Angular leaf spot (ALS) disease, caused by Xanthomonas fragariae , has emerged as a devastating bacterial pathogen, posing a serious threat to strawberry production. This study aimed to construct an effective synthetic microbial consortium using antagonistic bacteria and to elucidate its biocontrol mechanisms against ALS through an integrated approach including culturomics, real-time quantitative PCR (qPCR), and high-throughput sequencing of the phyllosphere microbiome. The main findings are as follows: Three synthetic microbial consortia were assembled following compatibility assessment. Among these, the combination of Paenibacillus polymyxa MY-J3 and Lysobacter antibioticus HY (designated M+H) demonstrated superior efficacy, achieving a relative control efficacy of 76.15% and 74.26% under greenhouse and field conditions, respectively. Using a tailored qPCR assay for X. fragariae detection, the M+H treatment reduced pathogen abundance by 99.99% compared to the control. The consortium M+H markedly up-regulated the expression of host defense-related genes while down-regulating key virulence genes of X. fragariae . The crude extract from the M+H consortium exhibited a minimum inhibitory concentration (MIC) of 80 mg/mL against X. fragariae and significantly disrupted bacterial biofilm formation, cell surface hydrophobicity, extracellular polysaccharide production, and reduced pathogenicity. Furthermore, treatment with the consortium notably altered the diversity and composition of the strawberry phyllosphere bacterial community. The microbial consortium M+H can suppress the occurrence of the ALS through multiple mechanisms, demonstrating promising application prospects.

Background The shift to bagless apple cultivation (i.e., fruit production without bagging) has increased the need for pest management during the summer and fall. The use of insect-repellent plants has become an effective biological control strategy in light of the increasing emphasis on reducing chemical pesticide applications and environmentally friendly integrated management strategies. Methods In this study, we evaluated the efficacy of three Tagetes species, marigold ( Tagetes erecta L.), peacockweed ( Tagetes patula L.), and Inca peacockweed ( Tagetes minuta L.), intercropped in apple orchards for controlling major pests: the peach fruit moth ( Carposina sasakii M.), fruit tree leaf roller ( Spilonota lechriaspis M.), and apple leaf miner ( Lithocolletis ringoniella M.). The relationship between pest suppression and volatile organic compound (VOC) profiles was further examined using gas chromatography–ion mobility spectrometry combined with principal component analysis. Results The results indicated that all three Tagetes species reduced lepidopteran pest damage, with Inca peacockweed providing the most pronounced and stable control effect after four consecutive years of planting. Specifically, Inca peacockweed decreased the damage incidence to 1.8%, 1.5%, and 2.4% for peach fruit moth, apple leaf miner, and fruit tree leaf roller, respectively, within a 3 m radius, and the level of control declined with increasing distance from the Tagetes rows. VOC profiling revealed that ketones were the dominant compounds in marigold and peacockweed, whereas esters predominated in Inca peacockweed; VOC diversity was greater in flowers than in leaves. Conclusion Overall, this work provides a scientific foundation for optimizing ecological planting designs and offers new perspectives for the development of plant-based pest control strategies in sustainable apple production systems.

Introduction Process-based crop models such as the Agricultural Production Systems sIMulator Next Generation (APSIM-NG) can simulate crop growth, phenology, and yield under diverse environmental and management conditions, supporting climate-smart agriculture strategies aimed at improving productivity and resilience. However, accurate calibration and validation are required to ensure reliable predictions across cultivars and nitrogen (N) management scenarios. Methods We evaluated APSIM-NG performance for simulating winter wheat cultivar responses to N rate using field experiments conducted in Nebraska during the 2020/21 and 2021/22 growing seasons. Trials followed a randomized complete block design with two cultivars (LCS and WB), four N rates (0, 56, 112, and 168 kg N ha⁻¹), and three replications. Observations included phenology, grain yield, protein content, shoot biomass, carbon-to-nitrogen ratio, soil nitrate and ammonium, soil moisture, and weather variables. Model calibration targeted cultivar-specific phenology, biomass, yield, and protein content. Validation was conducted using grain yield data from 29 site-year combinations across five Nebraska counties spanning six growing seasons (2017–2022). Model accuracy was evaluated using RMSE, RRMSE, and mean bias error. Results Calibration improved model performance, with well to moderate accuracy for phenology (RRMSE = 2.1–2.2%; RMSE = 3–5 days), grain yield (15–24%), protein content (8–11%), and grain N uptake (11–13%). APSIM-NG moderately captured cultivar differences in leaf N uptake, with RRMSE values of 27% for LCS and 33% for WB. Validation results showed good performance for grain yield in both cultivars (RRMSE = 14% for LCS and 19% for WB). Yield response to N was simulated well for LCS (RRMSE = 18% at the economic optimum N rate) and moderately for WB (32%). Discussion Overall, APSIM-NG demonstrated well to moderate performance in simulating phenology, yield, and grain N dynamics across winter wheat cultivars. These results highlight the model’s utility for evaluating N management strategies and supporting climate-smart decision-making aimed at improving nitrogen use efficiency and adaptation to climate variability in wheat systems.

Under climatic change and water scarcity conditions, the use of a precision irrigation system and crop-adapted irrigation strategy that takes into account climatic conditions is a major objective for scientists and farmers. The current study investigated the effects of different irrigation strategies, using low-cost smart tensiometers, on the vegetative growth, relative water content, gas exchange, and fruit quality of two young apple cultivars (Galaxy and Richared) in dry climate. Four irrigation treatments were considered: Control Irrigation [CI, 100% crop evapotranspiration (ETc)], Continuous Deficit Irrigation (CDI at 50% ETc), and Partial Root-zone Drying (PRD75 and PRD50 irrigated at 75% and 50% ETc, respectively). Results showed that shoot growth significantly decreased under CDI50% and PRD50% with a reduction of approximately 31.65% and 38.63% for Galaxy and Richared, respectively. A slight decrease in gas exchange parameters was recorded during the fruit ripening period, especially for Richared under CDI50% and PRD50%, while under PRD75%, the decrease was negligible. Moreover, a significant improvement in fruit quality was reported by an increase in firmness, TSS, that reached 19.50 and 17.50°Brix in Galaxy and Richared, respectively, under PRD75% and a decrease in acidity mainly in Richared. In conclusion, the deficit irrigation strategy PRD75% based on smart tensiometers data is the most recommended strategy in the region of the present study, considering its moderate effect on tree physiology and improvement in fruit quality.

Active canopy crop sensor commercialization offers producers the ability to vary nitrogen applications in real time based on crop reflectance measures. However, adoption of active canopy crop sensors has been limited due to inconsistent results, potential yield losses, and lack of information from field-scale trials under different management strategies. Therefore, the purpose of this study was to evaluate the capability of active crop canopy sensor system (OptRx™, Ag Leader, Ames, IA) in field trials on nine non-irrigated maize (Zea mays L.) sites in eastern Nebraska, where rainfall often limits yield (2019–2020). The sensor-based N management treatments were compared to each site’s grower treatment, examining the effects of N base rate, in-season application timing, and spatial variability technology performance. The sensor-based N management reduced N application by an average of 38.7 ± 20.8 kg Nha -1 without a yield penalty in 77% of the sites (n = 9). The base rate of N applied prior to the in-season, sensor-based application rate in-season, and timing of the in-season application influenced the N use efficiency (NUE) of the sensor-based N management approach. Partial factor productivity of N was improved by 16.8 ± 8.4 kg grain kg N−1 relative to growers’ current management. In terms of profit, 35% of sites demonstrated a profit advantage in sensor-based treatments. Field-scale research demonstrates that active canopy sensors can improve nitrogen management efficiency and profitability. These findings highlight the importance of evaluating active canopy crop sensors under variable field conditions to optimize sensor-based N management strategies.

In the context of altered climate regimes and escalating costs of cultivation, the conventional and non-cultivation practices have become economically untenable and unsustainable. This variability/fluctuations highlighting the need of adapting crop diversification strategies to promote sustainable agriculture practices and to maintain climate-resilient agroecosystems. Crop diversification helps to mitigate climate change impacts and supports the development of resilient and stable farming systems. Its underlying principles being systematic crop selection, resource conservation, optimal resource utilization, complementary crop combinations, own flourishing of the year-round planning of crop with various species of resources of surplus, not compromising yield and optimizing yield in resource-deprived drylands and rainfed areas. A systematic review of 134 diversified systems, obtained using a PRISMA guided meta synthesis from 2010 to 2025, shows that such systems produce average yields that are 20-38% better than monocropping and builds soil organic carbon by 9% with a reduction of 25-40% synthetic inputs. These study reveales that diversified systems viz. , intercropping and agroforestry reliably boost soil health, increase biodiversity, reduces dependence on chemical inputs and consequently improve climate adaptation capacity along with socio-economic conditions. Crop diversification generally reduces the incidence of pests and disease due to increased poplation of natural enemies which disrupts the pest activity compared to monoculturing which makes more susceptible to pests and diseases. Nevertheless, the effectiveness of the diversification is moderated by regional climatic conditions, policy frameworks and access to markets. Adoption is further hindered by knowledge deficit, infrastructural limitations and lack of risk aversion strategies amongst smallholders. This review addresses these gaps by offering a systematic global assessment of the benefits of diversification and the constraints to adoption, highlighting that the scaling of diversification processes requires context-specific policy incentives, knowledge transfer to farmers and value chain development for non-traditional crops.