Search Results (3,150)

Cover crops are vital for sustainable orchard management, but their differential impacts on soil microbial communities and inter-domain networks across soil profiles remain unclear. To address this, we conducted a field experiment in a semi-arid apple orchard on China’s Loess Plateau, comparing three treatments—clear tillage (CT), natural grass (NG), and Medicago sativa (MS)—across two soil depths (0–10 cm and 10–20 cm). Soil physicochemical properties and microbial biomass were analyzed, and bacterial (16S rDNA) and fungal (ITS2) communities were characterized via high-throughput sequencing. Results showed that MS treatment significantly increased key soil nutrients, elevating soil organic carbon (SOC) by 25% and total nitrogen (TN) by 30% in the topsoil relative to CT. Microbial α-diversity was significantly enhanced under cover crops, with the Shannon index increasing by 8.5% for bacteria and 15.2% for fungi in MS topsoil. Co-occurrence network analysis revealed that MS fostered the most complex and stable microbial networks, marked by a 40% increase in nodes and a 55% increase in edges compared to CT, alongside strengthened fungal–fungal associations. These findings quantitatively demonstrate that legume cover crops like Medicago sativa are most effective in enhancing soil fertility and microbial network complexity in semi-arid orchards, with benefits extending into the subsurface soil. Full article

Plant–pathogen interactions are complex biological processes characterized by dynamic changes in genes expression. In molecular plant pathology research, RT-qPCR has proven to be a valuable tool for investigating plant–pathogen interactions by examining gene expression changes in both plants and pathogens during infection. The choice of reliable reference genes is crucial, as this directly affects the robustness of normalization and the accuracy of analyzing the expression of genes of interest. A systematic literature search was conducted across relevant academic databases, resulting in the selection of 47 articles (38 on fungi and oomycetes, 7 on bacteria and 2 covering both bacteria, fungi and oomycetes) that evaluated the stability of 190 candidate reference genes. The most used reference genes in plant—fungal and oomycete pathosystems were GAPDH, ACT, TUB and EF, whereas UBQ, TUB, EF and ACT were most used in plant—bacterial pathosystems. Reference genes revealed considerable variability in their stability across different crops, pathogens and experimental conditions. Notably, several classical reference genes, traditionally assumed to maintain stable expression, exhibited considerable variability, supporting concerns regarding their reliability as universal references. Therefore, this review provides important insights for researchers seeking to identify suitable reference genes for their validation studies in plant–pathogen interaction. Full article

Understanding soybean cultivation in Northeast China is essential for informing policies related to national food security. However, long-term, high-resolution soybean maps are still lacking, largely due to persistent cloud cover, limited availability of high-quality field labels, and the difficulty of capturing crop phenological dynamics using traditional remote sensing methods. To address this gap, this study aims to develop a robust framework for generating decade-long soybean distribution maps by integrating medium-resolution Landsat imagery with advanced deep learning techniques. We mapped the soybean distribution across Northeast China from 2013 to 2022 by constructing a bi-monthly NDVI-based composite and applying a deep learning model that combines the Transformer architecture with fully connected neural networks. The model was trained using a large set of field-surveyed samples collected between 2017 and 2019. Validation results demonstrate strong classification performance, with a user accuracy of 89.77% and a producer accuracy of 88.59%, sufficient for reliable spatiotemporal analysis. When compared with prefecture-level statistical yearbook data, the predicted annual soybean areas show a high degree of agreement (R² = 0.9226). Overall, this study not only fills an important gap in long-term soybean mapping for Northeast China, but also provides a replicable methodological framework for large-scale, time-series crop mapping. The approach has strong potential for broader application in agricultural monitoring and food security assessment. Full article

The accurate identification of crop pests and diseases is critical for global food security, yet the development of robust deep learning models is hindered by the limitations of existing datasets. To address this gap, we introduce DLCPD-25, a new large-scale, diverse, and publicly available benchmark dataset. We constructed DLCPD-25 by integrating 221,943 images from both online sources and extensive field collections, covering 23 crop types and 203 distinct classes of pests, diseases, and healthy states. A key feature of this dataset is its realistic complexity, including images from uncontrolled field environments and a natural long-tail class distribution, which contrasts with many existing datasets collected under controlled conditions. To validate its utility, we pre-trained several state-of-the-art self-supervised learning models (MAE, SimCLR v2, MoCo v3) on DLCPD-25. The learned representations, evaluated via linear probing, demonstrated strong performance, with the SimCLR v2 framework achieving a top accuracy of 72.1% and an F1 score (Macro F1) of 71.3% on a downstream classification task. Our results confirm that DLCPD-25 provides a valuable and challenging resource that can effectively support the training of generalizable models, paving the way for the development of comprehensive, real-world agricultural diagnostic systems. Full article

The agroforestry system, which integrates the strategic intercropping of trees and grasses, is profoundly shaped by complex ecological interactions that dynamically reshape microclimatic environments and significantly impact the growth of understory forage species. Wolfberry–forage intercropping patterns have the potential to improve soil quality and orchard productivity, but their effects on forage cover crops are still unclear. Therefore, this study selects wolfberry and nine forage grass as research subjects to examine the effects of intercropping these species on the morphological characteristics, yield, quality, photosynthetic capacity, and plant physiology of forage grass. Based on experimental data, cover cropping facilitated plant growth, maintained fruit yield, and promoted leaf photosynthesis in forage compared with monocropping. This was exemplified by a notable increase in forage plants under the intercropping system, for the number of primary branches or tillers, and an improvement in the drying ratio of forage grasses, while reducing plant height, leaf-to-stem ratio, and photosynthetic rate (p < 0.05). Furthermore, the intercropping system significantly enhances the dry weight yield of alfalfa, ryegrass, and mangold, with increases of 60%, 64%, and 70%, respectively (p < 0.05). Additionally, it improves forage quality by increasing the crude protein content in ryegrass and mangold by 32% and 10%, respectively, and decreasing acid detergent fiber content by 10% and 18% (p < 0.05). Collectively, the results indicated that mangold, ryegrass, and alfalfa were the optimal cover crops for sustainable wolfberry production in the study area. The use of appropriate wolfberry–forage cover crops enhanced hay yield and the quality of forage by stimulating photosynthetic capacity and biotic stress resistance. Our research elucidates the mechanisms underlying the effects of intercropping systems on forage grass growth, aiming to provide a scientific basis for the development of animal husbandry and the rational utilization of land resources in the Ningxia region. Full article

Agriculture is still at serious risk from viral infections, particularly in light of climate change and more intensive farming practices. Small non-coding RNAs (sRNAs), in particular microRNAs (miRNAs) and circular RNAs (circRNAs), have emerged as crucial post-transcriptional regulators of plant antiviral defense in this setting. These molecules provide an essential RNA-based immunity layer by regulating hormones, autophagy, redox balance, immunological signaling, and programmed cell death. In this work, we examine the molecular processes through which circRNAs and miRNAs function during viral infection, focusing on how they affect autophagy and systemic acquired resistance (SAR). Through thorough searches of PubMed, Web of Science, and Scopus, we combined findings from peer-reviewed experimental and transcriptomic studies. Our study covers important crops as well as model species (Arabidopsis thaliana, Nicotiana benthamiana), providing a thorough understanding of sRNA synthesis, target control, and antiviral signaling. By combining previously disparate data, this review provides a coherent framework for understanding how short RNAs affect plant immune responses to viral infections. We highlight key regulatory relationships that need further study and propose that these results can be used as a foundation for new RNA-based biotechnological approaches. By carefully altering RNA regulatory mechanisms, scientists can use this information to help them create more resistant crops. Full article

Agriculture is one of the many sectors facing significant risks from climate change. To manage potential crop losses, whether climate-related or not, farmers widely rely on crop insurance to increase their resilience. However, farmers in Sri Lanka demonstrate a limited acceptance of crop insurance schemes. This study aimed to investigate the perceptions and Willingness-to-Pay (WTP) for crop insurance schemes among the paddy farmers in Kurunegala district. A total of 248 paddy farmers from the Kurunegala district were recruited as the study sample using the stratified random sampling approach. A pre-tested structured questionnaire and choice cards were used for primary data collection. The Conditional Logit Model (CLM) was used for data analysis. Around 77.8% of respondents were males engaged only in paddy farming, while the majority (62.5%) received an income of LKR 50,000 to 75,000. Complications experienced during the claim form-filling process (mean = 4.6), gaps in covering all crops on the crop land (mean = 4.6), and poor service quality (mean = 4.5) were perceived as the major limitations in existing crop insurance schemes. Outcomes of the CLM indicated that farmers with a positive attitude toward crop insurance significantly prefer plans with drought coverage (β = 0.823; p < 0.05), on-field assessments (β = 0.251; p < 0.05), and higher no-hazard returns (β = 0.318; p < 0.05) while showing a notable sensitivity to premium costs (β = −0.590; p < 0.05). The model also revealed an apparent willingness to switch from the status quo when presented with better-designed alternatives. The findings emphasized the need to implement responsive crop insurance schemes to enhance climate resilience and ensure the sustainability of paddy production in Sri Lanka. Full article

Alfalfa, as a high-quality forage crop, undergoes a drying process that is critical to its product quality and commercial value. This paper systematically reviews research progress on alfalfa drying technologies and equipment throughout the entire process. First, it proposes a comprehensive production technology model covering three core stages: drying pretreatment, drying conditioning and optimization, and product quality control. This model emphasizes adaptability to material characteristics, processing methods, product quality, and economic efficiency. Second, it delves into the drying mechanisms of alfalfa, detailing the forms of water presence (free water or bound water), migration pathways, and the three-stage water loss periods: constant rate, first falling rate, and second falling rate. It identifies “asynchronous drying of stems and leaves” as the core issue causing nutrient loss and technical challenges. Subsequently, a comprehensive review was conducted on pre-treatment equipment such as mowing and flattening, as well as various drying methods including natural drying, hot-air drying, solar drying, and microwave drying. The principles, characteristics, and impacts of these methods on alfalfa quality were evaluated. Additionally, a comprehensive quality assessment system for alfalfa hay was summarized, incorporating physical, chemical, and biological methods. Finally, future development directions are proposed: developing domestically produced, intelligent drying equipment; integrating clean energy to reduce energy consumption; and achieving precise control of drying processes through establishing multi-scale heat and mass transfer models. These efforts will advance China’s alfalfa drying industry toward standardization, integration, and intelligence, ensuring a stable supply of high-quality hay. Full article

We developed and evaluated a compact mushroom fruiting chamber equipped with Internet of Things technologies, designed to support decentralized urban agriculture. The system was constructed from a retrofitted glass-door refrigerator and integrated with Internet-connected sensors and a custom microcontroller to monitor and regulate temperature and humidity continuously. The control unit managed key variables, including temperature and relative humidity, during the cultivation of Pleurotus ostreatus mushrooms. Experimental trials assessed the effectiveness of the IoT-based system in maintaining optimal growth conditions by dynamically adjusting parameters tailored to the fungus’s specific physiological requirements during fruiting. The prototype successfully maintained a stable cultivation environment, achieving an average temperature of 25.0 °C (±0.7 °C) and relative humidity of 90% (±8%). Under optimized conditions (18 °C, with the cultivation block plastic cover preserved), mushroom yield reached 230 ± 2 g per block, corresponding to a biological efficiency of 44% and an estimated productivity of up to 612.04 kg m⁻² per year. Furthermore, the system achieved a water footprint of only 4.39 L kg⁻¹ of fresh mushrooms, significantly lower than that typically reported for conventional cultivation methods. These results demonstrate the feasibility of an efficient, compact, and water-saving controlled environment for mushroom cultivation, enabled by IoT-based technologies and organic residue substrates. Remote monitoring and control capabilities support urban food security, reduce transport-related emissions, optimize water use, and promote sustainable practices within a circular economy framework. The system’s adaptability suggests potential scalability to other crops and urban agricultural contexts. Full article

Solanum aculeatissimum is a medicinally and economically significant crop characterized by its aerial organs, which are densely covered with trichomes and spines. Trichomes serve as crucial sites for the synthesis of secondary metabolites in medicinal plants and represent important structural adaptations for resisting biotic and abiotic stresses. Elucidating the molecular mechanisms underlying trichome formation in S. aculeatissimum holds significant implications for enhancing both its medicinal value and stress resistance. The R2R3-MYB subfamily, the largest within the MYB transcription factor family, plays a pivotal role in regulating trichome development. Here, we present the first genome-wide identification of the R2R3-MYB gene family in S. aculeatissimum, characterizing 99 members. Phylogenetic analysis classified these SaMYBs into 10 groups. Cis-element predictions in their promoter regions revealed an abundance of light-responsive, phytohormone-responsive, and abiotic stress-responsive elements, suggesting roles in environmental adaptation. Furthermore, synteny analysis identified 25 segmentally duplicated gene pairs, and the purifying selection has been the dominant evolutionary force. Through comparative transcriptomic analysis of leaves from wild-type and sparse-trichome plants, we identified 16 differentially expressed SaMYB genes, comprising 3 upregulated and 13 downregulated genes. Subsequent qRT-PCR analysis showed that SaMYB1, SaMYB59, and SaMYB36 were highly expressed during early leaf development. Virus-induced gene silencing (VIGS) targeting these candidates demonstrated that silencing SaMYB59 significantly reduced trichome density, whereas silencing SaMYB1 or SaMYB36 produced no observable phenotypic change, confirming SaMYB59 as a key positive regulator of trichome formation. Our findings provide crucial molecular targets for elucidating the mechanisms of trichome development in S. aculeatissimum and establish a theoretical foundation for genetic improvement of this medicinal plant through regulation of the SaMYB59 gene. Full article

Temporal resolution of carbon dioxide (CO₂) release from the soil at the field scale is not completely understood. The objectives of this study were to identify trends, repetitive cycles, and residual patterns and structures with a time-series analysis from a furrow-irrigated corn (Zea mays L.) field with and without cover crops (i.e., CC and No-CC, respectively) over the course of one growing season in the Lower Mississippi River Basin. Carbon dioxide fluxes were measured from 5 May to 18 August 2024, four times a day (i.e., 0300, 0900, 1500, and 2100 h) from each of the two CC treatments. Linear trends were significant, but they were only able to explain 3 and 10% of the CO₂-flux variability for CC and No-CC, respectively. Spectral density analyses indicated the significant presence of repetitive patterns every four lags, the amplitude of which was numerically 25% greater for CC than for No-CC. The structure of the residual was best described by separate autoregressive-moving-average (ARMA) models for the CC and No-CC treatments. The current study provides preliminary yet fundamental information to improve the understanding of the dynamics of soil respiration processes from a row-crop production system. Full article

The introduction of living mulches into an orchard can be considered an agroecological practice that can provide several ecosystem services related to integrated crop protection, also in relation to the impact on soil microbiome. In this study, the introduction in an organic apple orchard of two plant mixtures designed as multifunctional living mulches to reduce weed competition and increase shelter for beneficial arthropods was evaluated in relation to their impact on soil nutrient content and bacterial activity indices. One mixture was composed of Trifolium repens (20%) and Festuca ovina (80%), the second made of 40 different plant species including legumes, flowering species and grasses. Both living mulches increased N-nitrate levels in spring, and the two-component plant mixture was also able to improve P and K levels in soil at the same time, in comparison to the natural cover (control). The two mixtures induced an increase in bacterial activity in the beginning (40 plant species mix) or middle of the growing season (two-component plant mix), without major effects on bacterial biodiversity at the phyla level, showing a high share of Proteobacteria and Actinobacteriota among treatments. Nevertheless, both plant mixtures modified the phenotypic profile of the bacterial population, measured with the Biolog method, of different classes of C sources including carbohydrates, amino acids and carboxylic acid. The results are pointing to possible benefits of the practice on soil microbial activity, which will have to be confirmed by longer studies. Full article

Soybeans (Glycine max (L.) Merr.) are a vital global crop; however, Thailand currently imports 99% of its domestic requirement, highlighting the critical need for enhanced domestic production. Morkhor 60, a new high-yielding variety, lacks optimized agronomic management for cultivation in the challenging sandy soils of Northeast Thailand. This study evaluated the effects of NPK fertilizer rates and plant spacing on Morkhor 60 growth and yield through two independent experiments conducted in sandy soils over a four-season period (2022–2023). Results demonstrated that 23.44 kg ha⁻¹ NPK provided optimal cost-effectiveness for Morkhor 60, achieving yields of 1238 kg ha⁻¹ statistically comparable to higher rates (1286 kg ha⁻¹) while reducing input costs by 50%. Plant spacing significantly affected productivity, with 30 × 20 cm spacing producing the highest yield (1775 kg ha⁻¹), representing 41% improvement over the narrow spacing (20 × 20 cm: 1257 kg ha⁻¹). The integrated management system (23.44 kg ha⁻¹ NPK with 30 × 20 cm spacing) achieved 87.6% ground cover for moisture conservation and delivered net profits of 29,850 THB ha⁻¹, with a benefit–cost ratio of 3.1. This research provides evidence-based agronomic recommendations for Morkhor 60 cultivation in sandy soil environments, contributing to Thailand’s soybean self-sufficiency through sustainable and economically viable production practices. Full article

Enhancing winter wheat yield in early spring relies on optimal soil temperature (ST) conditions and robust root systems, particularly in cold and dry areas. However, the long-term combined effects of conservation tillage and plastic film mulching (PFM) on the crop root system during early spring (the period of rejuvenation and jointing) remain unstudied. This study is based on a 22-year field experiment involving two long-term conservation tillage methods: mouldboard plowing with crop residue incorporation (MC) and no-tillage with crop residue cover (NC). The main treatments were further divided by applying black (B) and white (W) plastic films to each, resulting in MC with black (MCB) and white (MCW), and NC with black (NCB) and white (NCW) films. ST was recorded at depths of 0–40 cm during the afternoon, evening, and morning, while root characteristics (RCs) were measured at the peak flowering stage at depths of 0–60 cm, and crop yield and attributes were recorded at harvest during the 2023–2024 cropping season. Compared with MC and NC, MCB and MCW increased afternoon ST by 2.5 °C and 0.94 °C, and evening ST by 1.94 °C and 1.87 °C, while NCB and NCW decreased ST. MCB and MCW also increased accumulated ST during overwintering (131–161 °C) under the tilled system. PFM on MC increased the root length and weight densities by 10–17% and 25–32%, respectively; NCB and NCW decreased RCs by 8–15.2% across the soil depth. Additionally, afternoon and evening STs at 5–20 cm positively correlated with RCs and yield attributes (r > 0.84), whereas morning ST and a 40 cm depth were negatively correlated (r < −0.77). Under tilled conditions, both MCB and MCW substantially increased grain yield (10–12%) and biomass (31–38%) compared with MC. In contrast, NCB and NCW showed no yield and biomass advantage and even reductions (16–12% and 14–3%, respectively) compared with NC. FPM improved STs, RCs, and yield under tilled conditions but not in no-till systems, highlighting the need for supplementary practices to optimize ST in no-till systems. Full article

Cover crop mixes play an important role in enhancing soil health, nutrient turnover, and ecosystem resilience; yet, maintaining even seed dispersion and planting uniformity is difficult due to significant variances in seed physical and aerodynamic properties. These discrepancies produce non-uniform seeding and species separation in drill hoppers, which has an impact on stand establishment and biomass stability. The thousand-grain weight is an important measure for determining cover crop seed quality and yield since it represents the weight of 1000 seeds in grams. Accurate seed counting is thus a key factor in calculating thousand-grain weight. Accurate mixed-seed identification is also helpful in breeding, phenotypic assessment, and the detection of moldy or damaged grains. However, in real-world conditions, the overlap and thickness of adhesion of mixed seeds make precise counting difficult, necessitating current research into powerful seed detection. This study addresses these issues by integrating deep learning-based computer vision algorithms for multi-seed detection and counting in cover crop mixes. The Canon LP-E6N R6 5D Mark IV camera was used to capture high-resolution photos of flax, hairy vetch, red clover, radish, and rye seeds. The dataset was annotated, augmented, and preprocessed on RoboFlow, split into train, validation, and test splits. Two top models, YOLOv5 and YOLOv7, were tested for multi-seed detection accuracy. The results showed that YOLOv7 outperformed YOLOv5 with 98.5% accuracy, 98.7% recall, and a mean Average Precision (mAP 0–95) of 76.0%. The results show that deep learning-based models can accurately recognize and count mixed seeds using automated methods, which has practical applications in seed drill calibration, thousand-grain weight estimation, and fair cover crop establishment. Full article