Agronomy Journal最新文献

Identifying optimal fertilizer-sulfur (S) sources is crucial for effective soil-S fertility management. The study evaluated soybean [Glycine max (L.) Merr.] yield and tissue-S concentration responses to five fertilizer-S sources, ammonium sulfate, Sul4r-Plus/Gypsum, K-Mag, Tiger90CR, and Poly4, along with a no-S check across 9 site-years in Louisiana from 2023 to 2024. Each trial followed a randomized complete block design with four replications at 3 site-years and five replications at 6 site-years. Positive yield responses to fertilizer-S were recorded at only Site-Years 4 and 7, where soil-S concentrations at 0- to 15-cm depth were 9.1 and 12.0 mg kg⁻¹, respectively. At responsive site-years, Tiger90CR significantly increased yield compared to no-S check when applied at planting. Gypsum and K-Mag also consistently increased yield when applied at the full-flowering stage and showed the potential of boosting yield if applied at planting. Leaflet-S concentrations at the full-flowering stage align with the yield response from gypsum and K-Mag treatments. Due to the limited number of responsive site-years, it is challenging to identify a single fertilizer-S source that consistently improves soybean yield. However, ammonium sulfate, the most used S source in Louisiana and other states, did not consistently enhance yield or leaflet-S concentration in the responsive site-years. Furthermore, the lack of leaflet-S concentration increase for the preplant application of Tiger90CR at both responsive site-years suggests that Tiger90CR may require time to dissolve and release S for plant uptake. Continued research on S sources is essential to identify the most suitable options for soybean production in the Deep South climatic conditions.

Exposed soil, due to low vegetation cover or in open canopy crops, influences scene reflectance derived from remotely sensed data. An experiment was conducted in College Station, TX, to investigate the potential of six unmanned aerial systems (UASs)-derived and proximally sensed vegetation indices (VIs) in suppressing soil background brightness of four treatments in 2020 and 2021. The treatments were dry soil, dry soil with winter wheat (Triticum aestivum L.) crop residue, wet soil (WS), and wet soil with winter wheat crop residue (CRWS) in 2020. In 2021, WS and CRWS were replaced with dry sand and dry compost (DC). The VIs were calculated from remotely sensed data of treatment plots. Cotton (Gossypium hirsutum L.) canopy cover (%) on different dates of UAS flight was extracted using unsupervised classification. Factors such as shadows, crop residue, soil moisture, and uneven canopy growth influenced the scene reflectance. The shadow on the soil decreased the soil background reflectance to <10%. Soil background variations minimally impacted the UAS-derived VIs. Soil wetness resulted in higher normalized difference vegetation index (NDVI) than dry treatment plots at an estimated mean canopy cover > 30% in 2020. Similarly, higher NDVI was observed for DC treatment plots at an estimated mean canopy cover of <35% in 2021. The perpendicular vegetation index was least influenced by canopy cover or soil background variations. The study suggests that UAS can be used for large-scale research without being affected by soil variability when vegetation cover is above 30%.

Selecting the correct variety of a cereal crop is a vital first step, influencing all subsequent crop husbandry decisions and determining the potential successfulness of a crop in both economic and agronomic terms. Growers make this decision by considering farm location, local trial results and knowledge, and their priorities. Balancing these factors, information sources, and priorities is challenging for growers. Complex multi-factor analyses are required in the decision-making process; while computer-based decision support systems (DSSs) have been created to support this process, the localized nature and poor user-friendliness of the DSSs have resulted in limited grower adoption. This study aims to design a user-centric DSS to assist growers in selecting an optimal cereal variety for their priorities in each field. The system integrates local factors with independent cereal variety evaluation data, such as national “recommended lists.” It can be applied globally where such listings are available. Two use cases are described with Ireland's Department of Agriculture, Food, and the Marine and New Zealand's Foundation for Arable Research wheat (Triticum aestivum L.) recommended lists. Characteristic evaluation scores for each variety were normalized and weighted based on user priorities and field location using the simple additive weighting method. Growers’ local yield history of each variety, if available, can be included to create a site-specific DSS. The outcome is an overall ranking of the varieties present on the recommended list, enabling the user to make an informed decision on their chosen variety while accommodating market demands, seed availability, and their own preference.

Kernza intermediate wheatgrass (IWG) [Thinopyrum intermedium (Host) Barkworth & D.R. Dewey] is a perennial grain and forage crop with novel dual-use potential. Grazing IWG forage and/or intercropping IWG with legumes can increase total annual forage yields, but the effect of grazing timing on grain yield needs to be understood to maximize producer returns and the productivity of the perennial stand. In this study, we compared Kernza grain and forage yields under different cattle grazing timing treatments (spring, fall, or spring and fall) with ungrazed IWG stands, in both IWG monocultures and IWG–legume intercrops. We established the experiment in the fall of 2016 at Morris, MN, and Lancaster, WI, and collected data over 3 years. In the first grain production year, grazing spring vegetative regrowth reduced Kernza grain yield compared with ungrazed stands in both Minnesota (213 vs. 360 kg ha⁻¹, respectively) and Wisconsin (821 vs. 1030 kg ha⁻¹, respectively). However, grazing fall regrowth after summer grain and straw harvest did not negatively affect grain yield in the following year compared to the ungrazed control. Intercropping IWG with legumes increased accumulated forage vegetative regrowth in Wisconsin, but not in Minnesota. Overall, our study confirms IWG's potential as a dual-purpose crop under grazing management and recommends fall grazing to minimize adverse effects on subsequent grain yields. Future research should focus on refining grazing strategies to maximize dual-use productivity.

Intercropping has the potential to help adapt arable agriculture to climate change, but its effects on soil carbon and crop production in temperate agroecosystems remain uncertain. This study examined the effects of temperate intercropping systems on soil carbon and crop productivity. The study focused on two barley (Hordeum vulgare L.) cultivars with contrasting phenotypic traits (high-yielding vs. water stress-tolerant) intercropped with pea (Pisum sativum L.) and their three corresponding monoculture systems, grown without agrochemical inputs. During a 2-year field experiment in Scotland, soil properties were investigated on six occasions at two depths, upper (<5 cm) and lower (25–30 cm) topsoil. Crop yields and grain quality (i.e., grain carbon and nitrogen concentrations) were also assessed. Despite peas failing before harvest, intercropped barley exhibited yield gains of 90–132 g m⁻² based on net effect metric and up to 10% in partial land equivalent ratio compared to barley monocultures. By the second year, total carbon concentration increased by 8.1% in the upper topsoil in intercrop of high-yielding barley cultivar compared to its monoculture (23.6 g kg⁻¹). Also, grain carbon and nitrogen concentrations were higher for intercropped barley than monocultures, with the stress-tolerant cultivar increasing grain carbon concentration by 5.7% over its monoculture. These findings demonstrate for the first time in temperate agroecosystems that low-input barley-pea intercropping could at least maintain or increase soil carbon and grain quality without compromising yields but dependent on barley cultivar traits. Intercropping can therefore potentially help adaptation to and mitigation of climate change in agroecosystems.

Intercropping offers a sustainable path to yield stability and resource efficiency, but its success hinges on managing species-specific trade-offs. This study introduces a machine-learning framework to decode the complex interactions in a pea (Pisum sativum L.) and cucumber (Cucumis sativus L.) intercropping system. By applying Random Forest regression, Neural Network sensitivity analysis, and Pareto ranking to a comprehensive dataset, we identified the primary agronomic drivers. Our analysis revealed that crop biomass, nitrogen dynamics, and soil health were the most influential predictors of system performance. A key finding was the competitive asymmetry: pea emerged as the dominant species, as quantified by compatibility indices (competitive ratio, Aggressivity [aggressivity index]), yet this competition slightly reduced cucumber yield compared to its sole crop. Despite this, the system achieved a significant land-use advantage, with a land equivalent ratio >1, demonstrating that the overall synergy and yield benefit for pea result in superior resource use efficiency at the system level. Sensitivity analysis further highlighted crop water content and pest management as critical, manageable factors for optimizing outcomes. This work demonstrates the power of machine learning to move beyond trial-and-error, providing a data-driven blueprint for designing efficient and sustainable vegetable intercropping systems.

Agricultural systems that enhance soil health are essential for conserving biodiversity and ensuring the sustainability of food, water, and energy resources. Practices such as reduced tillage and cover cropping are increasingly adopted by farmers seeking long-term production efficiencies and conservation benefits. We conducted interviews with 100 maize and soybean farmers who had used conventional practices in the past but later (1) used reduced tillage but not cover crops or (2) used reduced tillage and cover crops. We assessed the comparative profitability of conventional farming versus reduced tillage with or without cover crops. A partial budget analysis revealed that reduced tillage with or without cover crops increased average net farm income by US$132 ha⁻¹ for maize (Zea mays L.) and $111 ha⁻¹ for soybean (Glycine max (L.) Merr.). Gains were from reduced production expenses—$60 ha⁻¹ for maize and $41 ha⁻¹ for soybean—and increased yields of 475 kg ha⁻¹ for maize and 197 kg ha⁻¹ for soybean. Farm size was not related to changes in net income, but, for maize, longer adoption was. Net expense changes were not significant between farmers using and not using cover crops. Yields remained similar, resulting in no significant difference in net income. Farmers using cover crops reported significantly lower fertilizer and pesticide expenses, which may reduce environmental externalities. This research suggests that reduced tillage with cover crops may provide economic and environmental benefits, with farmers reporting increased net income while simultaneously reducing input costs and potential environmental impacts through lower fertilizer and pesticide use.

Crop models are essential tools to understand risks, vulnerabilities, and uncertainties in agricultural systems. Advancements in digital data acquisition and accessibility of crop and land area data with larger spatial coverage and higher spatial resolutions necessitate corresponding developments in flexible multi-scale crop modeling application framework to facilitate decision-making and policy formulation from sub-national to global levels. While a few tools and approaches exist for spatial applications of crop models, their lesser flexibility owing to dependency on external programs, portability issues, complexity in files setup, and limited functionality thwarts users to employ crop models at larger scales. This paper aims to introduce Pythia, a novel gridded modeling framework for Decision Support System for Agrotechnology Transfer (DSSAT)-cropping system model (CSM), and demonstrate its application. The objectives are to explain Pythia design, execution workflow, and to show its main functionalities. Inputs to the Pythia framework include (i) point vector files that specify the sites of weather data and simulation points, (ii) a raster map of soil-profile identity numbers, (iii) a raster map of crop area (iv) a DSSAT FileX template, and (v) a configuration file to provide references to the required model input files and databases, and to set up the dynamic portions of the FileX template. A case study from maize cropping system in Ghana is used to demonstrate the applications of Pythia. Flexibilities in spatial coverage and parameterizing model inputs in Pythia provide DSSAT-CSM users a useful tool to run spatial simulations in local machines and in high-performance computing environment.

The accurate estimation of soybean (Glycine max) stand establishment is essential for evaluating crop emergence and informing early-season management practices. Recent advances in unmanned aerial vehicle (UAV) imagery and computer vision offer opportunities to automate plant population assessments; however, limited information exists on their accuracy in soybeans. This study evaluated two commercial UAV-based plant counting platforms, a point-based (convolutional neural network–derived) and a line-based (Hough transform–derived) approach across two growing seasons, three flight altitudes (15.2, 45.7, and 91.4 m), and seven plant removal treatments, including a control (no removal). UAV imagery was collected at 7- to 10-day intervals from 10 to 36 days after planting (DAP), and predictions were compared to manual on-ground counts. The point-based method provided the highest accuracy (within ±12% of ground-truth; R² = 0.81) when imagery was collected between 14 and 20 DAP at 15.2-m altitude. Accuracy declined beyond 27 DAP as canopy overlap increased. The line-based method remained more stable across altitudes and later growth stages but consistently overestimated plant counts, particularly in dense and narrow row canopies. Incorporating on-ground calibration areas improved accuracy by an average of 28% and up to 48% for the line-based approach in narrow rows. Row spacing and plant removal patterns had minimal effects on prediction error, although short repeating gaps were poorly detected by the line-based method. Overall, UAV-based plant counts in soybean are feasible and dependable when flights are timed during early vegetative growth and supported by calibration, providing a practical tool for in-season management and field-based crop monitoring.

Efficient nitrogen (N) fertilizer management is essential for advancing maize production, particularly in sandy soils of subtropical regions like Suwannee Valley, Florida, where NO₃─N leaching poses environmental risks. A 30-year simulation using a calibrated and evaluated Crop Environment Resource Synthesis-Maize model assessed the effects of N sources, rates, application timing, planting dates, and water regimes on grain yield (GY) and NO₃─N leaching. Results showed that moderate to high N rates (202–404 kg ha⁻¹) achieved the highest yields but increased NO₃─N leaching by 33%–38% compared to the recommended N rate (RNR) (269 kg N ha⁻¹). Early planting (March 1–22), split N applications, and precise irrigation (80%–90% of maximum available water [MAW]) improved yield up to 25% and reduced NO₃─N leaching up to 30%. Under rainfed conditions, a 50% reduction in the RNR led to a negligible change in yield but decreased NO₃─N leaching by 35%, while it reduced yield by 15%–30% under irrigated conditions. Optimal practices, including splitting N into five times, irrigation at 90% of MAW, and March 8 planting, achieved the highest yields (10,182–11,925 kg [dry matter] ha⁻¹) and the lowest NO₃─N leaching (60–63 kg ha⁻¹). Combined analysis revealed that the interaction between N rate and water management was significant for yield, N uptake, and NO₃─N leaching (p < 0.01). Irrigation at 90% of MAW with 100% of the RNR maximized yield (12,422 kg ha⁻¹) and N uptake (340 kg ha⁻¹), and reasonably lower NO₃─N leaching (40 kg ha⁻¹). These findings highlight the critical role of integrated management practices to improve GY while minimizing environmental impacts.