Search Results (3,588)

Weathered acidic soils are characterized by severe phosphorus (P) fixation and aluminum (Al³⁺) toxicity, limiting crop productivity. Integrated nutrient management (INM), combining mineral fertilizers, organic amendments, and biofertilizers, offers a promising strategy to overcome these constraints. Unlike conventional single-site fertilizer comparisons, this study examined how mineral, organic, and phosphate-solubilizing biological inputs interact with contrasting acidic soil environments across successive seasons, allowing simultaneous assessment of immediate crop productivity and residual soil fertility. Field experiments were conducted over two consecutive cropping seasons (2022–2023 and 2023–2024) at three sites in Tete Province, Mozambique: Ntengo-wa-mbalame, Rinzi, and Angónia Research and Technology Transfer Centre (CITTA). The experiment followed a 2 × 3 × 11 factorial arrangement in a randomized complete block design with four replications. Eleven treatments comprising mineral N, P, K, and S fertilizers, vermicompost, and a multifunctional multi-strain phosphate-solubilizing biofertilizer (Bio-Rock P) were evaluated. Soil chemical properties and crop performance were influenced by site and season (p ≤ 0.001). Ntengo-wa-mbalame had the most favorable fertility, achieving a maximum yield of 34.96 t ha⁻¹ in Year 1, whereas high acidity and exchangeable Al at Rinzi restricted canopy development and increased small, non-commercial tubers (<45 mm). Across sites, exclusive mineral fertilization showed numerical trends toward greater soil pH declines (up to 26%), whereas INM treatments maintained more stable soil pH levels (declines limited to 2.2–10.8%) and significantly reduced toxic exchangeable Al³⁺ down to 0.03 cmol_c kg⁻¹, likely through organo-metallic complexation. Treatments combining mineral inputs, vermicompost, and biofertilizers, particularly T7, showed strong residual effects, increasing available P from 55.34 to 87.02 mg kg⁻¹ by Year 2 while maintaining marketable tuber yields and nutrient recovery statistically comparable to or exceeding mineral fertilization. PCA showed that P agronomic efficiency (P-AE) was positively associated with biomass, tuber number, and total tuber yield. Overall, INM improved nutrient availability, moderated soil acidity and Al toxicity, and supported potato productivity, providing an ecologically sustainable strategy for fragile tropical agrosystems. Full article

Spreading dynamics have been extensively studied in social, communication, and epidemiological networks, leading to substantial advances in understanding spreading thresholds, outbreak patterns, temporal dynamics, and phase transitions. However, their extension to other complex systems, particularly agricultural systems, remains less systematically explored. In this mini-review, we summarize representative spreading models and theoretical approaches developed for complex networks. We first review spreading dynamics on single-layer networks and then discuss their extensions to multilayer, temporal, and higher-order networks, highlighting how increasingly complex interaction structures affect spreading thresholds, outbreak patterns, and phase transitions. We subsequently examine how these models and theoretical approaches can be transferred from social networks to complex agricultural systems. Representative applications include crop diseases and pests, livestock diseases, agricultural information and technology diffusion, and coupled disease–information processes. Finally, we discuss emerging directions for extending spreading theories to increasingly complex agricultural systems. This review may help clarify the generality and transferability of spreading theories across different networked systems and stimulate their further development in complex agricultural contexts. Full article

Smallholder farming systems in sub-Saharan Africa are highly heterogeneous, resulting in differences in farmers’ capacities, priorities, and responses to sustainable intensification (SI) innovations. This study assessed farmers’ perceptions of six SI innovations and evaluated their suitability across four farm typologies in the Mtunthama Extension Planning Area, Central Malawi. A mixed-methods participatory approach involving focus group discussions and farmer-led innovation ranking was used across 44 trial farmers. Qualitative data were analysed using thematic content analysis, while preference rankings were converted into weighted standardised scores for comparison across farm types. Kruskal–Wallis tests were applied to assess whether maize grain yield differed significantly across farm typologies by treatment. Farmers generally perceived SI innovations positively, identifying improved yields, soil fertility, food security, dietary diversity, and climate resilience as major benefits. Labour requirements, input constraints, management complexity, and limited training were identified as key barriers. Innovation preferences differed across farm typologies, reflecting variations in household resource endowment and livelihood strategies. Although conventional ridging with continuous sole maize received the highest proportion of first-choice rankings, conservation agriculture incorporating legumes, particularly maize-cowpea double-row strip cropping, achieved the highest overall preference scores across farm types. Yield analysis revealed significant differences across farm typologies for CA Maize-Lablab Rotation (H = 9.823, p = 0.020) and Conventional Maize (H = 12.834, p = 0.005), with resource-constrained farmers consistently recording lower yields. Field days attended by 253 independent observers confirmed community-level preference for cowpea-based CA innovations, with maize-cowpea strip cropping receiving the highest vote share at both sites. Post-project follow-up revealed that 68 out of 253 field day observers (26.9%) independently adopted cowpea-based conservation agriculture in the following season, of whom 58.8% were women. Adoption was driven by the early maturity of the cowpea variety under climate variability and access to a ready vendor market. These findings demonstrate that integrating farm typology analysis with participatory innovation assessment and post-project tracking improves the targeting of SI interventions and supports the design of context-specific innovation packages more likely to achieve sustained adoption than blanket technology recommendations. Full article

Huanglongbing (aka: citrus greening) associated with ‘Candidatus Liberibacter asiaticus’ (CLas) is one of the most impactful citrus diseases throughout the world. The aim of this project was to support the sensitive and rapid detection of CLas to prevent the continued spread and destruction of US citrus crops by this psyllid vector-borne disease. Two multicopy targets specific to CLas were selected for assay development: the five-copy nrdB (ribonucleotide reductase-RNR) gene and a four-copy locus 4cp (4CP). Published RNR RPA primers were used to develop a one-pot RPA-CRISPR assay, and RPA primers for 4CP were designed. crRNAs for both RNR- and 4CP-RPA-CRISPR/Cas assays were designed, evaluated through preliminary testing, and the best candidate primers and crRNA combinations were selected. The 4CP assay was selected and subjected to a battery of validation tests and determined fit-for-purpose. A field extraction protocol was also developed using commercial reagents, syringes and filters to bring kit sensitivity to the field. When combined with the assay, the complete workflow was able to detect CLas in infected citrus, down to a single infected leaf per subsample; the field workflow showed similar sensitivity to the laboratory-based workflow. While the extraction and assay proved useful, particularly for symptomatic leaves, its performance with asymptomatic leaves for early detection was not evaluated. The complexity of the assay in its current form hinders widespread practical applicability. In the future, revalidation of lyophilized reagents in a convenient kit can be performed for practical deployment; preliminary testing has shown success. Full article

Accurate and robust detection of pear leaf diseases and pests is important for early warning in orchards, precision control, and intelligent disease and pest management. In natural environments, this detection task is still difficult because target scales vary greatly, lesion boundaries are often blurred, disease symptoms may look similar, small-target features are usually weak, and cluttered backgrounds can reduce detection performance. We develop AMET-YOLO as a task-specific extension of YOLOv11n. ADown is a downsampling operator borrowed from YOLOv9 that replaces selected stride-convolution layers and thereby preserves local details during scale reduction. The Memory-guided Sparse Expert Compensation Module (MSECM) is a feature-enhancement design which couples learnable memory retrieval with four dilation-specific experts and input-dependent Top-2 routing. In the neck, the Efficient Multi-scale Aggregation Fusion module (EMAFuse) substitutes four concatenation nodes and performs fixed-width channel alignment, element-wise aggregation, and lightweight depthwise–pointwise mixing. The Task-Aligned Detection Head (TAHead) is a modified decoupled head that retains the YOLOv11n prediction structure while it routes localization responses through a one-way gating path to modulate intermediate classification features. Experiments on the six-class PearLeaf-DP6 dataset show that AMET-YOLO achieves a precision of 88.5 ± 1.3%, a recall of 83.1 ± 0.9%, an mAP@50 of 88.7 ± 0.7%, and an mAP@50:95 of 51.8 ± 0.5%. Compared with the YOLOv11n baseline, AMET-YOLO improves these four metrics by 4.8, 3.9, 3.3, and 2.3 percentage points, respectively. Experiments on a second public tea leaf disease dataset further show that the proposed model stays effective when it is trained and evaluated independently on a different crop disease detection task. AMET-YOLO is a model with 6.32 M parameters and 10.2 GFLOPs, which constitutes a moderate rise in complexity relative to YOLOv11n while it remains far more compact than RT-DETR-ResNet50. These workstation-based results position AMET-YOLO as an accuracy-oriented image-based detector under the evaluated protocol, and real-time deployment on resource-limited orchard devices is left for future validation. Full article

Heavy-metal contamination poses a serious threat to plant growth, productivity, and resilience, as it causes complex changes in cellular homeostasis, redox balance, element transport, and metabolism. This review summarizes current understanding of the molecular mechanisms by which plants adapt to heavy-metal stress, with a particular focus on transcriptional and omics approaches. The main detoxification and defense mechanisms are discussed, including chelation and vacuolar sequestration, antioxidant regulation, hormonal signaling, and ion transport. Particular attention is paid to the role of MAPK cascades and transcription factors in coordinating the expression of genes involved in metal transport, maintenance of redox homeostasis, and detoxification. Analysis of transcriptomic studies shows that the plant response is characterized by pronounced temporal, tissue-specific, and genotypic specificity and involves the reprogramming of genes involved in transport, antioxidant defense, secondary metabolism, and signaling pathways. At the same time, transcript levels do not always reflect the functional state of proteins and metabolic processes. Therefore, the integration of transcriptomics with genomics, epigenomics, proteomics, metabolomics, and single-cell and spatial approaches combined with functional validation is proposed as a key direction that may facilitate the identification of robust molecular markers and functionally validated genes for improving crop tolerance to heavy-metal contamination. Full article

The use of herbicides to control weeds in crops, especially in soybeans, corn, and sugarcane, is a common practice, but it poses a risk to nearby sensitive crops, since herbicide drift is common and has caused phytotoxicity, resulting in reduced crop yield. Thus, the determination of acid herbicides in plant leaves is important but challenging due to the complexity of the matrix and compound characteristics. Considering this, an analytical method was developed and validated for the determination of 22 acid herbicides in plant leaves, using a modified QuEChERS procedure and ultra-high performance liquid chromatography with tandem mass spectrometry (UHPLC-MS/MS) with a run time of 10 min. The optimized method involved the extraction of the hydrated sample with acetonitrile containing formic acid, partitioning with salts, and cleanup with graphitized carbon black. Validation presented recoveries from 71 to 118%, with RSD ≤ 19% and method limit of quantification of 5 µg kg⁻¹ for most of the herbicides indicating satisfactory performance. The proposed method was applied to 50 plant leaf samples, 36 of which contained residues of at least one of the evaluated herbicides. The developed method is rapid and efficient and can be applied in routine analyses of acid herbicides in plant leaves. Full article

Sorghum (Sorghum bicolor L.), the fifth-most important cereal crop globally, serves as a cornerstone crop for food security, forage production, and bioenergy feedstock on arid and semi-arid marginal lands, owing to its high water use efficiency derived from C4 photosynthesis and exceptional tolerance to abiotic stresses. Conventional sorghum breeding has long been constrained by insufficient genetic dissection of complex traits, the “black box” of genotype-to-phenotype mapping, and poorly understood genotype-by-environment interactions, resulting in stagnant genetic gain. In recent years, rapid advances in multi-omics technologies—including genomics, transcriptomics, epigenomics, single-cell omics, and microbiomics—have propelled sorghum research from single-gene mapping to systems-level dissection of regulatory networks, providing a novel paradigm for elucidating the molecular basis of agronomic traits across all molecular layers and breaking through the bottlenecks of conventional breeding. This review systematically synthesizes recent advances in sorghum multi-omics research, dissecting the molecular basis of key agronomic traits across distinct omics layers. We summarize the integrated multi-omics precision breeding technology system and elaborate on the practical applications of multi-omics approaches for four core breeding objectives: stress tolerance, yield, quality, and nutrient use efficiency. Finally, we discuss current challenges and future perspectives, aiming to provide a theoretical framework and technical reference for molecular design breeding in sorghum. Full article

Digital twin (DT) technology is increasingly recognized as a transformative tool in precision agriculture, yet its application to fruit cultivation remains fragmented and poorly systematized. This paper presents a systematic literature review of DT applications in fruit cultivation, following the PRISMA 2020 methodology. A structured search across Scopus and Web of Science databases yielded 38 studies published between 2019 and 2025 for review. Results revealed a strong concentration of research in the areas of sensing and Internet of Things, Cold Chain and Postharvest, and Modeling and Simulation. Also, it is indicated that DTs in this domain remain largely at the laboratory validation stage. Switzerland, the United States, and China lead in research output, while strawberries, oranges, and apples were the most studied crops. Key challenges identified included limited data availability, poor model transferability across cultivars, high implementation complexity, and insufficient integration across supply chain stages. This review highlights the need for standardized frameworks, open datasets, and cross-stage DT architectures to accelerate the transition from research prototypes to operational deployment in fruit cultivation. Full article

Heat stress disrupts protein homeostasis, yet the thermoregulatory roles of HSP10/CPN10 co-chaperonins in polyploid crops remain unexplored. This study conducted the first genome-wide characterization of the HSP10 gene family in allotetraploid Brassica juncea to identify heat tolerance candidates. Genome-wide identification of BjuHSP10 genes was performed across A and B subgenomes, followed by phylogenetic, gene structure, duplication, synteny, promoter, and structural analyses. Transcriptional responses were evaluated under moderate (28/24 °C) and severe (36/32 °C) heat stress conditions. Ten BjuHSP10 genes were identified, encoding six mitochondrial, three chloroplast-targeted, and one cytoplasmic protein. Mitochondrial members were small (~10.7 kDa) with three-exon structures, whereas chloroplast members were larger (15.5–27.1 kDa) with complex exon–intron organization. Segmental duplication drove family expansion (19 duplicated pairs), with differential retention of Arabidopsis homologs. Promoter regions contained 391 putative cis-regulatory elements associated with stress, hormonal, and developmental regulation. Under heat stress, BjuHSP10-4 and BjuHSP10-5 were induced under both treatments; BjuHSP10-1/3/6/9 responded only to severe stress, while four genes were repressed. Structural modeling confirmed conserved β-rich GroES/CPN10-like folds. These findings identify candidate BjuHSP10 genes, particularly BjuHSP10-4 and BjuHSP10-5, as promising targets for heat-tolerance improvement in mustard, providing a foundation for future breeding and functional studies in polyploid crops. Full article

Crop pests are a core bottleneck restricting the improvement and efficiency enhancement of agriculture. The traditional manual inspection mode has problems such as high missed detection rate and slow response, making it unable to meet the demands of modernized and precise agricultural pest control. Due to the limited computing resources of agricultural edge devices such as mobile phones and drones, as well as detection challenges in the field such as the high proportion of small targets and complex backgrounds, this paper proposes an improved model GAPyolo11 (GhostConv And C3K2 -PPA YOLO11), which is developed on the basis of YOLO (You Only Look Once) v11 and jointly improved by integrating the lightweight module GhostConv and the feature enhancement module C3K2-PPA. It reduces redundant computations through the “main feature and cheap transformation” strategy of GhostConv and strengthens the capture of pest features by incorporating the multi-branch attention mechanism of C3K2-PPA. The experiment shows that, using a real agricultural dataset containing 101 types of pests, GAPyolo11 achieves an mAP@0.5 (mean Average Precision at an Intersection over Union threshold of 0.5) improvement of 16.7% compared to the original YOLOv11n, with an 18% reduction in parameters, a 35% decrease in computational cost, and a detection speed of 20 ms per frame. Under the experimental conditions used in this study, the proposed GAPyolo11 model achieves feasible inference performance for deployment on edge devices such as mobile phones and agricultural drones and realizes real-time and precise pest identification within the test scenarios. It provides potential technical support for rapid field diagnosis and variable-rate pesticide application for precision plant protection, which may help reduce pest control costs and lower the risk of pesticide abuse. Full article

Accurate prediction of pest density and population levels in almond production is of great importance for crop yield, the prevention of quality losses, and the effectiveness of integrated pest management (IPM) strategies. Complex feature interactions in agricultural field data can affect both the predictive performance and interpretability of machine learning models. In this study, an explainable hybrid machine learning framework combining Mutated Particle Swarm Optimization (MPSO), XGBoost classification, and rule extraction was developed to classify pest insect density levels. The proposed approach was evaluated using two complementary validation settings. First, it was compared with 16 classification algorithms, including ensemble, deep learning, and rule-based methods, under stratified 5-fold cross-validation, achieving a mean accuracy of 79.63% and a mean Weighted F1-Score of 79.79%. In addition, nested stratified 5-fold cross-validation was employed to separate MPSO-based hyperparameter optimization from outer-fold performance evaluation. Under this more rigorous evaluation, the proposed approach achieved a mean accuracy of 79.27 ± 1.66% and a mean Weighted F1-Score of 79.29 ± 1.38%. Furthermore, interpretable IF–THEN decision rules were derived from the trained XGBoost decision structures to provide a transparent representation of the feature–threshold combinations associated with the classification decisions. Full article

Seeding constitutes a key crop-production operation that governs seed spatial arrangement, crop population structure, and potential yield formation, and forms the foundation of precise, efficient, and eco-friendly farming. However, field soil properties, regional climate, and crop agronomic requirements exhibit strong spatio-temporal heterogeneity. Conventional seeding operations based on manual experience and fixed preset parameters cannot meet the demands of large-scale precision agriculture. Enabled by progress in precision agriculture, intelligent sensing, artificial intelligence, and autonomous machinery, modern intelligent seeding systems integrate precision seed metering, high-precision environmental perception, and closed-loop dynamic self-regulation. Such systems can improve plant-spacing uniformity and enable precise seeding-depth control under standard open-field conditions, yet face noticeable performance limitations in GNSS-denied complex environments including dense crop canopies and greenhouses. This review outlines the evolutionary trajectory of seeding machinery and summarizes research progress regarding precision seeding, multi-functional equipment integration, multi-source information perception, and intelligent decision-making. Integrated design principles covering mechanical optimization, electronic control, and perception-driven decision systems are elaborated. Four developmental phases of seeding equipment are identified: mechanical precision operation, electronic intelligent regulation, multi-functional module integration, and intelligent cognitive integration. Current intelligent seeding technologies are constrained by limited adaptability to complex farmland conditions, unstable multi-source data fusion, insufficient long-term operational reliability, and high deployment costs across diverse scenarios, restricting their broad field-scale adoption. Future research should combine agronomic knowledge with artificial intelligence to improve environmental awareness and autonomous decision-making capability, develop low-cost, high-reliability integrated seeding equipment, and support the construction of intelligent agricultural machinery systems. Full article

Phosphogypsum (PG) is a bulk industrial solid waste generated during wet-process phosphoric acid production. Its agricultural use may improve specific soil properties but may also increase the risk of agricultural non-point-source pollution. Differences in phosphate-rock source, production process, degree of washing, stockpiling history, and treatment method result in substantial variation among PG materials in mineral composition and in the concentrations, modes of occurrence, and release potential of P, F, soluble salts, potentially toxic elements, and naturally occurring radionuclides. This review combines a structured literature search with a qualitative synthesis of risk evidence and uses agricultural soils, the root zone, and field boundaries as the principal assessment domains. It examines the release, transformation, immobilization, crop uptake, leaching below the root zone, and export through runoff, erosion, and drainage of PG-associated constituents. It also evaluates the mechanisms, pollutant partitioning, agronomic effects, and non-target risks of hazard-reduction and functionalization technologies. Available evidence indicates that the agronomic effects and environmental risks of PG cannot be assessed solely on the basis of total constituent concentrations, Ca and S supply, the results of a single leaching test, or short-term crop responses. They also depend on the releasable pollutant load, cumulative application rate, soil chemical and hydrological conditions, crop exposure, and long-term stability. Washing, leaching, separation, stabilization, thermal treatment, and biological treatment can reduce the concentrations or releasability of some pollutants in the treated solid but may also transfer pollutants to wastewater, sludge, residues, or gaseous streams. Following source traceability, risk screening, and treatment where necessary, PG may be used to ameliorate sodic soils, regulate nutrient availability, or stabilize some pollutants through ion exchange, adsorption, precipitation, surface complexation, and organic–mineral binding. Its effectiveness and associated risks depend on material properties, soil conditions, target pollutants, and application methods. Accordingly, this review proposes a conceptual framework for the safe utilization of PG that integrates source traceability, material classification, selective hazard-reduction treatment, functionalization design, soil–crop matching, tiered verification, and long-term monitoring. The framework identifies assessment priorities and management considerations for specific materials and application scenarios. Full article

Tropical crops are increasingly exposed to climate-driven, recurrent combinations of heat, excessive irradiance, high vapor pressure deficit (VPD), drought, flooding, salinity, and pathogen pressure that collectively impair photosynthesis and reduce crop productivity. Unlike individual stress events, these interacting stresses simultaneously disrupt photoprotection, electron transport, carbon assimilation, stomatal regulation, and chloroplast redox homeostasis, ultimately limiting whole-plant carbon gain under fluctuating environments. Nitric oxide (NO) has emerged as a central signaling molecule coordinating these adaptive responses, yet its roles in photosynthetic regulation remain dispersed across molecular, physiological, and crop-specific studies. This review presents an integrated framework linking chloroplast-level NO signaling with whole-plant photosynthetic performance in major tropical crops, including maize, sugarcane, banana, cassava, and oil palm. We propose that NO regulates photosynthetic resilience through three interconnected axes: (i) reversible protein S-nitrosylation that modulates photosynthetic complexes and Calvin–Benson cycle enzymes; (ii) maintenance of chloroplast redox homeostasis through coordinated reactive oxygen species (ROS) and reactive nitrogen species (RNS) signaling, antioxidant networks, and S-nitrosothiol metabolism; and (iii) ABA-mediated stomatal regulation that optimizes the trade-off between carbon assimilation, stomatal conductance, and water-use efficiency under environmental stress. We further discuss the emerging roles of S-nitrosothiols (SNOs) and S-nitrosoglutathione (GSNO) as central regulators of NO homeostasis and potential biochemical indicators linking chloroplast signaling with whole-plant carbon performance. By synthesizing current evidence across climate-relevant stress combinations, including heat + high light, drought + high VPD, flooding + hypoxia, salinity + pathogen interactions, this review provides a conceptual framework for understanding NO-mediated photosynthetic resilience and identifies key research priorities for developing NO-based strategies to enhance productivity and climate resilience in tropical cropping systems. Full article