Agriculture

24 pages, 4710 KB

Open AccessArticle

Precise Humidity Regulation for Grafted Seedling Plant Factories Based on Quadratic Regression Modeling and Machine Learning Prediction

by Jiaming Guo, Yizhi Ou, Xinyu Wei, Shan Hua, Jiahao Liu, Haishun Cao, Jie Li and Bin Li

Agriculture 2025, 15(23), 2523; https://doi.org/10.3390/agriculture15232523 - 4 Dec 2025

Viewed by 370

Abstract

This paper presents a valve-controlled pipeline humidification system aimed at achieving precise and uniform humidity regulation in the multi-layer cultivation environments of plant factories. Since grafted seedlings require stable humidity conditions for effective healing, the system was designed to enable fine-grained adjustments across [...] Read more.

This paper presents a valve-controlled pipeline humidification system aimed at achieving precise and uniform humidity regulation in the multi-layer cultivation environments of plant factories. Since grafted seedlings require stable humidity conditions for effective healing, the system was designed to enable fine-grained adjustments across different cultivation layers. A quadratic regression orthogonal rotational combination design was employed to investigate how valve opening angles affect mean relative humidity (MRH), and a regression prediction model was developed accordingly. The model exhibited strong predictive performance, achieving an R² of 0.9907 and an average relative error of only 0.67%. The optimal valve-opening angles were 60°, 50°, and 50°, respectively, which ensured that the MRH remained above 90% throughout operation. Experimental verification confirmed that the model accurately predicted humidity responses, while the proposed system improved uniformity by reducing the humidity variation from 6.1% to 0.3% and increasing the compliance rate from 58.3% to 100%. To enhance short-term humidity forecasting, three machine learning algorithms—Random Forest, XGBoost, and Transformer—were trained to predict humidity trends within a 6-h window. Among them, the RF model achieved the highest accuracy with an R² of 0.9543, outperforming the other models in both stability and precision. The main contribution of this study is the identification of the optimal valve-opening combination through a quadratic orthogonal rotation regression combination experiment. Additionally, the RF obtained the optimal machine learning model for predicting humidity within 6 h. Full article

(This article belongs to the Special Issue Research on Plant Production in Greenhouse and Plant Factory Systems—2nd Edition)

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43 pages, 7882 KB

Open AccessReview

Path Tracking Control in Autonomous Agricultural Vehicles: A Systematic Survey of Models, Methods, and Challenges

by Chuanhao Sun, Jinlin Sun, Shihong Ding, Qiushi Li and Li Ma

Agriculture 2025, 15(23), 2522; https://doi.org/10.3390/agriculture15232522 - 4 Dec 2025

Viewed by 713

Abstract

With the advancement of precision agriculture and agriculture 4.0, path tracking control technologies for autonomous agricultural vehicles (AAVs) have become essential for achieving efficient and automated operations. This paper begins by introducing the theoretical framework of AAV path tracking, including its applications across [...] Read more.

With the advancement of precision agriculture and agriculture 4.0, path tracking control technologies for autonomous agricultural vehicles (AAVs) have become essential for achieving efficient and automated operations. This paper begins by introducing the theoretical framework of AAV path tracking, including its applications across various working scenarios such as dry fields, paddy fields, and orchards, and establishes corresponding vehicle dynamics models suited to these environments. AAVs are classified into wheeled and tracked types based on structural characteristics and specific operational requirements. Subsequently, path tracking control methods are divided into linear and nonlinear approaches according to their system applicability, with detailed discussions on the implementation and adaptations of these strategies in real agricultural settings. Given its strong robustness and extensive adoption, sliding mode control receives particular emphasis in this review. Finally, the paper addresses persistent challenges in complex farmland environments and identifies future research directions aimed at enhancing practicality and adaptability. This review provides a comprehensive and structured analysis of AAV path tracking technologies, with a focus on environmental adaptability and operational feasibility, thereby offering valuable insights for further research and technological development in precision agriculture. Full article

(This article belongs to the Special Issue Perception, Decision-Making, and Control of Agricultural Robots)

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15 pages, 2540 KB

Open AccessArticle

Reduction of Pesticide Clothianidin, Thiamethoxam, and Propoxur Residues via Plasma-Activated Water Generated by a Pin-Hole Air Plasma Jet

by Suchintana Limkoey, Jitkunya Yuenyong, Chonlada Bennett, Dheerawan Boonyawan, Phumon Sookwong and Sugunya Mahatheeranont

Agriculture 2025, 15(23), 2521; https://doi.org/10.3390/agriculture15232521 - 4 Dec 2025

Viewed by 355

Abstract

This study explores the efficacy of plasma-activated water (PAW), produced using a laboratory-made pin-hole air plasma jet, in the reduction of pesticide residues, including clothianidin, thiamethoxam, and propoxur. The physicochemical analysis indicated that PAW’s pH decreased significantly with longer discharge times, while oxidation–reduction [...] Read more.

This study explores the efficacy of plasma-activated water (PAW), produced using a laboratory-made pin-hole air plasma jet, in the reduction of pesticide residues, including clothianidin, thiamethoxam, and propoxur. The physicochemical analysis indicated that PAW’s pH decreased significantly with longer discharge times, while oxidation–reduction potential (ORP) and electrical conductivity (EC) increased. Nitrogen and oxygen species in the plasma state were confirmed using optical emission spectroscopy. These results reflected the formation of rich reactive oxygen and nitrogen species (ROS and RNS), including hydroxyl radicals, hydrogen peroxide, and nitrate, contributing to its strong oxidative properties. The optimal PAW parameters for pesticide degradation were determined, and pesticide reduction was assessed using high-performance liquid chromatography (HPLC) and liquid chromatography–mass spectrometry (LC-MS). After 25 min of treatment, maximum reduction rates of 65%, 93%, and 88% were achieved for clothianidin, thiamethoxam, and propoxur, respectively. Only clothianidin yielded a single degradation product which is suggested to be formed by cyclic rearrangement following the loss of Cl and NO₂, while those of thiamethoxam and propoxur were not detected. PAW produced by atmospheric pin-hole air plasma jet demonstrated superior degradation efficiency with minimal toxic by-product formation. The findings contribute valuable insights into sustainable practices for environmental detoxification. Full article

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

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Graphical abstract

24 pages, 9152 KB

Open AccessArticle

Effect of Airflow Settings of an Orchard Sprayer with Two Individually Controlled Fans on Spray Deposition in Apple Trees and Off-Target Drift

by Grzegorz Doruchowski, Waldemar Świechowski, Ryszard Hołownicki, Artur Godyń and Andrzej Bartosik

Agriculture 2025, 15(23), 2520; https://doi.org/10.3390/agriculture15232520 - 4 Dec 2025

Cited by 1 | Viewed by 335

Abstract

Air-assisted sprayers are widely used in orchards to ensure deep canopy penetration and effective pesticide coverage, yet excessive or misdirected airflow often causes spray drift and ground losses. This study evaluated spray deposition efficiency, drift, and environmental performance of a novel double-tower orchard [...] Read more.

Air-assisted sprayers are widely used in orchards to ensure deep canopy penetration and effective pesticide coverage, yet excessive or misdirected airflow often causes spray drift and ground losses. This study evaluated spray deposition efficiency, drift, and environmental performance of a novel double-tower orchard sprayer (DIVENT) equipped with two independently driven axial fans allowing separate airflow adjustment on each side. Field experiments were conducted in apple orchards under crosswind conditions using the following three airflow emission scenarios (air volume to the LEFT/RIGHT side of sprayer): symmetrical (100%/100%), compensating crosswind (30%/100%), and one-sided (0%/100%). Measurements of spray deposition within the canopy, ground losses, and off-target deposition drift were performed using fluorescent tracer, and power consumption was recorded to estimate fuel use and CO₂ emissions. The compensating airflow setting significantly improved spray targeting, reducing both in-orchard ground losses and off-target drift by up to 60%, while maintaining uniform canopy coverage comparable to the conventional symmetrical mode. The one-sided emission scenario achieved the highest drift reduction (67.8%) and the lowest power and CO₂ emissions, though at the cost of reduced canopy deposition. Overall, the study demonstrates that independent fan control allows effective adaptation of spraying to weather and canopy conditions, providing substantial environmental and energy benefits without compromising spray efficiency. Full article

(This article belongs to the Section Agricultural Technology)

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17 pages, 702 KB

Open AccessArticle

Wheat Yield Prediction Based on Parallel CNN-LSTM-Attention with Transfer Learning Model

by Caixia Song, Tengao Liu, Weiguang Ning, Tong Xu, Shuhui Song, Zifei Li, Shuyun Ouyang, Xinquan Song, Taoyang Han, Zichen Zhang, Tianyu Chen and Jinbao Xie

Agriculture 2025, 15(23), 2519; https://doi.org/10.3390/agriculture15232519 - 4 Dec 2025

Viewed by 403

Abstract

Accurate wheat yield prediction is essential for ensuring food security and supporting governmental decision-making. However, the scarcity of long-term agricultural time-series data and the complex interplay between meteorological and socio-economic factors pose significant challenges. To address these issues, this study proposes a Transfer-Learning-Based [...] Read more.

Accurate wheat yield prediction is essential for ensuring food security and supporting governmental decision-making. However, the scarcity of long-term agricultural time-series data and the complex interplay between meteorological and socio-economic factors pose significant challenges. To address these issues, this study proposes a Transfer-Learning-Based Parallel CNN–LSTM–Attention (TPCLA) model for wheat yield forecasting. A cross-regional transfer learning strategy is employed to mitigate data scarcity by leveraging temporal patterns learned from regions with similar ecological characteristics. The proposed parallel architecture integrates one-dimensional convolutional neural networks and long short-term memory networks to jointly extract spatial and temporal features, while an attention mechanism is incorporated to highlight key influencing factors and enhance feature interpretability. Unlike conventional studies that primarily focus on climatic variables, this work considers both direct factors (e.g., average temperature and precipitation) and indirect socio-economic factors (e.g., agricultural mechanization level, total agricultural output value, grain production scale, cultivated land area, and disaster-affected area). Experimental results on multivariate wheat data from 1993 to 2024 demonstrate that several indirect indicators exert a more substantial influence on yield than traditional meteorological variables—reflecting the increasing ability of modern agricultural practices to buffer climatic variability. The proposed TPCLA model achieves an RMSE of 0.394, MAE of 0.326, and an

R^{2}

of 0.904, outperforming multiple benchmark models and confirming its robustness and predictive superiority under small-sample conditions. The findings not only validate the effectiveness of integrating indirect yield-influencing factors but also provide new insights for agricultural policy formulation and climate resilience strategies. Full article

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

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21 pages, 21928 KB

Open AccessArticle

HieraEdgeNet: A Multi-Scale Edge-Enhanced Framework for Automated Pollen Recognition

by Yuchong Long, Wen Sun, Ningxiao Sun, Wenxiao Wang, Chao Li and Shan Yin

Agriculture 2025, 15(23), 2518; https://doi.org/10.3390/agriculture15232518 - 4 Dec 2025

Cited by 1 | Viewed by 382

Abstract

Automated pollen recognition is a foundational tool for diverse scientific domains, including paleoclimatology, biodiversity monitoring, and agricultural science. However, conventional methods create a critical data bottleneck, limiting the temporal and spatial resolution of ecological analysis. Existing deep learning models often fail to achieve [...] Read more.

Automated pollen recognition is a foundational tool for diverse scientific domains, including paleoclimatology, biodiversity monitoring, and agricultural science. However, conventional methods create a critical data bottleneck, limiting the temporal and spatial resolution of ecological analysis. Existing deep learning models often fail to achieve the requisite localization accuracy for microscopic pollen grains, which are characterized by their minute size, indistinct edges, and complex backgrounds. To overcome this, we introduce HieraEdgeNet, a novel object detection framework. The core principle of our architecture is to explicitly extract and hierarchically fuse multi-scale edge information with deep semantic features. This synergistic approach, combined with a computationally efficient large-kernel operator for fine-grained feature refinement, significantly enhances the model’s ability to perceive and precisely delineate object boundaries. On a large-scale dataset comprising 44,471 annotated microscopic images containing 342,706 pollen grains from 120 classes, HieraEdgeNet achieves a mean Average Precision of 0.9501 (mAP@0.5) and 0.8444 (mAP@0.5:0.95), substantially outperforming state-of-the-art models such as YOLOv12n and the Transformer-based RT-DETR family in terms of the accuracy–efficiency trade-off. This work provides a powerful computational tool for generating the high-throughput, high-fidelity data essential for modern ecological research, including tracking phenological shifts, assessing plant biodiversity, and reconstructing paleoenvironments. At the same time, we acknowledge that the current two-dimensional design cannot directly exploit volumetric Z-stack microscopy and that strong domain shifts between training data and real-world deployments may still degrade performance, which we identify as key directions for future work. By also enabling applications in precision agriculture, HieraEdgeNet contributes broadly to advancing ecosystem monitoring and sustainable food security. Full article

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

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3 pages, 156 KB

Open AccessEditorial

Agronomic Practices for Enhancing Quality and Yield of Aromatic and Medicinal Crops

by Matěj Malík and Pavel Tlustoš

Agriculture 2025, 15(23), 2517; https://doi.org/10.3390/agriculture15232517 - 4 Dec 2025

Viewed by 283

Abstract

This Special Issue was created around a central question, a question that is increasingly difficult to resolve in practice, namely what should be included among agronomic practices to cultivate aromatic and medicinal crops so that they achieve both high yield and high value [...] Read more.

This Special Issue was created around a central question, a question that is increasingly difficult to resolve in practice, namely what should be included among agronomic practices to cultivate aromatic and medicinal crops so that they achieve both high yield and high value while remaining productive and economically feasible and delivering products that are chemically consistent and safe for consumers [...] Full article

(This article belongs to the Special Issue Agronomic Practices for Enhancing Quality and Yield of Aromatic and Medicinal Crops)

17 pages, 2565 KB

Open AccessArticle

Self-Supervised and Multi-Task Learning Framework for Rapeseed Above-Ground Biomass Estimation

by Pengfei Hao, Jianpeng An, Qing Cai, Junqin Cao, Zhanghua Hu and Baogang Lin

Agriculture 2025, 15(23), 2516; https://doi.org/10.3390/agriculture15232516 - 4 Dec 2025

Viewed by 443

Abstract

Accurate, high-throughput estimation of Above-Ground Biomass (AGB), a key predictor of yield, is a critical goal in rapeseed breeding. However, this is constrained by two key challenges: (1) traditional measurement is destructive and laborious, and (2) modern deep learning approaches require vast, costly [...] Read more.

Accurate, high-throughput estimation of Above-Ground Biomass (AGB), a key predictor of yield, is a critical goal in rapeseed breeding. However, this is constrained by two key challenges: (1) traditional measurement is destructive and laborious, and (2) modern deep learning approaches require vast, costly labeled datasets. To address these issues, we present a data-efficient deep learning framework using smartphone-captured top-down RGB images for AGB estimation (Fresh Weight, FW, and Dry Weight, DW). Our approach utilizes a two-stage strategy where a Vision Transformer (ViT) backbone is first pre-trained on a large, aggregated dataset of diverse, non-rapeseed public plant datasets using the DINOv2 self-supervised learning (SSL) method. Subsequently, this pre-trained model is fine-tuned on a small, custom-labeled rapeseed dataset (N = 833) using a Multi-Task Learning (MTL) framework to simultaneously regress both FW and DW. This MTL approach acts as a powerful regularizer, forcing the model to learn robust features related to the 3D plant structure and density. Through rigorous 5-fold cross-validation, our proposed model achieved strong predictive performance for both Fresh Weight (Coefficient of Determination, R² = 0.842) and Dry Weight (R² = 0.829). The model significantly outperformed a range of baselines, including models trained from scratch and those pre-trained on the generic ImageNet dataset. Ablation studies confirmed the critical and synergistic contributions of both domain-specific SSL (vs. ImageNet) and the MTL framework (vs. single-task training). This study demonstrates that an SSL+MTL framework can effectively learn to infer complex 3D plant attributes from 2D images, providing a robust and scalable tool for non-destructive phenotyping to accelerate the rapeseed breeding cycle. Full article

(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)

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18 pages, 1526 KB

Open AccessArticle

Eco-Efficiency Indicators in Traditional Iberian Pig Farms in the Dehesa Ecosystem: Integrated Economic and Environmental Performance

by Javier García-Gudiño, José Perea, Maria Font-i-Furnols, Elena Angón and Isabel Blanco-Penedo

Agriculture 2025, 15(23), 2515; https://doi.org/10.3390/agriculture15232515 - 4 Dec 2025

Viewed by 534

Abstract

The traditional Iberian pig production system in the dehesa ecosystem of southwestern Spain and Portugal represents a significant cultural and ecological model of extensive livestock farming currently facing sustainability challenges. This study aimed to identify eco-efficiency indicators by integrating economic and environmental dimensions [...] Read more.

The traditional Iberian pig production system in the dehesa ecosystem of southwestern Spain and Portugal represents a significant cultural and ecological model of extensive livestock farming currently facing sustainability challenges. This study aimed to identify eco-efficiency indicators by integrating economic and environmental dimensions across traditional Iberian pig farms. Structured surveys were conducted across 68 farms, complemented by life cycle assessment (LCA) to evaluate environmental impacts including climate change, acidification, eutrophication, energy demand and land occupation. Multivariate statistical analysis identified two distinct farm types: Mixed-orientation Farms (MF, 45.59% of farms), characterised by diversified production phases and greater reliance on external inputs, and Acorn-Fed Farms (AF, 54.41% of farms), specialised in acorn-based fattening with greater dehesa ecosystem integration. AF demonstrated significantly lower environmental impacts across all categories except land occupation, with reductions ranging from 9% to 18% compared to MF. Furthermore, AF achieved superior eco-efficiency with gross margins 15% higher than MF and economic returns per unit of environmental impact 32% to 59% higher across all indicators. These findings demonstrate that farrow-to-finish farms specialised in montanera systems can simultaneously achieve greater profitability and reduced environmental impacts, providing a replicable model for sustainable livestock production in Mediterranean agroecosystems. Full article

(This article belongs to the Section Farm Animal Production)

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22 pages, 33603 KB

Open AccessArticle

YOLO-AMAS: Maturity Detection of ‘Jiang’ Pomegranate in Complex Orchard Environments

by Chunxu Hao, Wenhui Dong, Huiqin Li, Jiangchen Zan and Xiaoying Zhang

Agriculture 2025, 15(23), 2514; https://doi.org/10.3390/agriculture15232514 - 3 Dec 2025

Viewed by 386

Abstract

In the era of smart agriculture, intelligent fruit maturity detection has become a critical task. However, in complex orchard environments, factors such as occlusion by branches and leaves and interference from bagging materials pose significant challenges to detection accuracy. To address this issue, [...] Read more.

In the era of smart agriculture, intelligent fruit maturity detection has become a critical task. However, in complex orchard environments, factors such as occlusion by branches and leaves and interference from bagging materials pose significant challenges to detection accuracy. To address this issue, this study focuses on maturity detection of ‘Jiang’ pomegranates and proposes an improved YOLO-AMAS algorithm. The method integrates an Adaptive Feature Enhancement (AFE) module, a Multi-Scale Convolutional Attention Module (MSCAM), and an Adaptive Spatial Feature Fusion (ASFF) module. The AFE module effectively suppresses complex backgrounds through dual-channel spatial attention mechanisms; the MSCAM enhances multi-scale feature extraction capability using a pyramidal spatial convolution structure; and the ASFF optimizes the representation of both shallow details and deep semantic information via adaptive weighted fusion. A SlideLoss function based on Intersection over Union is introduced to alleviate class imbalance. Experimental validation conducted on a dataset comprising 6564 images from multiple scenarios demonstrates that the YOLO-AMAS model achieves a precision of 90.9%, recall of 86.0%, mAP@50 of 94.1% and mAP@50:95 of 67.6%. The model significantly outperforms mainstream detection models including RT-DETR-1, YOLOv3 to v6, v8, and 11 under multi-object, single-object, and occluded scenarios, with a mAP50 of 96.4% for bagged mature fruits. Through five-fold cross-validation, the model’s strong generalization capability and stability were demonstrated. Compared to YOLOv8, YOLO-AMAS reduces the false detection rate by 30.3%. This study provides a reliable and efficient solution for intelligent maturity detection of ‘Jiang’ pomegranates in complex orchard environments. Full article

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

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19 pages, 559 KB

Open AccessArticle

The Influence of Grape Skin Flour on Reproductive Performance in Botoşani Karakul Rams

by Constantin Pascal, Claudia Pânzaru, Daniel Simeanu, Cristina-Gabriela Radu-Rusu and Ionică Nechifor

Agriculture 2025, 15(23), 2513; https://doi.org/10.3390/agriculture15232513 - 3 Dec 2025

Viewed by 374

Abstract

The present research aimed to analyze the influence of dietary supplementation with grape skin flour (GSF) in rams on body weight, body condition, semen quality, plasma testosterone levels, behavior, and fertility. The biological material consisted of four groups of rams (GSF0, GSF30, GSF60, [...] Read more.

The present research aimed to analyze the influence of dietary supplementation with grape skin flour (GSF) in rams on body weight, body condition, semen quality, plasma testosterone levels, behavior, and fertility. The biological material consisted of four groups of rams (GSF0, GSF30, GSF60, and GSF90), with each group comprising six adult individuals. The experimental period lasted 60 days and was carried out prior to the onset of the mating season. During this period, the experimental factor was represented by the supplementation level: GSF30 received 30 g GSF/kg dry matter (DM), GSF60 received 60 g GSF/kg DM, while GSF90 received 90 g GSF/kg DM. Although no significant differences in live body weight (LW) were observed among groups at the beginning of the mating period (MP), the additional supplementation with GSF supported a more consistent accumulation of body reserves. As a result, at the onset of the mating season (MS), body weight increased, though with different intensities: by 0.77% in L0 and by more than 6% in GSF90, with the difference between L0 and GSF90 being highly significant at p ≥ 0.01 (p value = 0.0028). Furthermore, GSF administration induced highly significant differences between GSF0 and GSF60 in body condition score (p ≤ 0.01), and high significant differences (p ≤ 0.001) between GSF0 and GSF90 in testicular circumference. Regarding ejaculate volume, differences were highly significant (p ≤ 0.01) only between GSF0 and GSF60, whereas sperm motility showed significant differences (p ≤ 0.05) between GSF0 and GSF60, and highly significant differences (p ≤ 0.001) between GSF0 and GSF90. The fertility of the rams, assessed by the total number of ewes fertilized, showed highly significant differences (p < 0.01) between GSF0 and GSF60, as well as between GSF0 and GSF90. Full article

(This article belongs to the Special Issue Advancements in Reproductive Biotechnology and Nutritional Strategies in Livestock Production)

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24 pages, 6588 KB

Open AccessArticle

Design and Performance Testing of a Motorized Machine-Mounted Self-Leveling Platform for Hilly Orchards

by Guangyu Xue, Haiyang Liu, Gongpu Wang, Yanyan Shi, Haiyang Shen, Zhou Zhou, Zihan Huan, Wenqin Ding and Lianglong Hu

Agriculture 2025, 15(23), 2512; https://doi.org/10.3390/agriculture15232512 - 3 Dec 2025

Viewed by 357

Abstract

To address issues such as attitude instability, insufficient adaptability, and poor operational quality of precision operation equipment caused by complex terrain conditions in hilly orchards, this study designed an electric carrier Self-Leveling Platform based on the 3-RRS parallel configuration. Focusing on the stability [...] Read more.

To address issues such as attitude instability, insufficient adaptability, and poor operational quality of precision operation equipment caused by complex terrain conditions in hilly orchards, this study designed an electric carrier Self-Leveling Platform based on the 3-RRS parallel configuration. Focusing on the stability requirements of the operation plane, an automatic leveling control strategy was proposed with the constant center height of the moving platform as an additional constraint condition. Based on the inverse kinematics solution of the 3-RRS Parallel Mechanism, the analytical mapping relationship between the fuselage attitude and the compensation angle of the leveling leg crank was derived, and based on this, the working space of the Self-Leveling Platform and the maximum compensation angles of the moving platform in the pitch and roll directions were calculated. Key structural parameters were optimized using a multi-objective genetic algorithm, followed by the completion of a 3D model design and modal simulation analysis to verify the effectiveness of the structural design. Finally, leveling performance tests were conducted on a prototype. The results showed that the platform can achieve omnidirectional automatic leveling, with a maximum leveling time of 1.593 s and a maximum steady-state error of 0.62° under typical slope and load conditions. Analysis of variance results further indicated that there are significant differences in the leveling performance of the 3-RRS parallel configuration of the Self-Leveling Platform in the pitch and roll directions, demonstrating anisotropic characteristics. This study provides an effective solution for attitude stability control of orchard operation equipment in hilly areas and offers theoretical reference and technical support for the application of the 3-RRS parallel configuration in the agricultural equipment field. Full article

(This article belongs to the Section Agricultural Technology)

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22 pages, 6232 KB

Open AccessArticle

Assessing the Combined Impacts of Future Climate and Land Use Changes on Soil Loss and Sediment Retention in the Lam Phra Phloeng Watershed, Thailand

by Uma Seeboonruang, Ranadheer Mandadi, Prapas Thammaboribal, Arlene L. Gonzales, Arun Kanchan and Satya Venkata Sai Aditya Bharadwaz Ganni

Agriculture 2025, 15(23), 2511; https://doi.org/10.3390/agriculture15232511 - 3 Dec 2025

Viewed by 478

Abstract

Soil erosion is a significant challenge to the environment, ecology, and economy, and areas that undergo fast land use change and climate change are the most affected. This research evaluates the effects that climate change and Land-Use/Land-Cover (LULC) change have, separately and together, [...] Read more.

Soil erosion is a significant challenge to the environment, ecology, and economy, and areas that undergo fast land use change and climate change are the most affected. This research evaluates the effects that climate change and Land-Use/Land-Cover (LULC) change have, separately and together, on soil loss and sediment retention in the Lam Phra Phloeng (LPP) watershed, Thailand. The InVEST Sediment Delivery Ratio (SDR) model was applied under the Shared from Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5), using projected LULC for 2050 and 2100. The Cellular Automata–Markov (CA–Markov) model has been utilized to generate future land use/land cover (LULC) scenarios demonstrating how land changes over spatial and temporal scale. Results show a marked decline in sediment retention and a rise in soil loss, especially under high-emission scenarios and cropland expansion. By 2100, cropland soil loss increased by 57.35%, while forest cover—a key determinant of sediment retention—declined from 45.41% in 2020 to 22.19%. When climate and land-use changes are considered together, they have a much greater effect on sediment loss, especially in cropland and built-up areas. These results highlight the vital role that forest conservation and adaptive land management, e.g., afforestation and sustainable agriculture, play in ensuring the continued availability of clean water in watersheds and in erosion control. The research provides policy-makers with real-life scenarios to draw on when sketching integrated watershed management plans aimed at reducing the negative effects of land use and climate change on soil stability and water resources in the LPP watershed. Full article

(This article belongs to the Section Agricultural Water Management)

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26 pages, 11096 KB

Open AccessArticle

Predicting Moisture in Different Alfalfa Product Forms with SWIR Hyperspectral Imaging: Key Wavelengths for Low-Cost Sensor Development

by Hongfeng Chu, Yanhua Ma, Chunmao Fan, He Su, Haijun Du, Ting Lei and Zhanfeng Hou

Agriculture 2025, 15(23), 2510; https://doi.org/10.3390/agriculture15232510 - 3 Dec 2025

Viewed by 418

Abstract

Rapid and accurate moisture detection is critical for alfalfa quality control, yet conventional methods are slow, and non-destructive techniques are challenged by different product forms. This study leveraged Short-Wave Infrared Hyperspectral Imaging (SWIR-HSI) to acquire spatially representative spectra, aiming to develop and validate [...] Read more.

Rapid and accurate moisture detection is critical for alfalfa quality control, yet conventional methods are slow, and non-destructive techniques are challenged by different product forms. This study leveraged Short-Wave Infrared Hyperspectral Imaging (SWIR-HSI) to acquire spatially representative spectra, aiming to develop and validate robust, form-specific moisture prediction models for compressed and powdered alfalfa. For compressed alfalfa, a full-spectrum Support Vector Regression (SVR) model demonstrated stable and good performance (mean Prediction Coefficient of Determination

R_{P}^{2}

= 0.880, Ratio of Performance to Deviation RPD = 2.93). In contrast, powdered alfalfa achieved superior accuracy (mean

R_{P}^{2}

= 0.953, RPD = 5.29) using an optimized pipeline of Savitzky–Golay’s first derivative, Successive Projections Algorithm (SPA) for feature selection, and an SVR model. A key finding is that the optimal model for powdered alfalfa frequently converged to an ultra-sparse, single-band solution near water absorption shoulders (~970/1450 nm), highlighting significant potential for developing low-cost, filter-based agricultural sensors. While this minimalist model showed excellent average accuracy, rigorous repeated evaluations also revealed non-negligible performance variability across different data splits—a crucial consideration for practical deployment. Our findings underscore that tailoring models to specific product forms and explicitly quantifying their robustness is essential for reliable NIR sensing in agriculture and provides concrete wavelength targets for sensor development. Full article

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

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29 pages, 6548 KB

Open AccessReview

Remote Sensing-Based Advances in Climate Change Impacts on Agricultural Ecosystem Respiration

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

Agriculture 2025, 15(23), 2509; https://doi.org/10.3390/agriculture15232509 - 3 Dec 2025

Viewed by 477

Abstract

Global climate change is exerting a growing impact on agricultural ecosystems. Accurately assessing the spatiotemporal dynamics of agricultural ecosystem respiration and its response mechanisms to climate has therefore emerged as a critical issue in agricultural carbon cycle research and climate change response. It [...] Read more.

Global climate change is exerting a growing impact on agricultural ecosystems. Accurately assessing the spatiotemporal dynamics of agricultural ecosystem respiration and its response mechanisms to climate has therefore emerged as a critical issue in agricultural carbon cycle research and climate change response. It should be noted that the ‘agro-ecosystem’ referred to in this study covers two major types: one is the farmland agro-ecosystem dominated by crop planting (such as farmland, orchard and other artificial management systems), and the other is the grassland agro-ecosystem dominated by herbaceous plants and managed by humans (such as grazing grassland and mowing grassland). Remote sensing technology provides a new way to break through the limitations of traditional ground observation by virtue of its advantages of large-scale and continuous monitoring. Based on the CiteSpace bibliometric method, this study focused on the key time window of 2021–2025, systematically searched the core collection of Web of Science, and finally included 222 related literature. This period marks the initial stage of the rise and rapid development of this interdisciplinary field, enabling us to capture the formation of its knowledge structure and the evolution of its research paradigm from the source. Through the quantitative analysis of this literature, it aims to reveal the research hotspots, development paths and frontier trends in this field. The results show that China occupies a dominant position in this field (135 articles). The evolution of research shows a three-stage development characterized by “technology-driven-method fusion-system coupling,” which is divided into the initial development period (2021–2022), the rapid growth period (2023–2024) and the deepening development period (2025) (because 2025 has not yet ended, this stage is a preliminary discussion). Keyword clustering analysis identified 13 important research directions, including machine learning (# 0 clustering), permafrost (# 1 clustering) and carbon flux (# 2 clustering). It is found that the deep integration of artificial intelligence and remote sensing data is promoting the transformation of research methods from traditional inversion to intelligent modeling. At the same time, the attention to alpine grassland and other ecosystems also reflects the trend that the research frontier extends to the interaction zone between the agricultural ecosystem and the natural environment. Future research should prioritize three key directions: building multi-scale monitoring networks, developing “grey box” models that integrate mechanisms and data fusion, and evaluating the carbon emission reduction efficiency of agricultural management practices. These efforts will provide a theoretical basis for carbon management and climate adaptation in agricultural ecosystems, as well as scientific and technological support for achieving global agricultural sustainable development goals (specifically, SDG13 on climate action and SDG15 on terrestrial ecosystem conservation). Full article

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

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12 pages, 2738 KB

Open AccessArticle

Effects of Queen Rearing Technology of Apis cerana by Cutting Comb on Reproductive Capacity and Productive Performance

by Yueyang Hu, Fangming Lu, Shuyun Li, Qizhong Pan, Yuyang Jiao, Yutong Jiang and Xiaobo Wu

Agriculture 2025, 15(23), 2508; https://doi.org/10.3390/agriculture15232508 - 2 Dec 2025

Viewed by 382

Abstract

The queen, as the reproductive core of a honeybee colony, has declining reproductive capacity with age, making it necessary to rear new queens to replace older ones. Traditional artificial queen-rearing methods face challenges, such as difficulties in larval grafting, particularly for Apis cerana [...] Read more.

The queen, as the reproductive core of a honeybee colony, has declining reproductive capacity with age, making it necessary to rear new queens to replace older ones. Traditional artificial queen-rearing methods face challenges, such as difficulties in larval grafting, particularly for Apis cerana. To address these issues, we developed a queen-rearing technology by cutting the comb. This study compared queen-rearing technology using comb cutting (CC) with larval grafting in A. cerana, measuring egg traits (length, width, weight), capped brood number, worker offspring initial weight, forager honey sac weight, worker morphology traits, and colony foraging efficiency. Queens reared using comb-cutting technology exhibited superior egg quality compared with those reared by larval grafting. The CC group showed significant improvements in egg length, egg weight, and number of capped brood cells (p < 0.05). Worker offspring from the CC group demonstrated significantly superior morphological traits—including forewing length, hindwing width, and lengths of the third and fourth tergites—as well as higher daily colony foraging activity, compared with those from the grafting larvae group (p < 0.05). Queen-rearing technology using CC effectively enhances the reproductive capacity and productive performance of colonies, promising high-quality queen rearing in A. cerana and sustainable beekeeping optimization. Full article

(This article belongs to the Special Issue Sustainable Beekeeping: Strategies for Enhancing Bee Stress Resistance)

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22 pages, 4615 KB

Open AccessArticle

Selection of Candidate Bacteria for Microbial Enrichment of Soil Amendments to Manage Contaminants of Emerging Concern in Agricultural Soils

by Rossana Sidari, Maria Teresa Rodinò, Giulio Scarpino, Stefano Mocali, Sara Del Duca, Elisabetta Loffredo and Antonio Gelsomino

Agriculture 2025, 15(23), 2507; https://doi.org/10.3390/agriculture15232507 - 2 Dec 2025

Viewed by 392

Abstract

Recycled bio-wastes such as compost and vermicompost, and bioenergy byproducts such as digestate and biochar are widely acknowledged for their role as soil conditioners capable of preserving soil fertility, maintaining soil health, and acting as a bio-adsorbent of organic soil pollutants (BIOSORs). Moreover, [...] Read more.

Recycled bio-wastes such as compost and vermicompost, and bioenergy byproducts such as digestate and biochar are widely acknowledged for their role as soil conditioners capable of preserving soil fertility, maintaining soil health, and acting as a bio-adsorbent of organic soil pollutants (BIOSORs). Moreover, they are attracting increasing attention for use as effective carriers of microbial consortia into arable soils. This study aims to combine selection of bacteria tolerating contaminants of emerging concern (CECs) and their use to fortify BIOSORs. Seventeen bacterial strains isolated from commercial bio-stimulant formulations were studied together with three strains previously isolated and identified as Bacillus subtilis, Bacillus licheniformis, and Serratia plymuthica. All the strains were tested in vitro for their ability to grow under increasing concentrations (0, 0.2, 0.5 and 1 mg L⁻¹) of CECs: bisphenol A, 4-nonylphenol, penconazole, and S-metolachlor. Results highlighted a variability in the tolerance of the bacteria to the tested CECs. The B. subtilis, B. licheniformis, and S. plymuthica were the most promising strains, individually or as consortium, to tolerate individual CECs and their mix. Moreover, they exhibited metabolic activity when inoculated in the BIOSORs. Nevertheless, additional investigations such as quantitative assessment of CECs are needed to validate the methodology. This work contributes to investigate the feasibility of stable and functionally active microbially enriched bio-sorbents (Me-BIOSORs) and provides preliminary evidence supporting the potential to be used in soil–plant systems at the field scale. Full article

(This article belongs to the Special Issue Feature Papers in Ecosystem, Environment and Climate Change in Agriculture)

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11 pages, 795 KB

Open AccessArticle

Severity of Leaf Spots Caused by Bipolaris maydis and Cercospora fusimaculans on Panicum maximum Forages Under Phosphate Fertilization and Limestone

by Néstor Eduardo Villamizar Frontado, Gelson dos Santos Difante, Alexandre Romeiro de Araújo, Manuel Claudio Motta Macedo, Denise Baptaglin Montagner, Marcio Martinello Sanches, Celso Dornelas Fernandes, Hitalo Rodrigues da Silva, Gustavo de Faria Theodoro, Mariane Arce Tico, Antonio Leandro Chaves Gurgel, Jéssica Gomes Rodrigues and Adrián Gonzalez

Agriculture 2025, 15(23), 2506; https://doi.org/10.3390/agriculture15232506 - 2 Dec 2025

Viewed by 405

Abstract

The study evaluated the severity of leaf spots caused by Bipolaris maydis and Cercospora fusimaculans in Panicum maximum subjected to different liming and phosphate fertilization levels. A randomized block design was used in a 6 × 2 × 5 factorial arrangement, considering six [...] Read more.

The study evaluated the severity of leaf spots caused by Bipolaris maydis and Cercospora fusimaculans in Panicum maximum subjected to different liming and phosphate fertilization levels. A randomized block design was used in a 6 × 2 × 5 factorial arrangement, considering six genetic materials of P. maximum, two cultivars (BRS Tamani and BRS Zuri) and four genotypes (PM422, PM408, PM414 and PM406), two phosphorus (P) doses (P19 and P116 mg dm³) and five limestone doses (0, 326, 653, 1306 and 2612 mg dm³). A significant interaction between Forage and P doses was observed for both pathogens (p < 0.0001 and p = 0.0001, respectively). The severity of C. fusimaculans decreased at P116 in the genotypes PM406, PM408, PM422 and Tamani. A P × Limestone interaction was detected for both pathogens (p = 0.0270 and p = 0.0077), with lower severity at P116. For B. maydis, limestone doses did not significantly differ. For C. fusimaculans, at P19, lower severity was observed at 1306 mg dm⁻³ limestone, while at P116, the lowest severity occurred at 2612 mg dm⁻³. No significant Forage × Limestone interaction was found. The Forage × P doses × Days interaction (p = 0.0005) influenced B. maydis severity, while the Forage × Days interaction (p < 0.0001) affected C. fusimaculans. Phosphate fertilization and liming reduce the severity of Bipolaris maydis and Cercospora fusimacula in different genotypes on Panicum maximum. Full article

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

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18 pages, 1374 KB

Open AccessArticle

Life Cycle Assessment of Reclaimed Water Irrigation in Organic Vineyards: Environmental Impacts and Water Stress Implications in La Rioja, Spain

by Adrián Agraso-Otero, Mar Vilanova de la Torre, María Malia Molleda, Ricardo Rebolledo-Leiva and Sara González-García

Agriculture 2025, 15(23), 2505; https://doi.org/10.3390/agriculture15232505 - 1 Dec 2025

Viewed by 447

Abstract

Agriculture puts significant pressure on freshwater sources, which motivates the use of reclaimed water for irrigation as a promising alternative to reduce freshwater demand while also providing nutrients. This study applies Life Cycle Assessment to determine the environmental impacts of irrigating a DOCa [...] Read more.

Agriculture puts significant pressure on freshwater sources, which motivates the use of reclaimed water for irrigation as a promising alternative to reduce freshwater demand while also providing nutrients. This study applies Life Cycle Assessment to determine the environmental impacts of irrigating a DOCa La Rioja vineyard with reclaimed water in the cultivation of organic grapes (scenario A) and compares it with an irrigation practice that uses canal water combined with organic extra-fertilisation (scenario B), accounting for differences in wastewater treatment processes. Results show that scenario A reduces impacts in categories such as global warming (16.2%) and freshwater eutrophication (25.6%) compared with scenario B, primarily due to the lower emissions associated with reclaimed water treatment. Additionally, a water balance was performed for the plot, which indicated that current inputs currently exceed losses in the region, so water stress is not observed; however, this situation may change in the near future due to population growth and climate change. These findings underscore the need to enhance the efficiency of the reclaimed water production, primarily by optimising its energy requirements, to support sustainable water use in agricultural systems. Full article

(This article belongs to the Special Issue Advances in Sustainable Viticulture)

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23 pages, 1114 KB

Open AccessArticle

Assessment of Competitiveness and Complementarity in Agri-Food Trade Between the European Union and Mercosur Countries

by Małgorzata Bułkowska and Łukasz Ambroziak

Agriculture 2025, 15(23), 2504; https://doi.org/10.3390/agriculture15232504 - 1 Dec 2025

Viewed by 750

Abstract

The EU–Mercosur agri-food trade is characterized by strong asymmetries reflecting long-standing structural differences between the two blocs. With the EU–Mercosur Agreement moving toward ratification, assessing these long-term trade patterns is essential for anticipating how liberalization may reshape comparative advantages and adjustment pressures in [...] Read more.

The EU–Mercosur agri-food trade is characterized by strong asymmetries reflecting long-standing structural differences between the two blocs. With the EU–Mercosur Agreement moving toward ratification, assessing these long-term trade patterns is essential for anticipating how liberalization may reshape comparative advantages and adjustment pressures in agri-food sectors. The analysis applies four quantitative indicators: the Revealed Comparative Advantage index (RCA), the Trade Complementarity Index (TCI), the Trade Intensity Index (TII), and the Export Similarity Index (ESI). Mercosur shows strong comparative advantages in raw and semi-processed commodities such as soybeans, meat, sugar and maize, while the EU specializes in higher value-added processed foods. High TCI values indicate strong alignment between Mercosur’s export structure and EU import demand, while low ESI values reveal limited direct competition. Low TII values suggest unrealized cooperation potential. Findings highlight both opportunities and vulnerabilities for agri-food sectors under future trade liberalization. Full article

(This article belongs to the Special Issue International Trade in Agri-Food Sector: Current State and Future Perspectives)

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21 pages, 13381 KB

Open AccessArticle

Research on Grassland Classification Method in Water Conservation Areas of the Qinghai–Tibet Plateau Based on Multi-Source Data Fusion

by Kexin Yan, Yueming Hu, Lu Wang, Xiaoyan Huang, Runyan Zou, Liangjun Zhao, Fan Yang and Taibin Wen

Agriculture 2025, 15(23), 2503; https://doi.org/10.3390/agriculture15232503 - 1 Dec 2025

Viewed by 378

Abstract

The Qinghai–Tibet Plateau is a crucial ecological security barrier in China and Asia. Its grassland ecosystem has high ecological service value. Scientific assessments and classifications of grasslands are crucial for determining the value of grassland resources and implementing refined management. Traditional grassland classification [...] Read more.

The Qinghai–Tibet Plateau is a crucial ecological security barrier in China and Asia. Its grassland ecosystem has high ecological service value. Scientific assessments and classifications of grasslands are crucial for determining the value of grassland resources and implementing refined management. Traditional grassland classification methods have used expert knowledge and linear models, which are subjective and cannot describe complex nonlinear relationships. We conducted a case study in Hongyuan County, Sichuan Province, in the water conservation area of the Qinghai–Tibet Plateau, using multi-source data including Landsat 8 (15 m/30 m), MOD15A2 (500 m), ALOS imagery (12.5 m), and 435 field survey samples, combined with machine learning models such as convolutional neural network (CNN), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), histogram gradient boosting (HistGradientBoosting), and random forest (RF). The objective was to develop a novel grassland classification method that integrates multi-source remote sensing data with machine learning algorithms. Based on the evaluation metrics of SHAP values, mean annual precipitation (MAP, 0.675), >0 °C Accumulated Temperature (AT, 0.591), and aspect (ASPECT, 0.548) were the most critical factors influencing alpine grasslands, revealing a driving mechanism characterized by climate dominance, topographic regulation, soil support, and vegetation response. The XGBoost model demonstrated the best performance (with an accuracy of 0.829, Precision of 0.818, Recall of 0.829, weighted F1-score of 0.820, and an AUC value of 0.870). The pixel-by-pixel absolute difference calculation between the model-predicted and the actual classification results showed that regions with no discrepancy (absolute value = 0) accounted for 75.82%, those with a minor discrepancy (absolute value = 1) accounted for 23.63%, and regions with a major discrepancy (absolute value = 2) accounted for only 0.54%. This study has established a replicable paradigm for the precise management and conservation of alpine grassland resources. Through the synergistic application of deep learning and machine learning, it generated superior baseline data, quantitatively uncovered a grassland differentiation mechanism dominated by hydrothermal factors and fine-tuned by topography in the complex Qinghai–Tibet Plateau, and delivered high-precision spatial distribution maps of grassland classes. Full article

(This article belongs to the Special Issue Feature Papers in Ecosystem, Environment and Climate Change in Agriculture)

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18 pages, 5598 KB

Open AccessArticle

Evaluation of Allyl Isothiocyanate and Ethylicin as Potential Substrate and Space Fumigants in Tomato Greenhouses

by Guangming Chen, Min Zhang, Zhaoai Shi, Aocheng Cao, Qiuxia Wang, Dongdong Yan, Wensheng Fang and Yuan Li

Agriculture 2025, 15(23), 2502; https://doi.org/10.3390/agriculture15232502 - 1 Dec 2025

Viewed by 409

Abstract

Continuous use of substrate cultivation can easily lead to the accumulation of crop pathogens, leading to widespread crop diseases. It is necessary to screen suitable and efficient substrate and space fumigants to keep the healthy development in substrate and greenhouses. This study systematically [...] Read more.

Continuous use of substrate cultivation can easily lead to the accumulation of crop pathogens, leading to widespread crop diseases. It is necessary to screen suitable and efficient substrate and space fumigants to keep the healthy development in substrate and greenhouses. This study systematically evaluated the effects of allyl isothiocyanate (AITC) and ethylicin fumigation on pathogens present on the substrate inside greenhouses. The average populations of Fusarium spp. and Phytophthora spp., bacterial and fungal community structures, tomato growth and yield were investigated and analyzed. The results demonstrated that both AITC and ethylicin exhibited significant inhibitory effects on Fusarium spp. and Phytophthora spp. in the substrate, with control efficiencies of 94.2% and 87.5%. Furthermore, these agents achieved 100% inhibition against Fusarium spp. while exceeding 90% Phytophthora spp. in the greenhouse space. Fumigation treatments significantly reduced pathogenic bacteria and increased beneficial microorganisms like Bacillus, Streptomyces and Brevibacillus in the substrate. Additionally, tomato yields increased significantly by over 45%. This study presents the first report on AITC and ethylicin as potential efficient fumigants easily used for both substrate and greenhouse space fumigation, which demonstrates excellent control effect on crop pathogens, with potential application in commercial tomato production in greenhouses to support sustainable agricultural practices. Full article

(This article belongs to the Special Issue Diseases Diagnosis, Prevention and Weeds Control in Crops—2nd Edition)

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20 pages, 543 KB

Open AccessArticle

Matrix-Based Assessment of Direct and Indirect Impacts of CAP Sectoral Interventions on Agricultural Production: An Ex-Ante Example of Poland

by Agnieszka Bezat and Włodzimierz Rembisz

Agriculture 2025, 15(23), 2501; https://doi.org/10.3390/agriculture15232501 - 1 Dec 2025

Viewed by 332

Abstract

Ex-ante appraisal of agricultural policy needs a transparent way to trace how sectoral interventions translate into production. We study the Polish CAP case and ask how much selected actions matter for livestock sectors. We assembled intervention-level budgets from the CAP Strategic Plan for [...] Read more.

Ex-ante appraisal of agricultural policy needs a transparent way to trace how sectoral interventions translate into production. We study the Polish CAP case and ask how much selected actions matter for livestock sectors. We assembled intervention-level budgets from the CAP Strategic Plan for Poland (2023–2027) and sectoral final output for milk, pigs, beef and poultry from Statistics Poland/Eurostat. We built matrices that map actions to sectors, normalized transfers by sectoral output, and separated dedicated from spillover effects. We report two cross-sections (2024, 2028) and a robustness test that perturbs I 1–I 2 allocation shares by ±10% under fixed envelopes. Horizontal income support dominates. In 2024, the cumulative effect of all analyzed actions equaled 16.68% of final output in milk, 14.43% in beef, 5.15% in pigs and 4.29% in poultry; by 2028, these values ease to 15.07%, 12.93%, 3.84% and 4.15%. Coupled payments to cows and young cattle add contributions in milk and beef. The ±10% reweighting of I 1–I 2 keeps the sector ranking unchanged; level changes are moderate (about 0.4–1.2 percentage points). A compact matrix approach provides a replicable map from interventions to sectors and highlights the preponderance of horizontal income support. The pattern—strongest relative support in milk and beef—appears robust to plausible allocation uncertainty. The main limitation is the use of final output as a revenue proxy; extending the matrix to all CAP actions and adding price–quantity feedback would be a natural next step. Policy implication: modest rebalancing of I 1–I 2 shares will not overturn sectoral exposure, but adjustments targeted at beef move levels the most. Full article

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

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29 pages, 3298 KB

Open AccessReview

Soil Aggregate Dynamics and Stability: Natural and Anthropogenic Drivers

by Ameer Hamza, Danutė Karčauskienė, Ieva Mockevičienė, Regina Repšienė, Mukkram Ali Tahir, Muhammad Zeeshan Manzoor, Shehnaz Kousar, Sumaira Salahuddin Lodhi, Nazima Rasool and Ikram Ullah

Agriculture 2025, 15(23), 2500; https://doi.org/10.3390/agriculture15232500 - 1 Dec 2025

Viewed by 1821

Abstract

Soil aggregate stability is a key indicator of soil health and is fundamental to soil processes such as water infiltration, nutrient cycling, carbon sequestration, erosion control, and ecosystem functionality. However, research concerning the impact of natural and anthropogenic factors on SAS across different [...] Read more.

Soil aggregate stability is a key indicator of soil health and is fundamental to soil processes such as water infiltration, nutrient cycling, carbon sequestration, erosion control, and ecosystem functionality. However, research concerning the impact of natural and anthropogenic factors on SAS across different climates, soil types, and management practices is lacking. This review synthesizes current understanding of physical, chemical, and biological mechanisms that govern the aggregate formation and stability and brings to light how the natural and anthropogenic drivers influence these processes. It highlights how clay mineralogy, root systems, microbial diversity, soil organic matter, and management practices shape the structure and turnover of aggregates essential for agricultural productivity. Key drivers of aggregate formation, categorized into natural (such as texture, clay mineral interaction, biota, and climate) and anthropogenic (such as tillage, land use changes, organic amendments) factors, have been critically evaluated. This review provides an insightful framework for soil management that may help enhance soil aggregation and promote sustainable agriculture and food security, especially under climate change. Full article

(This article belongs to the Topic Recent Advances in Soil Health Management)

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5 pages, 188 KB

Open AccessEditorial

Innovative Solutions for Sustainable Agriculture: From Waste to Biostimulants, Biofertilisers and Bioenergy

by Daniele Del Buono, Alberto Maria Gambelli and Giovanni Gigliotti

Agriculture 2025, 15(23), 2499; https://doi.org/10.3390/agriculture15232499 - 1 Dec 2025

Viewed by 350

Abstract

Due to its intensive nature [...] Full article

(This article belongs to the Special Issue Innovative Solutions for Sustainable Agriculture: From Waste to Biostimulants, Biofertilisers and Bioenergy)

16 pages, 4991 KB

Open AccessArticle

Simulation for Transversely Isotropic Citrus Tree Vibration Characteristics Based on the Frenet Frame

by Haobo Jiao, Weihong Liu, Liang Pan, Jiwei Dong, Guiying Ren, Chengsong Li, Lihong Wang, Chen Ma, Yipeng Wang, Bangtai Zhao and Xi Guo

Agriculture 2025, 15(23), 2498; https://doi.org/10.3390/agriculture15232498 - 30 Nov 2025

Viewed by 320

Abstract

Vibration technology is a commonly used method for detaching citrus fruits, and studying the vibrational properties of citrus trees can helpfully improve the effectiveness of vibrating harvesters. The existing mechanical properties of wood have shown that tree materials in nature have transversely isotropic [...] Read more.

Vibration technology is a commonly used method for detaching citrus fruits, and studying the vibrational properties of citrus trees can helpfully improve the effectiveness of vibrating harvesters. The existing mechanical properties of wood have shown that tree materials in nature have transversely isotropic characteristics instead of isotropic ones. However, in the study of the vibrational characteristics of fruit trees, the material of fruit trees is still defined as isotropic. This paper presents a vibration simulation approach for transversely isotropic citrus trees using the Frenet frame to reveal the true physical characteristics of fruit trees. A comparison was carried between the vibration spectrum obtained from experiments on citrus branches and the simulated spectra from transversely isotropic and isotropic material models. The findings reveal that the simulated vibration spectra for the transversely isotropic citrus branch can closely match the experimentally measured spectra. This supports the effectiveness of simulation method for transversely isotropic citrus trees. Furthermore, simulations of the vibration frequency response characteristics for citrus trees with both transversely isotropic and isotropic materials showed notable differences in their spectra. The proposed simulation method for transversely isotropic citrus trees offers a more precise depiction of their actual vibrational properties. This simulation technique is crucial for optimizing the parameters of citrus harvesting equipment, leading to enhanced machine performance. Full article

(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)

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25 pages, 2836 KB

Open AccessArticle

Synergistic Effects of Earthworm Size, Earthworm Application Timing, and Quantity on Brassica rapa var. chinensis Growth and Black Soil Pore Structure

by Baoguang Wu, Zhenyu Wang, Zhipeng Yin, Pu Chen, Yuping Liu, Shun Xu, Hao Pang and Qiuju Wang

Agriculture 2025, 15(23), 2497; https://doi.org/10.3390/agriculture15232497 - 30 Nov 2025

Viewed by 431

Abstract

Black soil, as a vital environment for food production, is currently facing severe degradation. Earthworm tillage is recognized as an effective approach to improving black soil structure; however, its optimal implementation strategy remains unclear. In this study, a pot experiment using Pak Choi [...] Read more.

Black soil, as a vital environment for food production, is currently facing severe degradation. Earthworm tillage is recognized as an effective approach to improving black soil structure; however, its optimal implementation strategy remains unclear. In this study, a pot experiment using Pak Choi (Brassica rapa L. ssp. chinensis) was conducted under an orthogonal design with three factors—earthworm size, application timing, and quantity. Combined with yield measurement, analysis of variance (ANOVA), and grey relational analysis (GRA), the effects of earthworm application on plant growth and soil structure were systematically evaluated. In addition, Computer Tomography (CT) scanning and three-dimensional reconstruction were employed to visualize the pore structures of representative soil samples. The results showed that large earthworms significantly enhanced both leaf and root biomass of Pak Choi, exhibiting a stronger promoting effect than small earthworms. Application at the sowing stage resulted in the greatest yield improvement, whereas applications at other growth stages had limited effects. The number of earthworms did not show a statistically significant impact under the experimental conditions, and its potential influence requires further verification under more refined density gradients. Overall, this study elucidates the mechanisms by which earthworm tillage improves soil structure and promotes crop growth, providing a theoretical basis for the restoration and sustainable utilization of degraded black soil. Full article

(This article belongs to the Section Agricultural Soils)

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34 pages, 127929 KB

Open AccessArticle

Integrating Grain–Carbon Synergy and Ecological Risk Assessment for Sustainable Land Use in Mountainous High-Risk Areas

by Qihong Ren, Shu Wang, Quanli Xu and Zhenheng Gao

Agriculture 2025, 15(23), 2496; https://doi.org/10.3390/agriculture15232496 - 30 Nov 2025

Viewed by 379

Abstract

Amid climate change and land-use transformation, the scientific identification of high-quality arable land reserves is critical for safeguarding both cropland quantity and quality. Conventional approaches, largely based on spatial autocorrelation and heterogeneity theories, inadequately capture the multi-scale integration of ecological functions and carbon [...] Read more.

Amid climate change and land-use transformation, the scientific identification of high-quality arable land reserves is critical for safeguarding both cropland quantity and quality. Conventional approaches, largely based on spatial autocorrelation and heterogeneity theories, inadequately capture the multi-scale integration of ecological functions and carbon cycling, particularly in ecologically high-risk areas where systematic identification and mechanism analysis are lacking. To address these challenges, this study introduces a geographically similar “grain-carbon” synergistic framework, paired with a “bidirectional optimization” strategy (negative elimination + positive selection), to overcome the shortcomings of traditional methods and mitigate grain–carbon trade-offs in high-risk areas. Using land-use data from Yunnan’s mountainous areas (2000–2020), integrated with InVEST-PLUS model outputs, multi-source remote sensing, and carbon pool datasets, we developed a dynamic land-use–carbon storage simulation framework under four policy scenarios: natural development, urban expansion, arable land protection, and ecological conservation. High-quality arable lands were identified through a geographic similarity analysis with the Geo detector, incorporating ecological vulnerability and landscape risk indices to delineate priority high-risk zones. Carbon storage degradation trends and land-use pressures were further considered to identify optimal areas for cropland-to-forest conversion, facilitating the implementation of the bidirectional optimization strategy. Multi-scenario simulations revealed an increase of 454.33 km² in high-quality arable land, with the optimized scenario achieving a maximum carbon storage gain of 23.54 × 10⁶ t, reversing carbon loss trends and enhancing both farmland protection and carbon sequestration. These findings validate the framework’s effectiveness, overcoming limitations of traditional methods and providing a robust strategy for coordinated optimization of carbon storage and arable land conservation in ecologically high-risk regions, with implications for regional carbon neutrality and food security. Full article

(This article belongs to the Section Ecosystem, Environment and Climate Change in Agriculture)

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16 pages, 2728 KB

Open AccessArticle

Adsorption Performance and Mechanisms of Copper by Soil Glycoprotein-Modified Straw Biochar

by Zhenyu Chen, Zhiyuan Gao, Yiyuan Xue, Xinchi Yao, Haiyan Shao and Qiang Wang

Agriculture 2025, 15(23), 2495; https://doi.org/10.3390/agriculture15232495 - 30 Nov 2025

Viewed by 400

Abstract

Biochar is one of the most promising crop straw utilization pathways. However, its capacity for adsorbing heavy metals is limited, and there is a potential risk of secondary pollution, highlighting the importance of developing efficient and environmentally friendly bio-modification methods. Here, we utilized [...] Read more.

Biochar is one of the most promising crop straw utilization pathways. However, its capacity for adsorbing heavy metals is limited, and there is a potential risk of secondary pollution, highlighting the importance of developing efficient and environmentally friendly bio-modification methods. Here, we utilized glomalin-related soil protein (GRSP), a byproduct from arbuscular mycorrhizal fungi, to modify straw biochar, developing a novel composite material and systematically evaluating its performance in removing copper ion (Cu²⁺) from aqueous solutions. Biochar samples derived from maize, wheat, and rice straw were prepared at three pyrolysis temperatures (300 °C, 500 °C, and 700 °C), followed by surface functionalization with GRSP to produce GRSP-modified straw biochar for Cu²⁺ adsorption experiments. The results demonstrated that the abundant functional groups (e.g., amino and carboxyl groups) in GRSP and the porous structure of the straw biochar exhibited a significant synergistic effect, enhancing the adsorption capacity for Cu²⁺. Notably, the GRSP-modified wheat straw biochar prepared at 700 °C achieved an adsorption capacity of 193.2 mg g⁻¹ for Cu²⁺, representing a 76% improvement over the unmodified material. Fourier transform infrared spectroscopy and scanning electron microscopy with energy-dispersive X-ray spectroscopy revealed that hydroxyl, carboxyl, and ether groups served as key adsorption sites for Cu²⁺, while the hydrophobic-acid precipitation characteristics of GRSP further enhanced the material’s recoverability. By systematically characterizing the material’s microstructure and its adsorption behavior toward Cu²⁺, this study elucidated the role of critical functional groups in the adsorption mechanism. This work not only offers a low-carbon and efficient strategy for agricultural waste valorization and heavy metal pollution control, but also advances the mechanistic understanding of “bio-abiotic” synergy in environmental remediation. Full article

(This article belongs to the Section Agricultural Technology)

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20 pages, 4558 KB

Open AccessArticle

Boosting Rice Disease Diagnosis: A Systematic Benchmark of Five Deep Convolutional Neural Network Models in Precision Agriculture

by Shu-Hung Lee, Qi-Wei Jiang, Chia-Hsin Cheng, Yu-Shun Tsai and Yung-Fa Huang

Agriculture 2025, 15(23), 2494; https://doi.org/10.3390/agriculture15232494 - 30 Nov 2025

Viewed by 379

Abstract

Rice diseases pose a critical threat to global food security. While deep learning offers a promising path toward automated diagnosis, clear guidelines for model selection in resource-constrained agricultural environments are still lacking. This study presents a systematic benchmark of five deep convolutional neural [...] Read more.

Rice diseases pose a critical threat to global food security. While deep learning offers a promising path toward automated diagnosis, clear guidelines for model selection in resource-constrained agricultural environments are still lacking. This study presents a systematic benchmark of five deep convolutional neural networks (CNNs)—Visual Geometry Group (VGG)16, VGG19, Residual Network (ResNet)101V2, Xception, and Densely Connected Convolutional Network (DenseNet)121—for rice disease identification using a public leaf image dataset. The models, initialized with ImageNet pre-trained weights, were rigorously evaluated under a unified framework, including 5-fold cross-validation and a challenging out-of-distribution (OOD) generalization test. Our results demonstrate a clear performance hierarchy, with DenseNet121 emerging as the superior model. It achieved the highest OOD accuracy and F1-score (both 85.08%) while exhibiting the greatest parameter efficiency (8.1 million parameters), making it ideally suited for edge deployment. In contrast, architectures with large fully connected layers (VGG) or less efficient feature learning mechanisms (Xception, ResNet101V2) showed lower performance in this specific task. This study confirms the critical impact of architectural design choices, provides a reproducible performance baseline, and identifies DenseNet121 as a robust, efficient, and highly recommendable CNN for practical rice disease diagnosis in precision agriculture. Full article

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

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Agriculture, Volume 15, Issue 23 (December-1 2025) – 122 articles

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