Agronomy Journal最新文献

The contribution rate of fertilizer nitrogen (N)—defined as the percentage of N uptake from fertilizer relative to total N uptake—is a fundamental parameter for establishing knowledge-based fertilization recommendations in crop production. This study aimed to compare the fertilizer N contribution rate for achieving maximum grain yield between double- and single-cropped rice (Oryza sativa L.). Data from four field experiments conducted between 2014 and 2023 were used to analyze the relationships of fertilizer N contribution rate and grain yield with N application rate in both double- and single-cropped rice, thereby estimating the fertilizer N contribution rate required to achieve maximum grain yield. The results showed that the fertilizer N contribution rate increased by 3.21%–3.58% and 1.92%–2.35% for each 10 kg ha⁻¹ increase in N application rate in double- and single-cropped rice, respectively. Maximum grain yields were achieved at N application rates of 190–208 kg ha⁻¹ per crop for double-cropped rice and 211–244 kg ha⁻¹ for single-cropped rice. Correspondingly, the fertilizer N contribution rates for achieving maximum grain yield ranged from 60.91% to 74.51% in double-cropped rice and from 46.90% to 49.65% in single-cropped rice. These results indicate that N fertilizers contribute more to grain yield in double- than in single-cropped rice, underscoring the importance of developing N management strategies and policies tailored to specific rice cropping systems.

Timely planting and uniform stands are prerequisites for optimal corn (Zea mays L.) production. However, frequent rainfall often limits corn acreage planted in the southeast region of the United States. Planting faster might offer a potential solution as new technology claims up to 19 km h⁻¹ planting speeds without sacrificing seed singulation or yield. The objective of this study was to evaluate corn response to varying planting speeds in Mississippi. Trials were arranged as a randomized complete block design during the 2023 and 2024 cropping seasons. A precision planter (John Deere bar and MaxEmerge 2 row units retrofitted with Ag Leader SureSpeed and SureForce) was tested at 9.7, 14.5, and 17.7 km h⁻¹ actual ground speeds. A mechanical planter (John Deere 1700 ground-driven planter equipped with eSet meters) at 9.7 km h⁻¹ was used as a standard check. Corn hybrid DKC 70-27 was planted at 81,800 and 85,000 seeds ha⁻¹ in 2023 and 2024, respectively. In both seasons, increased planting speed generally lowered plant population and quality of seed placement with increased skips and spacing variability. Planting at 14.5 km h⁻¹ optimized precision and reduced multiples using the precision planter. Moreover, planting speed beyond 14.5 km h⁻¹ did not affect corn yield. The precision planter at 17.7 km h⁻¹ exhibited improved performance over the mechanical planter at 9.7 km h⁻¹, particularly in maintaining lower miss and multiple indices. Using this technology, Mississippi corn producers can plant more land within the critical planting window at higher speeds without affecting yield.

Annual bluegrass (Poa annua L.) is a winter annual weed with limited herbicide control options in cool-season turfgrasses. This research evaluated the effect of irrigation frequency and mowing height on annual bluegrass cover in perennial ryegrass (Lolium perenne L.) in 2019 and 2020 on a 3-year-old mixed stand in “North Brunswick, NJ.” Treatments were arranged in a 2-by-2 factorial in a randomized split-plot design with mowing height (11 or 38 mm) as the main plot and irrigation frequency (once or thrice week⁻¹) as subplot. From June to October each year, both irrigation frequency treatments were irrigated to 60% reference evapotranspiration minus rainfall. Soil volumetric water content was consistently lower in once week⁻¹ irrigation treatments in both years. Annual bluegrass cover was affected by irrigation frequency and mowing height, but no interaction was detected. In October, annual bluegrass cover was reduced (47%) in once week⁻¹ treatments compared to thrice week⁻¹ treatments (59%). Additionally, annual bluegrass cover in October was reduced in treatments mown at 38 mm (46%) compared to 11 mm (60%). Irrigation frequency had no effect on turfgrass quality, green cover, or normalized difference vegetation index (NDVI); however, mowing height affected these response variables. When differences were present, all values were greater in the higher mown treatments. This research suggests reducing irrigation frequency reduces annual bluegrass cover without affecting turfgrass quality, green cover, or NDVI in the humid subtropical climate (near the Humid Continental climate zone). Additionally, increasing mowing height will reduce annual bluegrass cover.

Sodding is a method that provides immediate turfgrass cover and reduces the soil erosion potential at renovated sites. Because of its rhizomatous growth habit, Kentucky bluegrass (KB) (Poa pratensis L.) produces high-quality sod strength; however, tall fescue (TF) (Festuca arundinacea Shred.) is growing in popularity because of its superior heat and drought tolerance. The bunch-type growth habit of TF can result in weak sod strength and handling, which often requires plastic netting or the addition of KB at planting to improve sod strength during harvest and transplanting. Sod producers need more information on seeding ratios and classifications of KB when mixed with TF. Multiple field experiments in Kansas were conducted to evaluate the influence of seed mixture ratios (97:3, 95:5, and 90:10 w/w TF:KB) and KB classifications or growth aggressiveness labels on establishment speed, sod strength (maximum tensile strength and required work to tear), and sod handling (1–5 scale) at three harvests; 9, 10, and 12 months after planting. Experiment 1 results indicated 95:5 (w/w) TF:KB sod mixtures yielded similar establishment speed and sod strength across multiple harvests (12.1–15.9 N-m required work to tear sod), regardless of cultivar. Experiment 2 revealed some 95:5 and 90:10 (w/w) of TF:KB sod mixtures produced higher maximum tensile strength compared to 100% TF, but all 97:3 mixture ratios were similar in sod strength and established as quickly as 100% TF sod. Results will assist sod producers and turfgrass practitioners with information when mixing KB with TF for commercial sod.

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.