Agronomy Journal最新文献

Efficient nitrogen (N) management is critical for improving nitrogen use efficiency (NUE) and sustaining corn (Zea mays L.) yields. We evaluated summer cover crops planted after winter wheat (Triticum aestivum L.) to quantify biomass, N content, soil N dynamics, and subsequent corn performance under rainfed conditions in eastern Nebraska. Across 2 site-years (2020–2021 and 2023–2024), eight CC treatments were tested, including six legumes, one grass, one legume mixture, and two no-CC controls. Cover crop biomass ranged from 760 kg ha⁻¹ (cereal rye (Secale cereale L.), Austrian winter pea (Pisum sativum arvense L.)) to 8630 kg ha⁻¹ (sunn-hemp (Crotalaria juncea L.), legume mixture). Hairy vetch (Vicia villosa L.) and soybean (Glycine max (L.) Merr.) had high biomass N accumulation (176 and 188 kg N ha⁻¹, respectively) due to biological N-fixation. Hairy vetch significantly increased soil nitrate-N availability by 154% (preplant) and 111% (in-season) compared to the no-N and cereal rye controls. Corn yield improved significantly (p < 0.001) following legume CCs, with hairy vetch, sunn-hemp, and forage-pea (Pisum sativum L.) outperforming other species. Legumes increased corn yield by 2.50 Mg ha⁻¹ (2021) and 2.23 Mg ha⁻¹ (2024) compared to the N control treatment, contributing 29 and 44 kg ha⁻¹ of grain N, respectively. Apparent N credits from legumes ranged from 32 to 99 kg N ha⁻¹. In contrast, cereal rye reduced yield and N uptake, indicating net N immobilization. These results support legume CCs as a viable strategy to enhance NUE in rainfed corn systems.

Summer pak choi (Brassica chinensis L.) farming is difficult due to insect pressure. The preventive and physiological benefits of insect-proof netting with mesh sizes 20, 40, and 80 on pak choi cultivation were investigated in summer. We found that 20, 40, and 80 mesh nets significantly reduced insect activity (p < 0.05). Aphid populations decreased by 50.84% (20-mesh) and up to 80% (40/80-mesh), whereas Pieris rapae and Plutella xylostella infestations were completely eliminated (100% reduction) in 40- and 80-mesh nets. The net treatments increased shoot weight by 63.94%–67.91%, root weight by 52.63%–52.94%, and total biomass by 63.11%–66.67% over the uncovered control. The nets increased chlorophyll content (65.29%–67.25%) and improved antioxidant enzyme activity (superoxide dismutase, glutathione peroxidase: 85.51–104.38 µmol/min/mg protein; catalase: 41.16–55.62 µmol/min/mg protein) (p < 0.05). Magnesium and potassium concentrations increased by 117%–118% and 106%–121% over the uncovered control, respectively. The statistics show that insect-proof nets provide effective protection against key monitored pests and improve physiological performance in summer pak choi cultivation, with the 40-mesh net providing the best overall pest exclusion and growth promotion.

Plant tissue testing is an effective tool for diagnosing crop nutritional status; however, critical tissue-potassium (K) concentrations have not been established for modern cotton (Gossypium hirsutum L.) cultivars. We aimed to define critical leaf- and petiole-K concentrations at various growth stages for optimal cotton production. Nine fertilizer-K rate (0–187 kg K ha⁻¹) trials were conducted in a randomized complete block design on silt loam soils with very low to above optimum soil-test K in Arkansas, during 2023 and 2024. Leaf and petiole samples were collected from first square through the beginning of boll and fiber maturation, to quantify tissue-K concentrations. At maturity, cotton lint yield was measured. Lint yield was significantly affected by K fertilization (p ≤ 0.10) in seven out of nine trials, where the unfertilized control yield was 35%–79% of the yield-maximizing fertilizer-K rates (56, 75, and 112 kg K ha⁻¹). Leaf- and petiole-K concentrations increased with increasing K availability (soil or fertilization). Tissue-K concentrations peaked at first square and declined throughout cotton reproductive development. Critical leaf- and petiole-K concentrations to maximize yield were 11.1 and 47.3 g K kg⁻¹ at first square, 10.3 and 48.0 g K kg⁻¹ at first flower, and decreased to 5.1 and 7.5 g K kg⁻¹ at eight weeks after first flower, respectively. Petiole-K concentrations had a stronger relationship with relative yield than leaf-K (R² range of 0.31–0.65 for petioles and 0.19–0.53 for leaves). Our results suggest petioles are more accurate for monitoring cotton K nutrition, and growth-stage-specific critical K concentrations are key for accurately assessing cotton K nutritional status.

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.