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Agriculture
Volume 16
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Agriculture, Volume 16, Issue 2 (January-2 2026) – 150 articles

Cover Story (view full-size image): This figure illustrates the integrated, systems-based framework for enhancing environmental sustainability in ruminant production, as detailed in our comprehensive review. It highlights a holistic approach centered on the cow, where four key pillars work in synergy. These include advanced genetic and genomic selection, optimization of the rumen microbiome, innovative nutritional strategies, and the deployment of precision management technologies. This integrated model aims to achieve significant reductions in greenhouse gas emissions (e.g., CH4, N2O, CO2) and improve overall resource efficiency, including land, water, and feed, thereby promoting a more sustainable and economically viable future for the livestock industry. View this paper
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33 pages, 23667 KB  
Article
Full-Wave Optical Modeling of Leaf Internal Light Scattering for Early-Stage Fungal Disease Detection
by Da-Young Lee and Dong-Yeop Na
Agriculture 2026, 16(2), 286; https://doi.org/10.3390/agriculture16020286 - 22 Jan 2026
Viewed by 210
Abstract
Modifications in leaf architecture disrupt optical properties and internal light-scattering dynamics. Accurate modeling of leaf-scale light scattering is therefore essential not only for understanding how disease affects the availability of light for chlorophyll absorption, but also for evaluating its potential as an early [...] Read more.
Modifications in leaf architecture disrupt optical properties and internal light-scattering dynamics. Accurate modeling of leaf-scale light scattering is therefore essential not only for understanding how disease affects the availability of light for chlorophyll absorption, but also for evaluating its potential as an early optical marker for plant disease detection prior to visible symptom development. Conventional ray-tracing and radiative-transfer models rely on high-frequency approximations and thus fail to capture diffraction and coherent multiple-scattering effects when internal leaf structures are comparable to optical wavelengths. To overcome these limitations, we present a GPU-accelerated finite-difference time-domain (FDTD) framework for full-wave simulation of light propagation within plant leaves, using anatomically realistic dicot and monocot leaf cross-section geometries. Microscopic images acquired from publicly available sources were segmented into distinct tissue regions and assigned wavelength-dependent complex refractive indices to construct realistic electromagnetic models. The proposed FDTD framework successfully reproduced characteristic reflectance and transmittance spectra of healthy leaves across the visible and near-infrared (NIR) ranges. Quantitative agreement between the FDTD-computed spectral reflectance and transmittance and those predicted by the reference PROSPECT leaf optical model was evaluated using Lin’s concordance correlation coefficient. Higher concordance was observed for dicot leaves (Cb=0.90) than for monocot leaves (Cb=0.79), indicating a stronger agreement for anatomically complex dicot structures. Furthermore, simulations mimicking an early-stage fungal infection in a dicot leaf—modeled by the geometric introduction of melanized hyphae penetrating the cuticle and upper epidermis—revealed a pronounced reduction in visible green reflectance and a strong suppression of the NIR reflectance plateau. These trends are consistent with experimental observations reported in previous studies. Overall, this proof-of-concept study represents the first full-wave FDTD-based optical modeling of internal light scattering in plant leaves. The proposed framework enables direct electromagnetic analysis of pre- and post-penetration light-scattering dynamics during early fungal infection and establishes a foundation for exploiting leaf-scale light scattering as a next-generation, pre-symptomatic diagnostic indicator for plant fungal diseases. Full article
(This article belongs to the Special Issue Exploring Sustainable Strategies That Control Fungal Plant Diseases)
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14 pages, 1136 KB  
Article
Microclimate Effects on Quality and Polyphenolic Composition of Once-Neglected Autochthonous Grape Varieties in Mountain Vineyards of Asturias (Northern Spain)
by Susana Boso, José-Ignacio Cuevas, José-Luis Santiago, Pilar Gago and María-Carmen Martínez
Agriculture 2026, 16(2), 285; https://doi.org/10.3390/agriculture16020285 - 22 Jan 2026
Viewed by 162
Abstract
In the southwestern region of Asturias (Northern Spain) lies one of the few mountainous viticulture areas in the world, representing only 5% of global viticulture. The complex topography and differences in altitude, slope, and orientation of mountainous viticulture areas create highly variable microclimates [...] Read more.
In the southwestern region of Asturias (Northern Spain) lies one of the few mountainous viticulture areas in the world, representing only 5% of global viticulture. The complex topography and differences in altitude, slope, and orientation of mountainous viticulture areas create highly variable microclimates even among nearby plots, with distinct mean temperatures, relative humidity, and solar radiation. These factors strongly influence grape and wine quality, as well as polyphenol concentration. Several production parameters and basic chemical characteristics of must were analyzed over multiple years, along with polyphenol content, in grapes from the same clones of Albarín Blanco and Verdejo Negro (autochthonous genotypes of this viticultural area), grown in geographically close vineyards with different topographies and microclimates. The results revealed significant differences in all analyzed parameters. Both varieties showed polyphenol concentrations slightly higher than those reported in the scientific literature, which may be related to the typical conditions of mountain viticulture or intrinsic genetic factors of these varieties. The best grape and must quality, regardless of variety, was obtained in plots located in sunny, well-ventilated areas with steep slopes and low-fertility soils. These plots exhibited higher potential alcohol content and greater concentrations of anthocyanins, hydrocarbons, and total polyphenols. When comparing varieties, Verdejo Negro showed the highest levels of anthocyanins, flavonols, and total polyphenols, whereas Albarín Blanco exhibited the highest concentrations of total phenolics and hydrocarbons. Full article
(This article belongs to the Section Crop Production)
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13 pages, 1843 KB  
Article
Recruitment of Predator Cheilomenes sexmaculata by Active Volatiles from Lemon Plants Infested with Frankliniella intonsa
by Jie Zhang, Peng Huang, Rongxin Yi, Shuhan Huang, Jinai Yao and Deyi Yu
Agriculture 2026, 16(2), 284; https://doi.org/10.3390/agriculture16020284 - 22 Jan 2026
Viewed by 171
Abstract
The flower thrips, Frankliniella intonsa, is a major pest threatening citrus production. However, chemical control remains the primary management measure, which poses significant risks on ecosystems. Hence, it is urgent to prioritize more eco-friendly measures to efficiently control thrips. The ladybird, Cheilomenes [...] Read more.
The flower thrips, Frankliniella intonsa, is a major pest threatening citrus production. However, chemical control remains the primary management measure, which poses significant risks on ecosystems. Hence, it is urgent to prioritize more eco-friendly measures to efficiently control thrips. The ladybird, Cheilomenes sexmaculata, is a predominant natural enemy in the local citrus agroecosystem and could play a key role in suppressing thrips in agricultural landscapes. Although some ladybirds are known to be attracted to herbivore-induced plant volatiles (HIPVs), little is known about the specific attractive compounds and the effect of F. intonsa-infested lemon plants on the predatory response of C. sexmaculata. Here, we studied the chemical interaction between F. intonsa, C. sexmaculata, and lemon plants. In dual-choice behavioral assays, C. sexmaculata adults significantly preferred volatiles from F. intonsa-infested plants over those from healthy plants. Volatile collection and analysis identified six monoterpenes, five of which (α-pinene, β-pinene, sabinene, myrcene, and eucalyptol) individually attracted C. sexmaculata at specific concentrations. Moreover, a blend of these five compounds, formulated at their optimal attractive concentrations, elicited a stronger attraction in C. sexmaculata than individual compounds, indicating a synergistic interaction. This attractive blend can thus be used to develop a kairomone-based lure to enhance biological control and to complement existing integrated pest management approaches against thrips in lemon agroecosystems. Full article
(This article belongs to the Special Issue Sustainable Use of Pesticides—2nd Edition)
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23 pages, 11930 KB  
Article
DepthCL-Seg: Dual-Stream Feature Fusion for Green Fruit Instance Segmentation Based on Monocular Depth
by Yuelong Shang, Guodong Sun and Haiyan Zhang
Agriculture 2026, 16(2), 283; https://doi.org/10.3390/agriculture16020283 - 22 Jan 2026
Viewed by 193
Abstract
Accurate segmentation of target fruits is essential for automated field management. However, the challenge lies in the fact that many fruits remain green for extended periods, closely resembling the colors of leaves and branches, thus making accurate identification difficult. While current multi-modal methods [...] Read more.
Accurate segmentation of target fruits is essential for automated field management. However, the challenge lies in the fact that many fruits remain green for extended periods, closely resembling the colors of leaves and branches, thus making accurate identification difficult. While current multi-modal methods that utilize depth information can mitigate this problem, the high cost of equipment for acquiring such data limits the practical implementation of these techniques. To tackle this challenge, we introduce the monocular depth estimation technique Depth Anything V2 to fruit segmentation tasks, proposing a novel monocular depth-assisted instance segmentation framework, DepthCL-Seg. Within DepthCL-Seg, the Cross-modal Complementary Fusion (CCF) module effectively fuses RGB and depth information to enhance feature representation in low-contrast target regions. Additionally, a low-contrast adaptive refinement (LAR) module is designed to improve discrimination of easily confusable boundary pixels. Experimental results show that DepthCL-Seg achieves mAP scores of 74.2% and 86.0% on our self-constructed green fig and green peach datasets, respectively. These scores surpass the classical Mask R-CNN by 7.5% and 4.4%, and significantly outperform current mainstream methods. This framework provides novel technical support for automated management in fruit cultivation. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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16 pages, 2031 KB  
Article
Semitransparent Perovskite-Emulating Photovoltaic Covers for Lettuce Production
by Miriam Distefano, Giovanni Avola, Alessandra Alberti, Salvatore Valastro, Gaetano Calogero, Giovanni Mannino and Ezio Riggi
Agriculture 2026, 16(2), 282; https://doi.org/10.3390/agriculture16020282 - 22 Jan 2026
Viewed by 144
Abstract
Semitransparent perovskite photovoltaic (sPV) covers offer an attractive route for agrivoltaics, but their spectrally selective transmittance must be validated on plants cultivated under panel or in simulated conditions. Here, an AVA–MAPI perovskite module transmission profile was replicated using a programmable multi-channel LED platform [...] Read more.
Semitransparent perovskite photovoltaic (sPV) covers offer an attractive route for agrivoltaics, but their spectrally selective transmittance must be validated on plants cultivated under panel or in simulated conditions. Here, an AVA–MAPI perovskite module transmission profile was replicated using a programmable multi-channel LED platform and compared with a Reference McCree-adapted LED spectrum at identical photon flux density. Two lettuce cultivars (Lactuca sativa L.; ‘Canasta’ and ‘Trocadero’) were grown hydroponically in a light-sealed phytotron for 30 days (300 μmol m−2 s−1; 16/8 h photoperiod) under uniform temperature and humidity. Leaf gas exchange was quantified by fitting photosynthetic light-response curves, and plant performance was concurrently evaluated through growth metrics, biomass partitioning, and pigment-related traits (chlorophyll a/b, total carotenoids). The perovskite-emulated spectrum measurably reshaped net CO2 assimilation across the PAR domain—yielding higher AN at selected irradiances in post hoc contrasts—yet these physiological shifts did not translate into differences in leaf area, shoot or root biomass, or pigment concentrations—demonstrating spectral plasticity and agricultural compatibility of field-characterized perovskite transmission spectra. Overall, perovskite-emulated light sustained agronomically equivalent lettuce performance under moderate irradiance, supporting the feasibility of semitransparent perovskite PV covers, while underscoring the need for validation under natural sunlight. Full article
(This article belongs to the Section Agricultural Systems and Management)
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21 pages, 1492 KB  
Article
Soil Organic Carbon Dynamics in Contrasting Soil Types Under Short-Rotation Woody Crop Production
by Aistė Masevičienė and Lina Žičkienė
Agriculture 2026, 16(2), 281; https://doi.org/10.3390/agriculture16020281 - 22 Jan 2026
Viewed by 162
Abstract
Intensive agriculture, ecosystem degradation, and declining soil quality highlight the urgent need for sustainable land use strategies. The cultivation of short-rotation woody crops (SRC), combined with fertilization using sewage sludge digestate (SSD), offers a promising approach to recycle nutrient-rich waste and promote soil [...] Read more.
Intensive agriculture, ecosystem degradation, and declining soil quality highlight the urgent need for sustainable land use strategies. The cultivation of short-rotation woody crops (SRC), combined with fertilization using sewage sludge digestate (SSD), offers a promising approach to recycle nutrient-rich waste and promote soil organic carbon (SOC) accumulation. This study evaluated SOC concentrations, stocks and their spatial distribution in the 0–20 cm soil layer under SRC cultivation, with and without SSD fertilization, across contrasting soil types in Eastern Lithuania. The investigated soils included mineral (Luvisols (LV), Retisols (RT), Planosols (PL), Arenosols (AR)), organo-mineral (Gleysols (GL)), and organic soils (Histosols (HS)), representing textures from sand to peat and classified according to the World Reference Base for Soil Resources (WRB). Part I assessed baseline SOC variability in unproductive areas planted with hybrid poplars (Populus spp.) and hybrid aspen (Populus tremula × P. tremuloides) up to 20 years old. Part II examined SOC changes in three SRC fields of different ages (3–10 years), including unfertilized and SSD-fertilized stands. SOC concentrations increased consistently from mineral (1.14–1.80%) to organo-mineral (2.13–3.20%) and organic soils (6.37–17.53%). Heavier-textured soils accumulated more SOC than lighter soils, showing a strong positive correlation between SOC and soil texture (r = 0.82, p ≤ 0.01). SRC cultivation increased SOC across all soil types, while SSD fertilization further enhanced accumulation, with fertilized fields showing SOC increases of 0.50–1.07 percentage points and carbon stocks by 18.8–41.7 t ha−1, compared with smaller increases in unfertilized fields. Spatial visualization of SOC further highlighted long-term accumulation patterns across soil types, confirming the trends observed under SRC cultivation and SSD fertilization. Full article
(This article belongs to the Section Agricultural Soils)
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21 pages, 10584 KB  
Article
Multi-Temporal Point Cloud Alignment for Accurate Height Estimation of Field-Grown Leafy Vegetables
by Qian Wang, Kai Yuan, Zuoxi Zhao, Yangfan Luo and Yuanqing Shui
Agriculture 2026, 16(2), 280; https://doi.org/10.3390/agriculture16020280 - 22 Jan 2026
Viewed by 172
Abstract
Accurate measurement of plant height in leafy vegetables is challenging due to their short stature, high planting density, and severe canopy occlusion during later growth stages. These factors often limit the reliability of single-plant monitoring across the full growth cycle in open-field environments. [...] Read more.
Accurate measurement of plant height in leafy vegetables is challenging due to their short stature, high planting density, and severe canopy occlusion during later growth stages. These factors often limit the reliability of single-plant monitoring across the full growth cycle in open-field environments. To address this, we propose a multi-temporal point cloud alignment method for accurate plant height measurement, focusing on Choy Sum (Brassica rapa var. parachinensis). The method estimates plant height by calculating the vertical distance between the canopy and the ground. Multi-temporal point cloud maps are reconstructed using an enhanced Oriented FAST and Rotated BRIEF–Simultaneous Localization and Mapping (ORB-SLAM3) algorithm. A fixed checkerboard calibration board, leveled using a spirit level, ensures proper vertical alignment of the Z-axis and unifies coordinate systems across growth stages. Ground and plant points are separated using the Excess Green (ExG) index. During early growth stages, when the soil is minimally occluded, ground point clouds are extracted and used to construct a high-precision reference ground model through Cloth Simulation Filtering (CSF) and Kriging interpolation, compensating for canopy occlusion and noise. In later growth stages, plant point cloud data are spatially aligned with this reconstructed ground surface. Individual plants are identified using an improved Euclidean clustering algorithm, and consistent measurement regions are defined. Within each region, a ground plane is fitted using the Random Sample Consensus (RANSAC) algorithm to ensure alignment with the X–Y plane. Plant height is then determined by the elevation difference between the canopy and the interpolated ground surface. Experimental results show mean absolute errors (MAEs) of 7.19 mm and 18.45 mm for early and late growth stages, respectively, with coefficients of determination (R2) exceeding 0.85. These findings demonstrate that the proposed method provides reliable and continuous plant height monitoring across the full growth cycle, offering a robust solution for high-throughput phenotyping of leafy vegetables in field environments. Full article
(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)
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19 pages, 3108 KB  
Article
Enhancing Broiler Weight Prediction via Preprocessed Kernel Density Estimation
by Sangmin Yoo, Yumi Oh and Juwhan Song
Agriculture 2026, 16(2), 279; https://doi.org/10.3390/agriculture16020279 - 22 Jan 2026
Viewed by 139
Abstract
Accurate broiler weight estimation in commercial farms is hindered by noisy scale data and multi-broiler occupancy. To address this challenge, we propose a KDE-based framework enhanced with systematic preprocessing, including coefficient of variation (CV), relative change (ROC), and absolute change (AC). In this [...] Read more.
Accurate broiler weight estimation in commercial farms is hindered by noisy scale data and multi-broiler occupancy. To address this challenge, we propose a KDE-based framework enhanced with systematic preprocessing, including coefficient of variation (CV), relative change (ROC), and absolute change (AC). In this study, kernel density estimation (KDE) is employed not as a predictive model, but as a distributional tool to robustly extract representative flock weight from noisy, high-frequency scale measurements under commercial farm conditions. In the absence of physical ground-truth, our evaluation focused on the framework’s ability to consistently detect the single, representative peak in the KDE distribution. Weekly thresholds were empirically optimized for the preprocessing filters. Results show that the combined ROC + AC method consistently produced unimodal peak distributions and improved the Peak Detection Rate (PDR) from 91.2% (raw data) to 97.9%. Single-Entity Filtering, assisted by cameras, further mitigated density distortions caused by prolonged occupancy, while CV-only and ROC-only filtering yielded less stable representative values. These findings demonstrate that rigorous preprocessing is essential for reliable KDE-based weight estimation under real-world farm conditions. The proposed framework not only improves data quality and stabilizes distributions but also provides a practical foundation for real-time monitoring and AI-driven precision livestock farming models. Full article
(This article belongs to the Section Farm Animal Production)
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21 pages, 1059 KB  
Article
How Does the Digital Village Construction Affect the Urban–Rural Income Gap: Empirical Evidence from China
by Jin Xu and Hui Liu
Agriculture 2026, 16(2), 278; https://doi.org/10.3390/agriculture16020278 - 22 Jan 2026
Viewed by 173
Abstract
Digital rural construction (DRC), as a crucial intersection of the rural revitalization strategy and the construction of Digital China, is a key path to addressing the imbalance and inadequacy in the urban–rural income gap (URIG). Based on provincial panel data from 2011 to [...] Read more.
Digital rural construction (DRC), as a crucial intersection of the rural revitalization strategy and the construction of Digital China, is a key path to addressing the imbalance and inadequacy in the urban–rural income gap (URIG). Based on provincial panel data from 2011 to 2023, this paper systematically examines the relationship and mechanism of action between the two using an econometric model. This study finds that DRC significantly reduces the URIG overall, and this effect is achieved through increasing urbanization levels, accelerating employment, and promoting social consumption. Spatial effect tests indicate that DRC has a spatial spillover effect; construction in one province reduces the URIG in neighboring provinces. Further research shows that, against the backdrop of human capital level acting as a threshold variable, the effect of DRC on the URIG exhibits an inverted “U”-shaped characteristic, first increasing and then decreasing. Therefore, this paper proposes countermeasures and suggestions, including constructing a digital-enabled urban–rural integration mechanism, promoting cross-regional coordinated development of DRC, and implementing a tiered and categorized digital literacy improvement project. Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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27 pages, 23394 KB  
Article
YOLO-MSRF: A Multimodal Segmentation and Refinement Framework for Tomato Fruit Detection and Segmentation with Count and Size Estimation Under Complex Illumination
by Ao Li, Chunrui Wang, Aichen Wang, Jianpeng Sun, Fengwei Gu and Tianxue Zhang
Agriculture 2026, 16(2), 277; https://doi.org/10.3390/agriculture16020277 - 22 Jan 2026
Viewed by 165
Abstract
Segmentation of tomato fruits under complex lighting conditions remains technically challenging, especially in low illumination or overexposure, where RGB-only methods often suffer from blurred boundaries and missed small or occluded instances, and simple multimodal fusion cannot fully exploit complementary cues. To address these [...] Read more.
Segmentation of tomato fruits under complex lighting conditions remains technically challenging, especially in low illumination or overexposure, where RGB-only methods often suffer from blurred boundaries and missed small or occluded instances, and simple multimodal fusion cannot fully exploit complementary cues. To address these gaps, we propose YOLO-MSRF, a lightweight RGB–NIR multimodal segmentation and refinement framework for robust tomato perception in facility agriculture. Firstly, we propose a dual-branch multimodal backbone, introduce Cross-Modality Difference Complement Fusion (C-MDCF) for difference-based complementary RGB–NIR fusion, and design C2f-DCB to reduce computation while strengthening feature extraction. Furthermore, we develop a cross-scale attention fusion network and introduce the proposed MS-CPAM to jointly model multi-scale channel and position cues, strengthening fine-grained detail representation and spatial context aggregation for small and occluded tomatoes. Finally, we design the Multi-Scale Fusion and Semantic Refinement Network, MSF-SRNet, which combines the Scale-Concatenate Fusion Module (Scale-Concat) fusion with SDI-based cross-layer detail injection to progressively align and refine multi-scale features, improving representation quality and segmentation accuracy. Extensive experiments show that YOLO-MSRF achieves substantial gains under weak and low-light conditions, where RGB-only models are most prone to boundary degradation and missed instances, and it still delivers consistent improvements on the mixed four-light validation set, increasing mAP0.5 by 2.3 points, mAP0.5–0.95 by 2.4 points, and mIoU by 3.60 points while maintaining real-time inference at 105.07 FPS. The proposed system further supports counting, size estimation, and maturity analysis of harvestable tomatoes, and can be integrated with depth sensing and yield estimation to enable real-time yield prediction in practical greenhouse operations. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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16 pages, 2218 KB  
Article
Spatial Metabolomics Reveals the Biochemical Basis of Stipe Textural Gradient in Flammulina filiformis
by Xueqin Shu, Qian Dong, Qian Zhang, Jie Zhou, Chenchen Meng, Shilin Zhang, Sijun Long, Xun Liu, Bo Wang and Weihong Peng
Agriculture 2026, 16(2), 276; https://doi.org/10.3390/agriculture16020276 - 22 Jan 2026
Viewed by 129
Abstract
Flammulina filiformis is a widely cultivated edible mushroom valued for its taste and nutrition. However, its stipe often develops a fibrous and stringy texture that unpleasantly lodges between teeth during chewing. Texture analysis confirmed a distinct toughness gradient, with the upper stipe being [...] Read more.
Flammulina filiformis is a widely cultivated edible mushroom valued for its taste and nutrition. However, its stipe often develops a fibrous and stringy texture that unpleasantly lodges between teeth during chewing. Texture analysis confirmed a distinct toughness gradient, with the upper stipe being more brittle and less tough than the lower part. UHPLC-MS/MS-based metabolomics of these regions identified 953 metabolites, predominantly spanning lipids and lipid-like molecules, organic acids and derivatives, and nucleosides, nucleotides, and analogues. Comparative analysis revealed that the tender upper stipe was characterized by a widespread downregulation of primary metabolites, including severe depletion of key signaling molecules (cAMP, cGMP) and amino acids such as L-tryptophan. In contrast, the tough lower stipe was enriched with metabolites indicative of an oxidative environment, notably a broad spectrum of oxidized lipids and phenolic compounds. KEGG pathway analysis attributed this dichotomy to distinct metabolic programs. While the upper stipe exhibited downregulation in tryptophan and purine metabolism, the lower stipe was enriched for pathways associated with redox homeostasis and lipid peroxidation, including glutathione metabolism and lipid peroxidation. The co-accumulation of oxidized lipids and phenolics suggests a potential mechanism for oxidation-driven tissue fortification. This study reveals a spatially programmed metabolic basis for the textural differentiation in F. filiformis stipes, providing a framework for understanding tissue development and highlighting potential regulatory targets for breeding varieties with improved eating quality. Full article
(This article belongs to the Special Issue Edible and Medicinal Mushrooms: Molecular Biology, Cultivation, Active Compounds, Preservation and Processing)
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20 pages, 5998 KB  
Article
Soil Properties and Aging Processes Regulate Cr(VI) Toxicity to Caenorhabditis elegans
by Xiang Ao, Xiuli Dang, Long Zhao, Caiting Mai, Mengmeng Bao, Fengzhuo Geng, Roland Bol and Iseult Lynch
Agriculture 2026, 16(2), 275; https://doi.org/10.3390/agriculture16020275 - 21 Jan 2026
Viewed by 172
Abstract
Chromium (Cr) is a highly toxic heavy metal, yet its effects on soil invertebrates—particularly Caenorhabditis elegans (C. elegans)—remain insufficiently understood, especially regarding how soil properties and Cr speciation change regulate its bioavailability and toxicity. In this study, the toxicity of Cr(VI) [...] Read more.
Chromium (Cr) is a highly toxic heavy metal, yet its effects on soil invertebrates—particularly Caenorhabditis elegans (C. elegans)—remain insufficiently understood, especially regarding how soil properties and Cr speciation change regulate its bioavailability and toxicity. In this study, the toxicity of Cr(VI) to the growth, fertility, and reproduction of C. elegans was assessed in six representative agricultural soils following 7, 60, and 120 days of spiked soil aging, following ISO 10872 guidelines. Substantial differences in toxicity were observed among soils after 7 days of aging, with toxicity ranking from low to high as black soil < yellowish-red soil < red soil < yellow–brown soil < fluvo-aquic soil < purple soil. After 60 days of aging, Cr(VI) toxicity decreased markedly, with EC50 values for growth, fertility, and reproduction increasing by 1.04–2.32, 1.04–2.34, and 1.40–2.20 times, respectively. Organic matter (OM) and amorphous aluminum oxides (AlAO) were identified as the principal soil properties that were significantly correlated with Cr(VI) toxicity and were useful for explaining and estimating toxicity thresholds within the range of soils examined in this study. In addition, the magnitude of the aging effect showed significant positive correlations with both amorphous aluminum oxides (AlAO) and total aluminum (Altotal), suggesting that Al-bearing minerals may contribute to the time-dependent immobilization of Cr(VI) under the experimental conditions of this study. These findings expand the ecotoxicological database for chromium, improve the prediction of toxicity thresholds under diverse soil conditions, and provide a scientific basis for refining soil environmental quality standards and developing targeted management strategies for Cr-contaminated agricultural soils. Full article
(This article belongs to the Section Agricultural Soils)
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18 pages, 1264 KB  
Article
Milk Quality Dynamics in Romanian Black Spotted and Romanian Spotted Cattle Breeds Under Heat Stress
by Gabriela Amariții (Pădurariu), Claudia Pânzaru and Vasile Maciuc
Agriculture 2026, 16(2), 274; https://doi.org/10.3390/agriculture16020274 - 21 Jan 2026
Viewed by 160
Abstract
Milk production and quality are increasingly affected worldwide by rising ambient temperatures associated with climate change, with heat stress (HS) representing one of the major environmental challenges for dairy cattle. HS alters physiological and metabolic processes, leading to significant changes in milk composition, [...] Read more.
Milk production and quality are increasingly affected worldwide by rising ambient temperatures associated with climate change, with heat stress (HS) representing one of the major environmental challenges for dairy cattle. HS alters physiological and metabolic processes, leading to significant changes in milk composition, particularly in regions exposed to prolonged summer heat. The Temperature–Humidity Index (THI) is widely used to assess the degree of thermal discomfort and its impact on dairy performance. This study investigated the effects of heat stress on milk quality parameters in a dairy herd managed under identical conditions, comprising Romanian Black Spotted (RBS, Holstein strain) and Romanian Spotted (RS, Simmental strain) cows. Descriptive statistics were performed using the SAVC for Windows program, while differences between means were evaluated using the t-test in GraphPad Prism 9. Milk quality traits were significantly affected when THI values exceeded 73, with a consistent decline observed from early summer onwards. In the RBS breed, milk protein content decreased significantly compared with spring values, reaching 3.25% (p < 0.0001) in 2023 and 3.35% (p < 0.01) in 2024. Similar trends were recorded in the RS breed, with minimum protein values of 3.10% (p < 0.0001) and 3.19% (p < 0.0001). Fat content, casein concentration, and milk urea levels also showed highly significant HS-related changes (p < 0.0001). Overall, heat stress negatively affected milk quality, while the RS breed appears less affected under the studied conditions than the RBS breed. Full article
(This article belongs to the Special Issue Quality Assessment and Processing of Farm Animal Products)
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14 pages, 487 KB  
Article
A Life Cycle Costing of a Composting Facility for Agricultural Waste of Plant and Animal Origin in Southeastern Spain
by José García García, Begoña García Castellanos, Raúl Moral Herrero, Francisco Javier Andreu-Rodríguez and Ana García-Rández
Agriculture 2026, 16(2), 273; https://doi.org/10.3390/agriculture16020273 - 21 Jan 2026
Viewed by 183
Abstract
This study is an economic evaluation of a composting facility in southeastern Spain (applying Life Cycle Costing), a key region in European horticulture with a significant availability of agricultural biomass. Composting helps reduce dependence on inorganic fertilizers, aligning with European policies that promote [...] Read more.
This study is an economic evaluation of a composting facility in southeastern Spain (applying Life Cycle Costing), a key region in European horticulture with a significant availability of agricultural biomass. Composting helps reduce dependence on inorganic fertilizers, aligning with European policies that promote the transition toward organic fertilization practices. In addition, compost enhances soil health, increases soil organic carbon, and supports climate change mitigation. Despite its agronomic and environmental benefits, and the large availability of biomass in this region, there is a notable lack of literature addressing the economic costs of composting, which is the first step in assessing the sustainability of a production process. The proposed facility (production: 9000 tonnes of compost per year) utilizes pruning residues and manure to produce high-quality organic amendments. The analysis includes infrastructure, equipment, and every operational input. Likewise, the analysis also provides socio-economic indicators such as employment generation and contribution to the regional economy. Three scenarios were evaluated based on the pruning–shredding location: at the plant, at the farm with mobile equipment, and at the farm with conventional machinery. The most cost-effective option was shredding at the farm using mobile equipment, reducing the unit cost to EUR 65.19 per tonne due to the transport of a smaller volume of prunings and, therefore, lower fuel consumption. The plant also demonstrates high productivity per square metre and generates stable employment in rural areas. Overall, the findings highlight composting as a viable and competitive strategy within circular and low-carbon agricultural systems. Full article
(This article belongs to the Special Issue Economic and Environmental Assessment of Optimizing Management in Orchards)
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25 pages, 2891 KB  
Article
Automated Measurement of Sheep Body Dimensions via Fusion of YOLOv12n-Seg-SSM and 3D Point Clouds
by Xiaona Zhao, Xifeng Liu, Zihao Gao, Xinran Liang, Yanjun Yuan, Yangfan Bai, Zhimin Zhang, Fuzhong Li and Wuping Zhang
Agriculture 2026, 16(2), 272; https://doi.org/10.3390/agriculture16020272 - 21 Jan 2026
Viewed by 180
Abstract
Accurate measurement of sheep body dimensions is fundamental for growth monitoring and breeding management. To address the limited segmentation accuracy and the trade-off between lightweight design and precision in existing non-contact measurement methods, this study proposes an improved model, YOLOv12n-Seg-SSM, for the automatic [...] Read more.
Accurate measurement of sheep body dimensions is fundamental for growth monitoring and breeding management. To address the limited segmentation accuracy and the trade-off between lightweight design and precision in existing non-contact measurement methods, this study proposes an improved model, YOLOv12n-Seg-SSM, for the automatic measurement of body height, body length, and chest circumference from side-view images of sheep. The model employs a synergistic strategy that combines semantic segmentation with 3D point cloud geometric fitting. It incorporates the SegLinearSimAM feature enhancement module, the SEAttention channel optimization module, and the ENMPDIoU loss function to improve measurement robustness under complex backgrounds and occlusions. After segmentation, valid RGB-D point clouds are generated through depth completion and point cloud filtering, enabling 3D computation of key body measurements. Experimental results demonstrate that the improved model outperforms the baseline YOLOv12n-Seg: the mAP@0.5 for segmentation reaches 94.20%, the mAP@0.5 for detection reaches 95.00% (improvements of 0.5 and 1.3 percentage points, respectively), and the recall increases to 99.00%. In validation tests on 43 Hu sheep, the R2 values for chest circumference, body height, and body length were 0.925, 0.888 and 0.819, respectively, with measurement errors within 5%. The model requires only 10.71 MB of memory and 9.9 GFLOPs of computation, enabling real-time operation on edge devices. This study demonstrates that the proposed method achieves non-contact automatic measurement of sheep body dimensions, providing a practical solution for on-site growth monitoring and intelligent management in livestock farms. Full article
(This article belongs to the Special Issue Computer Vision Analysis Applied to Farm Animals)
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5 pages, 175 KB  
Editorial
Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring—2nd Edition
by Haikuan Feng, Yanjun Yang, Ning Zhang, Chengquan Zhou and Jibo Yue
Agriculture 2026, 16(2), 271; https://doi.org/10.3390/agriculture16020271 - 21 Jan 2026
Viewed by 165
Abstract
The integration of optical sensors and machine learning (ML) technologies has revolutionized agricultural monitoring, enabling precise, real-time insights into crop health, growth dynamics, and environmental interactions [...] Full article
(This article belongs to the Special Issue Novel Applications of Optical Sensors and Machine Learning in Agricultural Monitoring—2nd Edition)
20 pages, 6000 KB  
Article
A Study on the Interaction Mechanism Between Disc Coulters and Maize Root-Soil Composites Based on DEM-MBD Coupling Simulation
by Xuanting Liu, Zhanhong Guo, Zhenwei Tong, Miao He, Peng Gao, Yunhai Ma and Zihe Xu
Agriculture 2026, 16(2), 270; https://doi.org/10.3390/agriculture16020270 - 21 Jan 2026
Viewed by 127
Abstract
To solve the problems of high resistance and blockage in stubble-breaking operations, it is necessary to reveal the interaction mechanism between disc coulters and crop root–soil composites. This study developed a discrete element method–multi-body dynamics (DEM-MBD) coupling model of the stubble-breaking operation and [...] Read more.
To solve the problems of high resistance and blockage in stubble-breaking operations, it is necessary to reveal the interaction mechanism between disc coulters and crop root–soil composites. This study developed a discrete element method–multi-body dynamics (DEM-MBD) coupling model of the stubble-breaking operation and verified the accuracy of the model through soil bin tests (error < 20%) and field experiments (error < 32%). The model was used to investigate the effects of different design parameters (coulter type and disc radius) and operating parameters (tillage speed and depth) on the stubble-breaking operation. The results showed that due to the significant strengthening effect of roots on soil, the resistance of disc coulter stubble-breaking operation was high; the number of roots in contact with the blade edge and the amount of root deformation significantly affected the resistance of the disc coulter; irreversible deformation of roots and soil could easily lead to the holes and root hairpin effects in the seeding furrow; compared to plain disc coulters, the difference in the time of deformation and fracture of the roots made the resistance of the notched coulter lower. The wavy disc coulter with a longer edge curve made its resistance higher; the disc coulter with a greater radius, higher tillage speed, and deeper tillage depth significantly increased the tillage resistance. However, the disc coulter with a greater radius or a higher tillage speed was beneficial for improving stubble-breaking performance. This study revealed the interaction mechanism between disc coulters and maize root-soil composites, providing a theoretical basis for the optimization design of no-till stubble-breaking devices. Full article
(This article belongs to the Special Issue Intelligent Equipment and Automation Technology in Farmland Production)
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19 pages, 8625 KB  
Article
Study on Multi-Processing Vortex Core and Wall Shear Stress in Swirling Flow of a Guide-Vane Hydro-Cyclone for Agricultural Irrigation
by Yinghan Liu, Yiming Zhao and Yongye Li
Agriculture 2026, 16(2), 269; https://doi.org/10.3390/agriculture16020269 - 21 Jan 2026
Viewed by 148
Abstract
To investigate the spatiotemporal dynamics and wall shear stress patterns of a PVC (precessing vortex core) within a bounded swirling flow for agricultural irrigation, LES (Large Eddy Simulation) simulations based on a guide-vane hydro-cyclone were conducted and validated by physical experiments. Coherent structures [...] Read more.
To investigate the spatiotemporal dynamics and wall shear stress patterns of a PVC (precessing vortex core) within a bounded swirling flow for agricultural irrigation, LES (Large Eddy Simulation) simulations based on a guide-vane hydro-cyclone were conducted and validated by physical experiments. Coherent structures were extracted through flow modal decomposition, and a reduced-order model was established. The modal analysis of the flow reveals the following: A modal pairing phenomenon exists in the swirling flow, starting from the swirling section downstream of the guide-vane. The flow converts from a basic pipe flow to swirling flow. Compared to the vane section, the composite PVC in the swirling section exhibits mutual momentum exchange, leading to increasingly fragmented evolution of the vortex core over time and space. The application of vortex identification criteria to the reconstructed reduced-order model reveal that the precessing vortex core exhibits a tendency to spiral downstream along the guide-vane twist direction, with its rotation direction perfectly aligned with the guide-vane twist. As the Reynolds number of the bounded swirling flow increases, the circumferential precession of the PVC exhibits a linear weakening trend. As the relative length l/d of the guide-vane to the pipe increases, the circumferential precession of the PVC shows a linear strengthening trend. The wall shear stress analysis results indicate that the stress coefficient magnitude near the downstream location of the guide-vane is approximately zero, representing the lowest value across the entire flow. The region exhibits a rotational precession trend downstream. The stress coefficient magnitude between guide-vanes is relatively high, about 0.1 times dynamic pressure of approaching flow, and this trend also develops downstream with a rotational precession tendency. Full article
(This article belongs to the Section Agricultural Water Management)
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20 pages, 3835 KB  
Article
Impact of Water-Saving Irrigation on Agricultural Carbon Emissions in China
by Jingyu Wang, Xiaohu Qian and Yuanhua Yang
Agriculture 2026, 16(2), 268; https://doi.org/10.3390/agriculture16020268 - 21 Jan 2026
Viewed by 142
Abstract
This study analyzed the carbon reduction effects of water-saving irrigation based on panel data of Chinese provinces from 2010 to 2020. Carbon emissions from irrigation were calculated and decomposed using the Malmquist index and LMDI. Results indicate that, first, the accounting results show [...] Read more.
This study analyzed the carbon reduction effects of water-saving irrigation based on panel data of Chinese provinces from 2010 to 2020. Carbon emissions from irrigation were calculated and decomposed using the Malmquist index and LMDI. Results indicate that, first, the accounting results show a downward trend in estimated agricultural irrigation carbon emissions over the study period under a fixed-parameter framework. The average irrigation carbon intensity exhibits a declining pattern, particularly after the mid-2010s, with differences between provinces narrowing. Second, water-saving irrigation is associated with lower levels of estimated agricultural irrigation carbon emissions within the accounting framework by improving water-use efficiency and reducing irrigation water consumption per unit area, ultimately leading to a decrease in total carbon emissions. Finally, the carbon reduction effects are more pronounced and stable in major grain-producing regions. This study highlights regional heterogeneity in the emission-accounting outcomes associated with water-saving irrigation, which may provide descriptive evidence for discussions on region-specific irrigation management under different regional contexts. Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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14 pages, 4270 KB  
Article
Dual-Arm Coordination of a Tomato Harvesting Robot with Subtask Decoupling and Synthesizing
by Binhao Chen, Liang Gong, Shenghan Xie, Xuhao Zhao, Peixin Gao, Hefei Luo, Cheng Luo, Yanming Li and Chengliang Liu
Agriculture 2026, 16(2), 267; https://doi.org/10.3390/agriculture16020267 - 21 Jan 2026
Viewed by 147
Abstract
Robotic harvesters have the potential to substantially reduce the physical workload of agricultural laborers. However, in complex agricultural environments, traditional single-arm robot path planning methods often struggle to accomplish fruit harvesting tasks due to the presence of collision avoidance requirements and orientation constraints [...] Read more.
Robotic harvesters have the potential to substantially reduce the physical workload of agricultural laborers. However, in complex agricultural environments, traditional single-arm robot path planning methods often struggle to accomplish fruit harvesting tasks due to the presence of collision avoidance requirements and orientation constraints during grasping. In this work, we design a dual-arm tomato harvesting robot and propose a reinforcement learning-based cooperative control algorithm tailored to the dual-arm system. First, a deep learning-based semantic segmentation network is employed to extract the spatial locations of tomatoes and branches from sensory data. Building upon this perception module, we develop a reinforcement learning-based cooperative path planning approach to address inter-arm collision avoidance and end-effector orientation constraints during the harvesting process. Furthermore, a task-driven policy network architecture is introduced to decouple the complex harvesting task into structured subproblems, thereby enabling more efficient learning and improved performance. Simulation and experimental results demonstrate that the proposed method can generate collision-free harvesting trajectories that satisfy dual-arm orientation constraints, significantly improving the tomato harvesting success rate. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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13 pages, 1779 KB  
Article
Spatial Distribution and Oviposition Traits of Spodoptera eridania (Lepidoptera: Noctuidae) on Potato Plants Mediated by Chlorfenapyr
by Jhon Noel Gonzales Linares, José Bruno Malaquias, Jardel Lopes Pereira, João Batista Coelho Sobrinho, Luciana Barboza Silva, Luiz Leonardo Ferreira, José Magno Queiroz Luz and Alexandre Igor Azevedo Pereira
Agriculture 2026, 16(2), 266; https://doi.org/10.3390/agriculture16020266 - 21 Jan 2026
Viewed by 215
Abstract
Spodoptera eridania (Cramer, 1792) is increasingly reported from potato (Solanum tuberosum L., Solanaceae) in the Brazilian Cerrado, where infestations can cause substantial yield losses. Insecticides may alter the behavioral ecology of agricultural pests. The adaptability of S. eridania mediated by insecticides, [...] Read more.
Spodoptera eridania (Cramer, 1792) is increasingly reported from potato (Solanum tuberosum L., Solanaceae) in the Brazilian Cerrado, where infestations can cause substantial yield losses. Insecticides may alter the behavioral ecology of agricultural pests. The adaptability of S. eridania mediated by insecticides, especially regarding oviposition behavior, remains poorly understood. This study aimed to evaluate the spatial distribution and oviposition traits of S. eridania on potato plants under chlorfenapyr spraying. Egg masses were collected weekly, day after planting (DAP), totaling 322 collections up to the 91st DAP. Evaluations included the vertical plant strata (upper, middle and lower thirds), leaf surface (adaxial vs. abaxial), and density of scales covering egg masses (high, low, or absent). Results showed that nearly 90% of egg masses were deposited in the upper and middle thirds of the plants. Insecticide spraying modulated oviposition behavior because females preferred the middle third in treated plants, whereas oviposition predominated in the upper third of untreated plants. Moreover, under chlorfenapyr, 93.0 ± 1.2% of egg masses were placed on the abaxial surface. These findings highlight the role of insecticide-mediated behavioral shifts in shaping host-pest interactions and provide relevant insights for integrated pest management of S. eridania in potato field systems. Full article
(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)
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39 pages, 4728 KB  
Review
Advancing Sustainable Agriculture Through Aeroponics: A Critical Review of Integrated Water–Energy–Nutrient Management and Environmental Impact Mitigation
by Shen-Wei Chu and Terng-Jou Wan
Agriculture 2026, 16(2), 265; https://doi.org/10.3390/agriculture16020265 - 21 Jan 2026
Viewed by 370
Abstract
Aeroponics has emerged as a key technology for sustainable and resource-efficient food production, particularly under intensifying constraints on water availability, land use, and greenhouse gas (GHG) emissions. This review synthesizes recent advances in water–energy–nutrient integration, highlighting operational parameters—humidity (50–80%), temperature (18–25 °C), nutrient [...] Read more.
Aeroponics has emerged as a key technology for sustainable and resource-efficient food production, particularly under intensifying constraints on water availability, land use, and greenhouse gas (GHG) emissions. This review synthesizes recent advances in water–energy–nutrient integration, highlighting operational parameters—humidity (50–80%), temperature (18–25 °C), nutrient solution pH (5.5–6.5), and electrical conductivity (1.5–2.5 mS cm−1)—that critically influence system performance. Evidence indicates that closed-loop water recirculation and AI-assisted monitoring for environmental control and nutrient dosing can stabilize system dynamics and reduce water consumption by more than 90%. Reported yield improvements ranged from 45% to 75% compared with conventional soil-based cultivation. Moreover, systems powered by renewable energy demonstrated up to an 80% reduction in GHG emissions. Life-cycle assessment studies further suggest that aeroponics, coupled with low-carbon electricity in controlled-environment agriculture (CEA), can outperform traditional agricultural supply chains in climate and resource efficiency metrics. Additional technological innovations—including multi-tier vertical rack architectures, optimized misting intervals, and micronutrient-enriched fertigation formulations containing N, P, Ca, Mg, and K—were found to enhance spatial productivity and crop quality. Overall, aeroponics represents a promising pathway toward net-zero, high-performance agricultural systems. Full article
(This article belongs to the Section Agricultural Systems and Management)
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18 pages, 3256 KB  
Article
Macroaggregate–Microaggregate Interactions Drive Soil Carbon and Nitrogen Stabilization Under Rotational Tillage in Dryland Farming
by Sha Yang, Zhigang Wang, Jin Tong, Jing Xu, Juan Bai, Xingxing Qiao, Meichen Feng, Lujie Xiao, Xiaoyan Song, Meijun Zhang, Guangxin Li, Fahad Shafiq, Jiancheng Zhang, Chao Wang and Wude Yang
Agriculture 2026, 16(2), 264; https://doi.org/10.3390/agriculture16020264 - 21 Jan 2026
Viewed by 194
Abstract
Soil total carbon (TC) and total nitrogen (TN) are key indicators of soil fertility and ecosystem stability, particularly in dryland agroecosystems. However, how rotational tillage combined with straw return affects aggregate formation and aggregate-associated TC and TN stabilization remains insufficiently understood. In this [...] Read more.
Soil total carbon (TC) and total nitrogen (TN) are key indicators of soil fertility and ecosystem stability, particularly in dryland agroecosystems. However, how rotational tillage combined with straw return affects aggregate formation and aggregate-associated TC and TN stabilization remains insufficiently understood. In this study, we aimed to clarify how rotational tillage affects aggregate structure, stability, and the spatial distribution of TC and TN, thereby revealing internal processes driving nutrient stabilization in dryland farming systems. A long-term field experiment was conducted at the Shenfeng site of Shanxi Agricultural University, China, including three rotational tillage systems with straw return: T1 (two years of no tillage (NT) + one year of deep tillage (DT)), T2 (two years of conventional tillage (CT) + one year of DT), and T3 (two years of DT + one year of CT). Soil aggregates were separated into total mechanical aggregate (TMA), 0.25–2 mm MA, and 2–10 mm MA, and they were further fractionated into water-stable aggregates (WM, Wm, and Wf) for TC and TN analysis. The results showed that aggregate stability, TC, and TN were positively correlated and decreased with soil depth, indicating strong surface enrichment. TC was mainly enriched in 0.25–2 mm MA, whereas TN was concentrated in 2–10 mm MA, and water-stable macroaggregates (WM) acted as the dominant reservoirs for RC and RN. Relative to the 2016 baseline (CK), TC in 2022 tended to be higher under rotational tillage with straw return, while NT-containing systems better maintained TN across the 0–60 cm profile. Among the treatments, T1 provided the most balanced performance, with a higher MWD and GMD, lower D, and improved aggregate-associated TC and TN retention. These findings suggest that rotational tillage with straw return, particularly the NT–NT–DT sequence, can support aggregate stability and is associated with improved aggregate-mediated TC and TN retention in the Loess Plateau dryland winter wheat system. Full article
(This article belongs to the Topic Sustainable Energy Systems)
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25 pages, 2755 KB  
Article
Agroecology and Structural Performance of European Tomato Cropping Systems: A TAPE-Informed Cross-Country Analysis
by Roxana Ciceoi, Elena Cofas, Florin-Daniel Nitulescu and Paula Stoicea
Agriculture 2026, 16(2), 263; https://doi.org/10.3390/agriculture16020263 - 21 Jan 2026
Viewed by 204
Abstract
Tomato production is a strategic horticultural sector in Europe, yet it is increasingly exposed to climate variability, input-price volatility, and structural heterogeneity among national production models. This study provides a macro-level, cross-country assessment to benchmark structural performance and derive country typologies of tomato [...] Read more.
Tomato production is a strategic horticultural sector in Europe, yet it is increasingly exposed to climate variability, input-price volatility, and structural heterogeneity among national production models. This study provides a macro-level, cross-country assessment to benchmark structural performance and derive country typologies of tomato systems in 15 European countries over 2015–2024 using harmonized public statistics on cultivated area, production, and derived yields. A Tool for Agroecology Performance Evaluation (TAPE)—informed interpretive lens is used to frame yield level and interannual yield variability as transition-relevant performance signals, while acknowledging that farm- and territory-level TAPE scoring cannot be replicated with aggregated national data. The analysis combines descriptive benchmarking, trend-adjusted yield stability metrics, area–production relationship diagnostics, and multivariate classification (principal component analysis and Ward hierarchical clustering) to identify coherent national performance profiles. Results show pronounced cross-country contrasts and three recurring macro-patterns: (i) high-yield, low-dispersion systems with stable trajectories; (ii) transitional systems with lower yields and broader distributions; and (iii) high-dispersion systems indicating structural or climatic instability. The resulting typology supports differentiated policy discussion on adaptation, modernization priorities, and transition enabling conditions, and highlights the need to link macro-statistics with comparable agroecological indicators at farm and regional scale for stronger inference on transition pathways. Full article
(This article belongs to the Special Issue Agroecological Transition in Sustainable Food Systems)
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24 pages, 7972 KB  
Article
YOLO-MCS: A Lightweight Loquat Object Detection Algorithm in Orchard Environments
by Wei Zhou, Leina Gao, Fuchun Sun and Yuechao Bian
Agriculture 2026, 16(2), 262; https://doi.org/10.3390/agriculture16020262 - 21 Jan 2026
Viewed by 154
Abstract
To address the challenges faced by loquat detection algorithms in orchard settings—including complex backgrounds, severe branch and leaf occlusion, and inaccurate identification of densely clustered fruits—which lead to high computational complexity, insufficient real-time performance, and limited recognition accuracy, this study proposed a lightweight [...] Read more.
To address the challenges faced by loquat detection algorithms in orchard settings—including complex backgrounds, severe branch and leaf occlusion, and inaccurate identification of densely clustered fruits—which lead to high computational complexity, insufficient real-time performance, and limited recognition accuracy, this study proposed a lightweight detection model based on the YOLO-MCS architecture. First, to address fruit occlusion by branches and leaves, the backbone network adopts the lightweight EfficientNet-b0 architecture. Leveraging its composite model scaling feature, this significantly reduces computational costs while balancing speed and accuracy. Second, to deal with inaccurate recognition of densely clustered fruits, the C2f module is enhanced. Spatial Channel Reconstruction Convolution (SCConv) optimizes and reconstructs the bottleneck structure of the C2f module, accelerating inference while improving the model’s multi-scale feature extraction capabilities. Finally, to overcome interference from complex natural backgrounds in loquat fruit detection, this study introduces the SimAm module during the initial detection phase. Its feature recalibration strategy enhances the model’s ability to focus on target regions. According to the experimental results, the improved YOLO-MCS model outperformed the original YOLOv8 model in terms of Precision (P) and mean Average Precision (mAP) by 1.3% and 2.2%, respectively. Additionally, the model reduced GFLOPs computation by 34.1% and Params by 43.3%. Furthermore, in tests under complex weather conditions and with interference factors such as leaf occlusion, branch occlusion, and fruit mutual occlusion, the YOLO-MCS model demonstrated significant robustness, achieving mAP of 89.9% in the loquat recognition task. The exceptional performance serves as a robust technical base on the development and research of intelligent systems for harvesting loquats. Full article
(This article belongs to the Special Issue Cutting-Edge Technology in Agricultural Robotics: Sensing and Actuation)
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24 pages, 310 KB  
Article
A Study on the Nonlinear Impact of Agricultural Insurance on the Resilience of Agricultural Economy
by Yani Dong, Cheng Gui, Yan Zeng and Chunjie Qi
Agriculture 2026, 16(2), 261; https://doi.org/10.3390/agriculture16020261 - 20 Jan 2026
Viewed by 164
Abstract
With the deepening implementation of agricultural full-cost insurance and crop income insurance, agricultural insurance has gradually become a significant force in promoting agricultural and rural modernization and achieving the strategic goal of building a strong agricultural nation. Based on data from 30 provinces [...] Read more.
With the deepening implementation of agricultural full-cost insurance and crop income insurance, agricultural insurance has gradually become a significant force in promoting agricultural and rural modernization and achieving the strategic goal of building a strong agricultural nation. Based on data from 30 provinces in China from 2011 to 2023, a comprehensive evaluation index system for agricultural economic resilience was constructed, and the impact of agricultural insurance on agricultural economic resilience, along with its underlying mechanisms, was systematically analyzed. The findings reveal that: (1) There exists a nonlinear “U-shaped” relationship between agricultural insurance and agricultural economic resilience, a conclusion that remains robust after a series of tests; (2) Agricultural insurance can positively influence agricultural economic resilience by promoting agricultural technological progress; (3) When the level of industrial structure exceeds 7.108, agricultural insurance has a significant effect on agricultural economic resilience, and as the industrial structure level improves, the promoting effect of agricultural insurance becomes more pronounced; (4) The “U-shaped” impact of agricultural insurance on agricultural economic resilience is more prominent in the eastern, central, and northeastern regions, while it is not significant in the western region. Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
19 pages, 7125 KB  
Article
Identification and Characterization of the CRY Gene Family Involved in Safflower Flavonoid Biosynthesis
by Mamar Laeeq Zia, Debin Wang, Zixi Lin, Rubab Arshad, Xiaoyan Wang, Jiao Liu, Jianjiang Wei, Rui Qin and Hong Liu
Agriculture 2026, 16(2), 260; https://doi.org/10.3390/agriculture16020260 - 20 Jan 2026
Viewed by 183
Abstract
The cryptochromes (CRYs) perceive blue light to regulate various developmental and metabolic events. However, the role of CRYs in flavonoid biosynthesis and flower pigmentation in safflower (Carthamus tinctorius L.) remains unknown. In this study, we determined flower color diversity among 485 safflower [...] Read more.
The cryptochromes (CRYs) perceive blue light to regulate various developmental and metabolic events. However, the role of CRYs in flavonoid biosynthesis and flower pigmentation in safflower (Carthamus tinctorius L.) remains unknown. In this study, we determined flower color diversity among 485 safflower genotypes using the integrated CIELAB color space parameters and cluster analysis. On this basis, distinct colors were categorized into four groups, namely white (WW), yellow (YY), orange–red (OR), and yellow–red (YR). A genome-wide association study (GWAS) via 933,444 high-quality SNPs showed CtCRY2 as a flower color variation gene. Subsequently, genomic analysis identified three genes of the CRY family, including CtCRY1.1, CtCRY1.2, and CtCRY2. In silico analysis, such as gene structure, phylogeny and cis-acting elements, suggested CtCRY1.1 as a key candidate in pigment biosynthesis and was, therefore, selected for functional validation. Overexpression of CtCRY1.1 in Arabidopsis accumulated a high flavonoid content, particularly upregulating the expression of CHS, FLS, and ANS, proving its role as a positive regulator of flavonoid biosynthesis in safflower. These findings provide insights into the molecular mechanisms underlying flower color regulation in safflower and highlight CtCRY1.1 as a new target to enhance pigment-related traits in plants. Full article
(This article belongs to the Section Crop Genetics, Genomics and Breeding)
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17 pages, 3715 KB  
Article
A Two-Stage Farmer Assistant for Kidding Detection: Enhancing Farming Productivity and Animal Welfare
by João Ferreira, Pedro Gonçalves, Mário Antunes, Ana T. Belo and Maria R. Marques
Agriculture 2026, 16(2), 259; https://doi.org/10.3390/agriculture16020259 - 20 Jan 2026
Viewed by 345
Abstract
Kidding in goats is a highly significant event with major economic implications and strong impacts on the welfare of both the offspring and the mothers. Monitoring the process is extremely demanding, as it is impossible to predict precisely when it will occur. For [...] Read more.
Kidding in goats is a highly significant event with major economic implications and strong impacts on the welfare of both the offspring and the mothers. Monitoring the process is extremely demanding, as it is impossible to predict precisely when it will occur. For this reason, the automatic detection of kidding has the potential to generate substantial productivity gains while also improving animal well-being. Artificial intelligence techniques based on accelerometry data have been explored for identifying the event, but these approaches typically rely on data loggers, which cannot trigger real-time alerts or assistance. Embedding detection mechanisms directly into wearable devices enables much faster identification and supports energy-efficient operations. However, this approach also introduces considerable challenges, particularly due to the strict constraints of wearable devices in terms of weight, cost, and battery life. The present work documents the development of a real-time, automatic kidding-detection mechanism in which the detection workload is distributed between the collar and an edge device. System evaluation demonstrated the feasibility of this distributed architecture, confirming that both components can cooperate effectively to achieve reliable detection. The system achieved a Matthews Correlation Coefficient performance of 0.91, highlighting the robustness and practical viability of the proposed solution. Full article
(This article belongs to the Section Farm Animal Production)
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17 pages, 4376 KB  
Article
The FPF Gene Family in Tomato: Genome-Wide Identification and the Role of SlFPF1 in Gibberellin-Dependent Growth
by Yali Zhu, Yuanyuan Kong, Xingping Liu, Aiying Cui, Cuifang Chang, Xuemei Hou and Weibiao Liao
Agriculture 2026, 16(2), 258; https://doi.org/10.3390/agriculture16020258 - 20 Jan 2026
Viewed by 163
Abstract
Flowering promoting factor 1 (FPF1) is a key regulator of plant flowering time. While the functions of the FPF family have been characterized in species such as Arabidopsis and rice, systematic studies on the tomato FPF family remain limited. In this study, we [...] Read more.
Flowering promoting factor 1 (FPF1) is a key regulator of plant flowering time. While the functions of the FPF family have been characterized in species such as Arabidopsis and rice, systematic studies on the tomato FPF family remain limited. In this study, we comprehensively analyzed the FPF family in tomato (Solanum lycopersicum L.), identifying five SlFPF members in the tomato genome. Phylogenetic analysis classified these genes into five distinct subgroups, and chromosome mapping revealed their distribution across three chromosomes, with the highest density on chromosome 1. Promoter analysis identified a range of putative cis-acting elements related to abiotic stress and hormonal responses. Differential expression analysis of various tissues showed that the five SlFPF genes exhibit varying expression levels, where SlFPF1 had a significantly higher expression compared to the others. Following treatments with abiotic stresses (NaCl, PEG, dark, and low light) and phytohormones (GA, MeJA, ABA, and SA), SlFPF1 expression is notably higher under GA treatment than under other conditions. Based on these findings, SlFPF1 and GA treatments were selected for further functional analysis. The results show that GA treatment significantly promotes multiple morphological traits, including root length, stem diameter, leaf area, plant height, dry weight, and fresh weight. However, silencing SlFPF1 expression led to a reduction in all these traits. Moreover, in SlFPF1-silenced plants, GA treatment failed to enhance root length, leaf area, fresh weight, and dry weight, indicating that GA-dependent growth promotion in tomato plants relies on SlFPF1. This study provides a theoretical foundation for understanding the SlFPF gene family and its role in plant growth and stress responses. Full article
(This article belongs to the Section Crop Genetics, Genomics and Breeding)
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22 pages, 387 KB  
Article
The Impact of Digital Literacy on Farmers’ Green Production Behaviours: Evidence from Guizhou, China
by Li Zhu, Weiyong Yu and Jinxiu Yang
Agriculture 2026, 16(2), 257; https://doi.org/10.3390/agriculture16020257 - 20 Jan 2026
Viewed by 191
Abstract
The increasing momentum of agricultural digital transformation and green development necessitates investigations into how farmers’ digital literacy influences their engagement in green production behaviours, which is critical for achieving the high-quality development of modern agriculture. Utilising primary survey data collected from farmers in [...] Read more.
The increasing momentum of agricultural digital transformation and green development necessitates investigations into how farmers’ digital literacy influences their engagement in green production behaviours, which is critical for achieving the high-quality development of modern agriculture. Utilising primary survey data collected from farmers in rural areas of Guizhou Province, China, this study investigated how digital literacy affects farmers’ green production behaviours. The findings are as follows: (1) Digital literacy exerts a significant positive impact on farmers’ adoption of green production behaviours. Regarding the hierarchical effect, the order of influence is as follows: digital security awareness > basic digital skills > digital application and innovation. (2) The facilitating effect of digital literacy is primarily achieved through two pathways: the peer effect and the guidance effect. (3) Farmers with higher education levels are more impacted by digital literacy than farmers with lower education levels. (4) The impact of digital literacy is more positively significant for young and older farmers than for middle-aged groups. Based on these research findings, it is recommended that future policy formulation and technology extension efforts should prioritise support for specific regions and groups, such as mountainous areas, small-scale operations, low-education backgrounds, and the elderly. Such targeted approaches are crucial for encouraging wider adoption of green production behaviours among farmers. Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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