ACS agricultural science & technology最新文献

Gladiolus, a significant ornamental crop in global floriculture, frequently suffers substantial economic damage due to persistent Fusarium infections despite widespread fungicide applications. The present study systematically evaluated Fusarium diversity, pathogenicity, and fungicide resistance associated with Gladiolus crops, demonstrating the complexity in Fusarium pathogenesis. Initially, 730 Fusarium-like isolates were obtained from infected Gladiolus tissues and subsequently narrowed down to 30 isolates based on in vitro pathogenicity assays and fungicide resistance screening against carbendazim. Further selection identified 10 highly virulent isolates demonstrating resistance to carbendazim, propiconazole, and copper oxychloride. Molecular characterization using ITS and Tef1 gene sequencing classified the isolates into species complexes (SC) of Fusarium oxysporum (FOSC), Fusarium fujikuroi (FFSC), and Fusarium solani (FSSC). Isolates from the FFSC exhibited pronounced pathogenicity, with isolate GKF-6 (Fusarium circinatum) identified as the most aggressive pathogen for Gladiolus. Controlled pathogenicity assessments revealed rapid and extensive disease spread following aerial inoculation of spores, indicating that Fusarium dispersal is not limited to soil-borne mechanisms. Moreover, disease symptoms on Gladiolus plants differed with the mode and time of infection. These critical observations, along with the demonstrated fungicide resistance and genetic diversity of Fusarium isolates, highlight the necessity of revisiting current disease management strategies heavily reliant on fungicides.

Urea-formaldehyde (UF), a prominent slow-release nitrogen fertilizer, faces challenges in production optimization to efficiently meet the varying slow-release needs of different crops. This study employed response surface methodology (RSM) analysis combined with an artificial neural network-genetic algorithm (ANN-GA) prediction to refine the UF polymerization process. Key factors influencing the polymerization process and the slow-release properties of UF products were identified as the urea/formaldehyde molar ratio (U/F) and reaction pH. The ANN-GA model demonstrated superior prediction accuracy over the RSM model, achieving coefficient of determination (R²) values of 0.9968 for cold water-insoluble substances (CWI) and 0.9979 for hot water-insoluble substances (HWI), representing improvements of 0.6% and 0.43%, respectively. By utilizing a fitness function that incorporated the UF activity index as the objective, the model optimized process parameter combinations, yielding relative errors below 4% between predicted and experimental values. The ANN-GA model facilitated precise control over UF polymerization, enabling the synthesis of short-cycle slow-release UF derived from methylenediurea (MDU) for rapid nutrient delivery and long-cycle UF based on trimethylenetraurea (TMTU) for sustained nutrient release. This study introduces a novel framework for regulating fertilizer manufacturing processes in precision agriculture, employing a “demand-driven → algorithmic optimization → targeted synthesis” approach that provides quick and adaptive solutions.

This study evaluates the nematicidal potential of white mustard seeds (Sinapis alba (S. alba), family Brassicaceae) in vitro and as a biofumigant against root-knot nematodes, Meloidogyne spp., infecting tomato plants under field conditions. In vitro assays revealed that S. alba extract has effectively inhibited Meloidogyne incognita egg hatching after 4 days with 94% and increased the mortality rates of juveniles by 77 and 90% after 24 and 48 h. respectively. In field experiments, three doses of mustard seeds were applied, results showed a significant reduction in nematode populations, including galls, egg masses, and second-stage juveniles, as the treatment doses increased. In addition to nematode suppression, plant growth parameters, such as shoot and root length and fruit weight, were significantly enhanced with higher doses of white mustard seed treatment. The treatments also improved the defense mechanisms of the tomato plants, elevating antioxidant levels, antioxidant enzyme activities, and the content of flavonoids and phenolic compounds. Chemical profiling of the white mustard seeds by LC-HRESI-MS/MS revealed the presence of various compounds, mainly phenolic acids and their derivatives and flavonoids, which may contribute to the observed nematicidal effects.

Photoelectrochemical (PEC) sensors have demonstrated significant potential in agricultural detection due to their high sensitivity, rapid response, and low cost. While significant research efforts have been dedicated to optimizing photoelectrode architectures and designing efficient photoactive materials for agricultural detection, there remains a lack of systematic discussion on the mechanistic interplay between light–energy conversion and target recognition in photoelectrochemical (PEC) sensors. This review comprehensively summarizes recent advances in PEC agricultural sensors, focusing on three core design rationales: (1) enhancing light absorption (doping, nanostructures), (2) optimizing charge transport (surface plasmon resonance effect, quantum dot sensitization, 2D materials/metal–organic frameworks (MOFs)), and (3) developing specific recognition elements. PEC sensors achieve target detection by converting light energy into electrical signals through photoelectrodes and integrating specific recognition elements (e.g., enzymes, antibodies, aptamers, or molecularly imprinted polymers). Furthermore, the article summarizes typical application scenarios of PEC sensors in agricultural detection (e.g., soil component analysis, pesticide residue detection, and antibiotic and mycotoxin monitoring) and provides insights into future developments. These advancements offer crucial theoretical references and technical support for precision monitoring in smart agriculture.

The use of Azospirillum brasilense has been widely disseminated as a strategy to increase agricultural productivity, but its use in conjunction with other products is still not well studied. A promising compound for this purpose is nicotinamide, which positively influences the morphophysiological characteristics of plants and may result in biostimulant effects, in addition to enhancing the characteristics of inoculation with the bacteria. This study aimed to evaluate the effect of the foliar application of Azospirillum brasilense and nicotinamide as biostimulants on corn plants. The treatments consisted of T1: control, T2: foliar application of nicotinamide (200 mg L^–), T3: foliar application of Azospirillum brasilense (2 mL L^–), and T4: combined application of nicotinamide and A. brasilense. Gas exchange characteristics, vegetative growth, mass accumulation, and distribution were evaluated. It was found that applying these compounds increased the gas exchange capacity of the plants and improved growth and the accumulation of dry mass, mainly related to the root. There was also a correlation between root development and gas exchange capacity. The application of nicotinamide provides gains related to root development, positively impacting gas exchange characteristics. In addition, its combined use with Azospirillum brasilense results in a synergistic effect, producing more compact plants and increasing stem thickness and efficiency in terms of gas exchange.