Agriculture

18 pages, 8338 KB

Open AccessArticle

Genetic Analysis of Thai Centella asiatica Germplasm for Morphological, Biomass, and Centelloside Traits

by Jareerat Chunthawodtiporn, Thongchai Koobkokkruad, Samart Wanchana, Theerayut Toojinda, Paradee Thammapichai, Vinitchan Ruanjaichon and Kanokwan Romyanon

Agriculture 2025, 15(17), 1905; https://doi.org/10.3390/agriculture15171905 - 8 Sep 2025

Abstract

Centella asiatica (L.) Urban or Asiatic pennywort is an important medicinal plant used in traditional medicine, cosmetic and pharmaceutical industries; it is also an indigenous vegetable in Asian countries. A total of 169 Centella accessions were collected from all regions of Thailand and [...] Read more.

Centella asiatica (L.) Urban or Asiatic pennywort is an important medicinal plant used in traditional medicine, cosmetic and pharmaceutical industries; it is also an indigenous vegetable in Asian countries. A total of 169 Centella accessions were collected from all regions of Thailand and assessed for 29 traits, including morphological traits, plant biomass, and centelloside contents. Experiments were conducted in two growing systems, soil and hydroponics, with two harvests every month. The centelloside concentrations were determined by high-performance thin-layer chromatography (HPTLC) procedure. Analysis of variance (ANOVA), correlation matrix, genetic parameters, principal component analysis (PCA), and hierarchical clustering were determined both across all environments and for each growing system separately. The ANOVA revealed significant differences in genotypes and growing systems, along with their interactions. Hydroponic culture produced three- to four-fold higher biomass than soil system while triterpenoid sapogenin concentrations were notably greater in hydroponics. Biomass-related traits showed strong positive correlations with centelloside yields, and genetic analyses revealed moderate to high heritability for these characteristics. PCA and cluster analyses classified accessions into four distinct groups, identifying elite genotypes with both high biomass and centelloside yield. These findings provide a solid foundation for targeted selection in Centella breeding programs. Full article

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

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13 pages, 239 KB

Open AccessArticle

Phosphine Susceptibility Screening of Three Different Stored Product Beetle Species by Using Three Diagnostic Techniques

by Maria K. Sakka, Marie-Carolin Götze and Christos G. Athanassiou

Agriculture 2025, 15(17), 1904; https://doi.org/10.3390/agriculture15171904 - 8 Sep 2025

Abstract

Phosphine resistance represents a major challenge for stored product protection worldwide. In this study, we evaluated populations of Oryzaephilus surinamensis, Rhyzopertha dominica, and Cryptolestes ferrugineus collected from different regions using three diagnostic protocols: (i) the FAO test (30 ppm for 20 [...] Read more.

Phosphine resistance represents a major challenge for stored product protection worldwide. In this study, we evaluated populations of Oryzaephilus surinamensis, Rhyzopertha dominica, and Cryptolestes ferrugineus collected from different regions using three diagnostic protocols: (i) the FAO test (30 ppm for 20 h); (ii) a dose–response bioassay (50–1000 ppm for 3 d); (iii) the Phosphine Tolerance Test (3000 ppm for up to 270 min). Results indicated that while several O. surinamensis populations remained susceptible, all tested populations of R. dominica and C. ferrugineus were resistant. The three protocols produced comparable outcomes, supporting their reliability for diagnosing resistant populations. This is the first study to simultaneously compare three diagnostic approaches across multiple beetle species, providing the basis for a harmonized global diagnostic framework. These findings underscore the need for continued monitoring and highlight the importance of standardized tools for resistance management. Full article

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

24 pages, 26159 KB

Open AccessArticle

DAS-Net: A Dual-Attention Synergistic Network with Triple-Spatial and Multi-Scale Temporal Modeling for Dairy Cow Feeding Behavior Detection

by Xuwen Li, Ronghua Gao, Qifeng Li, Rong Wang, Luyu Ding, Pengfei Ma, Xiaohan Yang and Xinxin Ding

Agriculture 2025, 15(17), 1903; https://doi.org/10.3390/agriculture15171903 - 8 Sep 2025

Abstract

The feeding behavior of dairy cows constitutes a complex temporal sequence comprising actions such as head lowering, sniffing, arching, eating, head raising, and chewing. Its precise recognition is crucial for refined livestock management. While existing 2D convolution-based models effectively extract features from individual [...] Read more.

The feeding behavior of dairy cows constitutes a complex temporal sequence comprising actions such as head lowering, sniffing, arching, eating, head raising, and chewing. Its precise recognition is crucial for refined livestock management. While existing 2D convolution-based models effectively extract features from individual frames, they lack temporal modeling capabilities. Conversely, due to their high computational complexity, 3D convolutional networks suffer from significantly limited recognition accuracy in high-density feeding scenarios. To address this, this paper proposes a Spatio-Temporal Fusion Network (DAS-Net): it designs a collaborative architecture featuring a 2D branch with a triple-attention module to enhance spatial key feature extraction, constructs a 3D branch based on multi-branch dilated convolution and integrates a 3D multi-scale attention mechanism to achieve efficient long-term temporal modeling. On our Spatio-Temporal Dairy Feeding Dataset (STDF Dataset), which contains 403 video clips and 10,478 annotated frames across seven behavior categories, the model achieves an average recognition accuracy of 56.83% for all action types. This result marks a significant improvement of 3.61 percentage points over the original model. Among them, the recognition accuracy of the eating action has been increased to 94.78%. This method provides a new idea for recognizing dairy cow feeding behavior and can provide technical support for developing intelligent feeding systems in real dairy farms. Full article

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

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15 pages, 639 KB

Open AccessArticle

Arbuscular Mycorrhizal Fungi Inoculation Enhances Nutritional Quality of Prickly Pear (Opuntia ficus-indica) Fruits and Cladodes

by Sonia Labidi, Adrien Servent, Ghofran Bouzoumita, Tina Julien, Guillaume Cazals, Manel Ibrahim, Sofiène B. M. Hammami and Nawel Achir

Agriculture 2025, 15(17), 1902; https://doi.org/10.3390/agriculture15171902 - 8 Sep 2025

Abstract

The effects of arbuscular mycorrhizal fungi (AMF) inoculation on the chemical composition of the fruits and cladodes of two Opuntia ficus-indica cultivars, characterized by their red and yellow fruit color, were investigated under field conditions. AMF treatment was found to significantly influence the [...] Read more.

The effects of arbuscular mycorrhizal fungi (AMF) inoculation on the chemical composition of the fruits and cladodes of two Opuntia ficus-indica cultivars, characterized by their red and yellow fruit color, were investigated under field conditions. AMF treatment was found to significantly influence the concentration of phytonutrients in the fruits. The concentrations of betacyanin and betaxanthin increased by 1.2- and 1.9-fold in red and yellow fruits, respectively. The polyphenol content increased by 50%, with piscidic acid being the most abundant polyphenol in the red fruits. A similar increase in ascorbic acid was observed in the yellow fruits. Regarding the cladodes, AMF treatment was found to significantly affect macronutrient levels, with glucose and fructose contents being 90% and 34% higher, respectively. Additionally, cladodes from plants grown with AMF inoculation showed a 20% increase in ascorbic acid and phosphorus. These results demonstrate cultivar- and part-of-plant-dependent effects of AMF inoculation and confirm the nutritional and sustainable potential of Opuntia ficus-indica, particularly when coupled with mycorrhizal biofertilization practices. Full article

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

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28 pages, 6809 KB

Open AccessArticle

Application of Raman Spectroscopy-Driven Multi-Model Ensemble Modeling in Soil Nutrient Prediction

by Xiuquan Zhang, Juanling Wang, Zhiwei Li, Haiyan Song and Decong Zheng

Agriculture 2025, 15(17), 1901; https://doi.org/10.3390/agriculture15171901 - 8 Sep 2025

Viewed by 73

Abstract

Rapid and non-destructive acquisition of soil nutrient information is crucial for precision fertilization and soil quality monitoring. This study aims to establish a Raman spectroscopy-based framework for predicting key soil fertility indicators, including alkali-hydrolyzable nitrogen (AN), total nitrogen (TN), total phosphorus (TP), and [...] Read more.

Rapid and non-destructive acquisition of soil nutrient information is crucial for precision fertilization and soil quality monitoring. This study aims to establish a Raman spectroscopy-based framework for predicting key soil fertility indicators, including alkali-hydrolyzable nitrogen (AN), total nitrogen (TN), total phosphorus (TP), and organic matter (OM). The framework systematically integrates three typical spectral preprocessing methods (Standard Normal Variate transformation (SNV), Savitzky–Golay first derivative (SG_D1), and wavelet transform (Wavelet)), three feature selection strategies (Recursive Feature Elimination, XGBoost importance, and Random Forest importance), and 14 mainstream regression models to construct a multi-combination modeling system. Model performance was evaluated using five-fold cross-validation, with 80% of samples used for training and 20% for validation in each fold. Preprocessed Raman spectral features served as input variables, while the corresponding nutrient contents were used as outputs. Results showed significant differences in prediction performance across various combinations of preprocessing methods and regression algorithms for the four soil nutrient indicators. For AN prediction, the combination of Raw_SNV preprocessing with ElasticNet and BayesianRidge models achieved the best performance, with Test R² values of 0.713 and 0.721, and corresponding Test NRMSE as low as 0.092. For OM prediction, the same Raw_SNV preprocessing with ElasticNet and BayesianRidge also performed well, yielding Test R² values of 0.825 and 0.832, and Test NRMSE of 0.100 and 0.098, respectively. In TN prediction, both ElasticNet and BayesianRidge under Raw_SNV preprocessing achieved consistent Test R² of 0.74 and Test NRMSE around 0.20, indicating stable reliability. For TP prediction, the BayesianRidge model with Raw_SNV preprocessing outperformed all others with a Test R² of 0.71 and Test NRMSE of just 0.089, followed closely by ElasticNet (Test R² = 0.70, Test NRMSE = 0.092). Overall, the Raw_SNV preprocessing method demonstrated superior performance compared to SG_D1_SNV and Wavelet_SNV. Both BayesianRidge and ElasticNet consistently achieved high R² and low NRMSE across multiple targets, showcasing strong generalization and robustness, making them optimal model choices for Raman spectroscopy-based soil nutrient prediction. This study demonstrates that Raman spectroscopy, when combined with appropriate preprocessing and modeling techniques, can effectively predict soil organic matter and nitrogen in specific soil types under laboratory conditions. These results provide initial methodological insights for future development of intelligent soil nutrient diagnostics. Full article

(This article belongs to the Section Agricultural Soils)

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21 pages, 6402 KB

Open AccessArticle

Impact of Climate Change on the Climatic Suitability of Oilseed Rape (Brassica napus L.) Planting in Jiangsu Province, China

by Yuqing Shi, Qichun Zhu, Mengquan Zhu, Nan Jiang, Lixuan Ren and Yunsheng Lou

Agriculture 2025, 15(17), 1900; https://doi.org/10.3390/agriculture15171900 - 7 Sep 2025

Viewed by 840

Abstract

Climate change has caused considerable uncertainty to oilseed rape production. However, the climatic suitability for oilseed rape cultivation and its future changing trend remain unclear, specifically in Jiangsu Province—a major oilseed rape producing-region in China. Based on the past 50 years (1969–2018) of [...] Read more.

Climate change has caused considerable uncertainty to oilseed rape production. However, the climatic suitability for oilseed rape cultivation and its future changing trend remain unclear, specifically in Jiangsu Province—a major oilseed rape producing-region in China. Based on the past 50 years (1969–2018) of daily meteorological data from 13 meteorological stations in the province, this study established a climate suitability assessment model for oilseed rape cultivation. Temperature, precipitation, and sunlight were comprehensively analyzed, with suitable zones delineated through GIS spatial analysis and the natural break method. With the incorporation of SSP2-4.5 climatic scenario simulation data, the study projected the evolving trends of oilseed rape cultivation climatic suitability zones from 2024 to 2050 in the province. The findings reveal that over the past five decades, the climatic suitability for oilseed rape planting in the province has demonstrated the following patterns: temperature suitability increased by 0.02 per decade, precipitation suitability declined by −0.01 per decade, sunlight suitability decreased by −0.01 per decade, and comprehensive suitability rose by 0.01 per decade. High climatic suitability with the index of 0.80–1.00 was predominantly clustered in the central region, while moderate suitability zones with the index of 0.50–0.80 were mainly found in its northern and southern regions. Unsuitable zones with the index of 0.00–0.50 were mainly confined to the northern and southern extremities of the province. Under future climate scenarios, oilseed rape planting suitability is projected to improve significantly, with highly suitable zones expanding, particularly into the central and parts of the northern Jiangsu. Moderately suitable zones also will be extended, including potential areas such as the parts of Lianyungang and Wuxi. Unsuitable zones will be reduced, with only limited areas like southern Wuxi retaining lower suitability. Future temperature increases in Lianyungang are expected to be in favor of oilseed rape production. However, excessive precipitation in the southern region will require enhanced drainage measures. Improved temperature and precipitation conditions in Xuzhou are anticipated to boost the climatic suitability. Overall, oilseed rape planting climatic factors in the central and northern regions are projected to improve, enabling production expansion, while the southern region will face the challenge of excessive precipitation in Jiangsu Province. Full article

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

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26 pages, 7018 KB

Open AccessArticle

LiDAR-IMU Sensor Fusion-Based SLAM for Enhanced Autonomous Navigation in Orchards

by Seulgi Choi, Xiongzhe Han, Eunha Chang and Haetnim Jeong

Agriculture 2025, 15(17), 1899; https://doi.org/10.3390/agriculture15171899 - 7 Sep 2025

Viewed by 920

Abstract

Labor shortages and uneven terrain in orchards present significant challenges to autonomous navigation. This study proposes a navigation system that integrates Light Detection and Ranging (LiDAR) and Inertial Measurement Unit (IMU) data to enhance localization accuracy and map stability through Simultaneous Localization and [...] Read more.

Labor shortages and uneven terrain in orchards present significant challenges to autonomous navigation. This study proposes a navigation system that integrates Light Detection and Ranging (LiDAR) and Inertial Measurement Unit (IMU) data to enhance localization accuracy and map stability through Simultaneous Localization and Mapping (SLAM). To minimize distortions in LiDAR scans caused by ground irregularities, real-time tilt correction was implemented based on IMU feedback. Furthermore, the path planning module was improved by modifying the Rapidly-Exploring Random Tree (RRT) algorithm. The enhanced RRT generated smoother and more efficient trajectories with quantifiable improvements: the average shortest path length was 2.26 m, compared to 2.59 m with conventional RRT and 2.71 m with A* algorithm. Tracking performance also improved, achieving a root mean square error of 0.890 m and a maximum lateral deviation of 0.423 m. In addition, yaw stability was strengthened, as heading fluctuations decreased by approximately 7% relative to the standard RRT. Field results validated the robustness and adaptability of the proposed system under real-world agricultural conditions. These findings highlight the potential of LiDAR–IMU sensor fusion and optimized path planning to enable scalable and reliable autonomous navigation for precision agriculture. Full article

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

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18 pages, 3048 KB

Open AccessArticle

Estimation of Wheat Leaf Water Content Based on UAV Hyper-Spectral Remote Sensing and Machine Learning

by Yunlong Wu, Shouqi Yuan, Junjie Zhu, Yue Tang and Lingdi Tang

Agriculture 2025, 15(17), 1898; https://doi.org/10.3390/agriculture15171898 - 7 Sep 2025

Viewed by 226

Abstract

Leaf water content is a critical metric during the growth and development of winter wheat. Rapid and efficient monitoring of leaf water content in winter wheat is essential for achieving precision irrigation and assessing crop quality. Unmanned aerial vehicle (UAV)-based hyperspectral remote sensing [...] Read more.

Leaf water content is a critical metric during the growth and development of winter wheat. Rapid and efficient monitoring of leaf water content in winter wheat is essential for achieving precision irrigation and assessing crop quality. Unmanned aerial vehicle (UAV)-based hyperspectral remote sensing technology has enormous application potential in the field of crop monitoring. In this study, UAV was used as the platform to conduct six canopy hyperspectral data samplings and field-measured leaf water content (LWC) across four growth stages of winter wheat. Then, six spectral transformations were performed on the original spectral data and combined with the correlation analysis with wheat leaf water content (LWC). Multiple scattering correction (MSC), standard normal variate (SNV), and first derivative (FD) were selected as the subsequent transformation methods. Additionally, competitive adaptive reweighted sampling (CARS) and the Hilbert–Schmidt independence criterion lasso (HSICLasso) were employed for feature selection to eliminate redundant information from the spectral data. Finally, three machine learning algorithms—partial least squares regression (PLSR), support vector regression (SVR), and random forest (RF)—were combined with different data preprocessing methods, and 50 random partition datasets and model evaluation experiments were conducted to compare the accuracy of different combination models in assessing wheat LWC. The results showed that there are significant differences in the predictive performance of different combination models. By comparing the prediction accuracy on the test set, the optimal combinations of the three models are MSC + CARS + SVR (R² = 0.713, RMSE = 0.793, RPD = 2.097), SNV + CARS + PLSR (R² = 0.692, RMSE = 0.866, RPD = 2.053), and FD + CARS + RF (R² = 0.689, RMSE = 0.848, RPD = 2.002). All three models can accurately and stably predict winter wheat LWC, and the CARS feature extraction method can improve the prediction accuracy and enhance the stability of the model, among which the SVR algorithm has better robustness and generalization ability. Full article

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

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17 pages, 747 KB

Open AccessArticle

Factors Affecting China’s Tea Exports to Malaysia: An ARDL Analysis

by Yanqi Hu and Chin-Hong Puah

Agriculture 2025, 15(17), 1897; https://doi.org/10.3390/agriculture15171897 - 7 Sep 2025

Viewed by 152

Abstract

This study employed quarterly data spanning from 2005 to 2024 to investigate the factors affecting China’s tea exports to Malaysia using demand theory. The Autoregressive Distributed Lag (ARDL) approach and Granger causality test were applied to examine the long-run and short-run impacts of [...] Read more.

This study employed quarterly data spanning from 2005 to 2024 to investigate the factors affecting China’s tea exports to Malaysia using demand theory. The Autoregressive Distributed Lag (ARDL) approach and Granger causality test were applied to examine the long-run and short-run impacts of key variables, including the prices of China’s tea and coffee imported by Malaysia, Malaysia’s GDP, Malaysia’s tea production, and the international oil price. The ARDL bounds testing confirmed the existence of a long-run equilibrium among these variables. The empirical findings revealed that an increase in the price of China’s tea significantly reduced export volumes, whereas Malaysia’s GDP exerted a strong positive influence. The price of coffee exhibited a significantly negative effect, suggesting an unconventional substitution relationship with tea. Both Malaysia’s domestic tea production and the international oil price imposed downward pressures on China’s tea exports. Furthermore, the Granger causality analysis indicated that the price of China’s tea, the price of coffee, and Malaysia’s GDP all exerted short-run effects on China’s tea exports to Malaysia. These findings contribute to the export demand literature and offer implications for policies aiming to enhance bilateral tea trade between China and Malaysia. Full article

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

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18 pages, 4279 KB

Open AccessArticle

Soil Compaction Prediction in Precision Agriculture Using Cultivator Shank Vibration and Soil Moisture Data

by Shaghayegh Janbazialamdari, Daniel Flippo, Evan Ridder and Edwin Brokesh

Agriculture 2025, 15(17), 1896; https://doi.org/10.3390/agriculture15171896 - 7 Sep 2025

Viewed by 246

Abstract

Precision agriculture applies data-driven strategies to manage spatial and temporal variability within fields, aiming to increase productivity while minimizing pressure on natural resources. As interest in smart tillage systems expands, this study explores a central question: Can tillage tools be used to measure [...] Read more.

Precision agriculture applies data-driven strategies to manage spatial and temporal variability within fields, aiming to increase productivity while minimizing pressure on natural resources. As interest in smart tillage systems expands, this study explores a central question: Can tillage tools be used to measure soil compaction during regular field operations? To investigate this, vibration data measurements were collected from a cultivator shank in the northeast of Kansas using the AVDAQ system. The test field soils were Reading silt loam and Eudora–Bismarck Grove silt loams. The relationship between shank vibrations, soil moisture (measured by a Hydrosense II soil–water sensor), and soil compaction (measured by a cone penetrometer) was evaluated using machine learning models. Both XGBoost and Random Forest demonstrated strong predictive performance, with Random Forest achieving a slightly higher correlation of 93.8% compared to 93.7% for XGBoost. Statistical analysis confirmed no significant difference between predicted and measured values, validating the accuracy and reliability of both models. Overall, the results demonstrate that combining vibration data with soil moisture data as model inputs enables accurate estimation of soil compaction, providing a foundation for future in situ soil sensing, reduced tillage intensity, and more sustainable cultivation practices. Full article

(This article belongs to the Section Agricultural Soils)

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22 pages, 601 KB

Open AccessArticle

Farmers’ Attitudes Towards the Diversification of Agricultural Sustainable Production in Tourism in Vojvodina Province (Republic of Serbia)

by Maja Paunić, Dragan Tešanović, Vesna Vujasinović, Jasmina Lazarević, Snježana Gagić Jaraković, Miloš Ćirić, Gordana Vulić and Sreto Aleksić

Agriculture 2025, 15(17), 1895; https://doi.org/10.3390/agriculture15171895 - 6 Sep 2025

Viewed by 204

Abstract

This manuscript investigates the key factors driving the diversification of agricultural production towards tourism and analyzes the impact of economic business aspects and farmers’ identity on this process. The study involved 420 farm owners from the Autonomous Province of Vojvodina. Factor analysis identified [...] Read more.

This manuscript investigates the key factors driving the diversification of agricultural production towards tourism and analyzes the impact of economic business aspects and farmers’ identity on this process. The study involved 420 farm owners from the Autonomous Province of Vojvodina. Factor analysis identified four main factors: motivation, resources, market conditions, and accessibility. Results show that the average monthly income of farmers is €1350, and they recognize diversification potential as a tool to improve the economic performance of their farms. However, the prevailing traditional farmer identity limits this process. This study provides insights into farmers’ attitudes towards sustainable agricultural diversification into tourism within the context of a developing country. Full article

(This article belongs to the Special Issue Agricultural Citizen Science and Sustainable Regional Development—Second Edition)

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21 pages, 644 KB

Open AccessArticle

Competitiveness and Diversification in Grape Exports: Keys to Their Sustainability in Global Markets

by Hugo Daniel García Juárez, Jose Carlos Montes Ninaquispe, Sandra Lizzette León Luyo, Heyner Yuliano Marquez Yauri, Carlos Enrique Mendoza Ocaña, Nelly Victoria De La Cruz Ruiz, Sarita Jessica Apaza Miranda, Christian David Corrales Otazú, Antonio Rafael Rodríguez Abraham and Groover Valenty Villanueva Butrón

Agriculture 2025, 15(17), 1894; https://doi.org/10.3390/agriculture15171894 - 6 Sep 2025

Viewed by 267

Abstract

This study examined the sustainability of global table grape exports from 2020 to 2024, focusing on two key dimensions: market diversification and international competitiveness. Using data from Trade Map and applying the Herfindahl–Hirschman Index (HHI) and the Revealed Comparative Advantage Normalized Index (RCAN), [...] Read more.

This study examined the sustainability of global table grape exports from 2020 to 2024, focusing on two key dimensions: market diversification and international competitiveness. Using data from Trade Map and applying the Herfindahl–Hirschman Index (HHI) and the Revealed Comparative Advantage Normalized Index (RCAN), the research analyzed the export performance of major grape-exporting countries, including Peru, Chile, the Netherlands, Italy, the United States, South Africa, and China. The results showed significant differences in both market structure and competitive positioning. Countries like Peru and South Africa demonstrated rapid export growth and high competitiveness in certain markets, but faced elevated levels of market concentration, exposing them to external shocks. In contrast, Italy and the Netherlands maintained more diversified portfolios but showed modest competitiveness. The study concluded that no country achieved an ideal balance between diversification and competitiveness. As a result, it is recommended that governments pursue integrated trade strategies that promote geographic expansion alongside measures to enhance export competitiveness. Investments in logistics, quality certifications, and market intelligence are essential to reduce vulnerability and ensure long-term export sustainability. Full article

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

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18 pages, 2030 KB

Open AccessArticle

Land Use Changes Influence Tropical Soil Diversity: An Assessment Using Soil Taxonomy and the World Reference Base for Soil Classifications

by Selvin Antonio Saravia-Maldonado, Beatriz Ramírez-Rosario, María Ángeles Rodríguez-González and Luis Francisco Fernández-Pozo

Agriculture 2025, 15(17), 1893; https://doi.org/10.3390/agriculture15171893 - 5 Sep 2025

Viewed by 323

Abstract

The transformation of natural ecosystems into agroecosystems due to changes in land use/land cover (LULC) has been shown to significantly affect soil characterization and classification. The impact of LULC on soil taxonomy was assessed in a primary forest located in central–eastern Honduras, which [...] Read more.

The transformation of natural ecosystems into agroecosystems due to changes in land use/land cover (LULC) has been shown to significantly affect soil characterization and classification. The impact of LULC on soil taxonomy was assessed in a primary forest located in central–eastern Honduras, which had been deforested approximately forty years prior to the study. Morphological, physical, and physicochemical analyses were performed by describing 10 representative profiles, applying the Soil Taxonomy (ST) and World Reference Base for Soil Resources (WRB) nomenclatures. LULC resulted in physical degradation in agricultural areas, as evidenced by lighter-colored horizons (P02), reduced granular structure (P01, P02, P05), higher bulk densities (≤1.73 Mg m⁻³), and surface crusting (P02, P05); this phenomenon was also observed in pastures (P06–P09). SOC loss was 62% in croplands, 47–53% in agroforestry systems (P03) and fruit tree plantations (P04), and 25% in pastures. All profiles exhibited pH values between 6.5 and 8.4 and complete base saturation (BS), except for P08 and P09, which had pH values below 5.5, high levels of Al³⁺, and reduced BS (50–60%). Mollic epipedons and variability in the endopedons were also observed. According to the ST of the System of Soil Classification (SSC), the soils were classified as Mollisols, Entisols, Vertisols, and Alfisols; and as Phaeozems, Fluvisols, Gleysols, Anthrosols, Gypsisols, and Plinthosols by the WRB. We advocate for the inclusion of Anthropogenic Soils as a distinct Order within Soil Taxonomy (ST). The implementation of sustainable agricultural practices, in conjunction with the formulation of regulatory frameworks governing land use based on capacity and suitability, is imperative, particularly within the context of fragile tropical systems. Full article

(This article belongs to the Special Issue Factors Affecting Soil Fertility and Improvement Measures)

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18 pages, 2898 KB

Open AccessArticle

Exogenous Catalase Supplementation Alleviates Fusarium graminearum Mycotoxins-Induced Oxidative Stress in Weaned Piglets

by Shujie Liang, Yunfei Jiang, Chong Ling, Meitian Xian, Hui Ye, Qingyun Cao, Changming Zhang, Zemin Dong, Weiwei Wang and Jianjun Zuo

Agriculture 2025, 15(17), 1892; https://doi.org/10.3390/agriculture15171892 - 5 Sep 2025

Viewed by 279

Abstract

The objective of this study was to investigate the impact of exogenous catalase (CAT) on antioxidant properties, as well as on hepatic and intestinal health, in piglets exposed to Fusarium graminearum mycotoxins (FGM). Forty female weaned piglets were divided into five groups (eight [...] Read more.

The objective of this study was to investigate the impact of exogenous catalase (CAT) on antioxidant properties, as well as on hepatic and intestinal health, in piglets exposed to Fusarium graminearum mycotoxins (FGM). Forty female weaned piglets were divided into five groups (eight replicates per group). The pre-feeding period was 3 days, followed by a 28-day experimental period. The piglets in the control (CON) group were fed a diet without FGM contamination, while those in the FGM-exposed (TOX) group were fed a diet with FGM contamination. The LCAT, MCAT, and HCAT groups received an FGM-contaminated diet supplemented with 100, 200, and 400 U/kg of CAT, respectively. The results indicated that 400 U/kg CAT supplementation inhibited (p < 0.05, linear p < 0.05, quadratic p < 0.05) the decreases in average daily gain and average daily feed intake of piglets exposed to FGM. Moreover, all doses of supplemental CAT suppressed (p < 0.05) the increases in diarrhea rate and diarrhea index of FGM-exposed piglets. Additionally, supplemental CAT reversed (p < 0.05, linear and quadratic p < 0.05 in ileal tissue, quadratic p < 0.05 in ileal chyme) the decrease in ileal tissue and increase in ileal chyme of reactive oxygen species of piglets exposed to FGM. Supplemental CAT also enhanced the activities of ileal CAT (p < 0.05, quadratic p < 0.05) coupled with hepatic superoxide dismutase and CAT (p < 0.05, linear p < 0.05, quadratic p < 0.05) and elevated (p < 0.05) the expression of ileal and hepatic antioxidation-related genes of FGM-exposed piglets. Furthermore, the CAT supplementation increased (p < 0.05) the expression of Occludin and Claudin-1 in the ileum and colon of piglets exposed to FGM. The FGM-induced increase in the genus Staphylococcus and decrease in the genus Lactobacillus in the ileum of piglets were inhibited (p < 0.05) by supplemental 400 U/kg CAT, which also modulated the metabolite profiles involved in the glycerophospholipid metabolism pathway in hepatic portal vein blood. Exogenous CAT mitigates oxidative stress induced by FGM, along with improving intestinal and hepatic health of piglets, which can be associated with its ability to enhance intestinal microbiota and regulate hepatic glycerophospholipid metabolism, aside from its direct ability to scavenge oxygen radicals. The appropriate amount of supplemental CAT was 400 U/kg. Full article

(This article belongs to the Section Farm Animal Production)

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21 pages, 1689 KB

Open AccessReview

Reconsidering the Soil–Water–Crops–Energy (SWCE) Nexus Under Climate Complexity—A Critical Review

by Nektarios N. Kourgialas

Agriculture 2025, 15(17), 1891; https://doi.org/10.3390/agriculture15171891 - 5 Sep 2025

Viewed by 205

Abstract

Nowadays, sustainable agriculture is emerging as a critical framework within which food production, environmental protection and resilience to climate change must go hand in hand. At the core of this framework are the linkages between soil, water, crops, and energy (SWCE). As pressures [...] Read more.

Nowadays, sustainable agriculture is emerging as a critical framework within which food production, environmental protection and resilience to climate change must go hand in hand. At the core of this framework are the linkages between soil, water, crops, and energy (SWCE). As pressures from climate change, population growth and agricultural land degradation intensify, environmental management strategies are called upon to become more interdisciplinary, targeted and cost-effective. This review article synthesizes recent scientific findings shaping the contemporary understanding of hydro-environmental agriculture and critically examines the conceptual foundation of the SWCE nexus under climate complexity. In addition to reviewing methodological approaches, it highlights both successful global practice examples—such as integrated solar-powered irrigation and conservation-oriented soil–water management systems—and failed or problematic implementations where institutional fragmentation, unsustainable groundwater use, or energy trade-offs undermined outcomes. By analyzing these contrasting experiences, the article identifies key limiting factors and enabling conditions for scaling up nexus-based solutions. Finally, it provides recommendations for future research, integration, and policy-making, emphasizing the importance of adaptive governance, participatory approaches, and cross-sectoral collaboration to enhance the sustainability and resilience of agriculture. Full article

(This article belongs to the Topic Advances in Water and Soil Management Towards Climate Change Adaptation)

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19 pages, 371 KB

Open AccessArticle

Digital Literacy, Labor Force Characteristics and the Degree of Adoption of Agricultural Socialized Services: Empirical Evidence from Rural China

by Hong Tang, Zhiyou Liu and Feng Huang

Agriculture 2025, 15(17), 1890; https://doi.org/10.3390/agriculture15171890 - 5 Sep 2025

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Abstract

Under the strategic goal of agricultural modernization, agricultural socialization services have become an important means of enhancing agricultural efficiency and guaranteeing food security. Based on microdata from 3811 farm households in seven provinces, this paper integrates labor force structural characteristics with digital literacy [...] Read more.

Under the strategic goal of agricultural modernization, agricultural socialization services have become an important means of enhancing agricultural efficiency and guaranteeing food security. Based on microdata from 3811 farm households in seven provinces, this paper integrates labor force structural characteristics with digital literacy to construct a comprehensive analytical framework and empirically examines their effects on the degree of access to agricultural socialized services (DASS) through ordered logit model and moderated effects models. The results show that labor force characteristics significantly affect DASS, and the higher the degree of feminization, aging, and part-time employment, the higher the degree of access to services; digital literacy as a whole significantly improves DASS for farm households and shows heterogeneous moderating effects under different labor force characteristics. Therefore, this paper suggests formulating differentiated socialized service promotion strategies, deepening the digitalization of agricultural services, strengthening the digital technology training of rural laborers in various ways, enhancing DASS, effectively improving the efficiency of agricultural production, and supporting the dual goals of food security and rural revitalization. Full article

(This article belongs to the Topic Sustainability and Innovation in Agriculture for the Food Production of Tomorrow)

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15 pages, 2095 KB

Open AccessArticle

Exploring Genetic Variation in Root Traits and Root–Fungal Associations in Aegilops tauschii

by Ahmed Khaled Hassan Mohammedali, Yasir Serag Alnor Gorafi, Nasrein Mohamed Kamal, Izzat Sidahmed Ali Tahir, Hisashi Tsujimoto and Takeshi Taniguchi

Agriculture 2025, 15(17), 1889; https://doi.org/10.3390/agriculture15171889 - 5 Sep 2025

Viewed by 217

Abstract

Wheat domestication and selection for aboveground traits may have influenced belowground traits, reducing genetic diversity critical for adaptation to stress such as drought. However, the impacts on root system architecture and root–endophytic fungal interactions remain unclear. This study evaluated variation in root traits [...] Read more.

Wheat domestication and selection for aboveground traits may have influenced belowground traits, reducing genetic diversity critical for adaptation to stress such as drought. However, the impacts on root system architecture and root–endophytic fungal interactions remain unclear. This study evaluated variation in root traits and associations with arbuscular mycorrhizal fungi (AMF) and dark septate endophytes (DSE) among nine diploid Aegilops tauschii accessions (wild progenitor), one tetraploid Triticum turgidum cv. ‘Langdon’ (LNG), and one hexaploid Triticum aestivum cv. ‘Norin 61’ (N61). Root traits and fungal colonization varied significantly among genotypes. All Ae. tauschii accessions showed superior root development and lower DSE colonization compared to LNG and N61. AMF colonization was highest in accessions AT76 and KU-2126 (54% and 53%, respectively), while N61 exhibited the highest specific root length (SRL) and DSE colonization. AMF positively correlated with most root traits (except SRL), while DSE showed the opposite trend. Although Ae. tauschii accessions shared broadly favorable root traits, variation in their fungal interactions were more pronounced. A clustering heatmap incorporating both root and biotic traits clustered the genotypes into four groups, clearly separating the Ae. tauschii accessions into two clusters based on their root characteristics and root-fungal associations. These results highlight the hidden interspecific and intraspecific variations in Ae. tauschii and its potential as a genetic resource for optimizing root–endophytic fungal interactions, and improving wheat resilience to biotic and abiotic stress in a changing climate. Full article

(This article belongs to the Special Issue Arbuscular Mycorrhiza in Cropping Systems)

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23 pages, 9993 KB

Open AccessArticle

Morphological Characterization of Aspergillus flavus in Culture Media Using Digital Image Processing and Radiomic Analysis Under UV Radiation

by Oscar J. Suarez, Daniel C. Ruiz-Ayala, Liliana Rojas Contreras, Manuel G. Forero, Jesús A. Medrano-Hermosillo and Abraham Efraim Rodriguez-Mata

Agriculture 2025, 15(17), 1888; https://doi.org/10.3390/agriculture15171888 - 5 Sep 2025

Viewed by 358

Abstract

The identification of Aspergillus flavus (A. flavus), a fungus known for producing aflatoxins, poses a taxonomic challenge due to its morphological plasticity and similarity to closely related species. This article proposes a computational approach for its characterization across four culture media, [...] Read more.

The identification of Aspergillus flavus (A. flavus), a fungus known for producing aflatoxins, poses a taxonomic challenge due to its morphological plasticity and similarity to closely related species. This article proposes a computational approach for its characterization across four culture media, using ultraviolet (UV) radiation imaging and radiomic analysis. Images were acquired with a camera controlled by a Raspberry Pi and processed to extract 408 radiomic features (102 per color channel and grayscale). Shapiro–Wilk and Levene’s tests were applied to verify normality and homogeneity of variances as prerequisites for an analysis of variance (ANOVA). Nine features showed statistically significant differences and, together with the culture medium type as a categorical variable, were used in a supervised classification stage with cross-validation. Classification using Support Vector Machines (SVM) achieved 97% accuracy on the test set. The results showed that the morphology of A. flavus varies significantly depending on the medium under UV radiation, with malt extract agar being the most discriminative. This non-invasive and low-cost approach demonstrates the potential of radiomics combined with machine learning to capture morphological patterns useful in the differentiation of fungi with optical response under UV radiation. Full article

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

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16 pages, 4382 KB

Open AccessArticle

Identification and Comparative Analysis of Genetic Effects of 2Ns Chromosome Introgression from Psathyrostachys huashanica and Leymus mollis into Common Wheat

by Yuhui Pang, Jiaojiao Li, Wenjie Huo, Xueyou Hua, Jiayi Yuan, Xicheng Tang, Huanhuan Yang, Chongyang Jia, Jiachuang Li and Jixin Zhao

Agriculture 2025, 15(17), 1887; https://doi.org/10.3390/agriculture15171887 - 5 Sep 2025

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Abstract

Psathyrostachys huashanica (2n = 2x = 14, NsNs) and Leymus mollis (2n = 4x = 28, NsNsXmXm) are important wild relatives of common wheat. The Ns chromosomes from two species have been successfully introgressed into wheat through distant hybridization. To compare the genetic [...] Read more.

Psathyrostachys huashanica (2n = 2x = 14, NsNs) and Leymus mollis (2n = 4x = 28, NsNsXmXm) are important wild relatives of common wheat. The Ns chromosomes from two species have been successfully introgressed into wheat through distant hybridization. To compare the genetic effects and evolutionary relationship of Ns chromosomes from different genera in a wheat background, wheat-P. huashanica derivative WH15 and wheat-L. mollis derivative WM14-2 were selected. Sequential FISH-GISH showed that both WH15 and WM14-2 contained 40 wheat chromosomes (with 2D deletion) and two Ns chromosomes with different FISH karyotypes. Molecular markers and SNP array analysis revealed that the two lines both introduced 2Ns chromosomes. However, the P. huashanica 2Ns and L. mollis 2Ns had distinct sequence compositions, and the different SNPs between the two species 2Ns chromosomes were primarily clustered on the short arm. WH15 and WM14-2 exhibited significant differences in spike-related morphologies but shared leaf rust resistance and susceptibility to powdery mildew and Fusarium head blight. Cytogenetic analysis confirmed stable meiotic inheritance of the introduced 2Ns chromosomes. We further developed universal diagnostic markers for 2Ns chromosomes based on SLAF-seq. Therefore, substantial divergence likely exists between the Ns genomes of P. huashanica and L. mollis, and P. huashanica is probably not the direct Ns genome donor for Leymus. Our research-developed derivatives provide unique resources for comparative studies of the structural and functional evolution of homoeologous Ns chromosomes across genera, while offering valuable alleles for wheat improvement. Full article

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

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1 pages, 125 KB

Open AccessCorrection

Correction: Wan et al. Design and Performance Testing of a Multi-Variety Forage Grass Mixed-Sowing Seed Metering Device. Agriculture 2025, 15, 1788

by Qihao Wan, Wenxue Dong, Anbin Zhang, Fei Liu, Yingsi Wu, Yin Qi and Yuxing Ren

Agriculture 2025, 15(17), 1886; https://doi.org/10.3390/agriculture15171886 - 5 Sep 2025

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Abstract

In the published publication [...] Full article

(This article belongs to the Section Agricultural Technology)

19 pages, 22620 KB

Open AccessArticle

Mechanism Analysis of Soil Disturbance in Sodic Saline–Alkali Soil Tillage Based on Mathematical Modeling and Discrete Element Simulation

by Min Liu, Jinchun Sun, Dongyan Huang, Da Qiao, Meiqi Xiang, Weizhi Feng, Daping Fu and Jingli Wang

Agriculture 2025, 15(17), 1885; https://doi.org/10.3390/agriculture15171885 - 4 Sep 2025

Viewed by 256

Abstract

To elucidate the mechanism by which soil disturbance affects tillage performance during subsoiling remediation of northeastern primary sodic saline–alkali soil, this study established a mathematical prediction model linking subsoiler configuration parameters with draft force and soil porosity based on the soil dynamic equation [...] Read more.

To elucidate the mechanism by which soil disturbance affects tillage performance during subsoiling remediation of northeastern primary sodic saline–alkali soil, this study established a mathematical prediction model linking subsoiler configuration parameters with draft force and soil porosity based on the soil dynamic equation and the fourth strength theory. Discrete element simulation and field experiments demonstrated the model’s accuracy in predicting draft force and soil looseness (error < 5%). Among three configuration patterns evaluated, the “W”-type arrangement was selected for further simulation testing and predictive analysis through parameter adjustment. The simulation results aligned with the prediction results. Particle flow analysis revealed a quadratic relationship between subsoiler spacing variation, draft force, and soil looseness. At the particle scale, soil particle movement patterns were found to govern macroscopic effects, where soil clogging and repeated disturbances emerged as primary drivers of nonlinear variations in draft force and soil porosity. Finally, field experiments and simulations were performed using the parameter combinations predicted by the mathematical model, confirming the accuracy of these parameters. Through a tripartite validation approach combining mathematical modeling, DEM simulation, and field trials, this study systematically elucidates the complete mechanism whereby subsoiler arrangement parameters influence the tillage performance of sodic saline–alkali soil via soil–tool interactions, providing theoretical foundations for optimizing subsoiling equipment design and reducing energy consumption in saline–alkali land cultivation. Full article

(This article belongs to the Section Agricultural Technology)

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17 pages, 1006 KB

Open AccessArticle

Long-Term Production Performance and Stability of Alfalfa/Grass Mixtures in the Longdong Loess Plateau of China: Subjected to Various Species Combinations and Seeding Ratios

by Xiaojuan Wu, Junyu Zhang, Jiaojiao Zhang, Yixiao Lu, Ting Ye and Huimin Yang

Agriculture 2025, 15(17), 1884; https://doi.org/10.3390/agriculture15171884 - 4 Sep 2025

Viewed by 316

Abstract

Stable productivity is the basis for efficient and sustainable use of perennial grasslands, holding both ecological and economic importance. Alfalfa-based mixtures have great potential to achieve this goal. There was limited information on the impact of species combination and seeding ratio on their [...] Read more.

Stable productivity is the basis for efficient and sustainable use of perennial grasslands, holding both ecological and economic importance. Alfalfa-based mixtures have great potential to achieve this goal. There was limited information on the impact of species combination and seeding ratio on their long-term production performance and stability. We investigated forage yield, quality, and temporal stability over six years in alfalfa (Medicago sativa)/timothy (Phleum pretense) and alfalfa/smooth bromegrass (Bromus inermis) mixtures at varying seeding ratios. Alfalfa/grass mixtures showed a yield advantage over grass monocultures with greater yield at higher alfalfa seeding proportions (50% or more). The mixtures showed advantages in crude protein and neutral detergent fiber. Crude protein content tended to increase with increasing alfalfa seeding proportion, while fiber contents barely changed. As stands grew older, forage yield increased and then declined and showed greater stability in mixtures compared with monocultures. The percentage of alfalfa yield tended to increase over the life of the stand. In contrast, forage quality varied over the life of the stand, with greater variability in mixtures than monocultures. Considering forage yield, quality, and stability across years, smooth bromegrass would be more compatible with alfalfa in a mixture compared to timothy for the Longdong Loess Plateau of China and areas with similar climates. Full article

(This article belongs to the Section Crop Production)

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17 pages, 3939 KB

Open AccessArticle

Genome-Wide Identification and Cold Stress Response Analysis of the Rboh Gene Family in Pomegranate (Punica granatum L.)

by Yu Sheng, Xiaoyu Wang, Chenyu Wang, Xiaoyong Xu and Lijuan Jiang

Agriculture 2025, 15(17), 1883; https://doi.org/10.3390/agriculture15171883 - 4 Sep 2025

Viewed by 285

Abstract

Plant respiratory burst oxidase homolog (Rboh) genes are integral to the production of reactive oxygen species (ROS) and the regulation of stress responses. Here, bioinformatic techniques were employed to identify eight PgRboh genes (PgRbohA–H) in the genome of pomegranate [...] Read more.

Plant respiratory burst oxidase homolog (Rboh) genes are integral to the production of reactive oxygen species (ROS) and the regulation of stress responses. Here, bioinformatic techniques were employed to identify eight PgRboh genes (PgRbohA–H) in the genome of pomegranate (Punica granatum L.) and conduct a systematic analysis of this family. The findings showed that all PgRbohs proteins possess characteristic NADPH oxidase domains and are predicted to be localized on the cell membrane. Experimental verification confirmed the membrane localization of PgRbohD and PgRbohE proteins. Phylogenetic analysis categorized the PgRbohs proteins into six distinct groups, suggesting potential functional divergence among these groups. Promoter analysis revealed a significant presence of cis-acting elements responsive to low-temperature and methyl jasmonate (MeJA). The expression of PgRboh genes was found to be tissue-specific. Additionally, real-time PCR (RT-qPCR) was used to analyze expression patterns in response to low-temperature stress that involves multiple PgRboh genes in the cold response process. Overall, our results lay an important foundation for subsequent studies on the cold resistance function of pomegranate Rboh genes and provides new ideas for the breeding of new cold-resistant pomegranate varieties. Full article

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

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26 pages, 5144 KB

Open AccessArticle

Wine Tourism and Its Role in the Transformation of Wine Production and Consumption in Czechia: A Case Study

by David Průša, Karel Šrédl, Marie Prášilová, Anna Žovincová, Lenka Kopecká, Lucie Severová, Roman Svoboda, Dita Drozdová, Lasha Naveriani, Otakar Němec and Milan Robin Paták

Agriculture 2025, 15(17), 1882; https://doi.org/10.3390/agriculture15171882 - 3 Sep 2025

Viewed by 294

Abstract

The gradual decline in wine consumption in Czechia poses significant challenges for domestic winemakers. Moreover, the sector faces mounting pressure from climate change—most notably global warming—which is increasingly affecting viticulture and wine production across the region. Using advanced predictive models, we estimated developmental [...] Read more.

The gradual decline in wine consumption in Czechia poses significant challenges for domestic winemakers. Moreover, the sector faces mounting pressure from climate change—most notably global warming—which is increasingly affecting viticulture and wine production across the region. Using advanced predictive models, we estimated developmental trends and calculated forecasts for yield-generating components of grapevine cultivation. The results confirm stagnation or modest growth in the sector, with its development remaining strongly influenced by structural changes and external economic factors. While consumer demand is shifting toward white (or lighter) wines, climate change in Czechia is enhancing conditions for cultivating grape varieties suited to red wine production. This article examines the imbalance between supply and demand in the Czech wine market and identifies wine tourism as a possible solution for resolving the discrepancy. Full article

(This article belongs to the Special Issue Sustainability from Vineyard to Marketing: Innovative Frontiers in Wine Tourism)

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33 pages, 5925 KB

Open AccessArticle

Trajectory Tracking Control of an Orchard Robot Based on Improved Integral Sliding Mode Algorithm

by Yu Luo, Dekui Pu, Xiaoli He, Lepeng Song, Simon X. Yang, Weihong Ma and Hanwen Shi

Agriculture 2025, 15(17), 1881; https://doi.org/10.3390/agriculture15171881 - 3 Sep 2025

Viewed by 258

Abstract

To address the problems of insufficient trajectory tracking accuracy, pronounced jitter over undulating terrain, and limited disturbance rejection in orchard mobile robots, this paper proposes a trajectory tracking control strategy based on a double-loop adaptive sliding mode. Firstly, a kinematic model of the [...] Read more.

To address the problems of insufficient trajectory tracking accuracy, pronounced jitter over undulating terrain, and limited disturbance rejection in orchard mobile robots, this paper proposes a trajectory tracking control strategy based on a double-loop adaptive sliding mode. Firstly, a kinematic model of the orchard robot is constructed and a time-varying integral terminal sliding surface is designed to achieve global fast finite-time convergence. Secondly, a sinusoidal saturation switching function with a variable boundary is employed to suppress the high-frequency chattering inherent in sliding mode control. Thirdly, an improved double-power reaching law (Improved DPRL) is introduced to enhance disturbance rejection in the inner loop while ensuring continuity of the outer-loop output. Finally, Lyapunov stability theory is used to prove the asymptotic stability of the double-loop system. The experimental results show that attitude angle error settles within 0.01 rad after 0.144 s, while the position errors in both the x-axis and y-axis directions settle within 0.01 m after 0.966 s and 0.753 s, respectively. Regarding position error convergence, the Integral of Absolute Error (IAE)/Integral of Squared Error (ISE)/Integral of Time-Weighted Absolute Error (ITAE) are 0.7629 m, 0.7698 m, and 0.2754 m, respectively; for the attitude angle error, the IAE/ISE/ITAE are 0.0484 rad, 0.0229 rad, and 0.1545 rad, respectively. These results indicate faster convergence of both position and attitude errors, smoother control inputs, and markedly reduced chattering. Overall, the findings satisfy the real-time and accuracy requirements of fast trajectory tracking for orchard mobile robots. Full article

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

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20 pages, 4988 KB

Open AccessArticle

Use of Cellulose from Waste Paper to Improve the Water Capacity of Soils Within the Circular Economy

by Helena Raclavská, Michal Šafář, Konstantin Raclavský, Marek Kucbel, Pavel Kantor, Barbora Švédová, Karolina Slamová and Dalibor Matýsek

Agriculture 2025, 15(17), 1880; https://doi.org/10.3390/agriculture15171880 - 3 Sep 2025

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Abstract

The article focuses on verifying the potential of using cellulose obtained from waste cardboard to improve the soil’s water retention capacity, depending on its texture and type, in accordance with the principles of the circular economy. The study compares reference cellulose (RFC) and [...] Read more.

The article focuses on verifying the potential of using cellulose obtained from waste cardboard to improve the soil’s water retention capacity, depending on its texture and type, in accordance with the principles of the circular economy. The study compares reference cellulose (RFC) and waste carton-extracted cellulose (WCC) in terms of their structure and water-holding capacity (WHC), using FTIR spectroscopy and experiments across various soil types. Results showed that WCC has a significantly higher WHC (12.6 g/g) than RFC (0.75 g/g) due to its greater proportion of amorphous sections and the presence of lignin and hemicellulose. In contrast, the high crystalline content of RFC limits its water sorption capabilities. Soil texture and soil organic matter (SOM) play a crucial role in water retention. The highest WHC values were observed in fine-grained soils classified as silt loam. The study confirms that SOM has a stronger influence on WHC than texture alone. Applying WCC led to a linear increase in WHC across different soil types. Even soils with initially low WHC showed notable improvement with low doses of WCC (1%). The findings highlight the potential of waste carton-extracted cellulose as a soil amendment to enhance water retention in agricultural soils, especially in adapting to climate variability and drought conditions. Full article

(This article belongs to the Section Agricultural Soils)

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23 pages, 7482 KB

Open AccessArticle

DEM-Based Parameter Calibration of Soils with Varying Moisture Contents in Southern Xinjiang Peanut Cultivation Zones

by Wen Zhou, Hui Guo, Yu Zhang, Xiaoxu Gao, Chuntian Yang and Tianlun Wu

Agriculture 2025, 15(17), 1879; https://doi.org/10.3390/agriculture15171879 - 3 Sep 2025

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Abstract

To address the insufficient adaptability of imported peanut harvesting equipment’s soil-engaging components to the specific soil conditions in Xinjiang, this study conducted Discrete Element Method (DEM)-based calibration of soil mechanical parameters using field soil samples with 1–20% moisture content from typical peanut cultivation [...] Read more.

To address the insufficient adaptability of imported peanut harvesting equipment’s soil-engaging components to the specific soil conditions in Xinjiang, this study conducted Discrete Element Method (DEM)-based calibration of soil mechanical parameters using field soil samples with 1–20% moisture content from typical peanut cultivation areas in southern Xinjiang. Through the EDEM simulation platform, a comprehensive approach integrating the Hertz–Mindlin with the JKR adhesion model and Hertz–Mindlin with the Bonding model was employed to systematically calibrate nine key parameters: coefficient of restitution, static friction coefficient, rolling friction coefficient, JKR surface energy, normal/tangential stiffness per unit area, critical normal/tangential force, and soil bonding disk radius. Adopting static angle of repose (SAOR) and unconfined compressive force (UCF) as dual-response indicators, a hybrid experimental design strategy combining Central Composite Design (CCD), Plackett–Burman (PB) screening, and Box–Behnken Design (BBD) optimization was implemented. Regression models for SAOR and UCS were established, yielding six sets of soil parameters optimized for different moisture conditions through parameter optimization. Field validation demonstrated the following: ≤3.27% error in SAOR, ≤1.46% error in UCF, and ≤5.05% error in drawbar resistance validation for field digging shanks. Experimental results confirm that the model demonstrates strong prediction accuracy for soils in typical peanut harvesting regions of southern Xinjiang, thereby providing key parameter references for the future self-developed, highly adaptive soil-engaging components with drag reduction optimization in peanut harvesters for the Xinjiang region. Full article

(This article belongs to the Section Agricultural Soils)

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15 pages, 808 KB

Open AccessArticle

Djulis (Chenopodium formosanum) Stems as Sustainable Sawdust Alternative for Pleurotus sajor-caju Cultivation: A Feasibility Study

by Tzu-Huan Hung, Wee-Ann Ong, Wei-Sung Li, Yun-Yang Chao and Pearl Peichun Chang

Agriculture 2025, 15(17), 1878; https://doi.org/10.3390/agriculture15171878 - 3 Sep 2025

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Abstract

The heavy reliance of the mushroom industry on sawdust substrates is putting increasing pressure on already limited forest resources, forcing researchers to seek alternative materials. This study investigated the feasibility of using post-harvest djulis (Chenopodium formosanum Koidz.) stems, waste from this indigenous [...] Read more.

The heavy reliance of the mushroom industry on sawdust substrates is putting increasing pressure on already limited forest resources, forcing researchers to seek alternative materials. This study investigated the feasibility of using post-harvest djulis (Chenopodium formosanum Koidz.) stems, waste from this indigenous crop in Taiwan, to partially replace sawdust for Pleurotus sajor-caju cultivation. Initial screening with 0–100% djulis replacement revealed growth inhibition above 50% incorporation levels. Refined experiments focusing on 0–30% djulis ratios demonstrated that strain PT exhibited superior adaptation to djulis-containing substrates. Commercial scale grow bag trials showed that among djulis treatments, 25% djulis incorporation achieved the fastest mycelial colonization rate (1.0 cm/day), while 15% incorporation yielded the highest biological efficiency (76.17%), comparable to commercial controls (76.80%). Three-flush harvest cycles confirmed stable productivity across treatments, with total yields ranging from 286 to 320 g/bag. Nutritional analysis showed no major changes in amino acids and antioxidants, with djulis incorporation maintaining protein quality while some enhancement in total free amino acid content and reducing power at 25% incorporation. These findings demonstrate that 15–25% djulis stem substitution sustained commercial production parameters while contributing to sustainable agricultural waste management and reducing forest resource dependence. Full article

(This article belongs to the Special Issue The Role of Edible Mushrooms in Sustainable Food Systems)

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21 pages, 5022 KB

Open AccessArticle

GLL-YOLO: A Lightweight Network for Detecting the Maturity of Blueberry Fruits

by Yanlei Xu, Haoxu Li, Yang Zhou, Yuting Zhai, Yang Yang and Daping Fu

Agriculture 2025, 15(17), 1877; https://doi.org/10.3390/agriculture15171877 - 3 Sep 2025

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Abstract

The traditional detection of blueberry maturity relies on human experience, which is inefficient and highly subjective. Although deep learning methods have improved accuracy, they require large models and complex computations, making real-time deployment on resource-constrained edge devices difficult. To address these issues, a [...] Read more.

The traditional detection of blueberry maturity relies on human experience, which is inefficient and highly subjective. Although deep learning methods have improved accuracy, they require large models and complex computations, making real-time deployment on resource-constrained edge devices difficult. To address these issues, a GLL-YOLO method based on the YOLOv8 network is proposed to deal with problems such as fruit occlusion and complex backgrounds in mature blueberry detection. This approach utilizes the GhostNetV2 network as the backbone. The LIMC module is suggested to substitute the original C2f module. Meanwhile, a Lightweight Shared Convolution Detection Head (LSCD) module is designed to build the GLL-YOLO model. This model can accurately detect blueberries at three different maturity stages: unripe, semi-ripe, and ripe. It significantly reduces the number of model parameters and floating-point operations while maintaining high accuracy. Experimental results show that GLL-YOLO outperforms the original YOLOv8 model in terms of accuracy, with mAP improvements of 4.29%, 1.67%, and 1.39% for unripe, semi-ripe, and ripe blueberries, reaching 94.51%, 91.72%, and 93.32%, respectively. Compared to the original model, GLL-YOLO improved the accuracy, recall rate, and mAP by 2.3%, 5.9%, and 1%, respectively. Meanwhile, GLL-YOLO reduces parameters, FLOPs, and model size by 50%, 39%, and 46.7%, respectively, while maintaining accuracy. This method has the advantages of a small model size, high accuracy, and good detection performance, providing reliable support for intelligent blueberry harvesting. Full article

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

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16 pages, 3923 KB

Open AccessArticle

Research on Layered Fertilization Method of Fertilizer Applicator and Optimization of Key Parameters

by Yabo Zhang, Tongxi Li, Dong Zhang, Xiuwen Fan, Hong Zhang and Hao Niu

Agriculture 2025, 15(17), 1876; https://doi.org/10.3390/agriculture15171876 - 3 Sep 2025

Viewed by 333

Abstract

To address the challenges of layered fertilization in orchards and the lack of dedicated equipment, this study proposes a layered fertilization technique based on the three-dimensional distribution characteristics of jujube root systems and develops an orchard layered fertilizer applicator. First, the agronomic advantages [...] Read more.

To address the challenges of layered fertilization in orchards and the lack of dedicated equipment, this study proposes a layered fertilization technique based on the three-dimensional distribution characteristics of jujube root systems and develops an orchard layered fertilizer applicator. First, the agronomic advantages of layered fertilization were systematically elucidated by analyzing the spatial distribution patterns of jujube roots, as well as the mechanisms of fertilizer nutrient transport and uptake. Second, parametric design was conducted for key components (e.g., trenching–fertilizing unit), with emphasis on the structural design of the fertilizer-dividing box and the augerless spiral conveying mechanism. A three-factor, three-level experiment based on response surface methodology was implemented, where the coefficient of variation (CV) of fertilization uniformity and row consistency were selected as evaluation indices to optimize key parameters (forward speed, augerless spiral speed, and fertilizer gate opening). The optimal operational combination was determined as follows: forward speed of 2.62 km/h, augerless spiral speed of 29.87 r/min, and fertilizer gate opening of 3.49 cm. Field tests demonstrated that the CVs of fertilization uniformity and row consistency reached 7.77% and 8.46%, respectively, meeting the agronomic requirements for orchard fertilization. This study provides a reference for the development of orchard fertilization technologies and machinery. Full article

(This article belongs to the Section Agricultural Technology)

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