Planted fields, which are defined by areas containing different crop types, are essential for agricultural planning, crop mapping, and yield estimation. Accurate and timely information on planted fields is crucial. The Segment Anything Model 2 (SAM2) offers advanced image segmentation but is not directly suited for planted field segmentation. We propose ASAMPS, an Adaptive SAM2 model tailored for segmenting planted fields from single-date or time series remote sensing images. Unlike other SAM-based adaptations that require fine-tuning the model, ASAMPS incorporates three external modules to automatically generate and optimize prompt points, aligning SAM2 with the specific requirements of planted field segmentation without any retraining. Testing across six agricultural regions in China and the United States, ASAMPS demonstrated robust performance, achieving accuracy comparable to or surpassing state-of-the-art supervised methods. It demonstrated strong performance in complex landscapes and was effective across multiple satellite platforms, including Planet, GF-2, Sentinel-2, and Landsat 8 OLI. Additionally, ASAMPS efficiently extracted minimal planted fields from multi-temporal imagery. ASAMPS leverages SAM2's capabilities without requiring retraining and offers enhanced flexibility for optimizing prompts, making it suitable for diverse agricultural monitoring applications.

Modeling soil organic carbon stocks and changes in agricultural cropping systems using a decision support tool and process-based model.

ABSTRACT Zooplankton egg banks are crucial in rebuilding zooplankton populations in temporary water bodies when favourable conditions return after dry periods. However, their abundance, viability, and hatching success depend on many factors, with changes in land use and flooding regimes being potentially crucial drivers. Here, we studied a large‐river floodplain to compare the egg banks of soils across a land use gradient and at different elevations, the latter affecting inundation frequency and duration. We performed experimental manipulations at both the ecosystem scale (land use in the floodplain) and in the laboratory (incubation of soils of different origins). Six land use types were considered: (i) natural maple swamp, (ii) natural wet meadow, (iii) old forage cropping, (iv) recently sown forage cropping, (v) agri‐environmental corn/soybean cropping, and (vi) conventional corn/soybean cropping implemented in collaboration with agricultural producers. Egg banks were characterized through both (i) direct egg counts and (ii) by counting the individuals emerging following the incubation of soils in the laboratory, testing the effects of land use and flooding regime (duration, frequency) on hatching success. We found no significant difference in the abundance of resting eggs of different taxa in soils across the land use gradient in frequently inundated sites at low elevation. In contrast, at high elevation, where inundation periods are less frequent and shorter, rotifer and cladoceran egg density varied across land uses, with higher abundance in old forage cropping compared to agricultural cropping. Ostracods, rotifers and copepods were the most abundant taxa that hatched in the laboratory. Their abundance varied across land use types, with natural soils supporting higher abundances than agricultural soils, and forage cropping showing intermediate abundances. Frequently inundated sites (low elevation) had higher abundances of hatchlings than the less frequently inundated (high elevation) sites. Inundation duration in incubators strongly influenced ostracod hatching only, with an overall increase after 2 weeks of inundation and a decrease thereafter for most soil types. Different taxa dominated the egg and hatchling counts. For example, cladocerans were common in the egg bank but not in hatchling samples, while ostracods were absent from the former but dominated the latter. This apparent paradox suggests that egg viability varied along the land use gradient (e.g., due to agricultural practices during summer months), that the cues required for hatching differed across taxa, or that stress (land use) modulates the likelihood that ostracods enter dormancy as eggs versus juveniles/adults. Natural wetland habitats in floodplains favour the resilience of zooplankton communities facing a disturbance (i.e., the dry period) via a high potential recovery through the emergence of resting stages. In contrast, alterations to these habitats due to agricultural intensification in floodplains hampers zooplankton community resilience via decreased emergence success. Agricultural practices leading to changes in soil properties, which influence the viability of resting stages, are the plausible explanations for this phenomenon. It is paramount to conserve natural wetlands and implement sustainable land use practices in floodplains, to allow egg banks to rebuild zooplankton communities in floodplains and sustain their productivity and ecosystem services.

Can crop diversification buffer drought shocks? Evidence from Ethiopian smallholders.

Date seeds-derived microcrystalline cellulose reinforced films for food packaging applications: Development and characterization.

More than 130 thrips (Insecta, Thysanoptera) species are significant pests of agricultural crops, 16 of which are known vectors of economically important plant viruses from the Orthotospovirus genus (Bunyavirales: Tospoviridae). It is important to monitor and identify which thrips species are present in production systems so that appropriate controls can be deployed to prevent and manage thrips damage and virus spread. To overcome the challenges of traditional thrips monitoring using sticky traps and morphological identification, a DNA metabarcoding assay was developed, for accurate, high-throughput, and scalable identification of multiple thrips species. For this, a curated mitochondrial cytochrome c oxidase I (COI) database of thrips, based on combined morphological and molecular identification of 70 specimens, was developed and highlighted the importance of combined morphological and molecular identification with the correction of misidentified sequences present on public databases. The metabarcoding method can identify an individual target species within a thrips DNA mixture at a relative abundance of 0.1%, and it successfully identified up to nine different thrips species collected from sticky traps, weeds, and ornamental plants, even up to 2years after they were collected and kept on sticky traps. Metabarcoding from sticky traps has potential to greatly increase the efficiency of monitoring thrips and represents a significant biosecurity advance for the horticulture industry.

ABSTRACT Locusts, including migratory (Locusta migratoria) and desert locusts (Schistocerca gregaria), occasionally form large swarms and experience outbreaks. Large-scale locust outbreaks pose serious threats to agricultural crops. Early detection and termination are critical for mitigating damage. Currently, locusts are primarily monitored through ground surveys, which makes continuous observation over large areas challenging. Therefore, the development of remote-sensing technologies for estimating locust distribution is highly desirable. Hyperspectral (HS) remote sensing offers the potential to detect and identify ground targets such as locusts. Previous studies attempted to estimate the locust coverage ratio (LCR) using HS data through optimization algorithms based on numerical methods. A numerical optimization-based algorithm was developed previously to detect locusts and estimate their LCR using an HS sensor. Although the previous implementation executed the optimization numerically, in this study, an analytical solution to the optimization problem was derived to refine the algorithm. The proposed algorithm estimates the LCR with higher accuracy and greater computational efficiency than the previous method and eliminates pixel regions where the LCR is, by design, undefined under that method’s specification. We refer to this algorithm as the spectral optimization-based target abundance estimator (SOTAE). The SOTAE was verified through simulation. SOTAE can detect locusts with an area under curve of 0.9 when the ground LCR exceeds 0.02, a 2.5-fold lower LCR than in the previous study. Moreover, the computation duration was 0.005 times shorter than that of the previous method. Overall, SOTAE is a fast and accurate algorithm that shows potential for locust monitoring.

Seed coatings protect agricultural crops from pests, but they can expose pollinators to residues. We extended a dynamic model for pesticide uptake from soil into maize plants with a flower compartment, including nectar and pollen. Field experiments with seed/soil and spray applications were simulated successfully. Calibrated loss rates usually exceeded dissipation rates empirically fitted to declining concentrations due to continuous delivery of chemicals seen by the model (soil to plant components). Simulated dissipation consists of growth dilution and degradation (nonvolatile compounds) with half-lives fitted for imidacloprid in pollen of 0.2 to 0.9 days, close to observations following spray application. Our model predicts that mobile, persistent, and nonvolatile chemicals are potentially translocated to pollen if present in soil. This is relevant also for persistent, mobile, and toxic (PMT) chemicals released from reclaimed wastewater or via sewage sludge application. Our model can be incorporated into existing frameworks to estimate the exposure of pollinating insects.

ABSTRACT Purpose Access to agricultural extension and crop advisory services is critical, yet, in conflict-affected settings, their delivery and use are poorly understood. This paper has three objectives: first, to assess the distribution and types of agricultural extension services used by farmers in Myanmar; second, to examine changes in service use before and during conflict, focusing on the rise of digital agricultural services; and third, to analyze patterns of inclusion in accessing these services, comparing digital and in-person options. Design/methodology/approach We use a mixed-method approach for analysis. We rely on primary data from unique, large-scale, and repeated farm surveys conducted in Myanmar. This is complemented by a descriptive analysis of major digital extension platforms and contextual interviews with digital extension providers. Findings Since the onset of conflict, the use of in-person extension services has declined, while digital services have risen, with over 50 percent of farmers accessing them. Nonetheless, access remains uneven, with better-educated, wealthier, less remote, and more secure farmers benefiting disproportionately from both in-person and digital extension services. Practical implications Digital extension services have potential in conflict-affected settings but require targeted interventions to bridge access gaps. Policymakers must address digital literacy, connectivity, and affordability barriers. Theoretical implications Our research contributes to the literature by assessing the structure of digital extension services, focusing on their use in fragile settings, and advancing knowledge on the inclusiveness of agricultural extension in conflict-affected contexts. Originality/value This study provides timely evidence on digital agricultural advisory services in conflict settings, offering lessons for similar fragile environments globally.

The rapid growth of digital agriculture platforms, smart farming technologies, and web-based agricultural systems has significantly increased the need for intelligent agricultural monitoring and farm management solutions. Modern farming environments continuously face challenges such as unpredictable weather conditions, improper irrigation management, soil degradation, pest attacks, fertilizer misuse, and fluctuating market prices. Traditional agriculture management systems often struggle to efficiently handle large volumes of farming data, monitor crop conditions accurately, and provide real-time agricultural insights for effective decision-making. In large-scale agricultural environments, manual monitoring and analysis become time-consuming, inefficient, and less reliable. This paper presents the design and implementation of an AIbased Smart Agriculture Management Dashboard aimed at improving agricultural analysis, crop monitoring, and farming decision-making in modern agricultural environments. The proposed system integrates multiple smart farming functionalities including real-time weather monitoring, crop recommendation systems, fertilizer calculation modules, mandi price analytics, yield forecasting, and intelligent agricultural insights within a centralized dashboard platform. The system uses intelligent data processing and context-aware recommendation mechanisms to support efficient agricultural planning and resource management. The dashboard provides real-time visibility into farming activities through an interactive and user-friendly interface supporting weather visualization, crop tracking, AI-assisted recommendations, soil and fertilizer analysis, market price monitoring, multilingual accessibility, role-based access control, and centralized farm management workflows. Interactive graphs, analytical charts, and visualization modules improve operational monitoring and enable farmers to make faster and more accurate agricultural decisions. The system is developed using modern MERN Stack technologies including MongoDB, Express.js, React.js, Node.js, and Tailwind CSS to ensure scalability, modularity, responsive dashboard visualization, secure API communication, and efficient data management. The modular architecture of the system also supports future enhancements such as IoT integration, machine learning-based crop prediction, cloud deployment, and real-time agricultural analytics. The proposed Smart Agriculture Management Dashboard improves agricultural productivity, operational efficiency, resource optimization, and centralized farm monitoring compared to traditional agriculture management approaches, making it suitable for modern smart farming environments.

National governments and multilateral institutions face difficult challenges reconciling biodiversity, climate, and economic development goals. We integrated spatial biophysical and economic data with optimization methods to develop sustainable landscape efficiency frontiers that show maximally feasible combinations of biodiversity conservation, land-based climate mitigation, and net economic value from agricultural crops, livestock, and forestry production. We applied this approach in 146 countries and found large potential gains in biodiversity, climate, and economic development from improved land use and land management. Summing national-level results shows the potential to increase climate mitigation by more than 200 billion metric tons of CO2 equivalents (>20% increase) or net economic value by more than US$350 billion (>80% increase), without loss in other objectives.

Abiotic stresses such as drought and salinity significantly constrain the productivity of in vitro-grown wheat (Triticum aestivum L.) by disrupting its biochemical and physiological homeostasis. Rapid, non-destructive, and data-driven diagnostic approaches are therefore essential for the early detection of stress conditions and for supporting sustainable crop management. In this study, Raman spectroscopy (RS) was integrated with conventional biochemical assays to investigate wheat responses under controlled drought and salinity stress treatments. Distinct Raman spectral features associated with pigments, proteins, carbohydrates, and lipids were analyzed alongside biochemical indicators, including proline, chlorophyll, and malondialdehyde levels. Overall, the integration of RS with machine learning provides a rapid, robust, and non-invasive framework for the early detection of drought and salinity stress in wheat. Notably, Raman intensity variations observed at 737, 996, 1051, 1064, and 1518 [Formula: see text] exhibited consistent spectral trends that closely mirrored changes in conventional biochemical stress markers, confirming that these spectral shifts directly reflect underlying physiological stress responses. To classify stress levels and to identify key Raman-derived biomarkers associated with each stress type, a machine learning approach was implemented, achieving a classification accuracy exceeding 85% in discriminating control, drought-stressed, and salinity-stressed plants. Furthermore, characteristic Raman bands, particularly those associated with C-H and amide vibrational modes, showed strong correlations with established biochemical indicators, underscoring their potential as reliable, non-invasive stress biomarkers. Collectively, these findings provide mechanistic insight into stress-induced structural and biochemical alterations and support the application of RS-machine learning integration for precision agriculture and resilient crop management under changing environmental conditions.

Advances in 2-dimensional nanomaterials have driven the attention of researchers toward hexagonal boron nitride (hBN) for its applications like drug delivery and cosmetics. This work attempts to explore its application in agriculture for disease management in crops. In this regard, 2D-hBN nanosheets were exfoliated using the hydrothermal method, and a nanocomposite was prepared by incorporating phytofabricated TiO2 nanoparticles (NPs) to enhance antibacterial properties and analyze synergistic effects against phytopathogens. The prepared samples were characterized using UV-visible absorption spectroscopy, X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy and transmission electron microscopy (TEM). The TiO2 NPs synthesized using Azadirachta indica extract exhibited spherical NPs of ∼22 nm, while 2D-hBN formed ultrathin nanosheets. The antimicrobial activities of the prepared nanomaterials were evaluated against crop pathogens-Xanthomonas campestris pv. campestris (Xcc), Bacillus subtilis (BS), Pseudomonas fluorescens (PSFL), Fusarium graminearum, and Phytophthora spp. The outcomes of this study reveal that the synergistic effects of 2D-hBN and TiO2 NPs are more stronger, persistent, and long lasting than those of individual nanomaterials against plant pathogens for their potential application in the management of bacterial and fungal diseases during crop cultivation in agriculture.

AMCD: A multi-domain agricultural crop and flower image dataset for deep learning-based classification.

The family Rhabdoviridae comprises viruses with unsegmented, bi-segmented or tri-segmented negative-sense (-) RNA genomes of 10-16 kb. Virions are typically enveloped, with bullet-shaped or bacilliform morphology, but can also be non-enveloped filaments. Rhabdoviruses infect plants or animals, including vertebrates or invertebrates such as arthropods, which can serve as single hosts or act as biological vectors for transmission to animals or plants. Rhabdoviruses include important pathogens of humans, livestock, fish or agricultural crops. This is a summary of the International Committee on Taxonomy of Viruses (ICTV) Report on the family Rhabdoviridae, which is available at ictv.global/report/rhabdoviridae.

This article presents a Prairie-wide spatial vector dataset of agricultural field boundaries across Alberta, Saskatchewan, and Manitoba, Canada. The dataset was generated from Sentinel-2 Level-2A surface reflectance imagery (10 m spatial resolution) using an automated segmentation workflow based on the Segment Anything Model version 2 (SAM2). Sentinel-2 imagery was accessed in Google Earth Engine (GEE), filtered using cloud/quality masks, and aggregated into seasonal RGB composites representing key crop phenological periods (early-, mid-, and late-season) for large-scale segmentation input. The composite images were exported and processed with a SAM2 segmentation pipeline (tiled inference and mosaic-based post-processing) to delineate candidate field units without manually labeled training samples. Segmentation outputs were then post-processed using rule-based filtering and topology repair (removal of small artifacts/sliver polygons, hole filling, boundary cleaning, and geometry validity correction). Final vector outputs are distributed in ESRI Shapefile and GeoParquet formats with geometry attributes for downstream spatial analysis. The workflow code and processing scripts are provided to support reproducibility and adaptation to other regions. This dataset provides a consistent field-scale boundary reference layer for agricultural monitoring, crop and yield modeling, soil and environmental analysis, cropland mapping, land management, and machine-learning applications across the Canadian Prairies. •Prairie-wide field boundary datasets generated using automated segmentation of seasonal Sentinel-2 composites with the Segment Anything Model version 2, followed by vectorization and topological post-processing.•Vector outputs provided in both Shapefile and GeoParquet formats, enabling efficient use in traditional GIS, cloud-native, and big-data geospatial workflows.•A consistent, large-scale field boundary reference dataset supporting agricultural analysis, spatial modeling, cropland mapping, and machine learning applications across the Canadian Prairies.

Lead (Pb) contamination in soil significantly reduces crop productivity, presents considerable environmental and health hazards, and requires the development of effective remediation strategies. The use of biogas slurry derived from agricultural crop residues offers a promising approach to reduce Pb bioavailability in soil and alleviate its toxicity in maize seedlings, a topic that has not been extensively documented. Incorporating biogas slurry into soil immobilises Pb via adsorption. The biogas slurry reduced Pb uptake to plant aerial parts and ameliorated the emergence of maize seedlings by increasing germination rate. Consequently, Pb concentration in maize roots, shoots, and grains decreased by 68.17%, 55.17% and 62.10%, respectively. Furthermore, biogas slurry amendment markedly enhanced plant growth, stimulated antioxidant activity, and upregulated key non-enzymatic antioxidants, i.e., TPC, TFC, and DPPH. Moreover, organic addition ameliorated root architectural and functional traits like increased RL (99.01%), RPA (258.46%), and RSA (284.75%) more compared to the control. Conversely, it reduced BAC (15.57%) and BCF (35.75%). Analysis of organic osmolytes (TSP increased by 45.11%; TSS up to 51.04%) also elucidated that biogas slurry contribute to improved stress tolerance under Pb toxicity. These results offer new perspectives for sustainable agriculture by demonstrating that biogas slurry can decrease Pb mobility in the soil-plant system, thereby providing a potential solution to the ongoing issue of heavy metal contamination in agricultural soils.

Genetic resistance is the most economical and sustainable approach for crop protection, however, it is regularly overcome by pathogen virulence evolution. Polygenic resistance has greater durability, but unlinked genes are laborious to maintain in breeding programs. Introducing cloned resistance genes into the genome as gene stacks enables polygenic resistance with single locus inheritance. Fielder and Robin wheat plants were generated carrying two loci that each encode five wheat stem rust resistance transgenes, that is, Sr13c/Sr21/Sr22/Sr26/Sr33 and Sr22/Sr35/Sr45/Sr50/Sr55. Multiple transgenic events were combined and tested in the field. These lines, with unprecedented levels of transgenic resistance (i.e., 10 transgenes encoded on 90 kb of sequence), showed stable gene stack inheritance and transgene expression after eight generations and were highly resistant in the field. Importantly, in the absence of disease pressure no reproducible differences in the agronomic performance of transgenic lines that contained either one or both gene stacks was seen compared with control lines over two field trial seasons. These data confirm resistance gene stack efficacy and viability as a novel durable resistance strategy in agricultural crop production. Furthermore, this isogenic material that differs only by polygenic resistance provides significant insight into the broader question of the cost of disease resistance in host plants.

The global food system faces escalating risks to the production of major rainfed agricultural crops. Here we used modern portfolio theory to explore Pareto-optimal spatial arrangements of global rainfed cropping that explicitly account for trade-offs between total crop production and its interannual variability (risk). We analysed production/risk trade-offs under recent historical climate and yields for 2010, as well as under business-as-usual (RCP 6.0) and ambitious mitigation (RCP 2.6) climate futures for 2050. We found that optimizing the spatial distribution of rainfed crops could increase global production by 10.1% while maintaining the same level of risk as in 2010 or, alternatively, reduce production variability by 33.1% while maintaining the same level of production level as in 2010, all without additional land or water. Optimal global rainfed cropping patterns could meet future food demand with enhanced resilience under increased climate variability but only with yield-gap closure and more open and globalized trade, highlighting the need for coordinated production strategies, strengthened trade cooperation and sustainable intensification within climate adaptation policy.

Abstract This study, conducted during in 2023-2024 growing seasons on the experimental plots of the Bek-Dil-Bekh-Shokhsuvoriy farm in the Pastdargom district, Samarkand region, evaluated the effects of salt stress on the grow phases and productivity of winter and spring soft wheat varieties. A total of seven varieties were studied under field conditions using 1 m2 plots in triplicate. The assessment included phenological observations and measurements of key productivity traits: field germination, productive stem count (per m² and per plant), number of spikelets and grains per ear, grain weight per ear, and 1000-grain weight. Laboratory analyses followed the State Variety Testing of Agricultural Crops methodology. Data were statistically analyzed using Microsoft Excel and Statistica 6.0. Results indicated that salt stress negatively influenced seed germination, extended interphase periods, and reduced major yield components in both winter and spring wheat. Delays in the “germination–tillering” and “tillering–earing” phases were particularly pronounced under slightly saline soil conditions. A positive correlation (r = 0.720) was identified between yield and vegetation period duration, indicating that varieties with longer “shooting-to-earing” phases tended to form more productive ears and plants. Notably, winter wheat varieties demonstrated greater tolerance to salinity compared to spring varieties. Among the tested genotypes, Pahlavon, Ok Marvarid, Kayraktash, Es-1, and Es-4 showed superior performance in both control and slightly saline conditions, making them promising candidates for breeding programs aimed at improving salt tolerance and productivity. These findings are significant in the context of increasing soil salinization and global climate stress, as they contribute to efforts toward resilient crop production and sustainable food systems. The identified genotypes offer valuable material for future selection and cultivation on saline-prone soils.