Search Results (72)

Against the backdrop of global warming and a reshaped hydrothermal regime, the albic soil belt of the Sanjiang Plain, a major grain base, requires farm-scale evidence of how meteorological variability couples with staple-crop yields. Using meteorological and yield records from 2000 to 2023 at three large farms (859, 850, and 852), this study applied the Mann–Kendall test, wavelet and cross-wavelet coherence, Pearson correlation, gray relational analysis, and principal component analysis to track the evolution of air temperature, precipitation, evaporation, sunshine duration, relative humidity, and surface temperature, and to assess their multi-scale impacts on rice, corn, and soybean yields. The region warmed and became wetter overall, with dominant periodicities near 21a and 8a. Across the three farms, yields were significantly and positively associated with precipitation and air temperature (R > 0.60). Rice yield correlated strongly and negatively with evaporation at Farm 850 (R = −0.61) and at Farm 852 (R = −0.503). At Farm 859, gray relational analysis ranked precipitation highest for rice, corn, and soybean (γ = 0.853, 0.844, and 0.826), followed by air temperature. The first two principal components explained 67.66% of the variance; PC1 (41.80%) loaded positively for air temperature, and PC2 (25.86%) for precipitation and relative humidity. Cross-wavelet coherence indicated stable coupling between yields and hydrothermal variables, with the strongest coupling for rice with precipitation and air temperature, prominent coupling for corn with air temperature and sunshine duration, and stage-dependent responses of soybean to precipitation and evaporation. These results show that long-term trends together with phase-specific oscillations jointly shape yield variability. The findings support translating phase identification and sensitive windows into crop-specific rules for sowing or transplanting arrangements, irrigation timing, and early warning, providing a quantitative basis for climate-adaptive management on the study farms and, where soils, management, and microclimate are comparable, for the wider Sanjiang Plain. Full article

Understanding the forces that shape farmland rental prices in major agricultural regions such as the U.S. Corn Belt is essential for evaluating the economic and environmental resilience of agricultural regions. This study develops an integrated framework that combines spatial modelling with uncertainty-aware spatial analysis to examine how macroeconomic conditions influence rental dynamics across the core Corn Belt. Using geographically weighted regression, the analysis captures spatial variation in the sensitivity of rental prices to oil prices, interest rates, and economic activity, revealing substantial geographic heterogeneity in macroeconomic exposure. The results reveal pronounced spatial heterogeneity in rental price responses, with geographically weighted models consistently outperforming global linear specifications. Despite strong spatial variation in rental sensitivities, neither prediction uncertainty nor maize yield volatility displays a clear regional pattern, indicating that production stability and model reliability are highly localised. By linking spatially varying rent sensitivities with indicators of economic pressure and production instability, this study provides new insights into agricultural sustainability risk. The findings highlight the importance of place-based policy and region-specific risk management under increasing macroeconomic volatility. Full article

Accurate seed singulation is critical for uniform crop establishment and yield optimization in precision agriculture. This study presents the development and evaluation of a Hall sensor-based Seed Miss Prevention System (SMPS) integrated into a long-belt finger-clip precision metering device for corn (‘Dekalb DKC5032’) and sunflower (‘Astana’). The system utilizes neodymium magnets mounted on seed-picking fingers to trigger a Hall sensor that detects missed seeds in real time and initiates immediate compensation. Laboratory tests across rotational speeds from 10 to 80 rpm showed that the SMPS significantly reduced seed misses, especially within the 10–30 rpm range, where near-perfect singulation was achieved for corn (Miss Index < 0.01). For sunflower, although performance at very low speeds was limited by mechanical variability, the SMPS effectively reduced the miss index by up to 50 % at medium speeds. Statistical analysis (Tukey HSD) confirmed significant improvements in single and double miss prevention across both crops. The proposed Hall sensor-based approach demonstrated a robust, cost-effective, and dust-resistant solution for enhancing seed placement accuracy, providing a strong foundation for the development of intelligent and adaptive precision seeding systems. Full article

Rapid biofuel expansion has significantly reshaped agricultural land use in the United States, raising concerns about the conversion and long-term sustainability of marginal croplands. Understanding how policy incentives influence these land-use changes remains a key challenge in sustainable land management. This study aims to quantify the effects of the Renewable Fuel Standard on cropland expansion and subsequent abandonment in the U.S. Midwest using a probabilistic and spatially explicit framework. The analysis integrates geospatial datasets from USDA, USGS, gridMET, and the U.S. Energy Information Administration, combining indicators of soil productivity, slope, precipitation, temperature, and market accessibility. Bayesian logistic regression models were developed to estimate pre-policy baseline probabilities of corn cultivation and to generate counterfactual scenarios—hypothetical conditions representing land-use patterns in the absence of policy incentives. Results show that over one-quarter of marginal land cultivated in 2016 would likely not have been planted without biopower policy-related incentives, indicating that policy-driven expansion extended into less suitable areas. A second-stage analysis identified regions where such lands were later abandoned, revealing the role of climatic and economic constraints in shaping long-term sustainability. These findings demonstrate the effectiveness of integrating probabilistic modelling with high-resolution spatial data to evaluate causal policy effects and quantify counterfactual impacts—that is, the measurable differences between observed and simulated land-use outcomes. Full article

Exposure to per- and polyfluoroalkyl substances (PFASs) can cause detrimental health effects. The consumption of contaminated food is viewed as a major exposure pathway for humans, but the relationship between agriculture and PFASs has not been investigated thoroughly, and it is becoming a pressing issue since health advisories are continuously being reassessed. This semi-systematic literature review connects the release, environmental fate, and agriculture uptake of PFASs to enhance comprehension and identify knowledge gaps which limit accurate risk assessment. It focuses on the heavily agricultural state of Minnesota, USA, which is representative of the large Midwestern US Corn Belt in terms of agricultural activities, because PFASs have been monitored in Minnesota since the beginning of the 21st century. PFAS contamination is a complex issue due to the over 14,000 individual PFAS compounds which have unique chemical properties that interact differently with air, water, soil, and biological systems. Moreover, the lack of field studies and monitoring of agricultural sites makes accurate risk assessments challenging. Researchers, policymakers, and farmers must work closely together to reduce the risk of PFAS exposure as the understanding of their potential health effects increases and legacy PFASs are displaced with shorter fluorinated replacements. Full article

Accurate crop yield prediction is essential for stabilizing food supply chains and reducing the uncertainties in financial risks related to agricultural production. Yet, it is even more essential to understand how crop yield models make predictions depending on their relationship to Earth Observation (EO) indicators. This study presents a state-of-the-art explainable artificial intelligence (XAI) method to estimate corn yield prediction over the Corn Belt in the continental United States (CONUS). We utilize the recently introduced Kolmogorov–Arnold Network (KAN) architecture, which offers an interpretable alternative to the traditional Multi-Layer Perceptron (MLP) approach by utilizing learnable spline-based activation functions instead of fixed ones. By including a KAN in our crop yield prediction framework, we are able to achieve high prediction accuracy and identify the temporal drivers behind crop yield variability. We create a multi-source dataset that includes biophysical parameters along the crop phenology, as well as meteorological, topographic, and soil parameters to perform end-of-season and in-season predictions of county-level corn yields between 2016–2023. The performance of the KAN model is compared with the commonly used traditional machine learning (ML) models and its architecture-wise equivalent MLP. The KAN-based crop yield model outperforms the other models, achieving an R² of 0.85, an RMSE of 0.84 t/ha, and an MAE of 0.62 t/ha (compared to MLP: R² = 0.81, RMSE = 0.95 t/ha, and MAE = 0.71 t/ha). In addition to end-of-season predictions, the KAN model also proves effective for in-season yield forecasting. Notably, even three months prior to harvest, the KAN model demonstrates strong performance in in-season yield forecasting, achieving an R² of 0.82, an MAE of 0.74 t/ha, and an RMSE of 0.98 t/ha. These results indicate that the model maintains a high level of explanatory power relative to its final performance. Overall, these findings highlight the potential of the KAN model as a reliable tool for early yield estimation, offering valuable insights for agricultural planning and decision-making. Full article

Fall armyworm (FAW), Spodoptera frugiperda (J. E. Smith, 1797) (Lepidoptera: Noctuidae), is a major pest in the U.S. and has spread globally, causing severe agricultural losses in different countries. Due to its high mobility and potential for long-distance dispersal, understanding FAW migration is a key tool for forecasting outbreaks and implementing timely management measures. Recent studies using stable hydrogen isotopes indicated reverse (southward) migration of Helicoverpa zea Boddie (Lepidoptera: Noctuidae). Here, we tested the reverse migration hypothesis for FAW in North America. Estimation of the hydrogen isotopic ratio on 324 samples collected in Florida, an intermixing zone at the edge of the continental U.S., indicated evidence of reverse migration in samples of FAW moths. They showed a high probability of origin from the U.S. Corn Belt, with a greater probability of origin in Nebraska, South Dakota, Minnesota, Kansas and Wisconsin. This southward movement provides new insights into the risk of spreading pesticide resistance alleles in this species to southern regions and contributes to the improvement of integrated pest management and insect resistance management programs. Full article

Illinois is a leading state for agricultural production in the United States, and corn production in the state has rapidly increased since the 1970s. Intensification of agriculture has been shown to have impacts on the atmosphere by altering humidity, and changes in land cover and soil moisture have resulted in changes in stability and temperature in the planetary boundary layer. Using descriptive statistics and regression analysis, this study assessed changes in temperature and dew point across different land cover classes, parts of the growing season, and by the geographic location of the station (north vs. south) in Illinois from 2005–2022 using data from 58 hourly weather stations. Overall, dew points are not increasing more rapidly in cultivated agriculture areas compared to other land cover classes in the state. Dew points are increasing across land cover classifications, particularly in the later part of the growing season. Temperatures are not as consistent, with decreases in temperature observed in cultivated agricultural areas and during the peak of the growing season. While dew points are increasing in both the northern and southern regions of the state, temperature increases are only found in the north. Dew point increases in Illinois do not appear to be driven by changing agricultural practices. However, future work should examine additional regions inside and outside of the Corn Belt to determine if changes in land cover and agricultural practices have impacts on the climates of those regions. Full article

Interseeding pastures with annual warm-season grasses may increase forage accumulation and nutritive value. Our objective was to evaluate the effects of seeding rates of crabgrass [Digitaria ischaemum (Schreb.) Schreb. Ex Muhl], sorghum–sudangrass (Sorghum bicolor × S. bicolor var. sudanense), and teff [Eragrostis tef (Zuccagni) Trotter] on the forage accumulation and nutritive value of pastures of smooth bromegrass (Bromus inermis Leyss.), an introduced perennial cool-season grass cultivated for pasture and hay production in the U.S. Western Corn Belt. In spring, before interseeding, forage accumulation averaged 4.03 and 6.39 Mg ha⁻¹ in 2020 and 2021, respectively. In summer, after interseeding, forage accumulation averaged 3.52 Mg ha⁻¹ in 2020 but was not affected by treatment. In 2021, forage accumulation averaged 6.22 Mg ha⁻¹ in sorghum–sudangrass interseeded stands compared to 4.08 Mg ha⁻¹ in non-seeded smooth bromegrass. Interseeding crabgrass and teff had limited effects on forage accumulation and nutritive value. Increasing the seeding rate of sorghum–sudangrass linearly increased yield of crude protein, total digestible nutrients, and dry matter. In the next spring, forage accumulation averaged 8.01 Mg ha⁻¹, and the stands showed no residual effects of the one-time interseedings. Sorghum–sudangrass proved to be the optimum annual warm-season grass for interseeding. Full article

Sorting corn seeds before sowing is crucial to ensure the varietal purity of the seeds and the yield of the crop. However, most of the existing methods for sorting corn seeds cannot detect both varieties and defects simultaneously. Detecting seeds in motion is more difficult than at rest, and many models pursue high accuracy at the expense of model inference time. To address these issues, this study proposed a real-time detection model, YOLO-SBWL, that simultaneously identifies corn seed varieties and surface defects by using images taken at different conveyor speeds. False detection of damaged seeds was addressed by inserting a simple and parameter-free attention mechanism (SimAM) into the original “you only look once” (YOLO)v7 network. At the neck of the network, the path-aggregation feature pyramid network was replaced with the weighted bi-directional feature pyramid network (BiFPN) to increase the accuracy of classifying undamaged corn seeds. The Wise-IoU loss function supplanted the CIoU loss function to mitigate the adverse impacts caused by low-quality samples. Finally, the improved model was pruned using layer-adaptive magnitude-based pruning (LAMP) to effectively compress the model. The YOLO-SBWL model demonstrated a mean average precision of 97.21%, which was 2.59% higher than the original network. The GFLOPs were reduced by 67.16%, and the model size decreased by 67.21%. The average accuracy of the model for corn seeds during the conveyor belt movement remained above 96.17%, and the inference times were within 11 ms. This study provided technical support for the swift and precise identification of corn seeds during transport. Full article

During high-speed corn sowing at 10 km/h, the rapid seed discharge resulting from the high rotation speed of the seed disc escalates the impact force of seeds as they are released from the seed metering device into the seed guiding apparatus, consequently diminishing the overall seeding efficiency of the seeder. This study employed high-speed videography to conduct experiments and optimize parameters for the seed-receiving mechanism of a belt-driven seed guide tube. By changing the clamping wheel speed and seed-receiving angle, the speed change curve and displacement trajectory of seeds under different conditions were obtained and analyzed. The findings demonstrate that the seed speed fluctuation is more stable, and the seed displacement trajectory achieves greater stability at a clamping wheel speed of 560 r·min⁻¹. When the seed-receiving angle is set at 85°, the seed speed fluctuation becomes less apparent, resulting in a smoother seed displacement trajectory. Finally, the experimental results of high-speed cameras are confirmed by field tests. The findings of this study can act as a theoretical basis for the further optimization of the experimental belt-driven seed guide tube. Full article

The microwave polarization index (PI), defined as the difference between vertically polarized (V-pol) and horizontally polarized (H-pol) brightness temperature divided by their average, is independent of land surface temperature. Since soil emission is stronger at V-pol than H-pol and vegetation attenuates this polarized soil signal primarily because of liquid water stored in vegetation tissue, a lower PI will be indicative of more water in vegetation if vegetation emits a mostly unpolarized signal and changes in soil moisture within the emitting depth are small (like during periods of drought) or accommodated by averaging over long periods. We hypothesize that the L-band PI will reveal diurnal changes in vegetation water related to whether plants have adequate soil water. We compare 6 a.m. and 6 p.m. L-band PI from NASA’s Soil Moisture Active Passive (SMAP) satellite to the evaporative stress index (ESI) in the U.S. Corn Belt during the growing season. When $\mathrm{ESI}<0$ (there is not adequate plant-available water, also called plant water stress), the L-band PI is not significantly different at 6 a.m. vs. 6 p.m. On the other hand, when $\mathrm{ESI}\ge 0$ (no plant water stress), the L-band PI is greater in the evening than in the morning. This diurnal behavior can be explained by transpiration outpacing root water uptake during daylight hours (resulting in a decrease in vegetation water from 6 a.m. to 6 p.m.) and continued root water uptake overnight (that recharges vegetation water) only when plants have adequate soil water. Consequently, it may be possible to use L-band PI to identify plant water stress in the Corn Belt. Full article

Within the U.S., there is a growing need to integrate environmentally friendly practices into conventional agriculture. Agroforestry enhances environmental and resource stewardship in agricultural landscapes while offering potential economic benefits to farmers. Despite rising interest, limited information on its application in the U.S. hinders development efforts. A spatiotemporal analysis of current farm operations can provide crucial insights. This study examined patterns of agroforestry and tree crop adoption in the U.S. Corn Belt using USDA Census data (2012, 2017, and 2022) and spatial tools (Global Moran’s I, Local Moran’s I, and Moran scatterplots). The tree crops included in the analysis were chestnut (Castanea spp.), hazelnut (Corylus spp.), improved northern pecan (Carya illinoinensis), elderberry (Sambucus spp.), and pawpaw (Asimina triloba). The results showed increasing farm operations with agroforestry and tree crops over time for all census periods. Agroforestry had the strongest spatial cluster patterns, with Local Moran’s I revealing R2 values rising from 0.30 to 0.35 between 2017 and 2022. Chestnut, hazelnut, and improved pecan had clustered spatial patterns, but had decreasing spatial autocorrelations from 2012 to 2022, while elderberry clustered in 2017 but not 2022. This study reveals an upward trend in agroforestry adoption and the spatial expansion of certain tree crops in the U.S. Corn Belt, highlighting potential for region-specific agroforestry development. The findings offer insights to guide strategies and programs supporting sustainable agricultural practices. Full article

The objectives of this study were (1) to determine the net energy requirements for the maintenance of gestating sows based on indirect calorimetry, and (2) to explore the feasibility of predicting the net energy requirements for the maintenance of gestating sows based on daily heart rate monitoring. In Exp. 1, six Landrace × Yorkshire crossbred reproductive sows with an initial body weight of 229.5 ± 14.9 kg at d 56 of gestation were randomly assigned to six diverse energy feeding levels using a 6 × 6 Latin square design. The experimental diet was formulated using corn, soybean meal, and wheat bran as major ingredients, and the six feeding levels were set as 1.2, 1.4, 1.6, 1.8, 2.0, and 2.2 times metabolizable energy for maintenance (100 kcal ME/kg BW^0.75·d⁻¹), respectively. The animal trial lasted for six periods with 9 days per period, encompassing 5 days of adaptation, 3 days of calorimetry in fed state, and 1 day of calorimetry in fasting state. In Exp. 2, six Landrace × Yorkshire crossbred pregnant sows with an initial body weight of 232.5 ± 12.5 kg at d 64 were fed a corn–soybean meal diet. All sows were tested in a respiratory calorimetry chamber for a 4 day calorimetry test. The heat production of the gestation sows was measured every 5 min using indirect calorimetry, and the heart rate of the gestating sows was recorded every minute using a belt-shape monitor. The results showed that the net energy requirements for the maintenance of gestating sows significant increased as the gestational stage progressed (p < 0.05), and a linear regression model revealed the average net energy requirement for the maintenance of gestating sows was 410 kJ/BW^0.75 d⁻¹ during late gestation (days 70–110). Moreover, the average heart rate of the gestating sows was 84 bpm, and the mathematical model developed to predict the net energy requirements for the maintenance of gestating sows was ${NE}_m(\mathrm{k}\mathrm{c}\mathrm{a}\mathrm{l}/\mathrm{h})={\displaystyle \frac{1990}{1+\mathrm{e}\mathrm{x}\mathrm{p}\left[\frac{136-HR\left(\mathrm{b}\mathrm{p}\mathrm{m}\right)}{43}\right]}}$. In conclusion, the average net energy requirement for the maintenance of sows during late gestation was 410 kJ/BW^0.75 d⁻¹, and the utilization of the heart rate monitoring method was found to provide a relevant, accurate prediction for the net energy requirements of sows. Full article

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. Full article