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Background: The proportion of nitrogen (N) derived from the atmosphere (Ndfa) is a fundamental component of the plant N demand in legume species. To estimate the N benefit of grain legumes for the subsequent crop in the rotation, a simplified N balance is frequently used. This balance is calculated as the difference between fixed N and removed N by grains. The Ndfa needed to achieve a neutral N balance (hereafter θ ) is usually estimated through a simple linear regression model between Ndfa and N balance. This quantity is routinely estimated without accounting for the uncertainty in the estimate, which is needed to perform formal statistical inference about θ . In this article, we utilized a global database to describe the development of a novel Bayesian framework to quantify the uncertainty of θ . This study aimed to (i) develop a Bayesian framework to quantify the uncertainty of θ , and (ii) contrast the use of this Bayesian framework with the widely used delta and bootstrapping methods under different data availability scenarios. Results: The delta method, bootstrapping, and Bayesian inference provided nearly equivalent numerical values when the range of values for Ndfa was thoroughly explored during data collection (e.g., 6–91%), and the number of observations was relatively high (e.g., ≥ 100 ). When the Ndfa tested was narrow and/or sample size was small, the delta method and bootstrapping provided confidence intervals containing biologically non-meaningful values (i.e. < 0% or > 100%). However, under a narrow Ndfa range and small sample size, the developed Bayesian inference framework obtained biologically meaningful values in the uncertainty estimation. Conclusion: In this study, we showed that the developed Bayesian framework was preferable under limited data conditions ─by using informative priors─ and when uncertainty estimation had to be constrained (regularized) to obtain meaningful inference. The presented Bayesian framework lays the foundation not only to conduct formal comparisons or hypothesis testing involving θ , but also to learn about its expected value, variance, and higher moments such as skewness and kurtosis under different agroecological and crop management conditions. This framework can also be transferred to estimate balances for other nutrients and/or field crops to gain knowledge on global crop nutrient balances. [ABSTRACT FROM AUTHOR]

African yam bean (AYB; Sphenostylis stenocarpa) is an underutilized legume indigenous to Africa with great potential to enhance food security and offer nutritional and medicinal opportunities. However, low grain yield caused by fungal diseases, including pod blight and leaf tip dieback, deters farmers from large‐scale cultivation. To determine the prevalence of fungal diseases affecting leaves, pods and flowers of AYB, a survey was conducted in 2018 and 2019 in major AYB‐growing areas in Nigeria. Leaf tip dieback, flower bud rot and pod blight were the most common symptoms. Morphological and molecular assays were conducted to identify the causal agents of the observed diseases. In all the samples examined, fungi from eight genera were isolated from diseased leaves, buds and pods. Koch's postulates were fulfilled only for fungi belonging to the Colletotrichum genus. Fungi from the other seven genera did not produce disease symptoms in healthy AYB tissues. Several Colletotrichum isolates were characterized by sequencing the rDNA internal transcribed spacer (ITS), glyceraldehyde‐3‐phosphate dehydrogenase, calmodulin and ApMAT loci. A combined phylogenetic analysis revealed four Colletotrichum species: C. siamense, C. theobromicola and C. fructicola, which were recovered from diseased leaves, and C. truncatum, recovered from diseased pods and buds. Our results are useful to gear efforts to develop integrated management strategies to control diseases affecting AYB in Nigeria and elsewhere. Availability of such strategies may stimulate greater AYB cultivation, which can contribute to diet diversification, something repeatedly advocated by a range of stakeholders to increase food security and prosperity of smallholder farmers. [ABSTRACT FROM AUTHOR]

Information on the effects of changing agricultural management on crop and livestock performance is critical for developing evidence-based policies, investments, and programs. Evidence for Resilient Agriculture (ERA) v1.0.1 presents a dataset that harmonizes and aggregates 112,859 observations from 2,011 agricultural studies taken place in Africa between 1934 and 2018. The dataset includes information on the effect of 364 combinations of management practices and technologies on 87 environmental, social, and economic indicators of outcomes. Observations are geolocated and temporally tagged and thus can be linked to other datasets such as historical weather, soil properties, and road networks. ERA offers a new resource for understanding the impacts of changing farming practices under diverse environmental contexts, providing data to support strategic interventions aimed to enhance productivity, resilience, and sustainability of African agriculture. [ABSTRACT FROM AUTHOR]

Accurately measuring tobacco harvest maturity is crucial for optimizing quality and crop management. Traditional methods heavily rely on qualitative evaluation and chemical experiments, which introduce subjectivity and inherent limitations. This study proposes a novel method that combines deep learning with proximal hyperspectral imaging (HSI) to achieve precise recognition of tobacco leaf maturity. Unlike traditional techniques, HSI captures rich spectral and spatial data, enabling comprehensive analysis. It adopts a pixel-level annotation strategy to annotate each pixel based on its maturity level, thereby preserving spectral complexity. The dataset comprises 3000 randomly extracted cubes ( 5 × 5 × 176) from 150 original hyperspectral images, encompassing five maturity levels. Spectral and spatial features are extracted from hyperspectral data using a three-dimensional convolutional neural network (3D-CNN) architecture. This method effectively leverages complex spectral patterns for maturity recognition. During testing, the model demonstrated an impressive average accuracy of 99.93%. Visual predictions vividly illustrate the model's proficiency in maturity recognition, affirming its practical utility. This study pioneers the integration of deep learning and hyperspectral near-end sensing technology in tobacco maturity assessment, mitigating the constraints of traditional methods, and establishing the groundwork for real-time monitoring and quality control of tobacco. [ABSTRACT FROM AUTHOR]

Mason bees (Osmia. spp) can provide an alternative for crop pollination management in addition to the Western honeybees (Apis mellifera). Targeted evidence‐based guidelines can improve cost‐effective management of mason bees. Here, I test the effect of an attractant spray developed in the United States for improving the nesting preference for artificial trap nests of mason bees in Belgium. More specifically, this study investigates the effect of the attractant spray on the nesting preference and nesting performance of the European orchard bee (Osmia cornuta), as well as its effect on the infestation of nest‐associated macroparasites. The number of sealed nesting cavities was twice as high in the trap nests that were treated with the attractant spray. Nesting performance expressed as number of brood cells per cavity, successful cocoon formation, adult emergence, proportion of female bees in the offspring and the number of dead larvae was not different between the treated and the untreated nests. Overall infestation of macroparasites, and infestation of drosophilid flies in particular, were both twice as high in untreated nests. This study provides evidence that the attractant spray improves nesting preference and reduces macroparasite infestation in O. cornuta. The use of this attractant can provide growers and stakeholders with an effective management strategy to prevent infestation by macroparasites and promote efficient bee breeding. [ABSTRACT FROM AUTHOR]

Background and Objectives: This study explored the impact of nitrogen (N) application on yield and breadmaking as well as arabinoxylan (AX) content of wheat varieties Zhongmai 578 and Jimai 22 across varied environments, aiming to optimize N levels for improved outcomes. Findings: Significant N effects were observed on yield, flour properties, and breadmaking quality traits. Both varieties displayed high yield potential and stability, with Zhongmai 578 excelling in thousand kernel weight and Jimai 22 in spike number. Optimal N rates varied by location, with some achieving peak yields at reduced N levels. Processing traits like dough properties and bread‐making quality were influenced by E × N interactions. Arabinoxylans profiles was not affected by different N input. Conclusions: Customizing N applications to specific environment is critical for maximizing wheat yield potential and quality. Environmental considerations are integral for achieving desired processing traits and yield, highlighting the need for tailored N strategies in wheat cultivation. Significance and Novelty: This study emphasizes the importance of site‐specific optimization of N levels for enhanced yield and processing quality. It offers novel insights into site‐specific N management, showcasing its relevance in modern agricultural practices for sustainable crop production. [ABSTRACT FROM AUTHOR]

The current investigation endeavors to evaluate the prevalence of stubble burning in India, with a special focus on the state of Punjab. The study emphasizes the enormity of stubble burning by examining farm fire incidents, pollutant emissions, its detrimental impacts. It supports the effective management of crop residue along with proposing alternatives to stubble burning. The article conveys the message that stubble burning can result in deleterious effects on the environment, human health, crop growth, natural ecosystems, visibility, and physical infrastructure. The key solutions lie in education, functional literacy, a heightened awareness of environmental laws, rights and duties, stringent governance, and socially responsible public, promoting adherence to the National Green Tribunal's guidelines for managing crop residue and enlightening farmers about the ill effects of stubble burning on animal, soil, human health, crop biodiversity, and climate change. The available data of districts of Punjab indicates the recent waning trend in stubble burning, thus heralding a positive indication of environmental preservation. Decreased stubble burning is the reward of untiring government initiatives, support and subsidies, awareness programs, advanced research and technology, and enforcement of stringent regulations combined with recognition of the deleterious environmental impacts of stubble burning. This research article indicates that there is still a need for efforts to be made to eliminate stubble burning altogether. [ABSTRACT FROM AUTHOR]

Purpose: Rice production in Northeastern Vietnam is livelihood-oriented with a small farm size in the northern mountainous and midlands region. This study aims to determine the contribution of agronomic and input factors to the total carbon (C) footprint of rice production to identify solutions that can reduce greenhouse gas (GHG) emissions in agriculture in this region. Methods: We used a life cycle assessment method for the C footprint of rice production from planting to harvesting. Agronomic parameters and input data in both spring and summer crops were collected through interviews conducted with 50 rice-farming households in the Quang Minh commune, Viet Yen district, Bac Giang province, using questionnaires. Additionally, in-depth interviews were conducted to gather information on energy used for land preparation and harvesting. Results and discussion: The C footprint per hectare and per tonne of grain of the summer rice crop (15.6 ± 3.3 t CO2-eq/ha, 2.34 ± 0.54 t CO2-eq/t) was significantly higher than that of the spring crop (9.5 ± 1.8 t CO2-eq/ha, 1.3 ± 0.26 t CO2-eq/t). GHG emissions were due mainly to direct methane emissions during rice growth, accounting for 89.5% of the total C footprint of the summer crop and 80.5% of that of the spring crop. Fertilizer emissions also contributed significantly, accounting for 5.4% and 10.4%, respectively, nearly 75% from N-fertilizer emissions. A higher C footprint (per grain basis) was generally associated with straw incorporation, continuous flooded irrigation, longer rice growth duration, and lower rice yield, indicating the importance of agronomic management of crop residues, water and nutrients, choice of varieties, and growing seasons. Small farm size did not lead to higher carbon intensity. Conclusions: The solutions to avoid direct incorporation of rice straw, to conduct intermittent irrigation, to reduce the amount of N-fertilizer, and to use short-duration rice varieties (especially for summer crop) with high yield potential along with optimal agronomic management practices should be highlighted. Greater adoption of these measures will contribute significantly to reducing the C footprint of rice production in Northeastern Vietnam. [ABSTRACT FROM AUTHOR]

AbstractPhytoparasitic nematodes pose a widespread threat to agricultural crops worldwide. The escalating nematode damage has placed a substantial burden on growers, compelling heavy reliance on chemical nematicides. However, the use of these chemicals not only inflates production costs but also poses considerable risks to human health and the environment. In response to these challenges, researchers are actively exploring alternative nematode control techniques in agronomic crops, with a focus on environmentally friendly and economically viable solutions. The imperative to reduce dependence on chemical nematicides has spurred interest in developing strategies that are both agronomically effective and environmentally safe. Current nematode management methods often integrate biological and cultural approaches, taking into account ecological and financial considerations. Among these strategies, the utilization of antagonistic plants has emerged as particularly effective. Despite extensive research on the allelopathic properties of plants such as Tagetes, Crotalaria, Brassica, and Azadirachta indica against phytonematodes, the practical implementation of cover cropping with these antagonistic plants in commercial operations still requires attention. This review explores the impact of various antagonistic plants in combating phytonematodes, highlighting their significance in nematode management. [ABSTRACT FROM AUTHOR]

Declining natural pollinator populations are a potential threat to global food supplies. Mass flowering summer annual oilseeds can provide much needed crop diversity and floral resources to support pollinator communities. We evaluated how managing planting date affects floral phenology, floral accumulation (flower coverage time × flower area), pollinator visitation, pollinator diversity and yield of nine oilseed crops. Borage (Borago officinalis L.), calendula (Calendula officinalis L.), crambe (Crambe abyssinica Hochst), cuphea (Cuphea viscosissima Jacq. × Cuphea lanceolata W. T. Aiton), echium (Echium plantagineum L.), flax (Linum usitatissimum L.), spring camelina (Camelina sativa (L.) Crantz), spring canola (Brassica napus L.) and sunflower [Helianthus annuus L.] were grown in Morris, Minnesota during the summers of 2013 and 2014. Each crop was planted on a “standard” planting date of mid‐May and additional planting dates of early May, early June and early July. Shifting planting dates affected flowering phenology, floral accumulation, pollinator visitation and crop yield. Later planting dates led to later onset of flowering for all crops and affected total floral accumulation for crambe, cuphea and echium. Pollinator visitations changed with planting date and were generally greater with the later planting dates. At least two of the four planting dates supported high yield for each crop. The ability to maintain high crop yield across a window of planting dates affords growers management options for their fields and can be used to design complementary resource pairing and improve pollinator health through crop diversification and management. [ABSTRACT FROM AUTHOR]

Precision agriculture has revolutionized crop management and agricultural production, with LiDAR technology attracting significant interest among various technological advancements. This extensive review examines the various applications of LiDAR in precision agriculture, with a particular emphasis on its function in crop cultivation and harvests. The introduction provides an overview of precision agriculture, highlighting the need for effective agricultural management and the growing significance of LiDAR technology. The prospective advantages of LiDAR for increasing productivity, optimizing resource utilization, managing crop diseases and pesticides, and reducing environmental impact are discussed. The introduction comprehensively covers LiDAR technology in precision agriculture, detailing airborne, terrestrial, and mobile systems along with their specialized applications in the field. After that, the paper reviews the several uses of LiDAR in agricultural cultivation, including crop growth and yield estimate, disease detection, weed control, and plant health evaluation. The use of LiDAR for soil analysis and management, including soil mapping and categorization and the measurement of moisture content and nutrient levels, is reviewed. Additionally, the article examines how LiDAR is used for harvesting crops, including its use in autonomous harvesting systems, post-harvest quality evaluation, and the prediction of crop maturity and yield. Future perspectives, emergent trends, and innovative developments in LiDAR technology for precision agriculture are discussed, along with the critical challenges and research gaps that must be filled. The review concludes by emphasizing potential solutions and future directions for maximizing LiDAR's potential in precision agriculture. This in-depth review of the uses of LiDAR gives helpful insights for academics, practitioners, and stakeholders interested in using this technology for effective and environmentally friendly crop management, which will eventually contribute to the development of precision agricultural methods. [ABSTRACT FROM AUTHOR]

This review paper synthesizes the current understanding of greenhouse gas (GHG) emissions from field cropping systems. It examines the key factors influencing GHG emissions, including crop type, management practices, and soil conditions. The review highlights the variability in GHG emissions across different cropping systems. Conventional tillage systems generally emit higher levels of carbon dioxide (CO2) and nitrous oxide (N2O) than no-till or reduced tillage systems. Crop rotation, cover cropping, and residue management can significantly reduce GHG emissions by improving soil carbon sequestration and reducing nitrogen fertilizer requirements. The paper also discusses the challenges and opportunities for mitigating GHG emissions in field cropping systems. Precision agriculture techniques, such as variable rate application of fertilizers and water, can optimize crop production while minimizing environmental impacts. Agroforestry systems, which integrate trees and crops, offer the potential for carbon sequestration and reducing N2O emissions. This review provides insights into the latest research on GHG emissions from field cropping systems and identifies areas for further study. It emphasizes the importance of adopting sustainable management practices to reduce GHG emissions and enhance the environmental sustainability of agricultural systems. [ABSTRACT FROM AUTHOR]

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]

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]

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]

Large-scale yield estimation in the field or plot during wheat grain filling can contribute to high-throughput plant phenotyping and precision agriculture. To overcome the challenges of poor yield estimation at a large scale and for multiple species, this study employed a combination of multispectral and RGB drones to capture images and generation of time-series data on vegetation indices and canopy structure information during the wheat grubbing period. Five machine learning methods, partial least squares, random forest, support vector regression machine, BP neural networks, and long and short-term memory networks were used. The yield estimation of wheat grain filling period data was executed using a long and short-term memory network based on the preferred machine learning model, with a particular focus on distinguishing different heat-tolerant genotypes of wheat. The results unveiled a declining trend in the spectral reflectance characteristics of vegetation indices as the filling period progressed. Among the time-series data of the wheat filling period, the long and short-term memory network exhibited the highest estimation effectiveness, surpassing the BP neural network, which displayed the weakest estimation performance, by an impressive improvement in R2 of 0.21. The three genotypes of wheat were categorized into heat-tolerant genotype, moderate heat-tolerant genotype, and heat-sensitive genotype. Subsequently, the long and short-term memory network, which exhibited the most accurate yield estimation effect, was selected for regression prediction. The results indicate that the yield estimation effect was notably better than that achieved without distinguishing genotypes. Among the wheat genotypes, the heat-sensitive genotype demonstrated the most accurate prediction with an R2 of 0.91 and RMSE% of 3.25%. Moreover, by fusing the vegetation index with canopy structure information, the yield prediction accuracy (R2) witnessed an overall enhancement of about 0.07 compared to using the vegetation index alone. This approach also displayed enhanced adaptability to spatial variation. In conclusion, this study successfully utilized a cost-effective UAV for data fusion, enabling the extraction of canopy parameters and the application of a long and short-term memory network for yield estimation in wheat with different heat-tolerant genotypes. These findings have significant implications for informed crop management decisions, including harvesting and contingency forecasting, particularly for vast wheat areas. [ABSTRACT FROM AUTHOR]

Cover crops (CCs) are regarded as beneficial to agricultural practice as an option for soil quality improvement in field production systems. The main goal of this study was to assess the impact of spring phacelia (Phacelia tanacetifolia Benth.) and buckwheat (Fagopyrum Mill.) in a crop rotation (CC–leek–parsley, 2020–2021) on the physicochemical and biological properties of the soil in an organic high tunnel system. Soil analyses involved measurements of bulk density, water capacity, soil aggregation, soil organic carbon (SOC), available soil nutrients, as well as microbial abundance and diversity. Phacelia generated more aboveground biomass (58.2 t fresh matter ha−1) than buckwheat (33.0 t ha−1), and their biomass contained 161 kg N ha−1 and 67 kg N ha−1, respectively. A large quantity of elements, such as N, Ca, P, S, B, and Cu, were found in phacelia biomass. More Mg and Na were found in buckwheat plants. The results showed that CC biomass significantly improved some of the soil physical and chemical properties, such as soil organic carbon stock and wet aggregate stability, and decreased soil bulk density. Cover crop treatments changed the dynamics of soil bacterial and fungus populations in a high tunnel system. Phacelia increased the quantity of ammonifiers and nitrifiers in the soil substantially. Further research with a long-term focus is needed to assess the impact of cover crops on soil properties, soil quality, and subsequent crop yields in high tunnel crop rotation and management systems. [ABSTRACT FROM AUTHOR]

The acquisition of vegetation coverage information is crucial for crop field management, and utilizing visible light spectrum vegetation indices to extract vegetation coverage information is a commonly used method. However, most visible light spectrum vegetation indices do not fully consider the relationships between the red, green, and blue bands during their construction, making it difficult to ensure the accurate extraction of coverage information throughout the crop's entire growth cycle. To rapidly and accurately obtain potato vegetation coverage information, drones were used in this study to obtain high-resolution digital orthoimages of potato growth stages. Based on the differences in the grayscale values of potato plants, soil, shadows, and drip irrigation belts, this study presents a combination index of blue and green bands (BGCI) and a combination index of red and green bands (RGCI). The vegetation index intersection method was used with 10 vegetation information indices to extract vegetation coverage, and the differences in extraction accuracy were compared with those of the maximum entropy method and bimodal histogram method. Based on the high-precision fraction vegetation coverage (FVC) extraction results, the Pearson correlation coefficient method and random forest feature selection were used to screen 10 vegetation and 24 texture features, and the top six vegetation indices most strongly correlated with the FVC were selected for potato growth stage FVC estimation and accuracy verification. A high-precision potato vegetation coverage estimation model was successfully established. This study revealed that during the potato tuber formation and expansion stages, the BGCI combined with the vegetation index intersection method achieved the highest vegetation coverage extraction accuracy, with overall accuracies of 99.61% and 98.84%, respectively. The RGCI combined with the vegetation index intersection method achieved the highest accuracy, 98.63%, during the maturation stage. For the potato vegetation coverage estimation models, the model based on the BGCI achieved the highest estimation accuracy (R2 = 0.9116, RMSE = 5.7903), and the RGCI also achieved good accuracy in terms of vegetation coverage estimation (R2 = 0.8987, RMSE = 5.8633). In the generality verification of the models, the R2 values of the FVC estimation models based on the BGCI and RGCI were both greater than 0.94. A potato vegetation coverage estimation model was constructed based on two new vegetation information indices, demonstrating good accuracy and universality. [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]

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]

The rapid development of digital tools for crop management offers new opportunities to mitigate the effects of climate change on agriculture. This study examines the Normalized Difference Vegetation Index (NDVI) in coffee-growing areas of the province of Rodriguez de Mendoza, southern Peru, from 2001 to 2022. The objectives were the following: (a) to analyze NDVI trends in these areas; (b) to investigate trends in climatic variables and their correlations with altitude and NDVI; and c) to develop linear models tailored to each coffee-growing area. The study identified significant differences in NDVI trends among coffee plants, with mean NDVI values ranging from about 0.6 to 0.8. These values suggest the presence of stress conditions that should be monitored to improve crop quality, particularly in Huambo. Variability in rainfall, maximum and minimum temperatures, relative humidity, and altitude was also observed, with NDVI values showing a strong negative correlation with altitude. These results are crucial for making informed strategic decisions in integrated crop management and for monitoring crop health using vegetation indices. [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]

The climate-change-coupled fungal burden in crop management and the need to reduce chemical pesticide usage highlight the importance of finding sustainable ways to control Aspergillus flavus. This study examines the effectiveness of 50 Pseudomonas isolates obtained from corn rhizospheres against A. flavus in both solid and liquid co-cultures. The presence and quantity of aflatoxin B1 (AFB1) and AFB1-related compounds were determined using high-performance liquid chromatography–high resolution mass spectrometry analysis. Various enzymatic- or non-enzymatic mechanisms are proposed to interpret the decrease in AFB1 production, accompanied by the accumulation of biosynthetic intermediates (11-hydroxy-O-methylsterigmatocystin, aspertoxin, 11-hydroxyaspertoxin) or degradation products (the compounds C16H10O6, C16H14O5, C18H16O7, and C19H16O8). Our finding implies the upregulation or enhanced activity of fungal oxidoreductases and laccases in response to bacterial bioactive compound(s). Furthermore, non-enzymatic reactions resulted in the formation of additional degradation products due to acid accumulation in the fermented broth. Three isolates completely inhibited AFB1 or any AFB1-related compounds without significantly affecting fungal growth. These bacterial isolates supposedly block the entire pathway for AFB1 production in the fungus during interaction. Apart from identifying effective Pseudomonas isolates as potential biocontrol agents, this work lays the foundation for exploring new bacterial bioactive compounds. [ABSTRACT FROM AUTHOR]

Summary: 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]

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]

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]

The agricultural industry faces significant challenges in improving efficiency and productivity, particularly in monitoring crop health and environmental conditions. Traditional methods are often labor-intensive, timeconsuming, and lack real-time data, leading to suboptimal decision-making. Recent advancements in Internet of Things (IoT) and Artificial Intelligence (AI) technologies offer promising solutions. Long Range (LoRa) communication, a type of low-power wide-area network (LPWAN), enables long-distance data transmission with minimal power consumption, making it ideal for rural and expansive agricultural areas. When combined with deep learning, which can analyze large volumes of data to generate predictive insights, these technologies have the potential to revolutionize agricultural practices by providing farmers with timely and accurate information to optimize crop management and resource utilization. This study introduces an intelligent mote for agricultural applications, leveraging Long Range (LoRa) communication and deep learning techniques to improve precision farming. Traditional agricultural monitoring methods are labor-intensive and lack real-time insights. To address this, the mote is equipped with sensors to monitor temperature, humidity, soil moisture, and light intensity, transmitting real-time data over long distances with minimal power consumption using LoRaWAN. The collected data is processed by deep learning models to predict crop yield and identify potential issues. Field tests demonstrated a 15% improvement in yield prediction accuracy and a 20% reduction in water usage compared to traditional methods. These results highlight the effectiveness of integrating LoRa and deep learning in enhancing agricultural resource management and productivity. [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]

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]

Increasing areas of arable land, which is often heavily managed, negatively affect biological diversity in many ways, decreasing species richness and abundance. There is a substantial social demand for implementing agricultural management practices to preserve biological diversity locally. Here, we present the results of studies on the invertebrate diversity of woodland islets, which are small areas of forests surrounded by arable fields. Studies on invertebrate taxa show high values of diversity indices within such forest remnants, with a predominant presence of hymenopterans, collembolans, beetles, and spiders, which serve many ecosystem services, such as pollination or predation. A low abundance of herbivores and a high abundance of agile predators make such small woods a potential habitat for natural pest enemies. The results indicate a potential role for such woodland islets isolated from each other by a distance of ca. 1 km as an efficient substitute for extensive field management since they allow the maintenance of invertebrate diversity in the agricultural landscape. [ABSTRACT FROM AUTHOR]

Chlorophyll content is an important physiological indicator reflecting the growth status of crops. Traditional methods for obtaining crop chlorophyll content are time-consuming and labor-intensive. The rapid development of UAV remote sensing platforms offers new possibilities for monitoring chlorophyll content in field crops. To improve the efficiency and accuracy of monitoring chlorophyll content in maize canopies, this study collected RGB, multispectral (MS), and SPAD data from maize canopies at the jointing, tasseling, and grouting stages, constructing a dataset with fused features. We developed maize canopy chlorophyll content monitoring models based on four machine learning algorithms: BP neural network (BP), multilayer perceptron (MLP), support vector regression (SVR), and gradient boosting decision tree (GBDT). The results showed that, compared to single-feature methods, the MS and RGB fused feature method achieved higher monitoring accuracy, with R² values ranging from 0.808 to 0.896, RMSE values between 2.699 and 3.092, and NRMSE values between 10.36% and 12.26%. The SVR model combined with MS–RGB fused feature data outperformed the BP, MLP, and GBDT models in monitoring maize canopy chlorophyll content, achieving an R² of 0.896, an RMSE of 2.746, and an NRMSE of 10.36%. In summary, this study demonstrates that by using the MS–RGB fused feature method and the SVR model, the accuracy of chlorophyll content monitoring can be effectively improved. This approach reduces the need for traditional methods of measuring chlorophyll content in maize canopies and facilitates real-time management of maize crop nutrition. [ABSTRACT FROM AUTHOR]

The acceleration of the climate crisis and increased demand for water have caused water stress in many agricultural lands worldwide. This issue is of utmost importance as water stress represents one of the most crucial challenges for the agricultural sector and food security, affecting the growth and yield of crops. Developing agricultural strategies to mitigate the adverse effects of water stress and improve crop stress tolerance and crop yield is therefore crucial. This review aimed to analyze the effect of agricultural practices such as elicitation and biostimulation on mitigating the effects of water stress in vegetables. This manuscript provides relevant and recent information about the studied effects on various vegetable species and their responses under water deficit and agricultural and non-agricultural strategies to mitigate water stress, highlighting the use of elicitors and biostimulants. Inclusion criteria were scientific reports and book chapters published from 2000 to 2024, including keywords as follows: water stress + vegetables, water deficit + effects, drought stress management, agricultural strategies for water stress management, eustressors + water stress, elicitors and biostimulants + water stress mitigation. According to the reported literature, it was found that the physiological, biochemical, and molecular responses of vegetables to water stress depended on factors such as the severity and duration of the water deficit, the plant species, and the phenological state of the plants. Traditional agronomic strategies such as tillage, mulching, and intercropping for crop drought management were evaluated. Recently, alternative strategies for mitigating the effects of water stress have gained significant interest, such as the exogenous application of phytohormones and osmoprotectants, nutrient management, and the use of UV-B light, radiation, and acoustic waves, among others, whose eustressive effects (as biostimulants and elicitors) have been demonstrated. Among these eustressors, those of physical origin show great potential for mitigating water stress. To improve the individual potential of eustressors for water stress mitigation, we proposed the combination of practices such as tillage, mulching, application of hormones and osmoprotectants, and physical elicitors and biostimulants such as gamma rays, He-Ne laser, and UV-B. Further exploration is required to establish doses, application conditions, and effects on water stress mitigation and vegetable yield, underscoring the importance and ongoing nature of this research. [ABSTRACT FROM AUTHOR]

Horticultural crops are vulnerable to diverse microbial infections, which have a detrimental impact on their growth, fruit quality, and productivity. Currently, chemical pesticides are widely employed to manage diseases in horticultural crops, but they have negative effects on the environment, human health, soil physiochemical properties, and biodiversity. Additionally, the use of pesticides has facilitated the development and spread of resistant pathovars, which have emerged as a serious concern in contemporary agriculture. Nonetheless, the adverse consequences of chemical pesticides on the environment and public health have worried scientists greatly in recent years, which has led to a switch to the use of biocontrol agents such as bacteria, fungi, and insects to control plant pathogens. Biocontrol agents (BCAs) form an integral part of organic farming, which is regarded as the future of sustainable agriculture. Hence, harnessing the potential of BCAs is an important viable strategy to control microbial disease in horticultural crops in a way that is also ecofriendly and can improve the soil health. Here, we discuss the role of the biological control of microbial diseases in crops. We also discuss different microbial-based BCAs such as fungal, bacterial, and viral and their role in disease management. Next, we discuss the factors that affect the performance of the BCAs under field conditions. This review also highlights the genetic engineering of BCAs to enhance their biocontrol efficiency and other growth traits. Finally, we highlight the challenges and opportunities of biocontrol-based disease management in horticulture crops and future research directions to boost their efficacy and applications. [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]

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]

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]

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]

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]

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]

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]

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]

Agricultural practices and meteorological conditions affect soil structure and soil hydraulic properties. However, their temporal evolution is rarely studied, and even less in the field. Thus, their dynamics are rarely taken into account in models, often leading to inconsistent results and poor decision making. In this study, the temporal evolution of water retention properties and soil structure was monitored over a 3‐year period under several contrasting production systems. Soil Water Retention Curves (SWRCs) obtained directly in the field (with soil water content and potential sensors) were compared with theoretical SWRCs predicted by pedotransfer functions (PTFs) and laboratory SWRCs measured on undisturbed samples. Bulk densities were measured every 2 months. Results indicate a high degree of variability in SWRCs over time and between production systems. The results suggest that variations in the soil water retention behaviour can be induced by crop differentiation, weed control, crop residue management, compaction during harvest, or the introduction of temporary grassland. Contrasting climatic conditions between 2021 (water excess), 2022 (severe drought) and 2023 (intermediate) provided a unique opportunity to study the resilience of the crop systems to extreme climatic conditions. Different soil drying dynamics were observed and some agricultural practices were identified as influencing the soil water retention behaviour for at least 2 years. Comparison of SWRCs showed that the theoretical curves obtained from PTFs are not a good representation of the field SWRCs, especially for less conventional agricultural practices. The laboratory curves are closer with similar trends. However, these SWRCs are not optimal for investigating the temporal evolution of water retention properties. This research also shows that agricultural practices and crops can be levers for contributing to greater food resilience against future climatic conditions. Therefore, to assess the relevance of production systems for tomorrow's needs, studies should focus on the impact of multi‐cropping systems on water retention dynamics in the field. [ABSTRACT FROM AUTHOR]