Agronomy Journal最新文献

It is necessary to increase the agronomic use efficiency of urea to reduce ammonia volatilization and increase crop yield. However, relying solely on urea for the enhanced efficiency technologies development could harm fertilizer integrity, resulting in reduced application quality and fertilizer storage time. The authors aimed at developing and characterizing the physical, chemical, and physicochemical quality of a novel enhanced efficiency fertilizers, synthesized from urea plus boron (B), zinc (Zn), nickel (Ni), or molybdenum (Mo) addition, with or without N-(n-butyl) thiophosphoric triamide (NBPT) associated. Hygroscopicity, hardness, salt index (SI), pH, and thermogravimetric decomposition were the parameters evaluated. Fertilizer quality was assessed through microscopic X-ray fluorescence, microtomography, and scanning electron microscopy. Micronutrients were added by coating or granulation; they were homogeneously distributed over the fertilizer's granules. The fertilizers’ hardness increased up to 86% with Zn coated compared with uncoated. Granulated urea with B, Zn, Ni, or Mo had greater internal porosity, which resulted in lower hardness. Boron and Zn addition to the fertilizers increased the hygroscopicity in average 388% and 473%, respectively, compared with hygroscopicity observed for urea. Moreover, hygroscopicity was increased by an average of 56% with NBPT addition. Micronutrients addition to the urea granules increased the SI, while thermal decomposition stages of urea were unaffected by micronutrients addition. Enhanced efficiency fertilizers require characterization before agronomic efficiency tests due to changes in their physical, chemical, and physicochemical properties. Unfavorable changes could harm granules integrity and application efficiency in the field, resulting in economic losses to the industry and farmers.

Biostimulants play a crucial role in enhancing crop yields while promoting sustainability and environmental responsibility. Evaluating the efficacy of biostimulants on-farm requires rigorous, multiyear trials conducted across various locations and with different cultivars. This study was conducted in Uruguay from 2015 to 2023 to assess the impact of a single application of a humic biostimulant (HB) during the R3 phenological stage on irrigated rice (Oryza sativa L.). The study encompassed 103 farms situated in diverse cropping zones, each characterized by distinct cultivars, soil qualities, radiation, and temperature conditions across the East and North regions. Results revealed that the HB treatment elicited an average yield increase of 7.4% across all sites. Notably, 93% (97) of the trials exhibited a positive yield response, with an average increase of 8.5%, while only six trials (all in the eastern zone) showed a negative response to the HB treatment. A combined analysis of variance indicated that the biostimulant's effect did not significantly differ between production zones, years, or rice cultivars when negative responses were excluded. Furthermore, relationships with environmental variables were nonsignificant, underscoring the positive effect of the biostimulant regardless of location. These findings hold significant implications for Uruguay's rice sector, that is, integrating HBs into standard management practices could substantially boost irrigated rice yields in rice-producing areas.

The maturation process of coffee (Coffea arabica) trees exhibits inherent variability, producing fruits at various physiological maturity stages. This variability affects the resistance between the fruit and its peduncle, posing a challenge in mechanized harvesting: non-selective harvesting. A precise classification of coffee fruit detachment force is essential to address this challenge, ensuring coffee's quality and producer's profitability. This study assesses the efficacy of machine learning (ML) models in determining the detachment force across various coffee cultivars under drip-irrigated and rainfed conditions. The dataset included detachment force measurements from 24 cultivars—13 drip-irrigated and 11 rainfed—yielding 1152 data points. Variance analysis compared irrigation methods and three maturity stages: green, cherry, and dry. Detachment force was categorized into four classes based on the dataset's quartile distribution. The ML models utilized were random forest (RF), support vector machine (SVM), K-nearest neighbors, and artificial neural networks. The SVM model was notably effective in classifying detachment force for rainfed cultivars, with a Matthews correlation coefficient (MCC) of 0.78. In contrast, the RF model was particularly adept for drip-irrigated cultivars, with an MCC of 0.75. The highest classification accuracies were recorded for the extreme force classes I and IV, with precision values of 0.93 and 0.8, respectively, while classes II and III had lower precision at 0.57 and 0.69. Implementing these ML models for detachment force classification has been beneficial, improving decision-making in mechanized harvesting systems.

Efforts to address economic and environmental concerns surrounding nitrogen (N) have motivated attempts to improve estimates of plant-available N in soil. Several soil health indicators, including CO₂ burst, permanganate oxidizable carbon (C) (POXC), and autoclaved-citrate extractable (ACE) soil protein, assess labile C and N, and therefore may help to estimate soil N mineralization in long-term cover cropping systems (>3 years). This study evaluated the relationship of CO₂ burst, POXC, ACE-soil protein, and pre-sidedress nitrate test (PSNT) with agronomic optimum N rate (AONR) in corn (Zea mays L.). The study also looked at relationship between other soil test and corn yield parameters, relative yield (RY) and yield without N sidedress at 25 long-term cover crop sites across Virginia. Results showed relatively weak correlations between AONR and CO₂ burst, POXC, ACE-soil protein, and NO₃-N (r = 0.00 to −0.48), which indicates that these soil health tests may not reliably predict soil N availability and corn yield. Corn yield with zero-sidedress N rate had a negative relationship with cover crop C:N ratio (r = −0.66) and a positive relationship with cover crop N content (r = 0.59), and NO₃-N at pre-planting (r = 0.54) and sidedress (PSNT) (r = 0.63). The PSNT showed a better relationship (r = 0.65) compared to 72-h CO₂ burst (r = 0.48) with RY. Soil health indicators (CO₂ burst, POXC and ACE-soil protein) resulted in a poor or no relationship with AONR. Our results indicate that the PSNT was a more reliable indicator of the sidedress N rate in corn.

Physicochemical characteristics of soil, especially organic matter and soil texture, affect the optimal plant density in rice (Oryza sativa L.). A split-plot field experiment was done based on a randomized complete block design (RCBD) in four replications in Amol (Northern Iran) on the coastal strip of the Caspian Sea. The experimental treatments, that is, soil fertility (infertile, semi-fertile, and fertile) as the whole-plot factor and plant density (low, medium, and high by 15.2, 19.6, and 27.8 plants m⁻², respectively) as the split-plot factor, were studied over 2 years (2022 and 2023). The results indicated the greatest and smallest number of days from planting to tillering and from tillering to flowering for infertile soil, respectively. The maximum root fresh weight was measured during the tillering stage for infertile soil, whereas for fertile soil, the highest root fresh weight was recorded throughout the phases of panicle initiation and flowering. The greatest root length was measured at the tillering stage for 2022 in high-density infertile soil. The lowest number of panicles m⁻² and the percentage of full spikelets were obtained from infertile soil. The highest grain yield was obtained from high-density fertile soil. In mechanized rice cultivation, high density is suggested for fertile and semi-fertile soils and low density for infertile soil.

Ensuring rice (Oryza sativa) self-sufficiency in China relies significantly on achieving high grain yields in hybrid rice production. This study conducted field experiments across two site-years, comparing grain yield per unit of land area, grain yield generated per seedling, and associated yield traits for a hybrid rice variety under two combinations of hill spacing and the number of seedlings per hill. The combinations included a hill spacing of 30 cm × 14 cm with one seedling per hill (H14S1) and a hill spacing of 30 cm × 24 cm with three seedlings per hill (H24S3). The results revealed that H14S1 consistently outperformed H24S3, demonstrating a 7%–16% increase in grain yield per unit of land area and an impressive 88%–104% higher grain yield generated per seedling. H14S1 exhibited 56%–77% more panicles formed per seedling and 10%–15% more spikelets per panicle compared to H24S3. H14S1 produced 78%–115% higher biomass at heading and maturity per seedling and 8%–25% higher biomass at heading and maturity per tiller than H24S3. This study underscores the importance of promoting the individual growth and development of seedlings as a crucial strategy for achieving higher grain yield in hybrid rice production.

One strategy currently being developed to increase rice (Oryza sativa L.) productivity is using a multi-canopy cropping system in rice cultivation. This method involves planting tall and short rice genotypes in the same hill. The objective of this experiment was to estimate the genetic parameters and response to selection in multi-canopy rice. Each experiment was arranged in an augmented randomized complete block design with five replications for the checks. In the first planting season, 200 F₃ families from IPB196 and IPB197 populations were planted in monoculture and multi-canopy as the short genotypes. IPB187-F-40-1-1 was used in multi-canopy as the tall genotype. Selection of 25% based on grain weight per hill of short genotype in multi-canopy was performed, and 50 families were selected and their F₄ seeds were planted in the second season along with the same tall genotype. The results indicated the genotype × cropping system was significant for grain weight per hill in the F₃ and F₄ generations. Grain weight per hill has a similar realized h²_ns in the multi-canopy (0.58) with monoculture (0.54). Meanwhile, the response to selection in multi-canopy (3.60) was higher compared to monoculture (2.09), and therefore the selection of rice lines for a multi-canopy system should be conducted in the multi-canopy environment. A selection percentage of 5% resulted in a higher response to selection. These findings may provide insight into the acceleration of breeding rice varieties for the multi-canopy system.

Croplands have been the focus of substantial investigation due to their considerable potential for sequestering carbon. Understanding the potential for soil organic carbon (SOC) sequestration and necessary management strategies will be enabled with accurate process-based models. Accurately representing crop growth and agricultural practices will be critical for realistic SOC modeling. The MEMS 2 model incorporates a current understanding of SOC formation and stabilization, measurable SOC pools, and deep SOC dynamics and is seen as a highly promising tool to inform management intervention for SOC sequestration. Thus far, MEMS 2 has been developed to represent grasslands. In this study, we further developed MEMS 2 to model annual grain crops and common agricultural practices, such as irrigation, fertilization, harvesting, and tillage. Using four Ameriflux sites, we demonstrated an accurate simulation of crop growth and development. Model performance was strong for simulating aboveground biomass (index of agreement [d] range of 0.89–0.98) and green leaf area index (d from 0.90 to 0.96) across corn, soybean, and winter wheat. Good agreement with observations was also achieved for net ecosystem CO₂ exchange (d from 0.90 to 0.96), evapotranspiration (d from 0.91 to 0.94), and soil temperature (d of 0.96), while discrepancy with the available soil water content data remain (d from 0.14 to 0.81 at four depths to 100 cm). While we will continue model testing and improvement, MEMS 2 (version 2.14) has now demonstrated its ability to effectively simulate the growth of common grain crops and practices.

The herbicide dicamba (3,6-dichloro-2-methoxybenzoic acid) is commonly used to control broadleaf weeds in soybeans. Dicamba, however, is susceptible to volatilization and drift, thereby causing significant plant damage to nontarget crops downwind. Dicamba was reformulated to reduce volatility and off-target movement. The effectiveness of the dicamba reformulation was assessed by quantifying dicamba emissions following spray application and investigated how meteorological factors influenced the off-target movement. The experiments were conducted at the University of Minnesota Agricultural Experiment Station (UMORE Park) during the growing season of 2018, 2019, 2021, and 2022. Multiple high-flow polyurethane foam air samplers were used to measure dicamba concentrations downwind from a 4-ha soybean field sprayed with dicamba. Dicamba emissions were estimated using backward Lagrangian modeling constrained by the air sample observations. The results indicate that dicamba emissions and downwind transport were significant for several days following application. Further, non-traited soybeans located within 15–45 m showed substantial dicamba-related damage. In warmer, drier seasons, increased dicamba emissions caused more severe damage to downwind soybeans, likely worsened by drought stress preventing recovery. Favorable atmospheric conditions that reduced potential drift can be difficult to achieve in terms of the typical weather experienced over agricultural sites in the Upper Midwest. These results indicate that the dicamba reformulation has not adequately prevented significant post-spray volatilization losses and downwind transport.

Aflatoxin contamination in peanuts (Arachis hypogaea L.) is a significant public health risk. Aflatoxin is detected postharvest after inspection of loads associated with grading at peanut buying points, leaving growers and shellers in a precarious position. Stricter limits on aflatoxin contamination could restrict the United States access to international markets. Predicting aflatoxin risk remains challenging, but improved tools could help inform postharvest storage segregation decisions and alert industry stakeholders to seasonal threats. This study aimed to develop and evaluate multiple statistical models that estimate the regional status of peanut aflatoxin contamination based on preharvest weather conditions. Our approach expanded on an existing peanut aflatoxin model for which a new geographic area and time period were tested. Weather variables served as independent variables to predict the risk of aflatoxin as the proportion of samples with greater than 20 ppb and 4 ppb aflatoxin (PGT20 [the proportion of samples with greater than 20 ppb aflatoxin] and PGT4 [the proportion of samples with greater than 4 ppb aflatoxin], respectively) across 10 counties in Georgia for 2018–2022. Best-performing models were developed through multiple linear stepwise regression explaining more than 72% and 41% of the variability in PGT20 and PGT4, respectively. Model performance further varied whether it was a year of low or high aflatoxin incidence, with temperature observed as a key influencing factor across best-performing models. This study established an adaptive approach to monitoring and managing aflatoxin risk through statistical predictive modeling, with output targeting farmers, industry, regulators, and public health officials. Future model development will aim to improve interpretation and confidence with in-season aflatoxin prediction and efficacy testing of this approach across space and time.