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Gray mold, caused by Botrytis sp., is a significant disease in Colombian rose crops and its control depends primarily on the intensive use of chemically synthesized fungicides. Despite the importance of this pathogen, there is limited information in Colombian floriculture about molecular taxonomy of species, fungicide resistance of populations and their genetic mechanism of resistance. In this study, we analyze 12 isolates of this fungus collected from rose-producing crops in the Department of Cundinamarca and conducted phylogenetic analysis using HSP60, G3PDH, and RPB2 gene sequences. Additionally, we realize phenotypic and genotypic characterization of resistance to the fungicides fenhexamid, carboxin, and prochloraz, evaluating the in vitro EC50 and presence of mutations of target genes of each isolate. All isolates were characterized as Botrytis cinerea in the phylogenetic analysis and presents different levels of resistance to each fungicide. These levels are related to mutations in target genes, with predominancy of L195F and L400F in the ERG27 gene to fenhexamid resistance, H272R/Y in the SDHB gene for carboxin resistance, and Y136F in the CYP51 gene for prochloraz resistance. Finally, these mutations were not related to morphological changes. Collectively, this knowledge, presented for the first time to the Colombian floriculture, contribute to a better understanding of the genetic diversity and population of B. cinerea from rose-producing crops in the department of Cundinamarca, and serve as a valuable tool for making informed decisions regarding disease management, future research, and improving crop management and sustainability in the Colombian floriculture industry. [ABSTRACT FROM AUTHOR]

Background: In complex agricultural environments, the presence of shadows, leaf debris, and uneven illumination can hinder the performance of leaf segmentation models for cucumber disease detection. This is further exacerbated by the imbalance in pixel ratios between background and lesion areas, which affects the accuracy of lesion extraction. Results: An original image segmentation framework, the LS-ASPP model, which utilizes a two-stage Atrous Spatial Pyramid Pooling (ASPP) approach combined with adaptive loss to address these challenges has been proposed. The Leaf-ASPP stage employs attention modules and residual structures to capture multi-scale semantic information and enhance edge perception, allowing for precise extraction of leaf contours from complex backgrounds. In the Spot-ASPP stage, we adjust the dilation rate of ASPP and introduce a Convolutional Attention Block Module (CABM) to accurately segment lesion areas. Conclusions: The LS-ASPP model demonstrates improved performance in semantic segmentation accuracy under complex conditions, providing a robust solution for precise cucumber lesion segmentation. By focusing on challenging pixels and adapting to the specific requirements of agricultural image analysis, our framework has the potential to enhance disease detection accuracy and facilitate timely and effective crop management decisions. [ABSTRACT FROM AUTHOR]

In both plant breeding and crop management, interpretability plays a crucial role in instilling trust in AI-driven approaches and enabling the provision of actionable insights. The primary objective of this research is to explore and evaluate the potential contributions of deep learning network architectures that employ stacked LSTM for end-of-season maize grain yield prediction. A secondary aim is to expand the capabilities of these networks by adapting them to better accommodate and leverage the multi-modality properties of remote sensing data. In this study, a multi-modal deep learning architecture that assimilates inputs from heterogeneous data streams, including high-resolution hyperspectral imagery, LiDAR point clouds, and environmental data, is proposed to forecast maize crop yields. The architecture includes attention mechanisms that assign varying levels of importance to different modalities and temporal features that, reflect the dynamics of plant growth and environmental interactions. The interpretability of the attention weights is investigated in multi-modal networks that seek to both improve predictions and attribute crop yield outcomes to genetic and environmental variables. This approach also contributes to increased interpretability of the model's predictions. The temporal attention weight distributions highlighted relevant factors and critical growth stages that contribute to the predictions. The results of this study affirm that the attention weights are consistent with recognized biological growth stages, thereby substantiating the network's capability to learn biologically interpretable features. Accuracies of the model's predictions of yield ranged from 0.82-0.93 R2 ref in this genetics-focused study, further highlighting the potential of attention-based models. Further, this research facilitates understanding of how multi-modality remote sensing aligns with the physiological stages of maize. The proposed architecture shows promise in improving predictions and offering interpretable insights into the factors affecting maize crop yields, while demonstrating the impact of data collection by different modalities through the growing season. By identifying relevant factors and critical growth stages, the model's attention weights provide valuable information that can be used in both plant breeding and crop management. The consistency of attention weights with biological growth stages reinforces the potential of deep learning networks in agricultural applications, particularly in leveraging remote sensing data for yield prediction. To the best of our knowledge, this is the first study that investigates the use of hyperspectral and LiDAR UAV time series data for explaining/interpreting plant growth stages within deep learning networks and forecasting plot-level maize grain yield using late fusion modalities with attention mechanisms. [ABSTRACT FROM AUTHOR]

This study aimed to reconstruct the environmental conditions and the crop management practices and plant characteristics when agriculture appeared in western Europe. We analyzed oak charcoal and a large number of cereal caryopsides recovered from La Draga (Girona, Spain), an early (5300 to 4800 cal. BC) agricultural site from the Iberian Peninsula. The carbon isotope discrimination (Δ13C) values of oak, the dominant forest species in the region, indicates prevalence of a wet climate at the site. Further, we reconstructed crop management conditions, achievable yield, and crop characteristics through the analysis of Δ13C, nitrogen isotope composition (d15N), nitrogen content, and the reconstructed weight of wheat and barley caryopsides, following protocols developed by our team [Araus et al., Nat. Commun. 5, 3953 (2014)] and comparison of these parameters with present-day organic agriculture in the region. In parallel, a regional perspective was achieved through the study of wheat and barley grains of seventeen Neolithic sites from the western Mediterranean. The results suggest that rather than small-garden cultivation, a more extensive agriculture was practiced under good water availability and moderate manuring. Moreover, results from La Draga evidence that grain weight and spike morphology were comparable to contemporary cereals. Growing conditions and the prevalence of improved crop traits indicate that agriculture was fairly consolidated at the time it reached the western edge of Europe. [ABSTRACT FROM AUTHOR]

The root-knot nematode Meloidogyne izalcoensis was described parasitising coffee trees and causing damage in the Izalco volcano region, Sonsonate, El Salvador. In Brazil, it was detected in coffee roots in the municipality of Indianópolis, Minas Gerais state (MG). Intercropping cover crops is a traditional practice in Brazilian coffee orchards, and the use of plants that are non-hosts or antagonistic to the nematodes is a very promising strategy for coffee in infested areas. Due to the scarce information on the host status of M. izalcoensis, the aim of this study was to evaluate 24 plants of different species of cover crops and select the non-hosts or poor hosts. Two assays were conducted at different times, and all plants were inoculated with 10 000 eggs. After 90 days, the following parameters were determined: fresh root weight, reproduction factor (RF) and final reaction. Plants with RF < 1.0 were classified as non-hosts (NH), those with RF > 1.0 were classified as hosts and using statistical analyses as: good host (GH), intermediate host (IH) and poor host (PH). Most of the botanical species tested were classified as NH or PH to M. izalcoensis: cotton, rice, white oat, black oat, ryegrass, corn, three species of grass, two cultivars of millet, two wheat cultivars, Crotalaria breviflora, C. ochroleuca, C. spectabilis, jack bean, gray mucuna bean and two species of rice grass. Only tomato and bean were classified as GH, and C. juncea and soybean were classified as IH. The plants identified as non-hosts or poor hosts can be recommended for intercropping or for crop rotation in Brazilian coffee orchards in regions infested by M. izalcoensis. [ABSTRACT FROM AUTHOR]

Precision agriculture integrates multiple sensors and data types to support farmers with informed decision-making tools throughout crop cycles. This study evaluated Aboveground Biomass (AGB) estimates of Rye using attributes derived from PlanetScope (PS) optical, Sentinel-1 Synthetic Aperture Radar (SAR), and hybrid (optical plus SAR) datasets. Optical attributes encompassed surface reflectance from PS's blue, green, red, and near-infrared (NIR) bands, alongside the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). Sentinel-1 SAR attributes included the C-band Synthetic Aperture Radar Ground Range Detected, VV and HH polarizations, and both Ratio and Polarization (Pol) indices. Ground reference AGB data for Rye (Secale cereal L.) were collected from 50 samples and four dates at a farm located in southern Brazil, aligning with image acquisition dates. Multiple linear regression models were trained and validated. AGB was estimated based on individual (optical PS or Sentinel-1 SAR) and combined datasets (optical plus SAR). This process was repeated 100 times, and variable importance was extracted. Results revealed improved Rye AGB estimates with integrated optical and SAR data. Optical vegetation indices displayed higher correlation coefficients (r) for AGB estimation (r = +0.67 for both EVI and NDVI) compared to SAR attributes like VV, Ratio, and polarization (r ranging from −0.52 to −0.58). However, the hybrid regression model enhanced AGB estimation (R2 = 0.62, p < 0.01), reducing RMSE to 579 kg·ha−1. Using only optical or SAR data yielded R2 values of 0.51 and 0.42, respectively (p < 0.01). In the hybrid model, the most important predictors were VV, NIR, blue, and EVI. Spatial distribution analysis of predicted Rye AGB unveiled agricultural zones associated with varying biomass throughout the cover crop development. Our findings underscored the complementarity of optical with SAR data to enhance AGB estimates of cover crops, offering valuable insights for agricultural zoning to support soil and cash crop management. [ABSTRACT FROM AUTHOR]

miRNA are endogenous regulatory molecules comprising 20–24 nucleotides playing a crucial role in the growth and development of various organs in animals as well as plants. In plants, miRNAs regulate biotic and abiotic stress responses conferring tolerance by functioning as negative and positive regulators. Down-regulation of the target genes is accomplished by repressing translation or by cleavage of a transcript or by inhibiting transcription. Various studies on crop plants like rice, wheat, and sugarcane have revealed the importance of miRNA in conferring abiotic stress tolerance against salinity and drought and biotic resistance against pests such as plant pathogens and insects. The biotic and abiotic stress regulates the target gene expression through the differential expression of specific miRNAs. An in-depth study of miRNA as a molecular marker in plants under stress conditions, profiling miRNA expressions and thorough understanding of its role in gene regulation may help in enhancing crop productivity and crop management against various stress conditions. So, the main objective of this review is to summarize recent advances in miRNA regulation in plants with special emphasis on its role in stress tolerance. [ABSTRACT FROM AUTHOR]

Disease and nutrient deficiency disorders significantly impact the productivity of rice crops. Timely identification of these conditions is essential for effective mitigation of potential crop damage. To address this challenge, considerable research is happening in the field of rice crop monitoring and maintenance, using cutting-edge techniques like Machine learning (ML)/Deep learning (DL). This study aims to address critical aspects of the research landscape, including publication trends, data modalities, ML/DL models, pre-processing methods, segmentation techniques, and feature selection approaches in the context of rice crop's health. By presenting both research findings and existing gaps, this systematic literature review (SLR) offers valuable insights to direct future research endeavours in this domain. Our investigation involves a comprehensive review of articles sourced from Scopus, IEEE Xplore, Science Direct and Google Scholar resulting in a dataset of 91 unique articles spanning from the year 2013–2023. Following rigorous selection criteria, these 91 articles have been considered for in-depth analysis. Through an extensive examination of this corpus, our study seeks to provide answers to seven key questions pertaining to the past, present, and future directions of research of ML/DL application in rice crop health monitoring and disease/disorder diagnosis. The review adheres to the agricultural science-based PRISMA systematic review methodology and incorporates statistical analysis to explore relationships among variables such as dataset sample size, experimental accuracy, and classification models employed in various studies. • PRISMA-based review that covers both disease and nutrient deficiency disorders in rice plants in the context of ML/DL based diagnosis methods. • Highlights pros and cons of a wide range of methodologies, including both ML approaches and DL methodologies. • A meta-analysis has been performed for the understanding of the relationships between variables related to datasets and classification models. [ABSTRACT FROM AUTHOR]

Precision agriculture (PA) has great potential to increase agricultural productivity and profitability while reducing input costs and environmental impacts. Within PA, site‐specific crop management (SSCM) is considered the main premise, in which tillage operations and precise crop inputs (such as seed, fertiliser, water, pesticide and agrochemical) are applied according to field variability. The main aim of this review was to highlight the methods and tools used for spatial crop monitoring, soil and weather data influencing crop productivity and to support the adoption of SSCM technology. To achieve this goal: we discussed the main five components of SSCM, methods for monitoring crop and soil data, delineating field management zones (FMZs) and variable rate technologies (VRT) such as precision planting and digital smart sensors used for SSCM application. The review summarised that recent advances in plant and soil sensing systems, artificial intelligence (AI) and machine learning should be used in retrieving and analysing GIS big data for optimised crop inputs supply. Within VRT, light‐bar systems, automatic controllers and sensors are user‐friendly technologies that should be employed in SSCM solution. The authors highlight that adoption of PA can be increased through proper training and education of the farmers, and developing simple, affordable and efficient PA technologies. The review suggests five criteria that should be strictly adopted to get maximum benefits from SSCM: (i) all factors influencing crop yields can be identified; (ii) their effects on crop yields can be determined by using appropriate digital tools and crop modelling; (iii) variable rate crop inputs (VRCIs) should be calculated based on accurate information obtained from plant, soil and environment; (iv) targeted crop inputs should be exercised through global positioning system (GPS) enabled automatic controllers or wireless sensors network (WSN); and (v) right doses of crop inputs (e.g., nitrogen and irrigation) must be applied at the right time and place. [ABSTRACT FROM AUTHOR]

Temporal change in vegetation complexity and field management may interactively affect the structure of predator–prey networks in agroecosystems and consequently alter the biocontrol potential of predators. There is a limited number of studies that have addressed these questions for generalist predator–prey networks.We investigated how vegetation complexity during crop development and management type (conventional vs. organic) affect the trophic networks of orb‐weaving spiders and their prey in rainfed lowland rice ecosystems in southern Thailand. Specifically, we investigated orb‐weaving spiders and prey composition, prey selectivity and network structure.Overall, orb‐weaving spiders captured mostly detritus‐associated Diptera, aquatic‐associated Ephemeroptera and Hemiptera pests. The increasing vegetation complexity during rice development significantly restructured the network of orb‐weaving spiders and prey, while field management had only a marginal effect. The increased vegetation complexity during rice development led to an increased number of weak trophic interactions in comparison to few but strong interactions in simple vegetation.Our results indicate that increasing the number of spider species per prey taxon (prey vulnerability) in late rice season may enhance their top‐down control of prey including rice pests. This study also highlights that the network complexity and the stability of rice ecosystems increased during the rice growing season as the vegetation became more complex.Future practices could look for a way to support the densities of detritus and aquatic insects and to artificially increase habitat complexity during the early stage of rice development to improve the biocontrol services provided by the orb‐weaving spiders. [ABSTRACT FROM AUTHOR]

The monitoring of the tassel number and tasseling time reflects the maize growth and is necessary for crop management. However, it mainly depends on field observations, which is very labor intensive and may be biased by human errors. Tassel detection remains challenging due to the varying appearance of tassels across maize varieties, tasseling stages, and spatial resolutions. Moreover, the capability of the deep learning model for monitoring tassel number change and the time of entering tasseling stage has not been explored. In this study, we propose a novel approach for fast tassel detection using PConv (Partial Convolution) within YoloV8 series, named PConv-YoloV8 series. Compared to seven state-of-the-art deep learning methods, PConv-YoloV8 × 6 best trades off detection accuracy with the number of parameters (Parameters = 52.50 MB, AP = 0.950, R2 = 0.92, rRMSE = 9.08%). The potential of PConv-YoloV8 × 6 to provide an accurate detection of tassels in complex situations from near-ground and UAV images were comprehensively studied. PConv-YoloV8 × 6 maintained an excellent detection accuracy for maize at different tasseling stages (AP = 0.826–0.972, R2 = 0.83–0.92, RMSE = 1.94–3.01, rRMSE = 21.06%-7.09%), for different varieties (AP = 0.901–0.978, R2 = 0.77–0.97, RMSE = 1.39–3.16, rRMSE = 11.72%-5.06%), at different resolutions (AP = 0.921–0.956, R2 = 0.84–0.93, rRMSE = 8.72%-17.71%), and on UAV images with different resolutions (AP = 0.918–0.968, R2 = 0.98–0.99, rRMSE = 6.43%-12.76%), which proved the robustness of the model. The tasseling number and the time of entering tasseling stage detected from images were basically consistent with the trends observed in the manually labeled results. This study provides an effective method to monitor the tassel number and the time of entering the tasseling stage. A new maize tassel detection dataset (18260 tassels in 729 near-ground images and 20835 tassels in 144 UAV images) is created. Future studies will focus on making more lightweight models and achieving real-time detection capabilities. [ABSTRACT FROM AUTHOR]

Soil salinization poses a critical challenge to global food security, impacting plant growth, development, and crop yield. This study investigates the efficacy of deep learning techniques alongside chlorophyll fluorescence (ChlF) imaging technology for discerning varying levels of salt stress in soybean seedlings. Traditional methods for stress identification in plants are often laborious and time-intensive, prompting the exploration of more efficient approaches. A total of six classic convolutional neural network (CNN) models—AlexNet, GoogLeNet, ResNet50, ShuffleNet, SqueezeNet, and MobileNetv2—are evaluated for salt stress recognition based on three types of ChlF images. Results indicate that ResNet50 outperforms other models in classifying salt stress levels across three types of ChlF images. Furthermore, feature fusion after extracting three types of ChlF image features in the average pooling layer of ResNet50 significantly enhanced classification accuracy, achieving the highest accuracy of 98.61% in particular when fusing features from three types of ChlF images. UMAP dimensionality reduction analysis confirms the discriminative power of fused features in distinguishing salt stress levels. These findings underscore the efficacy of deep learning and ChlF imaging technologies in elucidating plant responses to salt stress, offering insights for precision agriculture and crop management. Overall, this study demonstrates the potential of integrating deep learning with ChlF imaging for precise and efficient crop stress detection, offering a robust tool for advancing precision agriculture. The findings contribute to enhancing agricultural sustainability and addressing global food security challenges by enabling more effective crop stress management. [ABSTRACT FROM AUTHOR]

In this short communication, we propose a framework that classifies methods for estimating crop nitrogen (N) input requirements along two dimensions: short-term versus long-term and current versus optimal crop management practices. The four resulting quadrants provide different perspectives on modelling methods, each distinctive in handling soil N supply, mineral versus organic N sources, management and yield levels. For each quadrant, study examples and data requirements are provided. We emphasise the importance of accurate and purpose-oriented selection of crop N requirement modelling methods and acknowledge the complexity and uncertainties inherent in N modelling, often due to a lack of data availability. The choice of methods to estimate N inputs strongly impacts model outcomes and conclusions, affecting agricultural practices, advisories, and policy strategies. Therefore, a decision tree is presented to support choices in assessing N input requirements so that models can help identify under and over-fertilisation hotspots, maintain soil fertility, and optimise crop yields, contributing to environmental protection and food security goals. [ABSTRACT FROM AUTHOR]

Viticulture is characterized by substantial pesticide applications, impacting natural enemies. New pest control strategies and management of plant diversity into agrosystems acting as reservoirs of natural enemies are assumed to limit pesticide use. Various studies support this hypothesis but gaps exist on the effect of diversification on Phytoseiidae mites, generalist predators reported as prevalent and efficient natural enemies in vineyards. This study focuses on the effect of cover crop management (no cover crop, spontaneous cover crops with or without agroforestry) and grape variety (resistant cv. Artaban and cv. Syrah) on predatory mites and prey communities, in a newly planted experimental vineyard in South-East France. Samplings were carried out three times a year on vine, cover crops, and co-planted trees. Phytoseiidae, Tydeiidae, Eriophyidae mites and thrips were characterized. Nine Phytoseiidae species were identified on vine, the main ones being Kampimodromus aberrans, Typhlodromus exhilaratus, Phytoseius finitimus and Euseius gallicus. Kampimodromus aberrans was prevalent on the cv. Syrah, highlighting a strong effect of variety. The low unexpected effect of system management observed outcome could be due to several factors, such as the experimental plot size or the influence of vine stress on Phytoseiidae communities in vines with cover crops. All phytoseiid species present on vine were identifed at least once on cover crops and co-planted trees, suggesting their potential role as reservoirs. Further studies should be performed investigating the evolution of communities in this newly-planted experimental system, as well as potential differences in trophic network interactions. [ABSTRACT FROM AUTHOR]

Purpose: The study's objective is to assess the environmental performance of rice production in Northern Italy, in particular in Piedmont, the first Italian and European district for the rice-growing area, and thus identify the most critical hotspots and agricultural processes. In particular, as a case study, a farm located in Vercelli (VC) has been chosen. Subsequently, the study results were compared with other different cultivation practices to evaluate the most sustainable choice. Methods: The application of the LCA has been performed, highlighting the phases of rice production that have the most significant impact. Then, uncertainty and sensitivity analyses have been made to estimate the robustness of the results and assess the influence of changing some input variables on emission reduction. Finally, multivariate statistical, specifically a principal component analysis (PCA), was conducted to aid the interpretation of the output dataset of this case study. LCA, uncertainty analysis, and sensitivity analysis were performed with SimaPro 9.2.0, using ReCiPe 2016 Midpoint (H) methodology, and PCA with R software. Results and discussions: The hotspot with the highest environmental load is irrigation, which compared to the other phases impacts more in 15 out of 18 categories, including 12 with impacts greater than + 75%. This is because irrigation causes direct impacts, related to the methanogenesis in rice fields, but also indirect impacts related mainly to the production of the energy mix required to move the large masses of irrigation water. Therefore, different water management systems were compared and results show that the irrigation systems based on intermittent paddy submergence (DSI) could result in − 40% lower impacts, resulting to be the preferable technique over the other irrigation systems analyzed, including the traditional one used in this study. Conclusions: In order to reduce the environmental impacts related to the irrigation process, a water management system characterized by intermittent flooding of the paddy field (DSI) could be used as it reduces the environmental impacts the most (− 40%), while the least suitable system is one characterized by continuous flooding without drought periods, as it causes the highest impacts. [ABSTRACT FROM AUTHOR]

The importance of soil management has been recognized internationally. In Japan, the interest in soil management has recently been increasing as the compost input into agricultural land has been decreasing. Owing to recent increases in chemical fertilizer prices, increased production costs in agriculture have become a challenge. Furthermore, the current strategy of the Japanese and European Commission for Sustainable Food Systems is to reduce the use of chemical fertilizers. From this perspective, there are considerable expectations for soil management and fertilizer reduction using organic matter. This review focuses on cover crops for soil management and for green manuring, an organic material that is advantageous in terms of transportation costs and the application of labor. First, the results of scientific research on the function of cover crops in improving the soil environment in the surface layer (organic matter accumulation, erosion control, and improvement of soil biological properties) and in the subsurface soil layer (physical properties) are reviewed. Next, the mechanisms of cover crops in supplying nutrients (nitrogen, potassium, and phosphorus) to cash crops as green manure are reviewed. Finally, I reviewed the ways in which the benefit of cover crops can be enhanced (species selection, growth stage of incorporation, and methods and timings of seeding the following crops). Because the effects of the introduction of cover crops on soil quality and cash crop production have been indicated quantitatively, the use of cover crops is expected to expand and contribute to reducing chemical fertilizer use and improving soil quality in the future. [ABSTRACT FROM AUTHOR]

Integrated nutrient management and crop rotation are important farming practices, which enhance the nutrient use efficiency of crops and reduce the incidence of diseases and insect pests. The study was carried out to address the gap in using integrated nutrient management in crop rotation systems for soil qualities and crop yield improvement. That was done by adjusting the balance ratio of dry bio-slurry and nitrogen fertilizers. The experiment was containing ten levels; Control (0,0), recommended nitrogen, 50% dry-bio slurry, 100% dry-bio slurry, 75% dry-bio slurry, 75% dry-bio slurry+25% recommended nitrogen, 50% dry-bio slurry+50% recommended nitrogen, 25% dry-bio slurry+75% recommended nitrogen, 100% dry-bio slurry + 25% recommended nitrogen and 100% dry-bio slurry + 50% recommended nitrogen that was laid out in randomized complete block design with three replications for three years. The data on soil properties and yield components of potatoes and wheat were collected and analyzed using statistical analysis system software 9.4. An application of dry bio-slurry with nitrogen fertilizer was significantly affected both crop yield and soil properties in the rotation system. The application of 25% dry bio-slurry with 75% recommended nitrogen gave the highest tuber yield of potato (27.6 tha-1) as compared to control. Similarly, using 100% and 75% sole dry bio-slurry resulted in the highest grain yield (3.85 tha-1) and above-ground biomass (9.59 tha-1) of wheat. The combination of 25% dry bio-slurry with 75% recommended nitrogen scored the highest net benefit (2889.2 US$) with an acceptable marginal return (4463.3%) via by improving crops yield in the system. So, an application of 25% dry bio-slurry with 75% recommended nitrogen could be promoted for yield-soil improvement in the study area and similar agroecology. [ABSTRACT FROM AUTHOR]

Cropping systems in semiarid regions have frequently relied on continuous tillage and irrigation, but declining groundwater resources have prompted a greater focus on conservation practices to improve soil health and water storage. We compared soil health responses from cotton production systems in semiarid, coarse‐textured soils with different crop management strategies under high or low irrigation levels. Management systems included continuous cotton with conventional tillage (CCCT) compared to no‐till cotton with a rye cover crop (NTCR) and no‐till cotton with a wheat‐fallow rotation (NTCW), including high or low irrigation zones within each system. Samples were collected annually from two bulk soil depths (0–10 cm and 10–20 cm) and root‐associated soils 7 years after systems were established and continued for 2 years. We found that cropping system, but not irrigation level, altered soil microbial communities and other soil health indicators. Despite variation between study years and sampling zones, the conservation systems had greater soil microbial community size via ester‐linked fatty acid methyl ester (EL‐FAME or FAME) analysis, extracellular enzyme activities, and soil organic matter than the CCCT system. The NTCW system also had greater arbuscular mycorrhizal fungi FAME abundance. Our study suggests that no‐till and conservation strategies such as cover cropping and rotation can improve biological soil health indicators in these sandy semiarid soils even with limited irrigation. [ABSTRACT FROM AUTHOR]

Plant-parasitic nematodes are a major problem for horticultural production, causing significant economic losses. Chemical nematicides are a common nematode control strategy but can negatively impact the environment and human health. In this review, we provide an overview of the use of fungi for the biological control of plant-parasitic nematodes in horticultural crops. Fungal bio-control represents an alternative, environmentally friendly strategy for managing nematodes. The use of fungi for the bio-control of nematodes has gained increasing attention due to the potential benefits of selectivity, sustainability, and long-term control. Here, we discuss the different types of fungi used for nematode management, the mechanisms of their action, the advantages and limitations of using fungi for bio-control, and the factors affecting their efficacy. Additionally, we provide several case studies of fungal bio-control against root-knot, cyst, and lesion nematodes in horticultural crops. The review concludes with a discussion of future directions and research recommendations for implementing fungal bio-control in horticultural crops. [ABSTRACT FROM AUTHOR]

The agriculture Internet of Things (IoT) has been widely applied in assisting pear farmers with pest and disease prediction, as well as precise crop management, by providing real-time monitoring and alerting capabilities. To enhance the effectiveness of agriculture IoT monitoring applications, clustering protocols are utilized in the data transmission of agricultural wireless sensor networks (AWSNs). However, the selection of cluster heads is a NP-hard problem, which cannot be solved effectively by conventional algorithms. Based on this, This paper proposes a novel AWSNs clustering model that comprehensively considers multiple factors, including node energy, node degree, average distance and delay. Furthermore, a novel high-performance cluster protocol based on Gaussian mutation and sine cosine firefly algorithm (GSHFA-HCP) is proposed to meet the practical requirements of different scenarios. The innovative Gaussian mutation strategy and sine–cosine hybrid strategy are introduced to optimize the clustering scheme effectively. Additionally, an efficient inter-cluster data transmission mechanism is designed based on distance between nodes, residual energy, and load. The experimental results show that compared with other four popular schemes, the proposed GSHFA-HCP protocol has significant performance improvement in reducing network energy consumption, extending network life and reducing transmission delay. In comparison with other protocols, GSHFA-HCP achieves optimization rates of 63.69%, 17.2%, 19.56%, and 35.78% for network lifespan, throughput, transmission delay, and packet loss rate, respectively. [ABSTRACT FROM AUTHOR]

A crucial determining factor in agricultural productivity is biotic stress. In addition, supply of quality food to the ever-increasing world's population has raised the food demand tremendously. Therefore, enhanced agricultural crop productivity is the only option to mitigate these concerns. It ultimately demanded the often and indiscriminate use of synthetic agrochemicals such as chemical fertilizers, pesticides, insecticides, herbicides, etc. for the management of various biotic stresses including a variety of plant pathogens. However, the food chain and biosphere are severely impacted due to the use of such harmful agrochemicals and their byproducts. Hence, it is need of hour to search for novel, effective and ecofriendly approaches for the management of biotic stresses in crop plants. Particularly, in plant disease management, efforts are being made to take advantage of newly emerged science i.e. nanotechnology for the creation of inorganic nanoparticles (NPs) such as metallic, oxide, sulphide, etc. through different routes and their application in plant disease management. Among these, green nanomaterials which are synthesized using environmentally friendly methods and materials reported to possess unique properties (such as high surface area, adjustable size and shape, and specific functionalities) making them ideal candidates for targeted disease control. Nanotechnology can stop crop losses by managing specific diseases from soil, plants, and hydroponic systems. This review mainly focuses on the application of biologically produced green NPs in the treatment of plant diseases caused due to bacteria, viruses, and fungi. The utilization of green synthesis of NPs in the creation of intelligent targeted pesticide and biomolecule control delivery systems, for disease management is considered environmentally friendly due to its pursuit of less hazardous, sustainable, and environmentally friendly methods. [ABSTRACT FROM AUTHOR]

Efficiently monitoring and quantifying the response of genotypes to water stress is critical in developing resilient crop cultivars in water-limited environments. The objective of this study was to assess water stress in cotton (Gossypium hirsutum L.) using high-resolution unmanned aerial system (UAS) images and identify water-stress-resistant cultivars in plant breeding. Various vegetation indices (VIs) and the crop water stress index (CWSI) derived from UAS images were applied to assess water stress in eight cotton cultivars under four irrigation treatments (90%, 60%, 30%, and 0% ET). The enhanced vegetation index (EVI), green normalized difference vegetation index (GNDVI), normalized difference red-edge index (NDRE), normalized difference vegetation index (NDVI), and crop water stress index (CWSI) were effective in detecting the effects of the irrigation treatments during the growing season. These VIs effectively differentiated cultivars in the middle and late seasons, while the CWSI detected cultivar differences in the mid–late growing season. The NDVI, GNDVI, NDRE, and EVI had a strong positive relationship with cotton yield starting from the mid-growing season in two years (R2 ranged from 0.90 to 0.95). Cultivars under each irrigation treatment were clustered into high-, medium-, and low-yielding groups based on the VIs at the mid–late growing seasons using hierarchical cluster analysis (HCA). The EVI derived from UAS images with high temporal and spatial resolutions can effectively screen drought-resistant cotton varieties under 30% and 60% irrigation treatments. The successful classification of cultivars based on UAS images provides critical information for selecting suitable varieties in plant breeding to optimize irrigation management based on water availability scenarios. This technology enables the targeted selection of water-stress-resistant cotton cultivars and facilitates site-specific crop management and yield prediction, ultimately contributing to precision irrigation and sustainable agriculture in water-limited environments. [ABSTRACT FROM AUTHOR]

Due to its readily soluble nature in groundwater and surface water, an excess accumulation of soluble salts in the soil, especially sodium chloride (NaCl), could pose serious threats to crop growth and development by disrupting physiological and biochemical processes in plants. The excess of soluble salt could originate from natural processes, such as weathering (physical or chemical) of parent rock materials, wind, and rainfall, and is thus termed as primary soil salinization. Besides, various anthropogenic activities, such as irrigation with brackish or saline water, poor land and water management, overuse of synthetic fertilizers, overextraction of groundwater, and saltwater intrusion into coastal aquifers as a result of sea level rise, could also create a salinity problem, which is known as secondary soil salinization. The salinity problem in soil and land of arid and semi-arid regions is even more severe. The concentration of salt is referred to as the sum of cation and anion or only cation or anion and is expressed as mg L-1. However, it is commonly measured as electrical conductivity (EC) of saturated soil extract (ECe) for scientific and analytical purposes and is expressed with a unit of decisiemens per meter (dS m-1). Salinity is a multi-dimensional stress for plants, impairing plant growth and development through cytotoxicity caused by excessive uptake of Na+ and Cl- ions, water stress, and nutritional imbalance. In addition, salinity is generally accompanied by oxidative stress because of the overproduction of reactive oxygen species. The water scarcity phase of salinity results in substantial yield loss through rapid inhibition of growth and development, while the ionic toxicity phase causes rapid leaf senescence through a variety of adverse impacts on cellular components simultaneously. Moreover, prooxidants generation at higher levels of salt stress threatens standard metabolic processes and poses a risk of irreversible damage on biomembranes, cellular organelles, nucleic acids, and proteins. Such constraints make cropping a great challenge in salt-affected areas; however, naturally salt-tolerant plants, also known as halophytes or semi-halophytes, can easily withstand comparatively higher salt concentrations because of their adaptive morpho-physiological and molecular mechanisms. Unlike halophytic or semi-halophytic plants, most of the commonly cultivated crops fail to tolerate salt stress as they fall under glycophyte or salt-susceptible category. However, the process of natural selection coupled with developing agricultural technologies has played a substantial role in imparting greater tolerance of crops to salt stress over the past few decades. Nevertheless, rapid sea level rise driven by anthropogenic climate change affecting the salinity of surface and groundwater in coastal areas and persistent spreading of salinity both in terms of land area and concentration call for a combined approach of compatible cultivar development and adoption of suitable management strategies. This necessitates a systematic and in-depth understanding of existing adaptations, tolerance mechanisms, and cultivation methods for stepping forward in strategizing future crop yield improvement programs. Therefore, the present review focuses on highlighting (i) the factors intensifying salinity problem in soils, (ii) salinity dynamics in plants including its uptake, transport, storage, and development of stress, (iii) salinity sensing mechanism in plants, (iv) naturally occurring adaptations, (v) genetic variabilities, and (vi) tolerance mechanisms available within cultivable crop germplasms with specific emphasis on morphological and anatomical tolerance, physiochemical tolerance, and role of genetic resources in enhancing tolerance against salinity. A number of agronomic, cultural, and technological management options to alleviate the adverse impacts of salt stress on crop plants are also discussed in this review, which could potentially offer invaluable information for the development of salt-tolerant crop improvement programs and optimized land and water management practices. [ABSTRACT FROM AUTHOR]

The impact of climate change on crops and agricultural yield is an actual threat while being a challenging issue due to the high complexity of factors that intervene at the local scale of the crop. Assessing it, requires the use of coupled models climate-phenology, meanwhile methods to identify management and genotypes suitable for local future conditions, in order to sustain adaptation strategies. We present the implementation and use of a new integrated climate-phenology adaptation support modeling system based on regional CORDEX climate models and the CERES Maize model from DSSAT platform, with new modules for optimal management and genotype identification using a hybrid method: deterministic modeling and -ML/ genetic algorithms. It was run as a regional pilot over Romania, operating in real-time in interaction with users, performing agro-climate projections (combination of fertilization, sowing date, soil) and providing best crop management simulated under climate change projections. Multi-model ensemble simulations for two climate scenarios RCP4.5 and RCP8.5 and twelve management scenarios show new results for the region. For the actual genotype we find a projected mean decrease in yield in both climate scenarios for all sowing dates and fertilization levels tested, response shown to be sensitive to initial soil parameters. This response was linked to two factors: a shorter growing season by up to 10 % and a loss of fertilization efficiency in a warmer climate. A warning points to results showing a narrowing of agro-management opportunities for crop yield but in opposite it is shown a significant role of optimal genotype-range identification that may provide crop solutions under climate change even in extreme years. Identifying best genotype under warmer climate along sets of six cross-parameter simulations show systematic lower values of the maximal yields, but emphasizes genotype windows of increases in the intermediate yield values in scenarios compared to actual climate. The highest harvest sensitivity to genotype is shown to be to changes in the thermal time to juvenil respectively to maturity stage under warmer climate. The results sustain using a deterministic coupled modeling system combined with data-driven modeling for identifying optimal adaptation including fertilization paths that contribute to climate change mitigation. [ABSTRACT FROM AUTHOR]

AbstractPrecision nutrient management is a modern approach for optimizing nutrient supply in fruit crops, ensuring that plants receive the actual amount of essential elements at the right time and place. Traditional nutrient management methods often undergo deficiencies, leading to over-fertilization, uneven distribution of nutrients, environmental pollution, and economic inefficiency. These review paper challenges can be addressed by providing real-time data of soil conditions, plant health and nutrient levels. This strategy depends on advanced technologies such as remote sensing, variable rate technology, fertigation, slow/control release fertilizer, and organic amendments to weave nutrient application to the specific needs of each crop and individual plant within a field. It is emphasized by its potential for plant growth and development, increased crop yield, optimized resource utilization and mitigated environmental concerns. By fine-tuning nutrient application, farmers can achieve better economic returns while promoting sustainable agriculture. Precision nutrient management for fruit crops is characterized by a scarcity of studies exploring the application of advanced technologies and data-driven approaches. There is a need for more in-depth investigation to develop and validate precision nutrient management strategies tailored to the unique requirements of different crops. Closing this research gap will contribute to sustainable and optimized fruit crop production. In conclusion, precision nutrient management represents a paradigm shift in agricultural practices, offering a more sustainable and efficient approach to nutrient application in fruit crops. By untried advanced technologies and data-driven insights, farmers can optimize their resource use, enhance crop performance, and contribute to the long-term sustainability of agriculture. [ABSTRACT FROM AUTHOR]

Babaco is a hybrid cultivar native to the Andean region of Ecuador and Colombia, commercially attractive for its fruit. Babaco production in Ecuador faces losses from plant pathogens like babaco mosaic virus (BabMV), an RNA virus that causes chlorosis, leaf mottling, and deformation. Phylogenetic studies link BabMV to papaya mosaic virus (PapMV), alternanthera mosaic virus , and senna mosaic virus. To address this threat, we developed novel species-specific primers to detect BabMV targeting a 165 bp region of the coat protein (CP). Genus-specific primers were designed to validate the species-specific primers and attest their ability to discriminate between BabMV and its closest relatives. These primers targeted a 175 bp fragment of the CP region. The most effective sets of primers were chosen for reverse transcription polymerase chain reaction (RT-PCR) and SYBR® Green-based quantitative reverse transcription polymerase chain reaction (RT-qPCR) in symptomatic and asymptomatic babaco plants. Among 28 plants tested, 25 were positive and 3 were negative for BabMV using species-specific and genus-specific primers in RT-PCR and RT-qPCR, while the PapMV positive control was detected with the genus-specific primers and was negative for the species-specific primers. These primers represent a valuable molecular tool for detecting BabMV, potentially enhancing crop management. [ABSTRACT FROM AUTHOR]

Cover crops (CCs) offer ecosystem benefits, yet their impact on subsequent crop yields varies with climate, soil, and management practices. Using the Decision Support System for Agrotechnology Transfer (DSSAT) at the University of Nebraska-Lincoln's Eastern Nebraska Research, Education, and Extension Center (ENREEC), we identified optimal cereal rye management strategies focusing on planting, termination, and the intervals between CC termination and corn planting. Results showed minimal impact of CC management variations on corn yield, underscoring corn's resilience to management changes. Delayed planting notably decreased CC biomass, nitrogen uptake, and biomass nitrogen content on average by 8.8%, 11%, and 9.2% for every five-day delay from 25 September. Every 5-day increase in the interval between CC termination and corn planting reduced biomass by 19.3%. Conversely, each 5-day delay in CC termination from 10 September to 10 October increased biomass by 30%, enhancing SOC accumulation. SOC changes over the 30-year simulation ranged from 5.8% to 7.7%, peaking with late May terminations. The earliest termination showed the highest nitrogen content in biomass (3.4%), with the lowest (0.69%) in mid-May. Our results demonstrate that strategic CC management supports soil health without negatively impacting corn yield in Eastern Nebraska, providing valuable insights for farmers and practitioners aiming to implement sustainable CC practices while preserving crop productivity. [ABSTRACT FROM AUTHOR]

This research focuses on the seasonal patterns of the ecophysiological recovery of four olive cultivars (Arbequina, Coratina, Frantoio, and Urano), both micropropagated and self-rooted, grown in a mature, irrigated, super high-density (SHD) orchard under Mediterranean conditions (Southern of Italy). The aim was to observe the impact of the micropropagation method on the ecophysiological responses. Ecophysiological parameters, including leaf water potential (LWP), stomatal conductance (gs), net photosynthetic rate (Pn), and transpiration rate (E) were assessed. Self-rooted trees consistently exhibited superior gs, E, and Pn recovery compared to the micropropagated ones. 'Arbequina' maintained elevated levels of Pn under water-deficit conditions. 'Coratina' exhibited increases in gs and E after irrigation. 'Frantoio' demonstrated recovery capabilities, with lower LWP and higher Pn under stress. 'Urano' micropropagated trees achieved higher gs values in mid-summer, while self-rooted trees sustained higher Pn later in the season. This field research highlighted the important role of the propagation method in optimizing the physiological performance of olive cultivars in SHD orchards. Furthermore, it highlighted the necessity of long-term studies on the effects of propagation methods and their interactions with other farming practices. [ABSTRACT FROM AUTHOR]

Weeds are unwanted and undesirable plants that interfere with cultivated crops for utilization of natural resources such as nutrients, water, and light, space, causing harbor of insect pests and disease adverse allelopathic effects and increasing cost of production indirectly, reducing crop yield. India is an agrarian country, where cropping systems form the backbone of Indian agriculture. Every cropping system has its associated weeds, and to manage these weeds requires a specific crop management approach. The main rice-based cropping systems in India, such as rice-wheat, rice-rice, rice-pulse, rice-maize, and rice-oilseed, have been pivotal in ensuring food security. However, these systems face significant challenges due to weed infestations, leading to yield losses ranging from 13% to 97%. Weeds like grass, sedge and broad-leaf weed varieties pose a substantial threat to these crops, particularly in direct-seeded conditions. To manage weeds effectively, various approaches are employed, including crop rotation, tillage, herbicide application, and intercropping. For instance, in rice-wheat systems, zero-tillage practices, crop residue mulch, and early sowing help to reduce weed emergence. In rice-pulse systems, pre-emergence herbicides, hand weeding, and intercropping prove effective. Maize-based systems benefit from tillage, mulching, and planting at closer spacing. Weed management is critical to sustaining rice-based cropping systems and ensuring food security in India. Tailored strategies that consider crop-specific challenges are essential to mitigate yield losses and maintain agricultural productivity. [ABSTRACT FROM AUTHOR]

The decline in seed quality over time due to natural aging or mishandling requires assessing seed vigor for resilience in adverse conditions. Accelerated aging (AA) methods simulate seed deterioration by subjecting seeds to high temperatures and humidity. Saturated salt accelerated aging (SSAA) is an AA method adopted for small seeds like lettuce (Lactuca sativa). In this study, we subjected seeds of two lettuce cultivars ('Muir' and 'Bauer') to SSAA by sealing them in a box containing 40 g/100 mL of a sodium chloride (NaCl) solution in a dark growth chamber at 41 °C for 24, 48, and 72 h with a control. We monitored their vigor using embedded computer cameras, tracking the projected canopy size (PCS) daily from sowing to harvest. The cultivar 'Muir' exhibited consistent PCS values across the treatments, while 'Bauer' showed PCS variations, with notable declines after prolonged aging. The germination rates dropped significantly after 48 and 72 h of SSAA. A nonlinear regression model revealed a strong relationship between PCS and shoot dry weight across harvests and cultivars (R2 = 0.93, RMSE = 0.15, p < 0.001). The research found that the projected canopy size and shoot dry weight increased over time with significant differences in treatments for the cultivar 'Bauer' but not for 'Muir,' with the canopy size being a strong predictor of dry weight and no significant impact from the SSAA treatments. This study highlights cultivar-specific responses to aging and demonstrates the efficacy of our imaging tool in predicting lettuce dry weight despite treatment variations. Understanding how aging affects different lettuce varieties is crucial for seed management and crop sustainability. [ABSTRACT FROM AUTHOR]

Global wheat production has remained stable in the last 20 years, benefiting from increased grain yields despite decline in harvested wheat area. Here, we conducted a comprehensive review of ca. 300 peer-reviewed studies worldwide to outline benefits of adding wheat to simple crop rotations (i.e., one to three rotational crops). We highlight the wheat's versatility for tactical in-season crop management (e.g., flexible sowing dates, crop type [winter vs spring], and nitrogen fertility) and strategic cropping system management (e.g., grazing and double-cropping) and provide evidence supporting the positive impact of wheat on the grain yield and yield stability of other rotational crops. The inclusion of wheat in simple cropping systems enhances agroecosystem diversity and improves resilience to biotic and abiotic stresses. The high carbon-to-nitrogen ratio (C:N) residue of wheat offers benefits and drawbacks on soil quality attributes, weed control, and climate change mitigation potential. The introduction of wheat to simple crop rotations can (i) interrupt pest population cycles by serving as a break crop; (ii) decrease N application requirements, thus reducing N losses, greenhouse gas emissions, soil acidification, and production costs; (iii) improve soil health and carbon sequestration; (iv) increase resource use-efficiency of the cropping system; (v) foment fauna population; and (vi) decrease variability in economic returns. This review highlights that wheat offers unique opportunities to increase diversification and foster more sustainable and resilient agroecosystems that will feed a growing global population while acting as a net carbon sink, helping to mitigate drivers of climate change. [ABSTRACT FROM AUTHOR]

Background: Climate influences the interaction between pathogens and their hosts significantly. This is particularly evident in the coffee industry, where fungal diseases like Cercospora coffeicola, causing brown‐eye spot, can reduce yields drastically. This study focuses on forecasting coffee brown‐eye spot using various models that incorporate agrometeorological data, allowing for predictions at least 1 week prior to the occurrence of disease. Data were gathered from eight locations across São Paulo and Minas Gerais, encompassing the South and Cerrado regions of Minas Gerais state. In the initial phase, various machine learning (ML) models and topologies were calibrated to forecast brown‐eye spot, identifying one with potential for advanced decision‐making. The top‐performing models were then employed in the next stage to forecast and spatially project the severity of brown‐eye spot across 2681 key Brazilian coffee‐producing municipalities. Meteorological data were sourced from NASA's Prediction of Worldwide Energy Resources platform, and the Penman–Monteith method was used to estimate reference evapotranspiration, leading to a Thornthwaite and Mather water‐balance calculation. Six ML models – K‐nearest neighbors (KNN), artificial neural network multilayer perceptron (MLP), support vector machine (SVM), random forests (RF), extreme gradient boosting (XGBoost), and gradient boosting regression (GradBOOSTING) – were employed, considering disease latency to time define input variables. Results: These models utilized climatic elements such as average air temperature, relative humidity, leaf wetness duration, rainfall, evapotranspiration, water deficit, and surplus. The XGBoost model proved most effective in high‐yielding conditions, demonstrating high precision and accuracy. Conversely, the SVM model excelled in low‐yielding scenarios. The incidence of brown‐eye spot varied noticeably between high‐ and low‐yield conditions, with significant regional differences observed. The accuracy of predicting brown‐eye spot severity in coffee plantations depended on the biennial production cycle. High‐yielding trees showed superior results with the XGBoost model (R2 = 0.77, root mean squared error, RMSE = 10.53), whereas the SVM model performed better under low‐yielding conditions (precision 0.76, RMSE = 12.82). Conclusion: The study's application of agrometeorological variables and ML models successfully predicted the incidence of brown‐eye spot in coffee plantations with a 7 day lead time, illustrating that they were valuable tools for managing this significant agricultural challenge. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Cereal crop (rice, wheat, maize etc.) residues mainly include the aboveground parts of plants that remain in an agricultural field after crop has been harvested. Nutritional value, organic matter content, and porous nature of cereal crop residues make them suitable for use on agricultural land to improve the soil physical, chemical, and biological properties. Application of crop residue into the soil needs proper strategies, which could sustain crop production as well as soil health. In this comprehensive review, efforts have been made to compile data on studies conducted the impact of residues on various soil health properties, current management practices and residue removing issues for a better understanding of soil health improvement strategies through cereal crop residue retention or incorporation. Further, the article discussed the relationship between cereal residue retention/incorporation and soil health properties by considering 113 peer-reviewed studies. We evaluated site-specific advantages and disadvantages involved on residue retention/incorporation. In addition, we found out the suitability of residue incorporation vs. retention, in respect to soil health improvement. This review found that residue retention or incorporation can positively influence soil physical, chemical, and biological characteristics associated with soil health. Significant influences of abiotic factors on residue management were observed in different locations. To our knowledge, this comprehensive review is the first one to present global view of current scenario of cereal crop residue management, their influence on soil health improvement, and residue removing issues with citing worldwide evidences. [ABSTRACT FROM AUTHOR]

Under global change and climate variations, determining the impacts of climate change and human activities on cropland net primary productivity (NPP) in Bangladesh, India and Myanmar (BIM) is of great significance for identifying yield-limiting factors, making adaptive agricultural management plans, and improving yields. Based on the GLOPEM-CEVSA model, through an integration of remote sensing data and LAI simulation, we investigated the impacts and spatiotemporal changes of water and human activities on BIM from 1982 to 2015. Three types of cropland NPPs were considered: actual NPP (NPPA), NPP affected by temperature and water (NPPWT), and NPP only affected by temperature (NPPT). Our analysis revealed that the water factor plays a predominant role in determining the NPP level in the BIM. Temperature variability was found to be conducive to NPPT, exhibiting an increasing trend of 10.66 g C m–2 yr–1. However, this trend was partially offset by precipitation variability, resulting in a net increase of 0.96 g C m–2 yr–1. In comparing temperature-driven NPP to temperature and water-driven NPP, water stress caused NPPT to decrease by 65.46% compared to NPPWT for the entire region. Cropland NPP in northwestern India and the central Deccan Plateau were significantly affected by water stress. Moreover, the influence of water on NPP in the BIM exhibited a substantial upward trend from 1982 to 2015, with Myanmar experiencing the most significant increase. The gap between NPPWT and NPPA in BIM demonstrated a notable decreasing trend during the same period, underscoring the positive impact of human activities on NPP. Inferences drawn from our findings suggest that with the implementation of rational and efficient crop management practices, there is a 36.80% potential improvement in NPPA compared to NPPWT in the BIM region, with India and Myanmar showing potential increases of 39.20% and 38.29%, respectively. These insights provide guidance for practical measures aimed at water resource management to enhance cropland productivity in the BIM, and they present a methodology for quantifying the effects of climatic changes and human activities at a regional scale. [ABSTRACT FROM AUTHOR]

Agriculture plays a crucial role in the economy, and there is an increasing global emphasis on automating agricultural processes. With the tremendous increase in population, the demand for food and employment has also increased significantly. Agricultural methods traditionally used to meet these requirements are no longer adequate, requiring solutions to issues such as excessive herbicide use and the use of chemical fertilizers. Integration of technologies such as the Internet of Things, wireless communication, machine learning, artificial intelligence (AI), and deep learning shows promise in addressing these challenges. However, there is a lack of comprehensive documentation on the application and potential of AI in improving agricultural input efficiency. To address this gap, a desk research approach was used by utilizing peer-reviewed electronic databases like PubMed, Scopus, Google Scholar, Web of Science, and Science Direct for relevant articles. Out of 327 initially identified articles, 180 were deemed pertinent, focusing primarily on AI's potential in enhancing yield through better management of nutrients, water, and weeds. Taking into account research findings worldwide, we found that AI technologies could assist farmers by providing recommendations on the optimal nutrients to enhance soil quality and determine the best time for irrigation or herbicide application. The present status of AI-driven automation in agriculture holds significant promise for optimizing agricultural input utilization and reducing resource waste, particularly in the context of three pillars of crop management, i.e. nutrient, irrigation, and weed management. [ABSTRACT FROM AUTHOR]

Soil is a heterogeneous medium that exhibits considerable variability in both spatial and temporal dimensions. Proper management of field variability using variable-rate fertilization (VRF) techniques is essential to maximize crop input–output ratios and resource utilization. Implementing VRF technology on a localized scale is recommended to increase crop yield, decrease input costs, and reduce the negative impact on the surrounding environment. This study assessed the agronomic and environmental viability of implementing VRF during the cultivation of summer maize using an on-the-go detector of soil total nitrogen (STN) to detect STN content in the test fields. A spatial delineation approach was then applied to divide the experimental field into multiple management zones. The amount of fertilizer applied in each zone was determined based on the sensor-detected STN. The analysis of the final yield and economic benefits indicates that plots that adopted VRF treatments attained an average summer maize grain yield of 7275 kg ha−1, outperforming plots that employed uniform-rate fertilization (URF) treatments, which yielded 6713 kg ha−1. Through one-way ANOVA, the yield p values of the two fertilization methods were 6.406 × 10−15, 5.202 × 10−15, 2.497 × 10−15, and 3.199 × 10−15, respectively, indicating that the yield differences between the two fertilization methods were noticeable. This led to an average yield increase of 8.37% ha−1 and a gross profit margin of USD 153 ha−1. In plots in which VRF techniques are utilized, the average nitrogen (N) fertilizer application rate is 627 kg ha−1. In contrast, in plots employing URF methods, the N fertilizer application rate is 750 kg ha−1. The use of N fertilizer was reduced by 16.4%. As a result, there is a reduction in production costs of USD 37.5 ha−1, achieving increased yield while decreasing the amount of applied fertilizer. Moreover, in plots where the VRF method was applied, STN was balanced despite the reduced N application. This observation can be deduced from the variance in summer maize grain yield through various fertilization treatments in a comparative experiment. Future research endeavors should prioritize the resolution of particular constraints by incorporating supplementary soil data, such as phosphorus, potassium, organic matter, and other pertinent variables, to advance and optimize fertilization methodologies. [ABSTRACT FROM AUTHOR]

The variability of crop load in Conference pears significantly influences fruit quality and postharvest losses. This study aimed to investigate and implement a novel technique for managing crop load in Conference pear orchards, with a focus on contributing to sustainable orchard management practices. High-russet pear cv. Conference fruit was harvested from trees categorized into two groups based on yield efficiency. A two-way ANOVA was used to test the effects of crop load level and year on yield properties and fruit quality parameters. Results demonstrated that low crop loads exerted a positive influence on fruit quality, resulting in increased fresh-market yield, enhanced fruit-mass parameters, and improved firmness and acidity. Additionally, fruit from low crop load levels exhibited a heightened level of characteristic skin russeting, a desirable attribute that enhances product appeal, compared to fruit from high crop load levels. Thus, crop load management in Conference pear orchards emerged as an effective preharvest organic strategy for mitigating postharvest quality losses during storage, thereby promoting overall orchard sustainability. [ABSTRACT FROM AUTHOR]

Mineral nutrition is essential for plant growth and the interaction of plants with biotic and abiotic stresses. Mango twig-tip dieback (MTTD) is a new type of mango decline, but its impact on trees' mineral nutrition is unknown. This study was conducted to determine the effect of MTTD infection on the nutrient status, balance, and resorption efficiency (RE) of mangoes. Leaf nutrient concentrations and deviation from the optimum percentage (DOP) indices of 'Kensington Pride' (KP) mango trees with low (LD) and high (HD) levels of MTTD infections were analyzed to compare the foliar nutrition status and nutrient balance between the LD and HD trees. Moreover, the nutrient resorption efficiency of MTTD-infected dried leaves (RED) was compared with the resorption efficiency of healthy (RES) leaves of KP mangoes. The concentrations of total Ca, Mg, Cu, Fe, Mn, and Zn were lower in the HD trees than in the LD trees. But the total K content was higher in the HD trees, and its DOP index was sufficient, while the total K concentration was of a low and deficient level in LD trees. Moreover, the DOP indices for total Ca, Mn, and Zn were less deficient in LD trees than in HD trees, and the overall nutrient imbalances were exacerbated in HD trees. The RED was significantly lower than RES for the total N, P, S, Cu, Fe, and Zn but significantly higher than RES for K. This study underscores the significant influence of MTTD on the mineral nutrition of KP mangoes, revealing distinct nutrient variations between trees with low and high MTTD infection levels. These findings have important implications for mango crop management, emphasizing the need for targeted nutrient interventions to address imbalances induced by MTTD and enhance the overall health and resistance of mango trees against MTTD infections. [ABSTRACT FROM AUTHOR]

African green monkeys (Chlorocebus sabaeus) are an established introduced species in the Federation of St. Kitts and Nevis, and the source of human-wildlife conflict. The Government of St. Kitts and Nevis has responsibility to manage problems caused by the monkeys, which include real and perceived social, economic, and ecological impacts, the most notable being agricultural damage to crops. These problems are widespread across the island and affect many community members in diverse ways. This study asked if the governmental policy environment enabled or impeded intersectoral management of monkeys. No formal policy addressed all harms or benefits the monkeys engender across multiple places and sectors, nor enabled an all-of-government approach. Government actions were guided by unwritten policies operating under the authorities of agriculture, conservation, or public health legislation and international agreements. No policies addressed harms while concurrently preserving the benefits the monkeys afford, such as their promotion and use in tourism. Private sector policies addressed animal welfare and exportation of animals used in biomedical research. Cross-sectoral governance was achieved through informal mechanisms. No metrics were found to evaluate effectiveness, acceptability or feasibility of government efforts. The Government of St. Kitts and Nevis has the intentions and components that could support an integrated One Health, healthy public policy approach to its monkey problems, but all actions to concurrently protect the health of monkeys, people, incomes and ecosystems were achieved through informal means which present certain implementation and assessment challenges. Written policies could increase transparency of decision making, aid in policy evaluation and ensure sustainability of action through variation in personnel and other organizational change. [ABSTRACT FROM AUTHOR]

Introduction: One of the non-biological stresses for crop plants is drought and lack of water, which is the most important factor limiting the growth of crop plants in arid and semi-arid areas, including Iran. Drought impairs normal growth, disturbs water relations, and reduces water use efficiency in plants. Plants, however, have a variety of physiological and biochemical responses at cellular and whole organism levels, making it a more complex phenomenon. The rate of photosynthesis is reduced mainly by stomatal closure, membrane damage, and disturbed activity of various enzymes, especially those involved in ATP synthesis. Plants display a range of mechanisms to withstand drought, such as reduced water loss by increased diffusive resistance, increased water uptake with prolific and deep root systems, and smaller and succulent leaves to reduce transpirational loss. Therefore, the effects of drought stress and seed nutritional priming on plants can play an important role in managing different irrigation regimes to deal with adverse environmental conditions and improve crop yield management. Materials and methods: Quinoa is one of the crops that has received special attention in recent years due to its high nutritional value and resistance to dehydration. For this purpose, an experiment was conducted in the form of a factorial split plot, with three rounds of irrigation of 7, 10 and 14 days as the main factor and seed priming at two levels without prime as a control and Iron sulfate + zinc sulfate combination as the second factor was applied on three genotypes (Q12, Q29 and Giza1) of quinoa as secondary factors, in the crop year of 2021-2022 in the regions of Neishabour and Kashmar. Results and discussion: Moisture stress decreased the relative water content and percentage of membrane stability. In Kashmir region, the highest relative water content was observed in cultivars Q12 and Giza under the treatment of irrigation once every 14 days. While in the Neishabur region, the highest value of the mentioned variable was obtained in the treatment of irrigation once every 7 days and cultivar Q29. Slicing analysis of the interaction effect of cultivar and irrigation treatments and the interaction effect of irrigation and priming in Kashmir region showed that the 7 and 10 day irrigation treatment was significant on cell membrane stability. Comparison of the average interaction effect of the three tested factors in Neishabur area showed that the variety Q12 (regardless of seed priming) along with applying 14 days of irrigation caused a significant increase in membrane stability in Neishabur. Although the lowest level of membrane stability was obtained in the priming treatment of Q12 and Giza cultivars with the application of 7 days of irrigation once. It was observed that the priming treatment of Q29 and Giza cultivars with irrigation every 10 and 7 days, respectively, showed the highest amount of total chlorophyll in the Kashmir region, while in the Neishabur region, the highest amount of total chlorophyll was obtained in non-priming of Giza cultivar with the application of 10 Irrigation once a day. The results of the experiment showed that seed priming of Q12 and Q29 cultivars under irrigation once every 7 days caused a significant increase in seed yield in Kashmir region. In Neishabur region, the highest amount of seed yield was obtained in the non-priming cultivar Giza under irrigation once in 10 days. Conclusion In general, seed priming with zinc sulfate and iron sulfate can improve the physiological characteristics of quinoa under water stress. Although in regarding the relative water content, the Q29 variety had a dominant effect on the priming treatment in Neishabour region. According to the results of the Q29 variety in the Kashmar region and the Giza variety In the Neishabour region, it will help plant growth by maintaining the chlorophyll content of the leaves under low irrigation conditions. [ABSTRACT FROM AUTHOR]

Planting date and seeding rate affect the microclimate within a crop field. They can influence lepidopteran insect infestation and fungal infections and are an important part of crop management strategy. Altering planting date and seeding rate can also influence yield and quality of silage corn (Zea mays L.). Field research was conducted to identify optimum planting time and seeding rate to minimize insect feeding, ear rot infections, and mycotoxin accumulation in silage corn without compromising yield and quality. Replicated field trials were conducted across multiple site‐years in Michigan with three planting dates (early: April 25–May 10; mid: May 11–25; and late: May 26–June 10) and/or four seeding rates (ranging from 69,160 to 113,620 seeds ha−1, in increments of 14,800 seeds ha−1). Mid‐planting yielded 12%–15% less forage than early and late plantings, partly due to greater insect feeding injury and fungal infections observed in the former. Neutral detergent fiber digestibility, starch, and crude protein concentration were greatest for early planting. Greater predicted milk per hectare and milk per megagram for early planting also indicated superior silage quality. Increasing seeding rate increased insect feeding and ear rot injury only when severity was >5% and >15%, respectively. The impact of increasing seeding rate on dry forage yield was specific to each site‐year. Overall, results showed that early planting of silage corn helps to escape insect and disease pressure and provides better yield and quality, while seeding rate response is variable and dependent on the field environment. Core Ideas: Escaping insect feeding and ear rots by altering the planting date is critical to protect yield and forage quality.Delaying silage corn planting reduced forage neutral detergent fiber digestibility.Insect feeding severity and ear rot increased at high seeding rates (≥98,800 seeds ha−1).Higher seeding rates led to reduction in neutral detergent fiber digestibility and predicted milk yield. [ABSTRACT FROM AUTHOR]

Crop models are valuable tools for simulating and assessing genotype‐by‐environment interactions. In most studies, these models are parameterized based on crop data from a few sites and years, which often limits their applicability to a broader geographic context. Therefore, we utilize countrywide multi‐environment variety trial data in this study to implement a genotype‐specific model parameterization for winter rye (Secale cereale L.) in Germany. We use the Crop and Environment REsource Synthesis (CERES) model originally used for wheat available in the decision support system for agrotechnology transfer (DSSAT) framework and adapt and evaluate it for rye. Calibration and evaluation involved a comprehensive agronomic trial datasets for the rye cultivar Palazzo, encompassing 194 site‐years of experiments covering various cereal production regions in Germany. The parameterization followed a structured approach, encompassing phenology, growth, and yield‐specific coefficients. The parameterized CSM‐CERES‐Rye (where CSM is cropping system model) demonstrated reasonable accuracy in simulating critical crop parameters, including aboveground biomass, leaf area index, tiller, grain number, unit seed weight, and grain yield. The model is available for diverse model‐based assessments of rye cultivation, including evaluating crop management, analyzing crop rotations, and assessing rye's suitability across varied environments, making it valuable for sustainable agriculture and decision‐making. Core Ideas: Adapting DSSAT‐CSM‐CERES‐Wheat (where DSSAT is decision support system for agrotechnology transfer, CSM is cropping system model, and CERES is crop and environment resource synthesis) for cereal rye using comprehensive data from German plant variety trials for the first time.A structured sequential method was employed for model parameterization.The model enables essential analyses, including crop management, rotations, and suitability assessments. [ABSTRACT FROM AUTHOR]

Climate change affects changes in rice-based systems of coastal Bangladesh. Both external pressures and system-immanent attributes influence farmers' adoption of new production practices, leading to the emergence of new land use patterns. Field and household surveys quantified recent and emerging change trends in crop yields and associated intensification-related practices in representative rice-based systems, using a diachronic approach (comparing the years 2011 and 2021). We analyzed 240 rice-producing farms, representing three rice-based cropping systems, namely extensive rice-fallow rotations, intensified irrigated rice double rotations, and diversified rice-mungbean rotations. The study sites contrasted favorable biophysical conditions (climate, soil type, water source and quality, soil fertility) in the Barishal district, and marginal (unfavorable biophysical environments) conditions at Patuakhali district in coastal Bangladesh. Soil fertility attributes were assessed at both study sites. The type, the extent, and the pathways of recent changes differed between locations, systems and seasons. We observed significant increases in aggregated yield (across crops and seasons), in individual crop yields, and in economic yields at both the marginal and the favorable sites. Crop yields varied widely (rice: 3.0-7.8 t ha-1, mungbean: 0.4-1.5 t ha-1), and rice yield was higher in dry than in the wet season. Farmers' adoption of intensification practices started earlier in the marginal study area, but the extent of the changes was larger at favorable sites. Most prominent was the mechanization of tillage operations and an increased use of mineral fertilizers, with the largest changes observed in irrigated rice and in dry season mungbean. Such site-, system-, and season-specific assessments will permit identifying drivers of change and can inform the assessment of potential future patterns of land system changes. [ABSTRACT FROM AUTHOR]

Deliberate open burning of crop residues emits greenhouse gases and toxic pollutants into the atmosphere. This study investigates the environmental impacts (global warming potential, GWP) and economic impacts (net cash flow) of nine agricultural residue management schemes, including open burning, fertilizer production, and biochar production for corn residue, rice straw, and sugarcane leaves. The environmental assessment shows that, except the open burning schemes, fossil fuel consumption is the main contributor of the GWP impact. The fertilizer and biochar schemes reduce the GWP impact including black carbon by 1.88–1.96 and 2.46–3.22 times compared to open burning. The biochar schemes have the lowest GWP (− 1833.19 to − 1473.21 kg CO2-eq/ton). The economic assessment outcomes reveal that the biochar schemes have the highest net cash flow (222.72—889.31 US$2022/ton or 1258.15–13409.16 US$2022/ha). The expenditures of open burning are practically zero, while the biochar schemes are the most costly to operate. The most preferable agricultural residue management type is the biochar production, given the lowest GWP impact and the highest net cash flow. To discourage open burning, the government should tailor the government assistance programs to the needs of the farmers and make the financial assistance more accessible. [ABSTRACT FROM AUTHOR]

[Display omitted] • Erosion-based prioritization using a comprehensive approach is vital. • The Shazand sub-Watersheds, Iran, were prioritized using the game theory. • The Shazand sub-watersheds were prioritized differently in various scenarios. • The game theory-based Condorcet approach holistically and successfully ranked sub-watersheds. The soil erosion of the watershed is different in temporal and spatial scales due to complex climatic and environmental conditions and their dynamic state over time. Accordingly, it is necessary to prioritize sub-watersheds based on their dynamic conditions to reduce the destructive effects of soil erosion. However, this issue has to be sufficiently reported on the scale of the watershed. Towards that, a game theory-based prioritization approach was planned concerning changes in vegetation cover and rainfall in the Shazand Watershed of the Markazi Province, Iran. To achieve the purpose of the study, the amount of soil erosion was first estimated in different scenarios resulting from the changes in rainfall erosivity and vegetation cover using the RUSLE model. Next, pairwise comparisons between 24 sub-watersheds in all scenarios were made based on the sub-watersheds' ranks in soil erosion in each scenario. Considering all scenarios, the priority of the sub-watersheds was assessed using the Condorcet approach. The results showed that the mean soil erosion in the watershed ranged from 5.4 and 36 t ha−1 y−1 while applying various scenarios. It is worth mentioning that the prioritization results obtained from the Condorcet algorithm and the current soil erosion rate are highly similar. On the other hand, sub-watersheds 2 and 9 were selected as the first and last priority, providing an appropriate roadmap for managers and relevant departments to adopt appropriate soil erosion inhibition measures. [ABSTRACT FROM AUTHOR]

BACKGROUND: Cassava is an important crop for the survival of smallholder farmers in Cameroon. However, the cassava sector has a low production per unit area compared to the technological potential in this country. In this context, breeders have developed varieties based mainly on their potential in terms of yield and disease resistance. These varieties have been widely disseminated in Cameroon within the framework of development projects. However, these releases have not achieved the expected adoption and yield levels at the national level. Therefore, it appears important to rethink the determinants of dissemination with a broader examination of the cassava production system. RESULTS: This paper analyses varietal complementarity as a key strategy in support of optimizing the experimental and continuous use of cassava varieties by farmers in the Central and Eastern regions of Cameroon. These two regions account for 50% of the country's production. A total of 111 semi‐structured interviews were conducted with farmers selected through purposive sampling in four villages in Central and Eastern Cameroon where improved varieties have been disseminated. The research revealed four types of complementarity, related to use, crop management, risk management and cultural complementarity. CONCLUSION: Our results argue for considering varietal complementarities practiced by farmers, within research and development programs to develop more effective breeding and dissemination approaches. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

This editorial discusses various studies related to the production of silage, focusing on factors that influence forage quality, physiology, and fermentation characteristics. The studies explore topics such as the effects of endogenous potassium and sodium on silage quality, the use of additives to improve the silage quality of corn in high-temperature environments, and the impact of magnesium and calcium on the production of kernel starch in corn. The document also mentions the use of plant essential oils as feed additives and the potential of natural woody plants as a sustainable feed source for livestock. The editorial concludes by emphasizing the need for further research in this area. [Extracted from the article]

Citrus tristeza virus (CTV) is a highly destructive viral pathogen posing a significant threat to citrus crops worldwide. Disease management and crop protection strategies necessitate the development of rapid and accurate detection methods. In this study, we employed Oxford Nanopore sequencing to detect CTV in Citrus unshiu samples. Subsequently, we developed a specific and sensitive detection assay combining CRISPR/Cas12a with reverse transcription-recombinase polymerase amplification. The CRISPR-Cas12a assay exhibited exceptional specificity for CTV, surpassing conventional RT-PCR by at least 10-fold in sensitivity. Remarkably, the developed assay detected CTV in field samples, with zero false negatives. This diagnostic approach is user-friendly, cost-effective, and offers tremendous potential for rapid onsite detection of CTV. Therefore, the CRISPR-Cas12a assay plays a significant role in managing and preserving citrus trees that are free from viruses in the industry. [ABSTRACT FROM AUTHOR]

Assessment of crop nitrogen status is essential for efficient crop growth management. Existing nitrogen measurements are accurate but destructive, laborious, and time-consuming. Therefore, the soil plant analysis development (SPAD) meter approach is commonly used to address these challenges along with locationspecific measurements. The study aims to develop a robust machine learning-based model for predicting rice crop SPAD values using spectral data and to generate spatial maps of SPAD values and nitrogen content. The SPAD meter data, UAV-based multispectral images, and spectroradiometer-based data were collected during Rabi 2021/22 and 2022/23 seasons. The red and rededge bands, Normalized Difference Vegetation Index, and Normalized Pigment Chlorophyll Index correlated well with SPAD values. The random forest regressor model performed well with UAV-based data compared to support vector regression and partial least square regression and achieved good accuracy with the ground truth spectroradiometer data. This generalized model demonstrates adaptability in precisely assessing crop nitrogen status. [ABSTRACT FROM AUTHOR]