Agriculture

20 pages, 8692 KiB

Open AccessArticle

Three Decades of Tillage Driven Topsoil Displacement and Soil Erosion Attenuation on Loess Plateau Slope Farmlands

by Shuanhu Li, Bohan Zhao, Huimin Wu, Rongbiao Li and Ping Wang

Agriculture 2025, 15(10), 1084; https://doi.org/10.3390/agriculture15101084 (registering DOI) - 17 May 2025

The slope lands of the Loess Plateau represent a critical region impacted by soil erosion, which directly contributes to the globally recognized high sediment concentration in the Yellow River. However, the extent to which sloped farmland contributes to soil loss remains scientifically contentious. [...] Read more.

The slope lands of the Loess Plateau represent a critical region impacted by soil erosion, which directly contributes to the globally recognized high sediment concentration in the Yellow River. However, the extent to which sloped farmland contributes to soil loss remains scientifically contentious. In this study, farmland with an initial slope gradient of 20° was selected for the experiment, and three decades of field monitoring data (1990s-2020s) and the Universal Soil Loss Equation (USLE) model were used for comparative calculation. The data indicated that the model-predicted soil loss rate in sloped farmland from the 1990s to the 2020s was calculated to be 62.48 t·ha⁻¹·yr⁻¹. Field-measured values averaged 45.67 t·ha⁻¹·yr⁻¹, whereas the current value is approximately 15.00 t·ha⁻¹·yr⁻¹. Anthropogenic disturbances, including tillage, manual weeding, and ovine grazing, mean that the topsoil of slope farmland has undergone cumulative displacement of 450~870 cm in 30 years, which is resulting in progressive slope gradient reduction from 20° to 5°. The soil erosion rates exhibited exponential decay characteristics, and finally gradually reached the level of flat farmland. When using the USLE model, the evolving slope gradient must be incorporated, rather than the slope angle extracted by DEM. Therefore, the key finding of this study is that the primary sources of soil loss in the Loess Plateau are non-agricultural slopes and gullies. Conversely, soil erosion on slope farmlands does not constitute a critical problem requiring urgent intervention. This finding should attract the attention of the local agricultural sector. Full article

(This article belongs to the Section Agricultural Soils)

22 pages, 1234 KiB

Open AccessArticle

Impact of Non-Agricultural Labor Transfer on the Ecological Efficiency of Cultivated Land: Evidence from China

by Weijuan Li, Jinyong Guo and Tian Xie

Agriculture 2025, 15(10), 1083; https://doi.org/10.3390/agriculture15101083 (registering DOI) - 17 May 2025

Abstract

The ecological efficiency of cultivated land utilization is closely related to food security and the sustainable development of agriculture. As an important actor in the utilization of cultivated land, the transfer of labor to non-agricultural sectors and its impact on ecological efficiency remain [...] Read more.

The ecological efficiency of cultivated land utilization is closely related to food security and the sustainable development of agriculture. As an important actor in the utilization of cultivated land, the transfer of labor to non-agricultural sectors and its impact on ecological efficiency remain underexplored. Taking China as an example, this study employs push–pull theory, technology factor substitution theory, and land scale economy theory to explore the motivations and mechanisms of non-agricultural labor transfer. An empirical analysis was conducted using provincial panel data from 2011 to 2023. The research methods include the super-efficiency SBM model, fixed effect model, mediating effect model, and threshold effect model. The results are as follows: (1) Non-agricultural labor transfer promotes improvements in the ecological efficiency of cultivated land utilization. A 1% growth in non-agricultural labor transfer is associated with a 0.615% improvement in the ecological efficiency of cultivated land utilization. The impact is especially evident in the main grain-producing areas and northern regions. (2) As a modern agricultural production factor, agricultural machinery plays a mediating role in factor substitution at the farmland stage, accounting for 39% of the effect. (3) The scale of agricultural land operation exhibits a single threshold effect with a threshold value of 1.1577. Against the backdrop of widespread non-agricultural labor transfer, this study provides a reference for further strengthening the utilization of agricultural machinery and promoting large-scale land operations. Full article

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

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19 pages, 2109 KiB

Open AccessArticle

Exploiting Chestnut Biochar as a Functional and Circular Ingredient in Weaned Piglet Diets

by Luciana Rossi, Sara Frazzini, Matteo Santoru, Benedetta Canala, Irene Ferri, Alessandra Moscatelli, Elisabetta Onelli, Matteo Dell’Anno, Salvatore Pilu and Serena Reggi

Agriculture 2025, 15(10), 1082; https://doi.org/10.3390/agriculture15101082 (registering DOI) - 17 May 2025

Abstract

Background: Achieving sustainable development in accordance with Agenda 2030 (Sustainable Development Goals 12, 13, and 17) has challenged the livestock sector and especially swine farming. Strategies focused on reducing the environmental impact and improving feed efficiency have therefore been explored. Due to its [...] Read more.

Background: Achieving sustainable development in accordance with Agenda 2030 (Sustainable Development Goals 12, 13, and 17) has challenged the livestock sector and especially swine farming. Strategies focused on reducing the environmental impact and improving feed efficiency have therefore been explored. Due to its beneficial properties, the application of biochar represents an interesting solution. This study therefore evaluates the effects of biochar supplementation on growth performance and health parameters in weaned piglets. Methods: A total of 223 piglets were divided into two experimental groups: the control (CTRL) group and the treatment (TRT group). The experiment involved two dietary treatments: the CTRL group was fed a standard diet, while the TRT group was fed the same diet supplemented with 1% chestnut biochar. Weekly measurements included body weight, feed intake, and fecal scores. Fecal samples were collected for microbiological analysis and evaluation of digestibility. Results: No significant differences were observed between the groups in terms of the principal zootechnical parameters. The TRT group showed lower E. coli counts in feces at 14 days and a significant decrease in diarrhea frequency at 28 days (32.14% CTRL vs. 3.23% TRT; p = 0.009). Protein digestibility was higher in the TRT group (79.5 ± 1.74%) compared to the CTRL group (75.0 ± 2.05%; p = 0.004). Additionally, the TRT group had significantly lower levels of derivates of reactive oxygen metabolites than the CTRL group (293.44 ± 59.28 vs. 553.98 ± 61.59 Carratelli units p ≤ 0.001). Conclusions: The inclusion of 1% biochar in the diets of post-weaning piglets can improve the health status of the animals. Biochar could thus be used as a valuable functional ingredient within an innovative nutritional strategy aimed at the management of gastrointestinal problems during the weaning period. Full article

(This article belongs to the Section Farm Animal Production)

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25 pages, 9450 KiB

Open AccessArticle

The Impact of Rural Population Decline on the Economic Efficiency of Agricultural Carbon Emissions: A Case Study of the Contiguous Karst Areas in Yunnan–Guizhou Provinces, China

by Weini Chen, Dejun Han, Yu Zhan and Bo Chen

Agriculture 2025, 15(10), 1081; https://doi.org/10.3390/agriculture15101081 (registering DOI) - 17 May 2025

Abstract

Amid global climate warming, agricultural low-carbon transition is critical for ecological governance. In China’s ecologically fragile contiguous karst areas of Yunnan–Guizhou, intensifying rural population decline poses unique challenges to emission reduction. This study analyzes population and agricultural production data from 25 cities (prefectures) [...] Read more.

Amid global climate warming, agricultural low-carbon transition is critical for ecological governance. In China’s ecologically fragile contiguous karst areas of Yunnan–Guizhou, intensifying rural population decline poses unique challenges to emission reduction. This study analyzes population and agricultural production data from 25 cities (prefectures) (2013–2022) to quantify rural population decline rates and agricultural carbon emission efficiency. We map their spatiotemporal evolution patterns, apply spatial autocorrelation models to assess spatial dependencies, and investigate mechanisms through a mediation model integrated with agricultural modernization’s three core systems: industrial, production, and management. Key findings reveal (1) divergent trajectories of carbon emission efficiency across regions with varying population decline types; (2) a global Moran’s I of −0.3519, indicating significant negative spatial correlation between population decline intensity and emission efficiency; and (3) dual impact mechanisms where population decline directly alters emission efficiency and indirectly modulates it through interactions with agricultural systems, with mechanism heterogeneity across decline patterns. To reconcile carbon reduction and agricultural growth, region-specific strategies must align population decline gradients with dynamic adjustments to agricultural systems, ensuring synchronized demographic transition and modernization. Full article

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

14 pages, 1709 KiB

Open AccessArticle

Influence of Sludge and Feed Mixtures on Metal Retention, Pathogen Reduction, and Nutritional Value in Black Soldier Fly (BSF) (Hermetia illucens) Larval Substrates

by Abeer Albalawneh, Heba Hasan, Sami Faisal Alarsan, Saja Abu Znaimah, Mai Diab, Ahmad Mohammed Alalwan, Yazan AlBalawnah, Ehab Alnaimat, Bilal Sharman and Musa Abu Dayyeh

Agriculture 2025, 15(10), 1080; https://doi.org/10.3390/agriculture15101080 (registering DOI) - 17 May 2025

Abstract

Black soldier fly (BSF) larvae are increasingly used in sustainable waste management, offering potential for the bioconversion of organic waste into insect-derived fertilizer and animal feed. This study investigates the impact of varied substrate mixtures percentages of sludge and chicken feed on heavy [...] Read more.

Black soldier fly (BSF) larvae are increasingly used in sustainable waste management, offering potential for the bioconversion of organic waste into insect-derived fertilizer and animal feed. This study investigates the impact of varied substrate mixtures percentages of sludge and chicken feed on heavy metal accumulation, pathogen reduction, and nutrient composition in BSF frass. Methods: The experiment was conducted with four substrate treatments (100% sludge, 75% sludge + 25% chicken feed, 25% sludge + 75% chicken feed, and 100% chicken feed) over a 20-day period. Chemical and microbiological analyses were performed on the feed mixture before adding larvae and on the frass produced in each treatment. Heavy metal concentrations, including cobalt (Co), chromium (Cr), nickel (Ni), and lead (Pb), pathogen levels (Escherichia coli, total coliform, and fecal coliform), and nutrient composition, including moisture content, pH, ash, nitrogen, phosphorus, calcium, potassium, sodium, magnesium, and chlorine, were assessed. Statistical analysis was used to determine significant differences across treatments. Results: Heavy metal levels in frass varied with substrate composition, with significantly higher concentrations of cobalt (Co), chromium (Cr), nickel (Ni), and lead (Pb) in sludge-dominant treatments (p < 0.05). Treatments with higher chicken feed content were associated with lower metal levels, indicating organic feed’s potential in limiting heavy metal accumulation (p < 0.001). Pathogen analysis showed high microbial levels in sludge-based treatments, while the 100% chicken feed treatment exhibited minimal contamination, highlighting its safety profile (p < 0.05). Nutrient characterization revealed that chicken feed-enhanced treatments produced frass with higher nitrogen and potassium levels, suggesting improved nutrient density and potential for agricultural use. Conclusions: Tailoring BSF substrates by combining sludge with organic feed can enhance the nutritional quality of frass while reducing environmental risks associated with heavy metal and pathogen presence. This study supports the potential of BSF as a sustainable bioconversion tool, promoting circular agriculture. Full article

(This article belongs to the Section Farm Animal Production)

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15 pages, 1358 KiB

Open AccessArticle

Research on Recognition of Green Sichuan Pepper Clusters and Cutting-Point Localization in Complex Environments

by Qi Niu, Wenjun Ma, Rongxiang Diao, Wei Yu, Chunlei Wang, Hui Li, Lihong Wang, Chengsong Li and Pei Wang

Agriculture 2025, 15(10), 1079; https://doi.org/10.3390/agriculture15101079 (registering DOI) - 16 May 2025

Abstract

The harvesting of green Sichuan pepper remains heavily reliant on manual field operations, but automation can enhance the efficiency, quality, and sustainability of the process. However, challenges such as intertwined branches, dense foliage, and overlapping pepper clusters hinder intelligent harvesting by causing inaccuracies [...] Read more.

The harvesting of green Sichuan pepper remains heavily reliant on manual field operations, but automation can enhance the efficiency, quality, and sustainability of the process. However, challenges such as intertwined branches, dense foliage, and overlapping pepper clusters hinder intelligent harvesting by causing inaccuracies in target recognition and localization. This study compared the performance of multiple You Only Look Once (YOLO) algorithms for recognition and proposed a cluster segmentation method based on K-means++ and a cutting-point localization strategy using geometry-based iterative optimization. A dataset containing 14,504 training images under diverse lighting and occlusion scenarios was constructed. Comparative experiments on YOLOv5s, YOLOv8s, and YOLOv11s models revealed that YOLOv11s achieved a recall of 0.91 in leaf-occluded environments, marking a 21.3% improvement over YOLOv5s, with a detection speed of 28 Frames Per Second(FPS). A K-means++-based cluster separation algorithm (K=1~10, optimized via the elbow method) was developed and was combined with OpenCV to iteratively solve the minimum circumscribed triangle vertices. The longest median extension line of the triangle was dynamically determined to be the cutting point. The experimental results demonstrated an average cutting-point deviation of 20 mm and a valid cutting-point ratio of 69.23%. This research provides a robust visual solution for intelligent green Sichuan pepper harvesting equipment, offering both theoretical and engineering significance for advancing the automated harvesting of Sichuan pepper (Zanthoxylum schinifolium) as a specialty economic crop. Full article

(This article belongs to the Special Issue Automation Strategy Using Machine Learning in Horticultural Crop Cultivation)

21 pages, 4385 KiB

Open AccessArticle

Germination Under Temperature Stress Facilitates Invasion in Indehiscent Lepidium Species

by Said Mohammed and Klaus Mummenhoff

Agriculture 2025, 15(10), 1078; https://doi.org/10.3390/agriculture15101078 (registering DOI) - 16 May 2025

Abstract

This study investigates the germination ecology of three Lepidium species, including the invasive, indehiscent-fruited Lepidium appelianum and Lepidium draba, and the invasive, dehiscent-fruited Lepidium campestre. The ability of Lepidium species to germinate under a wide range of temperature conditions is significant [...] Read more.

This study investigates the germination ecology of three Lepidium species, including the invasive, indehiscent-fruited Lepidium appelianum and Lepidium draba, and the invasive, dehiscent-fruited Lepidium campestre. The ability of Lepidium species to germinate under a wide range of temperature conditions is significant for understanding their potential invasiveness and establishment in novel and extreme environments. This study aims to clarify the germination behavior of L. appelianum, L. draba, and L. campestre, thereby enhancing our understanding of their invasive potential and ecological implications in the context of a changing climate. The base (Tb), optimum (To), and maximum temperatures for 50% germination (Tc(50)) were determined across a broad thermal gradient following standard protocols. Freshly harvested seeds and fruits of L. appelianum are non-dormant. In contrast, L. draba exhibit pericarp-mediated chemical dormancy, while L. campestre demonstrates physiological dormancy, which is released through after-ripening. The results indicate that L. appelianum and L. draba seeds and fruits germinate at a base temperature (Tb) of 1 °C and 4 °C, respectively. On the other hand, L. campestre seeds germinate at a Tb of 5.8 °C. The optimum temperature (To) for the germination of seeds and fruits in L. appelianum and L. draba ranges from 23 °C to 25 °C, while the To for L. campestre seed germination is 16 °C to 18 °C. Additionally, the maximum temperature for 50% germination (Tc(50)) for L. appelianum fruits is 39.8 °C, for L. draba it is 34.4 °C, and L. campestre reports a (Tc(50)) ranging from 27.4 °C to 33.3 °C for freshly harvested and after-ripened seeds, respectively. These results demonstrated that L. appelianum and L. draba can germinate across a broad temperature range, from very cold to very hot, unlike L. campestre. These findings suggest that the unique reproductive strategy of indehiscent fruits, coupled with a wide thermal germination niche, may contribute to the invasive success of L. appelianum and L. draba. Given the projected climate warming, the results highlight the potential for increased invasiveness of these species and suggest the need for targeted management strategies. Full article

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

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29 pages, 515 KiB

Open AccessArticle

Artificial Intelligence Models for Bankruptcy Prediction in Agriculture: Comparing the Performance of Artificial Neural Networks and Decision Trees

by Dominika Gajdosikova and Jakub Michulek

Agriculture 2025, 15(10), 1077; https://doi.org/10.3390/agriculture15101077 (registering DOI) - 16 May 2025

Abstract

Debt levels are a crucial factor when assessing the financial stability of agricultural firms, and excessive indebtedness is usually the most important indicator of financial distress. As agriculture is a capital-intensive sector with a high reliance on borrowed funds, firms in this sector [...] Read more.

Debt levels are a crucial factor when assessing the financial stability of agricultural firms, and excessive indebtedness is usually the most important indicator of financial distress. As agriculture is a capital-intensive sector with a high reliance on borrowed funds, firms in this sector are more vulnerable to insolvency. This study examines the performance of artificial neural networks (ANNs) and decision trees (DTs) in predicting the bankruptcy of Slovak agricultural enterprises. In an attempt to compare the models’ performances, the most consequential indebtedness ratios are investigated through machine learning approaches. ANN and DT models are found to perform significantly better than traditional forecast methods. ANN achieved an AUC of 0.9500, accuracy of 96.37%, precision of 96.60%, recall of 99.68%, and an F1-score of 98.12%, determining its robust predictive ability. DT performed a little better on AUC (0.9550) and achieved an accuracy of 97.78%, precision of 98.69%, recall of 99.01%, and an F1-score of 98.85%, determining its predictive ability and interpretability. These findings confirm the potential for applying AI-based models to enhance financial risk assessment. This study provides informative results for financial analysts, policymakers, and corporate managers in support of early intervention strategies. Additional research would be required to explore state-of-the-art AI techniques to further refine bankruptcy forecasting and financial decision-making in vulnerable sectors like agriculture. Full article

(This article belongs to the Special Issue Innovation and Sustainability in Agribusiness: Policies and Market Dynamics)

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15 pages, 2355 KiB

Open AccessArticle

Intelligent Detection and Automatic Removal Robot for Skinned Garlic Cloves

by Zhengbo Zhu, Xin Cao, Yawen Xiao, Li Xin, Lei Xin and Shuqian Li

Agriculture 2025, 15(10), 1076; https://doi.org/10.3390/agriculture15101076 - 16 May 2025

Abstract

After undergoing peeling-machine operations, skinned garlic cloves affect subsequent processing, and their manual removal is harmful to health. In this paper, an intelligent garlic-clove-removal test bench was designed, which mainly included a hopper, lifter, vibration conveyor, conveyor belt, visual system, removal robot, control [...] Read more.

After undergoing peeling-machine operations, skinned garlic cloves affect subsequent processing, and their manual removal is harmful to health. In this paper, an intelligent garlic-clove-removal test bench was designed, which mainly included a hopper, lifter, vibration conveyor, conveyor belt, visual system, removal robot, control cabinet, frame, etc. A technical method based on machine vision technology to distinguish whether or not garlic cloves had a skin was explored to ensure that the test bench could complete the recognition of the skinned garlic cloves, and to check that the test bench could also complete the removal of skinned garlic cloves. Tests were carried out to check the success rate of machine vision and the removal robot, and to optimize the parameters of the test bench. The results showed that the average success rate of machine vision was 99.15%, and the average success rate of the removal robot was 99.13%. The results also showed that the order of the three factors influence index was the conveying speed, the conveying volume, and the removal period. The regression analysis showed that when the conveying speed was 0.1 m·s⁻¹, the grasping period was 1.725 s, the conveying volume was 104.4 kg·h⁻¹, the qualified rate of the finished product was 97.15%, and the verification test result was 97.02%, which had no significant difference from the analysis result. The research results of this paper are conducive to the development of intelligent detection technology of garlic cloves, and to the development of garlic-planting technology and deep processing technology. Full article

(This article belongs to the Section Agricultural Technology)

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21 pages, 6337 KiB

Open AccessArticle

Characterization of Cowpea Genotypes for Traits Related to Early-Season Drought Tolerance

by Sujan Poudel, Lekshmy Valsala Sankarapillai, Bala Subramanyam Sivarathri, Vijaykumar Hosahalli, Richard L. Harkess and Raju Bheemanahalli

Agriculture 2025, 15(10), 1075; https://doi.org/10.3390/agriculture15101075 - 16 May 2025

Abstract

Cowpea (Vigna unguiculata (L.) Walp.) is a vital legume crop recognized for its nutritional value and adaptability to various growing conditions. However, exposure of cowpea to drought stress during the early growth stages can significantly restrict growth and yield potential. Therefore, identifying [...] Read more.

Cowpea (Vigna unguiculata (L.) Walp.) is a vital legume crop recognized for its nutritional value and adaptability to various growing conditions. However, exposure of cowpea to drought stress during the early growth stages can significantly restrict growth and yield potential. Therefore, identifying cowpea genotypes tolerant to drought during early growth and development is essential for maintaining yield potential. This study characterized 15 diverse cowpea genotypes for various physiological, pigment, and morphological traits that may contribute to drought tolerance. At the V2 stage, the cowpea genotypes were subjected to two moisture regimes: control (100% irrigation) and drought (50% irrigation) for 22 days to assess trait responses and their relationship to drought tolerance. Drought-stressed plants decreased stomatal conductance by 79%, negatively correlating with a 2.9 °C increase in canopy temperature. Under drought, the photochemical reflectance index (PRI) was strongly associated with the quantum yield of PSII and electron transport rate. Shoot biomass decreased by 51% and root biomass by 32% under drought. Leaf area and shoot weight were correlated with root traits such as total length, surface area, and weight. Among all genotypes, 280785-11 and UCR 1004 demonstrated superior rooting vigor under drought, emphasizing their efficiency in resource utilization. These findings highlight the relevance of utilizing drought-adaptive traits to improve early-season drought tolerance. Full article

(This article belongs to the Section Crop Production)

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15 pages, 5473 KiB

Open AccessArticle

Integrating Proximal Gamma Ray and Cosmic Ray Neutron Sensors to Assess Soil Moisture Dynamics in an Agricultural Field in Spain

by Leticia Gaspar, Trenton E. Franz and Ana Navas

Agriculture 2025, 15(10), 1074; https://doi.org/10.3390/agriculture15101074 - 16 May 2025

Abstract

Antecedent soil moisture is a critical driver of hydrological and erosive processes, directly affecting runoff generation and soil loss. An accurate assessment of soil water content (SWC) variability is therefore essential for sustainable land and water management, particularly in arid and semiarid regions. [...] Read more.

Antecedent soil moisture is a critical driver of hydrological and erosive processes, directly affecting runoff generation and soil loss. An accurate assessment of soil water content (SWC) variability is therefore essential for sustainable land and water management, particularly in arid and semiarid regions. This study explores the use of two emerging nuclear techniques, cosmic ray neutron sensors (CRNS) and proximal gamma ray spectroscopy (PGRS), to monitor SWC at the field scale in a semiarid agricultural field in NE Spain. Changes in soil moisture induced by a 16 mm rainfall event were monitored to evaluate the sensitivity and response of both techniques under dry and wet conditions. A stationary CRNS, located in the centre of the study field, recorded neutron counts at hourly intervals over a two-week period. Complementary PGRS surveys were conducted before and after the rainfall event, including (i) stationary measurements at the four corners of a 20 × 20 m plot, and (ii) mobile stop-and-go measurements along ten transects across the plot, with a spatial resolution of one metre. The results captured clear temporal dynamics in SWC, inferred from neutron count variations, as well as significant differences in ⁴⁰K (cps) measurements, between dry and wet conditions. These differences were observed when comparing the data from both stationary and mobile surveys conducted before and after the event. The integration of CRNS and PGRS offers complementary insights into scale, temporal dynamics and spatial variability, validating and highlighting the potential of these sensors for soil moisture monitoring. Both techniques demonstrated high sensitivity to variations in soil water content, and their complementary capabilities offer a robust, multi-scale approach with clear applications for precision agriculture and soil conservation. Full article

(This article belongs to the Special Issue Soil Chemical Properties and Soil Conservation in Agriculture)

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22 pages, 10649 KiB

Open AccessArticle

Enhanced Farmland Extraction from Gaofen-2: Multi-Scale Segmentation, SVM Integration, and Multi-Temporal Analysis

by Hang Yang, Hao Sun, Ke Wang, Jian Yang and Muhammad Hasan Ali Baig

Agriculture 2025, 15(10), 1073; https://doi.org/10.3390/agriculture15101073 - 16 May 2025

Abstract

In high-resolution remote sensing images, the combination of complex farmland plot features and limitations of manual and traditional classification methods hinders large-scale, automated, and precise farmland plot extraction. Key challenges include the following: (1) low accuracy and speckled noise (or salt-and-pepper noise) in [...] Read more.

In high-resolution remote sensing images, the combination of complex farmland plot features and limitations of manual and traditional classification methods hinders large-scale, automated, and precise farmland plot extraction. Key challenges include the following: (1) low accuracy and speckled noise (or salt-and-pepper noise) in pixel-based extraction methods; (2) difficulty in determining segmentation parameters for multi-scale algorithms; and (3) uncertainty about the optimal extraction period. This study proposes an object-oriented multi-scale segmentation method combined with a support vector machine, leveraging spectral reflectance, texture, and temporal differences between farmland and non-farmland plots. The method was validated across various types of farmland plots in the Xinbei and Jintan districts of Changzhou City, Jiangsu Province, China. Results indicate that there is (1) superior multi-scale segmentation during vegetative growth; (2) optimal segmentation parameters (scale 59, shape 0.2, compactness 0.6); (3) improved separation of farmland plots from large areas using road samples within farmland; and (4) enhanced extraction accuracy for irregular plots by increasing sample size. This approach effectively improves farmland plot extraction accuracy, supporting crop type identification and advancing digital agricultural management. Full article

(This article belongs to the Topic Advances in Smart Agriculture with Remote Sensing as the Core and Its Applications in Crops Field)

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27 pages, 12293 KiB

Open AccessArticle

Estimation of Leaf Chlorophyll Content of Maize from Hyperspectral Data Using E2D-COS Feature Selection, Deep Neural Network, and Transfer Learning

by Riqiang Chen, Lipeng Ren, Guijun Yang, Zhida Cheng, Dan Zhao, Chengjian Zhang, Haikuan Feng, Haitang Hu and Hao Yang

Agriculture 2025, 15(10), 1072; https://doi.org/10.3390/agriculture15101072 - 16 May 2025

Abstract

Leaf chlorophyll content (LCC) serves as a vital biochemical indicator of photosynthetic activity and nitrogen status, critical for precision agriculture to optimize crop management. While UAV-based hyperspectral sensing offers maize LCC estimation potential, current methods struggle with overlapping spectral bands and suboptimal model [...] Read more.

Leaf chlorophyll content (LCC) serves as a vital biochemical indicator of photosynthetic activity and nitrogen status, critical for precision agriculture to optimize crop management. While UAV-based hyperspectral sensing offers maize LCC estimation potential, current methods struggle with overlapping spectral bands and suboptimal model accuracy. To address these limitations, we proposed an integrated maize LCC estimation framework combining UAV hyperspectral imagery, simulated hyperspectral data, E2D-COS feature selection, deep neural network (DNN), and transfer learning (TL). The E2D-COS algorithm with simulated data was used to identify structure-resistant spectral bands strongly correlated with maize LCC: Big trumpet stage: 418 nm, 453 nm, 506 nm, 587 nm, 640 nm, 688 nm, and 767 nm; Spinning stage: 418 nm, 453 nm, 541 nm, 559 nm, 688 nm, 723 nm, and 767 nm. Combining the E2D-COS feature selection with TL and DNN significantly improves the estimation accuracy: the R² of the proposed Maize-LCNet model is improved by 0.06–0.11 and the RMSE is reduced by 0.57–1.06 g/cm compared with LCNet-field. Compared to the existing studies, this study not only clarifies the spectral bands that are able to estimate maize chlorophyll, but also presents a high-performance, lightweight (fewer input) approach to achieve the accurate estimation of LCC in maize, which can directly support growth monitoring nutrient management at specific growth stages, thus contributing to smart agricultural practices. Full article

(This article belongs to the Special Issue How Optical Sensors and Deep Learning Enhance the Production Management in Smart Agriculture)

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12 pages, 1247 KiB

Open AccessArticle

Insecticide Resistance and Plant Virus Status of Bemisia tabaci on Soybean in Suzhou

by Qi Li, Yao Ji, He Du, Shufang Ma, Jifei Zhu, Dehui Zhu, Natalia A. Belyakova, Youjun Zhang and Xin Yang

Agriculture 2025, 15(10), 1071; https://doi.org/10.3390/agriculture15101071 - 15 May 2025

Abstract

Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) is a super pest that seriously endangers the development of the agricultural economy worldwide. To prevent and control B. tabaci, insecticides have been used for many years, which has inevitably led to increased tolerance to chemical agents. To [...] Read more.

Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) is a super pest that seriously endangers the development of the agricultural economy worldwide. To prevent and control B. tabaci, insecticides have been used for many years, which has inevitably led to increased tolerance to chemical agents. To elucidate the development of field resistance and more scientifically and efficiently control B. tabaci, in December 2024, we conducted bioassays on B. tabaci on soybeans in Suzhou, Anhui Province, using 14 insecticides. These fourteen insecticides, namely, abamectin, spinetoram, thiamethoxam, flupyradifurone, imidacloprid, dinotefuran, acetamiprid, thiacloprid, nitenpyram, bifenthrin, deltamethrin, pyridaben, flonicamid, and emamectin benzoate, have multiple action sites and have all shown good control effects on B. tabaci. The results revealed that B. tabaci has developed high resistance to many insecticides and that some insecticides have even tended to fail, but B. tabaci is still sensitive to a small number of insecticides. Different biotypes of B. tabaci differ significantly in terms of insecticide resistance. We determined that the population of B. tabaci on soybean in Suzhou was the MED (Q) biotype. It carried the TYLCV virus, with a virus carrying rate of 60%, but did not carry ToCV or CCYV. Full article

(This article belongs to the Special Issue Sustainable Use of Pesticides—2nd Edition)

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22 pages, 4173 KiB

Open AccessArticle

Comprehensive Assessment of Soil Heavy Metal Contamination in Agricultural and Protected Areas: A Case Study from Iași County, Romania

by Camelia Elena Luchian, Iuliana Motrescu, Anamaria Ioana Dumitrașcu, Elena Cristina Scutarașu, Irina Gabriela Cara, Lucia Cintia Colibaba, Valeriu V. Cotea and Gerard Jităreanu

Agriculture 2025, 15(10), 1070; https://doi.org/10.3390/agriculture15101070 - 15 May 2025

Abstract

Soil contamination with heavy metals poses a significant risk to human health and ecological systems through multiple exposure pathways: direct ingestion of crops, dermal contact with polluted soil, and bioaccumulation within the food chain. This study analyses eleven composite soils, each collected in [...] Read more.

Soil contamination with heavy metals poses a significant risk to human health and ecological systems through multiple exposure pathways: direct ingestion of crops, dermal contact with polluted soil, and bioaccumulation within the food chain. This study analyses eleven composite soils, each collected in triplicate from different sites in Iași County, four of which are designated Natura 2000 protected areas (Mârzești Forest, Plopi Lake—Belcești, Moldova Delta, and Valea lui David). The assessment includes measurements of soil humidity by the gravimetric method, pH, and organic matter content, examined in relation to heavy metal concentrations due to their well-established interdependencies. For heavy metal determination, energy-dispersive X-ray spectroscopy (EDS) using an EDAX system (AMETEK Inc., Berwyn, PA, USA) and X-ray fluorescence spectrometry (XRFS) with a Vanta 4 analyser (Olympus, Waltham, MA, USA) were employed. Additionally, scanning electron microscopy (SEM) with a Quanta 450 microscope (FEI, Thermo Scientific, Hillsboro, OR, USA) was used primarily for informational purposes and to provide a broader perspective. In the case of chromium, 45.45% of the samples exceeded the permissible levels, with concentrations ranging from 106 mg/kg to 186 mg/kg, the highest value being nearly twice the alert threshold. Notably, not all protected areas maintain contaminant levels within safe limits. The sample from the Mârzești Forest protected site revealed considerably raised concentrations of mercury, arsenic, and lead, exceeding the alert thresholds (1 mg/kg—mercury, 15 mg/kg—arsenic, and 50 mg/kg—lead) established through Order no. 756/1997 issued by the Minister of Water, Forests, and Environmental Protection from Romania. On the other hand, the sample from Podu Iloaiei, an area with intensive agricultural activity, shows contamination with mercury and cadmium, highlighting significant anthropogenic pollution. The findings of this study are expected to raise public awareness regarding soil pollution levels, particularly in densely populated regions and protected ecological zones. Moreover, the results provide a scientific basis for policymakers and relevant authorities to implement targeted measures to manage soil contamination and ensure long-term environmental sustainability. Full article

(This article belongs to the Section Agricultural Soils)

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27 pages, 2421 KiB

Open AccessArticle

Sustainable Innovation Management Model (MGI) for Agro-Industrial Citrus Chain

by Jhon Wilder Zartha Sossa, Luis Horacio Botero Montoya, Juan Carlos Palacio Piedrahíta, Julio González Candia, Luis Fernando Gutiérrez Cano, Gina Lía Orozco Mendoza, Nolberto Gutiérrez Posada, Raúl Hernández Zarta, José Orlando Gómez Salazar and Juan Carlos Zapata Valencia

Agriculture 2025, 15(10), 1069; https://doi.org/10.3390/agriculture15101069 - 15 May 2025

Abstract

This paper proposes a sustainable innovation management model (hereinafter MGI) aimed at enhancing sustainability and leveraging open innovation opportunities within the Citrus agro-industrial chain in the Quindío Department, Colombia. The methodology combines surveys, consensus percentages, relevance and congruence indices, and a review of [...] Read more.

This paper proposes a sustainable innovation management model (hereinafter MGI) aimed at enhancing sustainability and leveraging open innovation opportunities within the Citrus agro-industrial chain in the Quindío Department, Colombia. The methodology combines surveys, consensus percentages, relevance and congruence indices, and a review of the literature from the last ten years, particularly in the Google Scholar and Scopus databases. A total of 97 documents directly related to innovation management in the citrus sector were reviewed, along with 58 indirect references. Through three questionnaires, 120 variables were identified, categorized into input (53), transformation (36), and output (31) stages. The findings, supported by sector analysis and foresight studies conducted for six regional agro-industrial chains, led to the development of three potential MGI models, one of which was selected for further application. The study highlights several challenges within the citrus value chain, including weak leadership, limited market competitiveness, outdated organizational structures, slow adoption of advanced technologies, and inadequate investment. The proposed MGI, with a focus on sustainable innovation, offers a generic interactive model that presents a dynamic and adaptable solution to drive competitiveness and value creation in the citrus sector. The chain studied requires not only the participation of different interest groups, but also the application of artificial intelligence to close the gaps and allow for sustainable innovation to be generated of sustainable innovation. Full article

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

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18 pages, 2395 KiB

Open AccessArticle

Functional Characteristics and Cellulose Degradation Genes of the Microbial Community in Soils with Different Initial pH Values

by Li Jiang, Boyan Xu and Qi Wang

Agriculture 2025, 15(10), 1068; https://doi.org/10.3390/agriculture15101068 - 15 May 2025

Abstract

Soil pH critically regulates microbial community structure and activity, thereby influencing carbon transformation processes in terrestrial ecosystems. However, the mechanisms underlying pH-mediated shifts in microbial metabolic functions and cellulose-degrading functional genes remain poorly understood. This study investigated the responses of bacterial communities, metabolic [...] Read more.

Soil pH critically regulates microbial community structure and activity, thereby influencing carbon transformation processes in terrestrial ecosystems. However, the mechanisms underlying pH-mediated shifts in microbial metabolic functions and cellulose-degrading functional genes remain poorly understood. This study investigated the responses of bacterial communities, metabolic profiles, and the abundance of cellobiohydrolase I (cbhI) and glycoside hydrolase family 48 (GH48) genes to varying pH levels in fluvo-aquic and red soils. High-throughput sequencing, PICRUSt-based metabolic prediction, and quantitative PCR were employed to analyze microbial composition, functional traits, and gene dynamics. Network analysis clarified linkages between functional genes, pathways, and taxa. The results revealed that elevated pH significantly increased CO₂ emissions and dissolved organic carbon (DOC) content in both soils. Dominant taxa, including Alphaproteobacteria, Bacteroidetes, Xanthomonadaceae, and Mycoplasma, exhibited pH-dependent enrichment. Metabolic predictions indicated that pH positively influenced genes linked to biodegradation and xenobiotic metabolism in fluvo-aquic soil but suppressed energy-metabolism-related genes. Contrastingly, in red soil, cbhI and GH48 gene abundance declined with rising pH, suggesting that acidic conditions favor cellulolytic activity. Network analysis identified strong positive correlations between CO₂ emissions and Caulobacteraceae, while cbhI and GH48 genes were closely associated with taxa such as Xanthomonadaceae, Comamonadaceae, and Micromonosporaceae, which drive organic matter decomposition. These findings underscore pH as a pivotal regulator of microbial community structure and functional gene expression, with soil-specific responses highlighting the need for tailored strategies to optimize carbon cycling and sequestration in agricultural ecosystems. Full article

(This article belongs to the Section Agricultural Soils)

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16 pages, 3519 KiB

Open AccessArticle

Effects of Exogenous Application of Phenolic Acid on Soil Nutrient Availability, Enzyme Activities, and Microbial Communities

by Yi Zhou, Yihang Liu, Chaoqiang Jiang, Zeinab El-Desouki, Muhammad Riaz, Chenlu Wang, Xueping Zhang, Jiayi Ding, Zhenghao Chen, Huaiwei Liu, Jia Shen and Hao Xia

Agriculture 2025, 15(10), 1067; https://doi.org/10.3390/agriculture15101067 - 15 May 2025

Abstract

Phenolic acids are important allelochemicals that contribute to obstacles in continuous cropping systems, significantly impacting soil nutrients, enzyme activities, and the composition of microbial communities. This study explored the effects of treatment time and the concentration of various phenolic acids (salicylic acid and [...] Read more.

Phenolic acids are important allelochemicals that contribute to obstacles in continuous cropping systems, significantly impacting soil nutrients, enzyme activities, and the composition of microbial communities. This study explored the effects of treatment time and the concentration of various phenolic acids (salicylic acid and p-hydroxybenzoic acid) on soil nutrients, enzyme activity, and soil microorganisms through cultivation experiments. The results indicated that high-concentration phenolic acid treatment negatively affected the availability of soil nutrients by acidifying the soil, as reflected in the low soil pH, compared to the untreated control. Moreover, the soil extracellular enzymes exhibited varying degrees of improvement when phenolic acids were added. Multi-element analysis revealed that treatment duration, concentration, and the type of phenolic acid significantly affected soil nutrient levels and enzyme activity. Additionally, structural equation modeling indicated a significant correlation between the concentration of phenolic acids and the diversity of microorganisms. Phenolic acids influence the soil ecological environment by altering the relative abundance of functional microorganisms (p_Patescibacteria and p_Mortierellomycota) in the soil. Thus, comprehensive regulation and control of continuous cropping obstacles can be achieved by adjusting the micro-ecological environment of the soil, which, in turn, affects phenolic acid substances present in the soil, thereby alleviating continuous cropping obstacles. Full article

(This article belongs to the Section Agricultural Soils)

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22 pages, 2483 KiB

Open AccessArticle

Effects of Biofertilizer on Yield and Quality of Crops and Properties of Soil Under Field Conditions in China: A Meta-Analysis

by Baolei Pei, Ting Liu, Ziyan Xue, Jian Cao, Yunpeng Zhang, Mulan Yu, Engang Liu, Jincheng Xing, Feibing Wang, Xuqin Ren and Zhenhua Zhang

Agriculture 2025, 15(10), 1066; https://doi.org/10.3390/agriculture15101066 - 15 May 2025

Abstract

Biofertilizers play a crucial role in promoting sustainable agriculture in China; however, comprehensive quantification of their effects and limitations in field conditions remain unclear. In this study, a meta-analysis encompassing 1818 comparisons from 107 studies was conducted to quantify their systematic effects in [...] Read more.

Biofertilizers play a crucial role in promoting sustainable agriculture in China; however, comprehensive quantification of their effects and limitations in field conditions remain unclear. In this study, a meta-analysis encompassing 1818 comparisons from 107 studies was conducted to quantify their systematic effects in field conditions in China. The results demonstrated that biofertilizers enhanced crop yields across 21 of the 23 investigated crops, with notable increases in millet (+65.42%), vegetables (e.g., Chinese cabbage +35.57%, ginger +39.18%), and legumes (kidney beans +54.03%), while cotton and rapeseed showed non-significant improvements. Nutritional quality was also improved, as evidenced by elevated levels of vitamin C (14.61%), protein (16.61%), and carotenoids (15.18%), alongside a reduction in nitrate content (21.94%). Soil health was significantly improved through increased organic matter (16.64%), enhanced enzymatic activities (urease: 57.60%; phosphatase: 43.51%), and a proliferation of beneficial microbes (bacteria: 157.10%; fungi: 30.28%), while pathogenic organisms were suppressed by 51.81%. The observed yield improvements were attributed to enhanced nutrient availability (total nitrogen: 16.67%; available phosphorus: 10.98%), optimized root growth (19.23% increase in volume), and a reduction in disease incidence (42.52%). The efficacy of biofertilizers was maximized when they were used in conjunction with organic amendments, resulting in a 29.20% increase in yield, particularly when applied prior to planting. These results show that biofertilizers boost productivity, quality, and soil functionality, depending on their production and field management practices. Their effectiveness is tied to optimizing soil properties and suppressing pathogens, providing strategies for sustainable agriculture in China. Full article

(This article belongs to the Section Agricultural Soils)

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