Agriculture

20 pages, 16616 KB

Open AccessArticle

Effect of Nitrogen on Interaction Between Carbon, Nitrogen and Phosphorus Cycles in High-Altitude Apple Orchards

by Wenqiang Huang, Lingchen Tong, Zheng Wu, Minghang Hu, Shuang Liu, Yanhui Ye and Yanying Han

Agriculture 2026, 16(11), 1214; https://doi.org/10.3390/agriculture16111214 (registering DOI) - 30 May 2026

Abstract

To elucidate the effects of nitrogen (N) addition on soil carbon (C), N, and phosphorus (P) cycling in high-altitude orchards on the Qinghai–Tibet Plateau, a three-year field experiment was conducted at an altitude of 3000 m with four N application rates (0, 150, [...] Read more.

To elucidate the effects of nitrogen (N) addition on soil carbon (C), N, and phosphorus (P) cycling in high-altitude orchards on the Qinghai–Tibet Plateau, a three-year field experiment was conducted at an altitude of 3000 m with four N application rates (0, 150, 300, and 450 kg N ha⁻¹, designated as CK, N150, N300, and N450, respectively). We determined soil physicochemical properties, 12 soil enzyme activities, and metagenomic characteristics, and further adopted partial least squares path modeling (PLS-PM) for data analysis and mechanism exploration. The results were as follows: (1) The N300 treatment yielded the maximum C-hydrolase activities and soil organic carbon content, with a 40.6% increase in soil organic carbon compared with the CK group. (2) The N450 treatment resulted in a 365.4% increase in soil nitrate content and significantly reduced the soil pH (from 6.32 to 5.86). Such environmental filtering significantly decreased the relative abundance of Nitrospirota and its core denitrification genes, including nosZ and narI. (3) Continuous N input induced secondary soil P limitation, leading to a more than 90% increase in phosphatase activities under the N450 treatment. Pseudomonadota activated soil P sources by enriching the functional potential of the phn gene cluster. Furthermore, the PLS-PM analysis revealed a significant negative statistical association between P-cycling enzymes and N-cycling functional potential (p < 0.01). This statistical linkage supports the observation of divergent metabolic responses among different element cycles. In conclusion, under the specific experimental conditions tested, an optimal N application rate of 300 kg N ha⁻¹ is recommended to balance agricultural productivity and soil ecological health. The microbiome of alpine apple orchards responds to elevated N input through metabolic trade-offs, namely reducing the functional potential for denitrification and enhancing the P recycling system. These findings provide vital molecular evidence to guide fertilizer reduction, optimize nutrient management, and promote the sustainable development of high-altitude agroecosystems. Full article

(This article belongs to the Section Agricultural Soils)

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27 pages, 7553 KB

Open AccessArticle

Research on Soil Salinity Inversion in Coastal Areas Based on UAV Multispectral Imagery and Ensemble Machine Learning

by Mengjia Zhang, Xinmiao Wu, Yu Hu, Jiajun Liu, Donglin Wang, Haonan Shen and Zhihong Qie

Agriculture 2026, 16(11), 1213; https://doi.org/10.3390/agriculture16111213 (registering DOI) - 30 May 2026

Abstract

Accurate and timely monitoring of soil salinity is of great significance for the ecological restoration of saline-alkali land and precision agricultural management. In this study, a typical coastal saline-alkali farmland located in Huanghua City, Hebei Province, China, in the Bohai coastal region, was [...] Read more.

Accurate and timely monitoring of soil salinity is of great significance for the ecological restoration of saline-alkali land and precision agricultural management. In this study, a typical coastal saline-alkali farmland located in Huanghua City, Hebei Province, China, in the Bohai coastal region, was selected as the study area. High-resolution images were acquired using an unmanned aerial vehicle (UAV) equipped with a multispectral sensor, and ground soil salinity samples were collected synchronously. Based on the construction of a feature library comprising spectral reflectance, vegetation indices, and salinity indices, three algorithms, PSO-SFLA, MultiSURF, and VIP, were employed for feature selection. Subsequently, an ensemble model was established, utilizing Ridge Regression (Ridge), Random Forest (RF), and Extra Trees (ET) as primary base learners, and Extreme Gradient Boosting (XGBoost) as the secondary meta-learner. This ensemble model was applied for soil salinity inversion. Furthermore, the coefficient of determination (R²), standardized root mean square error (S_RMSE), and the ratio of performance to interquartile distance (RPIQ) were introduced to comprehensively evaluate the accuracy of the models. Finally, the intrinsic physical responses of the features were explored through SHAP. The results showed that the optimization by the PSO-SFLA effectively reduced the impact of spectral multicollinearity, and 11 core features highly sensitive to salinity were selected from a vast number of indices. The ensemble model showed better predictive performance on the independent test set, achieving an R² of 0.758, an S_RMSE of 0.285, and an RPIQ of 3.382, outperforming the single Ridge, RF, and ET models under the current experimental conditions. Based on this model, the spatial distribution map of soil salinity in the experimental area was generated. The integrated and interpretable workflow proposed in this study, combining UAV multispectral imagery, PSO-SFLA-based feature selection, ensemble learning, and SHAP interpretation, provides a practical approach for accurate soil salinity inversion and dynamic agricultural monitoring in coastal saline-alkali lands. Full article

(This article belongs to the Section Agricultural Soils)

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19 pages, 6854 KB

Open AccessArticle

Effects of Electric Field Application in Heating, Thermophilic, Cooling and Maturation Phases on Greenhouse Gas Emissions of Biochar-Pig Manure Aerobic Composting

by Hongmei Zhang, Xiaoyun Lian, Qian Liu, Ling Zhou, Deguo Kong, Weiguo Xu, Lingling Chen and Zhisheng Wang

Agriculture 2026, 16(11), 1212; https://doi.org/10.3390/agriculture16111212 (registering DOI) - 30 May 2026

Abstract

Aerobic composting is an important technology for resource utilization, yet substantial greenhouse gas emissions and carbon–nitrogen losses limit its environmental benefits. Although biochar amendment has been widely applied, the stage-specific effects of electric field assistance on biochar-pig manure composting remain unclear. This study [...] Read more.

Aerobic composting is an important technology for resource utilization, yet substantial greenhouse gas emissions and carbon–nitrogen losses limit its environmental benefits. Although biochar amendment has been widely applied, the stage-specific effects of electric field assistance on biochar-pig manure composting remain unclear. This study focuses on the effects of electric field application on the transformation of carbon and nitrogen during different stages of biochar-pig manure composting. The physicochemical properties of the compost pile, as well as the concentrations and forms of carbon and nitrogen, were analyzed. Four electric-field application treatments were established: continuous application throughout the whole composting period (E1), weeks 0–2 (E2), weeks 3–4 (E3), and weeks 5–6 (E4). The results showed that the total nitrogen contents in the electric field treatment groups were 20.29–36.58% higher compared to the initial level and significantly higher compared to CK (10.91%). The cumulative NH₃ emissions reduced by 44.58–56.23%, while N₂O emissions declined by 8.99–49.29%. NH₄⁺-N content was higher in E3 (electric field application during weeks 3–4), while NO₃⁻-N content was higher in E4 (electric field application during weeks 5–6), indicating the optimal retention of nitrogen. In terms of carbon transformation, the cumulative emissions of CO₂ and CH₄ decreased by 6.22–33.49% and 3.5–60.35%, respectively. In addition, the electric field promoted organic matter degradation and aromatic substance accumulation, realizing efficient carbon sequestration. Meanwhile, it altered the structure of microbial communities, inhibited Pseudogracilibacillus, and activated norank_f_MWH-CFBk5, norank_f_Fodinicurvataceae, as well as aerobic degraders such as Pseudomonadota. This study confirms that electric field treatment facilitates efficient waste resource utilization by optimizing the composting microenvironment and cutting greenhouse gas emissions. Treatment E1 exhibited the optimal carbon retention capacity, while treatments E3 and E4 showed the best nitrogen retention performance. Full article

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

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27 pages, 4476 KB

Open AccessArticle

County-Level Assessment of Agricultural Vulnerability to Climate Extremes in Alabama (USA)

by Souleymane Fall, Joseph E. Quansah and Sara Gutierrez Diaz

Agriculture 2026, 16(11), 1211; https://doi.org/10.3390/agriculture16111211 (registering DOI) - 30 May 2026

Abstract

Climate extremes are increasingly affecting agricultural systems, but local conditions and different levels of resilience lead to varying degrees of vulnerability. In this study, composite indices were developed to assess county-level agricultural vulnerability to such extremes in Alabama (USA). Temperature and precipitation extreme [...] Read more.

Climate extremes are increasingly affecting agricultural systems, but local conditions and different levels of resilience lead to varying degrees of vulnerability. In this study, composite indices were developed to assess county-level agricultural vulnerability to such extremes in Alabama (USA). Temperature and precipitation extreme indices derived from long-term station observations were used to determine climate exposure, while sensitivity and adaptive capacity were characterized using agricultural and socioeconomic indicators. Two vulnerability indices were developed: a farm-based index that reflects agricultural production systems, and a hybrid index that includes both farm-system and socioeconomic adaptive capacities. The farm system vulnerability results showed that highly vulnerable counties are concentrated in northern Alabama, while low-vulnerability counties form clusters in the southeast and include several Black Belt counties, reflecting differences in exposure, sensitivity, and farm-based adaptive capacity. Incorporating socioeconomic adaptive capacity expands the distribution of highly vulnerable counties across Alabama and increases vulnerability in many Black Belt counties that previously exhibited low farm-based vulnerability, while more urbanized counties with stronger socioeconomic capacity exhibit lower vulnerability. Overall, the results suggest that focusing solely on farm-system resilience may misrepresent the capacity of agricultural communities to deal with climate extremes. The framework used in this study led to the development of a flexible tool that can help policymakers plan targeted agricultural strategies and support efficient climate adaptation across Alabama. Full article

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

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16 pages, 1004 KB

Open AccessArticle

Estimation of Ramie Key Phenotypic Traits Based on UAV Remote Sensing

by Hongyu Fu, Wei Wang, Jihao Nie, Guoxian Cui, Wei She and Tao Xue

Agriculture 2026, 16(11), 1210; https://doi.org/10.3390/agriculture16111210 - 29 May 2026

Abstract

UAV-based phenotyping enables efficient high-throughput measurement of field crops. Phenotypic monitoring of ramie is critical for its cultivation management and variety breeding. However, ramie exhibits characteristics including multiple annual harvests, short growth cycles and rapid dynamic growth change, all of which increase the [...] Read more.

UAV-based phenotyping enables efficient high-throughput measurement of field crops. Phenotypic monitoring of ramie is critical for its cultivation management and variety breeding. However, ramie exhibits characteristics including multiple annual harvests, short growth cycles and rapid dynamic growth change, all of which increase the difficulty of growth monitoring and yield estimation. This study aims to utilize UAV-based multispectral remote sensing to estimate ramie plant height (PH), leaf area index (LAI), and above-ground biomass (AGB) over multiple time series, and to assess the influence of seasonal effects and different data processing strategies on the accuracy of ramie digital phenotyping. Over three ramie growth cycles, a total of 15 UAV flights were conducted over an experimental field consisting of 72 plots. The structure from motion (SfM) algorithm was applied to estimate PH. Remote sensing features derived from UAV imagery were used with background segmentation and machine learning to estimate LAI. The AGB was estimated by combining remote sensing-derived PH, LAI, and climate data. The results showed that the estimated and measured phenotypes were highly correlated, with optimal coefficients of determination of 0.961 for PH and 0.873 for LAI. Background segmentation improved LAI accuracy. Integrating climate data, remote sensing-derived PH and LAI significantly enhanced the accuracy of AGB estimation. In conclusion, this study provides a feasible method for extracting ramie phenotypes from UAV remote sensing imagery, providing methodological support for large-scale management of the crop industry and intelligent, precise monitoring of crop growth. Full article

(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)

29 pages, 991 KB

Open AccessArticle

The Impact of Artificial Intelligence on Agricultural Carbon Sinks and Net Carbon Sinks in the Yellow River Basin: Evidence from Panel Data from 97 Chinese Cities

by Lei Nie, Xuerong Wang, Zhifang Wu, Bin He, Yuanyuan Wei and Xiaohang Yue

Agriculture 2026, 16(11), 1209; https://doi.org/10.3390/agriculture16111209 - 29 May 2026

Abstract

A scientific understanding of the relationship between artificial intelligence (AI) and agricultural carbon sinks (ACS) is essential for promoting the low-carbon transformation of agriculture in the Yellow River Basin, accelerating the achievement of the “dual carbon” goals, and enhancing the high-quality development of [...] Read more.

A scientific understanding of the relationship between artificial intelligence (AI) and agricultural carbon sinks (ACS) is essential for promoting the low-carbon transformation of agriculture in the Yellow River Basin, accelerating the achievement of the “dual carbon” goals, and enhancing the high-quality development of the regional economy. Using panel data from 97 cities in the Yellow River Basin in China from 2001 to 2023, this study measures the levels of ACS and agricultural net carbon sinks (ANCS), and further examines the mechanisms, regional heterogeneity, and moderating effects associated with the impact of AI on both indicators. The results indicate that: (1) AI significantly improves ACS and ANCS in cities within the Yellow River Basin. Specifically, for every 1% increase in AI development, ACS and ANCS increase by approximately 0.0111 million tons and 0.0138 million tons, respectively. This effect is more pronounced in the upper and lower reaches of the basin, while remaining insignificant in the middle reaches; (2) AI promotes improvement in ACS and ANCS by increasing the level of agricultural mechanization and the intensity of chemical fertilizer application; (3) Grain yield per unit area and agricultural planting structure positively moderate the relationship between AI and both ACS and ANCS. Overall, the findings suggest that AI plays a significant positive role in enhancing ACS and ANCS. Therefore, greater emphasis should be placed on the cultivation, diffusion, and application of AI technologies to achieve the sustained improvement of ACS and ANCS capacity in the Yellow River Basin. Full article

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

29 pages, 4970 KB

Open AccessArticle

The Design and Optimization of a Multi-Channel Fertilizer Spreading System Based on EDEM Simulation and the CNN-LSTM–Attention Algorithm

by Xiangan Chen, Xuemin Zhang, Yajuan Wang and Shuangjie Zhang

Agriculture 2026, 16(11), 1208; https://doi.org/10.3390/agriculture16111208 - 29 May 2026

Abstract

To achieve the objectives of a variable spreading width and uniform fertilizer distribution in different operating environments, such as orchards and greenhouses, this study proposes a multi-channel fertilizer spreading method and designs a multi-channel fertilizer spreading system. The system mainly consists of a [...] Read more.

To achieve the objectives of a variable spreading width and uniform fertilizer distribution in different operating environments, such as orchards and greenhouses, this study proposes a multi-channel fertilizer spreading method and designs a multi-channel fertilizer spreading system. The system mainly consists of a fertilizer delivery device and multi-channel spreading fertilizer disc. Based on the fertilizer delivery requirements, the fertilizer delivery device was designed using kinematic theories and the EDEM software. To address the optimization problem of the fin angle (λ), EDEM simulation is combined with machine learning methods, the multi-channel fertilizer spreading system was simulated using EDEM, and a spreading model was established based on the CNN-LSTM–Attention algorithm using simulation data. The model was employed to optimize the fin angle (λ), with spreading uniformity and width as the evaluation metrics. The optimization yielded a fin angle of λ = 34.15°, a coefficient of variation (Cv) of transverse distribution uniformity of 9.29%, and a spreading width of 3.0 m. The optimization results were validated through a combination of EDEM simulation and field tests. The EDEM simulations indicated a Cv of transverse distribution uniformity of 7.66% and an effective spreading width of 2.95 m. Field test results show a spreading width of 2.97 m and an average Cv of transverse distribution uniformity of 10.21%. The model optimization results align well with both the EDEM simulation and field test outcomes, providing new insights into researching fertilizer spreading methods. Full article

(This article belongs to the Section Agricultural Technology)

22 pages, 676 KB

Open AccessArticle

Does Agricultural Production Trusteeship Promote Pesticide Reduction? A Case Study of Rice Farmers in Sichuan Province, China

by Ziwei Peng, Dingde Xu and Gang Fu

Agriculture 2026, 16(11), 1207; https://doi.org/10.3390/agriculture16111207 - 29 May 2026

Abstract

Under the dual pressures of ensuring food security and advancing the green transformation of agriculture, effectively guiding farmers toward pesticide reduction has become a core issue in China’s sustainable agricultural development. As a new form of agricultural socialized service, agricultural production trusteeship offers [...] Read more.

Under the dual pressures of ensuring food security and advancing the green transformation of agriculture, effectively guiding farmers toward pesticide reduction has become a core issue in China’s sustainable agricultural development. As a new form of agricultural socialized service, agricultural production trusteeship offers a potential solution to the challenge of farmers’ excessive pesticide input through specialized services. Based on the Ordinary Least Squares (OLS) method, this study employs micro-level survey data from 978 rice-growing farmers in Sichuan Province, China, to empirically examine the impact of agricultural production trusteeship on farmers’ pesticide reduction. The findings are as follows: (1) Agricultural production trusteeship significantly promotes pesticide reduction among farmers. (2) The moderating effect results of land endowments indicate that both cultivation scale and land fragmentation exert a negative moderating effect on the relationship between agricultural production trusteeship and farmers’ pesticide reduction. (3) The heterogeneity results reveal that, from the perspective of trusteeship mode selection, full-process trusteeship is more effective in promoting farmers’ pesticide reduction. From the perspective of farmer differentiation, the pesticide-reducing effect of agricultural production trusteeship is more pronounced among non-farm-dominated farmers and middle and new generation farmers. Accordingly, this study proposes that agricultural production trusteeship may serve as a promising policy pathway for pesticide reduction initiatives, and that differentiated promotion strategies and incentive mechanisms are recommended to be implemented based on farmer types and land endowments. Full article

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

22 pages, 1846 KB

Open AccessArticle

Control System Based on Tuna Swarm Optimization, Fuzzy PID, and MQTT Protocol for Remote Fertilization

by Xun Wu, Lixin Zhang, Jiawei Zhao and Xue Hu

Agriculture 2026, 16(11), 1206; https://doi.org/10.3390/agriculture16111206 - 29 May 2026

Abstract

Aiming to address the problems of low flow-control accuracy, slow response speed, and difficulties in remote monitoring during precision agricultural fertilization, this study proposes a fertilization flow control method based on the tuna swarm optimization algorithm (TSO) combined with fuzzy PID control. Remote [...] Read more.

Aiming to address the problems of low flow-control accuracy, slow response speed, and difficulties in remote monitoring during precision agricultural fertilization, this study proposes a fertilization flow control method based on the tuna swarm optimization algorithm (TSO) combined with fuzzy PID control. Remote monitoring and cloud-based management of the system are achieved through the MQTT protocol. The proposed method uses the TSO to globally optimize the initial PID parameters and the outputs of fuzzy rules, while fuzzy logic is employed to realize real-time adaptive adjustment of control parameters under nonlinear operating conditions. The experimental results show that the proposed TSO-Fuzzy-PID controller exhibits excellent dynamic performance in fertilization flow control. Compared with the conventional PID controller, the maximum overshoot of the system is reduced from 28.37% to 9.15%, and the settling time is shortened from 111 s to 76 s. The remote communication test results indicate that, during 20 consecutive tests lasting 60 min each, the average communication delay of the MQTT communication link remains within 162–215 ms, the maximum delay does not exceed 300 ms, and the message transmission success rate reaches 99.4%, verifying the good real-time performance and reliability of the system. Full article

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

26 pages, 22791 KB

Open AccessArticle

Farmland Visual Navigation with Semantic Segmentation Under Leaf Occlusion

by Jiahao Liang, Chao Liu, Yuting Zhai, Mingfu Zhang and Yanlei Xu

Agriculture 2026, 16(11), 1205; https://doi.org/10.3390/agriculture16111205 - 29 May 2026

Abstract

In agricultural machinery visual navigation, accurately identifying the navigation line extraction region (NLER) at the center of the field of view is crucial for obtaining a precise navigation centerline. Although deep learning is the predominant method for NLER extraction, existing approaches face challenges [...] Read more.

In agricultural machinery visual navigation, accurately identifying the navigation line extraction region (NLER) at the center of the field of view is crucial for obtaining a precise navigation centerline. Although deep learning is the predominant method for NLER extraction, existing approaches face challenges in farmland environments characterized by densely distributed and irregularly extended leaves. These challenges result in unstable predictions, slow inference, and large model sizes that impede real-time applications. To address these issues, we propose a lightweight navigation segmentation residual network (LNS-ResNet), which integrates an inhibition–enhancement module (IEM) and a global convolutional residual block (GCRB). The IEM uses row–column one-dimensional convolutions to enhance vertical features between crop rows and suppress leaf-edge interference, producing more robust input features. The GCRB incorporates a full convolutional global attention (FCGA) mechanism to capture global context while preserving local spatial information. LNS-ResNet effectively reduces foliage interference and achieves accurate segmentation, with intersection over union (IoU) scores of 84.71% for crop row and 93.77% for path regions. Based on the segmentation output, we further propose a mask region determination-based navigation line extraction algorithm (MRD-Line), which directly identifies the NLER and connects the centerline within the mask without relying on line fitting. Deployed experiments on the Jetson TX2 demonstrate that the proposed method achieves both accuracy and efficiency, with mean angular deviations of 0.138

^{\circ}

(path) and 0.425

^{\circ}

(crop row), with average processing times of 64.1 ms (path) and 62.6 ms (crop row). Full article

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

14 pages, 235 KB

Open AccessArticle

Study on the Factors Influencing the Adoption of Intelligent Agricultural Machinery by Farmers in Changsha County, Hunan Province, Based on the Ordered Logit Model

by Junyi Peng, Minli Yang and Zhuo Li

Agriculture 2026, 16(11), 1204; https://doi.org/10.3390/agriculture16111204 - 29 May 2026

Abstract

In order to better promote the use of intelligent agricultural machinery, enhance the efficiency of grain production, optimize resource utilization, and effectively address the practical problem of the reduction in the rural labor force, while theoretically clarifying the mechanism that affects the adoption [...] Read more.

In order to better promote the use of intelligent agricultural machinery, enhance the efficiency of grain production, optimize resource utilization, and effectively address the practical problem of the reduction in the rural labor force, while theoretically clarifying the mechanism that affects the adoption of intelligent agricultural machinery by farmers in Changsha County. Based on a questionnaire survey of farmers in Changsha County, Hunan Province, the ordered logit model was used to identify the significant factors influencing farmers’ adoption of intelligent agricultural machinery. The empirical results show that male farmers, farmers with a non-agricultural occupation, and farmers with a lower education level (below high school) have a lower willingness to adopt intelligent agricultural machinery. As the risk of purchasing intelligent agricultural machinery decreases, market demand increases, and the number of agricultural services provided by the government increases, the likelihood of farmers adopting intelligent agricultural machinery also increases. Based on these findings, this paper proposes targeted suggestions aimed at increasing the adoption of intelligent agricultural machinery by farmers in Changsha County, Hunan Province. Full article

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

26 pages, 4661 KB

Open AccessArticle

Design and Field Experiment of a High-Speed Sliding-Cutting Device for Xiangsha Taro Stems in Viscoplastic Soil

by Xiaoying He, Qi He, Tiantian Jing, Meng Fang, Jiahao Shen, Jun Zhang and Zhong Tang

Agriculture 2026, 16(11), 1203; https://doi.org/10.3390/agriculture16111203 - 29 May 2026

Abstract

To address technical challenges such as equipment clogging and tuber damage during the mechanized harvesting of Xiangsha taro, this study designed a high-speed sliding-cutting device and conducted preliminary field performance evaluations. Based on the preliminary morphological baseline of Xiangsha taro and the distribution [...] Read more.

To address technical challenges such as equipment clogging and tuber damage during the mechanized harvesting of Xiangsha taro, this study designed a high-speed sliding-cutting device and conducted preliminary field performance evaluations. Based on the preliminary morphological baseline of Xiangsha taro and the distribution of soil penetration resistance, a multi-tooth rotary disc cutting device was developed. Kinematic and dynamic modelling indicated that a velocity ratio of 3.5–5.5 facilitate a ‘cycloidal loop’ trajectory, which theoretically reduces the potential for root disturbance by mitigating forward pushing forces. Initial field tests under specific orderly ridge conditions yielded a cutting qualification rate exceeding 96% and an estimated field capacity of 0.025 ha/h. While these results offer a preliminary technical reference for segmented harvesting equipment, the current validation is limited by the idealized row alignment of the experimental plot. Future research must evaluate the system’s adaptability to field irregularities and conduct direct controlled comparisons with commercial manual devices to fully substantiate its practical superiority. Full article

(This article belongs to the Section Agricultural Technology)

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18 pages, 7912 KB

Open AccessArticle

Multi-Source Remote Sensing Collaboration Reveals Spatiotemporal Differentiation and Driving Mechanisms of Soil Organic Matter in Cultivated Land of Anhui Province

by Mengmeng Tang, Shang Han, Wenlong Cheng, Shan Tang, Rongyan Bu, Min Li, Hui Wang, Rui Zhu, Fahui Jiang, Changai Lu and Ji Wu

Agriculture 2026, 16(11), 1202; https://doi.org/10.3390/agriculture16111202 - 29 May 2026

Abstract

The spatial heterogeneity and dynamic changes in soil organic matter (SOM) are key indicators for assessing cultivated land quality and the carbon cycle. Currently, large-scale SOM monitoring relies primarily on limited ground sampling, making it difficult to capture continuous spatiotemporal variation patterns. Taking [...] Read more.

The spatial heterogeneity and dynamic changes in soil organic matter (SOM) are key indicators for assessing cultivated land quality and the carbon cycle. Currently, large-scale SOM monitoring relies primarily on limited ground sampling, making it difficult to capture continuous spatiotemporal variation patterns. Taking Anhui Province, China as the study area, this research integrates multi-source remote sensing and geostatistical methods to construct a multi-source collaborative SOM inversion model and analyze its spatiotemporal evolution patterns, thereby achieving high-precision, continuous spatiotemporal monitoring of SOM. A total of 3026 sampling points in Huangshan, Chuzhou and Fuyang cities in Anhui Province were selected as model training samples. The study divided the terrain into three elevation zones (<20 m, 20–40 m, >40 m) and employed the Synthetic Minority Oversampling Technique (SMOTE) method to optimize sample distribution. Based on MODIS data, this study screened spectral bands and key phenological periods significantly correlated with SOM. By integrating spectral information from Landsat 8/9 OLI imagery, meteorological data and topographic factors, a random forest (RF) inversion model incorporating multi-source environmental variables was constructed. The results indicate that (1) the RF-based SOM inversion model exhibits moderate predictive accuracy acceptable for regional-scale SOM mapping, with a coefficient of determination (R²) of 0.55 and a root-mean-square error (RMSE) of 3.3 g/kg, effectively enabling the quantitative estimation of SOM at a regional scale. (2) The model’s inversion results reflect the spatial distribution of SOM in cultivated land in Anhui Province for the years 2019, 2022 and 2024. The provincial average SOM value shows an upward trend, with SOM content exhibiting a pattern of higher levels in the south and lower levels in the north, higher levels in the west and lower levels in the east, as well as a tendency to cluster. (3) Analysis using GeoDetector indicates that topography and precipitation are the primary drivers influencing SOM distribution, and the interaction between these two factors provides significantly greater explanatory power for SOM distribution than either factor alone. Through the integration of multi-source remote sensing data and model optimization, this study has validated the feasibility of multi-scale remote sensing-based SOM inversion, revealed the spatial differentiation characteristics and driving mechanisms of SOM in Anhui Province’s cultivated land, and provided a scientific basis for improving cultivated land quality and soil carbon sink management. Full article

(This article belongs to the Section Agricultural Soils)

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27 pages, 10340 KB

Open AccessArticle

Design and Application of a Cloud Platform for Broiler Inspection Robots

by He Zhu, Guangyu Zhao, Haiyang Wang, Sai Luo, Jingkun Sun, Deqi Hao and Changxi Chen

Agriculture 2026, 16(11), 1201; https://doi.org/10.3390/agriculture16111201 - 29 May 2026

Abstract

To reduce the labor intensity of manual inspection and support centralized robot management in commercial caged broiler farming, this study designed and implemented a cloud platform for broiler inspection robots based on a layered IoT architecture. The platform supports robot task scheduling, remote [...] Read more.

To reduce the labor intensity of manual inspection and support centralized robot management in commercial caged broiler farming, this study designed and implemented a cloud platform for broiler inspection robots based on a layered IoT architecture. The platform supports robot task scheduling, remote control, real-time status monitoring, multimodal data acquisition, hierarchical cloud storage, and structured warning-event management. Dead chicken detection was selected as the primary field-validated anomaly-analysis task. Open-mouth behavior recognition, thermal anomaly analysis, and abnormal sound identification were integrated as auxiliary screening functions. A three-month field trial was conducted in a commercial four-tier caged broiler house. The platform achieved a robot command success rate of 99%, a real-time status refresh delay below 3 s, backend service availability of 99.95%, and an average robot offline frequency of 0.2 times per week. Field validation of dead chicken detection achieved a precision of 90.6%, recall of 90.4%, F1 score of 90.5%, and cage-level localization accuracy of 96%. These results indicate that the proposed platform can support routine robot inspection, warning-event review, carcass localization, and targeted farm management response in commercial caged broiler production. Full article

(This article belongs to the Topic Digital Agriculture, Smart Farming and Crop Monitoring)

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4 pages, 171 KB

Open AccessEditorial

Price and Trade Dynamics in Agricultural Commodity Markets

by Mariusz Hamulczuk, Karolina Pawlak and Katarzyna Czech

Agriculture 2026, 16(11), 1200; https://doi.org/10.3390/agriculture16111200 - 29 May 2026

Abstract

Agricultural commodity markets are currently shaped by overlapping economic, climatic, geopolitical, and policy-related shocks [...] Full article

(This article belongs to the Special Issue Price and Trade Dynamics in Agricultural Commodity Markets)

31 pages, 17485 KB

Open AccessArticle

Spatial Mismatch Between Agricultural Heritage Systems and Eco-Cultural Service Provision in Zhejiang Province, China

by Fei Ju and Zunling Zhu

Agriculture 2026, 16(11), 1199; https://doi.org/10.3390/agriculture16111199 - 29 May 2026

Abstract

Agricultural heritage systems are traditional agroecosystems formed through long-term ecological adaptation, farming practices, and local knowledge transmission. Their conservation depends not only on formal recognition but also on ecological support and effective links with contemporary cultural service networks. Yet it remains unclear whether [...] Read more.

Agricultural heritage systems are traditional agroecosystems formed through long-term ecological adaptation, farming practices, and local knowledge transmission. Their conservation depends not only on formal recognition but also on ecological support and effective links with contemporary cultural service networks. Yet it remains unclear whether they are spatially aligned with the eco-cultural service conditions required for socio-ecological resilience and agroecological transition. Using 205 important agricultural heritage systems in Zhejiang Province, China, this study integrates nearest neighbor analysis, kernel density estimation, the InVEST model, a cultural service index, and spatial autocorrelation analysis. Results show that agricultural heritage systems are significantly clustered in northern and southwestern Zhejiang. Ecosystem service values are concentrated in the mountainous and hilly areas of southwestern and south-central Zhejiang, whereas cultural service provision is concentrated in the northern Zhejiang Plain and urbanized areas around Hangzhou Bay. Agricultural heritage systems show weak but statistically detectable spatial associations with ecosystem services, cultural service provision, and their eco-cultural synergy pattern, indicating limited spatial correspondence rather than strong spatial coupling. These findings indicate a spatial mismatch between historically evolved agricultural heritage systems, ecological support conditions, and contemporary cultural service provision. This study contributes a spatial diagnostic framework for identifying ecological-support gaps, cultural-service gaps, and eco-cultural mismatch areas, thereby informing differentiated agricultural heritage governance and regional planning. Full article

(This article belongs to the Special Issue Agroecological Transitions and Socio-Ecological Resilience in Traditional Agricultural Landscapes)

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24 pages, 2891 KB

Open AccessReview

Precision Tools for Forage Assessment and Nutritional Decision Support in Grazing-Ruminant Systems: A Narrative Review

by Cristiana Maduro Dias and Alfredo Borba

Agriculture 2026, 16(11), 1198; https://doi.org/10.3390/agriculture16111198 - 29 May 2026

Abstract

Spatial and temporal heterogeneity in pasture quantity and nutritive value remains a major constraint to efficient nutritional management in grazing-ruminant systems. This critical narrative review was based on targeted searches of peer-reviewed literature on pasture heterogeneity, forage quality assessment, grazing management, animal monitoring, [...] Read more.

Spatial and temporal heterogeneity in pasture quantity and nutritive value remains a major constraint to efficient nutritional management in grazing-ruminant systems. This critical narrative review was based on targeted searches of peer-reviewed literature on pasture heterogeneity, forage quality assessment, grazing management, animal monitoring, and data integration in grazing-ruminant systems, with emphasis on both recent studies and conceptually foundational work. Precision technologies have emerged as complementary tools that can improve the characterization of pasture resources, animal responses, and grazing dynamics, but their value depends on whether they support nutritionally relevant decisions under field conditions. This review examines current precision approaches, such as portable near-infrared spectroscopy, proximal and remote sensing, geospatial tools, animal-mounted sensors, and grazing-control technologies, and their capacity to improve decisions related to supplementation, stocking rate, grazing rotation, and pasture allocation. Across technologies, performance and applicability vary substantially with observational scale, calibration requirements, and validation context. This review also highlights persistent constraints, including calibration robustness, transferability across systems, field validation, interoperability, economic feasibility, and barriers to routine adoption. Precision tools can improve pasture-based nutritional management, but their practical contribution depends on how effectively they are validated, integrated, and translated into decision-support logic under commercial grazing conditions. Full article

(This article belongs to the Special Issue Impact of Forage Quality and Grazing Management on Ruminant Nutrition)

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35 pages, 81315 KB

Open AccessArticle

Tomato Pedicel Picking-Point Localization via Improved YOLOv8n-EED-Seg and RGB-D Fusion

by Liping Wu, Lilin Liu and Dongdong Teng

Agriculture 2026, 16(11), 1197; https://doi.org/10.3390/agriculture16111197 - 29 May 2026

Abstract

Accurate and rapid localization of tomato pedicel picking points presents a significant challenge for automated harvesting, due to factors such as occlusion by dense foliage, overlapping fruits, variable lighting conditions, and the slender morphology of pedicels. To address these, we propose an integrated [...] Read more.

Accurate and rapid localization of tomato pedicel picking points presents a significant challenge for automated harvesting, due to factors such as occlusion by dense foliage, overlapping fruits, variable lighting conditions, and the slender morphology of pedicels. To address these, we propose an integrated picking decision system combining enhanced instance segmentation with RGB-D fusion. In this study, a lightweight detection model named YOLOv8n-EED-seg is introduced. An optimized EfficientRep backbone is integrated to enhance computational efficiency, while the EMAttention mechanism and a refined DynamicHead module strengthen multi-scale feature representation for slender pedicels. The model further incorporates the Zhang–Suen algorithm for skeleton extraction and a large-neighborhood mean method for depth restoration, enabling precise 3D localization. Experiments are conducted on a dataset of 3310 images collected in a greenhouse environment. Compared with the baseline YOLOv8n-seg, our model improves precision, recall, F1 score, and mAP₅₀ by 5.09%, 2.78%, 3.63%, and 4.31%, respectively. The system achieves an inference speed of 4.8 ms per frame, enabling real-time performance, while attaining a 93.88% success rate in 3D picking-point localization. Furthermore, the proposed model demonstrates superior robustness in complex environments compared with common segmentation models, effectively balancing accuracy, speed, and model complexity. This study provides a reliable technical pathway for high-precision, vision-based tomato-harvesting robots. Full article

(This article belongs to the Special Issue Artificial Intelligence in Precision Agriculture: Applications in Crop Management)

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14 pages, 3643 KB

Open AccessArticle

Non-Invasive Sex Identification of Early-Stage Poultry Eggs Using Machine Vision

by Joel Andersson, Per Magnusson and Fredrik Frisk

Agriculture 2026, 16(11), 1196; https://doi.org/10.3390/agriculture16111196 - 29 May 2026

Abstract

The routine culling of day-old male chicks represents a major ethical concern in the poultry industry. This practice has been banned in Germany, and a similar ban is being considered by the European Union. Each year, hundreds of millions of day-old male chicks [...] Read more.

The routine culling of day-old male chicks represents a major ethical concern in the poultry industry. This practice has been banned in Germany, and a similar ban is being considered by the European Union. Each year, hundreds of millions of day-old male chicks are culled in the EU, with several billion culled worldwide. Various methods have been developed to determine the sex of chicks before hatching; however, most are invasive and identify sex relatively late, potentially after the onset of pain perception in embryos. Existing approaches include polymerase chain reaction analysis, spectroscopy, analysis of volatile organic compounds, morphological analysis, and machine vision. Previous studies have shown that machine vision can achieve accuracies of up to 89.25% by analyzing blood vessel patterns during early incubation. Despite this potential, research remains limited, particularly regarding different chicken breeds and the temporal development of embryos. In this study, we investigate the impact of both breed variation and temporal information on early-stage sex identification. Image data were collected on incubation days 4, 5, and 6 from a total of 208 chicken eggs. A convolutional neural network (CNN) and a hybrid convolutional neural network–recurrent neural network (CNN–RNN) model were evaluated to analyze spatial and temporal features. The results show that the CNN model achieved an accuracy of up to 71.43%, while the hybrid CNN–RNN model reached 67.85%. These findings indicate that incorporating temporal information did not improve performance compared to the baseline CNN. However, due to the limited size and quality of the dataset, no definitive conclusions can be drawn. Full article

(This article belongs to the Section Farm Animal Production)

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