Agriculture

22 pages, 3975 KB

Open AccessArticle

Calibration of V2 Discrete Element Model Parameters for Simulation of Atlantic Potato Slicing and Sorting

by Hui Geng, Jingming Hu, Quan Feng, Wei Sun, Mei Yang, Haohua Wang, Weihao Qiao and Pan Wang

Agriculture 2026, 16(4), 489; https://doi.org/10.3390/agriculture16040489 (registering DOI) - 22 Feb 2026

Abstract

To address the lack of contact and breakage parameters in the discrete element method (DEM) simulation of potato seed cutting and sorting processes, this study took the ‘Atlantic’ potato seed as the research object and constructed a crushable potato model using EDEM. Through [...] Read more.

To address the lack of contact and breakage parameters in the discrete element method (DEM) simulation of potato seed cutting and sorting processes, this study took the ‘Atlantic’ potato seed as the research object and constructed a crushable potato model using EDEM. Through physical experiments, the mean average diameter, moisture content, density, Poisson’s ratio, and elastic modulus were measured. The coefficients of collision restitution, static friction, and rolling friction between the potato seed and the Q235 steel plate were determined as 0.576, 0.559, and 0.073, respectively. Using the actual repose angle of 22.89° as the response target, and combining the steepest ascent test with the Box–Behnken design, the non-cohesive contact parameters between potato seed particles were calibrated. The resulting coefficients of collision restitution, static friction, and rolling friction between particles were determined as 0.404, 0.412, and 0.0156, respectively. Finally, based on physical shear tests (maximum shear force 23.56 N), significant influencing factors were identified through Plackett–Burman screening as the bonding radius ratio r and the normal stiffness per unit area Kn. Using the steepest ascent test and the Box–Behnken response surface method, the key bonding parameters of the Bonding V2 model were calibrated as follows: r = 1.098, K_n = 8.597 × 10⁷ N·mm⁻³, tangential stiffness per unit area K_t = 3.250 × 10⁶ N·mm⁻³, critical compressive stress σ_n = 5.500 × 10⁵ Pa, and shear strength τ_t = 3.000 × 10⁴ Pa. The relative error between the simulated and actual maximum shear forces was 0.89%, which is small. The calibrated flexible model accurately represents the physical characteristics of potato seeds and provides a reliable reference for the design of mechanized potato seed cutting and sorting equipment. Full article

(This article belongs to the Section Agricultural Technology)

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23 pages, 2608 KB

Open AccessArticle

Designing Predictive Models: A Comparative Evaluation of Machine Learning Algorithms for Predicting Body Carcass Fat in Ewes at Weaning

by Ahmad Shalaldeh, Mosleh Abualhaj, Ahmad Adel Abu-Shareha, Ayman Elshenawy, Yassen Saoudi, Muzammil Hussain, Ahmad Shubita, Majeed Safa and Chris Logan

Agriculture 2026, 16(4), 488; https://doi.org/10.3390/agriculture16040488 (registering DOI) - 22 Feb 2026

Abstract

Accurate estimation of Body Carcass Fat (BCF) is essential for evaluating the physiological condition of ewes. Traditional assessment via Body Condition Score (BCS) through palpation is inaccurate and subjective. BCF can now be predicted more precisely using objective measurements. This study presents a [...] Read more.

Accurate estimation of Body Carcass Fat (BCF) is essential for evaluating the physiological condition of ewes. Traditional assessment via Body Condition Score (BCS) through palpation is inaccurate and subjective. BCF can now be predicted more precisely using objective measurements. This study presents a comparative analysis of eight machine learning (ML) models for predicting BCF in Coopworth ewes, using weight and RGB-image-based body measurements. Four non-linear regression methods and four neural network architectures were evaluated using a dataset of 74 ewes with 13 independent variables. The dataset was partitioned into training (52 ewes), validation (11 ewes), and testing (11 ewes) sets. The Gradient Boosting Regression achieved the highest predictive accuracy with an R² value of 0.9434 using body weight and width, followed by Ensemble Neural Network (R² = 0.9371) using body weight. The findings demonstrate the effectiveness of the Gradient Boosting Regression, Ensemble Neural Network and Random Forest tree-based approaches for morphometric prediction tasks in biological applications. BCF values obtained from image analysis were validated against those derived from computerized tomography (CT), considered the gold standard. These findings highlight the potential of image-guided, ML-driven models for objective, non-invasive, cost-effective assessment of ewe body composition in modern livestock systems. Full article

(This article belongs to the Special Issue Machine Learning in Precision Livestock Farming: From Animal Activity Forecasting to Environmental Control)

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29 pages, 20184 KB

Open AccessArticle

Estimation of Canopy Traits and Yield in Maize–Soybean Intercropping Systems Using UAV Multispectral Imagery and Machine Learning

by Li Wang, Shujie Jia, Jinguang Zhao, Canru Liang and Wuping Zhang

Agriculture 2026, 16(4), 487; https://doi.org/10.3390/agriculture16040487 (registering DOI) - 22 Feb 2026

Abstract

Strip intercropping of maize and soybean is a key practice for improving land productivity and ensuring food and oil security in the hilly regions of the Loess Plateau. However, complex interspecific interactions generate highly heterogeneous canopy structures, making it difficult for traditional linear [...] Read more.

Strip intercropping of maize and soybean is a key practice for improving land productivity and ensuring food and oil security in the hilly regions of the Loess Plateau. However, complex interspecific interactions generate highly heterogeneous canopy structures, making it difficult for traditional linear models to capture yield variability within mixed pixels. Based on a single-season (2025) field experiment, this study developed a UAV multispectral imagery-based yield estimation framework integrating multiple machine-learning algorithms. Shapley additive explanations (SHAP) and partial dependence plots (PDP) were used to interpret the spectral–yield relationships under different spatial configurations. The predictive performance of linear regression and eight nonlinear algorithms was compared using 20 spectral features. Ensemble learning outperformed linear approaches in all intercropping scenarios. In the maize–soybean 3:2 pattern, the GBDT model delivered the highest accuracy (R² = 0.849; NRMSE = 9.28%), whereas in the 4:2 pattern with stronger shading stress on soybean, the random forest model showed the greatest robustness (R² = 0.724). Interpretation results indicated that yield in monoculture systems was mainly driven by physiological traits characterized by visible-band indices, while yield in intercropping systems was dominated by structural and stress-response traits represented by near-infrared and soil-adjusted vegetation indices. The generated centimeter-scale yield maps revealed clear strip-like spatial variability driven by interspecific competition. Overall, explainable machine learning combined with UAV multispectral data shows promise for within-season yield estimation in intercropping systems and can support spatially differentiated precision management under the sampled conditions. Full article

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

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25 pages, 4958 KB

Open AccessArticle

ViaNet: Interpretable and Lightweight Deep Hyperspectral Classification of Pepper Seed Viability

by Lei Zhu, Yeminzi Zhou, Yueming Zhu, Ling Zou, Bin Li, Siqiao Tan, Feng Liu and Fuchen Chen

Agriculture 2026, 16(4), 486; https://doi.org/10.3390/agriculture16040486 (registering DOI) - 22 Feb 2026

Abstract

Seed viability fundamentally determines crop establishment, stress resilience, and yield stability in pepper (Capsicum annuum L.), yet conventional assessment remains destructive, labor-intensive, and poorly scalable, while existing spectral learning approaches largely lack physiological interpretability, limiting their reliability for industrial seed quality management. [...] Read more.

Seed viability fundamentally determines crop establishment, stress resilience, and yield stability in pepper (Capsicum annuum L.), yet conventional assessment remains destructive, labor-intensive, and poorly scalable, while existing spectral learning approaches largely lack physiological interpretability, limiting their reliability for industrial seed quality management. Here, we present ViaNet, a lightweight, interpretable deep hyperspectral classification framework for 1038 naturally aged pepper seeds labeled via standardized 14-day germination tests. ROI-averaged hyperspectral reflectance vectors are modeled as a binary classification task, and ViaNet integrates Successive Projections Algorithm (SPA)-based wavelength sparsification with Efficient Channel Attention (ECA)-driven spectral weighting within a compact 1D-CNN architecture, enabling physiologically grounded feature learning under strict computational constraints. The model achieves recall for germinable seeds (79.75%) and outperforms classical machine learning methods. In addition, ViaNet consistently highlights reproducible spectral bands associated with natural-aging-related biochemical changes as reported in the literature (e.g., carotenoid-related absorption features in the near-UV region). By coupling spectral feature selection with attention-guided wavelength focusing, ViaNet establishes a closed analytical chain from spectral compression to physiologically interpretable inference. This framework balances predictive accuracy, interpretability, and deployability and provides a scalable, non-destructive, and biologically informed paradigm for hyperspectral seed viability assessment. Full article

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

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14 pages, 3127 KB

Open AccessArticle

Multi-Output Gaussian Process Regression for Rapid Multi-Nutrient Prediction in Soil Using Near-Infrared Spectroscopy

by Yan-Rui Dai and Zheng-Guang Chen

Agriculture 2026, 16(4), 485; https://doi.org/10.3390/agriculture16040485 (registering DOI) - 22 Feb 2026

Abstract

The concentrations of nitrogen (N), phosphorus (P), potassium (K), organic matter (OM), and pH in soil are critical markers of fertility that influence crop growth and yield. Traditional wet-chemical analyses are labor-intensive, time-consuming, and costly, thereby constraining timely soil information acquisition for precision [...] Read more.

The concentrations of nitrogen (N), phosphorus (P), potassium (K), organic matter (OM), and pH in soil are critical markers of fertility that influence crop growth and yield. Traditional wet-chemical analyses are labor-intensive, time-consuming, and costly, thereby constraining timely soil information acquisition for precision agriculture. This study evaluates whether multi-output Gaussian process regression (MOGPR) can enhance the prediction accuracy of multiple soil nutrients by exploiting their intrinsic correlations, in comparison with single-output Gaussian process regression (SOGPR). Near-infrared (NIR) spectroscopy was applied to 622 typical black soil samples collected from the Farm 855 (45°43′ N, 131°35′ E), Heilongjiang Province, China. Corresponding MOGPR and SOGPR models were developed for systematic performance comparison. Results indicated that MOGPR significantly outperformed SOGPR for nutrients exhibiting moderate-to-strong intercorrelations (N, P, K, and OM), yielding R² improvements of 0.070.28 and RPD increases of 16–40%, whereas only limited gains were observed for pH due to its weak correlations with other nutrients. These findings indicate that combining NIR spectroscopy with MOGPR offers significant potential for rapid, nondestructive assessment of multiple soil nutrients. This study further establishes a correlation-aware multi-output modeling framework that links shared spectral responses with an inter-nutrient dependency structure, providing methodological guidance for multi-nutrient soil prediction. Full article

(This article belongs to the Section Agricultural Soils)

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12 pages, 776 KB

Open AccessArticle

Optimisation of Whole-Plant Corn Silage Harvesting Methods Based on Silage Quality in Northeast China: Interaction of Latitude, Harvesting Time, and Stubble Height

by He Wang, Long Zhang, Xiangyu Wang, Zhihao Zhang, Xue Han, Xuepeng Wang, Songze Li, Zhe Sun, Tao Wang, Yuguo Zhen and Xuefeng Zhang

Agriculture 2026, 16(4), 484; https://doi.org/10.3390/agriculture16040484 (registering DOI) - 22 Feb 2026

Abstract

Factors such as latitude, harvesting stage, and stubble height influence silage quality and harvesting decisions. We aimed to examine how harvest stage and stubble height affect the quality of whole-plant corn silage across different latitudes in Northeast China. Experiments were conducted in five [...] Read more.

Factors such as latitude, harvesting stage, and stubble height influence silage quality and harvesting decisions. We aimed to examine how harvest stage and stubble height affect the quality of whole-plant corn silage across different latitudes in Northeast China. Experiments were conducted in five different latitude regions (Shenyang, Changchun, Tongliao, Harbin, and Qiqihar) and assessed three stubble heights (20, 40, and 60 cm) at each harvest maturity stage: milk, initial wax, middle wax, late wax, and full maturity. After fermentation, whole-plant corn silage samples were collected and evaluated for nutritional content, fermentation quality, and toxin levels. Increasing the stubble height increased the dry matter (DM), crude protein, starch, and deoxynivalenol content in the whole-plant corn silage (p < 0.01), but decreased the acid detergent fibre and neutral detergent fibre concentrations (p < 0.01). Delayed harvest increased the DM and vomitoxin content (p < 0.01). The pH decreased initially and then increased as the harvest was delayed (p < 0.01). Meanwhile, NH₃–N and acetic acid content did not differ significantly with delayed harvesting (p > 0.05). At higher latitudes, the optimal harvest period is correspondingly delayed, shortening the harvest time. To maintain silage quality without affecting yield or economics, a 40 cm stubble height is recommended. If delayed, incrementally increasing the stubble height to 60 cm may be warranted to maintain silage quality. We provide data-driven insights to optimise silage production and ruminant nutrition. Full article

(This article belongs to the Special Issue Silage Preparation, Processing and Efficient Utilization—2nd Edition)

19 pages, 2981 KB

Open AccessArticle

Physiological and Transcriptomic Responses of Xinjiang Wheat ‘Xindong 22’ (Triticum aestivum L.) to Drought Stress During Early Development

by Kunkun Wu, Xiaoya Li, Chen Gao, Xin Li, Yuhao Zhao, Xinyu Li and Weihong Sun

Agriculture 2026, 16(4), 483; https://doi.org/10.3390/agriculture16040483 (registering DOI) - 21 Feb 2026

Abstract

The Xinjiang wheat variety ‘Xindong 22’ was used as experimental material. Two soil moisture treatments were established: control (CK, 70–75% field capacity), drought (X1, 60–65%). The photosynthetic characteristics and resistance physiological indexes of wheat leaves under different stress levels were analyzed, and RNA-Seq [...] Read more.

The Xinjiang wheat variety ‘Xindong 22’ was used as experimental material. Two soil moisture treatments were established: control (CK, 70–75% field capacity), drought (X1, 60–65%). The photosynthetic characteristics and resistance physiological indexes of wheat leaves under different stress levels were analyzed, and RNA-Seq technology was used to conduct transcriptome sequencing and analysis were performed on wheat leaves. The results showed that under drought stress, superoxide dismutase (SOD) activity was significantly enhanced, while peroxidase (POD) activity decreased. Soluble sugar and proline contents also increased. These changes likely enhanced reactive oxygen species scavenging, thereby reducing the content of malondialdehyde in the leaves. Meanwhile, under the X1 treatment, stomatal conductance and transpiration rate of wheat leaves showed a slow decreasing trend, the intercellular CO₂ concentration decreased slightly, the decline in Fv/Fm was relatively small, and the value of the non-photochemical quenching coefficient gradually increased. Transcriptome analysis identified 1881 differentially expressed genes (DEGs). Notably, drought stress induced the up-regulation of key genes involved in the ABA signaling pathway (e.g., SnRK2 and ABF) and the MAPK cascade, suggesting their crucial roles in mediating drought responses in this wheat variety. In the jasmonic acid signaling pathway, MYC2 functions as a positive regulator by interacting with JAZ proteins. These findings demonstrate that Xinjiang wheat employs integrated physiological and molecular strategies to cope with drought stress. Full article

(This article belongs to the Special Issue Crop Responses and Adaptations to Environmental Stresses: New Insights and Approaches)

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27 pages, 5403 KB

Open AccessArticle

Comprehensive Quality and Volatile Profile Analysis of Loquat Fruit Across Different Harvest Times

by Siyue Luo, Mingfeng Qiao, Xuemei Cai, Lili Duan, Xinxin Zhao and Baohe Miao

Agriculture 2026, 16(4), 482; https://doi.org/10.3390/agriculture16040482 (registering DOI) - 21 Feb 2026

Abstract

To assess the impact of developmental stages on the postharvest quality of loquat, fruits at five distinct maturity phases (T1–T5) were systematically analyzed. This study employed a range of analytical techniques to conduct a comprehensive examination of the variations in quality, nutritional content, [...] Read more.

To assess the impact of developmental stages on the postharvest quality of loquat, fruits at five distinct maturity phases (T1–T5) were systematically analyzed. This study employed a range of analytical techniques to conduct a comprehensive examination of the variations in quality, nutritional content, and volatile compounds of loquat across different developmental stages. Sensory evaluation indicated that the T4 stage achieved the highest score (93.21 ± 1.13), with significant differences observed in a* (6.56–13.08) and b* values (16.91–22.16). Both moisture content (45.45–86.94 g/100 g) and fruit firmness (8.65–3.31 N) exhibited a decline with delayed harvest (p < 0.05). Scanning electron microscopy (SEM) analysis revealed that, as the developmental period progressed, the pores in the loquat cell walls enlarged, and the cellular structure became increasingly relaxed. GC–MS and GC–IMS detected 48 and 47 volatile compounds, respectively. Using orthogonal partial least squares discriminant analysis (OPLS–DA), we identified 14 key compounds that distinguish the five developmental stages. E-nose and E-tongue analyses showed significant changes in the smell and taste profiles of loquats over time, with the T1 stage notably different from later stages. This study offers important insights and guidance on the postharvest quality of loquats at various developmental stages. Full article

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

22 pages, 4254 KB

Open AccessArticle

Design and Research of Soil Disinfection Pesticide Application Control System Based on PSO-PID Algorithm

by Mengdi Xu, Zhichong Wang, Xiangjie Niu, Zhen Wang, Wei Zou, Changyuan Zhai and Si Li

Agriculture 2026, 16(4), 481; https://doi.org/10.3390/agriculture16040481 (registering DOI) - 21 Feb 2026

Abstract

Soil-borne diseases and continuous cropping obstacles have become critical factors constraining sustainable agricultural development. Traditional pesticide application methods commonly suffer from uneven dosage distribution, significant chemical waste, and environmental pollution. To enhance the precision and system stability of soil disinfection, this paper designs [...] Read more.

Soil-borne diseases and continuous cropping obstacles have become critical factors constraining sustainable agricultural development. Traditional pesticide application methods commonly suffer from uneven dosage distribution, significant chemical waste, and environmental pollution. To enhance the precision and system stability of soil disinfection, this paper designs a precision pesticide application system for soil disinfection based on the Particle Swarm Optimization Proportional-Integral-Derivative algorithm (PSO-PID). Centered on a C37 controller, the system integrates the application pipeline, pumps, electric ball valves, multiple sensors, and a control terminal. The PSO-PID algorithm was deployed on the Codesys V2.3 platform, achieving precise flow control by adjusting electric ball valve openings in conjunction with velocity feedback. Simulink simulations showed that the PSO-PID algorithm outperforms conventional PID control in terms of settling time, overshoot, and steady-state error. Bench tests further validated the effectiveness of the proposed algorithm. Compared to conventional PID, the PSO-PID algorithm demonstrates superior control accuracy, faster response time, and enhanced application stability, with relative errors of 2.33%, 1.25%, and 1.20% respectively, while those of the conventional PID algorithm reach 3.67%, 3.35% and 4.88% respectively, representing a reduction of approximately 50% compared with the conventional PID algorithm. The results of the system application uniformity test indicated that the PSO-PID algorithm reduces relative error by approximately 40% compared to conventional PID, with the coefficients of variation being 2.02%, 1.73% and 1.81% respectively, which represented a significant improvement over those of the conventional PID algorithm (3.36%, 3.13% and 3.81%). Both application uniformity and stability outperform conventional PID algorithms, effectively minimizing application deviation. It outperforms conventional PID in both application uniformity and stability, effectively minimizing application deviation. The findings demonstrate that the proposed PSO-PID application control method achieves high control accuracy and application stability, providing reliable technical support for precision soil disinfection application. Full article

(This article belongs to the Special Issue Integrated Management of Soil-Borne Diseases—Second Edition)

21 pages, 1501 KB

Open AccessArticle

Agricultural Resilience Under Threat: Assessing Technical Efficiency Across Conflict Contexts in the Sahara–Sahelian Region

by Youssouf Traore and Zhongfeng Qin

Agriculture 2026, 16(4), 480; https://doi.org/10.3390/agriculture16040480 (registering DOI) - 20 Feb 2026

Abstract

Agriculture serves as a critical foundation for livelihoods, food security, and sustainable development across the Sahara–Sahelian region. However, this vital sector faces mounting pressures from recurrent armed conflicts that systematically undermine its resilience and long-term sustainability. This study provides a comprehensive analysis of [...] Read more.

Agriculture serves as a critical foundation for livelihoods, food security, and sustainable development across the Sahara–Sahelian region. However, this vital sector faces mounting pressures from recurrent armed conflicts that systematically undermine its resilience and long-term sustainability. This study provides a comprehensive analysis of agricultural technical efficiency across 23 African countries in the Sahara–Sahelian region from 2009 to 2021, employing a robust bias-corrected bootstrap Data Envelopment Analysis approach. The findings reveal a concerning regional deterioration, with technical efficiency declining at an average annual rate of 1.7% throughout the study period. Conflict-affected countries demonstrated distinctive vulnerability patterns, exhibiting both higher average efficiency levels (0.875) and greater volatility, with annual declines of 1.8%. Sub-regional analysis highlights the Sahel’s particular fragility, where efficiency decreased by 2.2% yearly, nearly double the decline rate observed in North Africa. The most severe efficiency losses were recorded in countries experiencing intense and protracted conflict, notably Burkina Faso (4.0%) and Mali (3.5%), underscoring the severe association between conflict exposure and the erosion of agricultural productive capacity. These findings underscore the importance of developing integrated strategies that simultaneously address security challenges, climate adaptation, and institutional reform for effective resilience-building. Policy recommendations highlight the importance of enhanced regional connectivity, knowledge transfer, and targeted investments in agricultural capacity building—all aligned with both Sustainable Development Goals and the African Union’s Agenda 2063 objectives for achieving sustainable agricultural transformation in conflict-affected regions. Full article

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

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29 pages, 7042 KB

Open AccessArticle

Design and Motion Control Analysis of a Dual-Claw Seedling Pick-and-Throw Mechanism for an Automatic Transplanter with Multi-Layer Tray Handling

by Mengjiao Yao, Jianping Hu, Wei Liu, Jiawei Shi, Junpeng Lv, Jinhong Li, Yongwang Jin, Shuangxia Zhang, Dan Liu and Jiahui Chen

Agriculture 2026, 16(4), 479; https://doi.org/10.3390/agriculture16040479 (registering DOI) - 20 Feb 2026

Abstract

To address the existing problems of frequent manual tray handling, poor continuity, and insufficient coordination in fully automatic transplanters, this study designed an integrated multi-layer tray-handling and dual-claw coordinated seedling pick-and-throw mechanism. Through continuous tray conveying and multi-layer tray-handling mechanisms, automatic replacement of [...] Read more.

To address the existing problems of frequent manual tray handling, poor continuity, and insufficient coordination in fully automatic transplanters, this study designed an integrated multi-layer tray-handling and dual-claw coordinated seedling pick-and-throw mechanism. Through continuous tray conveying and multi-layer tray-handling mechanisms, automatic replacement of multiple seedling trays was achieved. A dual-claw coordinated seedling picking and planting mechanism was designed, and the seedling picking trajectory was optimized based on path planning and RecurDyn kinematic simulation. Six-segment and seven-segment S-shaped acceleration and deceleration motion control curves and planning strategies that can be switched according to the target displacement and dynamic parameters were proposed, and a PLC-based software and hardware control system was constructed. The simulation and experimental results show that the dual-module parallel motion mode is more efficient and has a smoother trajectory than the serial mode. The average positioning absolute error of tray conveying is 1.09 mm, the average horizontal and vertical positioning absolute errors of seedling picking are 1.07 mm and 1.09 mm, respectively, and the horizontal and vertical positioning absolute errors of seedling planting are 1.50 mm and 1.51 mm, respectively. The success rate of seedling picking is 97.01%, the success rate of seedling planting is 96.39%, and the qualified rate of planting is 96%. The experimental results meet the actual operation requirements. This study provides a theoretical basis and technical support for the high-efficiency coordinated operation of fully automatic transplanters. Full article

(This article belongs to the Section Agricultural Technology)

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28 pages, 2393 KB

Open AccessArticle

How the Digital Economy Reduces Agricultural Carbon Emissions: Mechanisms, Threshold Effects, and Policy Implications

by Huaijin Li, Kexin Li, Paravee Maneejuk and Jianxu Liu

Agriculture 2026, 16(4), 478; https://doi.org/10.3390/agriculture16040478 - 20 Feb 2026

Abstract

The problem of agricultural environmental pollution is increasingly serious, and carbon emissions have become an important form of pollution that must be controlled. This study aims to explore the impact mechanism and heterogeneity of the digital economy on China’s agricultural carbon emission intensity. [...] Read more.

The problem of agricultural environmental pollution is increasingly serious, and carbon emissions have become an important form of pollution that must be controlled. This study aims to explore the impact mechanism and heterogeneity of the digital economy on China’s agricultural carbon emission intensity. Based on the panel data of 30 provinces in China from 2012 to 2022, an empirical analysis was conducted using two-way fixed effect models, moderating effect models, and panel threshold models, revealing that the development of the digital economy is significantly and negatively associated with agricultural carbon emission intensity. However, the emission reduction effect is restricted by a complex moderation and threshold framework. Specifically, the improvement of human capital may lead to a decreasing trend in the emission reduction effect of the digital economy, implying the existence of a potential “efficiency rebound” effect. The regional innovation environment can significantly enhance the emission reduction effect of the digital economy, and this effect is most significant when there is both high human capital and a superior innovation environment. In addition, the emission reduction effect of the digital economy exhibits threshold characteristics and is optimal when agricultural technology progress reaches an intermediate level; an institutional environment can play an effective role at the intermediate level, but its independent emission reduction effect tends to be saturated under a highly perfect institutional environment. These findings provide new evidence for understanding the complex relationship between the digital economy and agricultural carbon emissions and provide a theoretical basis and practical guidance for the formulation of differentiated agricultural low-carbon development policies. Full article

(This article belongs to the Topic Environmental Pollution in Modern Agriculture: Causes, Effect, and Control)

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31 pages, 1791 KB

Open AccessArticle

Behavioral Factors Influencing Agro-Ecological Strategy Adoption: A UTAUT-Based Analysis of Organic Farmers in Małopolska, Poland

by Masoomeh Shemshad, Agnieszka Klimek-Kopyra, Marcin Kopyra and Ewa Szpunar-Krok

Agriculture 2026, 16(4), 477; https://doi.org/10.3390/agriculture16040477 - 20 Feb 2026

Abstract

The transition toward sustainable agriculture has increased interest in agroecological strategies (AS), which aim to reduce chemical inputs while enhancing environmental and socio-economic resilience. Despite growing policy support, adoption remains limited, suggesting that farmers’ behavioral intention (BI) alone may not fully capture the [...] Read more.

The transition toward sustainable agriculture has increased interest in agroecological strategies (AS), which aim to reduce chemical inputs while enhancing environmental and socio-economic resilience. Despite growing policy support, adoption remains limited, suggesting that farmers’ behavioral intention (BI) alone may not fully capture the complexity of agroecological uptake. This study aims to identify and validate key behavioral constructs associated with farmers’ intention to use AS, applying the Unified Theory of Acceptance and Use of Technology (UTAUT) as a conceptual and measurement framework. A cross-sectional survey was conducted among 188 farmers engaged in agroecological farming in the Małopolska region of Poland. Confirmatory factor analysis (CFA) was employed to validate the measurement model and assess the reliability and validity of four latent constructs: Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FCs). Following model refinement, 17 measurement items were retained. All constructs demonstrated strong internal consistency and convergent validity (Composite Reliability > 0.85; Average Variance Extracted > 0.70). The highest standardized factor loadings were observed for “ease of learning” within EE (λ = 0.995), “reduction of production costs” within PE (λ = 0.990), and “access to organizational support” within FC (λ = 0.985). BI exhibited a very high factor loading (BI2, λ = 0.998), indicating strong commitment among current agroecological farmers. Descriptive findings further point to limited institutional participation and extension support, highlighting the prominence of structural conditions within the validated measurement framework. The main contribution of this study lies in the empirical validation of the UTAUT-based measurement instrument for agroecological contexts and in emphasizing the salience of institutional and facilitating dimensions in relation to farmers’ BI toward agroecological transitions. Full article

(This article belongs to the Special Issue Sustainable Strategies for Enhancing Farmers' Income in Rural Development)

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17 pages, 1090 KB

Open AccessArticle

Evaluating Wheat Seed Quality: Performance, Stability, and Genetic Control Across Six Greek Environments Using Multiple Selection Designs

by Vasileios Greveniotis, Elisavet Bouloumpasi, Adriana Skendi, Dimitrios Kantas and Constantinos G. Ipsilandis

Agriculture 2026, 16(4), 476; https://doi.org/10.3390/agriculture16040476 - 19 Feb 2026

Abstract

Wheat seed quality is a key factor of end-use performance and nutritional value, yet it is strongly influenced by both genetic and environmental factors. The present study evaluated the performance, stability, and genetic control of wheat seed quality traits across six contrasting environments [...] Read more.

Wheat seed quality is a key factor of end-use performance and nutritional value, yet it is strongly influenced by both genetic and environmental factors. The present study evaluated the performance, stability, and genetic control of wheat seed quality traits across six contrasting environments in Greece, focusing on genotypes derived from three selection designs (McGinnis & Shebeski, honeycomb, and gridding) and a local landrace. The measured traits included crude protein, fat, ash, starch, crude fibre, Zeleny sedimentation, carbohydrate, soluble fraction, non-starch fraction, and moisture. A combined ANOVA revealed significant effects of genotype, environment, and their interaction on all traits. Crude protein, fat, ash, and carbohydrate were predominantly governed by genotype, while starch, Zeleny sedimentation, soluble fraction, non-starch fraction, and moisture were more influenced by environmental factors, while crude fiber showed balanced genotype × environment effects. Stability analysis identified genotypes with consistent expression of key quality traits across environments, demonstrating the relevance of stability parameters for reliable selection. Correlation analysis indicated positive associations among protein, fat, Zeleny sedimentation, and crude fiber, and negative relationships with starch, carbohydrate, soluble fraction, and non-starch fraction, revealing trade-offs among wheat seed quality components. Selection method influenced trait expression, with gridding-derived lines excelling in protein and fat, McGinnis & Shebeski lines in Zeleny sedimentation and fiber, and honeycomb-derived lines in starch, carbohydrate, soluble, and non-starch fractions. Overall, the results support the use of multi-environment evaluation and stability-based selection to improve wheat seed quality in a predictable and targeted manner. Full article

(This article belongs to the Section Seed Science and Technology)

14 pages, 3198 KB

Open AccessArticle

Rootstock-Dependent Regulation of Tomato Yield and Fruit Quality Revealed by Widely Targeted Metabolomics

by Tianyun Han, Zhihao Liang and Yuan Huang

Agriculture 2026, 16(4), 475; https://doi.org/10.3390/agriculture16040475 - 19 Feb 2026

Abstract

In tomato production, grafting enhances stress resistance, increases yield, and improves fruit quality. However, the selection of rootstock types limits its broader adoption. This study systematically evaluated the effects of grafting with 16 different rootstocks on tomato survival rate and yield. Fruits from [...] Read more.

In tomato production, grafting enhances stress resistance, increases yield, and improves fruit quality. However, the selection of rootstock types limits its broader adoption. This study systematically evaluated the effects of grafting with 16 different rootstocks on tomato survival rate and yield. Fruits from four rootstocks, Gangshi 319 self-grafted (CK), Gangshi 319 seedlings (A), Torubam (T), and Fanzhen No. 1 (F), were further selected for fruit quality analysis and broad target metabolomics. The results showed that, except for Qiezhen No. 3 (QZ3), the graft survival rates of all rootstocks exceeded 95%. Grafting with rootstock F significantly increased yield per plant and soluble solids content, whereas rootstock T significantly reduced both traits. Broad target metabolomics analysis identified 18 major metabolite categories, including lipids, ketoaldehydes and esters, and terpenoids. KEGG pathway enrichment analysis revealed that differentially accumulated metabolites between the F and T treatments were primarily enriched in pathways such as the citric acid cycle, phenylpropanoid biosynthesis, glyoxylate and dicarboxylate metabolism, flavonoid biosynthesis, cysteine and methionine metabolism, and glycerophospholipid metabolism. These findings indicate that rootstock F effectively enhances tomato fruit yield and soluble solids accumulation by coordinating primary and secondary metabolism. This study provides important metabolic level insights for the selection and application of high quality and high yield tomato rootstocks in grafting. Full article

(This article belongs to the Section Crop Genetics, Genomics and Breeding)

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17 pages, 763 KB

Open AccessArticle

Bio-Efficiency of Blue Diode Laser Treatment on Weed Seedlings and Seeds Under Controlled Conditions

by Mattie De Meester, Tim de Theije, Simon Cool, David Nuyttens, Lieven Delanote and Benny De Cauwer

Agriculture 2026, 16(4), 474; https://doi.org/10.3390/agriculture16040474 - 19 Feb 2026

Abstract

Laser radiation constitutes a promising technological advancement within the integrated weed management toolbox but is hindered by low energy use efficiency. This study investigated the efficiency of a pulsed blue diode laser for controlling small weed seedlings and seeds under controlled conditions. Dose–response [...] Read more.

Laser radiation constitutes a promising technological advancement within the integrated weed management toolbox but is hindered by low energy use efficiency. This study investigated the efficiency of a pulsed blue diode laser for controlling small weed seedlings and seeds under controlled conditions. Dose–response experiments were conducted on three grasses (Poa annua, Echinochloa crus-galli, Digitaria sanguinalis) and three dicotyledonous species (Solanum nigrum, Chenopodium album, Senecio vulgaris). For seedlings, the effects of species, growth stage (cotyledon, 2-leaf), and leaf wetness (dry, wet) were tested. For seeds, burial depth (0 mm, 2 mm) and imbibition status (non-imbibed, imbibed) were examined. Biological efficiency was assessed through plant survival, aboveground dry biomass, leaf area, and seed viability. Laser application caused significant, dose-dependent reductions in biomass accumulation and plant survival, with up to 100% mortality. Seedlings were most sensitive at the cotyledon stage and when foliage was dry, requiring up to 68 and 52% lower energy doses compared to older or wet targets, respectively. Species-specific responses were observed, with dicotyledonous species generally requiring 80 to 99% lower energy doses than grasses. Laser exposure was also effective in reducing the viability of non-imbibed, surface-exposed seeds, requiring up to 64 and 99% lower energy doses than imbibed or buried seeds, respectively. These results confirm that laser efficiency is strongly influenced by species traits, developmental stage, surface moisture, and seed water status. Optimising and tailoring laser parameters to these factors enhances weed control efficacy while maximising energy efficiency, improving the performance and sustainability of laser-based weeding. Full article

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

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20 pages, 3733 KB

Open AccessArticle

Dynamic Response Analysis and Multi-Objective Optimization of a Potato–Soil Separation Conveyor Based on DEM–MBD Coupling and Field Validation

by Yongfei Pan, Jian Zhang, Ang Zhao, Shiting Lv, Wanru Liu and Ranbing Yang

Agriculture 2026, 16(4), 473; https://doi.org/10.3390/agriculture16040473 - 19 Feb 2026

Abstract

Potato combine harvesters often face the challenge of balancing efficient potato–soil separation with minimizing tuber mechanical damage, which significantly affects harvest quality and economic returns. To address this issue, a dual-vibration potato–soil separation conveyor was designed based on agronomic planting parameters and soil [...] Read more.

Potato combine harvesters often face the challenge of balancing efficient potato–soil separation with minimizing tuber mechanical damage, which significantly affects harvest quality and economic returns. To address this issue, a dual-vibration potato–soil separation conveyor was designed based on agronomic planting parameters and soil physical characteristics. A high-fidelity DEM-MBD coupling simulation model was developed to analyze soil clod breakage behavior and potato collision-induced jumping dynamics, and to identify key operational factors influencing separation performance. The porosity was verified using computer vision combined with CT technology to ensure the model’s fidelity. Single-factor simulations and a central composite design (CCD) response surface experiment were conducted using potato damage rate and soil removal efficiency as evaluation indices. The results showed that the inclination angle α, conveying line speed V_f, and vibration frequency f were the dominant factors affecting separation efficiency and tuber integrity. Multi-objective optimization determined optimal operating parameters of α = 18.51°, V_f = 1.995 km·h⁻¹, and f = 6.22 Hz, under which soil removal efficiency reached 98.43% and the minimum damage rate was 1.60%. Field experiments using a 4U-1000 combine harvester verified the simulation results, with an average soil removal efficiency of 97.8% and an average damage rate of 1.62%. These findings confirm the accuracy of the DEM-MBD simulation model and provide theoretical guidance for optimizing separation devices in large-scale potato harvesting equipment. Full article

(This article belongs to the Section Agricultural Technology)

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25 pages, 995 KB

Open AccessReview

Research Progress and Application Advances of Controlled-Release Nitrogen Fertilizers in Crop Production

by Kaiwen Zhang, Lingxiao Zhu, Hongchun Sun, Yongjiang Zhang, Ke Zhang, Zhiying Bai, Zhanbiao Wang, Liantao Liu and Cundong Li

Agriculture 2026, 16(4), 472; https://doi.org/10.3390/agriculture16040472 - 19 Feb 2026

Abstract

Against the backdrop of global population and food security challenges, improving crop nitrogen use efficiency (NUE) is essential for sustainable agriculture. Conventional nitrogen fertilizers suffer from low utilization rates and significant environmental pollution. In contrast, controlled-release nitrogen fertilizers (CRNFs) synchronize nutrient supply with [...] Read more.

Against the backdrop of global population and food security challenges, improving crop nitrogen use efficiency (NUE) is essential for sustainable agriculture. Conventional nitrogen fertilizers suffer from low utilization rates and significant environmental pollution. In contrast, controlled-release nitrogen fertilizers (CRNFs) synchronize nutrient supply with crop demand, offering significant advantages in enhancing yield, efficiency, and environmental sustainability. This review systematically outlines the developmental types of CRNFs, with a focus on their agronomic and ecological benefits. Key quantitative outcomes include yield increases of 3.0–15.3% in winter wheat, 12.38–22.67% in cotton, and maintained or improved maize yield even with a 20% reduction in nitrogen input. CRNFs also reduce ammonia volatilization by 20–43% in paddy fields. The review further elucidates the synergistic mechanisms through which CRNFs optimize root growth, enhance photosynthetic efficiency, and improve NUE. Major challenges such as high costs, release control precision, and coating material sustainability are critically assessed. Future directions include developing biodegradable coatings, smart fertilization systems, and stronger policy frameworks to facilitate broader adoption. This work provides a comprehensive theoretical and practical foundation for advancing the efficient and sustainable use of CRNFs in modern crop production. Full article

(This article belongs to the Section Crop Production)

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27 pages, 4560 KB

Open AccessArticle

Digital Village Construction and High-Quality Development of Grain Production Under the Background of Population Shrinkage: Evidence from China’s Major Grain-Producing Areas

by Jinrui Chang, Jiaxuan Yu, Jianbo Liu and Huiming Jiang

Agriculture 2026, 16(4), 470; https://doi.org/10.3390/agriculture16040470 - 19 Feb 2026

Abstract

Digital village (DV) construction is the core driving force for high-quality development of the rural economy, and is a key strategy for achieving coordinated progress in urban development and rural revitalization. This study empirically analyzes the direct effect and enhancement mechanisms of DV [...] Read more.

Digital village (DV) construction is the core driving force for high-quality development of the rural economy, and is a key strategy for achieving coordinated progress in urban development and rural revitalization. This study empirically analyzes the direct effect and enhancement mechanisms of DV construction on the high-quality development of grain production (HDGP) by panel data from 170 cities in China’s major grain-producing areas spanning 2013–2022; this study uses the CRITIC-EWM combined evaluation, two-way fixed effects, mediating effect and moderating effect model. The results show that: (1) HDGP appears more sluggish compared to the orderly growth of DV construction, but the level of DV construction and the level of HDGP are mismatched in spatial distribution. (2) DV construction has a significant promoting effect on HDGP, and the digitalization of economy and digitalization of life play more efficiently motivating role in HDGP. (3) This promoting effect is stronger in the population-shrinking regions than in the non-population-shrinking regions. (4) Approximately 8% of the promoting impact of DV construction on the HDGP is achieved indirectly through the scale of new agricultural business entities. (5) Government innovation planning exerts a significant enhancing moderating effect on the influence of DV construction on HDGP. Full article

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

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