Amid the dual pressures of ecological conservation and livelihood sustainability on the Qinghai–Tibet Plateau, investigating the economic effects of herders’ adaptation strategies holds practical relevance. Focusing on grass-based livestock husbandry, this study examines 327 pastoral households in Xinghai County, Qinghai Province, using endogenous switching regression models to empirically analyze the determinants, economic effects, and underlying mechanisms of herders’ production transformation. The main contribution is providing new empirical evidence for understanding herders’ adaptive strategies and informing policy design. The findings reveal that: (1) Transformation decisions are rational choices shaped by household resource endowments. Households with more labor and larger pasture areas are more likely to transform, while non-pastoral employment partially substitutes for such transformation. (2) Production transformation significantly increases herders’ pastoral income. Under the counterfactual framework, the income enhancement effect amounts to 21,509.08 Yuan for the transformed group and 741.30 Yuan for the non-transformed group. Income growth in the transformed group mainly stems from specialized livestock production, whereas the non-transformed group relies more on gradual improvements and policy compensation. (3) Production transformation promotes large-scale breeding without affecting livestock mortality rates. Efficiency gains from transformation are significant only for the transformed group; forcing non-transformers to adopt transformation under current endowments may lead to efficiency losses. These findings suggest that the government should prioritize supporting herders with both the capacity and willingness to transform, address barriers faced by vulnerable groups, and emphasize productivity enhancement and moderate-scale operations to facilitate sustainable income growth. Full article

Chronic nutrient stress, whether deficiency or excess, alters trees’ physiology and disrupts their growth. Because growth and hydraulics co-determine stem diameter dynamics, we hypothesize that stem diameter variation (SDV, measured by point dendrometers) carries identifiable signatures of nutrient stress that can be detected and classified. We evaluated our hypothesis by an SDV time series from a controlled experiment of nitrogen (N), phosphorus (P), and potassium (K) deficiency, optimal levels, and excess in citrus trees. We extracted temporal features from hourly SDV measurements and trained a hierarchical machine learning framework that first detected stress, then separated deficiency from excess, and finally attributed the nutrient axis (if possible). The framework substantially outperformed flat baselines. It achieved more than 70% precision for nutrient-specific classification under the full hierarchy and nearly 90% accuracy with a modified variant in which the deficiency and excess branches were each decomposed into potassium vs. {nitrogen, phosphorus} without further subdivision of nitrogen and phosphorus. Accuracy stabilized within one to two weeks of temporal aggregation, indicating an agronomically actionable detection horizon. Dendrometer-derived SDV enables noninvasive nutrient stress detection. Hierarchical ML outperforms flat baselines for NPK stress classification. Two-week temporal voting stabilizes accuracy at actionable timescales. Full article

Cucumber Fusarium wilt, which is induced by the soil-borne pathogen Fusarium oxysporum f. sp. Cucumerinum (FOC), represents a highly destructive disease. Cucumber seedling grafted onto figleaf gourd (Cucurbita ficifolia Bouché) rootstock (CFC) demonstrated better resistance to FOC. However, the molecular mechanism underlying this enhanced disease resistance capability is largely unknown. To elucidate this, we performed transcriptome, small RNA, and degradome sequencing for leaves from CFC and self-grafted cucumbers (SGC) as controls, with and without FOC infections, respectively. Our results indicated that three NAC genes, all predicted as targets of csa-miR164, were significantly up-regulated in CFC after FOC infection. Co-transformation assay in Nicotiana benthamiana confirmed that csa-miR164f directly inhibits NAC2, and transient overexpression of NAC2 in cucumber enhanced resistance to FOC, supporting its positive role in defense. Therefore, our results suggest that three NACs, upregulated in CFC, as an alternative pathway, enhance the reactive oxygen species burst and hypersensitive response, which further elevates the resistance to FOC infection. These results provide new insights into the molecular basis for improved FOC resistance in CFC. Full article

Red clover (Trifolium pratense L.) is a crop used as a forage that possesses an exceptional nutritional profile and digestibility. Unfortunately, this crop has low seed yield. Within the framework of the “Legume Generation” EC-funded project, our team aimed to investigate the role of foliar boron application on pollen viability and pollen tube development, and to assess its overall effect on red clover cultivation. Plants of six commercial diploid red clover cultivars, Nika 11, Sofia 52, AberClaret, Milvus, Global, and S123, were field-grown and boron-treated by spraying with the commercial product “Lebasol”, 11% active water-soluble boron. To reach our purpose, the transcript levels of genes related to flower, pollen, and pollen tube development and boron transport were measured by qRT-PCR; pollen grain viability and count were assessed microscopically. For this research, eight genes were selected: Auxin Response factor (TprARF17); TprAPETALA3; Walls are thin (TprWAT1 and TprWAT2); NIPs genes (Nodulin Intrinsic Protein) TprNIP4;2, TprNIP7;1, TprNIP5;1, and TprNIP6;1. Additionally, total nitrogen content in leaves detached from field-grown boron-treated and untreated plants was assessed and compared with the expression levels of two TprNIP5;1 and TprNIP6;1 transporters. The fresh and dry biomass weight from the first and second cuts was evaluated, as well as the seed collected from the red clover plants. Seed germination percentage and vigor of seedlings were examined in vitro for both boron-treated and untreated groups of two specific cultivars. Collected data confirm that foliar application of boron affects pollen viability and plant development of red clover in the cultivation conditions of South East Europe. Full article

Collective action serves as a critical mechanism for addressing deficiencies in small-scale irrigation infrastructure and fostering a virtuous cycle of their operation and maintenance. Village leaders, as central figures in organizing and mobilizing farmers toward collective action, play a pivotal role in shaping participatory irrigation management (PIM) outcomes through their public leadership. Drawing on micro-survey data from 723 farm households across Ningxia, Shanxi, and Shandong provinces in China’s Yellow River basin, this study employed a multi-group structural equation model (SEM) to analyze the impact of public leadership on collective action in PIM. The findings indicate that: (1) public leadership is directly associated with collective action, with a direct effect of 0.530; (2) public leadership indirectly enhances collective action through mediating variables—cadre–mass relationship, institutional trust, and grassroots democracy—with an indirect effect of 0.045; and (3) the personal characteristics of village leaders moderate the influence of public leadership on collective action. Specifically, public leadership exerts a strong effect when leaders belong to the village elite, possess a least a high school education, or are not members of the village’s major clan. These insights suggest that policymakers should explicitly consider public leadership in fostering collective action within the PIM framework. Full article

Rapid non-destructive evaluation of the moisture content of freshly harvested paddy rice in the field is essential for determining the optimal harvest timing, ensuring high-quality rice production and energy savings. This study developed a non-destructive prediction model for the moisture content of paddy rice using near-infrared (NIR) spectroscopy combined with machine learning and deep learning techniques. Rice samples were collected weekly during the ripening period after heading, and NIR reflectance spectra were acquired in the range of 950–2200 nm. Seven spectral preprocessing techniques were applied; and the prediction models developed, using partial least squares regression, support vector regression, deep neural network, and one-dimensional convolutional neural networks (1D-CNNs) based on VGGNet and EfficientNet architectures. Among these, the EfficientNet-based 1D-CNN combined with Savitzky–Golay 1st order derivative preprocessing showed the highest performance, achieving an ${\mathrm{R}}_{\mathrm{p}}^2$ of 0.999 and an RMSE_P of 0.001 (Friedman test, p < 0.001; Kendall’s W = 0.97), significantly outperforming previous traditional machine learning models. The results demonstrate that the proposed prediction model enables highly accurate estimation of moisture content in freshly harvested paddy rice without requiring drying or milling. The proposed approach can be implemented across various agricultural operations, enabling optimal harvest timing, quality control during storage, energy efficient drying, and real-time monitoring via on-combine sensor systems. Full article

Against the backdrop of China’s rural revitalization, understanding the factors influencing farmers’ agricultural technology adoption behavior is crucial for enhancing such adoption. Therefore, exploring the decision-making logic behind farmers’ agricultural technology adoption behavior is of paramount importance. This study, conducted among 482 typical farming households in the Chengdu Plain of Sichuan Province, China, introduced the Random Forest (RF) algorithm into an Improved TAM. Combined with SHAP and PDP techniques, it identified 21 influencing factors and their nonlinear interaction mechanisms. Key findings include the following: (1) Adoption rates stood at only 14.3%, exhibiting a pronounced “advantage-oriented” pattern favoring male farmers, middle-aged/young adults, and higher-income groups; (2) Level of agricultural production tools and technology (C1) and Agricultural product sales channels (C2) emerged as core drivers, with C1 presenting a significant “technology threshold effect”—adoption probability surged from 0.1 to over 0.35 during intelligent technology transitions; (3) Monthly household income level (B4) effectively mitigates risk aversion among elderly farmers, revealing the critical role of Age (A2) in decision-making and enabling a complementary relationship between experience and technology; (4) Self-learning and training proficiency in agricultural technology (F1) reflects that excessive technological complexity triggers resistance and blocks adoption, while Highest educational attainment in the household (B1) and Number of educated family members (B2) exhibit nonlinear peak characteristics influenced by “brain drain” due to labor migration. These findings not only expand the theoretical application of machine learning in studying farmer behavior but also provide granular insights for overcoming the “last mile” bottleneck in agricultural technology dissemination. Full article

Evapotranspiration and crop coefficients are key variables for designing efficient irrigation strategies in tree crops, yet standard tabulated coefficients derived for mature, fully covering orchards often fail to represent the water use of young, high-density hazelnut systems. In recent years, updated crop coefficients for temperate fruit trees, including hazelnut, and transpiration-based models have been proposed, while several studies have successfully linked Vegetation Indices and thermal metrics to single and basal crop coefficients in vineyards, orchards and field crops. However, no information is available on the use of UAV-derived spectral and thermal indices to estimate crop coefficients in high-density hazelnut orchards. This study compares crop coefficients obtained from traditional approaches (the FAO56 single crop coefficient, a transpiration-based coefficient, and ground cover reduction factors) with coefficients estimated from UAV-derived Normalized Difference Water Index (NDWI) and Crop Water Stress Index (CWSI) in a subsurface-drip-irrigated hazelnut orchard (cv. Tonda Francescana^®) with two planting densities (625 and 1250 trees ha⁻¹) in central Italy. Multispectral and thermal UAV surveys carried out between 2021 and 2024 were used to derive canopy geometrical traits, ground cover, NDWI, and CWSI, while a local weather station provided reference evapotranspiration. Empirical relationships were calibrated between crop coefficients and ground cover, NDWI, and CWSI, and mid-season coefficients were applied to estimate daily crop evapotranspiration, which was then compared with the irrigation volumes supplied during the 2024 season. The standard FAO56 crop coefficient (Kc = 0.9) overestimated evapotranspiration, especially at the lower planting density, whereas ground cover-based reduction factors recalibrated for hazelnut and the transpiration-based coefficient provided estimates more consistent with the applied irrigation. UAV-based NDWI- and CWSI-derived crop coefficients produced mid-season values close to those obtained with the transpiration-based method for both planting densities, confirming that spectral and thermal information can effectively capture the combined effects of canopy development and water status. These results indicate that combining traditional methods with UAV-derived indices offers a flexible framework to refine crop coefficients in high-density hazelnut orchards and support more accurate and spatially explicit irrigation scheduling. Full article

Erosion in intensively farmed landscapes threatens above- and below-ground biodiversity. While impacts on soil physical and chemical properties (which affect soil inhabiting biota) are well documented, effects on ground-associated fauna (distribution, diversity, abundance) remain less understood. A likely very strong factor is the direct transport of epigeon together with the eroded soil. We assessed how water-erosion processes shape communities of epigeic invertebrates along agricultural slopes in the Chernozem region of South Moravia (Czech Republic). Ground-dwelling invertebrates were sampled over five years (May–September) in conventionally managed maize fields using pitfall traps across 18 sloping fields. Three slope positions were compared per field (control, erosional, depositional; 54 positions in total). Community patterns were evaluated using Canonical Correspondence Analysis with covariates (month, year, slope position, site), and species responses to key drivers were analysed using Generalised Additive Models. Across the full dataset, Shannon diversity and species richness did not differ significantly among slope positions; however, total invertebrate abundance was significantly lower in erosional parts. Interannual variation was pronounced and linked to precipitation: wet conditions increased diversity and richness at depositional positions, whereas dry conditions reduced diversity downslope. Ordination and GAM results identified erosion intensity and relative precipitation/temperature anomalies as important predictors, with most dominant species showing higher abundances under low to moderate erosion. These findings indicate that epigeic invertebrate communities along slopes can serve as indicators of erosion force. Full article

Comprehensively integrating eco-farms and rural tourism represents a crucial pathway for advancing rural revitalization and sustainable development; however, existing research has pre-dominantly focused on the net effects of individual factors, failing to reveal the underlying complexity of multiple co-occurring factors and their interactive logics. With the aim of addressing this theoretical gap, we employ a configurational approach that integrates Necessity Condition Analysis (NCA) with fuzzy set qualitative comparative analysis (fsQCA), and data was collected from 1041 Chinese ecological farms (ecological farm operators) using a structured questionnaire, to systematically explore the integrated complex configurational driving logic. Our findings reveal that no single necessary condition independently causes high-level integration. The fsQCA results further reveal that high-level integration is attainable via two distinct, yet equivalent pathways. First, the “Endogenous–Technological–Economic Synergistic Drive Model” emphasizes the intrinsic development needs of business entities, requiring extensive synergy with external technological empowerment and the regional economic environment; second, the “re-source–market–integration linkage-driven” pathway leverages unique resource endowments and achieves value transformation through efficient resource integration capabilities, guided by clear market demand. Both pathways exhibit functional substitutability among their conditions, demonstrating strategic systemic flexibility. Additionally, in the analysis of non-high-integration configurations, we draw upon structural hole theory to categorize systemic failures caused by missing key connections or factor misalignment. Full article

Soil nitrogen availability is a major constraint to crop productivity in rainfed arid and semi-arid regions. The influence of straw strip mulching on nitrogen availability and transformation across soil layers remains unclear. This study investigates the effect of straw strip mulching on soil nitrogen dynamics and crop-specific variation in wheat- and potato-cultivated soils under rainfed semi-arid conditions. This study consisted of five mulching treatments, including without mulching (Tck), black plastic film mulching (Tp), straw strip mulching (Tss), plant strip without mulch (Tps), and composite strip of straw strip mulching and plant strip without mulch (Tcs) applied in wheat and potato cultivation during 2019 and 2020, and soil nitrogen fractions were determined across different soil depths. Tss mulching showed the highest increase in urease activity (48%), nitrite reductase activity (48%), microbial biomass nitrogen (52%), NH₄ (11%), acid-hydrolyzed total nitrogen (10%), acid-soluble NH₄ (6%), acid-hydrolyzed amino sugar (16%) and acid-hydrolyzable unknown nitrogen (59%) relative to Tck without mulching. While total nitrogen (11%) and acid-hydrolyzed amino acid (9%) were highest in the Tps treatment compared to Tck treatment, the mulching treatment had no significant effect on soil organic nitrogen-derived functional traits. Across all treatments, the 0–20 cm soil layer consistently showed the highest concentrations of observed soil traits, which declined with increasing soil depth. Furthermore, potato-cultivated soils showed consistently higher concentrations of these traits than wheat-cultivated soils, and the concentrations of these traits in 2020 exceeded those observed in 2019. This study highlights that maize straw mulching in strips significantly promotes soil organic nitrogen fractions, particularly in the upper soil layers, and promotes higher nitrogen availability in potato than in wheat-cultivated soils, and is recommended as an effective soil management practice to improve soil nitrogen availability in rainfed semi-arid Loess Plateau conditions. Full article

The LOCAL project, ‘Biodiversity and enhancement of local sheep and goat genotypes with a predominant aptitude for milk production’, was developed in the Apulia region of Southern Italy. It adopted a multidisciplinary scientific approach to address the conservation of native sheep and goat breeds, and it aimed to engage a wide and diverse audience to contribute to the development of the territory. This work outlines some of the project’s objectives and, in particular, the activities relating to the historical documentation, census and morphological characteristics of four breeds: the Gentile di Puglia sheep and the Grigia del Subappennino Dauno, Capestrano Pugliese and Antica Murgiana goat breeds. The project’s results enabled the four breeds to be registered in the Regional Register of Animal Genetic Resources, paving the way for further initiatives aimed at implementing in situ and ex situ conservation of the breeds’ genetic heritage. Furthermore, the paper presents actions aimed at raising awareness of the importance of animal biodiversity and native populations, with a particular focus on education, tourism, and productive services. Full article

Accurate identification of the cotton top bud is a prerequisite for automated cotton topping. However, the detection of the cotton top bud is low due to the small target size and a similar background. Therefore, a method named CFSS-YOLO was proposed to detect the cotton top bud based on YOLOv12 with an attention mechanism. Firstly, a Convolutional Block Attention Module (CBAM) was introduced into the neck structure of YOLOv12 to suppress background interference and improve target recognition accuracy. Secondly, a new loss function, FSSLoss, was designed where the Shape-IoU (Intersection over Union) optimized by Focaler-IoU was used for the part of localization loss, and Slideloss was integrated to improve the classification loss. The improvement of the loss function aimed to balance the relationship between classification loss and localization loss and accelerate the convergence speed of the model. The experimental results show that the precision, recall and mAP50 of the proposed CFSS-YOLO are 87.6%, 75.3% and 84.8%, respectively. The detection performance of the proposed method is superior to mainstream object detection models such as YOLOv12s, YOLOv5s, SSD, RT-DETR, and DEIM-R18 in outer farmland. These results demonstrate that the proposed CFSS-YOLO has high potential for application and promotion in the cotton top bud recognition task. Full article

To estimate leaf area, the length-width method, also called the Montgomery equation, has been widely used. It is an empirical formula stating that within a given species, the area of a leaf is proportional to the product of its length and width. Although the formula is known to be applicable to a variety of simple leaves and leaflets, its applicability to compound leaves has only been investigated on a limited range of leaf forms and economically important crops. In this study, we investigated whether this method is broadly applicable to compound leaves of diverse forms. We measured 20 compound-leaved species including various leaf shapes (ternate, biternate, triternate, palmate, pedate, and pinnate leaves) as well as life forms (trees, herbs, and woody and herbaceous lianas). Our data cover diverse taxa, including both Ranunculales and core eudicots (Fabales, Rosales, Fagales, Vitales, Apiales, Lamiales, Asterales, and Dipsacales). The results show that the length-width method is applicable to all types of compound leaves investigated (slope [i.e., Montgomery parameter]: 0.298–1.035; R² = 0.928–0.996). These results indicate that a compound leaf can be considered equivalent to a simple lobed leaf when applying the length-width method. Full article

In structured orchard environments, harvesting robots operate where rigid bodies (e.g., trunks, poles, and wires) coexist with flexible foliage. Strict avoidance of all obstacles significantly compromises operational efficiency. To address this, this study proposes an end-to-end autonomous harvesting framework characterized by an “avoid-rigid, push-through-soft” strategy. This framework explicitly propagates uncertainties from sensor data and reconstruction processes into the planning and policy phases. First, a multi-task perception network acquires 2D semantic masks of fruits and branches. Class probabilities and instance IDs are back-projected onto a 3D Gaussian Splatting (3DGS) representation to construct a decision-oriented, semantically enhanced 3D scene model. The policy network accepts multi-channel 3DGS rendered observations and proprioceptive states as inputs, outputting a continuous preference vector over eight predefined motion primitives. This approach unifies path planning and action decision-making within a single closed loop. Additionally, a dynamic action shielding module was designed to perform look-ahead collision risk assessments on candidate discrete actions. By employing an action mask to block actions potentially colliding with rigid obstacles, high-risk behaviors are effectively suppressed during both training and execution, thereby enhancing the robustness and reliability of robotic manipulation. The proposed method was validated in both simulation and real-world scenarios. In complex orchard scenarios, the proposed AE-TD3 algorithm achieves a harvesting success rate of 77.1%, outperforming existing RRT (53.3%), DQN (60.9%), and TD3 (63.8%) methods. Furthermore, the method demonstrates superior safety and real-time performance, with a collision rate reduced to 16.2% and an average operation time of only 12.4 s. Results indicate that the framework effectively supports efficient harvesting operations while ensuring safety. Full article