Simultaneous determination of fumonisins and hydrolyzed fumonisins in corn products and animal feed by automated dual-magnetic solid phase extraction.

A probabilistic analysis of cost-benefit among nitrogen fertilizer application, corn production and drinking water nitrate treatment.

ABSTRACT Heavy reliance on synthetic nitrogen (N) fertilizers has resulted in soil degradation, declining soil productivity, and lower N use efficiency (NUE). Sustainable alternatives such as biochar (BC) and biofertilizers (BFs) offer promising solutions to enhance soil health and support yield improvement. This study investigates the individual and combined effects of BC and microbial BFs on soil properties, corn growth, and yield under controlled greenhouse conditions. A randomized experiment with seven treatments consisting of granular urea, BC, BFs, and their combinations were conducted using clay loam soil. Soil samples collected at four growth stages were analyzed for inorganic N forms, including ammonium-N (NH4 +–N) and nitrate-N (NO3 −–N), as well as organic matter (OM), phosphorus (P), potassium (K), and other physicochemical properties, while plant growth traits, chlorophyll content, biomass, and grain yield were measured. Treatments containing BC and/or BFs significantly increased soil OM and nutrient availability, including N, P, and K. In addition, these treatments improved plant height, leaf number, and chlorophyll concentration, resulting in 14–17% greater biomass and 14–19% higher yield compared to the control. Notably, the integrated application of BC, BFs, and 50% urea treatment increased yield and biomass by 25% and 28%, respectively, values comparable to full urea application, indicating that N fertilizer use can be reduced without compromising productivity. Overall, the results highlight strong synergistic benefits of BC and BFs for sustainable corn production.

Carbon Isotope Ratios as Biomarkers for Added Sugar Intake: Fundamental Limitations that Constrain Valid Application.

Implementing an extensive pest survey is expensive and time-consuming, so optimizing site selection is critical to success. Efficiently allocating resources and maximizing survey coverage in areas where crops are at risk from pests could alleviate economic losses and threats to food security. Therefore, we developed a "site score metric" to quantitatively assess the value of each existing site to the survey. The site score metric is based on (i) historic corn production intensity, (ii) pest count per site, and (iii) distance from the closest site. By evaluating each site based on these factors, we were able to identify which sampling locations are most valuable to the survey. A k-nearest neighbor model was developed to interpolate pest populations from individual trap counts corn fields across the primary corn producing region in the state of Wisconsin. By evaluating the interpolated map of Wisconsin with the site scoring metric, the value of hypothetical additional survey locations can be assessed. An additional k-nearest neighbor model was used to classify similarly scoring regions to identify clusters where additional surveillance would be most valuable. This scoring metric can be used to guide new survey efforts, thereby improving the quality of data collected. Additionally, this could save costs by eliminating low scoring locations.

Sustainable fertilization strategy to enhance phosphorus availability and sweet corn productivity

Nicosulfuron (NIF) stress poses a significant constraint on sweet corn production. Graphene oxide (GO), as an environmentally friendly nanomaterial, serves as a safe agent to enhance crop stress resistance. However, the molecular mechanisms of GO-mediated sweet corn to tolerate NIF remain largely unexplored. This study aimed to investigate the regulatory effects of GO on sweet corn's response to NIF through physiological assessments, comparative transcriptomic analysis, and metabolomic profiling. The findings indicate that the application of GO mitigates the detrimental effects of NIF stress on sweet corn seedlings, as evidenced by enhanced growth metrics, improved chlorophyll fluorescence parameters, and photosynthetic indicators. GO predominantly influences genes and metabolites related to the synthesis of flavonoids, phenylpropanoids, and calcium (Ca2+) signaling pathways to counteract NIF stress in sweet corn. Additionally, key genes associated with peroxidases (POD) and cytochrome P450 monooxygenases (CYP) were identified through the analysis of weighted gene co-expression networks (WGCNA). These genes play crucial roles in the response to NIF stress orchestrated by graphene oxide. In summary, this study demonstrates the synergistic regulatory effect of GO on NIF tolerance in sweet corn, providing valuable insights for developing NIF-resistant sweet corn varieties.

Water and nutrients plays an important role for optimum growth and yield of sweetcorn under semi-arid conditions. During the growing seasons of 2019–20 and 2020–21, an experimental trial was conducted at Rajendranagar, Hyderabad, Telangana, to examine the responsiveness of high-density summer sweet corn to drip irrigation and fertigation regimes. The treatment comprises of three drip irrigation levels (0.6, 0.8 and 1.0 Epan throughout the crop growth period) and four fertigation levels (100 and 125% recommended dose of nitrogen and potassium [RDNK] according to recommendation and crop coefficient curve). Results revealed that growth, cob and green fodder yields of sweet corn increased significantly with increasing irrigation levels up to 1.0 Epan combined with fertigation at 125% RDNK applied according to the crop coefficient curve (I 3 F 4 ) during both years of experimentation. Total crop evapotranspiration (ETc) ranged from 182.3 to 278.1 mm and 170.3 to 245.3 mm across irrigation treatments during 2019–20 and 2020–21, respectively. Crop water use efficiency (CWUE) was significantly higher under the lowest irrigation level (0.6 Epan) compared to 0.8 and 1.0 Epan. Crop coefficient (Kc) values at 1.0 Epan were estimated as 0.44, 0.82, 0.96 and 0.79 during 0–20, 21–40, 41–60 DAS and from 61 DAS to harvest, respectively, in 2019–20, and 0.45, 0.80, 0.95 and 0.79 during the corresponding stages in 2020–21. The physical optimum water requirement was 594.6 and 469.5 mm, producing yields of 13,393 and 12,752 kg ha −1 during 2019–20 and 2020–21, respectively. The economic optimum was slightly lower at 585.7 and 464.6 mm, resulting in yields of 13,389 and 12,749 kg ha −1 during the respective years. The study concludes that drip irrigation at 1.0 Epan combined with fertigation at 125% RDNK applied as per crop coefficient curve is optimal for maximizing high-density summer sweet corn productivity under semi-arid conditions.

Agricultural production constitutes a primary source of rural income in the Sistan region, with its sustainability contingent upon the ecological resilience of both agricultural systems and their supporting ecosystems. In this study, we employed emergy footprint analysis as a methodological framework to assess the ecological sustainability of diverse cropping systems—including wheat, barley, corn (grain and forage), alfalfa, canola, watermelon, melon, mung bean, lentil, onion, garlic, fennel, cumin, and black seed—using data from the 2021 agricultural year. Input and output data were collected through farmer interviews and meteorological records using a stratified random sampling approach. By converting material and energy flows into solar emjoules (sej), we evaluated key sustainability indicators: emergy biocapacity (EBC), emergy footprint (EFP), ecological surplus/deficit (EED/EES), ecological pressure index (EPI), ecological footprint intensity (EFI), and the ecological-economic coordination index (EECI). These indicators collectively provide a comprehensive assessment of the balance between ecological supply and human demand. Results revealed substantial variability across counties and crop types. Wheat production in Zahak exhibited the highest emergy carrying capacity, while mung bean in Hamoun showed the lowest. Wheat systems in Zahak also demonstrated the largest ecological footprint due to elevated input consumption. Notably, Zahak wheat production showed the greatest ecological surplus, whereas grain corn production in Hirmand was characterized by robust safety margins. Comparative analysis across counties identified garlic production in four counties and wheat in Hirmand as ecologically deficit systems under high pressure, whereas grain corn in Hirmand and green cumin in other counties emerged as more sustainable alternatives. These findings highlight a lack of ecological and economic balance across most examined systems. The emergy footprint framework proves effective for diagnosing sustainability challenges, and we recommend enhanced resource efficiency—particularly through precision nutrient management and location-specific guidelines—to mitigate both ecological and economic costs in these systems.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-026-49480-5.

Seasonal and diurnal variations in soil temperature play a critical role in baby corn production by influencing nutrient uptake and crop development. This study evaluated the effects of sowing windows, crop geometry and mulching on soil temperature dynamics and yield of baby corn. A field experiment was conducted at Tamil Nadu Agricultural University, Coimbatore, during the winter (January–April) and kharif (June–September) seasons of 2022 using a split-plot design with 3 replications. Results revealed that sowing windows, crop geometry and mulching significantly affected soil temperature at both the surface and sub-surface (15 cm depth) during both seasons. Late sowing (D₃) consistently recorded the highest surface soil temperatures (27.5–29.1 °C at 7:22 hr and 40.1–41.2 °C at 14:22 hr) during winter and kharif, followed by mid (D₂) and early sowing (D₁). Wider spacing (60 × 30 cm) without mulching resulted in higher surface and sub-surface soil temperatures compared to closer spacing and mulched treatments. Sub-surface soil temperature at 15 cm depth was also highest under late sowing and wider spacing without mulching across both seasons. Correlation analysis indicated a significant negative relationship between soil temperature and baby corn yield, with correlation coefficients ranging from -0.57* to -0.98** in winter and -0.60* to -0.94** in kharif 2022. The study demonstrates that appropriate adjustment of sowing time, crop geometry and mulching practices can effectively regulate soil temperature and enhance baby corn productivity under varying seasonal conditions.

Nitrogen management and detasseling are the most important agro-techniques of baby corn for achieving higher yields. Detasseling is a recommended technique in baby corn reduces competition between tassel and silk for nutrients and improves yield. Baby corn is a fast-growing crop requires more nitrogen (N); the duration and yields of winter crop is usually higher than other seasons. We investigated the combination of different detasseling times and various nitrogen levels on baby corn yield and profitability. The experiment was laid out in winter season including three detasseling time and four nitrogen levels. Results indicate that detasseling immediately after tassel emergence with 33 % more N (200 kg N ha-1) over the recommended dose of N (RDN) improved the productivity traits, quality and monetary returns. Detasseling immediately after tassel emergence enhanced the baby cob and baby corn yield by 11.4% and 15.2%, respectively over no detasseling. Results suggest that early detasseling with application of higher nitrogen is advantageous to augment the productivity and profitability of winter baby corn under irrigated ecosystem of eastern Uttar Pradesh.

Tar spot, caused by Phyllachora maydis Maubl, has significantly impacted U.S. corn production since its first detection in 2015. The elusive nature of this fungus has hindered advancement in disease characterization, especially pertaining to its spatiotemporal development. This study provides an in-depth analysis of field-scale epidemics by considering both temporal and vertical dynamics to ascertain the spatiotemporal intensification of this disease. The analysis involved generating high-resolution severity data across four growing seasons (2021 to 2024) in two production-style corn fields in northwest Indiana. We then applied population growth models and Markov chains to parameterize the temporal and vertical dynamics of tar spot progression within the corn canopy. From this, we found three distinct epidemiological phases: an establishment phase characterized by sporadic onset with <0.5% severity, a lag phase with widespread low severity (0.5 to 1%), and an exponential phase with rapid severity increases exceeding 40% in some cases. Beyond the conventional "bottom-up" paradigm, we observed diverse infection-like patterns influenced by canopy position, onset timing, and corn growth stage. Exponential growth models described severity intensification with an average apparent infection rate of 0.20/day, which was applicable across canopy positions, locations, and years. An aggregated Markov chain model, built from the 2021-2023 data, accurately estimated the 2024 epidemic's vertical-temporal progression and thus validated the framework developed from this study. Ultimately, this approach supports improved surveillance for targeted management by establishing a foundation for real-time detection and probabilistic modeling to aid in the enhancement of crop production.

This article presents the results of a comparative study of impact of variable fertilization on forage corn productivity in 2022 and 2025 in the production conditions in Western Siberia. The relevance of this study conditioned by the need for rational fertilizer use in the face of rising fertilizer costs and climate variability. The aim of research was to comparatively evaluate the effectiveness of traditional uniform and variable (precision) fertilizer application methods based on agrochemical field mapping. Three field experiment options compared: control (no fertilizer), traditional application of organic and mineral fertilizers and variable fertilization with dose adjustment based on spatial heterogeneity of soil fertility. It was found that the differentiated system provided the maximum relative yield increase (+36%) in the drought-prone of 2025, demonstrating high adaptability to stressful weather conditions. Furthermore, this technology significantly reduced within-field yield variability (coefficient of variation 1.83% versus 10.90% in the control), ensuring a more uniform agricultural background. The results confirm that differentiated fertilization is a resource-saving and environmentally friendly element of precision farming, contributing to the stabilization of forage crop production in a variable climate and cost optimization.

Soil quality (SQ) is a key determinant of agricultural productivity and environmental sustainability, yet its assessment is challenged by the diverse functions of soil and the absence of universally accepted indicators. This study aimed to develop a crop yield-correlated minimum dataset (MDSCorr) for SQ assessment and evaluate its performance across multiple U.S. regions. Over a five-year period, data (n = 576) from geo-referenced composite soils at 0–30 cm depth were collected from gypsum amended cover crop integrated corn-soybean rotation experimental sites at Shorter (Alabama), Farmland (Indiana), and Hoytville and Piketon (Ohio). Using the available soil and crop yield data, six scoring functions (four linear and two nonlinear) and three indexing approaches (additive, weighted additive, and Nemoro) were evaluated to calculate the SQ index (SQI). The MDSCorr identified a reduced set of key soil properties most strongly associated with corn productivity, including total organic carbon, microbial biomass carbon, active carbon, total nitrogen, and aggregate-related physical indicators explaining SQ. Using different scoring and indexing approaches, the calculated SQI values at the Indiana site, used as a reference ranged from 0.31 to 0.6. Among the approaches, linear scoring with threshold limits and additive indexing produced the most consistent SQI values, reducing variability to within ±1% compared to the total dataset (TDS). The MDSCorr-based SQI showed strong positive correlations with the TDS-derived SQI (R² = 0.53 to 0.93) and outperformed the principal component analysis-based MDS (MDSPCA) in terms of reliability and consistency. Based on MDSCorr-derived SQI values, the relative SQ rankings for the four study sites were: Hoytville > Indiana > Alabama > Piketon. While calibration and validation are recommended across geographic regions and cropping systems, the MDSCorr approach, when combined with linear scoring and additive indexing, has the potential to provide a simplified and transferable framework for SQ assessment.

Soil quality (SQ) is a key determinant of agricultural productivity and environmental sustainability, yet its assessment is challenged by the diverse functions of soil and the absence of universally accepted indicators. This study aimed to develop a crop yield-correlated minimum dataset (MDSCorr) for SQ assessment and evaluate its performance across multiple U.S. regions. Over a five-year period, data (n = 576) from geo-referenced composite soils at 0-30 cm depth were collected from gypsum amended cover crop integrated corn-soybean rotation experimental sites at Shorter (Alabama), Farmland (Indiana), and Hoytville and Piketon (Ohio). Using the available soil and crop yield data, six scoring functions (four linear and two nonlinear) and three indexing approaches (additive, weighted additive, and Nemoro) were evaluated to calculate the SQ index (SQI). The MDSCorr identified a reduced set of key soil properties most strongly associated with corn productivity, including total organic carbon, microbial biomass carbon, active carbon, total nitrogen, and aggregate-related physical indicators explaining SQ. Using different scoring and indexing approaches, the calculated SQI values at the Indiana site, used as a reference ranged from 0.31 to 0.6. Among the approaches, linear scoring with threshold limits and additive indexing produced the most consistent SQI values, reducing variability to within ±1% compared to the total dataset (TDS). The MDSCorr-based SQI showed strong positive correlations with the TDS-derived SQI (R² = 0.53 to 0.93) and outperformed the principal component analysis-based MDS (MDSPCA) in terms of reliability and consistency. Based on MDSCorr-derived SQI values, the relative SQ rankings for the four study sites were: Hoytville > Indiana > Alabama > Piketon. While calibration and validation are recommended across geographic regions and cropping systems, the MDSCorr approach, when combined with linear scoring and additive indexing, has the potential to provide a simplified and transferable framework for SQ assessment.

Cornsilk flies, Euxesta eluta and Chaetopsis massyla (Diptera: Ulidiidae), are serious economic pests affecting sweet corn production in Florida. As a basis for development of enhanced trapping strategies, the sensory and behavioral basis for response to color was examined. Using electroretinograms, spectral sensitivity curves for both species revealed broad curves with a peak in the UV (350 nm) and the green region (500–550 nm) of the spectrum. Curves for males and females of each species were relatively similar in shape. Using pigment templates, the measured curve for E. eluta was matched by pigment combinations with maximum sensitivity at 350, 430, 500 and 560 nm in a ratio of 25:21:25:29. Similarly, the curve for C. massyla was best matched by pigments with maximum sensitivity at 350, 430, 500 and 560 nm in a 28:18:27:27 ratio. Laboratory behavioral assays were conducted to evaluate if attraction occurred in response to the color or the brightness of a target. Attraction responses were evaluated in paired tests to blue, green and yellow cards paired with gray cards of matching brightness. Despite relatively similar visual pigments, the two species differed greatly in their behavioral attraction. Euxesta eluta was only attracted to yellow cards at low or high brightness levels, to mid-level bright blue cards and not attracted to green cards at any brightness level. In contrast, C. massyla was highly attracted to yellow and green cards compared to gray cards of the same brightness but avoided blue cards. These differences in response to color and brightness are important for interpretation of surveillance results as well as development visual traps targeted for these species.

Atrazine, a triazine-class herbicide widely used in U.S. Corn and soybean production is highly soluble, persistent and prone to runoff. It poses risks such as reduced aquatic productivity and biological endocrine disruption. Observation-driven geostatistical models can extend sparse monitoring networks by estimating contaminant levels across space and time. We evaluated all available Iowa surface water atrazine measurements from 1986 to 2025 ( n = 13,247), identifying sparse and inconsistent sampling before 2000 and after 2004, with a period of sustained high sampling intensity from 2000 to 2004 (&gt;1,000 samples per year). In this study, we applied a geostatistical, Bayesian Maximum Entropy (BME) framework to model log-transformed atrazine concentrations in Iowa surface waters from 2000 to 2004 using 6,478 observations from 673 monitoring sites. We compared a traditional spatial-only geostatistical framework with a fully spatiotemporal BME model incorporating both spatial and temporal data. Spatiotemporal dependence was modeled using an additive, nested space–time, exponential covariance structure. This allows spatial and temporal autocorrelation to jointly inform prediction while preserving localized contamination patterns. Model performance was evaluated using leave-one-out cross-validation. Results show spatiotemporal BME significantly outperformed spatial-only approaches, improving predictive power ( R 2 = 0.73 vs. 0.64) and reducing mean squared error (0.318 vs. 0.432) log µg 2 /L. Performance gains were largest for error metrics sensitive to extreme values, with spatiotemporal BME substantially reducing mean squared error relative to spatial-only models, indicating improved representation of episodic high-concentration events. Our findings demonstrate atrazine exposure risk appears to be predictable and likely aligned with seasonal application, chemical half-life, and delayed runoff. These processes likely produce localized hotspots often exceeding the U.S. EPA’s 3 µg/L drinking water standard. Atrazine correlation showed strong spatial (mobility 25–150 km) and temporal persistence (~20–50 days). This approach complements mechanistic models, offering a flexible, data-driven framework for environmental health monitoring where data are sparse or irregular.

A disease complex: crop losses and economic impact of corn stunt diseases on Brazilian corn production

Government intervention through fertilizer subsidies is a pivotal instrument for national food security, particularly for corn commodities in Gorontalo. This study evaluates the effectiveness of fertilizer subsidy policy implementation based on the "6T" principles (Right Type, Quantity, Price, Place, Time, and Quality) and its impact on corn production in Pulubala District. Using a quantitative explanatory design, 99 corn farmers were surveyed through proportional random sampling. Data were analyzed using Spearman Rank correlation to assess relationships between systemic variables: Targeting Accuracy (), Quantity Accuracy (), Timeliness (), and Physical Availability (). Findings reveal that () and () have significant positive correlations with production. Conversely, timeliness () and physical availability () showed no statistical significance (). This anomaly suggests a "Governance Paradox" where physical stock availability does not guarantee accessibility due to bureaucratic rigidities within the e-RDKK digital system. The study recommends database synchronization and logistics debureaucratization to ensure sustainable agricultural output.

This article analyzes how global political instability is reshaping the structure of the international grain market. The study focuses on the geopolitical factors currently driving production specialization among major grain and oilseed exporters. It traces the evolution of agricultural policies in the United States and the People's Republic of China, examining their impact on the production volumes of soybeans, corn, and other key commodities. Based on Chicago Mercantile Exchange (CME) futures price trends for soybeans and corn, the research finds that soybeans currently offer higher economic returns than corn. Furthermore, the rising cost of mineral fertilizers is exerting significant downward pressure on future corn production, undermining its profitability and competitive standing. Analysis of U.S. planting patterns over the last decade, based on USDA data, indicates an increase of about 5% in corn’s share of total acreage. This structural distortion suggests an imminent need for acreage correction to restore a more sustainable distribution. Analysis of classic indicators on the distribution of area structure between corn and soybean crops suggests a moderate advantage in the future in favor of soybeans, although long-term demand for this crop remains in doubt due to a persistent change in the structure of global trade flows. The article specifically highlights the implications of these global shifts for the Ukrainian grain market. As global producers pivot toward soybeans and the corn supply tightens, reduced competition in international markets is expected to create favorable conditions for Ukrainian grain exports. The competitiveness of Ukrainian producers is further bolstered by quality advantages, including non-GMO status, ISCC certification, and the ability to differentiate products by end-use (feed, biofuel, or food grade). These findings emphasize the need for a strategic review of Ukraine’s grain production structure in light of global geopolitical and economic shifts. The findings may support the development of agricultural policies aimed at strengthening Ukraine’s position in the global grain market.