Background The practice of tobacco monoculture usually leads to increased occurrence of soil-borne diseases and reduced yield and quality, posing a significant obstacle to sustainable farming. The soil microbiome is central to soil health, but the mechanisms by which crop rotation alleviates monoculture-related obstacles by reconstructing microbial communities and their interaction networks remain poorly understood. Methods A field study was conducted in two regions to compare tobacco grown on soils after rotation (with wheat or rapeseed) and tobacco grown on soils with continuous tobacco cropping. A comprehensive analysis was performed on soil physicochemical properties, enzyme activities, microbial biomass, 16S rRNA bacterial and ITS fungal gene sequencing, microbial co-occurrence networks and phenotypic data, including tobacco yield, disease index and chemical quality of cured tobacco. A multi-omics coupling framework was constructed to integrate environmental variables, community structure and phenotype. Results Crop rotation notably increased the content of available potassium in the soil and enhanced the activities of key enzymes involved in carbon and phosphorus cycling, such as β-glucosidase, sucrase and acid phosphatase. These changes led to improved nutrient availability. Across the two regions, crop rotation consistently restructured both bacterial and fungal communities, enriching beneficial taxa, including Chitinophagaceae , Rhodanobacteraceae , Xanthobacteraceae , Cercophora and Montagnula , while suppressing the proliferation of potential fungal pathogens. The microbial co-occurrence networks under rotation conditions were more intricate, cooperative and functionally integrated. Path analysis, incorporating composite variables, revealed a comprehensive causal pathway: crop rotation enhances the abundance of beneficial microbes and increases network complexity, which, in turn, improves the soil health indexes. This ultimately results in higher tobacco yield, reduced disease incidence and superior chemical and sensory quality of the cured tobacco.

This paper reports on the daily crop coefficient dynamics from budbreak to harvest over a 4-year period. This study determines the actual evapotranspiration of pecans in the hot and humid growing conditions of Georgia. It does so by using a combination of eddy-covariance and micro-lysimeter measurements. Furthermore, this paper evaluated the actual evapotranspiration by comparing it with both the actual irrigation practices used by commercial growers and the current recommended irrigation schedule. The present study found that the intraseasonal variability observed in pecan water use is more significant than the interannual variability. From bud break through harvest, the daily pecan water use ranged from 0.4 to 6.2 mm with daily crop coefficient (Kc) values varying between 0.3 and 1.0 during that period. In this 4-year study, the water demand of pecan trees in Georgia typically was found to hover around 680 mm. During the study, the total evapotranspiration of pecans during the growing season (Mar–Oct) was found to be 34% higher for 7- and 10-year-old trees in 2020 and 2023, respectively, than the current Extension Service recommendation of irrigation in Georgia, US, whereas the water supplementation was ~42% higher than the actual evapotranspiration of pecan trees. These results regarding water requirements may suggest a revision of the true water needs of pecan orchards in Georgia.

Viruses are numerically the most abundant forms on Earth, and most are present in soil. Even though viruses are highly abundant in soil and critical to rhizosphere function, visualizing the diverse morphotypes within soil has been challenging. The difficulty is primarily due to the heterogenous nature of isolated suspensions that typically contain nanometer to micron scale debris which renders protein crystallography for structural studies unfeasible and hinders cryo-electron microscopy due to ice thickness and contrast issues. Here we employed and compared a simple spin filtration method to cleanup solutions of extracted viruses for direct observation with cryo-electron microscopy. The method employs common physical biochemical separation steps to remove large and small debris which dramatically improves image quality and preservation of structural features to permit visualizing morphotypes not typically seen with conventional negative stain approaches. In addition to tailed and non-tailed polyhedral phages, several under reported or novel morphotypes of soil viruses are directly visualized as a particle library with both 2D and 3D information.

Introduction The multiple nutritional disorders producing the early decline of citrus productivity are commonly observed across the citrus belts of northeast India. This situation is further compounded by a mismatch between annual addition and consumption of fertilizers, in the backdrop of an erroneous diagnosis of nutrient imbalance. In this background, we attempted to diagnose nutrient balance in Khasi mandarin ( Citrus reticulata Blanco) orchards using several diagnostic tools comprising machine learning (ML) tools. Methods A database of soil available nutrients (KMn0 4 -N, Brays-P, NH 4 0Ac-K, DTPA-Fe, DTPA-Mn, DTPA-Cu, DTPA-Zn) and fruit yield documenting 180 Khasi mandarin orchards of seedling origin (10–30 years old with row-to-row distance of 4 m and trees 6 m apart) raised under rainfed conditions in the Meghalaya state of northeast India. Diagnosis methods were compared: the sufficiency level of available nutrients (SLAN), the basic cation saturation ratio (BCSR), the compositional nutrient diagnosis (CND), the diagnosis and recommendation integrated system (DRIS) and ML tools like random forest and xgboost. Results Soil test interpretation of a low-yielding and nutritionally imbalanced orchard differed among diagnostic methods. DRIS predicted deficient-to-low concentrations of Zn, Ca, P, N, and K; other nutrients like Fe, Cu, Mn, and Mg were at optimum-to-high concentrations. CND standards diagnosed Zn deficiency and Cu excess with potential agronomic manifestations for early decline in productivity. SLAN interpretation was highly skewed for Ca; moderately skewed for Mg, Cu and Zn, and unskewed for N, P, K, Fe and Mn. The accuracy of ML regression models relating nutrient expressions to fruit yield was invariably high, followed by SLAN. The ML xgboost regression model exhibited the highest accuracy in predicting fruit yield from soil test. Conversely, the BCSR, which considers only three cationic dual ratios, was inaccurate. There were distortions when relating concentration values to DRIS indices to determine ‘optimum’ concentration ranges. The ML classification models showed that concentration values were also less accurate than clr to classify data as true negative (TN) or true positive (TP). The xgboost classification model showed optimum ranges for N, P, Ca, Mg, Mn, and Cu; whereas K, Fe, and Zn fell below the lower limits. Conclusion ML, hence, as a classification approach, aided in discarding cases of poor yields and high yields showing luxury nutrient consumption or suboptimal nutrient levels. The soil test standards and site-by-site comparisons can further support site-specific nutrient management and precision fertilization. Considering Zn as the most deficient nutrient, Zn biofortification interventions are recommended to increase fruit yield and quality in Khasi mandarin.

The accelerating impacts of climate change continue to challenge global agriculture, increasing the need for adaptive and sustainable technological solutions. In this context, Unmanned Aerial Vehicles (UAVs) have evolved from experimental tools to key components of precision agriculture, offering advanced capabilities for monitoring, diagnosis, and targeted intervention. This Research Topic explores how UAVs, combined with remote sensing, AI, and operational strategies, can advance climate-smart agriculture. The topic focuses on innovative technologies and UAV applications in precision agriculture for mitigating climate change, highlighting recent advancements.The four contributions in this collection illustrate UAVs' potential to improve resource-use efficiency, enhance plant protection, enable early detection of biotic stress, and support scalable, data-driven decision-making. Together, these studies underline both the versatility of UAV technologies and the need for continued innovation to address environmental variability, operational challenges, and integration with complementary sensing platforms.Regarding UAV for spraying, the: operational variability and deposition efficiency,

This paper presents the learnings from a study that used qualitative research methods to explore how to promote agroecological transitions (AETs), conducted over four years with Bolivian farmers and consumers in Cochabamba. The study examines how different motivations and production practices shape farmers’ engagement with agroecology. The research applied thematic analysis to construct farmer typologies and identify factors influencing perceptual and behavioural transformation towards AE practices. The results show three farmer typologies based on motivations and production practices: i) conventional production and offer; ii) mixed production and offer; and iii) AE production and offer. Thematic analysis identified a set of primary and secondary factors common across typologies that affect transformations towards AE, with the main factors including risk-aversion, plot size and family tradition, and secondary ones such as health and new AE knowledge versus family tradition. A gap emerged in how these factors affect changes in perspectives and behaviours across farmer types. Understanding the role of context-specific factors in AE transformations is essential to improve AE training and implementation approaches, and the discussion interprets farmers’ risk perceptions through behavioural economics, particularly Prospect Theory, which highlights loss aversion and reference-dependent decision-making.

Sewage water and sludge provide a viable option to meet crop water and nutrient demands in the face of rising climatic stress. Thus, a 2-year field study (2018 and 2019) was conducted to evaluate the effect of sewage water and sludge on soil properties and the growth response of green chili. The experiment comprised nine treatment combinations involving three types of irrigation water: normal water (I 1 ), treated sewage water (I 2 ), untreated sewage water (I 3 ) along with three soil amendments: farmyard manure (FYM) at 25 t ha −1 (SA 1 ), sewage sludge at 25 t ha −1 (SA 2 ), and a mix of sewage sludge at 12.5 t ha −1 + FYM at 12.5 t ha −1 (SA 3 ). Soil-available nutrient status of N, P, and K increased by ˜10%–15%, ˜14%–20%, and ˜13%–18%, respectively, in I 3 and SA 2 . Sewage water and sludge application further improved soil microbial populations, which included actinomycetes, fungi, and bacteria. Concurrently, multivariate analysis of variance (MANOVA) demonstrated a positive influence of irrigation and soil amendments on soil properties. Across both study years, I 3 and SA 2 recorded a higher mean green chili yield, with an improvement of approximately 25% over I 1 and SA 1 . Hence, the findings reveal the feasibility of harnessing sewage water resources as sustainable inputs, advancing both resource efficiency and short-term agricultural sustainability in the region.

Strawberry powdery mildew, caused by Podosphaera aphanis , is a major disease of strawberry, capable of leading to yield losses of up to 70%. Its management typically relies on frequent chemical fungicide applications due to the extended infection window of the pathogen. Podosphaera aphanis overwinters as mycelium and chasmothecia on infected plant material, which may act as primary inoculum in spring. This two-year study investigated the effectiveness of post-winter removal of infected leaf and plant debris in disease control and quantified the role of chasmothecia in early season infection under high-tunnel conditions. Complete removal of infected tissues delayed disease onset and reduced severity by up to 88%. In the two years, chasmothecium formation increased in late summer following 8 and 16 cumulative hours below 13 °C, respectively. In spring, ascosporic infection events occurred after the accumulation of 21 and 18 cumulative hours with temperatures above 10 °C combined with at least two consecutive hours of leaf wetness, consistently with previous findings. The efficacy of the sanitation practice was further supported by excluding the possibility that chasmothecia and/or chasmothecia on debris fragments potentially fallen into the substrate could trigger infection. In addition, we investigated the timing of ascospore-derived infections by monitoring disease progression on individual leaves. Infection rate was quantified across different leaf ages to assess their role in epidemic development and their potential contribution to secondary inoculum. The highest infection rate was observed on 15-day-old class age, with a crucial role in supporting early pathogen development. These results indicate the importance of sanitation practices, such as overwintering infected leaf removal in reducing early-season infections and informing more targeted control strategies.

Introduction Agriculture is an indispensable practice with a long history that dates back millennia. The cultivation of Chinese yam ( Dioscorea polystachya Turczaninow) is of social importance but not yet optimized; the process is currently complex and labor-intensive. Notwithstanding, the plant is regarded as a promising supplementation for ensuring food security, even in the face of climate change. This is due to its nutritional value and its diverse contribution to the cultivation of food crops. Method The EKO-YAM project, presented in this work, is thus concerned with the implementation and evaluation of four different cultivation methods with regard to the content of primary and secondary compounds, as well as economic factors. Results This study shows insights into the scientific monitoring of growing and the impact of different cultivation techniques on starch and secondary metabolites. Discussion The superordinate goal was the improvement of the growing of Dioscorea polystachya in terms of diet, metabolites, costs, and sustainability.

With the rapid increase in drone adoption, the global agricultural drone market is forecasted to grow to $11.9 billion USD by 2028 from an approximate worth of $1.4 billion USD in 2021 (Research and Markets, 2023). Butts et al. explored the combined effects of various hydraulic nozzle types and spray volumes on coverage and deposits from remotely piloted aerial application systems (spray drones). They observed increased spray recovery in the center of the spray swath compared to ground spray equipment, and identified specific nozzles to improve deposits while mitigating off-target movement potential. Paul et al. observed similar weed control in rice from a spray drone application compared to a knapsack manual sprayer despite significant reductions in spray volume, coverage, and droplet deposits. Their findings highlighted the potential of spray drones to reduce both labor and pesticide exposure in direct-seeded rice production.Proper weed identification is a critical first step towards effective weed management efforts. Venkataraju et al. explored multiple algorithms, including machine learning, deep learning, and object detection, to correctly identify troublesome weeds such as Palmer amaranth (Amaranthus palmeri S. Wats.) and waterhemp [Amaranthus tuberculatus (Moq.) J. D. Sauer]. Their deep learning model was most successful, achieving 93% accuracy in identifying the two species.Innovative techniques also involve viable strategies for improving future weed management efforts. Somala et al. explored the capabilities of a fungal extract (Diaporthe spp.) to reduce growth characteristics of barnyardgrass (Echinochloa crus-galli P. Beauv.). Their laboratory research demonstrated greater than 80% reduction in barnyardgrass seed germination, root length, and shoot length. In other laboratory allelochemical research, Trespidi et al. identified that root exudates from Baccharis halimifolia L. reduced germination by 50-75% and reduced root length by more than 85% across all tested species. However, they noted the challenges of consistently translating laboratory allelochemical results to field applications, and thus further research is needed to refine the use of allelochemicals for effective weed management. Niklolić et al. investigated biological characteristics, including germination thresholds and seed properties, of common ragweed (Ambrosia artemisiifolia L.) across different European climatic conditions. Their research demonstrated high adaptability of common ragweed to varying weather patterns, further exemplifying the need for site-specific weed management strategies.Finally, understanding practitioners' perceived needs, current practices, and thoughts about novel technologies is vital for establishing stakeholder-driven research and providing meaningful outreach. Ugljic et al. surveyed 128 stakeholders across the U.S. Midwest to assess these values. Results revealed more than 75% of respondents were unsure of adopting innovative targeted spray technologies. Survey results also showed that nearly 50% of respondents indicated they needed additional information on targeted spray equipment, underscoring the importance of further innovative technology research in conjunction with effective outreach efforts.This collection of research works serves as a critical example of the innovative weed management research being conducted globally, and the need for continued exploration. Longterm career forecasts have predicted that there will be a need for professionals who understand weed biology, ecology, and management principles, while simultaneously comprehending sensors, automation, and engineering technologies (Westwood et al., 2018). Additional funding resources and support are also required in this critical agricultural research crossroad. Collaborations with industry partners are a necessity, but there is also a pressing requirement for state, federal, and international funding resources to evaluate innovative weed management technologies and techniques. These novel tools will be released commercially regardless of the research background; therefore, funding not tied to a specific company will be crucial to validate marketing claims, supply non-biased data, and disseminate recommendations through engagement and Extension outreach for the successful implementation of these tools.