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Agriculture
Volume 15
Issue 16
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Agriculture, Volume 15, Issue 16 (August-2 2025) – 82 articles

Cover Story (view full-size image): The impact of climate change on the prevalence and severity of powdery mildew in cucurbit crops is increasing prominently. While significant progress has been made in understanding the molecular basis of host resistance and pathogen virulence, the complex interaction between genetic, environmental, and epidemiological factors necessitates a multifaceted approach. Incorporating climate-resilient breeding targets, molecular diagnostics, and adaptive disease management strategies is essential for mitigating future disease outbreaks. The convergence of genomics, gene editing, and predictive modeling offers hope for the development of long-lasting, environmentally responsive resistance in cucurbits. Combining genetic resistance with agronomic practices and predictive disease modeling could provide sustainable solutions for controlling powdery mildew under changing climate conditions. View this paper
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20 pages, 3484 KB  
Article
Monitoring Fertilizer Effects in Hardy Kiwi Using UAV-Based Multispectral Chlorophyll Estimation
by Sangyoon Lee, Hongseok Mun and Byeongeun Moon
Agriculture 2025, 15(16), 1794; https://doi.org/10.3390/agriculture15161794 - 21 Aug 2025
Viewed by 388
Abstract
This study addresses the need for efficient and non-destructive monitoring of the nutrient status of hardy kiwi (Actinidia arguta), a plantation crop native to East Asia. Traditional nutrient monitoring methods are labor-intensive and often destructive, limiting their practicality in precision agriculture. [...] Read more.
This study addresses the need for efficient and non-destructive monitoring of the nutrient status of hardy kiwi (Actinidia arguta), a plantation crop native to East Asia. Traditional nutrient monitoring methods are labor-intensive and often destructive, limiting their practicality in precision agriculture. To overcome these challenges, we deployed a rotary-wing unmanned aerial vehicle (UAV) equipped with a multispectral camera to capture monthly images of 10 hardy kiwi orchards in South Korea from June to October 2019. We extracted spectral bands (i.e., red, red-edge, green, and near-infrared) to generate normalized difference vegetation index and canopy chlorophyll content index maps, which were correlated with in situ chlorophyll measurements using a chlorophyll meter. Strong positive correlations were observed between vegetation indexes and actual chlorophyll content, with canopy chlorophyll content index achieving the highest predictive accuracy (average correlation coefficient > 0.84). Regression models based on multispectral data enabled reliable estimation of leaf chlorophyll across months and regions, with an average RMSE of 3.1. Our results confirmed that UAV-based multispectral imaging is an effective, scalable approach for real-time monitoring of nutrient status, supporting timely, site-specific fertilizer management. This method has the potential to enhance fertilizer efficiency, reduce environmental impact, and improve the quality of hardy kiwi cultivations. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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23 pages, 3539 KB  
Article
Design and Experimental Analysis of a Grinding Disc Buckwheat Dehulling Machine
by Ning Zhang, Wang Li, Lihong Li and Decong Zheng
Agriculture 2025, 15(16), 1793; https://doi.org/10.3390/agriculture15161793 - 21 Aug 2025
Viewed by 308
Abstract
Buckwheat is a highly nutritious coarse grain crop, yet its industrial processing has long faced two major challenges: the low whole-kernel rate of domestic dehullers and the poor local adaptability of imported equipment. To address these problems, a novel grinding disc-type dehulling machine [...] Read more.
Buckwheat is a highly nutritious coarse grain crop, yet its industrial processing has long faced two major challenges: the low whole-kernel rate of domestic dehullers and the poor local adaptability of imported equipment. To address these problems, a novel grinding disc-type dehulling machine was developed, featuring upper and lower discs with alternating deep–shallow composite textures to reduce kernel breakage and improve whole kernel rate. A 0–10 mm adjustable gap mechanism was incorporated to suit different buckwheat varieties and particle sizes, enhancing dehulling efficiency. Buckwheat grains were classified into four size ranges: 4.0–4.5 mm, 4.5–5.0 mm, 5.0–5.3 mm, and 5.3–5.7 mm. For all sizes, the optimal rotational speed was 12 r/min, with corresponding optimal gaps of 2.53 mm, 2.80 mm, 3.20 mm, and 3.40 mm, respectively. The whole-kernel rates under these conditions were 32.9%, 37.5%, 45.6%, and 55.1%, respectively, all above 30%, showing substantial improvement. For the 4.5–5.0 mm fraction, orthogonal tests revealed that a small gap (2.859 mm) achieved a dehulling rate of 89.9% and a whole-kernel rate of 38.03%, making it suitable for mass production. A larger gap (3.288 mm) combined with secondary dehulling increased the cumulative whole kernel rate to 50.26%, which is advantageous for producing high value-added products. The novel grinding disc structure balanced frictional and compressive forces on kernels, while the adjustable gap design improved adaptability. Combined with size classification and parameter optimization, this approach provides precise processing schemes for various buckwheat varieties and offers both theoretical and practical value for industrial application. Full article
(This article belongs to the Section Agricultural Technology)
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14 pages, 1520 KB  
Article
Spatiotemporal Analysis of Ventilation Efficiency in Single-Span Plastic Greenhouses in Hot-Humid Regions of China: Using Validated CFD Modeling
by Song Wang, Naimin Kong, Lirui Liang, Yuexuan He, Wenjun Peng, Xiaohan Lu, Chi Qin, Zijing Luo, Wei Zhao, Chengyao Jiang, Mengyao Li, Yangxia Zheng and Wei Lu
Agriculture 2025, 15(16), 1792; https://doi.org/10.3390/agriculture15161792 - 21 Aug 2025
Viewed by 292
Abstract
To characterize the spatiotemporal distribution of temperature and airflow in single-span plastic-film greenhouses, we coupled field experiments with three-dimensional computational fluid dynamics (CFD) simulations in a warm–temperate region of China. Model reliability and validity were evaluated against field measurements. The average and maximum [...] Read more.
To characterize the spatiotemporal distribution of temperature and airflow in single-span plastic-film greenhouses, we coupled field experiments with three-dimensional computational fluid dynamics (CFD) simulations in a warm–temperate region of China. Model reliability and validity were evaluated against field measurements. The average and maximum relative errors between simulated and measured values were 6% and 9%, respectively. Significant spatial heterogeneity in both temperature and airflow was observed. Vertically, temperature rose with height; horizontally, it declined from the center toward the sidewalls. Under prevailing meteorological conditions, the daily maxima occurred at distinct elevations above the fan-vent outlets. Airflow was most vigorous near the vents, whereas extensive stagnant zones aloft reduced overall ventilation efficiency. These findings provide a quantitative basis for designing single-span plastic film greenhouses in China’s hot–humid regions, informing ventilation improvements, and guiding future optimization efforts. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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18 pages, 2207 KB  
Article
Fermentation Regulation: Revealing Bacterial Community Structure, Symbiotic Networks to Function and Pathogenic Risk in Corn Stover Silage
by Zhumei Du, Shaojuan Cui, Yifan Chen, Yunhua Zhang, Siran Wang and Xuebing Yan
Agriculture 2025, 15(16), 1791; https://doi.org/10.3390/agriculture15161791 - 21 Aug 2025
Viewed by 284
Abstract
Improving agricultural by-product utilization can alleviate tropical feed shortages. This study used corn stover (CS, Zea mays L.) at the maturity stage as the material, with four silage treatments: control, lactic acid bacteria (LAB, Lactiplantibacillus plantarum), cellulase (AC, Acremonium cellulolyticus), and [...] Read more.
Improving agricultural by-product utilization can alleviate tropical feed shortages. This study used corn stover (CS, Zea mays L.) at the maturity stage as the material, with four silage treatments: control, lactic acid bacteria (LAB, Lactiplantibacillus plantarum), cellulase (AC, Acremonium cellulolyticus), and LAB+AC. After 60 days fermentation in plastic drum silos, the silos were opened for sampling. PacBio single-molecule real-time sequencing technology was used to study bacterial community structure, symbiotic network functionality, and pathogenic risk to clarify CS fermentation regulatory mechanisms. The CS contained 59.9% neutral detergent fiber and 7.1% crude protein. Additive-treated silages showed better quality than the control: higher lactic acid (1.64–1.83% dry matter, DM), lower pH (3.62–3.82), and reduced ammonia nitrogen (0.54–0.81% DM). Before ensiling, the CS was dominated by Gram-negative Rhizobium larrymoorei (16.30% of the total bacterial community). Functional prediction indicated that the microbial metabolism activity in diverse environments was strong, and the proportion of potential pathogens was relatively high (14.69%). After ensiling, Lactiplantibacillus plantarum as Gram-positive bacteria were the dominant species in all the silages (58.39–84.34% of the total bacterial community). Microbial additives facilitated the establishment of a symbiotic microbial network, where Lactiplantibacillus occupied a dominant position (p < 0.01). In addition, functional predictions showed an increase in the activity of the starch and sucrose metabolism and a decrease in the proportion of potential pathogens (0.61–1.95%). Among them, the synergistic effect of LAB and AC inoculants optimized the silage effect of CS. This study confirmed that CS is a potential high-quality roughage resource, and the application of silage technology can provide a scientific basis for the efficient utilization of feed resources and the stable development of animal husbandry in the tropics. Full article
(This article belongs to the Special Issue Silage Preparation, Processing and Efficient Utilization—2nd Edition)
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16 pages, 7723 KB  
Article
Mycorrhizal Regulation of Core ZmSWEET Genes Governs Sugar Accumulation in Maize
by Guang-Xia He, Feng-Ling Zheng, Ying-Ning Zou, Xiu-Bing Gao, Qiang-Sheng Wu and Can Guo
Agriculture 2025, 15(16), 1790; https://doi.org/10.3390/agriculture15161790 - 21 Aug 2025
Viewed by 307
Abstract
Mycorrhizal symbiosis relies on the host’s supply of carbohydrates, while sugar transport within plants is governed by the SWEET sugar transporter family. Although the symbiotic association between arbuscular mycorrhizal fungi (AMF) and maize is critical for its growth and sugar regulation, different AMF [...] Read more.
Mycorrhizal symbiosis relies on the host’s supply of carbohydrates, while sugar transport within plants is governed by the SWEET sugar transporter family. Although the symbiotic association between arbuscular mycorrhizal fungi (AMF) and maize is critical for its growth and sugar regulation, different AMF species have varying impacts on the host. The aim of this study was to analyze the effects of inoculating six different AMF species [Diversispora epigaea (De), Rhizophagus intraradices (Ri), Paraglomus occultum (Po), Entrophospora etunicata (Ee), Glomus heterosporum (Gh), and Funneliformis mosseae (Fm)] on plant growth, leaf photosynthetic capacity, glomalin-related soil protein content, leaf sugar content, and SWEET gene expression of maize under potted conditions for two months. AMF species colonize maize roots and showed significant species-specific variation, where Ri and Fm colonized treatment had the greatest rates (66~68%). All six fungi significantly increased biomass and stem diameter, with Ee treatment yielding the thickest stems, and enhanced leaf photosynthetic performance and glomalin-related soil protein fractions to some extent, with species-specific enhancements. All AMF species in particular significantly increased leaf sucrose; all except Ri treatment significantly increased fructose; while only Po and Fm treatments significantly increased glucose. AMF inoculations consistently upregulated the expression of ZmSWEET1b/3a/3b/4a/4b/14a and 16 genes, consistently downregulated the expression of ZmSWEET6b/11b/12a/13a/13b/13c and 17b genes, and induced treatment-specific regulation in the other gene expression. Root AMF colonization clustered with sugars and specific ZmSWEETs, with ZmSWEET4a/15b and 14b central to sucrose/glucose based on principal component analysis, indicating that these genes have specific regulatory effects in response to AMF treatments. In short, AMF inoculation reprogrammed ZmSWEET expression in a species-specific manner, with core ZmSWEET genes mediating sugar accumulation to support symbiosis. Full article
(This article belongs to the Special Issue Beneficial Microbes for Sustainable Crop Production)
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20 pages, 2008 KB  
Article
Using APSIM Model to Optimize Nitrogen Application for Alfalfa Yield Under Different Precipitation Regimes
by Yanbiao Wang, Haiyan Li, Yuanbo Jiang, Yaya Duan, Yi Ling, Minhua Yin, Yanlin Ma, Yanxia Kang, Yayu Wang, Guangping Qi, Guoyun Shen, Boda Li, Jinxi Chen and Huile Lv
Agriculture 2025, 15(16), 1789; https://doi.org/10.3390/agriculture15161789 - 21 Aug 2025
Viewed by 325
Abstract
Scientific nitrogen management is essential for maximizing crop growth potential while minimizing resource waste and environmental impacts. Alfalfa (Medicago sativa L.) is the most widely cultivated high-quality leguminous forage crop globally, and is capable of providing nitrogen through nitrogen fixation. However, there [...] Read more.
Scientific nitrogen management is essential for maximizing crop growth potential while minimizing resource waste and environmental impacts. Alfalfa (Medicago sativa L.) is the most widely cultivated high-quality leguminous forage crop globally, and is capable of providing nitrogen through nitrogen fixation. However, there remains some disagreement regarding its nitrogen management strategies. This study conducted a three-year field experiment and calibrated the APSIM-Lucerne model. Based on the calibrated model, three typical precipitation year types (dry, normal, and wet years) were selected. Combining field experiments, eight nitrogen application scenarios (0, 80, 120, 140, 160, 180, 200, and 240 kg·ha−1) were set up. With the objectives of increasing alfalfa yield, nitrogen partial productivity, and nitrogen agronomic efficiency, this study investigates the appropriate nitrogen application thresholds for alfalfa under different precipitation year types. The results showed the following: (1) Alfalfa yield increased first and then decreased with the increase in nitrogen application level. The annual yield of the N160 treatment was the highest (13.39 t·ha−1), which was 5.15% to 32.39% higher than that of the other treatments. (2) The APSIM-Lucerne model could well reflect the growth process and yield of alfalfa under different precipitation year types. The R2 and NRMSE between the simulated and observed values of the former were 0.85–0.91 and 5.33–7.44%, respectively. The R2 and NRMSE between the simulated and measured values of the latter were 0.74–0.96 and 2.73–5.25%, respectively. (3) Under typical dry, normal, and wet years, the optimal nitrogen application rates for alfalfa yield increases were 120 kg·ha−1, 140 kg·ha−1, and 160 kg·ha−1, respectively. This study can provide a basis for precise nitrogen management of alfalfa under different precipitation year types. Full article
(This article belongs to the Special Issue Advancements in Best Management Practices for Enhancing Soil Health and Water Quality)
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19 pages, 7264 KB  
Article
Design and Performance Testing of a Multi-Variety Forage Grass Mixed-Sowing Seed Metering Device
by Wenxue Dong, Anbin Zhang, Qihao Wan, Fei Liu, Yingsi Wu, Yin Qi and Yuxing Ren
Agriculture 2025, 15(16), 1788; https://doi.org/10.3390/agriculture15161788 - 21 Aug 2025
Viewed by 274
Abstract
Traditional fluted roller seed metering devices exhibit unstable seeding rates during forage seed mixed sowing. To address this issue, a new seed metering device was designed based on the agronomic requirements of forage seed mixing and the structural characteristics of fluted roller mechanisms. [...] Read more.
Traditional fluted roller seed metering devices exhibit unstable seeding rates during forage seed mixed sowing. To address this issue, a new seed metering device was designed based on the agronomic requirements of forage seed mixing and the structural characteristics of fluted roller mechanisms. The discrete element method (DEM) was employed to numerically simulate the movement of particles within the seed metering device. Single-factor experiments identified optimal parameter ranges for the seed metering device: a metering shaft speed of 10–20 r/min, a seed inlet width of 8–24 mm, and a seed outlet height of 10–20 mm. A response surface methodology (RSM) experiment was then designed using Design-Expert 13 software. The results yielded optimal operating parameters: a metering shaft speed of 18.9 r/min, a seed inlet width of 9.3 mm, and a seed outlet height of 14.4 mm. The field experiment validated the seeding performance with the optimal parameter combination. The coefficient of variation (CV) for the first-class seed (CV1) was 4.16%, and for the second-class seed (CV2) it was 2.98%, both of which met the requirements for mixed sowing of forage. Full article
(This article belongs to the Section Agricultural Technology)
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20 pages, 1882 KB  
Review
Active Chlorophyll Fluorescence Technologies in Precision Weed Management: Overview and Perspectives
by Jin Hu, Yuwen Xie, Xingyu Ban, Liyuan Zhang, Zhenjiang Zhou, Zhao Zhang, Aichen Wang and Toby Waine
Agriculture 2025, 15(16), 1787; https://doi.org/10.3390/agriculture15161787 - 21 Aug 2025
Viewed by 393
Abstract
Weeds are among the primary factors that adversely affect crop yields. Chlorophyll fluorescence, as a sensitive indicator of photosynthetic activity in green plants, provides direct insight into photosynthetic efficiency and the functional status of the photosynthetic apparatus. This makes it a valuable tool [...] Read more.
Weeds are among the primary factors that adversely affect crop yields. Chlorophyll fluorescence, as a sensitive indicator of photosynthetic activity in green plants, provides direct insight into photosynthetic efficiency and the functional status of the photosynthetic apparatus. This makes it a valuable tool for assessing plant health and stress responses. Active chlorophyll fluorescence technology uses an external light source to excite plant leaves, enabling the rapid acquisition of fluorescence signals for real-time monitoring of vegetation in the field. This technology shows great potential for weed detection, as it allows for accurate discrimination between crops and weeds. Furthermore, since weed-induced stress affects the photosynthetic process of plants, resulting in changes in fluorescence characteristics, chlorophyll fluorescence can also be used to detect herbicide resistance in weeds. This paper reviews the progress in using active chlorophyll fluorescence sensor technology for weed detection. It specifically outlines the principles and structure of active fluorescence sensors and their applications at different stages of field operations, including rapid classification of soil and weeds during the seedling stage, identification of in-row weeds during cultivation, and assessment of herbicide efficacy after application. By monitoring changes in fluorescence parameters, herbicide-resistant weeds can be detected early, providing a scientific basis for precision herbicide application. Full article
(This article belongs to the Special Issue Multi- and Hyper-Spectral Imaging Technologies for Crop Monitoring—2nd Edition)
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20 pages, 16392 KB  
Article
PCC-YOLO: A Fruit Tree Trunk Recognition Algorithm Based on YOLOv8
by Yajie Zhang, Weiliang Jin, Baoxing Gu, Guangzhao Tian, Qiuxia Li, Baohua Zhang and Guanghao Ji
Agriculture 2025, 15(16), 1786; https://doi.org/10.3390/agriculture15161786 - 21 Aug 2025
Viewed by 299
Abstract
With the development of smart agriculture, the precise identification of fruit tree trunks by orchard management robots has become a key technology for achieving autonomous navigation. To solve the issue of tree trunks being hard to see against their background in orchards, this [...] Read more.
With the development of smart agriculture, the precise identification of fruit tree trunks by orchard management robots has become a key technology for achieving autonomous navigation. To solve the issue of tree trunks being hard to see against their background in orchards, this study introduces PCC-YOLO (PENet, CoT-Net, and Coord-SE attention-based YOLOv8), a new trunk detection model based on YOLOv8. It improves the ability to identify features in low-contrast situations by using a pyramid enhancement network (PENet), a context transformer (CoT-Net) module, and a combined coordinate and channel attention mechanism. By introducing a pyramid enhancement network (PENet) into YOLOv8, the model’s feature extraction ability under low-contrast conditions is enhanced. A context transformer module (CoT-Net) is then used to strengthen global perception capabilities, and a combination of coordinate attention (Coord-Att) and SENetV2 is employed to optimize target localization accuracy. Experimental results show that PCC-YOLO achieves a mean average precision (mAP) of 82.6% on a self-built orchard dataset (5000 images) and a detection speed of 143.36 FPS, marking a 4.8% improvement over the performance of the baseline YOLOv8 model, while maintaining a low computational load (7.8 GFLOPs). The model demonstrates a superior balance of accuracy, speed, and computational cost compared to results for the baseline YOLOv8 and other common YOLO variants, offering an efficient solution for the real-time autonomous navigation of orchard management robots. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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25 pages, 1559 KB  
Article
Influence of Information Sources on Technology Adoption in Apple Production in China
by Linjia Yao, Gang Zhao, Changqing Yan, Amit Kumar Srivastava, Qi Tian, Ning Jin, Junjie Qu, Ling Yin, Ning Yao, Heidi Webber, Eike Luedeling and Qiang Yu
Agriculture 2025, 15(16), 1785; https://doi.org/10.3390/agriculture15161785 - 21 Aug 2025
Viewed by 364
Abstract
China holds the largest apple cultivation area globally, yet yields per hectare remain relatively low. Despite substantial government investment in modern orchard technologies, adoption remains limited among farmers. This study investigates the economic and sociological drivers of technology uptake, focusing on how information [...] Read more.
China holds the largest apple cultivation area globally, yet yields per hectare remain relatively low. Despite substantial government investment in modern orchard technologies, adoption remains limited among farmers. This study investigates the economic and sociological drivers of technology uptake, focusing on how information sources shape adoption behavior. Based on 382 farmer surveys across major apple-producing provinces, the study examines (1) farmers’ preferences for agricultural information sources, (2) the influence of demographic characteristics on those preferences, and (3) the differential effects of specific sources on the adoption of key technologies, including dwarf rootstocks and virus-free seedlings. Results show that agri-chemical dealers (ACDs) and farmer peers (FPs) are the most commonly used information channels. Access to advice from local experts (EXPs) significantly increases the likelihood of adopting dwarf rootstocks, while information from ACDs promotes the use of virus-free seedlings. In contrast, reliance on personal farming experience is negatively associated with technology uptake. These findings highlight the need to strengthen formal information dissemination systems and better integrate trusted local actors like ACDs and EXPs into agricultural extension. Targeted information delivery can improve adoption efficiency, promote evidence-based decision-making, and support the modernization and sustainability of China’s apple sector. Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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23 pages, 2173 KB  
Article
Competitiveness and Nitrogen Fixation Efficiency Analysis of Rhizobium leguminosarum Strains in Different Field Pea (Pisum sativum L.) Genotypes
by Justina Kaziūnienė, Audrius Gegeckas, Laura Lapinskienė, Kristyna Razbadauskienė, Raimonda Mažylytė and Skaidrė Supronienė
Agriculture 2025, 15(16), 1784; https://doi.org/10.3390/agriculture15161784 - 20 Aug 2025
Viewed by 439
Abstract
The uneven effectiveness of rhizobia inoculants has increased interest in developing specific inoculants for each genotype. This study investigated the biological nitrogen fixation efficiency and competition between different Rhizobium leguminosarum strains in different pea genotypes, namely, “Egle DS” and “Respect”. The results showed [...] Read more.
The uneven effectiveness of rhizobia inoculants has increased interest in developing specific inoculants for each genotype. This study investigated the biological nitrogen fixation efficiency and competition between different Rhizobium leguminosarum strains in different pea genotypes, namely, “Egle DS” and “Respect”. The results showed that plant genotype was a significant factor determining competition and nitrogen fixation among R. leguminosarum strains. The most competitive R. leguminosarum LIN06 strain in the pea genotype “Egle DS” was characterized by a low nitrogen fixation efficiency, while the most competitive R. leguminosarum EGLE10 strain in the “Respect” genotype was characterized by a high biological nitrogen fixation efficiency. It was also found that the “Respect” genotype may prefer and form symbiotic relationships with more efficient nitrogen fixing strains, while the “Egle DS” genotype formed symbiotic relationships with less efficient strains. However, even less efficient strains had a significant positive effect on nitrogen accumulation in plants under natural conditions. Finally, our study showed that sophisticated tests and methods are not necessary to analyze the competitiveness of rhizobia; it is sufficient to analyze the effectiveness of bacterial strains on plants in unsterilized soil. Full article
(This article belongs to the Special Issue Physiological and Molecular Mechanism for Effective Methods to Enhance Crop Yield and Quality)
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18 pages, 3628 KB  
Article
Extraction of Cotton Cultivation Areas Based on Deep Learning and Sentinel-2 Image Data
by Liyuan Li, Hongfei Tao, Yan Xu, Lixiran Yu, Qiao Li, Hong Xie and Youwei Jiang
Agriculture 2025, 15(16), 1783; https://doi.org/10.3390/agriculture15161783 - 20 Aug 2025
Viewed by 331
Abstract
Cotton is a crucial economic crop, and timely and accurate acquisition of its spatial distribution information is of great significance for yield prediction, as well as for the formulation and adjustment of agricultural policies. To accurately and efficiently extract cotton cultivation areas at [...] Read more.
Cotton is a crucial economic crop, and timely and accurate acquisition of its spatial distribution information is of great significance for yield prediction, as well as for the formulation and adjustment of agricultural policies. To accurately and efficiently extract cotton cultivation areas at a large scale, in this study, we focused on the Santun River Irrigation District in Xinjiang as the research area. Utilizing Sentinel-2 satellite imagery from 2019 to 2024, four cotton extraction models—U-Net, SegNet, DeepLabV3+, and CBAM-UNet—were constructed. The models were evaluated using metrics, including the mean intersection over union (mIoU), precision, recall, F1-score, and over accuracy (OA), to assess the models’ performances in cotton extraction. The results demonstrate that the CBAM-UNet model achieved the highest accuracy, with an mIoU, precision, recall, F1-score, and OA of 84.02%, 88.99%, 94.75%, 91.78%, and 95.56%, respectively. The absolute error of the extracted cotton areas from 2019 to 2024 ranged between 923.69 and 1445.46 hm2, with absolute percentage errors of less than 10%. The coefficient of determination (R2) between the extracted results and statistical data was 0.9817, indicating the best fit. The findings of this study provide technical support for rapid cotton identification and extraction in large- and medium-sized irrigation districts. Full article
(This article belongs to the Special Issue Intelligent Sensing and Edge AI-Driven Systems for Precision Agriculture)
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19 pages, 6878 KB  
Article
LiDAR-Assisted UAV Variable-Rate Spraying System
by Xuhang Liu, Yicheng Liu, Xinhanyang Chen, Yuhan Wan, Dengxi Gao and Pei Cao
Agriculture 2025, 15(16), 1782; https://doi.org/10.3390/agriculture15161782 - 20 Aug 2025
Viewed by 287
Abstract
In wheat pest and disease control methods, pesticide application occupies a dominant position, and the use of UAVs for precise pesticide application is a key technology in precision agriculture. However, it is difficult for existing UAV spraying systems to accurately achieve variable spraying [...] Read more.
In wheat pest and disease control methods, pesticide application occupies a dominant position, and the use of UAVs for precise pesticide application is a key technology in precision agriculture. However, it is difficult for existing UAV spraying systems to accurately achieve variable spraying according to crop growth conditions, resulting in pesticide waste and environmental pollution. To address this issue, this paper proposes a LiDAR-assisted UAV variable-speed spraying system. Firstly, a biomass estimation model based on LiDAR data and RGB data is constructed, LiDAR point cloud data and RGB data are extracted from the target farmland, and, after preprocessing, key parameters including LiDAR feature variables, canopy cover, and visible-light vegetation indices are extracted from the two types of data. Using these key parameters as model inputs, multiple machine learning methods are employed to build a wheat biomass estimation model, and a variable spraying prescription map is generated based on the spatial distribution of biomass. Secondly, the variable-speed spraying system is constructed, which integrates a prescription map interpretation module and a PWM control module. Under the guidance of the variable spraying prescription map, the spraying rate is adjusted to achieve real-time variable spraying. Finally, a comparative experiment is designed, and the results show that the LiDAR-assisted UAV variable spraying system designed in this study performs better than the traditional constant-rate spraying system; while maintaining equivalent spraying effects, the usage of chemical agents is significantly reduced by 30.1%, providing a new technical path for reducing pesticide pollution and lowering grain production costs. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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15 pages, 4584 KB  
Article
Effect of Cutting Age on Seed Production of Flemingia Macrophylla for the Optimisation of Cropping Systems, Cotopaxi-Ecuador
by Ricardo Luna-Murillo, Joselyne Solórzano, Idalia Pacheco-Tigselema, Jairo Dueñas-Tovar, Lady Bravo-Montero and María Jaya-Montalvo
Agriculture 2025, 15(16), 1781; https://doi.org/10.3390/agriculture15161781 - 20 Aug 2025
Viewed by 366
Abstract
The tropical shrub legume Flemingia macrophylla is a specie that influences higher forage production, increases protein content, and reduces nitrogen fertiliser and animal protein supplement use. However, there is little scientific literature on the influence of the cutting age of Flemingia macrophylla on [...] Read more.
The tropical shrub legume Flemingia macrophylla is a specie that influences higher forage production, increases protein content, and reduces nitrogen fertiliser and animal protein supplement use. However, there is little scientific literature on the influence of the cutting age of Flemingia macrophylla on the nutritional-productive behaviour of the plant and soil microbiology. Therefore, this study addresses the interaction between high-value forages and coffee cropping systems under agroecological management. The study aims to evaluate the seed production of Flemingia macrophylla and its association with the crops of “Geisha Coffee” and “Sarchimor Coffee” at the Sacha Wiwa Experimental Centre (Cotopaxi-Ecuador) through the analysis of growth and bromatology of the seeds at cutting ages of 30, 45, 60, and 75 days for their potential use in the local agro-industry. The methodology was composed of three phases: (i) crop experimental design, (ii) crop sampling, and (iii) agroecological management strategies. The results suggest that Flemingia macrophylla can be integrated into agroforestry systems with coffee, reducing dependence on chemical fertilisers and improving seed productivity. Seed production peaked at 60 days, with the highest levels of protein (31.44%), nitrogen (5.03%), potassium (1.17%), and calcium (0.78%), making it an excellent forage source. Fibre content, however, was highest at 75 days (11.20%), making this cycle preferable when higher fibre is required. Notably, soil organic matter depletion in plots associated with Sarchimor coffee suggested higher nutrient demands. This study demonstrated the potential of Flemingia macrophylla to diversify agroecological systems with improved productivity and nutritional quality. Full article
(This article belongs to the Special Issue Strategies for Resilient and Sustainable Agri-Food Systems)
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19 pages, 13383 KB  
Article
Differential Responses of Two Sorghum Genotypes to Drought Stress at Seedling Stage Revealed by Integrated Physiological and Transcriptional Analysis
by Manhong Wang, Irshad Ahmad, Muhi Eldeen Hussien Ibrahim, Bin Qin, Hailu Zhu, Guanglong Zhu and Guisheng Zhou
Agriculture 2025, 15(16), 1780; https://doi.org/10.3390/agriculture15161780 - 20 Aug 2025
Viewed by 386
Abstract
Drought stress significantly limits crop growth and yield, and the mechanisms underlying genotypic variation in drought tolerance remain unclear. This study investigated the growth and transcriptomic responses of two sorghum varieties, drought-sensitive Jinza 35 (V1) and drought-tolerant Longza 24 (V2), under drought conditions. [...] Read more.
Drought stress significantly limits crop growth and yield, and the mechanisms underlying genotypic variation in drought tolerance remain unclear. This study investigated the growth and transcriptomic responses of two sorghum varieties, drought-sensitive Jinza 35 (V1) and drought-tolerant Longza 24 (V2), under drought conditions. Comparative transcriptomic analysis, along with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, revealed distinct molecular mechanisms between the two varieties. Both varieties exhibited drought-responsive changes in photosynthesis-related pathways. However, the drought-tolerant V2 showed significant enrichment in phenylpropanoid biosynthesis, starch-sucrose metabolism, and plant hormone signaling pathways, suggesting enhanced metabolic flexibility under stress. In contrast, V1 primarily activated ribosome metabolism and cell cycle regulation pathways, indicating a less adaptive response focused on basic cellular processes. These findings highlight key metabolic and regulatory differences underlying drought tolerance in sorghum. The study provides valuable molecular insights and candidate pathways for future functional studies and the breeding of drought-resistant sorghum varieties. Full article
(This article belongs to the Special Issue Effects of Salt Stress on Crop Production—2nd Edition)
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23 pages, 2428 KB  
Review
Cabbage Stink Bug (Eurydema ventralis Kolenati, 1846) (Hemiptera: Pentatomidae)—An Increasingly Important Pest in Europe
by Sergeja Adamič Zamljen, Tanja Bohinc and Stanislav Trdan
Agriculture 2025, 15(16), 1779; https://doi.org/10.3390/agriculture15161779 - 19 Aug 2025
Viewed by 373
Abstract
Eurydema ventralis Kolenati, 1846 (Hemiptera: Pentatomidae), commonly known as the cabbage stink bug, is an increasingly important pest in Brassicaceae crops across Europe, including Slovenia. This review provides a comprehensive synthesis of current knowledge on the taxonomy, biology, distribution, and economic impact of [...] Read more.
Eurydema ventralis Kolenati, 1846 (Hemiptera: Pentatomidae), commonly known as the cabbage stink bug, is an increasingly important pest in Brassicaceae crops across Europe, including Slovenia. This review provides a comprehensive synthesis of current knowledge on the taxonomy, biology, distribution, and economic impact of Eurydema ventralis, with a focus on cabbage (Brassica oleracea L. var. capitata) cultivation. Various monitoring and population assessment methods are discussed as foundational tools for implementing integrated pest management (IPM). The focus of this study is on the available control strategies, including chemical, biological, cultural, and mechanical approaches. While synthetic insecticides remain a commonly used option, their environmental impact, potential for resistance development, and non-target effects raise concerns. Increasing research attention is being given to biological control agents, such as egg parasitoids, generalist predators (e.g., Coccinellidae, Carabidae, Nabidae), and entomopathogenic fungi. These agents show considerable promise but are not being fully utilized at present. A further review of cultural practices and mechanical control methods is also undertaken for their role in reducing pest populations. The compatibility of different strategies within an IPM framework is examined in detail. In conclusion, this review identifies existing knowledge gaps and puts forward a number of recommendations for future research directions. The purpose of these recommendations is to support the development of more sustainable and ecological pest management solutions for E. ventralis in cabbage cultivation. Full article
(This article belongs to the Special Issue Occurrence and Control Strategies of Agrophagous of Horticultural Crops)
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18 pages, 2204 KB  
Article
Controlled-Release Urea Coordinates Maize Physiology with Soil Nitrogen Retention: Balancing High Yield and Environmental Sustainability
by Wei Yan, Meng Huang, Huiqing Yang, Zhonghua Wang, Shujuan Sun, Yinshan Xie, Jinbian Sun, Qiong Li, Bo Liu, Chengcheng Gao, Yanfang Xue and Kaichang Liu
Agriculture 2025, 15(16), 1778; https://doi.org/10.3390/agriculture15161778 - 19 Aug 2025
Viewed by 413
Abstract
Controlled-release urea (CRU) can improve nitrogen (N) use efficiency and yield, but comprehensive evaluations of its agronomic, physiological, and environmental impacts remain limited. Through a two-year field experiment comparing three CRU types with conventional urea at five N rates (0-280 kg N ha [...] Read more.
Controlled-release urea (CRU) can improve nitrogen (N) use efficiency and yield, but comprehensive evaluations of its agronomic, physiological, and environmental impacts remain limited. Through a two-year field experiment comparing three CRU types with conventional urea at five N rates (0-280 kg N ha−1), we demonstrate that CRU at 180 kg N ha−1 maintained high maize yields (13.9 Mg ha−1) while improving N use efficiency, with thermosetting polymer-coated samples (TCU) showing superior performance. There was a significant increase in the net photosynthetic rate by 7.9–32.7% and intercellular CO2 concentration by 20.6–40.0% under CRU treatments during the silking and milking stages. The CRU treatments also sustained optimal levels of hormones, N metabolism enzymes, and sucrase and urease activities. Compared to common urea, life cycle assessment indicates that CRU has achieved a 47.5% reduction in reactive N losses and an 18.7% decrease in greenhouse gas emissions. Economically, CRU outperformed common urea, with TCU providing the highest net benefit through yield stability and labor savings. These findings establish TCU at 180 kg N ha−1 as an optimal strategy of maize production in the North China Plain, balancing productivity, profitability, and environmental protection. Full article
(This article belongs to the Special Issue Technologies for Achieving High-Yield Maize of High Nutritional Quality)
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17 pages, 671 KB  
Article
Price Integration of the Ukrainian and EU Corn Markets in the Context of the Russian—Ukrainian War
by Mariusz Hamulczuk and Denys Cherevyk
Agriculture 2025, 15(16), 1777; https://doi.org/10.3390/agriculture15161777 - 19 Aug 2025
Viewed by 475
Abstract
Russia’s full-scale aggression against Ukraine has led to profound disruptions in local and global agri-food markets. Since Ukraine is one of the world’s largest maize exporters, the war also contributed to considerable changes in trade reallocation, as well as an increase in the [...] Read more.
Russia’s full-scale aggression against Ukraine has led to profound disruptions in local and global agri-food markets. Since Ukraine is one of the world’s largest maize exporters, the war also contributed to considerable changes in trade reallocation, as well as an increase in the significance of the European Union in Ukrainian exports. This study analyses the effects of the Russian–Ukrainian war on horizontal maize price transmission between Ukraine and the EU countries. The panel autoregressive distributed lag model (ARDL) was applied to investigate the impact of the war on the price pass-through between those countries. The econometric analysis was performed on a weekly feed maize export price series for Ukraine and 14 selected EU countries. The time frame of research, January 2019 to December 2024, was split into pre-war and war periods. The study indicates that with the outbreak of the war, the long-term relationship between Ukraine and the EU’s maize prices has weakened. At the same time, there was an increase in the short-run maize price transmission between Ukraine and the Eastern EU countries. This proves that in the face of the conflict, market participants in these countries are increasingly guided by the market situation in Ukraine when making economic decisions. Full article
(This article belongs to the Special Issue Price and Trade Dynamics in Agricultural Commodity Markets)
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25 pages, 3969 KB  
Article
Geographical Variation in Cover Crop Management and Outcomes in Continuous Corn Farming System in Nebraska
by Andualem Shiferaw, Girma Birru, Tsegaye Tadesse, Brian Wardlow, Tala Awada, Virginia Jin, Marty Schmer, Ariel Freidenreich and Javed Iqbal
Agriculture 2025, 15(16), 1776; https://doi.org/10.3390/agriculture15161776 - 19 Aug 2025
Viewed by 394
Abstract
Cover crops (CCs) are widely recognized for their numerous benefits, including enhancing soil health, mitigating erosion, and promoting nutrient cycling, among many others. However, their outcomes vary significantly depending on site-specific biophysical conditions and agronomic management practices. This study investigates the geographic variations [...] Read more.
Cover crops (CCs) are widely recognized for their numerous benefits, including enhancing soil health, mitigating erosion, and promoting nutrient cycling, among many others. However, their outcomes vary significantly depending on site-specific biophysical conditions and agronomic management practices. This study investigates the geographic variations in cover crop outcomes across Nebraska, focusing on three critical management factors: seeding rate, termination timing, and termination-to-corn planting intervals. Using Decision Support System for Agrotechnology Transfer (DSSAT) simulations, we evaluated the effects of these practices on cover crop biomass, growth stages, and subsequent corn yield across seven sites. The results revealed that corn yield remained resilient across all sites, with no statistically significant differences (p > 0.05) across termination timings, seeding rates, or termination-to-planting intervals. A CC seeding rate analysis showed that biomass tended to increase with higher seeding densities, particularly from 200 to 250 plants m−2, but the gains diminished beyond that, and few pairwise comparisons reached statistical significance. Termination timing had a significant effect on biomass and growth stages, with delayed termination resulting in greater biomass accumulation and advanced phenological development (e.g., Zadoks > 45), which may complicate termination efficacy. Increasing termination-to-planting intervals led to reduced biomass due to shorter growing periods, though these reductions were not associated with significant corn yield penalties. This study highlights the importance of tailoring CC management strategies to local environmental conditions and agronomic objectives. By addressing these site-specific factors, the findings offer actionable insights for farmers and land managers to optimize both ecological benefits and productivity in Nebraska’s no-till systems. Full article
(This article belongs to the Special Issue Optimizing Crop Management Strategies for Climate Resilience Using Advanced Modeling Techniques)
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30 pages, 5453 KB  
Review
Advances in Hyperspectral and Diffraction Imaging for Agricultural Applications
by Li Chen, Yu Wu, Ning Yang and Zongbao Sun
Agriculture 2025, 15(16), 1775; https://doi.org/10.3390/agriculture15161775 - 19 Aug 2025
Viewed by 511
Abstract
Hyperspectral imaging and diffraction imaging technologies, owing to their non-destructive nature, high efficiency, and superior resolution, have found widespread application in agricultural diagnostics. This review synthesizes recent advancements in the deployment of these two technologies across various agricultural domains, including the detection of [...] Read more.
Hyperspectral imaging and diffraction imaging technologies, owing to their non-destructive nature, high efficiency, and superior resolution, have found widespread application in agricultural diagnostics. This review synthesizes recent advancements in the deployment of these two technologies across various agricultural domains, including the detection of plant diseases and pests, crop growth monitoring, and animal health diagnostics. Hyperspectral imaging utilizes multi-band spectral and image data to accurately identify diseases and nutritional status, while combining deep learning and other technologies to improve detection accuracy. Diffraction imaging, by exploiting the diffraction properties of light waves, facilitates the detection of pathogenic spores and the assessment of cellular vitality, making it particularly well-suited for microscopic structural analysis. The paper also critically examines prevailing challenges such as the complexity of data processing, environmental adaptability, and the cost of instrumentation. Finally, it envisions future directions wherein the integration of hyperspectral and diffraction imaging, through multisource data fusion and the optimization of intelligent algorithms, holds promise for constructing highly precise and efficient agricultural diagnostic systems, thereby advancing the development of smart agriculture. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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17 pages, 2159 KB  
Article
Eco-Friendly Suppression of Grapevine Root Rot: Synergistic Action of Biochar and Trichoderma spp. Against Fusarium equiseti
by Sabrina Esposito Oliveira da Mota, Jamilly Alves de Barros, Kedma Maria Silva Pinto, José Eduardo Cordeiro Cezar Santos, Alberto dos Passos Vieira, Elisiane Martins de Lima, Diogo Paes da Costa, Gustavo Pereira Duda, José Romualdo de Sousa Lima, Mairon Moura da Silva, Carlos Alberto Fragoso de Souza, Rafael José Vilela de Oliveira, Claude Hammecker and Erika Valente de Medeiros
Agriculture 2025, 15(16), 1774; https://doi.org/10.3390/agriculture15161774 - 19 Aug 2025
Viewed by 408
Abstract
The application of biochar and beneficial microorganisms has gained attention as a sustainable strategy to enhance soil health and plant resistance to pathogens. Trichoderma spp. play critical roles in nutrient mobilization, rhizosphere colonization, and suppression of soilborne diseases. However, little is known about [...] Read more.
The application of biochar and beneficial microorganisms has gained attention as a sustainable strategy to enhance soil health and plant resistance to pathogens. Trichoderma spp. play critical roles in nutrient mobilization, rhizosphere colonization, and suppression of soilborne diseases. However, little is known about the interactive effects of biochar and Trichoderma on the suppression of Fusarium equiseti (P1I3)-induced root rot in grapevine seedlings. In this study, we investigated the effects of two Trichoderma aureoviride strains (URM 6668 and URM 3734), with and without grapevine pruning-derived biochar (BVP), on disease severity, plant growth, and soil properties. Our results revealed that the combination of biochar and Trichoderma significantly reduced disease incidence and promoted biomass accumulation. Notably, BVP and T. aureoviride URM 3734 were the most effective at reducing leaf disease severity, resulting in a 53% decrease. Conversely, the combination of BVP and T. aureoviride URM 6668 led to the greatest reduction in root disease severity, with a 56% decrease. These findings suggest a synergistic relationship between biochar and beneficial fungi, reinforcing the role of organic soil amendments in promoting plant health. The integrated use of biochar and Trichoderma strains offers a viable, environmentally sound approach for managing grapevine root rot and enhancing seedling health in sustainable viticulture systems. Full article
(This article belongs to the Section Agricultural Systems and Management)
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22 pages, 457 KB  
Article
The Impact of National-Level Modern Agricultural Industrial Parks on County Economies: The Analysis of Lag Effects and Impact Pathways
by Xinzi Yang and Jun Wen
Agriculture 2025, 15(16), 1773; https://doi.org/10.3390/agriculture15161773 - 19 Aug 2025
Viewed by 332
Abstract
County economies are the cornerstone of China’s economic and social development but face challenges such as a singular industrial structure and the outflow of production factors. As an important policy tool for rural revitalization, the impact mechanism of National-Level Modern Agricultural Industrial Parks [...] Read more.
County economies are the cornerstone of China’s economic and social development but face challenges such as a singular industrial structure and the outflow of production factors. As an important policy tool for rural revitalization, the impact mechanism of National-Level Modern Agricultural Industrial Parks (NMAIPs) on county economies remains inadequately explored. This study aims to quantify the dynamic economic effects of the NMAIP policy through rigorous empirical analysis and elucidate the core pathways driving county economic growth. Based on panel data from 44 counties in six central Chinese provinces from 2014 to 2024, this study employs a Multi-Period Difference-in-Differences (DID) model and finds a significant one-year lag effect of the NMAIP policy: in the year following park establishment, county GDP increased by an average of 8.5%, and this positive effect persisted until the fourth year but showed a trend of marginal diminution. Pathway analysis reveals that agricultural scale expansion (measured by gross output value of agriculture, forestry, animal husbandry, and fishery) and production efficiency improvement (measured by the ratio of output value to agricultural expenditure) are the core driving mechanisms, accounting for 48% and 35% of the total effect, respectively. In contrast, the mediating roles of industrial integration (comprehensive index) and industrial structure upgrading (share of agricultural services) were not statistically significant in the short run. The policy lag primarily arises from the conversion cycle of infrastructure investment to economic output, while pathway differences are closely related to the maturity of the county’s agricultural industrial chain and resource allocation efficiency. This study provides robust empirical evidence for optimizing the timing and pathways of the NMAIP policy design: policy effect evaluations require a 1–2 year “window period”; resources should be prioritized for projects that can rapidly enhance scale and efficiency (e.g., scaled planting, technology-driven efficiency gains), laying a solid agricultural foundation before gradually fostering industrial integration. This aligns with the spirit of “avoiding industrial hollowing-out” proposed in the 2024 Central “Thousand Villages Project” and provides the Chinese experience for the policy evaluation and path selection of global agricultural parks. Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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16 pages, 1873 KB  
Article
Peak Soil Erosion Risk in Mixed Forests: A Critical Transition Phase Driven by Moso Bamboo Expansion
by Jie Wang, Xin Wang, Youjin Yan, Liangjie Wang, Haibo Hu, Bing Ma, Hongwei Zhou, Jiacai Liu, Fengling Gan and Yuchuan Fan
Agriculture 2025, 15(16), 1772; https://doi.org/10.3390/agriculture15161772 - 18 Aug 2025
Viewed by 312
Abstract
Driven by climate change and human activities, the expansion of highly invasive moso bamboo (Phyllostachys edulis) into coniferous forests induces a serious ecological imbalance. Its rapidly spreading underground roots significantly alter soil structure, yet the mechanisms by which this expansion affects [...] Read more.
Driven by climate change and human activities, the expansion of highly invasive moso bamboo (Phyllostachys edulis) into coniferous forests induces a serious ecological imbalance. Its rapidly spreading underground roots significantly alter soil structure, yet the mechanisms by which this expansion affects soil detachment capacity (Dc), a key soil erosion parameter, remain unclear. While bamboo expansion modifies soil physicochemical properties and root characteristics, influencing Dc and, consequently, soil erosion resistance, the underlying mechanisms, particularly stage-specific variations, are not thoroughly understood. In this study, we examined Japanese white pine (Pinus parviflora Siebold & Zucc.) forest (CF), moso bamboo–Japanese white pine mixed forest (MF), and moso bamboo forest (BF) as representative stages of bamboo expansion. By integrating laboratory-controlled measurements of soil physicochemical properties and root traits with field-based flume experiments, we comprehensively investigate the effects of moso bamboo expansion into CF on soil detachment capacity. The results of the study can be summarized as follows: (1) Expansion of moso bamboo significantly changed soil physicochemical properties and root characteristics. Soil bulk density was the highest in the MF (1.13 g·cm−3), followed by the CF (1.08 g·cm−3) and BF (1.03 g·cm−3); non-capillary porosity increased significantly with expansion (CF 0.03% to MF 0.10%); and although the stability of aggregates (MWD) increased by 24.5% from the CF to MF, root mass density (RMD) in the MF (0.0048 g·cm−3) was much higher than that in the CF (0.0009 g·cm−3). This intense root competition between forest types, combined with increased macroporosity development, compromised overall soil structural integrity. This weakening may lead to a looser soil structure during the transition phase, thereby increasing erosion risk. (2) There were significant stage differences in Dc: it was significantly higher in the MF (0.034 kg·m−2·s−1) than in the CF (0.023 kg·m−2·s−1) and BF (0.018 kg·m−2·s−1), which revealed that the MF was an erosion-sensitive stage. (3) Our Partial Least Squares Structural Equation Modeling (PLS-SEM) results revealed that soil physicochemical properties (soil moisture content and soil total nitrogen) dominated Dc changes through direct effects (total effect −0.547); in comparison, root properties indirectly affected Dc by modulating soil structure (indirect effect: −0.339). The results of this study reveal the dynamics and mechanisms of Dc changes during bamboo expansion, and for the first time, we identify a distinct Dc peak during the mixed forest transition phase. These findings provide a scientific basis for moso bamboo forest management, soil erosion risk assessment, and optimization of soil and water conservation strategies. Full article
(This article belongs to the Section Agricultural Soils)
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25 pages, 13274 KB  
Article
Design and Experiment of Monomer Profiling Strip Tillage Machine with Straw-Strip-Collecting and Subsoiling Functions
by Baoci Qiu, Qiyue Zhang, Hanyu Yang, Jin He, Quanyu Wang, Hang Li, Lu Tan, Xianliang Wang and Han Lin
Agriculture 2025, 15(16), 1771; https://doi.org/10.3390/agriculture15161771 - 18 Aug 2025
Viewed by 292
Abstract
Aiming at the problems of intensified soil compaction under the conditions of no-tillage operations and machine blockage caused by large-scale straw returning to the field, an operation mode of “straw strip collecting-strip subsoiling” was proposed, and a Monomer Profiling Strip Tillage Machine (MPSTM) [...] Read more.
Aiming at the problems of intensified soil compaction under the conditions of no-tillage operations and machine blockage caused by large-scale straw returning to the field, an operation mode of “straw strip collecting-strip subsoiling” was proposed, and a Monomer Profiling Strip Tillage Machine (MPSTM) with Straw-Strip-Collecting and Subsoiling Functions was designed to achieve anti-blocking operation and three-dimensional soil compaction reduction. The principle and mechanism parameters of monomer profiling in strip tillage are analyzed, and the effective profiling conditions are clarified. It is determined that the deflection angle, inclination angle, and installation spacing have a key influence on the straw clearance effect. The theory of soil failure and soil compaction reduction under the operation of the subsoiling and strip tillage mechanism is studied, and a combination of a medium-sized Subsoiler shovel handle and a 150 mm double-wing shovel is adopted. Using the EDEM discrete element method, taking the spatial parameters of the stubble clean disc (SCD) as the test factors and the straw removal rate (SRR) as the test indicator, a quadratic orthogonal rotation test is conducted to clarify the influence of each parameter on the straw clearance. The optimal SCD spatial parameters were determined as a deflection angle of 16.5°, an inclination angle of 25°, and an installation spacing of 100 mm, achieving a maximum SRR of 95.34%. Field test results demonstrated stable machine operation. Post-operation measurements yielded the following results: the width of the straw-cleaning band (WSCB) in the sowing strip is 193.7 mm; the overall straw removal rate (OSRR) is 84.82%, which is basically consistent with the simulation results; the subsoiling depth (SD) is 271.7 mm; the subsoiling depth stability (SDS) is 91.85%; the soil fragmentation rate (SFR) is 81.19%; and the reduction of soil compaction in the 0–10, 10–20, and 20–30 cm soil layer is 50.08%, 21.78%, and 40.83%, respectively. These results confirm that the machine effectively cleaned straw within the seeding band and reduced soil compaction, meeting the agronomic and technical requirements for strip tillage. Full article
(This article belongs to the Section Agricultural Technology)
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20 pages, 2992 KB  
Article
Multi-Scale Spatiotemporal Characteristics Assessment of Water and Land Resources Ecological Security in China’s Main Grain-Producing Areas
by Kun Cheng, Bao Zhu, Nan Sun and Xingyang Zhang
Agriculture 2025, 15(16), 1770; https://doi.org/10.3390/agriculture15161770 - 18 Aug 2025
Viewed by 286
Abstract
Water and land resources, as the material foundation of food production, are essential for national food security. Current research has not yet explored the spatiotemporal features of water and land resources ecological security (WLRES) at the urban scale. To fill this gap, this [...] Read more.
Water and land resources, as the material foundation of food production, are essential for national food security. Current research has not yet explored the spatiotemporal features of water and land resources ecological security (WLRES) at the urban scale. To fill this gap, this study evaluated WLRES across 180 cities in China’s main grain-producing areas (MGPAs) from 2005 to 2020. A WLRES evaluation system was developed based on the DPSIR framework and the CRITIC method. The Moran’s I and kernel density estimation were utilized to analyze the spatial distribution, variation trends, and spatial autocorrelation of WLRES from different scales. The results demonstrate the following: (1) WLRES in the MGPAs exhibited a fluctuating upward trend, transitioning from “relatively low ecological security” to “moderate ecological security.” (2) The spatial distribution of WLRES was characterized by higher values in the northeast and southwest regions and lower values in the central region, with spatial heterogeneity gradually intensifying. (3) From 2005 to 2016, WLRES exhibited significant positive spatial autocorrelation: cities with high ecological-security levels were concentrated in the northern region, whereas those with low ecological-security levels were clustered in the central and southern of Huang-Huai-Hai Basin. Over time, this positive spatial autocorrelation weakened and eventually vanished. Our research can provide feasible policy references for improving the sustainable development of WLRES in the MGPAs. Full article
(This article belongs to the Section Agricultural Water Management)
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20 pages, 3620 KB  
Article
Participatory Selection of Bambara Groundnut Landraces in Burkina Faso: Effects of Gender and Participant Diversity
by Zakaria Kiebre, Mariam Kiebre, Romaric Kiswendsida Nanema, Fanta Reine Sheirita Tietiambou, Clémence Zerbo, Ignace Tonde, Pasquale De Muro, Hamid El Bilali, Filippo Acasto and Jacques Nanema
Agriculture 2025, 15(16), 1769; https://doi.org/10.3390/agriculture15161769 - 18 Aug 2025
Viewed by 404
Abstract
The centre of origin of Bambara groundnut (BGN; Vigna subterranea L.) is Western Sub-Saharan Africa. Due to its high nutritional value and tolerance to biotic and abiotic stresses, this neglected and underutilised species has recently gained significant attention. However, BGN production faces several [...] Read more.
The centre of origin of Bambara groundnut (BGN; Vigna subterranea L.) is Western Sub-Saharan Africa. Due to its high nutritional value and tolerance to biotic and abiotic stresses, this neglected and underutilised species has recently gained significant attention. However, BGN production faces several challenges, including a lack of quality varieties. This study describes a selected core collection based on phenotypic traits, investigates relevant selection criteria and identifies a set of landraces according to participants’ preferences. A core collection of landraces was generated, described, and then subjected to participatory varietal selection. Through individual semi-structured interviews, key selection criteria were identified. Focus group discussions were organised to explore group criteria and to support and validate information from personal interviews. The varietal selection involved choosing three landraces per participant. The results highlighted that seed colour, seed size, cultural value, market value, seed taste, storage, and seed cooking duration were the main selection criteria; however, specific trait preferences varied by gender and participants. Two of 14 selected BGN landraces were considered by the panel of evaluators to be most suitable for recommendation to growers and breeders. They can be disseminated in BGN production regions and used for plant breeding. Full article
(This article belongs to the Special Issue Advances in the Cultivation and Production of Leguminous Plants)
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19 pages, 20000 KB  
Article
Investigating the Potential Mechanism of Methane Mitigation in Seaweed Gracilaria lemaneiformis via 16S rRNA Gene Sequencing and LC/MS-Based Metabolomics
by Yi Sun, Shuai Li, Tongjun Guo, Xiong Tong, Zhifei Zhang, Yufeng Yang, Qing Wang, Dagang Li and Li Min
Agriculture 2025, 15(16), 1768; https://doi.org/10.3390/agriculture15161768 - 18 Aug 2025
Viewed by 329
Abstract
Methane (CH4), originating from ruminants, is a major source of greenhouse gas emissions in the agriculture industry. This study aimed to determine the potential of red seaweed Gracilaria lemaneiformis (G. lemaneiformis) as an anti-methanogenic feed additive for cattle. Three [...] Read more.
Methane (CH4), originating from ruminants, is a major source of greenhouse gas emissions in the agriculture industry. This study aimed to determine the potential of red seaweed Gracilaria lemaneiformis (G. lemaneiformis) as an anti-methanogenic feed additive for cattle. Three supplementation levels of seaweed (2%, 5%, and 10% of dry matter) were evaluated for their effects on gas production and rumen fermentation characteristics during 48 h in vitro fermentation. The results revealed a significant decrease in total gas production (TGP), CO2, CH4, ammonia nitrogen (NH3-N), and volatile fatty acid (VFA) concentrations, with no differences in pH or dry matter disappearance (DMD). Notably, compared with the control group without seaweed, supplementation with 2% G. lemaneiformis effectively reduces CH4 emissions by 27.5% (p < 0.05). Supplementation with 2% G. lemaneiformis decreased the abundance of methanogens g_norank_f_Methanomethylophilaceae, responsible for CH4 generation, and increased the populations of bacteria (Kandleria and Succinivibrio) that compete with methanogens for substrates. Furthermore, upregulating the levels of 13(S)-HOTrE and 9(S)-HOTrE (polyunsaturated fatty acids) could inhibit methanogenic activity. Additionally, lower VFA concentrations will provide less raw materials for methane synthesis, thus further inhibiting methanogenesis. In summary, G. lemaneiformis, as a red seaweed with important economic value, can not only be applied to enhance marine carbon sinks but can also serve as a promising candidate for mitigating biomethane emissions in cattle. Full article
(This article belongs to the Special Issue Impact of Forage Quality and Grazing Management on Ruminant Nutrition)
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19 pages, 3086 KB  
Article
Foliar Ascorbic Acid Enhances Postharvest Quality of Cherry Tomatoes in Saline Hydroponic Substrate System
by Fellype Jonathar Lemos da Silva, Hans Raj Gheyi, Geovani Soares de Lima, Lauriane Almeida dos Anjos Soares, Vera Lúcia Antunes de Lima, Francisco Jean da Silva Paiva, André Alisson Rodrigues da Silva, Denis Soares Costa, Rafaela Aparecida Frazão Torres, Allesson Ramos de Souza, Vitor Manoel Bezerra da Silva, Maria Amanda Guedes, Valeska Karolini Nunes Oliveira, Brencarla de Medeiros Lima and Reynaldo Teodoro de Fátima
Agriculture 2025, 15(16), 1767; https://doi.org/10.3390/agriculture15161767 - 18 Aug 2025
Viewed by 440
Abstract
Ascorbic acid is a non-enzymatic antioxidant compound essential for plant defense under salt stress conditions. It can induce salt stress tolerance and enable the use of saline water in hydroponic cultivation with substrates. This study evaluated the effect of foliar application of ascorbic [...] Read more.
Ascorbic acid is a non-enzymatic antioxidant compound essential for plant defense under salt stress conditions. It can induce salt stress tolerance and enable the use of saline water in hydroponic cultivation with substrates. This study evaluated the effect of foliar application of ascorbic acid on the yield and postharvest quality of ‘Laranja’ cherry tomatoes grown in saline nutrient solutions under a substrate-based hydroponic system. The experiment was conducted in a greenhouse in Campina Grande, Paraíba, Brazil, in a randomized block design in a 5 × 5 factorial arrangement, corresponding to five levels of electrical conductivity of the saline nutrient solution—SNS (2.1—Control, 2.8, 3.5, 4.2, and 4.9 dS m−1) and five concentrations of ascorbic acid—AA (0, 150, 300, 450, and 600 mg L−1), with four replications. Salinity above 2.1 dS m−1 reduced yield components and phenolic compound content. However, the saline nutrient solution of 4.9 dS m−1 combined with 600 mg L−1 foliar application of AA increased fruit firmness, soluble solids, and titratable acidity. Additionally, SNS of 4.9 dS m−1 enhanced the levels of vitamin C, flavonoids, and anthocyanins. While AA improved postharvest quality of cherry tomatoes, it did not increase production under salt stress. Foliar application is thus a promising approach for enhancing fruit quality of cherry tomatoes grown in hydroponic systems using saline water, supporting sustainable production in semiarid regions. Full article
(This article belongs to the Section Agricultural Systems and Management)
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28 pages, 7272 KB  
Article
Dynamic Object Detection and Non-Contact Localization in Lightweight Cattle Farms Based on Binocular Vision and Improved YOLOv8s
by Shijie Li, Shanshan Cao, Peigang Wei, Wei Sun and Fantao Kong
Agriculture 2025, 15(16), 1766; https://doi.org/10.3390/agriculture15161766 - 18 Aug 2025
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Abstract
The real-time detection and localization of dynamic targets in cattle farms are crucial for the effective operation of intelligent equipment. To overcome the limitations of wearable devices, including high costs and operational stress, this paper proposes a lightweight, non-contact solution. The goal is [...] Read more.
The real-time detection and localization of dynamic targets in cattle farms are crucial for the effective operation of intelligent equipment. To overcome the limitations of wearable devices, including high costs and operational stress, this paper proposes a lightweight, non-contact solution. The goal is to improve the accuracy and efficiency of target localization while reducing the complexity of the system. A novel approach is introduced based on YOLOv8s, incorporating a C2f_DW_StarBlock module. The system fuses binocular images from a ZED2i camera with GPS and IMU data to form a multimodal ranging and localization module. Experimental results demonstrate a 36.03% reduction in model parameters, a 33.45% decrease in computational complexity, and a 38.67% reduction in model size. The maximum ranging error is 4.41%, with localization standard deviations of 1.02 m (longitude) and 1.10 m (latitude). The model is successfully integrated into an ROS system, achieving stable real-time performance. This solution offers the advantages of being lightweight, non-contact, and low-maintenance, providing strong support for intelligent farm management and multi-target monitoring. Full article
(This article belongs to the Topic Precision Feeding and Management of Farm Animals, 3rd Edition)
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Article
Wheat Head Detection in Field Environments Based on an Improved YOLOv11 Model
by Yuting Zhang, Zihang Liu, Xiangdong Guo, Congcong Li and Guifa Teng
Agriculture 2025, 15(16), 1765; https://doi.org/10.3390/agriculture15161765 - 17 Aug 2025
Viewed by 617
Abstract
Precise wheat head detection is essential for plant counting and yield estimation in precision agriculture. To tackle the difficulties arising from densely packed wheat heads with diverse scales and intricate occlusions in real-world field conditions, this research introduces YOLO v11n-GRN, an improved wheat [...] Read more.
Precise wheat head detection is essential for plant counting and yield estimation in precision agriculture. To tackle the difficulties arising from densely packed wheat heads with diverse scales and intricate occlusions in real-world field conditions, this research introduces YOLO v11n-GRN, an improved wheat head detection model founded on the streamlined YOLO v11n framework. The model optimizes performance through three key innovations: This study introduces a Global Edge Information Transfer (GEIT) module architecture that incorporates a Multi-Scale Edge Information Generator (MSEIG) to enhance the perception of wheat head contours through effective modeling of edge features and deep semantic fusion. Additionally, a C3k2_RFCAConv module is developed to improve spatial awareness and multi-scale feature representation by integrating receptive field augmentation and a coordinate attention mechanism. The utilization of the Normalized Gaussian Wasserstein Distance (NWD) as the localization loss function enhances regression stability for distant small targets. Experiments were, respectively, validated on the self-built multi-temporal wheat field image dataset and the GWHD2021 public dataset. Results showed that, while maintaining a lightweight design (3.6 MB, 10.3 GFLOPs), the YOLOv11n-GRN model achieved a precision, recall, and mAP@0.5 of 92.5%, 91.1%, and 95.7%, respectively, on the self-built dataset, and 91.6%, 89.7%, and 94.4%, respectively, on the GWHD2021 dataset. This fully demonstrates that the improvements can effectively enhance the model’s comprehensive detection performance for wheat ear targets in complex backgrounds. Meanwhile, this study offers an effective technical approach for wheat head detection and yield estimation in challenging field conditions, showcasing promising practical implications. Full article
(This article belongs to the Section Artificial Intelligence and Digital Agriculture)
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