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YOLO11-ARAF: An Accurate and Lightweight Method for Apple Detection in Real-World Complex Orchard Environments
Preparation and Characterization of Liquid Fertilizers Produced by Anaerobic Fermentation
Employment of Biodegradable, Short-Life Mulching Film on High-Density Cropping Lettuce in a Mediterranean Environment: Potentials and Prospects
Emerging Trends in AI-Based Soil Contamination Monitoring and Prevention
The Influence of Weather Conditions and Available Soil Water on Vitis vinifera L. Albillo Mayor in Ribera del Duero DO (Spain) and Potential Changes Under Climate Change: A Preliminary Analysis

Journal Description

Agriculture

Agriculture is an international, scientific peer-reviewed open access journal published semimonthly online by MDPI.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), PubAg, AGRIS, RePEc, and other databases.
Journal Rank: JCR - Q1 (Agronomy) / CiteScore - Q1 (Plant Science)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 18 days after submission; acceptance to publication is undertaken in 1.9 days (median values for papers published in this journal in the first half of 2025).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Companion journals for Agriculture include: Poultry, Grasses and Crops.

Impact Factor: 3.6 (2024); 5-Year Impact Factor: 3.8 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2077-0472

Latest Articles

27 pages, 1236 KB

Open AccessArticle

Does Institutional Quality Shape Agricultural Credit Orientation? Evidence from D-8 Nations

by Ömer Keskin, Batuhan Medetoğlu, Yusuf Bahadır Kavas and Musa Gün

Agriculture 2025, 15(18), 1975; https://doi.org/10.3390/agriculture15181975 (registering DOI) - 19 Sep 2025

The agricultural sector, which has long been overshadowed by industrialization, has reemerged with renewed strategic significance in the face of global crises, including pandemics and armed conflicts. This study examines the causal relationship between institutional quality and agricultural credit orientation in the Developing-Eight [...] Read more.

The agricultural sector, which has long been overshadowed by industrialization, has reemerged with renewed strategic significance in the face of global crises, including pandemics and armed conflicts. This study examines the causal relationship between institutional quality and agricultural credit orientation in the Developing-Eight countries from 2002 to 2023. Using the agriculture orientation index for credit as a key indicator, this study investigates how disaggregated institutional dimensions—control of corruption, government effectiveness, political stability and absence of violence, rule of law, regulatory quality, and voice and accountability—affect the allocation of commercial bank credit to agriculture. Both the standard Kónya panel causality test and its time-varying extension are employed to capture static and dynamic causal patterns. The findings demonstrate that institutional quality exerts a substantial effect on credit orientation, although the magnitude and characteristics of this influence differ across countries. Türkiye, Indonesia, Nigeria, and Egypt exhibit consistent causal relationships, whereas other countries reveal episodic or latent effects linked to specific political or legal shifts. By combining dynamic methodology with a policy-relevant indicator, this study offers novel insights into how governance shapes agricultural finance. The results underscore the need for country-specific and institution-sensitive credit strategies to increase resilience and equity in financial systems. Full article

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

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21 pages, 3101 KB

Open AccessArticle

GIS-Based Land Suitability Analysis for Sustainable Almond Cultivation in Lebanon

by Pascale Elbared, Nadine Nassif, Georges Hassoun and Maurizio Mulas

Agriculture 2025, 15(18), 1974; https://doi.org/10.3390/agriculture15181974 (registering DOI) - 19 Sep 2025

Almonds are one of the major products that are economically competent and compatible with the Mediterranean climate, a key characteristic that distinguishes Lebanon. The present study aims to examine the suitability of land use and land cover on the Lebanese territory for sustainable [...] Read more.

Almonds are one of the major products that are economically competent and compatible with the Mediterranean climate, a key characteristic that distinguishes Lebanon. The present study aims to examine the suitability of land use and land cover on the Lebanese territory for sustainable almond cultivation, based on the FAO land suitability criteria. The research explored the existing areas of almond cultivation and the land possessing the potential for almond cultivation in Lebanon using an analysis model developed on GIS. The evaluation of Land Suitability (LS) based on GIS and Multi-Criteria Evaluation methods (MCE) with Weighted Overlay (WO) was applied, and the almond suitability map was rendered using the seven following parameters: temperature, rainfall, slope, elevation, soil pH, soil texture, and soil depth. These variables were integrated through GIS and were allocated to different weights to each thematic layer, as per its relevance. Ultimately, the almond suitability map was established, comprising four categories: highly suitable, moderately suitable, marginally suitable, and not suitable. The obtained results indicated that almond cultivation areas were around 5500 ha in 2010, while more than 60% of the study area can be planted with almonds in accordance with the almond suitability map. In closing, the targeted decision-makers will potentially deem this study as a valid source of knowledge for planning land use, and a tool to mitigate land degradation. Full article

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

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21 pages, 4780 KB

Open AccessArticle

Influence of Soil Physical and Hydraulic Properties on Cacao Productivity Under Agroforestry Systems in the Amazonian Piedmont

by Fabio Buriticá, José Iván Vanegas and Juan Carlos Suárez

Agriculture 2025, 15(18), 1973; https://doi.org/10.3390/agriculture15181973 (registering DOI) - 19 Sep 2025

In the Amazonian piedmont, cacao-based agroforestry systems (cAFSs) were significantly influenced by the soil’s physical, hydraulic, and structural characteristics, which largely determined agricultural productivity. A total of 122 plots with cocoa-based agroforestry systems measuring 1000 m² were randomly selected from different farms [...] Read more.

In the Amazonian piedmont, cacao-based agroforestry systems (cAFSs) were significantly influenced by the soil’s physical, hydraulic, and structural characteristics, which largely determined agricultural productivity. A total of 122 plots with cocoa-based agroforestry systems measuring 1000 m² were randomly selected from different farms located in the Amazonian foothills in the department of Caquetá. Different variables related to soil physics and hydrology, as well as production, were determined for each plot. Soil characteristics explain 33% of the total variance in cocoa yield. Sand content (71.2%) correlated positively with yield, while clay (22.62%) and silt (23.99%) correlated negatively. Three soil types were identified: sandy loam (high productivity, yield 1129.07 g) and two variants of sandy clay loam (lower yield, 323.97 g). Hydraulic properties were important, with total porosity of 56.04% and hydraulic conductivity of 20.45 mm h⁻¹. The CCN-51 and ICS-60 clones performed better in sandy loam soils, while ICS-95 and TSH-565 adapted better to sandy clay loam soils with medium stability. The physical and hydric soil properties are crucial factors that directly influence cocoa productivity in agroforestry systems of the Amazon piedmont, where the appropriate selection of clones according to soil characteristics is fundamental to optimize crop productivity and sustainability. Full article

(This article belongs to the Section Agricultural Soils)

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25 pages, 15287 KB

Open AccessArticle

Research on a UAV-Based Litchi Flower Cluster Detection Method Using an Improved YOLO11n

by Baoxia Sun, Yanggang Ou, Jiatong Tang, Shuqin Cai, Yutao Chen, Wenyi Bao, Juntao Xiong and Yanan Li

Agriculture 2025, 15(18), 1972; https://doi.org/10.3390/agriculture15181972 (registering DOI) - 18 Sep 2025

The number of litchi flower clusters is an important indicator for predicting the fruit set rate and yield of litchi trees. However, their dense distribution, scale variation, and occlusion make it very challenging to achieve high-precision intelligent detection of litchi flower clusters in [...] Read more.

The number of litchi flower clusters is an important indicator for predicting the fruit set rate and yield of litchi trees. However, their dense distribution, scale variation, and occlusion make it very challenging to achieve high-precision intelligent detection of litchi flower clusters in natural scenes. This study proposes a UAV-based litchi flower cluster detection method using an improved YOLO11n. First, the backbone introduces a WTConv-improved C3k2 module (C3k2_WTConv) to enhance feature extraction capability; then, the neck adopts a SlimNeck structure for efficient multi-scale fusion and parameter reduction; and finally, the DySample module replaces the original up-sampling to mitigate accuracy loss caused by scale variation. Experimental results on UAV-based litchi flower cluster detection show that the model achieves an mAP@0.5 of 87.28%, with recall, precision, F1-score, and mAP@0.5 improved by 6.26%, 4.03%, 5.14%, and 5.16% over YOLO11n. Computational cost and parameters decrease by 7.69% and 2.37%, respectively. In counting tasks, MAE, RMSE, MAPE, and R² reach 5.23, 6.89, 9.72%, and 0.9205, indicating excellent performance. The proposed method offers efficient and accurate technical support for intelligent litchi blossom management and yield estimation, and provides optimization strategies applicable to dense multi-scale object detection tasks. Full article

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

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26 pages, 5633 KB

Open AccessArticle

AGRI-YOLO: A Lightweight Model for Corn Weed Detection with Enhanced YOLO v11n

by Gaohui Peng, Kenan Wang, Jianqin Ma, Bifeng Cui and Dawei Wang

Agriculture 2025, 15(18), 1971; https://doi.org/10.3390/agriculture15181971 - 18 Sep 2025

Corn, as a globally significant food crop, faces significant yield reductions due to competitive growth from weeds. Precise detection and efficient control of weeds are critical technical components for ensuring high and stable corn yields. Traditional deep learning object detection models generally suffer [...] Read more.

Corn, as a globally significant food crop, faces significant yield reductions due to competitive growth from weeds. Precise detection and efficient control of weeds are critical technical components for ensuring high and stable corn yields. Traditional deep learning object detection models generally suffer from issues such as large parameter counts and high computational complexity, making them unsuitable for deployment on resource-constrained devices such as agricultural drones and portable detection devices. Based on this, this paper proposes a lightweight corn weed detection model, AGRI-YOLO, based on the YOLO v11n architecture. First, the DWConv (Depthwise Separable Convolution) module from InceptionNeXt is introduced to reconstruct the C3k2 feature extraction module, enhancing the feature extraction capabilities for corn seedlings and weeds. Second, the ADown (Adaptive Downsampling) downsampling module replaces the Conv layer to address the issue of redundant model parameters; The LADH (Lightweight Asymmetric Detection) detection head is adopted to achieve dynamic weight adjustment while ensuring multi-branch output optimization for target localization and classification precision. Experimental results show that the AGRI-YOLO model achieves a precision rate of 84.7%, a recall rate of 73.0%, and a mAP50 value of 82.8%. Compared to the baseline architecture YOLO v11n, the results are largely consistent, while the number of parameters, G FLOPs, and model size are reduced by 46.6%, 49.2%, and 42.31%, respectively. The AGRI-YOLO model significantly reduces model complexity while maintaining high recognition precision, providing technical support for deployment on resource-constrained edge devices, thereby promoting agricultural intelligence, maintaining ecological balance, and ensuring food security. Full article

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

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22 pages, 30314 KB

Open AccessArticle

Knowledge-Enhanced Deep Learning for Identity-Preserved Multi-Camera Cattle Tracking

by Shujie Han, Alvaro Fuentes, Jiaqi Liu, Zihan Du, Jongbin Park, Jucheng Yang, Yongchae Jeong, Sook Yoon and Dong Sun Park

Agriculture 2025, 15(18), 1970; https://doi.org/10.3390/agriculture15181970 (registering DOI) - 18 Sep 2025

Accurate long-term tracking of individual cattle is essential for precision livestock farming but remains challenging due to occlusions, posture variability, and identity drift in free-range environments. We propose a multi-camera tracking framework that combines bird’s-eye-view (BEV) trajectory matching with cattle face recognition to [...] Read more.

Accurate long-term tracking of individual cattle is essential for precision livestock farming but remains challenging due to occlusions, posture variability, and identity drift in free-range environments. We propose a multi-camera tracking framework that combines bird’s-eye-view (BEV) trajectory matching with cattle face recognition to ensure identity preservation across long video sequences. A large-scale dataset was collected from five synchronized 4K cameras in a commercial barn, capturing both full-body movements and frontal facial views. The system employs center point detection and BEV projection for cross-view trajectory association, while periodic face recognition during feeding refreshes identity assignments and corrects errors. Evaluations on a two-day dataset of more than 600,000 images demonstrate robust performance, with an AssPr of 84.481% and a LocA score of 78.836%. The framework outperforms baseline trajectory matching methods, maintaining identity consistency under dense crowding and noisy labels. These results demonstrate a practical and scalable solution for automated cattle monitoring, advancing data-driven livestock management and welfare. Full article

(This article belongs to the Special Issue Computer Vision Analysis Applied to Farm Animals)

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23 pages, 4996 KB

Open AccessArticle

Simulation and Experiment on Parameters of an Airflow-Guiding Device for a Centrifugal Air-Assisted Sprayer

by Sibo Tian, Hao Guo, Jianping Li, Yang Li, Zhu Zhang and Peng Wang

Agriculture 2025, 15(18), 1969; https://doi.org/10.3390/agriculture15181969 - 18 Sep 2025

Orchard air-assisted sprayers have become key equipment for the prevention and control of fruit tree diseases and pests. However, centrifugal fans are rarely used in orchard air-assisted sprayers. To address the issue that the airflow generated by single-duct centrifugal air-assisted sprayers is insufficient [...] Read more.

Orchard air-assisted sprayers have become key equipment for the prevention and control of fruit tree diseases and pests. However, centrifugal fans are rarely used in orchard air-assisted sprayers. To address the issue that the airflow generated by single-duct centrifugal air-assisted sprayers is insufficient to cover the lower canopy, a flow-guiding device for the lower canopy of fruit trees was designed. The Flow Simulation software was used to simulate the airflow field, and various structural parameters of the air outlet were analyzed to determine the optimal configuration of the upper edge inclination angle, the position of the upper air outlet, and the length of the upper air outlet. The results showed that the position of the upper air outlet had the most significant impact on the uniformity of the external flow field, followed by the upper edge inclination angle and the length of the upper air outlet. The optimal parameter settings for the air supply guiding device were determined as follows: upper edge inclination angle of 79°, upper air outlet position of 307 mm, and upper air outlet length of 190 mm. The verification test showed that the relative error between the simulated and actual airflow velocity measurements did not exceed 10%, confirming the accuracy of the simulation. The orchard field test showed that the average deposition density in the inner canopy of fruit trees was 78 particles/cm², indicating strong penetration ability; the distribution of spray droplets in the vertical direction of the canopy was uniform, meeting the requirements of fruit tree pesticide application operations. This technology provides a new approach for the application of centrifugal fans in fruit tree pesticide spraying. Full article

(This article belongs to the Section Agricultural Technology)

25 pages, 13160 KB

Open AccessArticle

LWCD-YOLO: A Lightweight Corn Seed Kernel Fast Detection Algorithm Based on YOLOv11n

by Wenbin Sun, Kang Xu, Dongquan Chen, Danyang Lv, Ranbing Yang, Songmei Yang, Rong Wang, Ling Wang and Lu Chen

Agriculture 2025, 15(18), 1968; https://doi.org/10.3390/agriculture15181968 (registering DOI) - 18 Sep 2025

As one of the world’s most important staple crops providing food, feed, and industrial raw materials, corn requires precise kernel detection for seed phenotype analysis and seed quality examination. In order to achieve precise and rapid detection of corn seeds, this study proposes [...] Read more.

As one of the world’s most important staple crops providing food, feed, and industrial raw materials, corn requires precise kernel detection for seed phenotype analysis and seed quality examination. In order to achieve precise and rapid detection of corn seeds, this study proposes a lightweight corn seed kernel rapid detection model based on YOLOv11n (LWCD-YOLO). Firstly, a lightweight backbone feature extraction module is designed based on Partial Convolution (PConv) and an efficient multi-scale attention module (EMA), which reduces model complexity while maintaining model detection performance. Secondly, a cross layer multi-scale feature fusion module (MSFFM) is proposed to facilitate deep feature fusion of low-, medium-, and high-level features. Finally, we optimized the model using the WIOU bounding box loss function. Experiments were conducted on the collected Corn seed kernel detection dataset, and LWCD-YOLO only required 1.27 million (M) parameters and 3.5 G of FLOPs. Its precision (P), mean Average Precision at 0.50 (mAP_0.50), and mean Average Precision at 0.50:0.95 (mAP_0.50:0.95) reached 99.978%, 99.491%, and 99.262%, respectively. Compared to the original YOLOv11n, the model size, parameter count, and computational complexity were reduced by 50%, 51%, and 44%, respectively, and the FPS was improved by 94%. The detection performance, model complexity, and detection efficiency of LWCD-YOLO are superior to current mainstream object detection models, making it suitable for fast and precise detection of corn seeds. It can provide guarantees for achieving seed phenotype analysis and seed quality examination. Full article

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

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23 pages, 63827 KB

Open AccessArticle

A Two-Stage Weed Detection and Localization Method for Lily Fields Targeting Laser Weeding

by Yanlei Xu, Chao Liu, Jiahao Liang, Xiaomin Ji and Jian Li

Agriculture 2025, 15(18), 1967; https://doi.org/10.3390/agriculture15181967 - 18 Sep 2025

The cultivation of edible lilies is highly susceptible to weed infestation during its growth period, and the application of herbicides is often impractical, leading to the rampant growth of diverse weed species. Laser weeding, recognized as an efficient and precise method for field [...] Read more.

The cultivation of edible lilies is highly susceptible to weed infestation during its growth period, and the application of herbicides is often impractical, leading to the rampant growth of diverse weed species. Laser weeding, recognized as an efficient and precise method for field weed management, presents a novel solution to the weed challenges in lily fields. The accurate localization of weed regions and the optimal selection of laser targeting points are crucial technologies for successful laser weeding implementation. In this study, we propose a two-stage weed detection and localization method specifically designed for lily fields. In the first stage, we introduce an enhanced detection model named YOLO-Morse, aimed at identifying and removing lily plants. YOLO-Morse is built upon the YOLOv8 architecture and integrates the RCS-MAS backbone, the SPD-Conv spatial enhancement module, and an adaptive focal loss function (ATFL) to enhance detection accuracy in conditions characterized by sample imbalance and complex backgrounds. Experimental results indicate that YOLO-morse achieves a mean Average Precision (mAP) of 86%, reflecting a 3.2% improvement over the original YOLOv8, and facilitates stable identification of lily regions. Subsequently, a ResNet-based segmentation network is employed to conduct semantic segmentation on the detected lily targets. The segmented results are utilized to mask the original lily areas in the image, thereby generating weed-only images for the subsequent stage. In the second stage, the original RGB field images are first converted into weed-only images by removing lily regions; these weed-only images are then analyzed in the HSV color space combined with morphological processing to precisely extract green weed regions. The centroid of the weed coordinate set is automatically determined as the laser targeting point.The proposed system exhibits superior performance in weed detection, achieving a Precision, Recall, and F1-score of 94.97%, 90.00%, and 92.42%, respectively. The proposed two-stage approach significantly enhances multi-weed detection performance in complex environments, improving detection accuracy while maintaining operational efficiency and cost-effectiveness. This method proposes a precise, efficient, and intelligent laser weeding solution for weed management in lily fields. Although certain limitations remain, such as environmental lighting variation, leaf occlusion, and computational resource constraints, the method still exhibits significant potential for broader application in other high-value crops. Full article

(This article belongs to the Special Issue Plant Diagnosis and Monitoring for Agricultural Production)

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21 pages, 664 KB

Open AccessArticle

The Role of Food Safety in Sustainable Gastronomic Tourism: Insights from Farm-Stay Tourist Experiences

by Dragan Vukolić, Mladen Radišić, Maja Radišić, Dušan Pevac, Srđan Milošević and Tamara Gajić

Agriculture 2025, 15(18), 1966; https://doi.org/10.3390/agriculture15181966 - 18 Sep 2025

In contemporary tourism, gastronomic offerings increasingly go beyond the boundaries of mere taste enjoyment, becoming an important element of the sustainable development of destinations. At the same time, food safety is gaining importance as a key aspect of the tourist experience and trust [...] Read more.

In contemporary tourism, gastronomic offerings increasingly go beyond the boundaries of mere taste enjoyment, becoming an important element of the sustainable development of destinations. At the same time, food safety is gaining importance as a key aspect of the tourist experience and trust in a destination. The research was conducted in Serbia, focusing specifically on agritourism farm stays known for their local food production and sustainable hospitality practices. This study highlights the crucial link between local agricultural practices and tourists’ perceptions of food safety, positioning food safety as a key dimension of both sustainable gastronomy and rural development. The research was conducted on a sample of 650 tourists in farm stays, using a structured survey questionnaire, with data analysed through descriptive statistics, factor analysis, Pearson correlation, ANOVA, and multiple regression analysis. The results indicate that tourists highly value food safety, particularly in the context of local and traditional gastronomy, and that there is a significant correlation between the perception of food safety and the intention to revisit or recommend a destination. This study suggests that the integration of food safety standards into sustainable gastronomic practices is essential for enhancing competitiveness and building long-term trust among individuals of various sociodemographic profiles. Full article

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

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16 pages, 1995 KB

Open AccessArticle

Powdery Mildew Resistance Gene (Pm) Stability and Blumeria graminis f. sp. avenae Virulence Trends in Poland (2021–2023): Challenges to Durable Resistance in Oat

by Weronika Grzelak, Aleksandra Nucia and Sylwia Okoń

Agriculture 2025, 15(18), 1965; https://doi.org/10.3390/agriculture15181965 - 18 Sep 2025

Oat (Avena sativa L.) is a widely cultivated cereal crop valued for both its nutritional benefits and agricultural versatility. However, oat production is increasingly challenged by powdery mildew, which is caused by Blumeria graminis f. sp. avenae (Bga) and can [...] Read more.

Oat (Avena sativa L.) is a widely cultivated cereal crop valued for both its nutritional benefits and agricultural versatility. However, oat production is increasingly challenged by powdery mildew, which is caused by Blumeria graminis f. sp. avenae (Bga) and can lead to considerable yield losses. Genetic resistance remains the most sustainable and environmentally friendly method of disease control. This study aimed to evaluate the effectiveness of 14 oat genotypes carrying known resistance genes (Pm1–Pm12) and Avena strigosa accessions against Bga populations collected across four regions of Poland between 2021 and 2023. Host–pathogen assays were used to assess resistance levels, virulence frequency, and pathotype diversity. Resistance genes were categorized into three groups based on performance: highly effective (Pm2, Pm4, Pm5, Pm7 in APR122 and A. strigosa), variably effective (Pm7 in ‘Canyon’ and Pm9–Pm12), and moderately effective (Pm1, Pm3, Pm6 and Pm3+8). Pathogen populations exhibited decreasing virulence complexity and diversity over time, with substantial regional variation. There were few dominant pathotypes, but most were rare and transient. This study confirms the long-term effectiveness of several resistance genes and the necessity of continuous resistance monitoring. It supports the use of gene pyramiding to ensure durable, regionally adapted protection. These results highlight the importance of combining resistance breeding with integrated disease management to ensure sustainable oat production under changing environmental conditions. Full article

(This article belongs to the Special Issue Fungal Plant Pathogens in Agricultural Crops: Diversity, Detection, and Control)

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25 pages, 4730 KB

Open AccessArticle

Fertility-Based Nitrogen Management Strategies Combined with Straw Return Enhance Rice Yield and Soil Quality in Albic Soils

by Qiuju Wang, Xuanxuan Gao, Baoguang Wu, Jingyang Li, Xin Liu, Jiahe Zou and Qingying Meng

Agriculture 2025, 15(18), 1964; https://doi.org/10.3390/agriculture15181964 - 17 Sep 2025

Low productivity in albic soils often results in excessive nitrogen input, while straw return further increases nitrogen accumulation through decomposition. To address this issue, a three-year field experiment was conducted in albic soils of high, medium, and low fertility. Two nitrogen management strategies [...] Read more.

Low productivity in albic soils often results in excessive nitrogen input, while straw return further increases nitrogen accumulation through decomposition. To address this issue, a three-year field experiment was conducted in albic soils of high, medium, and low fertility. Two nitrogen management strategies were assessed: nitrogen addition and reduction. Addition treatments included conventional nitrogen application rate alone (N), straw return (8250 kg ha⁻¹) with conventional nitrogen application rate (SN), and straw return with increased nitrogen (SN₊). Reduction treatments comprised SN and straw return with 10%, 20%, and 30% reduced nitrogen (SN_0.9, SN_0.8, and SN_0.7). Soil physical properties, nutrient content, and rice yield were evaluated. Results showed that SN_0.9 exhibited advantages in high-fertility albic soils, as it increased rice yield and improved some soil quality while reducing the nitrogen input by 10%. However, yield under SN_0.9 declined progressively over the three years, indicating limitations of long-term application. SN performed better than both N and SN₊ in medium- and low-fertility albic soils, offering better yield and soil quality improvements. However, nitrogen overaccumulation risk under continuous application should not be overlooked. These findings highlight that fertility-based nitrogen adjustment combined with straw return can simultaneously improve rice productivity and soil quality while reducing nitrogen input in albic soils. Full article

(This article belongs to the Section Agricultural Soils)

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20 pages, 13450 KB

Open AccessArticle

An AI-Based System for Monitoring Laying Hen Behavior Using Computer Vision for Small-Scale Poultry Farms

by Jill Italiya, Ahmed Abdelmoamen Ahmed, Ahmed A. A. Abdel-Wareth and Jayant Lohakare

Agriculture 2025, 15(18), 1963; https://doi.org/10.3390/agriculture15181963 - 17 Sep 2025

Small-scale poultry farms often lack access to advanced monitoring tools and rely heavily on manual observation, which is time-consuming, inconsistent, and insufficient for precise flock management. Feeding and drinking behaviors are critical, as they serve as early indicators of health and environmental issues. [...] Read more.

Small-scale poultry farms often lack access to advanced monitoring tools and rely heavily on manual observation, which is time-consuming, inconsistent, and insufficient for precise flock management. Feeding and drinking behaviors are critical, as they serve as early indicators of health and environmental issues. With global poultry production expanding, raising over 70 billion hens annually, there is an urgent need for intelligent, low-cost systems that can continuously and accurately monitor bird behavior in resource-limited farm settings. This paper presents the development of a computer vision-based chicken behavior monitoring system, specifically designed for small barn environments where at most 10–15 chickens are housed at any time. The developed system consists of an object detection model, created on top of the YOLOv8 model, trained with an imagery dataset of laying hen, feeder, and waterer objects. Although chickens are visually indistinguishable, the system processes each detection per frame using bounding boxes and movement-based approximation identification rather than continuous identity tracking. The approach simplifies the tracking process without losing valuable behavior insights. Over 700 frames were annotated manually for high-quality labeled data, with different lighting, hen positions, and interaction angles with dispensers. The images were annotated in YOLO format and used for training the detection model for 100 epochs, resulting in a model having an average mean average precision (mAP@0.5) metric value of 91.5% and a detection accuracy of over 92%. The proposed system offers an efficient, low-cost solution for monitoring chicken feeding and drinking behaviors in small-scale farms, supporting improved management and early health detection. Full article

(This article belongs to the Special Issue Application of Intelligent Technologies in Farm Animal Disease, Feeding and Building Environmental Control)

16 pages, 1646 KB

Open AccessArticle

Suitability of Slovakian Landscapes for Vegetable Growing

by Jozef Vilček, Štefan Koco, Adam Kupec, Stanislav Torma and Matúš Maxin

Agriculture 2025, 15(18), 1962; https://doi.org/10.3390/agriculture15181962 - 17 Sep 2025

The cultivation of vegetables in Slovakia has traditionally occurred in the vicinity of human settlements, predominantly in allotments. Large-scale vegetable production requires not only intensification measures but also a strategic selection of regions with optimal soil and climatic conditions. In Slovakia, this selection [...] Read more.

The cultivation of vegetables in Slovakia has traditionally occurred in the vicinity of human settlements, predominantly in allotments. Large-scale vegetable production requires not only intensification measures but also a strategic selection of regions with optimal soil and climatic conditions. In Slovakia, this selection is limited by the availability of arable land suitable for vegetable cultivation. This study quantifies and delineates areas that are very suitable, suitable, poorly suitable, and unsuitable for the major vegetable species grown in the region. The findings indicate that the largest proportion of very suitable arable land is best suited for the cultivation of cauliflower (35%), celery (33%), beans (31%), and beetroot (28%). Conversely, the analysis reveals that a significant proportion of arable soils possess potentially unsuitable conditions for specific crops, with asparagus (94%), peppers (80%), and cucumbers (71%) exhibiting the highest percentages. In addition, an analysis of actual vegetable cultivation between the years 2020 and 2024 indicates that a substantial portion of certain crops, specifically 75% of celery, 59% of tomatoes, 56% of cauliflower, and 54% of carrots are cultivated in areas that are very suitable for their growth. In contrast, 81% of pumpkin, 79% of beetroot, and 47% of beans are produced under unsuitable conditions. By optimizing the selection of suitable areas and soils, the potential of the Slovak landscape can be utilized more efficiently for domestic vegetable production. Full article

(This article belongs to the Special Issue Agronomic Practices for Improving Growth, Quality, and Yield of Vegetables)

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14 pages, 1877 KB

Open AccessArticle

Silicon as a Tool to Manage Diaphorina citri and Relation Soil and Leaf Chemistry in Tahiti Lime

by Ana Maria Restrepo-García, Alejandro Hurtado-Salazar and Alberto Soto-Giraldo

Agriculture 2025, 15(18), 1961; https://doi.org/10.3390/agriculture15181961 - 17 Sep 2025

Silicon (Si) is gaining recognition as a sustainable alternative to reduce insecticide use in the management of the Asian citrus psyllid and huanglongbing (HLB). This study aimed to evaluate the effects of two silicon sources and three application methods on Diaphorina citri incidence, [...] Read more.

Silicon (Si) is gaining recognition as a sustainable alternative to reduce insecticide use in the management of the Asian citrus psyllid and huanglongbing (HLB). This study aimed to evaluate the effects of two silicon sources and three application methods on Diaphorina citri incidence, soil chemical properties, and foliar nutrient uptake in a Tahiti lime orchard. Using a randomized block design, treatments were applied six times over three months. Foliar and combined applications of diatomaceous earth reduced vegetative flushing and decreased natural psyllid incidence by up to 75% in the first 30 days. While silicon did not affect oviposition in induced infestations, it disrupted the nymph-to-adult transition. Silicon also improved soil conditions, increasing pH, organic matter, and the availability of phosphorus, calcium, and magnesium. In leaf tissue, higher levels of nitrogen, phosphorus, potassium, iron, and silicon (0.28–0.50%) were observed. Fruit quality improved with silicon, showing greater fresh weight (134 g) and juice content (44.7%) compared to the control (95.33 g and 28.5%). The results suggest that silicon’s effectiveness depends more on its availability and application method than its source. Incorporating silicon, especially diatomaceous earth, into fertilization programs supports pest control, enhances soil and plant nutrition, and improves fruit quality. Full article

(This article belongs to the Special Issue Strategies to Enhance Nutrient Use Efficiency and Crop Nutrition)

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22 pages, 6218 KB

Open AccessArticle

Exogenous Application of Applied Microbial Agents to Alleviate Salt Stress on ‘Pinot Noir’ Grapes and Improve Fruit Yield and Quality

by Zhilong Li, Lei Ma, Guojie Nai, Zhihui Pu, Jingrong Zhang, Sheng Li, Bing Wu and Shaoying Ma

Agriculture 2025, 15(18), 1960; https://doi.org/10.3390/agriculture15181960 - 17 Sep 2025

Microbial inoculants, as a new type of product that combines economic efficiency with ecological sustainability, play an important role in promoting plant growth and development, increasing crop yields, and enhancing plant resistance to abiotic stress. This study used the wine grape cultivar ( [...] Read more.

Microbial inoculants, as a new type of product that combines economic efficiency with ecological sustainability, play an important role in promoting plant growth and development, increasing crop yields, and enhancing plant resistance to abiotic stress. This study used the wine grape cultivar (Vitis vinifera ‘Pinot Noir’) as experimental material to systematically investigate the effects of microbial inoculants on the soil–leaf–fruit system during the late growth stage of grapes under salt stress conditions (200 mM NaCl). This study analyzed the regulatory effects of microbial inoculants on soil physicochemical properties, leaf physiological and biochemical characteristics, as well as fruit yield and quality. The results showed that salt stress significantly inhibited the growth of Pinot Noir grapes. However, the application of microbial inoculants effectively alleviated the negative effects of salt stress. By enhancing the plant’s antioxidant defense capacity and regulating physiological metabolic pathways such as osmotic balance, the inoculants significantly mitigated the inhibitory effect of salt stress on fruit development. Notably, the S+JH treatment group demonstrated particularly outstanding results, with hundred-berry weight, single-bunch weight, and yield per plant increasing significantly by 15.96%, 12.47%, and 28.93%, respectively, compared to the salt stress group (S). Additionally, this treatment also stabilized free amino acid content and suppressed excessive organic acid synthesis. This study provides new technical insights into the application of microbial inoculants for saline-alkali land improvement and stress-resistant cultivation of horticultural crops such as grapes, holding significant practical value for promoting the sustainable development of the grape industry in saline-alkali regions. Full article

(This article belongs to the Special Issue Abiotic Stress Responses in Horticultural Crops)

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19 pages, 9179 KB

Open AccessArticle

Linking the Changes of Soil Organic Carbon with Rare Bacterial Diversity in Sagebrush Desert Grassland Under Grazing Exclusion

by Bingjie Yu, Zongjiu Sun, Yuxuan Cui and Huixia Liu

Agriculture 2025, 15(18), 1959; https://doi.org/10.3390/agriculture15181959 - 17 Sep 2025

Grazing exclusion is an effective and economical tool for restoring degraded grasslands. Yet, less attention is paid to the changes of rare and abundant bacterial taxa and their connections with soil organic carbon changes after grazing exclusion (GE). Using high-throughput sequencing and multiple [...] Read more.

Grazing exclusion is an effective and economical tool for restoring degraded grasslands. Yet, less attention is paid to the changes of rare and abundant bacterial taxa and their connections with soil organic carbon changes after grazing exclusion (GE). Using high-throughput sequencing and multiple statistical methods, we assessed shifts in rare and abundant bacterial taxa and contributions to soil organic carbon in five typical sagebrush (Xinyuan, Bole, Qitai, Hutubi, Manasi) desert experimental plots in Xinjiang, northwest China. The results demonstrated that rare bacterial α-diversity decreased significantly in Xinyuan, Bole, and Qitai plots, while Hutubi and Manasi plots significantly increased during GE (p < 0.05). GE increased the edges/nodes ratio from 29.60% to 44.90% and changed network complexity by shifting the nodes and topological properties, cohesion, and robustness in the bacterial network. The changes in rare bacterial diversity are tightly correlated with changes in soil organic carbon. The results not only underline the pivotal role of rare bacterial taxa in response to GE and soil organic carbon changes but also provide novel insights into the mechanisms of soil organic carbon changes after GE. Full article

(This article belongs to the Section Agricultural Soils)

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27 pages, 5441 KB

Open AccessArticle

Design and Experiment of a Multi-Duct Air-Delivered Sprayer for Closed Apple Orchards

by Juxia Wang, Fengzi Zhang, Yuanmeng Wang, Haoran Li, Yusheng Jin, Yanqing Zhang, Zhiyong Zhang and Qingliang Cui

Agriculture 2025, 15(18), 1958; https://doi.org/10.3390/agriculture15181958 - 17 Sep 2025

A self-propelled multi-duct air-delivered sprayer was developed to address the challenges of dense canopies and low pesticide utilization in closed-canopy apple orchards. It featured an intelligently adjustable spray bar and formed a directional air curtain via a centrifugal fan and a duckbill air [...] Read more.

A self-propelled multi-duct air-delivered sprayer was developed to address the challenges of dense canopies and low pesticide utilization in closed-canopy apple orchards. It featured an intelligently adjustable spray bar and formed a directional air curtain via a centrifugal fan and a duckbill air outlet to improve droplet penetration. Using CFD simulations, the air duct size and the air outlet distance were optimized, and the field orthogonal test was carried out with driving speed, nozzle pressure, and nozzle type as factors. The results showed that the optimal parameters were an air duct size of 230 × 110 mm, an air outlet distance of 350 mm, and a fan speed of 2160 r/min. Compared to liquid pump independent operation, liquid pump–fan cooperative operation significantly increased droplet deposition density (p < 0.05) and reduced the degree of dispersion. All three factors significantly influenced deposition density (p < 0.05), and nozzle type had the greatest influence on deposition density, followed by nozzle pressure, and then driving speed. Optimal performance was obtained at a 0.3 m/s driving speed, a 3 MPa nozzle pressure, and a 6502 nozzle type. Under the optimal combination of operating parameters, field verification tests demonstrated that cooperative operation achieved higher average coverage (60.54% vs. 48.30%) and average deposition density (71.34 vs. 60.54 droplets/cm²), with a more uniform coefficient of variation in droplet coverage on leaves (a range of 13.37–19.07% vs. 9.70–22.67%). These results indicate that the sprayer exhibits strong penetration and provides good uniform coverage, effectively increasing droplet deposition across different canopy heights. Full article

(This article belongs to the Section Agricultural Technology)

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20 pages, 2611 KB

Open AccessArticle

Effects of Phenanthrene Soil Pollution on Cadmium Bioaccumulation and Metabolic Responses in Maize (Zea mays L.)

by Guangwei Zhang, Guohui Ning, Yukun Zhang, Qingyu Meng, Jiahui Li, Mingyue Qi, Liqian Chen, Liang Mi, Jiayuan Gao, Meng Zhang, Xiaoxue Zhang, Xiaomin Wang and Zhixin Yang

Agriculture 2025, 15(18), 1957; https://doi.org/10.3390/agriculture15181957 - 16 Sep 2025

Co-contamination of cadmium (Cd) and polycyclic aromatic hydrocarbons (PAHs) in agricultural soils poses a critical threat to crops and food safety, but how PAHs affect Cd uptake and plant metabolism is still unclear. Maize (Zea mays L.) of the variety Hanyu 702 [...] Read more.

Co-contamination of cadmium (Cd) and polycyclic aromatic hydrocarbons (PAHs) in agricultural soils poses a critical threat to crops and food safety, but how PAHs affect Cd uptake and plant metabolism is still unclear. Maize (Zea mays L.) of the variety Hanyu 702 (HY702) was previously identified by our group asaccumulating Cd at low levels when grown in soil containing Cd and phenanthrene (Phe). These contaminants were used here as model pollutions, alone and in combination, to assess the accumulation, growth, physiological, and metabolic responses of HY702 seedlings. Four treatments were compared, including a control without pollution, single Phe pollution, single Cd pollution, and Cd and Phe combined pollution. The experiments followed a completely randomized design with three replicates per treatment. The results revealed that Cd accumulation in the plants was significantly reduced when Phe was present as well (9% reduction in roots and 44% in stems and leaves compared to Cd single pollution). The combined Cd-Phe pollution had no significant impact on the height or chlorophyll content of the maize plants but markedly reduced their malondialdehyde (MDA) content. In addition, it increased the proline content by 56% and antioxidant enzyme activity by 15% (peroxidase, POD), 24% (superoxide dismutase, SOD), and 57% (catalase, CAT) compared to the control treatment. Metabolomics analysis revealed that the coexistence of Phe and Cd activated four key metabolic pathways: (a) alanine, aspartate, and glutamate metabolism; (b) valine, leucine, and isoleucine biosynthesis; (c) aminoacyl-tRNA biosynthesis; and (d) histidine metabolism. This activation resulted in increased levels of six differential metabolites: L-asparagine, L-methionine, L-glutamate, (S)-2-acetyl-2-hydroxybutanoic acid, urocanic acid, and 2-isopropylmalic acid. These metabolites induced detoxification pathways and reduced Cd accumulation. The findings reported here offer new insights into how plants metabolically adapt to the combined pollution of Cd and PAHs and provide an important scientific basis for pollution control strategies. Full article

(This article belongs to the Section Agricultural Soils)

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15 pages, 4599 KB

Open AccessArticle

Effects of Biodegradable Film Mulching and Water-Saving Irrigation on Soil Moisture and Temperature in Paddy Fields of the Black Soil Region

by Jizhen Li, Yuning He, Jilong Liu, Yinqi Wang, Yunze Guo and Yuchen Lu

Agriculture 2025, 15(18), 1956; https://doi.org/10.3390/agriculture15181956 - 16 Sep 2025

Paddy cultivation in the black soil region of northeast China is faced with the problems of low irrigation water use efficiency (IWUE) and low temperature stress during sowing. Therefore, the combinations of film mulching and water-saving irrigation methods were adopted to adjust the [...] Read more.

Paddy cultivation in the black soil region of northeast China is faced with the problems of low irrigation water use efficiency (IWUE) and low temperature stress during sowing. Therefore, the combinations of film mulching and water-saving irrigation methods were adopted to adjust the balance between water and yield under the condition of suitable soil water and heat environment, and to quantify the relationship between irrigation water and yield formation. This study investigated the mechanisms of two kinds of biodegradable film mulching combined with two water-saving irrigation on soil hydrothermal conditions in cold-region paddy fields. The results show that film mulching improved the water retention capacity of the soil at different depths, with black film exhibiting better moisture conservation than white film. Overall, controlled irrigation resulted in higher soil moisture than ridge irrigation before the heading–flowering stage, but lower values in heading–flower stage and the later stage. Film mulching also increased soil temperature across different layers, with black film showing a more warming effect in the 0–5 cm soil layer. All combinations of biodegradable film mulching and water-saving irrigation enhanced the IWUE, with the ridge irrigation combined with black film mulching showing the most significant improvement. This research provides technical references for water-efficient rice cultivation in cold regions. Full article

(This article belongs to the Section Agricultural Water Management)

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