Agronomy Journal最新文献

In the United States, agriculture accounts for approximately 10% of total greenhouse gas (GHG) emissions, including contributions from potato (Solanum tuberosum L.) production, a staple crop in the American diet. However, limited research has focused on recent trends in US potato production, particularly the contribution of different agricultural inputs and their role in GHG emissions. This study analyzes trends in US potato production using over two decades (1999/2000–2022) of annual survey data from the United States Department of Agriculture/National Agricultural Statistical Service. Key areas of analysis include planted and harvested area, yields, total and unit sale prices, and input usage. The data are further used to estimate GHG from potato production through the Cool Farm Tool for 2000 and 2022. Our findings reveal a 34% and 32% decline in planted and harvested area, respectively, alongside a 22% reduction in total production across all market segments. Notably, yield increased by 15% in 2022 compared to 2000. The overall decrease in potato production aligns with sharp increases in unit price and total potato sales after adjusting for inflation, which rose by 54% and 20%, respectively. Inputs such as nitrogen, phosphorus, herbicides, and insecticides showed consistent reductions in per-hectare and total annual application, whereas potassium and fungicide usage increased. Yield improvements and reduced input usage led to a 39% decrease in total estimated emissions and a 20% reduction in emissions intensity by 2022 compared to 2000. The study highlights site-specific nutrient management and technologies like low-emission fertilizers, renewable energy, carbon sequestration practices, and breeding as future investment priorities.

Alabama, located in the northern subtropics, is the third-largest producer of peanut [Arachis hypogaea (L.)] in the United States. Historically, herbicides have been the primary means of weed control in peanut. However, increasing cases of herbicide-resistant weeds and a lack of commercially available herbicide-tolerant technology have limited the herbicide options for weed control in this crop. There is an urgent need to integrate non-chemical tools to prolong the effectiveness of the existing weed management program in peanut. A 2-year study in a split-split plot design was conducted at the Wiregrass Research and Extension Center, Alabama, for investigating integrative and individual effects of row orientation, mulch, and row spacing, in conjugation with a uniform, standard herbicide program, on weed control and yield in peanut. In this study, crop rows planted in east-west orientation allowed least weed emergence in both years, closely followed by the northeast-southwest (NE-SW), as compared to other row orientations. However, the NE-SW orientation yielded greatest across the years. Row spacing did not influence weed density but affected weed biomass by influencing canopy closure timing as revealed by leaf area index and normalized difference vegetation index (NDVI) data. Mulching influenced both weed density and biomass, especially early in the season. Based on the current study, the NE-SW row orientation along with mulch or cover optimized early-season weed suppression and yield in Alabama peanut fields.

Agriculture is the essential human activity and the most widespread human interaction with the environment. It connects all—through the food we eat, the land we rely on, and the people who produce it. The purpose of this paper is to begin a conversation on the role ethics has and ought to play in preparing future agricultural professionals.

Machine learning (ML) is increasingly being used to enhance yield predictions and optimize agronomic practices in sub-Saharan Africa. Yet, understanding how these models generalize across heterogenous ecological context remains unresolved. This study, conducted in Ghana, evaluates the predictive performance of four ML models, namely, random forest (RF), support vector machine (SVM), k-nearest neighbors (KNN), and extreme gradient boosting (XGBoost) for predicting maize yield and agronomic efficiency—defined as the increase in yield per unit of nutrient applied. It also compares variable importances identified by these models and how they influence yield and agronomic efficiency. The analysis used 4496 georeferenced maize trial datasets from various agroecological zones across Ghana, incorporating 35 variables related to soil properties, climate, topography, crop management, and fertilizer application. Model performance was assessed using three cross-validation techniques: leave-one-out, leave-site-out, and leave-agroecological-zone-out. Accuracy was measured using mean error, root mean square error (RMSE), and model efficiency coefficient. When evaluated under leave-one-out cross-validation, XGBoost consistently achieved the highest predictive accuracy with the lowest RMSE for yield (639.5 kg ha⁻¹) and for agronomic efficiency of nitrogen (11.6 kg kg⁻¹), which is moderate given the high variability in on-farm nutrient response. RF also performed well, while KNN and SVM showed poor extrapolation under stringent validation. Nitrogen application rate, rainfall, and crop genotype were consistently identified as the most influential explanatory variables across all models, providing insight into key drivers of productivity. These findings demonstrate the power of ML techniques in supporting agricultural planning and improving maize production in sub-Saharan Africa.

Soybean [Glycine max (L.) Merr.] farmers have shown increasing interest in using substances or microorganisms purported to enhance plant growth and development as plant biostimulant for seed treatment (BST). Field tests of soybean biostimulants in Brazil and the United States have shown inconsistent results in increasing crop yield. Additionally, there are substantial differences in the BST registration and regulation processes in Brazil compared to the United States. Therefore, the objectives of this literature review are to (1) synthesize published research articles on the influence of biostimulant products that contain the commonly used microorganisms of the genera Azospirillum, Bacillus, and Bradyrhizobium for seed treatment on soybean seed yield in Brazil and the United States and (2) compare the BST registration differences between the two countries. After synthesizing 40 papers, we found that biostimulants more frequently increased soybean yields in Brazil compared to the US field trials. One existing limitation is the absence of a clearly defined, unified, science-based regulatory pathway for BST products in the United States. Thus, the lack of regulation in the United States opens space for commercializing products without supporting data. In Brazil, the Ministry of Agriculture and Livestock has established legislation for registering, producing, and commercializing BST. Overall, some of the inconsistent benefits identified in the US literature may be partially attributed to the need for improvements in product registration and quality tests. Additionally, the quality tests should be not only at the microbiological level but also at the agronomic level using research-based evidence from independent field trials.

This study applies explainable artificial intelligence (XAI) to analyze the impact of inter-year variation in weather conditions on yields of oilseed sunflower (Helianthus annuus L.) across the United States. By integrating historical county-level yield data from 1976 to 2022 with monthly meteorological data over the same period, we identified key weather predictors influencing sunflower yields at national and state levels along with critical yield-sensitive threshold temperature and precipitation values that predict reduced yield. Across the sunflower production range, the most critical climate variables identified are July and August maximum temperatures and total precipitation, reflecting yield vulnerability to summer heat waves and drought during budding and flowering. Secondarily, overly cool temperatures during spring planting and establishment (May–June) reduce yields, as do overly cool end-of-season temperatures during seed maturation and harvest (September–October), indicating risk of frost or insufficient growing degree days to support plant development. Winter precipitation and temperatures were also detected as important to overall yield dynamics, in particular where wetter winters benefitted yields. Specific temperature and precipitation tipping points vary across the geographic extent of production, but align with existing agronomic knowledge. Our XAI approach enhances model transparency, offering valuable insights for farmers and policymakers to develop adaptive strategies for sunflower cultivation under climate change. Future research incorporating additional factors like soil characteristics and agricultural practices can further refine yield predictions.

Quantitative morphological parameters of the rice root system under drought stress in juvenile differentiation stage is pivotal for optimizing water management and breeding of drought-tolerant varieties in rice (Oryza sativa L.). This study aims to quantify responses of rice root morphological parameters to varying drought intensities (DI) and durations (DD) in juvenile differentiation stage by proposing a novel drought impact factor, including IF_Bi-DI (drought impact factor for total biomass under drought intensity), IF_Bi-DD (drought impact factor for total biomass under drought duration), IF_RBi-DI (drought impact factor for root biomass under drought intensity), and IF_RBi-DD (drought impact factor for root biomass under drought duration). Pot experiments were conducted during 2018 and 2019 rice growing seasons using two rice cultivars. Nanjing 9108 (conventional) and Huaidao 5 (hybrid), under different DI (including T1, T2, T3, and T4—four levels) and DDs (including W1, W2, W3, W11, and W12—five levels). The results showed that the ratio of scanned root length to the scanned root biomass, and the partition coefficient of total root biomass followed exponential functions, while the partition coefficient of scanned root biomass exhibited an S-curve relationship. IF_Bi-DI, IF_Bi-DD, IF_RBi-DI, and IF_RBi-DD correlated linearly and logarithmically with time index, respectively. Root surface and volume models adhered to S-curve functions, whereas root average diameter displayed a linear decline with root length. The validation of models developed by us demonstrated strong correlations between simulated and observed values (r > 0.73, p < 0.001), with mean absolute difference (d_a) and root mean square errors consistently below 5% and 6.095 g plant⁻¹, respectively. This study establishes first biomass-driven framework to predict root morphological parameters under drought stress in juvenile differentiation stage, offering breeders actionable insights for developing drought-resilient cultivars and enabling precision irrigation strategies to mitigate yield losses in water-limited environments.

Peanut (Arachis hypogaea L.) is a globally important crop; however, its productivity is increasingly threatened by heat stress, exacerbated by global warming. Developing heat-tolerant peanuts is crucial for sustainable production amidst rising temperatures. Unlike commercial cultivars, wild-derived peanuts possess broader genetic diversity, being naturally adapted to an array of challenging climatic conditions. Antioxidant activity and reactive oxygen species (ROS) regulation are potential indicators of heat tolerance. Studies on enzymatic activity in peanuts have focused on commercial cultivars, leaving a research gap regarding the antioxidant defense mechanism in wild relatives. This study aimed to identify peanut genotypes with superior antioxidant performance and classify their response to heat stress by increasing activity of specific enzymes to scavenge ROS. The experiment was conducted in growth chambers, using 20 peanut genotypes, 12 wild-derived and eight commercial cultivars. Heat stress (35/22°C, day/night) was imposed for 7 days at 60 days after planting, following pre- and post-stress conditions of 30/20°C (day/night). Leaf samples were collected before, during, and after heat stress. Enzymatic activities of superoxide dismutase, catalase, and ascorbate peroxidase, alongside hydrogen peroxide levels, were analyzed. Upregulation of antioxidant activities under heat stress and recovery periods highlighted their role in detoxifying ROS. AU NPL 17, BatKemp1, IpaCor2, IpaDur2, IpaDur3, MagDur1, and ValSten1 exhibited superior antioxidant enzyme activity, suggesting their potential for heat tolerance. Results also indicated different mechanisms used by peanut genotypes to scavenge ROS, such as balanced ROS scavenging, prioritization of peroxisomal or chloroplast/cytosol detoxification, and compensatory mechanisms.

Viriato, V., Rodrigues, G. S., Nunes, M. R., Adege, A. B., & Bonfim, F. P. G. (2025). On-farm observations of socioenvironmental impacts of Humulus lupulus L. cultivation in Brazil. Agronomy Journal, 117, e70175. https://doi.org/10.1002/agj2.70175

The funding statement for this article was missing. The following funding statement has been added to the article in the Acknowledgments section:

The article was funded by Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), Brazil (Grant Number: 2023/12485-0). The Article Processing Charge for the publication of this research was funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Brazil (ROR identifier: 00x0ma614).

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Agroecosystem benefits provided by a winter cover crop are proportional to residue quantity and decomposition rate. For growers who plant cover crops to suppress weeds and conserve soil moisture during the cash crop growing season, it is important to understand how management decisions such as termination method impact cover crop residue quantity and quality over time. A decomposition study was conducted in Maryland at two field sites with differing soil textures in 2022 and 2023 to test the impact of two broad-spectrum herbicides frequently used for cover crop termination before cash crop planting. At anthesis, cereal rye (Secale cereale L.) plots were either mechanically terminated with a roller-crimper or left standing. One week later, chemical termination treatments (glyphosate and paraquat) were applied to half of both the rolled and standing plots. After plant death, samples of the terminated cereal rye biomass were placed in mesh litterbags, which were affixed to the soil surface between corn rows. The litterbags were then retrieved at 2, 4, 6, 8, and 12 weeks after chemical termination treatments were sprayed and at corn harvest. No differences in decomposition rates were observed when biomass loss was calculated by calendar date or by heat units. In some site-years, roller-crimped cereal rye had higher concentrations of lignin and holocellulose. No differences in residue chemistry between the chemical termination herbicides were detected. Residue of mature cereal rye terminated late in the spring will decompose slowly regardless of termination method, maintaining a persistent mulch during the cash crop season.