United States Corn Belt Research Articles

First Report of Dalbulus maidis (DeLong and Wolcott) (Hemiptera: Cicadellidae) in Oklahoma.

The corn leafhopper, Dalbulus maidis (DeLong and Wolcott) (Hemiptera: Cicadellidae), is an invasive insect that can cause damage to maize (Zea mays L.) in two ways: by direct feeding and the transmission of several plant pathogens. Dalbulus maidis is an invasive and serious economic pest of maize that has spread from its center of origin in Mexico to the southernmost parts of the United States. Prior to 2024, corn leafhoppers had not been documented in Oklahoma, and their spread northward toward the United States corn belt is of significant concern. Here, we provide the first reports of the insect in maize in several Oklahoma counties. Insect specimens were collected at various commercial and experimental field sites by Oklahoma State University research and extension personnel. The identity of the insect species was validated through morphological and molecular taxonomy. The presence records for the corn leafhopper presented here provide valuable information for future monitoring and management efforts of this economically important pest and disease.

Observational evidence for groundwater influence on crop yields in the United States

As climate change shifts crop exposure to dry and wet extremes, a better understanding of factors governing crop response is needed. Recent studies identified shallow groundwater-groundwater within or near the crop rooting zone-as influential, yet existing evidence is largely based on theoretical crop model simulations, indirect or static groundwater data, or small-scale field studies. Here, we use observational satellite yield data and dynamic water table simulations from 1999 to 2018 to provide field-scale evidence for shallow groundwater effects on maize yields across the United States Corn Belt. We identify three lines of evidence supporting groundwater influence: 1) crop model simulations better match observed yields after improvements in groundwater representation; 2) machine learning analysis of observed yields and modeled groundwater levels reveals a subsidy zone between 1.1 and 2.5 m depths, with yield penalties at shallower depths and no effect at deeper depths; and 3) locations with groundwater typically in the subsidy zone display higher yield stability across time. We estimate an average 3.4% yield increase when groundwater levels are at optimum depth, and this effect roughly doubles in dry conditions. Groundwater yield subsidies occur ~35% of years on average across locations, with 75% of the region benefitting in at least 10% of years. Overall, we estimate that groundwater-yield interactions had a net monetary contribution of approximately $10 billion from 1999 to 2018. This study provides empirical evidence for region-wide groundwater yield impacts and further underlines the need for better quantification of groundwater levels and their dynamic responses to short- and long-term weather conditions.

Impact of large kernel size on yield prediction: a case study of corn yield prediction with SEDLA in the U.S. Corn Belt

Predicting agricultural yields is imperative for effective planning to sustain the growing global population. Traditionally, regression-based, simulation-based, and hybrid methods were employed for yield prediction. In recent times, there has been a notable shift towards the adoption of Machine Learning (ML) methods, with Deep Learning (DL), particularly Convolutional Neural Networks (CNNs) and Long-Short Term Memory (LSTM) networks, emerging as popular choices for their enhanced predictive accuracy. This research introduces a cost-effective DL architecture tailored for corn yield prediction, considering computational efficiency in processing time, data size, and NN architecture complexity. The proposed architecture, named SEDLA (Simple and Efficient Deep Learning Architecture), leverages the spatial and temporal learning capabilities of CNNs and LSTMs, respectively, with a unique emphasis on exploring the impact of kernel size in CNNs. Simultaneously, the study aims to exclusively employ satellite and yield data, strategically minimizing input variables to enhance the model’s simplicity and efficiency. Notably, the study demonstrates that employing larger kernel sizes in CNNs, especially when processing histogram-based Surface Reflectance (SR) and Land Surface Temperature (LST) data from Moderate Resolution Imaging Spectroradiometer (MODIS), allows for a reduction in the number of hidden layers. The efficacy of the architecture was evaluated through extensive testing on corn yield prediction across 13 states in the United States (U.S.) Corn Belt at county-level. The experimental results showcase the superiority of the proposed architecture, achieving a Mean Absolute Percentage Error (MAPE) of 6.71 and Root Mean Square Error (RMSE) of 14.34, utilizing a single-layer CNN with a 15 × 15 kernel in conjunction with LSTM. These outcomes surpass existing benchmarks in the literature, affirming the efficacy and potential of the suggested DL framework for accurate and efficient crop yield predictions.

Being a woman with the “skills of a man”: negotiating gender in the 21st century US Corn Belt

There has been broad interest in the so-called rise of women farmers in United States (US) agriculture. Researchers have elucidated the diverse ways farmers ‘perform’ gender, while also examining how engaging in a masculine-coded industry like agriculture shapes individuals’ gendered identities as well as their social and mental wellbeing. While illuminating, this work is mostly focused on sustainable or direct-market farmers, with surprisingly little research examining women on conventional row crops operations. This paper works to fill this empirical gap and further theorize gender-agriculture intersections through analyzing interviews with Iowa women row-crop farmers to understand the ways they perceive their gendered identities, and how they see them shaping their farming experience and mental wellbeing. Deploying a conceptual understanding of gender as both discursive and embodied as well as relational and fluid, I find respondents see themselves operating in a somewhat liminal gender identity, where they feel adept at moving between masculinized spaces of agriculture and more feminized domains of homes and office jobs. Critically, while women rarely expressed stress about doing “masculine” coded agricultural labor, they had more complex feelings towards either disliking or imperfectly completing feminized care and reproductive labor. Younger women expressed particular ambivalence about assuming the identity of farmer while also fulfilling gendered norms around (heterosexual) marriage and childbearing. The liminality of women’s gender performance also cut both ways, and while they feel able to access different gendered spaces some feel they are not fully accepted in either. I conclude by reflecting on what these particular forms of gendered subjectivity might mean for women’s mental wellbeing and how agencies might better support gender equity in agriculture.

Corn and the range: rethinking ranching, agriculture and the feedlot

ABSTRACT Debates about modern beef production suffer from a major historical blind spot. For over a century, the feedlot relied on a closed nutrient loop to sustain soil fertility in the United States Corn Belt. Driven by industrial capitalist urbanization, a cattle-grain-beef complex shaped land use, tenure systems, management practices and environmental politics on the expanding frontier, sacrificing the tall-grass prairies and fracturing agrarian resistance to corporate concentration and the Union Stock Yards. Concentrated Animal Feeding Operations (CAFOs) emerged after 1950, breaking the nutrient cycle of the original feedlot and turning the relationship between ranching and agriculture into a dysfunctional one.

Susceptibility of northern corn rootworm (Diabrotica barberi) populations to Cry3Bb1 and Gpp34/Tpp35Ab1 proteins in seedling and diet overlay toxicity assays.

The northern corn rootworm, Diabrotica barberi Smith & Lawrence (Coleoptera: Chrysomelidae) is a major pest of maize in the United States Corn Belt. Recently, resistance to Bacillus thuringiensis (Bt) maize was reported in North Dakota and increased use of Bt maize hybrids could facilitate resistance evolution in other maize-producing states. In this study, susceptibility to Bt proteins was evaluated in wild D. barberi populations from 8 fields collected in 5 different states (Minnesota, Missouri, Nebraska, Iowa, and North Dakota). Field populations were compared to a susceptible D. barberi colony in seedling and diet toxicity assays conducted with 3 concentrations of Cry3Bb1 (0.4, 4.0, and 40.0 µg/cm2) and Gpp34/Tpp35Ab1 (previously called Cry34/35Ab1; 1.4, 14.0, and 140.0 µg/cm2). The 2019 population from Meeker Co., Minnesota (MN-2019), exhibited the lowest mortality to Cry3Bb1 and also had nominally lowest mortality to Gpp34/Tpp35Ab1 at the highest concentrations tested in diet toxicity assays. Percent second instar was also highest for larvae of the Minnesota population surviving Cry3Bb1. In seedling assays, MN and IA-2018 populations exhibited the highest proportion survival and dry weight to both proteins expressed in corn. No significant differences in mortality, percent second instar, and dry weight were observed at the highest concentration for both proteins among the populations collected in in 2020. Most D. barberi populations were still highly susceptible to Cry3Bb1 and Gpp34/Tpp35Ab1 proteins based on diet and seedling assays, but resistance appears to be developing in some D. barberi populations. Now that methods are available, resistance monitoring may also be needed for D. barberi in some regions.

Prevalence and Risk Assessment of Aflatoxin in Iowa Corn during a Drought Year.

Warm temperatures and drought conditions in the United States (US) Corn Belt in 2012 raised concern for widespread aflatoxin (AFL) contamination in Iowa corn. To identify the prevalence of AFL in the 2012 corn crop, the Iowa Department of Agriculture and Land Stewardship (IDALS) conducted a sample of Iowa corn to assess the incidence and severity of AFL contamination. Samples were obtained from grain elevators in all of Iowa's 99 counties, representing nine crop reporting districts (CRD), and 396 samples were analyzed by IDALS using rapid test methods. The statewide mean for AFL in parts per billion (ppb) was 5.57 ppb. Regions of Iowa differed in their incidence levels, with AFL levels significantly higher in the Southwest (SW; mean 15.13 ppb) and South Central (SC; mean 10.86 ppb) CRD (p < 0.05) regions of Iowa. This sampling demonstrated high variability among samples collected within CRD and across the entire state of Iowa in an extreme weather event year. In years when Iowa has AFL contamination in corn, there is a need for a proactive and preventive strategy to minimize hazards in domestic and export markets.

The Women Farmer Stress Inventory: Examining women farmer stress in the United States Corn Belt.

While women identifying as primary farmers have increased in the United States, there has not been research focused on the antecedents of stress and quality of life among women farmers in particular. This study set out to construct a Women Farmer Stress Inventory (WFSI), test its dimensionality, and assess its criterion-related validity by looking at its relationship with subjective wellbeing as measured by the Satisfaction with Life Scale (SWLS). We then examined sociodemographic and farm-level correlates to assess their relationship with stress. We utilized responses from a random sample of 592 Iowan women farmers who responded to a mailout survey that included the WFSI. We conducted exploratory factor analysis to identify the factorial structure of the WFSI, and used linear regression to evaluate how sociodemographic and farm-level characteristics were related to each factor. The analysis revealed 5 unique factors that reflected different aspects of women farmer stress: time pressures and workload, environmental concern, external stressors from governments and market, interpersonal relationships, and rural amenities. All factors except rural amenities had high levels of internal consistency (Cronbach's alpha >0.80) and were validated using the external criteria of SWLS measures. Young age, being married, and engagement in off-farm work, and smaller farm size were associated with greater levels of stress across most domains. The WFSI is a promising tool that shows high internal consistency and is validated with life satisfaction. Our study also finds certain sociodemographic and farm characteristics associated with different stress domains, which could inform both future research and community-based interventions.

Effects of water surplus on prevented planting in the US Corn Belt for corn and soybeans

Record-high prevented planting of staple crops such as corn and soybeans in the United States (US) Corn Belt due to heavy rainfall in recent years has spurred concern over crop production, as growing evidence suggests winter and spring precipitation extremes will occur more frequentlyin the coming decades. Using county-level data, we examine the effects of planting-season water surplus—precipitation minus evaporative demand—on prevented planting of corn and soybeans in the US Corn Belt. Using monthly water surplus data, we show significant impacts of excess moisture on preventing planting and suggest a 58%–177% increase in prevented planting during the months of April–June per standard deviation increase in water surplus. Downscaled climate change projections are used to estimate future changes in prevented planting during the mid-century (2036–2065) under the moderate emission scenario (RCP4.5). Our model predicts a decrease in prevented planting of approximately 111,000 acres (12%) for corn and 80,000 acres (16%) for soybeans in the US Corn Belt, relative to historical levels from 1950 to 2005. However, if we consider only precipitation and disregard evaporative demand, the alternative model indicates an increase of approximately 260,000 acres (30%) for corn and 86,000 acres (19%) for soybeans. Geographically, we find that prevented planting will slightly increase in some parts of Iowa, Minnesota, and Wisconsin and generally decrease in the other parts of the US Corn Belt. This work collectively highlights the value of incorporating water surplus data in assessing prevented-planting impacts and is the first known study to examine changing risk of prevented planting under future climate scenarios that may help inform adaptation efforts to avoid losses.

Effects of SmartStax® and SmartStax® PRO maize on western corn rootworm (Diabrotica virgifera virgifera LeConte) larval feeding injury and adult life history parameters.

Field-evolved resistance of the western corn rootworm (WCR), Diabrotica virgifera virgifera LeConte, to Bacillus thuringiensis Berliner (Bt) proteins Cry3Bb1 and Cry34/35Ab1 (now classified as Gpp34Ab1/Tpp35Ab1) expressed in the pyramid SmartStax® has been documented in areas of the United States (U.S.) Corn Belt. SmartStax® PRO is a recently registered rootworm-active pyramid containing the same Bt proteins expressed in SmartStax® plus DvSnf7 dsRNA. Little to no published data is available comparing efficacy of the technologies or potential effects of dietary exposure on adult WCR fitness. Therefore, experiments were conducted to compare effects of adult WCR dietary exposure to SmartStax® and SmartStax® PRO on life history parameters and efficacy of the technologies in the field with both Bt-susceptible and Bt-resistant WCR populations. WCR life history parameters evaluated included adult longevity, head capsule width, egg production, and egg viability. Results of small-plot field trials indicated that both technologies provided a high level of root protection when a Bt-susceptible WCR population was present. Root protection was reduced on SmartStax® but maintained on SmartStax® PRO when WCR Bt resistance occurred. Lifetime egg production was the key life history parameter that was significantly reduced when either Bt-susceptible or Bt-resistant adult WCR were fed SmartStax® or SmartStax® PRO diet. A potential fitness advantage was apparent as egg production was significantly higher in the Bt-resistant than Bt-susceptible population. The similar response by the Bt-susceptible WCR population to SmartStax® and SmartStax® PRO indicates that results were caused by sublethal dietary exposure to Bt proteins. Adult size (males < females) and egg viability (high: >95%) were not significantly different among treatments but longevity results were inconsistent between years. Collectively, the field efficacy and life history parameter data expand existing knowledge of SmartStax® and SmartStax® PRO technologies, which will inform practical WCR resistance management programs.

An unsupervised domain adaptation deep learning method for spatial and temporal transferable crop type mapping using Sentinel-2 imagery

Accurate crop type mapping is essential for crop growth monitoring and yield estimation. Recently, various machine learning methods have been increasingly used for crop type mapping, but they often lose their validity when directly applied to other regions and years due to differences in the distribution of source and target data, that is, domain shift. To address the problem, we developed a deep adaptation crop classification network (DACCN) based on the idea of unsupervised domain adaptation (UDA). The proposed DACCN mainly consists of two parts, a feature extractor that converts the original input into high-level representations, and a domain aligner where the discrepancy between source and target distributions is measured using multiple kernel variant of maximum mean discrepancy (MK-MMD). Four states in the United States (U.S.) Corn Belt and two provinces in northeastern China were used as study areas, where samples used for model building and accuracy evaluation were collected based on time-series Sentinel-2 imagery and reference maps in 2018 and 2019. Then, three experiments were designed to verify the transferability of DACCN across space, time, and space–time, respectively. In each experiment, the proposed DACCN was compared to deep crop classification network (DCCN), a model with a similar structure to DACCN but without the domain adaptation mechanism, and two machine learning methods, random forest (RF) and support vector machines (SVM). The experimental results showed that DACCN outperformed other models in most transfer cases with overall classification accuracies ranging from 0.835 to 0.922. The DACCN also performed better in spatially continuous mapping with its predicted crop type maps more consistent with the reference ones. As an innovative application of transfer learning in crop type mapping, the methodology proposed in this study effectively addressed the problem of missing labels in target domains and alleviated the negative impact of domain shift.

Field-scale dynamics of planting dates in the US Corn Belt from 2000 to 2020

Crop planting dates are a dynamic feature of agricultural systems that respond to short- and long-term climate signals, crop and cultivar selection, and technology changes. Planting date records are essential for yield gap analyses, accurate crop modeling, and tracking farmer adaptations to weather and climate change. Although planting dates have high variation at local scales due to heterogeneity in farm resources and decision-making, available long-term data on planting dates are largely restricted to aggregated regional statistics or, at best, satellite-derived datasets with limited spatiotemporal extent and at resolutions unable to distinguish individual fields. Here, we generated retrospective annual field-scale (30 m) planting date maps for both maize and soybeans spanning 2000–2020 across a 12-state region in the United States Corn Belt based on Landsat satellite data and a large ground sample of over 28,000 maize and soybean fields. Using training data from 2015 to 2020 for model selection, we found that planting date predictions improved with harmonic regression of Landsat data and additional annual weather covariates. The preferred random forest model approximately doubled performance compared to a null model based on state median planting dates, capturing 47% of field-level variation for maize (mean absolute error, MAE = 7.4 days) and 44% for soybeans (MAE = 7.5 days) against held-out ground truth test data for 2008–2014. We also evaluated the full 2000–2020 dataset with state agricultural statistics, finding strong agreement with median planting dates for maize (R2 = 0.76, MAE = 4.4 days) and slightly lower agreement for soybeans (R2 = 0.65, MAE = 5.4 days) when aggregated to the state level. We then used this new dataset to analyze environmental determinants of planting dates at a finer-scale than previously possible, controlling for unobserved variation at the sub-state district level. We found that during 2000–2020, each standard deviation increase in early-season rainfall delayed planting by ∼2.5 days, and fields with higher soil productivity ratings tended to be planted earlier. We did not find meaningful trends over the last two decades in planting dates for maize or soybeans, in contrast to trends towards earlier planting dates late last century and predicted for this period in climate adaptation studies. We hypothesize increases in early season rainfall or persisting early-season frost risks may have inhibited these shifts towards earlier planting. Remotely sensed planting dates will be a useful tool for yield gap analyses, crop simulation modeling, and ongoing assessment of climate adaptation.

Reduced susceptibility of western corn rootworm (Diabrotica virgifera virgifera LeConte) populations to Cry34/35Ab1-expressing maize in northeast Nebraska

The western corn rootworm (WCR; Diabrotica virgifera virgifera LeConte) is a significant pest of maize (Zea mays L.) across the United States Corn Belt. Transgenic maize hybrids expressing insecticidal proteins derived from Bacillus thuringiensis (Bt) have been used to manage WCR since 2003. Widespread resistance to Cry3Bb1 (and associated cross-resistance to mCry3A and eCry3.1Ab) has placed increased selection pressure on Cry34/35Ab1 in single-protein and pyramided transgenic maize hybrids. Data on the susceptibility of Nebraska WCR populations to Cry34/35Ab1 has not been published since 2015 and plant-based bioassays conducted in 2017–2018 confirmed resistance to Cry3Bb1 + Cry34/35Ab1 maize, suggesting resistance to Cry34/35Ab1 has evolved in the Nebraska landscape. Therefore, plant-based bioassays were conducted on F1 progeny of WCR populations collected from northeast Nebraska in 2018 and 2019. Larval survival and development were used to classify resistance to Cry34/35Ab1 in each WCR population. Bioassays confirmed incomplete resistance to Cry34/35Ab1 maize in 21 of 30 WCR populations; 9 of 30 WCR populations remained susceptible to Cry34/35Ab1. Collectively, results indicate that northeast Nebraska WCR populations were in the initial stages of resistance evolution to Cry34/35Ab1 during 2018–2019. Appropriate resistance management strategies are needed to mitigate resistance and preserve efficacy of rootworm-active products containing Cry34/35Ab1.

Maize Inbred Mp708 is Highly Susceptible to Western Corn Rootworm, Diabrotica virgifera virgifera (Coleoptera: Chrysomelidae), in Field and Greenhouse Assays.

The western corn rootworm (WCR), Diabrotica vifgirera virgifera LeConte, (Coleoptera: Chrysomelidae) causes significant economic damage in corn production each year. Resistance to insecticides and transgenic corn with Bacillus thuringiensis (Bt), Berliner toxins have been reported throughout the United States Corn Belt. Corn breeding programs for natural resistance against WCR larvae could potentially assist in rootworm management. Root damage and root regrowth of eight corn lines were evaluated in field assays at three different locations. Results indicated the inbred 'Mp708' had the greatest root damage and was significantly greater than damage for the susceptible control, B37×H84. In greenhouse assays, we evaluated four of these lines plus a hybrid expressing the mCry3A Bt toxin for damage. The results indicated that Mp708 had significantly higher root damage when compared to 'CRW3(S1)C6' and 'MIR604'. Despite previous work suggesting otherwise, we conclude that Mp708 is highly susceptible to the WCR larvae based on root damage in field and greenhouse plant assays.

Impacts of different types of El Niño events on water quality over the Corn Belt, United States

Abstract. The United States Corn Belt region, which primarily includes two large basins, namely, the Ohio–Tennessee River basin (OTRB) and the Upper Mississippi River basin (UMRB), is responsible for the Gulf of Mexico hypoxic zone. Climate patterns such as El Niño can affect the runoff and thus the water quality over the Corn Belt. In this study, the impacts of eastern Pacific (EP) and central Pacific (CP) El Niño events on water quality over the Corn Belt region were analyzed using the Soil and Water Assessment Tool (SWAT) models. Our results indicated that, at the outlets, annual total nitrogen (TN) and total phosphorus (TP) loads decreased by 13.1 % and 14.0 % at OTRB and 18.5 % and 19.8 % at UMRB, respectively, during the EP El Niño years, whereas during the CP El Niño years, they increased by 3.3 % and 4.6 % at OTRB and 5.7 % and 4.4 % at UMRB, respectively. On the subbasin scales, more subbasins showed negative (positive) anomalies of TN and TP during EP (CP) El Niño. A seasonal study confirmed that water quality anomalies showed the opposite patterns during EP and CP El Niño years. At the outlet of OTRB, seasonal anomalies in nutrients matched the El Niño–Southern Oscillation (ENSO) phases, illustrating the importance of climate variables associated with the two types of El Niño events on water quality in the region. At the UMRB, TN and TP were also influenced by agricultural activities within the region, and their anomalies became greater in the growing seasons during both EP and CP El Niño years. A quantitative analysis of precipitation, temperature, and their effects on nutrients suggested that precipitation played a more important role than temperature did in altering the water quality in the Corn Belt region during both types of El Niño years. We also found specific watersheds (located in Iowa, Illinois, Minnesota, Wisconsin, and Indiana) that faced the greatest increases in TN and TP loads and were affected by both the precipitation and agricultural activities during the CP El Niño years. The information generated from this study may help proper decision-making for water environment protection over the Corn Belt.

Process-based modeling of soil nitrous oxide emissions from United States corn fields under different management and climate scenarios coupled with evaluation using regional estimates

Direct emissions of soil nitrous oxide during a growing season (N2Ogs) can be quantified with process-based models considering interactions between management, climate, and soil moisture when key data are available. We used an adapted “parameterized CENTURY/DAYCENT-model” (pCENTURY) calibrated with crop growth and soil organic matter decay coefficients at the county-level for the estimation of N2Ogs in the United States Corn Belt. Model estimated N2O-emissions from corn-based biofuels scenarios considering crop rotation, fertilizer inputs, tillage, and weather were compared against meta-summary of field observations from 55 studies. Both model and meta-summary ranked N2Ogs-emissions to be corn &amp;gt; wheat &amp;gt; soybean phase while model likely underestimated cover crop N2Ogs-emissions. The N2Ogs-emissions and the associated emission factors (EFs) were modeled and summarized to be greater after anhydrous ammonia than urea application and from conventional tilled than non-tilled fields. Modeled and observed N2Ogs-emissions after organic and inorganic fertilizer amendment did not differ due to high variability associated with the treatments. However, the organic fertilizer associated EFs were greater according to meta-summary data because of N input rates. Regionalized weather scenarios indicate hotspots for N2Ogs-emissions can occur where crop N uptake is limited during dry years and in eastern states also during normal or wet seasons. The pCENTURY-derived N2Ogs EFs (0.91 ± 0.19%) for counties investigated were only slightly lower than literature (1.07 ± 0.57%) or Tier-1 (1%) values. Our preliminary evaluation of regional soil moisture estimates showed reasonable agreement between monthly soil moisture estimates and the North American Soil Moisture Dataset during the growing season, but overestimation of soil moisture in winter-spring can influence the estimates of annual N2O emissions so future work is needed to calibrate soil moisture-associated model parameters. Our work provided scenario-based estimates of climate and management impacts on soil N2Ogs-emissions together with valuable spatial insights into EFs that will be improved by more accurate information of fertilizer inputs and more temporally refined model evaluation.

Plant Foraging Strategies Driven by Distinct Genetic Modules: Cross-Ecosystem Transcriptomics Approach.

Plants have evolved diverse strategies for foraging, e.g., mycorrhizae, modification of root system architecture, and secretion of phosphatase. Despite extensive molecular/physiological studies on individual strategies under laboratory/greenhouse conditions, there is little information about how plants orchestrate these strategies in the field. We hypothesized that individual strategies are independently driven by corresponding genetic modules in response to deficiency/unbalance in nutrients. Roots colonized by mycorrhizal fungi, leaves, and root-zone soils were collected from 251 maize plants grown across the United States Corn Belt and Japan, which provided a large gradient of soil characteristics/agricultural practice and thus gene expression for foraging. RNA was extracted from the roots, sequenced, and subjected to gene coexpression network analysis. Nineteen genetic modules were defined and functionally characterized, from which three genetic modules, mycorrhiza formation, phosphate starvation response (PSR), and root development, were selected as those directly involved in foraging. The mycorrhizal module consists of genes responsible for mycorrhiza formation and was upregulated by both phosphorus and nitrogen deficiencies. The PSR module that consists of genes encoding phosphate transporter, secreted acid phosphatase, and enzymes involved in internal-phosphate recycling was regulated independent of the mycorrhizal module and strongly upregulated by phosphorus deficiency relative to nitrogen. The root development module that consists of regulatory genes for root development and cellulose biogenesis was upregulated by phosphorus and nitrogen enrichment. The expression of this module was negatively correlated with that of the mycorrhizal module, suggesting that root development is intrinsically an opposite strategy of mycorrhizae. Our approach provides new insights into understanding plant foraging strategies in complex environments at the molecular level.

Characterizing the sublethal effects of SmartStax PRO dietary exposure on life history traits of the western corn rootworm, Diabrotica virgifera virgifera LeConte.

The western corn rootworm (WCR), Diabrotica virgifera virgifera LeConte, is an economically important pest of field corn (Zea mays L.) across the United States (U.S.) Corn Belt. Repeated use of transgenic hybrids expressing Bacillus thuringiensis (Bt) proteins has selected for field-evolved resistance to all current rootworm-active Bt proteins. The newest product available for WCR management is SmartStax® PRO, a rootworm-active pyramid containing Cry3Bb1, Cry34/35Ab1 [now reclassified as Gpp34Ab1/Tpp35Ab1] and a new mode of action, DvSnf7 dsRNA. Understanding the fitness of adult WCR after dietary exposure to SmartStax® PRO will identify potential impacts on WCR population dynamics and inform efforts to optimize resistance management strategies. Therefore, the objective of the present study was to characterize the effect of SmartStax® PRO dietary exposure on WCR life history traits. Adult WCR were collected during 2018 and 2019 from emergence tents placed over replicated field plots of SmartStax® PRO or non-rootworm Bt corn at a site with a history of rootworm-Bt trait use and suspected resistance to Cry3Bb1 and Cry34/35Ab1. Adult survival was reduced by 97.1–99.7% in SmartStax® PRO plots relative to the non-rootworm Bt corn plots during the study. Individual male/female pairs were fed different diets of ear tissue to simulate lifetime or adult exposure. Life history parameters measured included adult longevity, adult head capsule width, lifetime female egg production, and egg viability. Results indicate that lifetime or adult exposure to SmartStax® PRO significantly reduced adult longevity and lifetime egg production. Larval exposure to SmartStax® PRO significantly reduced WCR adult size. Results from this study collectively suggest that SmartStax® PRO may negatively impact WCR life history traits, which may lead to reduced population growth when deployed in an area with WCR resistance to Bt traits.

Maize Leaf Appearance Rates: A Synthesis From the United States Corn Belt.

The relationship between collared leaf number and growing degree days (GDD) is crucial for predicting maize phenology. Biophysical crop models convert GDD accumulation to leaf numbers by using a constant parameter termed phyllochron (°C-day leaf−1) or leaf appearance rate (LAR; leaf oC-day−1). However, such important parameter values are rarely estimated for modern maize hybrids. To fill this gap, we sourced and analyzed experimental datasets from the United States Corn Belt with the objective to (i) determine phyllochron values for two types of models: linear (1-parameter) and bilinear (3-parameters; phase I and II phyllochron, and transition point) and (ii) explore whether environmental factors such as photoperiod and radiation, and physiological variables such as plant growth rate can explain variability in phyllochron and improve predictability of maize phenology. The datasets included different locations (latitudes between 48° N and 41° N), years (2009–2019), hybrids, and management settings. Results indicated that the bilinear model represented the leaf number vs. GDD relationship more accurately than the linear model (R2 = 0.99 vs. 0.95, n = 4,694). Across datasets, first phase phyllochron, transition leaf number, and second phase phyllochron averaged 57.9 ± 7.5°C-day, 9.8 ± 1.2 leaves, and 30.9 ± 5.7°C-day, respectively. Correlation analysis revealed that radiation from the V3 to the V9 developmental stages had a positive relationship with phyllochron (r = 0.69), while photoperiod was positively related to days to flowering or total leaf number (r = 0.89). Additionally, a positive nonlinear relationship between maize LAR and plant growth rate was found. Present findings provide important parameter values for calibration and optimization of maize crop models in the United States Corn Belt, as well as new insights to enhance mechanisms in crop models.

Mapping corn and soybean phenometrics at field scales over the United States Corn Belt by fusing time series of Landsat 8 and Sentinel-2 data with VIIRS data

Accurate and timely crop phenology is essential for efficient crop management and precise modelling of crop yield and productivity. It has been commonly mapped from moderate or coarse resolution (≥500 m) satellite data at a national or global scale, which generally represents the mixture of multiple crops or between crops and natural vegetation. Crop phenological progress at field scales (≤30 m) is critical for monitoring the growth of specie-specific crop types, but it is very challenging at large scales because of limited cloud-free observations in the satellite time series. To map corn and soybean phenometrics at a field scale (30 m) over entire Corn Belt in the United States, this study for the first time develops a new approach that fuses high temporal resolution Visible Infrared Imaging Radiometer Suite (VIIRS) observations (500 m) and high spatial resolution Harmonized Landsat and Sentinel-2 (HLS) data (30 m). The fused HLS-VIIRS time series (daily 30 m) is applied to map crop phenometrics of greenup onset, mid-greenup phase, maturity onset, senescence onset, mid-senescence phase, and dormancy onset. These six phenometrics are correlated to crop progress (CP) of seven corn and six soybean growth stages from planting to harvest that are observed by the National Agricultural Statistics Service (NASS) of the United States Department of Agriculture (USDA) and are further validated using PhenoCam near-surface time series imagery. The result indicates that the HLS-VIIRS crop phenometrics are capable of tracking the CP very well. Although their biophysical definitions of CP stages and sample coverage were not the same in our comparisons, the HLS-VIIRS crop phenometrics and NASS CP at a state level are closely comparable with a difference<4 days for corn and soybean emergence, 1 day for soybean dropping leaves, and 6–10 days for other corn and soybean growth stages. Their significant correlations (R2 ranging from 0.81 to 0.99, p < 0.01) suggest that the HLS-VIIRS crop phenometrics are robust proxies for predicting NASS CP with a high accuracy. Moreover, the HLS-VIIRS phenometrics aligns closely with PhenoCam phenological timings at various crop growth stages with an absolute average difference from 3 days to 5.3 days and bias from 1.5 to 3.5 days. The result demonstrates that the proposed approach could be implemented for monitoring field scale crop progress at a national or global scale.