial crop modeling to quantify the biophysical impact of weather extremes. More specifically, the study analyzes the weather extreme which affected maize production in the USA in 2012; it also estimates the effect of a similar weather extreme in 2050, using future climate scenarios. The secondary impact of the weather extreme on food security in the developing world is also assessed using trend analysis.Many studies have reported on the significant reduction in maize production in the USA due to the extreme weather event (combined heat wave and drought) that occurred in 2012. However, most of these studies focused on yield and did not assess the potential effect of weather extremes on food prices and security. The overall goal of this study was to use geo-spatial crop modeling and trend analysis to quantify the impact of weather extremes on both yield and, followed food security in the developing world.We used historical weather data for severe extreme events that have occurred in the USA. The data were obtained from the National Climatic Data Center (NCDC) of the National Oceanic and Atmospheric Administration (NOAA). In addition we used five climate scenarios: the baseline climate which is typical of the late 20th century (2000s) and four future climate scenarios which involve a combination of two emission scenarios (A1B and B1) and two global circulation models (CSIRO-Mk3.0 and MIROC 3.2). DSSAT 4.5 was combined with GRASS GIS for geo-spatial crop modeling. Simulated maize grain yield across all affected regions in the USA indicates that average grain yield across the USA Corn Belt would decrease by 29% when the weather extremes occur using the baseline climate. If the weather extreme were to occur under the A1B emission scenario in the 2050s respectively, average grain yields would decrease by 38% and 57%, under the CSIRO-Mk3.0 and MIROC 3.2 global climate models, respectively.The weather extremes that occurred in the USA in 2012 resulted in a sharp increase in the world maize price. In addition, it likely played a role in the reduction in world maize consumption and trade in 2012/13, compared to 2011/12. The most vulnerable countries to the weather extremes are poor countries with high maize import dependency ratios including those countries in the Caribbean, northern Africa and western Asia. Other vulnerable countries include low-income countries with low import dependency ratios but which cannot afford highly-priced maize. The study also highlighted the pathways through which a weather extreme would affect food security, were it to occur in 2050 under climate change.Some of the policies which could help vulnerable countries counter the negative effects of weather extremes consist of social protection and safety net programs. Medium- to long-term adaptation strategies include increasing world food reserves to a level where they can be used to cover the production losses brought by weather extremes.

Intercontinental effect on sexual shape dimorphism and allometric relationships in the beetle pest Diabrotica virgifera virgifera LeConte (Coleoptera: Chrysomelidae)

Hind wing shape variation was examined in 686 adult Diabrotica virgifera virgifera collected from maize plants in Europe and the USA Corn Belt, using geometric morphometric techniques. Sexual dimorphism at an Intercontinental scale was assessed using canonical variates analysis, a multivariate statistical method used to find the shape characters that best distinguish among groups of specimens. Our results showed that each of the populations of D. v. virgifera investigated in this study showed high levels of sex based hind wing shape dimorphism. In particular a stronger and more obvious pattern of hind wing shape variation was found in the USA than in Europe. These results support previous studies on D. v. virgifera wing shape that show that female D. v. virgifera have more elongated wings than males. These differences raise the question of whether sexual dimorphism may be modulated by natural selection.

The Myth of Nitrogen Fertilization for Soil Carbon Sequestration

Intensive use of N fertilizers in modern agriculture is motivated by the economic value of high grain yields and is generally perceived to sequester soil organic C by increasing the input of crop residues. This perception is at odds with a century of soil organic C data reported herein for the Morrow Plots, the world's oldest experimental site under continuous corn (Zea mays L.). After 40 to 50 yr of synthetic fertilization that exceeded grain N removal by 60 to 190%, a net decline occurred in soil C despite increasingly massive residue C incorporation, the decline being more extensive for a corn-soybean (Glycine max L. Merr.) or corn-oats (Avena sativa L.)-hay rotation than for continuous corn and of greater intensity for the profile (0-46 cm) than the surface soil. These findings implicate fertilizer N in promoting the decomposition of crop residues and soil organic matter and are consistent with data from numerous cropping experiments involving synthetic N fertilization in the USA Corn Belt and elsewhere, although not with the interpretation usually provided. There are important implications for soil C sequestration because the yield-based input of fertilizer N has commonly exceeded grain N removal for corn production on fertile soils since the 1960s. To mitigate the ongoing consequences of soil deterioration, atmospheric CO(2) enrichment, and NO(3)(-) pollution of ground and surface waters, N fertilization should be managed by site-specific assessment of soil N availability. Current fertilizer N management practices, if combined with corn stover removal for bioenergy production, exacerbate soil C loss.

Simulation of moderate levels of slug injury to soybean

Slugs became a significant problem in soybean, Glycine max (L.) Merrill, in the eastern USA cornbelt as growers adopted conservation tillage production practices. Little information is known about the consequences from slug injury on soybean growth and yield. This study examined the effects of moderate levels of simulated slug injury, i.e., stand loss and defoliation to the unifoliate and 1st trifoliate leaflets, to the crop. Soybean stands were established at levels at and above those requiring replanting, and defoliation was done by removing 50% of the unifoliate and 1st trifoliate leaflets. Plant growth measurements were taken during the summer and yields recorded. The greatest impact on yield was from reduced plant stands, with 50% defoliation of the unifoliate leaflet having a much lesser impact. There was little effect from injury to the 1st trifoliate. Results suggest that growers should be most concerned when slug injury causes reduced stands and from defoliation to the unifoliate leaflet. Early planting that allows for maximum growth of the plant before slugs become active in later spring would lower the potential for economic losses.

An Improved Woodruff Buffer for Estimation of Lime Requirements

AbstractThe dominant quick‐test method of estimating soil lime requirements in the USA Corn Belt and adjacent area is the Shoemaker, McLean, Pratt (SMP) buffer method. This work was done to compare an improved Woodruff buffer with the SMP buffer to determine which most accurately estimated soil lime requirement. This study used a Ca(OH)2‐CaCl2 soil titration procedure and incubation of soils with finely ground, chemically pure CaCO3 to compare the accuracy of the original Woodruff procedure, the SMP single buffer procedure, and the new Woodruff procedure in estimating the lime requirements of 89 soil samples. The results supported 1961 findings by Ohio workers that the original Woodruff procedure drastically underestimated lime requirements. The results also supported the literature that the SMP procedure underestimates low lime requirements. The SMP tended to overestimate high lime requirements. Based upon the results of this study, the new Woodruff procedure was a better predictor of lime requirements than the SMP procedure.