Agriculture

33 pages, 1291 KB

Open AccessReview

Coffee Pulp Recycling in Coffee Cultivation: Agronomic Effects and Bean Quality Responses

by Rongjie Gui, Xinyu Tang, Lin Yan, Qingyun Zhao, Xingjun Lin, Huan Yu, Yunping Dong, Zixin Chen, Yulan Li, Kejing Zhao, Jiayi Shi, Yijiaqi Zhang, Yanli Huang and Ang Zhang

Agriculture 2026, 16(15), 1691; https://doi.org/10.3390/agriculture16151691 - 6 Aug 2026

Viewed by 437

Abstract

Improper disposal of coffee-processing by-products can cause environmental pollution, greenhouse gas emissions, and resource loss, whereas their reuse in coffee plantations may support sustainable production. This review systematically examines the material properties, stabilization methods, field application pathways, agronomic effects, quality responses, and environmental [...] Read more.

Improper disposal of coffee-processing by-products can cause environmental pollution, greenhouse gas emissions, and resource loss, whereas their reuse in coffee plantations may support sustainable production. This review systematically examines the material properties, stabilization methods, field application pathways, agronomic effects, quality responses, and environmental risks of coffee-pulp-type by-products in cultivation. Relevant studies published up to June 2026 were retrieved from Web of Science, Scopus, ScienceDirect, SpringerLink, Google Scholar, and CNKI and qualitatively synthesized along the soil–plant–quality continuum. Current evidence suggests that properly stabilized materials, applied at appropriate rates, can improve soil organic matter, structure, water and nutrient retention, microbial activity, plant growth, photosynthesis, and crop yield in plantations. They may also indirectly influence green bean quality by regulating sugars, amino acids, chlorogenic acids, and caffeine. However, these effects depend strongly on material properties, maturity, application rate, coffee genotype, soil and climatic conditions, and management practices. Excessive or insufficiently decomposed materials may cause soil acidification, phytotoxicity, oxygen depletion, nutrient imbalance, and yield–quality trade-offs. Overall, recycling within plantations can turn processing waste into farm inputs, reinforce on-farm carbon and nutrient cycles, ease disposal burdens, and advance BCG and wider circular-economy principles in practice. Full article

(This article belongs to the Section Agricultural Product Quality and Safety)

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17 pages, 7719 KB

Open AccessArticle

Effects of Post-Wheat Sequential Green Manure Cropping on Soil Water Balance and Potato Productivity in a Loess Plateau Rotation System

by Yuanhong Zhang, Huizhi Hou and Jiade Yin

Agriculture 2026, 16(15), 1690; https://doi.org/10.3390/agriculture16151690 - 6 Aug 2026

Viewed by 379

Abstract

The Loess Plateau is a typical dryland agricultural region where water scarcity constrains crop production. Plastic film mulching is widely used to mitigate this limitation, but its long-term application has caused soil degradation and environmental pollution, highlighting the need for sustainable alternatives. This [...] Read more.

The Loess Plateau is a typical dryland agricultural region where water scarcity constrains crop production. Plastic film mulching is widely used to mitigate this limitation, but its long-term application has caused soil degradation and environmental pollution, highlighting the need for sustainable alternatives. This study evaluated the effects of post-wheat sequential green manure cropping—common vetch (CV) and winter rape (CR)—on soil water balance, potato yield, water use efficiency (WUE), and economic returns within a winter wheat-potato rotation system. A field experiment (2019–2023) was conducted on the Loess Plateau, comparing CV, CR, plastic film mulching (PM), and no mulching (NM). Soil water content (0–200 cm), evapotranspiration (ET), tuber yield, WUE, and economic performance were measured. Although CV and CR depleted soil water in the 0–100 cm layer during their growth, subsequent fallow precipitation (86 mm) and low soil water loss replenished the deficit, resulting in comparable soil water storage at potato sowing among CV, CR, and NM (518.8, 508.8, and 518.9 mm, respectively). Over the full rotation cycle, a positive soil water balance (63.1–147.5 mm) was maintained across all treatments, indicating no detectable net depletion within the measured 0–200 cm profile over the four potato seasons. Potato tuber yield increased by 61.7% (CV), 52.4% (CR), and 74.8% (PM) relative to NM. The WUE of CV (73.5 kg ha⁻¹ mm⁻¹) and CR (71.5 kg ha⁻¹ mm⁻¹) was comparable to that of PM (74.2 kg ha⁻¹ mm⁻¹) and significantly higher than that of NM (46.1 kg ha⁻¹ mm⁻¹). Net incomes under CV and CR did not differ significantly from PM, while their input costs were lower. Collectively, the integrated system combining post-wheat sequential green manure cropping—particularly with common vetch—and straw mulching represents a technically feasible, economically viable, and environmentally beneficial alternative to plastic film mulching, contributing to sustainable dryland agriculture on the Loess Plateau and analogous semi-arid regions. Full article

(This article belongs to the Special Issue Enhancing Soil Health and Water Use Efficiency in Sustainable Agriculture—2nd Edition)

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20 pages, 3778 KB

Open AccessArticle

Diagnosing the Value-Chain Constraints and Opportunities for Smallholder Arabica Coffee in Saudi Arabia

by Bernard M. Gichimu, Bandar H. Alfaifi, Kakoli Ghosh, Khalid M. Al-Rohily, Naser B. Al-Marri and Nizar Haddad

Agriculture 2026, 16(15), 1689; https://doi.org/10.3390/agriculture16151689 - 6 Aug 2026

Cited by 1 | Viewed by 444

Abstract

Saudi Arabia’s Arabica coffee is produced almost entirely by smallholder farmers. Although the local production meets only a small share of national consumption, the sector has expanded substantially in recent years under national development initiatives and strategic investment. This study diagnoses the smallholder [...] Read more.

Saudi Arabia’s Arabica coffee is produced almost entirely by smallholder farmers. Although the local production meets only a small share of national consumption, the sector has expanded substantially in recent years under national development initiatives and strategic investment. This study diagnoses the smallholder value chain in Jazan, Asir and Al Baha through a cross-sectional survey of 347 farms in 14 governorates (May–November 2023). Mean green-bean yield was 0.37 kg per tree (median 0.25), which is within the global smallholder Arabica range, and the top 6.5% of the farms exceeded 1 kg per tree. Water scarcity was the leading production constraint (84.9% of respondents). This prompted a separate, exploratory irrigation survey of 57 Jazan farms (January 2024), which found true drip irrigation to be about 50% more water-productive than basin or stripped-emitter systems, a finding that would need confirmation through controlled trials. Although membership is self-selected and unmeasured confounding cannot be excluded, farmers who belonged to a producer organisation consistently performed better across the value chain. Their yields were higher (0.40 versus 0.23 kg per tree, p = 0.003), and this advantage held after adjustment for farm and farmer characteristics (+78%, 95% CI + 33 to +137). In addition, 24% of members undertook on-farm roasting against 3.9% of non-members. These findings point to wider producer-organisation coverage, more efficient irrigation and improved post-harvest handling as priority areas for intervention and future evaluation. Full article

(This article belongs to the Section Agricultural Systems and Management)

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24 pages, 1599 KB

Open AccessArticle

Assessing the Impacts of Population Shrinkage on Agricultural Water Resource Utilization and Environmental Carrying Capacity in Northeast China

by Yuan Ji and Wenxin Liu

Agriculture 2026, 16(15), 1688; https://doi.org/10.3390/agriculture16151688 - 6 Aug 2026

Viewed by 342

Abstract

Investigating the dynamic interplay between population shrinkage and water resource carrying capacity holds critical implications for safeguarding national food security and ecological resilience. This study systematically examines the underlying mechanisms and spatiotemporal evolution of their coupling relationship in Northeast China, thereby advancing the [...] Read more.

Investigating the dynamic interplay between population shrinkage and water resource carrying capacity holds critical implications for safeguarding national food security and ecological resilience. This study systematically examines the underlying mechanisms and spatiotemporal evolution of their coupling relationship in Northeast China, thereby advancing the theoretical framework of human–land systems and delivering empirically grounded insights for sustainable regional development. Drawing on balanced panel data from 34 prefecture-level cities in Northeast China over the period 2010–2023, this study develops a population shrinkage index grounded in registered population dynamics. Building upon the DPSIR (driving forces–pressures–state–impacts–responses) conceptual framework, we construct a comprehensive evaluation system for agricultural water resource carrying capacity, explicitly operationalizing each of its five dimensions. Employing a spatial Durbin model (SDM), we rigorously estimate the direct effect of population shrinkage on carrying capacity, while simultaneously testing the mediating pathways through human capital accumulation and fiscal policy interventions. The empirical analysis reveals that (1) over the study period (2010–2023), population shrinkage in Northeast China intensified progressively, expanding spatially from initially localized pockets to widespread, regionally contiguous areas. Concurrently, agricultural water resource carrying capacity displayed pronounced spatial heterogeneity and statistically significant spatial clustering. (2) Overall, the degree of population decline significantly negatively affects agricultural water resource carrying capacity. Further heterogeneity tests showed that this negative effect exhibits significant variation across different population-contracted regions and provinces. Among the control variables, urbanization rate, per capita GDP, and total water resources availability exhibit statistically significant positive associations with agricultural water resource carrying capacity (3) Over the study period, higher human capital endowment and greater fiscal intervention intensity significantly attenuated the adverse effect of population shrinkage on agricultural water resource carrying capacity. Full article

(This article belongs to the Special Issue Water Resource Management and Policy in Agriculture: Challenges and Opportunities)

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23 pages, 9613 KB

Open AccessArticle

Grain Production Efficiency in Shandong Province, China: Spatial–Temporal Patterns, Influencing Factors, and Improvement Strategies

by Ye Sun and Bei Jin

Agriculture 2026, 16(15), 1687; https://doi.org/10.3390/agriculture16151687 - 6 Aug 2026

Viewed by 401

Abstract

Against the backdrop of increasing global food security pressures and tightening resource–environment constraints, enhancing grain production efficiency has become a focal international concern. Based on panel data from 16 cities in Shandong Province, China, spanning 2013 to 2022, this study employs the DEA–Malmquist [...] Read more.

Against the backdrop of increasing global food security pressures and tightening resource–environment constraints, enhancing grain production efficiency has become a focal international concern. Based on panel data from 16 cities in Shandong Province, China, spanning 2013 to 2022, this study employs the DEA–Malmquist index, SBM model, and Spatial Durbin model to measure grain production efficiency and analyze its spatiotemporal evolution and influencing factors. The findings reveal that Shandong’s grain production efficiency has generally improved but exhibits a spatial differentiation pattern of “higher in Western Shandong, lower in Eastern Shandong,” with significant positive spatial correlation and agglomeration effects. Mechanization significantly boosts efficiency, while urbanization, excessive fertilizer and pesticide use, and labor surplus exert notable negative impacts. This research clarifies the spatial spillover mechanisms and key constraints of efficiency, providing scientific evidence and practical guidance for optimizing agricultural resource allocation, promoting regional collaborative innovation, and formulating differentiated food security policies in Shandong Province and other regions with similar natural–economic conditions. Full article

(This article belongs to the Special Issue Sustainable Farmland Transfer and Food Production: Economic and Policy Perspectives in Agriculture)

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24 pages, 2625 KB

Open AccessArticle

ShuffleNetV2-hSimKD: A Lightweight Network for Plant Disease Detection

by Qiuxin Si, Yoojeong Song and Sang Ik Han

Agriculture 2026, 16(15), 1686; https://doi.org/10.3390/agriculture16151686 - 6 Aug 2026

Viewed by 411

Abstract

Early and accurate plant disease detection is essential for reducing crop losses and supporting sustainable agricultural management. Although deep learning-based approaches have achieved strong performance in plant disease analysis, many existing models require substantial computational resources, which limits their use in resource-constrained agricultural [...] Read more.

Early and accurate plant disease detection is essential for reducing crop losses and supporting sustainable agricultural management. Although deep learning-based approaches have achieved strong performance in plant disease analysis, many existing models require substantial computational resources, which limits their use in resource-constrained agricultural environments. This study proposes ShuffleNetV2-hSimKD, a lightweight integration framework for plant disease detection. It adopts ShuffleNetV2 as the backbone and incorporates the parameter-free SimAM attention mechanism to enhance disease-related feature representation without introducing additional learnable parameters. In addition, the standard ReLU activation function is replaced with h-swish to improve nonlinear feature extraction and preserve informative feature responses. A hybrid knowledge distillation strategy is further employed to transfer both output-level and feature-level knowledge from a high-capacity teacher model to the lightweight student network during training. Unlike previous studies that apply these techniques in isolation, ShuffleNetV2-hSimKD synergistically integrates parameter-free SimAM, h-swish optimization, and hybrid KD to overcome the representation limitations of lightweight backbones in subtle disease symptom detection. The proposed framework was evaluated on a balanced subset of the PlantVillage dataset, in which leaf images were categorized as healthy or diseased. ShuffleNetV2-hSimKD achieved an accuracy of 90.41% with only 1.4M parameters and 151M FLOPs. Compared with representative lightweight Convolutional Neural Networks (CNNs), the proposed model achieved improved accuracy and recall while maintaining low computational complexity. These results demonstrate that ShuffleNetV2-hSimKD provides an effective balance between detection performance and computational efficiency, highlighting its potential as a lightweight candidate for plant disease detection in resource-constrained agricultural scenarios. Full article

(This article belongs to the Special Issue Smart Sensor-Based Systems for Crop Monitoring)

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21 pages, 4124 KB

Open AccessArticle

Multi-Scale Driving Mechanisms of the Spatiotemporal Distribution of Soil pH in High-Standard Farmland Within the Hilly Regions of Southern China

by Zhengxiang Zeng, Fanghao Li, Meiqiu Chen, Yujun Cai and Yefeng Jiang

Agriculture 2026, 16(15), 1685; https://doi.org/10.3390/agriculture16151685 - 5 Aug 2026

Viewed by 417

Abstract

Elucidating the multi-scale driving mechanisms of the spatiotemporal distribution of soil pH is critical for regional soil ecological regulation and the preservation of cultivated land fertility. However, systematic elucidation of the multi-scale driving mechanisms of the spatiotemporal distribution of soil pH remains limited. [...] Read more.

Elucidating the multi-scale driving mechanisms of the spatiotemporal distribution of soil pH is critical for regional soil ecological regulation and the preservation of cultivated land fertility. However, systematic elucidation of the multi-scale driving mechanisms of the spatiotemporal distribution of soil pH remains limited. Here, we integrated random forest, XGBoost, and geographically interpretable SHAP (GeoShapley) to systematically investigate the spatiotemporal changes in soil pH and their driving mechanisms across three analytical scales (global, raster, and point-level) before and after high-standard farmland construction, based on 149 paired soil sampling points collected in 2008 and 2023 in Taihe County, Jiangxi Province. The mean soil pH in the study area increased from 5.12 in 2008 to 5.53 in 2023, suggesting mitigation of soil acidification. XGBoost outperformed random forest in both periods, with R² values of 0.71 and 0.67 for the two periods, respectively, and the spatial predictions indicated that soil pH in the western and southeastern regions increased following high-standard farmland construction. At the global scale, feature importance based on XGBoost indicated that climate contributed most to soil pH variation in both periods, followed by anthropogenic activities. At the raster scale, GeoShapley revealed a shift from a combination of anthropogenic activities and climate in 2008 to an interplay among climate, vegetation, and topography in 2023, suggesting a decreasing influence of anthropogenic disturbance. At the point level, the factors primarily influencing soil pH shifted from topographic and anthropogenic ones in 2008 to topographic and climatic ones in 2023, regardless of whether the sampling points exhibited low or high pH values, further corroborating the diminishing role of anthropogenic influence. Interaction analyses indicated that topography and vegetation consistently modulated the effects of anthropogenic and climatic factors throughout the study period, suggesting that these two factors were consistently associated with soil pH dynamics across the study area. In summary, this study provides a scientific basis for the targeted improvement of cultivated land fertility and the formulation of differentiated soil management strategies for high-standard farmland in the hilly regions of southern China. Full article

(This article belongs to the Special Issue Smart Farming Practices and Technologies for Agricultural Soil Management)

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34 pages, 10258 KB

Open AccessArticle

FruitDet: A Multi-Module Lightweight Detector for Young Apple Fruits Under Day–Night Orchard Conditions

by Jipeng Chen, Jinzheng Yu, Langyu Tang, Rong Zhang, Jinyan Li, Hongda Chen, Zhiyuan Zhang, Yang Liu and Hongfei Yang

Agriculture 2026, 16(15), 1684; https://doi.org/10.3390/agriculture16151684 - 5 Aug 2026

Viewed by 373

Abstract

Reliable perception of young apple fruits in natural orchards is a prerequisite for automated thinning and intelligent orchard management, yet remains difficult in real field conditions due to small fruit size, dense distribution, branch–leaf occlusion, background similarity, and severe illumination degradation at night. [...] Read more.

Reliable perception of young apple fruits in natural orchards is a prerequisite for automated thinning and intelligent orchard management, yet remains difficult in real field conditions due to small fruit size, dense distribution, branch–leaf occlusion, background similarity, and severe illumination degradation at night. This study presents FruitDet, a lightweight multi-module detector designed for robust day–night young apple fruit detection in complex orchard environments. A field dataset was established in a high-density apple orchard in Aksu, Xinjiang, covering daylight and low-light night-time scenes with diverse occlusion, scale, and illumination variations. To improve detection robustness without sacrificing computational efficiency, FruitDet combines three complementary mechanisms: an inverted-bottleneck-based multi-scale feature enhancement module for preserving small-fruit details, a channel–spatial attention module for suppressing foliage and illumination interference, and a lightweight Transformer-based context module for modeling long-range dependencies between fruits and surrounding orchard structures. In daytime scenes, FruitDet achieved 91.904% precision, 77.557% recall, 83.254% mAP50, and 66.427% mAP50–95; in night-time scenes, it maintained 90.107% precision, 75.135% recall, 80.544% mAP50, and 64.719% mAP50–95. Compared with mainstream detectors including YOLOv5n, YOLOv8n, YOLO11n, YOLO26n, Faster R-CNN, RT-DETR, and RT-DETRv2, FruitDet consistently delivered higher accuracy across lighting conditions. Ablation, visualization, public-dataset testing, and edge-deployment experiments verified that the proposed modules jointly improve small-object representation, background discrimination, low-light robustness, and real-time applicability. With 2.960 M parameters, 3.726 G FLOPs, and approximately 180 FPS, FruitDet offers a practical and efficient visual perception approach for Young fruit monitoring was conducted under both daytime and night-time orchard conditions covered in this study. All-weather orchard monitoring and robotic young-fruit thinning. The shareable data are available Full article

(This article belongs to the Special Issue Advances in Precision Agriculture in Orchard)

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33 pages, 23794 KB

Open AccessArticle

Navigation Line Extraction Method for Alfalfa Crops Based on RACG-RandLA Point Cloud Segmentation Model

by Kehua Dang, Jiachen Cao, Pengjie Pan, Zijie Niu, Zehan Lu, Dongyan Zhang and Yongjie Cui

Agriculture 2026, 16(15), 1683; https://doi.org/10.3390/agriculture16151683 - 5 Aug 2026

Viewed by 421

Abstract

Early-stage alfalfa navigation faces challenges like low plants, narrow rows, and weed interference, causing camera–LiDAR colored point clouds to suffer from sparsity, discontinuous boundaries, and varying illumination. Standard point-level semantic segmentation struggles to support stable crop row allocation and navigation line fitting under [...] Read more.

Early-stage alfalfa navigation faces challenges like low plants, narrow rows, and weed interference, causing camera–LiDAR colored point clouds to suffer from sparsity, discontinuous boundaries, and varying illumination. Standard point-level semantic segmentation struggles to support stable crop row allocation and navigation line fitting under these conditions. To address this, we propose RACG-RandLA, a multi-output row-aware color-geometric point cloud segmentation model. Pseudo-labels (crop, background, ‘ignore’) are generated using color and spatial priors, alongside transverse offset and point-level confidence labels for crop points. Built on RandLA-Net, the multi-task network simultaneously outputs crop semantics, transverse offsets, and confidences. It features a color-geometry residual fusion module that adaptively integrates 3D geometric and RGB/ExG features via zero-initialized scaling to handle complex lighting and missing data. Additionally, a late Row-cued Local Feature Aggregation (LFA) module embeds longitudinal continuity and transverse offset constraints into deep layers, effectively mitigating cross-row feature aliasing. During inference, a multi-output pipeline utilizes these predictions for reliable crop point filtering, center refinement, and navigation line fitting. Experiments show that the xyzrgb_exg input achieves an optimal balance between accuracy and conciseness. RACG-RandLA achieves a Test IoU of 0.8590, outperforming PointNet variants, and secures the highest Row Count Accuracy of 0.8761. Furthermore, it reduces lateral navigation jitter to 0.0241 m while maintaining an 85.04% success rate. Ultimately, the proposed method demonstrates a superior balance of semantic accuracy, structural consistency, and navigation stability, providing a robust perception solution for agricultural robots. Full article

(This article belongs to the Section Artificial Intelligence and Digital Agriculture)

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24 pages, 1144 KB

Open AccessArticle

Heterogeneous Behavioral Mechanisms Behind Farmers’ Compensation Expectations for Fertilizer Reduction: A TPB-Based Analysis Using Choice Experiment Data

by Xueyu Tang, Hao Wu, Zhaotong Zhang and Liuyang Yao

Agriculture 2026, 16(15), 1682; https://doi.org/10.3390/agriculture16151682 - 5 Aug 2026

Viewed by 374

Abstract

The compensation a farmer requires to reduce fertilizer use reflects the behavioral factors behind the decision to participate. This study examines how they are associated with compensation expectations, and whether these associations differ across operator types, by integrating the Theory of Planned Behavior [...] Read more.

The compensation a farmer requires to reduce fertilizer use reflects the behavioral factors behind the decision to participate. This study examines how they are associated with compensation expectations, and whether these associations differ across operator types, by integrating the Theory of Planned Behavior with a discrete choice experiment. Using survey data from 1066 rice farmers in Jiangsu Province, China, we recover each farmer’s willingness-to-accept (WTA) for fertilizer reduction as a money-metric proxy for the intention to participate, and model it as a function of behavioral attitude, social-institutional norm, and perceived behavioral control separately for conventional smallholders, specialized large-scale planters, and registered family farms. The pattern of associations differs across operator types. A favorable attitude is associated with a lower WTA only among smallholders, the social-institutional norm among smallholders and family farms, and perceived behavioral control among large-scale planters and family farms; the evidence for the attitude association among smallholders and for the norm association among family farms is relatively weaker. Each construct is thus associated with compensation expectations in a distinct subset of operator types, a structure that pooled estimation obscures. The findings suggest that differentiated policy could match attitudinal, capability-building, or institutional levers to the channel associated with each operator type, rather than relying on uniform payment increases. Full article

(This article belongs to the Section Agricultural Economics, Policies and Rural Management)

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48 pages, 22386 KB

Open AccessArticle

A Reinforcement Learning-Based Multi-Strategy Differential Evolution Algorithm for Agricultural UAV Path Planning

by Pengyu Chen, Chengzhi Qu, Zihan Meng and Yaji Tang

Agriculture 2026, 16(15), 1681; https://doi.org/10.3390/agriculture16151681 - 4 Aug 2026

Viewed by 439

Abstract

In the realm of precision agriculture, agricultural UAV path planning is challenging because the UAV must avoid obstacles, follow uneven terrain, and satisfy multiple flight constraints simultaneously. Differential evolution (DE) has been widely adopted for this problem because of its simple structure and [...] Read more.

In the realm of precision agriculture, agricultural UAV path planning is challenging because the UAV must avoid obstacles, follow uneven terrain, and satisfy multiple flight constraints simultaneously. Differential evolution (DE) has been widely adopted for this problem because of its simple structure and effective optimization capability. However, existing DE-based methods often become trapped in local optima and cannot effectively balance exploration and exploitation in complex search environments. To address these issues, this paper proposes a reinforcement learning-based multi-strategy differential evolution algorithm, named PPOMSDE. By introducing Proximal Policy Optimization (PPO) to construct a multi-dimensional state pool and an action pool, PPOMSDE enables adaptive strategies for individuals, improving strategy selection during the search process. An independent multi-buffer is adopted to ensure strict data isolation and efficient learning to avoid strategy confusion. In addition, an adaptive triplet mechanism which partitions the population into fitness-based tiers (best, medium, and worst) assigns different control parameters and mutation strategies to individuals with different fitness levels, improving the balance between global exploration and local exploitation. Extensive experiments on the CEC’2014 and CEC’2017 benchmark suites demonstrate the effectiveness of PPOMSDE. The proposed method achieves the lowest average performance ranks of 1.39 on the combined 10-D and 30-D CEC’2014 benchmarks and 1.03 on the 10-D CEC’2017 benchmarks. In agricultural UAV path planning, PPOMSDE generates safer and smoother flight paths while maintaining accurate terrain-following flight, reducing the overall cost by an average of 22.42% compared with ISDE, L-SHADE, SHADE, and ISHACDE. Full article

(This article belongs to the Section Artificial Intelligence and Digital Agriculture)

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19 pages, 15115 KB

Open AccessArticle

Bacillus velezensis B313-6 as a Potential Biocontrol Strain Against Anthracnose of Panax quinquefolius, and Its Fermentation Optimization, and Field Efficacy on American Ginseng

by Shuang Feng, Yue Shi, Ruijie Liu, Xin Li, Shuai Shao, Nan Yin, Yuejia Song, Haoxin Zhan, Yuxin He, Tom Hsiang, Changqing Chen, Liping Liu and Jie Gao

Agriculture 2026, 16(15), 1680; https://doi.org/10.3390/agriculture16151680 - 4 Aug 2026

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Abstract

Anthracnose of American ginseng is caused by Colletotrichum panacicola. In this study, Bacillus velezensis B313-6, isolated from the rhizosphere soil of American ginseng, was identified based on morphological, physiological, biochemical, 16S rRNA, and the gyrase subunit A protein (gyrA) regions’ [...] Read more.

Anthracnose of American ginseng is caused by Colletotrichum panacicola. In this study, Bacillus velezensis B313-6, isolated from the rhizosphere soil of American ginseng, was identified based on morphological, physiological, biochemical, 16S rRNA, and the gyrase subunit A protein (gyrA) regions’ analyses. B. velezensis B313-6 showed strong antagonistic activity against C. panacicola with an inhibition rate of 94.4% in dual culture assay, and exhibited broad-spectrum antifungal activity against eight other plant pathogenic fungi (inhibition rates 70–94%). Using single-factor experiments combined with response surface methodology, the fermentation conditions were optimized, increasing the inhibition rate against C. panacicola from 93.9% to 97.4%. In field trials, the B. velezensis B313-6 fermentation broth at a low dose (33.3 mL/m²) provided a control efficacy of 67.6% after the third spray, comparable to a commercial B. subtilis product used as a positive control. The low-dose treatment also significantly promoted American ginseng growth, increasing plant height, root length, shoot fresh weight and root fresh weight by 9.7%, 11.3%, 10.5% and 15.5%, respectively, compared to the treatment of water. These results indicate that B. velezensis B313-6 has great potential as a biocontrol agent against the anthracnose of Panax quinquefolius and also promotes the growth of American ginseng. Full article

(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)

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28 pages, 7243 KB

Open AccessArticle

Genome-Wide Identification of the PgFLA Gene Family and Association Analysis with Ginsenoside Biosynthesis in Panax ginseng

by Aimin Wang, Xiaoqian Han, Xinyue Hu, Lin Xu, Mingzhu Zhao, Yi Wang, Kangyu Wang and Meiping Zhang

Agriculture 2026, 16(15), 1679; https://doi.org/10.3390/agriculture16151679 - 4 Aug 2026

Viewed by 345

Abstract

Panax ginseng is an economically important medicinal plant, and the biosynthetic regulatory network of ginsenosides, its core bioactive constituents, is extremely complex. Fasciclin-like arabinogalactan proteins (FLAs) are a class of glycoproteins widely involved in plant growth and development that modulate secondary cell wall [...] Read more.

Panax ginseng is an economically important medicinal plant, and the biosynthetic regulatory network of ginsenosides, its core bioactive constituents, is extremely complex. Fasciclin-like arabinogalactan proteins (FLAs) are a class of glycoproteins widely involved in plant growth and development that modulate secondary cell wall biosynthesis, phytohormone signal transduction, and stress responses in plants. In this study, we performed a genome-wide identification of the FLA gene family in P. ginseng and identified 21 PgFLA family members. Comprehensive analyses were subsequently conducted to characterize their gene structures, chromosomal distributions, collinearity relationships, phylogenetics, expression patterns, and cis-acting regulatory elements. Subsequently, combined with SNP/InDel-ginsenoside association analysis based on 344 ginseng accessions, correlation analysis between gene expression and ginsenoside content, as well as co-expression network analysis linking PgFLA genes with key enzyme genes of the ginsenoside biosynthetic pathway, eight candidate PgFLA genes associated with ginsenoside biosynthesis were identified. Further mediation effect analysis identified six PgFLA members that potentially affect ginsenoside biosynthesis by modulating the expression of key enzymes involved in the ginsenoside biosynthetic pathway. Quantitative real-time PCR (qRT-PCR) was used to validate the expression of these pivotal candidate genes in methyl jasmonate (MeJA)-treated ginseng adventitious roots. The results revealed that the transcript abundances of five genes (PgFLA02, PgFLA05-01, PgFLA05-02, PgFLA06, and PgFLA09-06) were significantly negatively correlated with the levels of protopanaxadiol-type ginsenosides (Rb1, Rb2, Rb3, Rc, and Rd). These findings provide a theoretical foundation for further dissecting the molecular regulatory network underlying ginsenoside biosynthesis and supply valuable candidate genes for marker-assisted breeding and quality improvement of cultivated P. ginseng. Full article

(This article belongs to the Special Issue Genetic Breeding Strategies for Field Crops Responding to Abiotic Stress)

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15 pages, 6344 KB

Open AccessArticle

PsAOS3 Enhances High-Temperature Tolerance of Tree Peony by Promoting JA Accumulation

by Xuanyu Huo, Jun Luo, Ying Chen, Daqiu Zhao and Yuhan Tang

Agriculture 2026, 16(15), 1678; https://doi.org/10.3390/agriculture16151678 - 4 Aug 2026

Viewed by 277

Abstract

High-temperature stress severely restricts plant growth and development by inducing oxidative damage and photosynthetic inhibition. Allene oxide synthase (AOS) is a key enzyme in jasmonic acid (JA) biosynthesis, yet its role in the high-temperature stress response of woody ornamental plants remains largely uncharacterized. [...] Read more.

High-temperature stress severely restricts plant growth and development by inducing oxidative damage and photosynthetic inhibition. Allene oxide synthase (AOS) is a key enzyme in jasmonic acid (JA) biosynthesis, yet its role in the high-temperature stress response of woody ornamental plants remains largely uncharacterized. In this study, PsAOS3 was isolated and characterized from tree peony (Paeonia suffruticosa Andr.), an economically important ornamental species susceptible to high-temperature stress. PsAOS3 was localized to the plasma membrane and was strongly induced by high-temperature stress. Functional analyses using virus induced gene silencing in tree peony showed that silencing of PsAOS3 accelerated chlorophyll loss, increased reactive oxygen species (ROS) accumulation, impaired photosystem II efficiency and membrane integrity, and reduced antioxidant enzymes and endogenous JA contents under high-temperature stress. Conversely, overexpression of PsAOS3 in tobacco (Nicotiana tabacum L.) alleviated chlorophyll loss, decreased ROS accumulation, improved photosystem II efficiency and membrane integrity, and elevated antioxidant enzymes and endogenous JA contents under high-temperature stress. Collectively, this study reveals a potential role of PsAOS3 in regulating high-temperature responses in tree peony. Full article

(This article belongs to the Special Issue Propagation, Cultivation and Quality Regulation of Ornamental Plants)

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17 pages, 2681 KB

Open AccessArticle

Mixed Seeding of Annual Ryegrass–Chinese Milk Vetch Sustains High Aboveground Biomass and Soil Fertility in Southern China

by Min Huang, Hao Yang, Ting Wang, Xinyu Luo, Jing Liu, Ming Sun, Chanjuan Wu, Shiqie Bai, Ping Li, Lixia Zhang and Wenlong Gou

Agriculture 2026, 16(15), 1677; https://doi.org/10.3390/agriculture16151677 - 4 Aug 2026

Viewed by 391

Abstract

Winter fallow fields are widely distributed in southern China, where legume–grass mixtures are considered a useful approach for improving land-use efficiency and forage productivity while maintaining agroecosystem functions. Previous studies on the use of winter fallow fields have generally focused either on monoculture [...] Read more.

Winter fallow fields are widely distributed in southern China, where legume–grass mixtures are considered a useful approach for improving land-use efficiency and forage productivity while maintaining agroecosystem functions. Previous studies on the use of winter fallow fields have generally focused either on monoculture or on crops subjected to a single cut. These studies have primarily assessed aboveground productivity, whereas soil bacterial community responses have received comparatively little attention. Consequently, limited information is available on how different seeding combinations and cutting times affect aboveground biomass and soil bacterial community structure in mixed-cropping systems. A fixed-site field experiment involving annual ryegrass (Lolium multiflorum L.) and Chinese milk vetch (Astragalus sinicus L.) was conducted on yellow clay soil. Five annual ryegrass-Chinese milk vetch seeding combinations (100:0, 75:25, 50:50, 25:75, and 0:100) were established as proportions of their respective monoculture seeding rates. The same plots were maintained for five consecutive winter growing seasons, from autumn 2021 to spring 2026. Plant and soil samples were collected only during the final growing season (2025–2026), at four cutting times in January, March, April, and May. These samples were used to assess the effects of seeding combination and cutting time on forage productivity, root traits, soil fertility, and soil bacterial community structure. Among all groups, the 75% annual ryegrass + 25% Chinese milk vetch group (R75M25) exhibited the best overall performance, with cumulative yields after four cuttings of 17,274.21 kg·ha⁻¹ dry matter, 2717.02 kg·ha⁻¹ crude protein, and 12,220.46 kg·ha⁻¹ digestible dry matter. In addition, the R75M25 group maintained relatively high contents of alkali-hydrolyzable nitrogen, available phosphorus, and available potassium in soil. The top three phyla of soil samples were Proteobacteria, Actinobacteria, and Acidobacteria, with a total relative abundance of >60%. Among these, Proteobacteria showed the greatest relative abundance in monoculture, whereas the seeding combination had a reduced proportion of it. In addition, its relative abundance rose steadily with cutting time. At the genus level, RB41, Gemmatimonas, and Sphingomonas dominated the soil bacterial community. The seeding combinations did not alter soil bacterial alpha diversity. Cutting times significantly reduced the phylogenetic diversity index and drove the temporal differentiation of taxa such as Actinobacteria and Bacteroidetes. The inclusion of an appropriate proportion of Chinese milk vetch improved forage nutritional value while maintaining high biomass accumulation in southern China, with the R75M25 group showing the best overall performance in winter fallow fields. Full article

(This article belongs to the Topic Soil Health and Nutrient Management for Crop Productivity)

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35 pages, 1367 KB

Open AccessReview

Plant-Derived Bioactive Compounds in Agricultural Waste Anaerobic Digestion: Mechanisms of Inhibition, Process Stability and Methane Production

by Anna Rygało-Galewska and Kinga Borek

Agriculture 2026, 16(15), 1676; https://doi.org/10.3390/agriculture16151676 - 3 Aug 2026

Viewed by 526

Abstract

Anaerobic digestion (AD) plays a key role in the circular bioeconomy by converting organic waste into renewable energy and facilitating the sustainable utilisation of waste materials. Agricultural and agro-industrial by-products are increasingly recognised as valuable AD feedstocks due to their widespread availability and [...] Read more.

Anaerobic digestion (AD) plays a key role in the circular bioeconomy by converting organic waste into renewable energy and facilitating the sustainable utilisation of waste materials. Agricultural and agro-industrial by-products are increasingly recognised as valuable AD feedstocks due to their widespread availability and significant bioenergy potential. However, many of these substrates contain plant-derived bioactive compounds, such as polyphenols, tannins, flavonoids and terpenes, which can influence microbial communities and process performance. Depending on their concentration and chemical characteristics, these compounds may inhibit microbial activity, impair process stability, and ultimately decrease methane production. This review critically synthesises current knowledge on the occurrence, bioavailability and biological activity of plant-derived bioactive compounds in agricultural feedstocks used for anaerobic digestion, with particular emphasis on their implications for process performance and reactor stability. The principal mechanisms through which phytochemicals influence anaerobic digestion include enzyme inhibition, membrane disruption, interference with syntrophic interactions and trace metal chelation. The available evidence demonstrates a pronounced dose-dependent response, whereby low concentrations may exert neutral or selective modulatory effects. In contrast, elevated concentrations disrupt microbial activity, leading to volatile fatty acid accumulation, prolonged lag phases and reduced methane production. Current mitigation strategies include substrate pretreatment, co-digestion, microbial adaptation, adsorbent-assisted detoxification and the use of DIET-promoting materials. An integrated evidence matrix is proposed to link phytochemical composition with reactor configuration, operational parameters and mitigation strategies, thereby providing a practical framework for feedstock-specific process optimisation. Overall, the available evidence demonstrates that reliable evaluation of agricultural feedstocks should extend beyond conventional biochemical methane potential assessment to incorporate phytochemical composition, microbial functional responses and key operational parameters. Such an integrated approach can improve the prediction of methane recovery and support evidence-based optimisation of anaerobic digestion within circular bioeconomy systems. Full article

(This article belongs to the Special Issue Anaerobic Fermentation of Agricultural Waste and Sustainable Bioenergy Recovery)

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17 pages, 7391 KB

Open AccessArticle

Improved YOLOv8 Weed Segmentation Method Based on Dual-ViT

by Weihan Wu, Kaiwen Huang, Haonan Ji, Tujia Chen and Xueshen Chen

Agriculture 2026, 16(15), 1675; https://doi.org/10.3390/agriculture16151675 - 3 Aug 2026

Viewed by 383

Abstract

To address inaccurate weed segmentation under crop overlap, occlusion, and complex field backgrounds, this study developed a combined method integrating DViT-YOLOv8-seg with confidence-guided SLIC voting. The dataset contained 1872 field images (800 × 600 pixels) of Guangzhou soft-stem lettuce and four common weed [...] Read more.

To address inaccurate weed segmentation under crop overlap, occlusion, and complex field backgrounds, this study developed a combined method integrating DViT-YOLOv8-seg with confidence-guided SLIC voting. The dataset contained 1872 field images (800 × 600 pixels) of Guangzhou soft-stem lettuce and four common weed species: Eleusine indica, Digitaria sanguinalis, Portulaca oleracea, and Amaranthus blitum. All weed species were merged into one weed class, while lettuce, soil, and other field regions were treated as non-weed. Real-ESRGAN and data augmentation enhanced the training samples; Dual-ViT strengthened global–local feature interaction; GSConv reduced redundant computation; BiFPN improved multi-scale fusion; and SLIC refined ambiguous boundaries. After super-resolution preprocessing and three-fold expansion, baseline mPA increased by 10.9 percentage points. The improved network achieved 88.3% mPA at 7.9 GFLOPs, corresponding to +3.6 percentage points and -1.0 GFLOPs relative to the baseline. SLIC voting increased FWIoU to 95.6%, 4.1 percentage points above the network without SLIC. Compared with YOLOv5-seg and Fast-SCNN, mPA improved by 2.0 and 6.5 percentage points, respectively; GFLOPs were 87.7% and 95.5% lower than those of YOLOv5-seg and DeepLabv3+, respectively. The method therefore provides a favorable trade-off between segmentation accuracy and theoretical network computation for complex lettuce field imagery. Full article

(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)

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23 pages, 7715 KB

Open AccessArticle

CFD-Based Simulation and Optimization of Summer Environmental Conditions in Laying Hen Houses

by Lili Zhang, Shanjie Zhang, Miaomiao Xie, Jun Li, Xianwang Liu, Zhirun Ma, Qiang Zhang and Hualong Li

Agriculture 2026, 16(15), 1674; https://doi.org/10.3390/agriculture16151674 - 3 Aug 2026

Viewed by 351

Abstract

To address uneven temperature and relative humidity distributions, localized heat accumulation, and insufficient air velocity in an enclosed stacked-cage laying hen house, a three-dimensional computational fluid dynamics (CFD) model of the laying hen house was developed using field-measured structural and environmental data, and [...] Read more.

To address uneven temperature and relative humidity distributions, localized heat accumulation, and insufficient air velocity in an enclosed stacked-cage laying hen house, a three-dimensional computational fluid dynamics (CFD) model of the laying hen house was developed using field-measured structural and environmental data, and a porous-media model was established for the cage zone. Model validation showed that the normalized mean square error (NMSE) values for temperature, relative humidity, and air velocity were all below 0.25, confirming the reliability of the CFD model. Through visualization analysis of the contour maps, the problems of uneven airflow distribution in the original ventilation system and significant heat accumulation at the fan end were identified. On this basis, numerical simulations were conducted for six air-inlet configurations by varying two key parameters: air-inlet spacing and air-inlet number. The simulation results showed that, compared with the original model, the configuration with an air-inlet spacing of 1.14 m and a total of 32 air inlets on the two gable walls improved the uniformity of temperature, air velocity, and relative humidity by 18.00%, 10.54%, and 18.38%, respectively, while reducing the mean effective temperature index (ETI) in the cage zone by 0.5 °C. This configuration effectively alleviated localized heat accumulation and improved air-velocity uniformity. These findings provide a theoretical basis and technical support for the structural optimization and environmental regulation of enclosed stacked-cage laying hen houses. Full article

(This article belongs to the Section Farm Animal Production)

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21 pages, 21693 KB

Open AccessArticle

A Novel Telescopic Cartesian Manipulator for Kiwifruit Harvesting with Hybrid Model–Vision Error Compensation

by Bo Jia, Shuolin Kong, Juncai Huang, Xiaoyu Ma, Jiwei Zhang, Rui Li, Chen Li, Majeed Yaqoob, Shen Hin Lim and Longsheng Fu

Agriculture 2026, 16(15), 1673; https://doi.org/10.3390/agriculture16151673 - 3 Aug 2026

Viewed by 372

Abstract

Crops cultivated on trellis systems, such as kiwifruit, grapes, et al., create a partially structured workspace; this environment is highly suitable for Cartesian robotic harvesting. However, limited extension range of current Cartesian manipulators necessitates a large vertical space, which directly conflicts with the [...] Read more.

Crops cultivated on trellis systems, such as kiwifruit, grapes, et al., create a partially structured workspace; this environment is highly suitable for Cartesian robotic harvesting. However, limited extension range of current Cartesian manipulators necessitates a large vertical space, which directly conflicts with the height constraints of trellis canopies. This paper presented a hollow telescopic Cartesian manipulator with a belt-driven cascaded differential transmission for single-degree-of-freedom kiwifruit operations. The two-stage nested carbon fiber structure offers an extension ratio of 1.83:1 and a 549 mm retracted length, requiring 45.36% less vertical space than its single-stage architecture. The hybrid model–vision error compensation (HMVEC) strategy is proposed to address nonlinear positioning errors inherent in the cantilever telescopic configuration. It combines a polynomial–Fourier kinematic model for feedforward correction with YOLO11n AprilTag detection. The results showed the HMVEC strategy reduced the positioning root mean square error (RMSE) by 42.28% (from 7.90 mm to 4.56 mm). The designed manipulator achieved a 95% kiwifruit-transfer success rate at a mean cycle time of 4.8 s per fruit. These results demonstrate the feasibility of the proposed structural design and HMVEC strategy for precise manipulator positioning and post-detachment fruit transfer. Full article

(This article belongs to the Special Issue Advances in Robotic Systems for Precision Orchard Operations)

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21 pages, 38128 KB

Open AccessReview

Comprehensive Exploitation of Uncaria rhynchophylla: Cultivation Optimization, Whole-Plant Utilization, and By-Product Valorization Toward an Ecological Circular Economy

by Xiaoming Tian, Guangfeng Xiang, Hao Lv, Bin Yuan, Yiting Shen, Lan Zhou, Lu Zhu, Gaofei Li, Chao Liu, Xiangpeng Li, Xin Li, Xiaoming Fan and Juyang Liao

Agriculture 2026, 16(15), 1672; https://doi.org/10.3390/agriculture16151672 - 3 Aug 2026

Viewed by 482

Abstract

Uncaria rhynchophylla is an important medicinal plant widely used for treating neurological and cardiovascular diseases. However, current utilization primarily focuses on its hooked stems (Uncariae Ramulus cum Uncis), while its by-products remain underutilized. A systematic understanding of its cultivation and whole-plant [...] Read more.

Uncaria rhynchophylla is an important medicinal plant widely used for treating neurological and cardiovascular diseases. However, current utilization primarily focuses on its hooked stems (Uncariae Ramulus cum Uncis), while its by-products remain underutilized. A systematic understanding of its cultivation and whole-plant utilization is lacking, limiting its industrial and ecological potential. We comprehensively evaluated the cultivation, processing, by-product utilization, and ecological value of U. rhynchophylla within an industrial crop framework. This review synthesizes recent advances in agronomic practices, phytochemical composition, environmental influences, and product development strategies by integrating findings from experimental and applied studies. The hooked stems are rich in alkaloids such as rhynchophylline, while other plant parts contain valuable active compounds suitable for bio-based products, including functional foods and medicinal materials. Environmental factors (light, soil, altitude) and cultivation strategies significantly influence yield and active compound accumulation. Whole-plant utilization and by-product valorization can enhance resource-use efficiency and economic returns. U. rhynchophylla has significant potential as an industrial medicinal crop. Integrating optimized cultivation, processing technologies, and by-product utilization strategies supports sustainable production and rural development. However, limitations include standardization and limited large-scale validation. Future research should focus on mechanistic studies, industrial-scale processing, and development of high-value products to promote a circular bioeconomy. Full article

(This article belongs to the Special Issue Medicinal and Aromatic Crops: Cultivation, Quality, Processing, Application)

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21 pages, 840 KB

Open AccessArticle

Socio-Economic and Institutional Predictors of Climate Change Adaptation Among Smallholder Dairy Farmers in Plateau State, Nigeria

by Hosea Istifanus Finangwai, Peter Pano Barje, Samuel Emmanuel Danbauchi, Luka Yangka Guluwa, Shedrach Benjamin Pewan and Dashe Yakubu Gunya

Agriculture 2026, 16(15), 1671; https://doi.org/10.3390/agriculture16151671 - 3 Aug 2026

Viewed by 313

Abstract

Climate change presents significant challenges for smallholder dairy farmers, especially in developing countries where production relies heavily on natural resources. This study examined factors linked to climate change adaptation among smallholder dairy farmers in Plateau State, Nigeria. A cross-sectional survey was conducted with [...] Read more.

Climate change presents significant challenges for smallholder dairy farmers, especially in developing countries where production relies heavily on natural resources. This study examined factors linked to climate change adaptation among smallholder dairy farmers in Plateau State, Nigeria. A cross-sectional survey was conducted with 400 farmers selected through multistage sampling across eight major dairy-producing Local Government Areas. Data collection involved structured questionnaires, key informant interviews, and focus group discussions. Analysis included descriptive statistics, Pearson’s chi-square test, one-way ANOVA, and multiple linear regression. The results showed that dairy farming is mostly practised by economically active male farmers. Many respondents noticed changes in rainfall and rising temperatures, but awareness of climate change and its impact on dairy production was generally low. Farmers identified seasonal feed shortages, declining forage quality, water scarcity, heat stress, and reduced feed intake as key challenges. While some adaptation measures, such as adjusting grazing practices and water management, had been adopted, many found them to be ineffective. Regression analysis indicated that education, climate change awareness, access to extension services, and feed availability significantly influenced adaptation outcomes, with the model explaining 61.0% of the variation (R² = 0.610). Access to extension services had the strongest impact. The results highlight the need to strengthen institutional support to boost smallholder farmers’ adaptive capacity. Enhancing livestock extension programmes, raising awareness about climate change, improving access to information, and promoting climate-smart dairy management and sustainable feed practices can improve the resilience and sustainability of dairy systems in Plateau State and similar regions. Full article

(This article belongs to the Special Issue Biodiversity and Ecosystem Resilience in Agricultural Landscapes Under Land-Use and Climate Change)

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23 pages, 7393 KB

Open AccessArticle

Long-Term Green Manure Incorporation with Reduced Chemical Fertilizer Enhances Soil Quality and Rice Yield by Altering Soil Microbial Communities’ Structure and Functions in Paddy Soils

by Jishi Zhang, Min Tao, Chunfeng Zheng, Guanghui Du, Lin Zhang, Yuhu Lv, Weidong Cao and Chunzeng Liu

Agriculture 2026, 16(15), 1670; https://doi.org/10.3390/agriculture16151670 - 3 Aug 2026

Viewed by 314

Abstract

Excessive use of chemical fertilizers degrades soil health and threatens sustainable crop production, which can be mitigated by partially substituting chemical fertilizers with Chinese milk vetch (Astragalus sinicus L., MV, as green manure). However, the mechanisms through which MV incorporation alters soil [...] Read more.

Excessive use of chemical fertilizers degrades soil health and threatens sustainable crop production, which can be mitigated by partially substituting chemical fertilizers with Chinese milk vetch (Astragalus sinicus L., MV, as green manure). However, the mechanisms through which MV incorporation alters soil microbial community structure and function, enhances soil quality and crop productivity, as well as its long-term effects in paddy soils, are still not fully understood. In this study, we investigated the responses of soil physical, chemical, and biological properties (to comprehensively evaluate soil quality); microbial community structure and function; rice productivity; and the sustainable yield index (SYI) to five fertilizer treatments based on a 13-year field experiment in a paddy’s soil in Henan, China. The treatments included: CK (no chemical fertilizer and no MV), F100 (100% chemical fertilizer), MVF80, MVF60 and MVF40 (80%, 60%, and 40% of the chemical fertilizer rate combined with MV, respectively). Compared with the F100 treatment, MVF60 slightly increased rice yield by 1.71% and significantly improved SYI by 5.10%. All MV treatments significantly increased soil organic carbon (SOC, by 14.4–16.3%) and microbial biomass carbon (MBC, by 16.7–20.1%). MVF60 and MVF40 significantly reduced bulk density, and increased macroaggregate content and mean weight diameter (MWD). MVF80 significantly enriched soil total phosphorus (TP), total potassium (TK), mineral nitrogen (Nmin), and urease (UE). The improvement in these soil properties resulted in a marked increase (by 11.6–20.1%) in the soil quality index (SQI) under all MV treatments. Random forest analysis identified MBC and Nmin as the most important predictors of SQI. Moreover, MV incorporation increased the relative abundance of beneficial taxa (Firmicutes, Clostridium_sensu_stricto_1, Bradyrhizobium, and Nigrospora), which were positively correlated with SQI (p < 0.05), while reducing the relative abundance of pathogenic fungal genera such as Fusarium. Furthermore, regression analysis revealed strong positive correlations between SQI and both rice yield and SYI. In summary, long-term MV incorporation with a 40% reduction in chemical fertilizer (MVF60) constitutes an effective and sustainable nutrient management approach for rice production in southern China. This practice enhances soil quality through improved physical structure, nutrient cycling, and microbial community structure and function, ultimately resulting in higher and more stable yields. Full article

(This article belongs to the Special Issue Organic Fertilizer Substitution: Effects on Soil Fertility, Crop Productivity, and Environmental Sustainability)

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18 pages, 8725 KB

Open AccessArticle

Short-Term Agricultural Commodity Price Forecasting: A Metaheuristic-Optimized LSTM–Attention Framework

by Chang Su, Yuanping Zhang, Xiang Li, Songsong Hou, Yukai Chang and Yan Guo

Agriculture 2026, 16(15), 1669; https://doi.org/10.3390/agriculture16151669 - 3 Aug 2026

Viewed by 443

Abstract

Accurate short-term agricultural commodity price forecasting is important for market monitoring, procurement planning, inventory management, and price–risk awareness. This study develops a data-driven forecasting framework, namely CMACS–ARO–LSTM–Attention (CALA), which combines an LSTM–Attention predictor with an improved Artificial Rabbits Optimization algorithm. The original reported [...] Read more.

Accurate short-term agricultural commodity price forecasting is important for market monitoring, procurement planning, inventory management, and price–risk awareness. This study develops a data-driven forecasting framework, namely CMACS–ARO–LSTM–Attention (CALA), which combines an LSTM–Attention predictor with an improved Artificial Rabbits Optimization algorithm. The original reported CALA results are retained unchanged. This minimal revision adds persistence/random-walk and ARIMA references on the archived comparison blocks. For wheat, the reported CALA result is

R^{2} = 0.995

, RMSE

= 0.008

, MAE

= 0.004

, and MAPE

= 0.021 %

; the newly added persistence/random-walk and ARIMA references have RMSE values of 0.102 and 0.092, respectively. The soybean application is presented as supplementary mixed evidence rather than proof of broad generalization. The high goodness of fit is interpreted as short-horizon price-level approximation in a persistent series, not as proof of directional trading profitability. Repeated CALA runs, formal predictive-accuracy tests, equal-budget ablations, and multi-step interval forecasts remain outside this minimal revision. Full article

(This article belongs to the Section Agricultural Economics, Policies and Rural Management)

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33 pages, 1922 KB

Open AccessSystematic Review

Smart Urban Agriculture in Transition: A Systematic Review of ICT Integration, Applications, and Challenges

by Ruhang Wei, Dan Wu, Wulijiang Mulati, Qifeng Hou and Shengxi Xin

Agriculture 2026, 16(15), 1668; https://doi.org/10.3390/agriculture16151668 - 3 Aug 2026

Viewed by 508

Abstract

This paper systematically reviews the emerging field of smart urban agriculture, defined as the integration of information and communication technologies (ICTs) into food production practices within and around built-up urban areas. Although urban agriculture and digital agriculture have each generated substantial scholarship, their [...] Read more.

This paper systematically reviews the emerging field of smart urban agriculture, defined as the integration of information and communication technologies (ICTs) into food production practices within and around built-up urban areas. Although urban agriculture and digital agriculture have each generated substantial scholarship, their intersection remains conceptually fragmented and empirically uneven. Following the PRISMA 2020 guidelines, this study reviews 143 English-language articles indexed in Web of Science and Scopus between 2016 and 2026, combining bibliometric mapping with structured thematic coding. The analysis shows that smart urban agriculture has expanded rapidly since 2021, but remains geographically concentrated and disciplinarily dispersed. Current research is organized mainly around IoT and sensor networks, machine learning, soilless cultivation, vertical farming, plant factories, and controlled-environment agriculture. ICT applications are most mature in enclosed, data-rich, and technically controllable systems, where they support monitoring, prediction, automation, and resource optimization. By contrast, community-based, open-space, and governance-oriented forms of urban agriculture remain underexplored. By systematically linking ICT families with different urban agriculture production settings, this review clarifies the field’s emerging knowledge structure and demonstrates that technological development remains uneven across agricultural forms and socio-institutional contexts. Full article

(This article belongs to the Special Issue Integrated Precision Technologies for Sustainable Agro-Environmental Management in Climate-Smart Agriculture)

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21 pages, 978 KB

Open AccessArticle

Nutritional Status Enhances Honey Bee Colony Development and Soybean Yield, While Exposure to a Triple-Action Fungicide During Pollination Does Not Compromise Social Stability

by Matheus Franco Trivellato, Yara Martins Molina Ferraz, Cássia Regina de Avelar Gomes, Aline Yukari Kato, Samir Moura Kadri, Ricardo de Oliveira Orsi, David De Jong and Daniel Nicodemo

Agriculture 2026, 16(15), 1667; https://doi.org/10.3390/agriculture16151667 - 3 Aug 2026

Viewed by 439

Abstract

Adequate nutrition is essential for honey bee colony development and may influence pollination services provided to agricultural crops. In contrast, fungicides commonly used in agriculture have been associated with physiological and behavioral alterations in individual bees, although their consequences at the colony level [...] Read more.

Adequate nutrition is essential for honey bee colony development and may influence pollination services provided to agricultural crops. In contrast, fungicides commonly used in agriculture have been associated with physiological and behavioral alterations in individual bees, although their consequences at the colony level remain poorly understood. This study evaluated the effects of colony nutritional status and exposure to a commercial fungicide containing bixafen, prothioconazole, and trifloxystrobin on honey bee colony development and soybean pollination efficiency. Twelve honey bee colonies were maintained under nutritional supplementation or restriction (n = 6) for seven weeks. Subsequently, each group was subdivided according to fungicide exposure or no exposure while colonies were confined in soybean pollination cages. Colony weight, food reserves, hygienic behavior, brood area, and Varroa destructor infestation were monitored throughout the experiment, and soybean yield was assessed at harvest. Nutritional supplementation improved colony performance, increasing weight gain, food reserves, hygienic behavior efficiency, and soybean yield, which increased by 31.8% (3076.7 vs. 2334.2 kg ha⁻¹) compared with pollinator-excluded plots. Fungicide exposure did not affect colony development, food reserves, hygienic behavior, or mite infestation, regardless of nutritional status. Confinement reduced colony weight and brood area, but supplemented colonies resumed weight gain after returning to the apiary, whereas food restricted colonies maintained stable weights. These findings demonstrate that nutritional status is a key determinant of honey bee colony performance and pollination efficiency, whereas no measurable adverse effects of fungicide exposure on colony performance were detected. Full article

(This article belongs to the Special Issue New Insights into Improving Pollinator Health and Productivity)

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2 pages, 253 KB

Open AccessObituary

A Tribute to Prof. Danilo Florentino Pereira

by Eduardo Festozo Vicente

Agriculture 2026, 16(15), 1666; https://doi.org/10.3390/agriculture16151666 - 3 Aug 2026

Viewed by 490

Abstract

Exceptionally, this space is now dedicated to a tribute to Prof. Dr. Danilo Florentino Pereira (in memoriam), who passed away on 30 May 2026, during the organization period for the Special Issue “Application of Antimicrobial Peptides and Natural Products as Feed Additives in [...] Read more.

Exceptionally, this space is now dedicated to a tribute to Prof. Dr. Danilo Florentino Pereira (in memoriam), who passed away on 30 May 2026, during the organization period for the Special Issue “Application of Antimicrobial Peptides and Natural Products as Feed Additives in Animal Production” of the journal Agriculture, in which he was contributing as a Guest Editor [...] Full article

(This article belongs to the Special Issue Application of Antimicrobial Peptides and Natural Products as Feed Additives in Animal Production)

26 pages, 2385 KB

Open AccessArticle

Natural Risk Shocks and Rural Household Livelihood Resilience: Does Digital Transformation Make a Difference?

by Bin Yang, Tianshu Quan, Jia Li and Hui Zhang

Agriculture 2026, 16(15), 1665; https://doi.org/10.3390/agriculture16151665 - 2 Aug 2026

Viewed by 542

Abstract

The natural disasters caused by climate change are increasingly threatening the livelihood sustainability of rural households. How to enhance the adaptability of farmers has become a major issue that urgently needs to be addressed in rural development. An increasing amount of research suggests [...] Read more.

The natural disasters caused by climate change are increasingly threatening the livelihood sustainability of rural households. How to enhance the adaptability of farmers has become a major issue that urgently needs to be addressed in rural development. An increasing amount of research suggests that the digital transformation in rural areas may provide solutions to this problem. This study selected large sample data from the China Family Panel Studies (CFPS) from 2014 to 2020 to empirically test the role of digital economy in mitigating the adverse impact of natural disasters on livelihood resilience of rural households in China. The empirical results indicate that although natural disasters have a significant negative impact on the livelihood resilience of rural households, the embedding of digital technology can alleviate this negative impact to some extent by expanding non-agricultural employment opportunities for rural households, enhancing households’ access to credit, and improving agricultural production strategies. During this process, the buffering capacity, self-organization ability, and learning ability of rural families have been significantly improved. Moreover, the mitigating effect of the digital economy exhibits heterogeneity. It is more pronounced in the central and western regions than in the eastern regions, and also more evident among high-income families relative to low-income families. The key to improving the benefits of digital technology may lie in tailored policy interventions and digital skills training for farmers. Finally, this study provides empirical evidence from rural China on the role of the digital economy in strengthening farmers’ adaptive capacity against disaster risks. While these findings are context-specific, they may still provide valuable insights for other agriculture-dependent developing countries facing persistent climate risks. Full article

(This article belongs to the Section Agricultural Economics, Policies and Rural Management)

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44 pages, 7688 KB

Open AccessReview

Deep Learning for Coffee Leaf Disease Detection: Opportunities and Challenges for Quality Traceability in Agricultural E-Commerce

by Wuxin Zhang, Rui Shi, Mingjie Xue and Baoquan Yin

Agriculture 2026, 16(15), 1664; https://doi.org/10.3390/agriculture16151664 - 2 Aug 2026

Viewed by 349

Abstract

Coffee leaf diseases impair photosynthesis, thereby degrading the chemical composition and flavor quality of coffee beans. However, these resulting quality defects are often not visible from the appearance of green beans alone, compelling e-commerce quality control to trace back to leaf disease detection [...] Read more.

Coffee leaf diseases impair photosynthesis, thereby degrading the chemical composition and flavor quality of coffee beans. However, these resulting quality defects are often not visible from the appearance of green beans alone, compelling e-commerce quality control to trace back to leaf disease detection at the production origin. This focus on “quality traceability” imposes requirements on computer vision technologies that fundamentally differ from those of conventional pesticide-application-oriented detection: prioritizing high precision over high recall, replacing simple classification with severity grading, and necessitating the integration of variety and origin metadata. This paper conducts a systematic review of 53 relevant studies published from January 2020 to March 2026, examining existing data resources, model architectures, and industrial adaptability through the lens of e-commerce quality traceability. Our review highlights three major findings: (1) existing datasets could be further enriched in variety labeling, severity scoring, and origin metadata; (2) current models, predominantly focused on classification, have room for closer alignment with traceability requirements regarding optimization objectives, task definitions, and output formats; and (3) cutting-edge technologies, including semantic segmentation, multimodal fusion, and visual foundation models, offer viable pathways to bridge these gaps. This review provides standardized technical evaluation criteria and clear optimization directions for origin inspection, batch grading and whole-chain traceability management of coffee agricultural e-commerce platforms. Full article

(This article belongs to the Section Artificial Intelligence and Digital Agriculture)

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19 pages, 3728 KB

Open AccessArticle

Long-Term Pepper (Capsicum annuum L.) Monoculture Reshapes Rhizosphere Soil Chemistry, Microbiota, and Metabolite Profiles

by Fan Yang, Zihang Han, Ying Zhang, Xin Wang, Yuting Hong, Xiaoke Chang, Wenrui Yang, Yaxian Zhao and Qiuju Yao

Agriculture 2026, 16(15), 1663; https://doi.org/10.3390/agriculture16151663 - 1 Aug 2026

Viewed by 334

Abstract

Continuous monoculture alters rhizosphere soil conditions and microbial community structure, but the integrated soil biochemical, microbial, and metabolomic responses of pepper (Capsicum annuum L.) rhizosphere soils remain insufficiently characterized. Here, we compared uncropped/non-continuously cropped pepper soil (Y0) with soil under 10 years [...] Read more.

Continuous monoculture alters rhizosphere soil conditions and microbial community structure, but the integrated soil biochemical, microbial, and metabolomic responses of pepper (Capsicum annuum L.) rhizosphere soils remain insufficiently characterized. Here, we compared uncropped/non-continuously cropped pepper soil (Y0) with soil under 10 years of pepper monoculture (Y10) using soil physicochemical assays, enzyme measurements, 16S rRNA and ITS amplicon sequencing, untargeted UHPLC-Q Exactive HFX metabolomics, and predictive functional profiling. Long-term monoculture markedly separated Y10 from Y0 in multivariate analyses. Y10 soils showed higher organic matter, available nitrogen, available phosphorus, available potassium, and electrical conductivity in soil-water extracts, whereas microbial biomass carbon and pH were lower. Soil enzyme profiles also differed between treatments. Microbial alpha diversity declined under Y10, and bacterial and fungal community structures were clearly separated between treatments. At the taxonomic level, Acidobacteriota and several oligotrophic bacterial taxa were relatively enriched in Y0, whereas Proteobacteria, Bacteroidota, Chloroflexi, Bacillota, Pseudomonas, Bacillus, and several fungal genus-level taxa increased in Y10. Untargeted metabolomics revealed extensive remodeling of rhizosphere metabolites, with 413 up-regulated and 21 down-regulated differential metabolites in Y10. Differential metabolites were mainly associated with carboxylic acids and derivatives, benzene and substituted derivatives, fatty acyls, aromatic-compound transformation, sulfur metabolism, alkaloid biosynthesis, and microbial metabolism. Correlation analyses further linked key microbial taxa with soil pH, microbial biomass carbon, available nutrients, electrical conductivity, and enzyme activities. These results indicate that long-term pepper monoculture is associated with coordinated shifts in soil chemical status, microbial community composition, and metabolite profiles. The study provides an integrated basis for understanding rhizosphere changes under pepper continuous-cropping systems while recognizing that functional predictions and metabolite annotations require experimental validation. Full article

(This article belongs to the Special Issue Soil Management and Interdisciplinary Approaches to Global Challenges)

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Open AccessArticle

Effects of Precipitation Regimes on Ecosystem Respiration in Agricultural Regions of the Southern Tibetan Plateau

by Fengqiuli Zhang, Keding Sheng, Tongde Chen, Jiarong Hou and Xingshuai Mei

Agriculture 2026, 16(15), 1662; https://doi.org/10.3390/agriculture16151662 - 1 Aug 2026

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Abstract

Understanding how the spatiotemporal variability of precipitation affects ecosystem respiration (RE) is central to carbon–climate feedback in climate-smart agriculture, yet remains unresolved for the alpine agricultural region of the southern Qinghai–Tibet Plateau, where flux observations are sparse. Using 25 years (2000–2024) of monthly [...] Read more.

Understanding how the spatiotemporal variability of precipitation affects ecosystem respiration (RE) is central to carbon–climate feedback in climate-smart agriculture, yet remains unresolved for the alpine agricultural region of the southern Qinghai–Tibet Plateau, where flux observations are sparse. Using 25 years (2000–2024) of monthly gridded climate and remote sensing data for the Yarlung Zangbo River Basin and Its Two Tributaries Basin, we developed a flux tower-constrained reference–respiration (R_ref) environment-matching model in which R_ref varies with the enhanced vegetation index (EVI) and land surface temperature (LST) to correct the Lloyd–Taylor parameterization. The correction reduced the RE root mean square error by 54.8% (1.04 → 0.47 gC·m⁻²·month⁻¹) and eliminated systematic bias (+0.80 → −0.001) relative to an independent gridded RECO product. We then constructed a multidimensional index of precipitation variability (intra-annual concentration, interannual variability, long-term trend, spatial clustering) and combined random forest, spatial regression, lag analysis, and structural equation modeling (SEM) to disentangle direct and indirect pathways from precipitation variability to RE. The central finding is an indirect-conduction mechanism: precipitation concentration (PCI) affects RE almost entirely through vegetation productivity (PCI → GPP → RE, indirect effect −0.676) rather than directly (direct effect +0.076, opposite in sign), because low temperature and high soil water holding capacity buffer the immediate soil moisture response. The basin functions as a net carbon source (mean NEP = −0.550 gC·m⁻²·month⁻¹) with a significant warming-driven interannual RE increase (Sen’s slope = 0.0025 yr⁻¹, p = 0.022) that is independent of the stable precipitation total. The framework offers a transferable paradigm for carbon flux attribution in alpine regions under sparse observation. Full article

(This article belongs to the Special Issue Assessing Ecosystem Respiration in Global Carbon Cycle: Digital Application in Agriculture)

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