Agronomy Journal最新文献

This study examined the effects of exogenous trehalose on the development and stress tolerance of Chinese chives (Allium tuberosum) under high-temperature conditions during summer. Foliar applications of trehalose at several dosages significantly improved morphological traits, including leaf length, plant height, and yield. Trehalose treatments enhanced photosynthetic efficiency (net photosynthetic rate, stomatal conductance, transpiration rate) and chlorophyll content (soil plant analysis development values), while reducing intercellular CO₂ concentration. Furthermore, the treatment of trehalose augmented the activity of antioxidant enzymes (superoxide dismutase, peroxidase, and catalase) and diminished malondialdehyde levels, indicating a decrease in oxidative damage. The buildup of osmolytes, such as proline, soluble carbohydrates, and proteins, was significantly increased, hence boosting stress resilience. Among the treatments, 5 mmol/L trehalose showed the most pronounced benefits in growth, physiological, and biochemical indicators. The data demonstrate that trehalose functions as an effective biostimulant for enhancing the summer acclimatization and yield of Chinese chives.

Agriculture is undergoing a shift driven by the need to address sustainability challenges linked to environmental degradation, resource inefficiency, and uneven technological access across farm structures. This study examines how the twin transformation is defined as the concurrent adoption of digital technologies and ecological practices and supports the development of a sustainable bioeconomy. An approach integrating quantitative farm survey data with descriptive and econometric analysis was employed, combining survey data from 573 farms with detailed financial assessments for a representative targeted subset selection of farms. Key indicators, including a digital technology adoption index and organic production share, were developed to evaluate adoption levels. Statistical analysis of the profile-level data showed that neither digital technology adoption nor organic orientation had a significant effect on net economic benefit per hectare. A strong inverse correlation (r = −0.97) was found between digital adoption and organic farming intensity, indicating that farms with higher digital uptake tended to have a lower share of organic production. This research contributes to understanding the farm-level dynamics of sustainability transitions and emphasizes the importance of aligning technological and ecological innovations to ensure broader participation in the sustainable bioeconomy.

Digital agriculture is emerging as the next green revolution, helping farmers to make data-informed decisions that increase productivity and optimize resource use. Understanding farmers' perceptions of current barriers and future opportunities for technology adoption is necessary for sustainable agriculture. This study aimed to identify trends and patterns in farmers' perceptions of digital agriculture technology adoption. A survey was distributed across most of the Midwest and adjacent region of the United States, collecting 247 responses. Results showed 93% used digital agricultural technology, with long-term (>10 years) adoption of auto-guidance (59%), yield mapping (56%), and variable rate technologies (36%). Perceived financial profitability was a key driver of technology adoption (36%), followed by input optimization (18%) and productivity (16%). The main barrier was high cost relative to perceived benefit (31%), followed by small farm size (16%) and equipment incompatibility (14%). Environmental benefits emerged as a tertiary motivator, and their lower prioritization suggests that farmers focus more on the economic dimension of sustainability when adopting new technologies. Fertilizer efficiency (27%), pest management (18%), and water management (13%) were top challenges to address. Findings suggest that digital agriculture adoption is primarily driven by economic considerations, with cost-benefit analysis and entry costs as key determinants in US Midwest and adjacent region farming systems.

Impacts of cover crop mixtures on essential nutrient availability after termination are not well understood in the Mid-South. This study's goal was to evaluate cover crop biomass degradation and nutrient availability in soils. Experiments were conducted at the Macon Ridge Research Station (MRRS) and Dean Lee Research Station (DLRS) in Louisiana. At MRRS, treatments included seven cover crop (including mono- and polycultures of legumes, grasses, and a brassica) and a fallow as the main plot, with two N rates (0 and 179 kg N ha⁻¹) as the subplot. DLRS used four cover crop treatments. Cover crop biomass was collected at termination in mid-February and placed in nylon mesh bags on the soil surface. Soil samples and nylon bags were collected at 0, 1, 2, 3, 4, 6, and 8 weeks post-termination and used to assess biomass degradation and nutrient release over time. Polyculture cover crop mixes tended to produce more biomass and N assimilation. The optimum timing of inorganic N availability was 6 weeks after cover crop termination, which resulted in greater soil NO₃⁻-N, allowing for synchronous release of N to meet the main crop N demand in early spring. Soil P, K, and S were not significantly different among cover crop treatments. A mix of black oats (Avena strigosa) + crimson clover (CC, Trifolium incarnatum) + radish (RD, Raphanus sativus) showed the most rapid N degradation rate while CC + hairy vetch (Vicia villosa Roth) + RD had the greatest N released from biomass. Findings emphasized the importance of selecting proper cover crop mixtures and termination timing to improve nutrient cycling in no-till systems.

A study on the growth of roots could improve the efficiency of varietal selection. This study aims to investigate the performance on root and yield of eight cassava (Manihot esculenta Crantz) genotypes grown under three different water management levels during the early growth phase. A strip–plot design with four replications was used. Three levels of irrigation during 30–180 days after planting (DAP) were factor A (W1 = 100%, W2 = 60%, and W3 = 20% of the crop water requirement), whereas the eight cassava genotypes were factor B. The cassava genotypes were grown in October 2019 and October 2020. The data were recorded for soil physical and chemical properties prior to planting, meteorological data, and crop data. The results from both years indicated that the W1 level produced higher root length, root volume, root surface area, chlorophyll fluorescence, and storage root dry weight than the W2 and W3 levels at 180 DAP. The genotype CMR38–125–77, grown under W1 during the early growth phase, exhibited greater root length, root volume, root surface area, and chlorophyll fluorescence, total crop biomass (excluding roots), and storage root yield compared to the other genotypes at 180 and 330 DAP. Under water-limited conditions, the genotypes Rayong 11 and Rayong 9 had good performance for root length, root volume, and root surface area at 180 and 330 DAP. Measurements of root length, root volume, and root surface area based on the auger method could identify a superior genotype in terms of cassava biomass.

Under Mediterranean irrigated conditions cover cropping (CC) and double cropping (DC) are diversification/intensification strategies that can increase grain yield and resource use efficiency of the traditional winter fallow-maize (Zea mays L.) system. Four cropping systems were evaluated in terms of productivity, water and nitrogen use efficiency (WUE and NUE) under sprinkler-irrigated conditions during three growing seasons in the Ebro Valley, Spain: (1) long-season maize with winter fallow (F-LSM), (2) long-season maize after a leguminous cover crop (common vetch, Vicia sativa L.) (CC-LSM), (3) short-season maize after a cereal crop (barley, Hordeum vulgare L.) (B-SSM), (4) short-season maize after a leguminous crop (winter peas, Pisum sativum L.) (P-SSM). The introduction of the vetch winter cover crop required an additional 5% irrigation water but allowed to reduce the nitrogen fertilizer applied by 20%, increasing the system grain yield synthetic nitrogen use efficiency (NUEsynt-g) by 27% without affecting the cropping system grain yield and WUE. DC systems required 12% more irrigation water than the traditional F-LSM but produced more grain. The B-SSM was the most productive system (21.9 Mg grain ha⁻¹) and increased the WUE by 32% compared to the F-LSM system, but required a 39% more nitrogen fertilizer. Compared to the traditional F-LSM system, the P-SSM cropping system increased the grain yield (+16%), protein yield (+66%), NUEsynt-g (+20%), and the WUE (+10%). The diversification and intensification of the traditional F-LSM system increased yield (with the DC systems) and resource use efficiency (WUE with the DC systems; NUE with CC-LSM and P-SSM cropping systems).

McNally, B. C., Elmore, M. T., Kowalewski, A. R., Braithwaite, E. T., & Cain, A. B. (2025). Irrigation frequency and mowing height influence annual bluegrass in perennial ryegrass. Agronomy Journal, 117, e70232. https://doi.org/10.1002/agj2.70232

North Brunswick, NJ was mistakenly placed in quotes throughout the article. It has now been corrected in the following places: in the second sentence of the abstract; in the caption of Table 1, and in the second sentence under Table 1's first footnote; in the fourth sentence under Section 3.1; in the captions of Tables 2, 3, 4, and 5; in the last sentence of the second paragraph under Section 3.3; and in the last sentence of first paragraph under Section 3.4.

Rutgers University Horticulture Farm No. 2 in North Brunswick, NJ was mistakenly placed in quotes in both the third sentence in Section 2.1 and the first sentence in Section 3.1.

In addition, in the last sentence in the fifth paragraph in the Introduction, in the fifth sentence under Section 3.1, and in the second to last sentence in the second paragraph in Section 3.4, New Jersey should not have been in quotes.

We apologize for these errors.

Soybean (Glycine max L. Moench) yield is influenced by fluctuations in weather throughout the growing season and across the planting dates. Therefore, for growers, predicting soybean yield early in the season and addressing yield variability is essential for strategic decisions and resource utilization. The objective of our study was to capture soybean yield variability under three different planting dates (early, mid, and late), for two seeding rates (low, ∼247,100 and high, ∼370,650 seeds ha⁻¹) and two maturity groups (MGs 3 and 4), using machine learning models to predict soybean yield. Agronomic and meteorological data from the Kansas Mesonet and high-resolution (3 m) PlanetScope satellite imagery were used to predict and address soybean yield variability. Results showed that early-planted soybeans have demonstrated higher mean yield potential with a higher coefficient of variation than mid- and late-planted soybeans. Therefore, to quantify and model this variability, four models, including Random Forest (RF), Adaptive Boosting (AdaBoost), K-Nearest Neighbor, and Least Absolute Shrinkage and Selection Operator, were evaluated. The RF and AdaBoost algorithms performed comparatively better (R²: 0.79–0.80; root mean square error: 0.38–0.39 Mg ha⁻¹; mean absolute error: 0.31 Mg ha⁻¹; mean squared error: 0.14–0.15 Mg ha⁻¹; mean absolute percentage error: 0.08%). Moreover, we have observed that the accuracy percentage (10% error threshold) and R² were relatively higher as the crop matured, with the highest during the late vegetative and reproductive stages. This highlights the importance of in-season monitoring of the resources and market planning.

Nearly 1 billion ha of soils affected by salinization have been identified worldwide (8.7% of the planet's soils). These soils are mainly found in naturally arid or semi-arid environments. The map also shows that 20%–50% of irrigated soils across all continents are too saline. Thus, soil salinity is one of the most critical threats to food security. It adversely affects the growth and productivity of agricultural crops. Tomato is the most important horticultural plant and an essential annual crop for human food worldwide. The effects of salinity on tomato (Solanum lycopersicum L.) plants have been studied in recent years by several researchers. Attempts to improve tomato salinity tolerance through conventional breeding programs have had limited success due to the complexity of the trait. Thus, various cultural techniques, in addition to varietal selection, are applied to mitigate the harmful effects of salinity, such as seed pretreatments through priming methods, chemical fertilizers, and organic amendments like the use of beneficial soil microorganisms, including plant growth-promoting rhizobacteria and arbuscular mycorrhizal fungi. This review paper provided valuable information on the behavior of tomato cultivars under saline conditions. The review also provides a synthetic overview of current and relevant scientific advances allowing the improvement of salinity tolerance of tomato plants. However, natural seed or soil treatments to combat salinization have not been widely developed. Nevertheless, the strategies developed in this review, combined with recent advances in emerging biotechnological solutions, could allow mitigating the effects of salinity on tomato plants.

While vegetation indices (VIs)-based machine learning (ML) techniques have been developed for predicting crop yield, limited research has focused on how VI selection impacts ML model predictions or on identifying optimal VI combinations. In this study, three ML models, including Distributed Random Forest (DRF), Gradient Boosting Machine (GBM), and Deep Neural Network (DNN), were established to predict rice (Oryza sativa L.) yield using eight VIs: difference vegetation index (DVI), land surface wetness index, normalized difference vegetation index (NDVI), normalized difference (red − blue)/(red + blue) vegetation index, ratio vegetation index (RVI), soil adjusted vegetation index (SAVI), transformed vegetation index (TVI), and Keetch–Byram drought index (KBDI), extracted at five key growth stages: re-greening, tillering, stem elongation, preliminary heading, and full heading. The feature attribution method was used to quantify the relative contributions of input variables to yield predictions. The results are as follows: (1) The three ML models produce accurate rice yield predictions using DVI, NDVI, RVI, and SAVI, with root mean square error (RMSE) ranging from 174.80 to 291.83 kg/ha, R² from 0.56 to 0.84, and Nash Sutcliffe efficiency (NSE) from 0.56 to 0.84. But three models produce poor predictions with KBDI, with RMSE ranging from 344.01 to 404.73 kg/ha, R² from 0.31 to 0.44, and NSE from 0.14 to 0.38. (2) The DNN model performs better than the GBM and DRF models for rice yield prediction. (3) Note that 80% of the most important input variables are associated with the rice preliminary heading stage for the DNN models, whose importance values ranged from 0.65 to 1.00, and the average TVI at this growth stage is the most important variable. Therefore, the DNN technique, when integrated with VIs from the preliminary heading stage, is recommended for rice yield prediction.