Agriculture

16 pages, 746 KiB

Open AccessArticle

Effects of Seed Fraction on Sowing Quality and Yield of Three-Line Hybrid Maize

by Katarzyna Panasiewicz, Rafał Sobieszczański, Karolina Ratajczak, Agnieszka Faligowska, Grażyna Szymańska, Jan Bocianowski, Anna Kolanoś and Rafał Pretkowski

Agriculture 2025, 15(9), 972; https://doi.org/10.3390/agriculture15090972 (registering DOI) - 29 Apr 2025

Abstract

Maize is one of the most productive cereal crops, and is increasingly being grown over large areas. Using the right cultivar of high-quality selected seeds for sowing can be crucial for its productivity. The aim of this study was to investigate the effect [...] Read more.

Maize is one of the most productive cereal crops, and is increasingly being grown over large areas. Using the right cultivar of high-quality selected seeds for sowing can be crucial for its productivity. The aim of this study was to investigate the effect of kernel fraction on the seed quality, seed vigor, morphological traits, and seed yield of the trilinear hybrid maize cv. ‘Lokata’. The research factor was the kernel fraction, categorized based on the thousand-kernel weight (TKW) into four groups: I—small; II—medium; III–large; and IV–very large. A three-year experiment showed that increases in the TKW resulted in increases in germination and vigor up to fraction III (large seeds) in maize. Sowing maize seeds with a higher TKW resulted in plants with higher fresh and dry weights in the early stages of maize development; however, this response decreased as growth progressed. The seed yield was significantly correlated with plant height and the number of kernels per cob for all fractions sown, but the fraction did not significantly modify the seed yield of ‘Lokata’ maize. Full article

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

20 pages, 6169 KiB

Open AccessArticle

Developing an Uncrewed Aerial Vehicle (UAV)-Based Prediction Model for the Rice Harvest Index Using Machine Learning

by Zhaoyang Pan, Zhanhua Lu, Liting Zhang, Wei Liu, Xiaofei Wang, Shiguang Wang, Hao Chen, Haoxiang Wu, Weicheng Xu, Youqiang Fu and Xiuying He

Agriculture 2025, 15(9), 971; https://doi.org/10.3390/agriculture15090971 (registering DOI) - 29 Apr 2025

Abstract

(1) Background: The harvest index is important for measuring the correlation between grain yield and aboveground biomass. However, the harvest index can only be measured after a mature harvest. If it can be obtained in advance during the growth period, it will promote [...] Read more.

(1) Background: The harvest index is important for measuring the correlation between grain yield and aboveground biomass. However, the harvest index can only be measured after a mature harvest. If it can be obtained in advance during the growth period, it will promote research on high harvest indices and variety breeding; (2) Methods: In this study, we proposed a method to predict the harvest index during the rice growth period based on uncrewed aerial vehicle (UAV) remote sensing technology. UAV obtained visible light and multispectral images of different varieties, and the data such as digital surface elevation, visible light reflectance, and multispectral reflectance were extracted after processing for correlation analysis. Additionally, characteristic variables significantly correlated with the harvest index were screened out; (3) Results: The results showed that TCARI (correlation coefficient −0.82), GRVI (correlation coefficient −0.74), MTCI (correlation coefficient 0.83), and TO (correlation coefficient −0.72) had a strong correlation with the harvest index. Based on the above characteristics, this study used a variety of machine learning algorithms to construct a harvest index prediction model. The results showed that the Stacking model performed best in predicting the harvest index (R² reached 0.88) and had a high prediction accuracy. (4) Conclusions: Therefore, the harvest index can be accurately predicted during rice growth through UAV remote sensing images and machine learning technology. This study provides a new technical means for screening high harvest index in rice breeding, provides an important reference for crop management and variety improvement in precision agriculture, and has high application potential. Full article

(This article belongs to the Section Digital Agriculture)

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29 pages, 19793 KiB

Open AccessArticle

Design of a Conveyer Trough Bolt Signal Acquisition System and Bayesian Ensemble Identification Method for Working State

by Yi Lian, Bangzhui Wang, Meiyan Sun, Kexin Que, Sijia Xu, Zhong Tang and Zhilong Huang

Agriculture 2025, 15(9), 970; https://doi.org/10.3390/agriculture15090970 (registering DOI) - 29 Apr 2025

Abstract

Rice combine harvester conveyor troughs and their bolted connections are susceptible to vibration-induced failure due to operational and environmental excitations. Addressing the challenge of predicting the state of the combine harvester’s conveyor trough bolted structure prior to vibration-induced failure, this study addresses this [...] Read more.

Rice combine harvester conveyor troughs and their bolted connections are susceptible to vibration-induced failure due to operational and environmental excitations. Addressing the challenge of predicting the state of the combine harvester’s conveyor trough bolted structure prior to vibration-induced failure, this study addresses this by investigating signal analysis, system design, and condition identification for these critical components. Firstly, multi-point vibration signals from the conveyor trough were acquired and analyzed in the time-frequency domain. The analysis pinpointed the X-direction at the trough-frame connection (Point 5) as the most responsive location, with RMS peaking at 6.650 during header start-up (vs. 0.849 idle). Significant responses were also noted at Point 3 (Y-dir, 4.628) and Point 6 (X-dir, 3.896) under certain conditions (where Z-direction responses were minimal), identifying critical points that form the basis for condition assessment. Secondly, a vibration acquisition system was developed using a high-performance AD7606 ADC and A39C wireless technology. It features 16-bit resolution (0.00076 mm/s theoretical sensitivity), 8-channel synchronous sampling up to 200 kSPS, and rapid (0.8 s) wireless data transmission. This system meets the demands for high-frequency, high-precision monitoring of the bolted structure. Finally, after comparing machine learning algorithms, Support Vector Machine was chosen for its superior performance. Using a one-vs.-one strategy and data from critical points, an operational condition identification model was developed. Validation with field data confirmed high accuracy (96.9–99.7%) for principal states and low misclassification rates (<5%). This allows for precise identification of the bolted structure’s working status. The research presented in this study offers effective methodologies and technical underpinning for the condition monitoring of critical structural components in rice combine harvesters. Full article

(This article belongs to the Section Agricultural Technology)

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16 pages, 7273 KiB

Open AccessArticle

An AI-Based Open-Source Software for Varroa Mite Fall Analysis in Honeybee Colonies

by Jesús Yániz, Matías Casalongue, Francisco Javier Martinez-de-Pison, Miguel Angel Silvestre, Beeguards Consortium, Pilar Santolaria and Jose Divasón

Agriculture 2025, 15(9), 969; https://doi.org/10.3390/agriculture15090969 (registering DOI) - 29 Apr 2025

Abstract

Infestation by Varroa destructor is responsible for high mortality rates in Apis mellifera colonies worldwide. This study was designed to develop and test under field conditions a new free software (VarroDetector) based on a deep learning approach for the automated detection and counting [...] Read more.

Infestation by Varroa destructor is responsible for high mortality rates in Apis mellifera colonies worldwide. This study was designed to develop and test under field conditions a new free software (VarroDetector) based on a deep learning approach for the automated detection and counting of Varroa mites using smartphone images of sticky boards collected in honeybee colonies. A total of 204 sheets were collected, divided into four frames using green strings, and photographed under controlled lighting conditions with different smartphone models at a minimum resolution of 48 megapixels. The Varroa detection algorithm comprises two main steps: First, the region of interest where Varroa mites must be counted is established. From there, a one-stage detector is used, namely YOLO v11 Nano. A final verification was conducted counting the number of Varroa mites present on new sticky sheets both manually through visual inspection and using the VarroDetector software and comparing these measurements with the actual number of mites present on the sheet (control). The results obtained with the VarroDetector software were highly correlated with the control (R² = 0.98 to 0.99, depending on the smartphone camera used), even when using a smartphone for which the software was not previously trained. When Varroa mite numbers were higher than 50 per sheet, the results of VarroDetector were more reliable than those obtained with visual inspection performed by trained operators, while the processing time was significantly reduced. It is concluded that the VarroDetector software Version 1.0 (v. 1.0) is a reliable and efficient tool for the automated detection and counting of Varroa mites present on sticky boards collected in honeybee colonies. Full article

(This article belongs to the Special Issue Recent Advances in Bee Rearing and Production)

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27 pages, 1867 KiB

Open AccessArticle

MACA-Net: Mamba-Driven Adaptive Cross-Layer Attention Network for Multi-Behavior Recognition in Group-Housed Pigs

by Zhixiong Zeng, Zaoming Wu, Runtao Xie, Kai Lin, Shenwen Tan, Xinyuan He and Yizhi Luo

Agriculture 2025, 15(9), 968; https://doi.org/10.3390/agriculture15090968 (registering DOI) - 29 Apr 2025

Abstract

The accurate recognition of pig behaviors in intensive farming is crucial for health monitoring and growth assessment. To address multi-scale recognition challenges caused by perspective distortion (non-frontal camera angles), this study proposes MACA-Net, a YOLOv8n-based model capable of detecting four key behaviors: eating, [...] Read more.

The accurate recognition of pig behaviors in intensive farming is crucial for health monitoring and growth assessment. To address multi-scale recognition challenges caused by perspective distortion (non-frontal camera angles), this study proposes MACA-Net, a YOLOv8n-based model capable of detecting four key behaviors: eating, lying on the belly, lying on the side, and standing. The model incorporates a Mamba Global–Local Extractor (MGLE) Module, which leverages Mamba to capture global dependencies while preserving local details through convolutional operations and channel shuffle, overcoming Mamba’s limitation in retaining fine-grained visual information. Additionally, an Adaptive Multi-Path Attention (AMPA) mechanism integrates spatial-channel attention to enhance feature focus, ensuring robust performance in complex environments and low-light conditions. To further improve detection, a Cross-Layer Feature Pyramid Transformer (CFPT) neck employs non-upsampled feature fusion, mitigating semantic gap issues where small target features are overshadowed by large target features during feature transmission. Experimental results demonstrate that MACA-Net achieves a precision of 83.1% and mAP of 85.1%, surpassing YOLOv8n by 8.9% and 4.4%, respectively. Furthermore, MACA-Net significantly reduces parameters by 48.4% and FLOPs by 39.5%. When evaluated in comparison to leading detectors such as RT-DETR, Faster R-CNN, and YOLOv11n, MACA-Net demonstrates a consistent level of both computational efficiency and accuracy. These findings provide a robust validation of the efficacy of MACA-Net for intelligent livestock management and welfare-driven breeding, offering a practical and efficient solution for modern pig farming. Full article

(This article belongs to the Special Issue Modeling of Livestock Breeding Environment and Animal Behavior)

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17 pages, 1281 KiB

Open AccessReview

Sustainable Food Systems Through Livestock–Pasture Integration

by Monde Rapiya, Mthunzi Mndela and Abel Ramoelo

Agriculture 2025, 15(9), 967; https://doi.org/10.3390/agriculture15090967 (registering DOI) - 29 Apr 2025

Abstract

The world’s population is projected to rise significantly, which poses challenges for global food security due to increased demand for food, especially from livestock products. As incomes grow in lower-income countries, there is a shift towards more diverse diets that include meat and [...] Read more.

The world’s population is projected to rise significantly, which poses challenges for global food security due to increased demand for food, especially from livestock products. As incomes grow in lower-income countries, there is a shift towards more diverse diets that include meat and dairy, stressing our agricultural systems. Livestock plays a crucial role in food production, contributing about 16% of dietary energy, and effective pasture management is vital for enhancing livestock productivity. This review explores how integrating pasture and livestock management can create sustainable food systems and improve nutrition and livelihoods. It assesses the economic viability of pasture-based livestock systems and examines how climate change affects both pasture productivity and livestock performance. The review also identifies innovative practices, such as improved grazing management and technological advancements, that can improve pasture health and livestock output. The findings underscore the importance of well-managed pastures, which can restore degraded lands, improve animal welfare, and support food security. It also highlights that adaptation strategies are necessary to address the challenges posed by climate change, ensuring that livestock systems remain sustainable. By focusing on innovative practices and better management, we can meet the growing demand for animal products while preserving ecosystems and promoting economic stability. Overall, this review emphasizes the need for a holistic understanding of how livestock and pasture management can work together to enhance food security in a changing world. Full article

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

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18 pages, 7074 KiB

Open AccessArticle

Estimating Pruning Wood Mass in Grapevine Through Image Analysis: Influence of Light Conditions and Acquisition Approaches

by Stefano Puccio, Daniele Miccichè, Gonçalo Victorino, Carlos Manuel Lopes, Rosario Di Lorenzo and Antonino Pisciotta

Agriculture 2025, 15(9), 966; https://doi.org/10.3390/agriculture15090966 (registering DOI) - 29 Apr 2025

Abstract

Pruning wood mass is crucial for grapevine management, as it reflects the vine’s vigor and balance. However, traditional manual measurement methods are time-consuming and labor-intensive. Recent advances in digital imaging offer non-invasive techniques, but limited research has explored pruning wood weight estimation, especially [...] Read more.

Pruning wood mass is crucial for grapevine management, as it reflects the vine’s vigor and balance. However, traditional manual measurement methods are time-consuming and labor-intensive. Recent advances in digital imaging offer non-invasive techniques, but limited research has explored pruning wood weight estimation, especially regarding the use of artificial backgrounds and lighting. This study assesses the use of image analysis for estimating wood weight, focusing on image acquisition conditions. This research aimed to (i) evaluate the necessity of artificial backgrounds and (ii) identify optimal daylight conditions for accurate image capture. Results demonstrated that estimation accuracy strongly depends on the sun’s position relative to the camera. The highest accuracy was achieved when the camera faced direct sunlight (morning on the northwest canopy side and afternoon on the southeast side), with R² values reaching 0.90 and 0.93, and RMSE as low as 44.24 g. Artificial backgrounds did not significantly enhance performance, suggesting that the method is applicable under field conditions. Leave-One-Group-Out Cross-Validation (LOGOCV) confirmed the model’s robustness when applied to Catarratto cv. (LOGOCV R² = 0.86 in NB and 0.84 in WB), though performance varied across other cultivars. These findings highlight the potential of automated image-based assessment for efficient vineyard management, using minimal effort adjustments to image collection that can be incorporated into low-cost setups for pruning wood weight estimation. Full article

(This article belongs to the Section Digital Agriculture)

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Graphical abstract

19 pages, 3455 KiB

Open AccessArticle

Identification of Cotton Defoliation Sensitive Materials Based on UAV Multispectral Imaging

by Yuantao Guo, Hu Zhang, Wenju Gao, Quanjia Chen, Qiyu Chang, Jinsheng Wang, Qingtao Zeng, Haijiang Xu and Qin Chen

Agriculture 2025, 15(9), 965; https://doi.org/10.3390/agriculture15090965 (registering DOI) - 29 Apr 2025

Abstract

(1) Background: This study aims to analyze the defoliation and boll opening performance of 123 upland cotton germplasm resources after spraying defoliant, using multispectral data to select relevant vegetation indices and identify germplasm resources sensitive to defoliants, providing methods for cotton variety improvement [...] Read more.

(1) Background: This study aims to analyze the defoliation and boll opening performance of 123 upland cotton germplasm resources after spraying defoliant, using multispectral data to select relevant vegetation indices and identify germplasm resources sensitive to defoliants, providing methods for cotton variety improvement and high-quality parental resources. (2) Methods: 123 historical upland cotton germplasm resources from Xinjiang were selected, and the defoliation and boll opening of cotton leaves were investigated at 0, 4, 8, 12, 16, and 20 days after defoliant application. Simultaneously, multispectral digital images were collected using drones to obtain 12 vegetation indices. Based on defoliation rate, the optimal vegetation index was selected, and defoliant-sensitive germplasm resources were identified. (3) Results: The most significant difference in defoliation rate of cotton germplasm resources occurred 16 days after application. Cluster analysis grouped the 123 breeding materials into three categories, with Class I showing the best defoliation effect. Among the 12 vegetation indices, the Plant Senescence Reflectance Index (PSRI) has the highest correlation coefficient with the defoliation rate; and when the PSRI value is higher, the defoliation effect of the material is better. By comparing the traditional investigation method with the unmanned aerial vehicle multispectral technology, 15 cotton materials sensitive to defoliants were determined, with a defoliation rate of over 85%, a lint percentage ranging from 76.67% to 98.04%, and a PSRI value ranging from 0.1607 to 0.1984. (4) Conclusions: The study found that the vegetation index with sensitive response can be used as an effective indicator to evaluate the sensitivity of cotton breeding materials to defoliants. Using an unmanned aerial vehicle (UAV) equipped with vegetation indices for screening shows a high consistency with the manual investigation and screening method in screening excellent defoliation materials; it proves that it is feasible to screen cotton breeding materials with excellent defoliation effects using UAV multispectral technology. Full article

(This article belongs to the Section Digital Agriculture)

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24 pages, 2085 KiB

Open AccessReview

A Review on the Evolution of Air-Assisted Spraying in Orchards and the Associated Leaf Motion During Spraying

by Guanqun Wang, Ziyu Li, Weidong Jia, Mingxiong Ou, Xiang Dong and Zhengji Zhang

Agriculture 2025, 15(9), 964; https://doi.org/10.3390/agriculture15090964 (registering DOI) - 29 Apr 2025

Abstract

Air-assisted spraying is vital in modern orchard pest management by enhancing droplet penetration and coverage on complex canopies. However, the interaction between airflow, droplets, and flexible foliage remains unclear, limiting spray efficiency and environmental sustainability. This review summarizes recent advances in understanding leaf [...] Read more.

Air-assisted spraying is vital in modern orchard pest management by enhancing droplet penetration and coverage on complex canopies. However, the interaction between airflow, droplets, and flexible foliage remains unclear, limiting spray efficiency and environmental sustainability. This review summarizes recent advances in understanding leaf motion dynamics in wind and droplet fields and their impact on pesticide deposition. First, we review orchard spraying technologies, focusing on air-assisted systems and their contribution to more uniform coverage. Next, we analyze mechanisms of droplet deposition within canopies, highlighting how wind characteristics, droplet size, and canopy structure influence pesticide distribution. Special attention is given to leaf aerodynamic responses, including bending, vibration, and transient deformation induced by wind and droplet impacts. Experimental and simulation studies reveal how leaf motion affects droplet retention, spreading, and secondary splashing. The limitations of static boundary models in deposition simulations are discussed, along with the potential of fluid-structure interaction (FSI) models. Future directions include integrated leaf-droplet experiments, intelligent airflow control, and incorporating plant biomechanics into precision spraying. Understanding leaf motion in spray environments is key to enhancing orchard spraying efficiency, precision, and sustainability. Full article

(This article belongs to the Section Agricultural Technology)

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24 pages, 3252 KiB

Open AccessReview

Plant- and Microbial-Based Organic Disease Management for Grapevines: A Review

by Mereke Alimzhanova, Nurkanat Meirbekov, Yerkanat Syrgabek, Rebeca López-Serna and Saltanat Yegemova

Agriculture 2025, 15(9), 963; https://doi.org/10.3390/agriculture15090963 (registering DOI) - 29 Apr 2025

Abstract

This review compares 32 studies (2000–2024) on plant- and microbial-based organic disease management to control grapevine pests and diseases. A systematic literature search provided 24 studies on microbial agents and 8 on plant treatments. Their effectiveness against key pathogens, including downy mildew, powdery [...] Read more.

This review compares 32 studies (2000–2024) on plant- and microbial-based organic disease management to control grapevine pests and diseases. A systematic literature search provided 24 studies on microbial agents and 8 on plant treatments. Their effectiveness against key pathogens, including downy mildew, powdery mildew, and gray mold, was compared. Microbial agents such as Candida sake inhibited Botrytis cinerea by up to 80% in the lab and Pseudomonas sp. dramatically reduced grapevine lesion lengths by 32–52% in field conditions, while Bacillus subtilis reduced powdery mildew by 96% in greenhouse conditions and A. pullulans reduced Ochratoxin A infection by 99% in field conditions. In laboratory conditions, C. guilliermondii A42 reduced grape rot to 8–22% and A. cephalosporium B11 reduced it to 16–82%, confirming A42’s greater efficacy. Plant-derived agents and essential oils, including lavender and cinnamon, suppressed 100% of pathogens in vitro, whereas copper coupled with plant-derived agents reduced disease incidence by up to 92% under field conditions. While promising, plant-derived agents are plagued by formulation instability, which affects shelf life and effectiveness, while microbial agents must be kept under stringent storage conditions and can be variable under different vineyard conditions. These limitations identify the requirement for a stronger formulation strategy and large field validations. Organic disease management offers several important benefits, such as environmental safety, biodegradability, compatibility with organic cultivation, and low pesticide dependence. The application of these agents in pest management systems is ecologically balanced, improves soil health, and enables sustainable vineyard management. Full article

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

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9 pages, 520 KiB

Open AccessArticle

Environmental and Colony-Related Factors Linked to Small Hive Beetle (Aethina tumida) Infestation in Apis mellifera

by Camilla Di Ruggiero, Andrea Gyorffy, Francesco Artese, Alessandra De Carolis, Angelo De Simone, Marco Pietropaoli, Camilla Pedrelli and Giovanni Formato

Agriculture 2025, 15(9), 962; https://doi.org/10.3390/agriculture15090962 (registering DOI) - 29 Apr 2025

Abstract

The small hive beetle (SHB) was first detected in Italy in 2014 and remains confined to the regions of Calabria and Sicily (Italy). The environmental and colony-related factors favorable to the development of SHBs are widely studied, but mainly at the laboratory level; [...] Read more.

The small hive beetle (SHB) was first detected in Italy in 2014 and remains confined to the regions of Calabria and Sicily (Italy). The environmental and colony-related factors favorable to the development of SHBs are widely studied, but mainly at the laboratory level; it is not yet clear whether these factors also apply in the field within apiaries in regions affected by SHBs. In 2022, we conducted a study in the province of Reggio Calabria, Italy, to investigate if these factors influence SHB infestation levels in honey bee colonies. Data were collected from 67 hives in late winter and 81 hives in autumn, inspecting each hive three times per season. Overall, SHB infestation levels were low (an average 0.83 SHB/hive). Our analysis revealed a significant relationship between the SHB infestation level and the following six factors: the number of combs covered by adult bees, the total number of combs, combs surveillance, the previous month’s infestation, sun exposure, and season. GLM analysis predicted a higher number of SHBs in colonies with fewer combs covered by adult bees (2.543), with a greater number of combs (1.877), with lower comb surveillance (0.935), with a higher SHB infestation level in the previous month (1.192), in shaded locations compared to sunny ones (0.207), and in autumn compared to late winter (0.258), with peak infestations in September. These findings provide insights to inform surveillance plans, optimise sentinel apiaries setup in SHB-free regions, and offer practical guidance for beekeepers on implementing biosecurity measures to minimise infestation levels and enhance early detection. Future research should examine whether these factors have similar effects in regions with higher SHB infestation rates. Full article

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

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21 pages, 1338 KiB

Open AccessArticle

Effect of Ascophyllum nodosum, Sideritis scardica and Fucus vesiculosus Extracts on Germination, Initial Growth and Antioxidant Potential of Red Russian Kale Microgreens

by Barbara Drygaś, Ewa Szpunar-Krok, Joanna Kreczko, Tomasz Piechowiak, Czesław Puchalski and Marta Jańczak-Pieniążek

Agriculture 2025, 15(9), 961; https://doi.org/10.3390/agriculture15090961 (registering DOI) - 28 Apr 2025

Abstract

Natural plant- and algae-based extracts used in crop cultivation offer numerous advantages, including the potential to positively affect plant growth, exhibit hormonal activity, increase stress resistance, improve crop quality as environmentally benign alternatives to synthetic agrochemicals and help combat oxidative stress. The presented [...] Read more.

Natural plant- and algae-based extracts used in crop cultivation offer numerous advantages, including the potential to positively affect plant growth, exhibit hormonal activity, increase stress resistance, improve crop quality as environmentally benign alternatives to synthetic agrochemicals and help combat oxidative stress. The presented experiments aimed to compare the effectiveness of extracts from brown algae such as Ascophyllum nodosum and Fucus vesiculosus, as well as the plant Sideritis scardica, on the germination and initial growth of red kale (Brassica napus var. Pabularia) microgreens. Microgreens treated with aqueous extracts of A. nodosum, F. vesiculosus, as well as the control group, had the highest growth, whereas the lowest growth was observed in plants treated with water–ethanol extracts at the highest tested concentration (10%). The 10% water–ethanol extracts of brown algae reduced plant biomass, while aqueous extracts increased it. Applying water extracts of algae at concentrations (10, 1, 0.1%), as well as the water extract of S. scardica (10, 1%), led to an increase in the total phenolic content in the tested experimental groups. A significant influence on increasing total flavonoid content was noted for water extracts of F. vesiculosus at concentrations ranging from 0.1% to 10%. An opposite effect was observed for the water–ethanol extracts, where the lowest TFC was found in plants grown on mats soaked with 0.1% F. vesiculosus and 1% A. nodosum. All water–ethanol extracts tended to reduce the antioxidant activity of the tested red kale microgreens. In microgreens treated with water extracts of F. vesiculosus at concentrations of 1% and 10%, an increase in antioxidant activity was observed. Examining the impact of plant and algae extracts on kale germination and growth may provide valuable information on ways to improve the quality and health-promoting properties of kale microgreens. Full article

(This article belongs to the Section Crop Production)

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12 pages, 232 KiB

Open AccessArticle

Yield and Seed Quality of Faba Bean (Vicia faba L. var. minor) as a Result of Symbiosis with Nitrogen-Fixing Bacteria

by Magdalena Serafin-Andrzejewska, Agnieszka Falkiewicz, Wiesław Wojciechowski and Marcin Kozak

Agriculture 2025, 15(9), 960; https://doi.org/10.3390/agriculture15090960 (registering DOI) - 28 Apr 2025

Abstract

Faba bean is a high-protein legume that can be successfully grown in most climates around the world. It is one of the most popular pulses cultivated in Poland. Its seeds are a source of plant protein, used most often in feed production. Field [...] Read more.

Faba bean is a high-protein legume that can be successfully grown in most climates around the world. It is one of the most popular pulses cultivated in Poland. Its seeds are a source of plant protein, used most often in feed production. Field experiments and laboratory seed analyses were carried out in 2022 and 2023 to assess the effect of the application of nitrogen-fixing bacteria on the yield and seed quality of a low-tannin faba bean cultivar. The factor was tested at four levels: control, seed inoculation with Rhizobium leguminosarum bv. viceae, foliar spraying with Methylobacterium symbioticum, and seed inoculation and spraying (double application). The application of N-fixing bacteria had a positive effect on faba bean seed yield. In 2022, plants responded most effectively to a double application, increasing seed yield by 25.4%, while, in 2023, the highest seed yield was obtained after inoculation (12.3% increase). Although the single application of bacteria caused a decrease in seed protein content, the double application (inoculation and spraying) significantly enhanced seed protein content. The protein productivity per hectare was compensated by the higher seed yield and increased by 41.7% in 2022 and 14.9% in 2023 compared to plots where N-fixing bacteria were not applied. This work shows that it is possible to use different strains of N-fixing bacteria in faba bean cultivation and this can significantly improve yields while reducing the need for synthetic nitrogen fertilizers, which supports sustainable production. Full article

(This article belongs to the Special Issue Advances in the Cultivation and Production of Leguminous Plants)

26 pages, 20750 KiB

Open AccessArticle

Leveraging Text Mining and Network Analysis for the Diffusion of Agricultural Science and Technology Policies in China

by Xiaohe Liang, Yu Wu, Jiajia Liu, Jiayu Zhuang, Tong Yuan, Ying Chen, Lizhen Cui, Ailian Zhou, Jiajia Zhou and Tong Li

Agriculture 2025, 15(9), 959; https://doi.org/10.3390/agriculture15090959 (registering DOI) - 28 Apr 2025

Abstract

Agricultural science and technology policies (ASTPs) have played a pivotal role in shaping agricultural innovation, sustainability, and cleaner production practices. Understanding how ASTPs diffuse is essential for optimizing policy design and advancing the green transition in agriculture. This study aims to investigate the [...] Read more.

Agricultural science and technology policies (ASTPs) have played a pivotal role in shaping agricultural innovation, sustainability, and cleaner production practices. Understanding how ASTPs diffuse is essential for optimizing policy design and advancing the green transition in agriculture. This study aims to investigate the diffusion of ASTPs in China, using a quantitative citation-based approach. The goal is to explore diffusion patterns, topic characteristics, and historical trajectories of ASTPs, thereby providing insights into policy transmission mechanisms that can inform future policy improvements. We analyze 3207 ASTP documents, focusing on policy citation links to examine the distribution, diffusion characteristics, and dynamics of policies. The analysis includes tracking topic evolution and identifying key policies while estimating the main diffusion paths. The results show that the top-down diffusion model is the dominant pattern of policy transmission, exhibiting the highest diffusion speed and both short- and long-term impacts. ASTPs have progressively expanded toward industrialization, informatization, and green development, with increased policy transmission efficiency. The diffusion process has formed three primary pathways: (i) enhancing agricultural innovation capacity, (ii) accelerating the transformation of technological achievements, and (iii) improving the agricultural science and technology innovation system. These pathways are critical to advancing sustainable and cleaner agricultural production. This study provides valuable insights into the diffusion of ASTPs and highlights key pathways for policy optimization. The findings suggest that enhancing policy frameworks and improving policy implementation efficiency will be crucial for facilitating the transition toward sustainable, low-carbon, and environmentally friendly agricultural practices. Future research should refine data sources and incorporate semantic analysis to capture more detailed policy transmission mechanisms. Full article

(This article belongs to the Topic Ecological Protection and Modern Agricultural Development)

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26 pages, 2343 KiB

Open AccessArticle

Crossformer-Based Model for Predicting and Interpreting Crop Yield Variations Under Diverse Climatic and Agricultural Conditions

by Ruolei Zeng, Jialu Li, Zihan Li and Qingchuan Zhang

Agriculture 2025, 15(9), 958; https://doi.org/10.3390/agriculture15090958 (registering DOI) - 28 Apr 2025

Abstract

Crop yield prediction is critical for agricultural decision making and food security. Traditional models struggle to capture the complex interactions among meteorological, soil, and agricultural factors. This study introduces Crossformer, a Transformer-based model with a Local Perception Unit (LPU) for local dependencies and [...] Read more.

Crop yield prediction is critical for agricultural decision making and food security. Traditional models struggle to capture the complex interactions among meteorological, soil, and agricultural factors. This study introduces Crossformer, a Transformer-based model with a Local Perception Unit (LPU) for local dependencies and a Cross-Window Attention Mechanism for global dependencies. Experiments on winter wheat, rice, and corn datasets show that Crossformer outperforms CNN, LSTM, and Transformer in Test Loss, R², MSE, and MAE. For instance, on the corn dataset, Crossformer achieves a Test Loss of 0.0271 and an R² of 0.9863, compared to 0.7999 and 0.1634 for LSTM, respectively, demonstrating a substantial improvement in predictive performance. Interpretability analysis highlights the importance of temperature and precipitation in yield prediction, aligning with agricultural insights. The results demonstrate Crossformer’s potential for precision agriculture. Full article

(This article belongs to the Section Digital Agriculture)

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23 pages, 664 KiB

Open AccessArticle

The Role of Agricultural Socialized Services in Unlocking Agricultural Productivity in China: A Spatial and Threshold Analysis

by Yu Bai, Yuheng Wei, Ruofan Liao and Jianxu Liu

Agriculture 2025, 15(9), 957; https://doi.org/10.3390/agriculture15090957 (registering DOI) - 28 Apr 2025

Abstract

Amid global economic transformation, a persistent productivity gap exists between developed and developing nations in agriculture sector, shaped by technological advancements and shifting resource allocation patterns. Agricultural socialized services (ASS), defined as organized systems providing technical support, mechanization assistance, information services, market linkages, [...] Read more.

Amid global economic transformation, a persistent productivity gap exists between developed and developing nations in agriculture sector, shaped by technological advancements and shifting resource allocation patterns. Agricultural socialized services (ASS), defined as organized systems providing technical support, mechanization assistance, information services, market linkages, and resource optimization to farmers, have emerged as critical mechanisms for agricultural development. In developing economies, these services catalyze gains in agricultural labor productivity through the integration of advanced technologies and the mechanization of farming practices. Using panel data from 30 Chinese provinces during 2011 to 2022, this study investigates the relationship between ASS and ALP, focusing on regional heterogeneity, threshold effects, and spatial spillovers. The combination of spatial econometric methods and threshold analysis was selected for its unique capacity to capture both the geographic interdependencies and nonlinear relationships that characterize agricultural development processes. These thresholds at 5.254 and 8.478 represent critical points where the impact of ASS on ALP significantly changes in magnitude, revealing a nonlinear relationship that evolves across different stages of agricultural development. The study highlights notable regional disparities in the impact of ASS. Specifically, ASS is more effective on ALP in eastern, central and key food-producing regions, while its impact is relatively weak in western and non-food-producing regions. Spatial spillover analysis indicates that advancements in ASS create positive externalities, extending beyond their immediate implementation zones and facilitating inter-provincial agricultural cooperation and development. These findings provide crucial guidance for policymakers and agricultural service providers to optimize resource allocation and service delivery strategies. By identifying critical development thresholds and regional variations, this research offers evidence-based support for government officials designing targeted agricultural policies and enterprises developing region-specific service models to foster sustainable agricultural growth across diverse regional landscapes. Full article

(This article belongs to the Topic Novel Studies in Agricultural Economics and Sustainable Farm Management)

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25 pages, 3281 KiB

Open AccessArticle

Agricultural and Industrial Heritage as a Resource in Frontier Territories: The Border Between the Regions of Andalusia–Extremadura (Spain) and Alentejo (Portugal)

by Ainhoa Maruri Arana and María Teresa Pérez Cano

Agriculture 2025, 15(9), 956; https://doi.org/10.3390/agriculture15090956 (registering DOI) - 28 Apr 2025

Abstract

The border effect on heritage protection, shaped by historical and physical factors, contributes to the formation of socio-territorial systems, particularly in relation to productive landscapes. This study focuses on the Portuguese–Spanish border between Andalusia and Extremadura, a region where inter-regional dynamics mirror international [...] Read more.

The border effect on heritage protection, shaped by historical and physical factors, contributes to the formation of socio-territorial systems, particularly in relation to productive landscapes. This study focuses on the Portuguese–Spanish border between Andalusia and Extremadura, a region where inter-regional dynamics mirror international tensions due to the coexistence of differing legislative frameworks. The area is characterized by shared agricultural and ecological systems and fragmented transport networks, which complicate territorial integration. Methodologically, the study involves a selection of seven municipalities based on demographic vulnerability and rural identity, followed by historical and spatial analysis using legal sources, historical dictionaries, and digital platforms for heritage mapping. One of the key components was the identification and documentation of historical mills linked to the Ardilla River and its tributaries, using a combination of official heritage databases and user-generated platforms like Wikiloc and local websites. The twenty-one mills found highlight a significant presence of unprotected yet generally well-preserved mills that exemplify the agricultural and industrial legacy of the region. These assets, often overlooked in formal inventories, underline the potential for cross-border heritage recognition and call for a rethinking of protection strategies through the lens of cultural landscapes and community engagement. Full article

(This article belongs to the Special Issue Economic Development of Rural Areas in Border Territories: Threats and Opportunities)

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9 pages, 212 KiB

Open AccessCommunication

Ventilation Fans Offset Potential Reductions in Milk Margin from Heat Stress in Wisconsin Dairy Farms

by Neslihan Akdeniz and Leonard Polzin

Agriculture 2025, 15(9), 955; https://doi.org/10.3390/agriculture15090955 (registering DOI) - 28 Apr 2025

Abstract

Heat stress is becoming an increasing concern for dairy farmers due to elevated temperatures and wind shadow caused by rural development. Mechanical ventilation helps mitigate heat stress; however, transitioning from natural to mechanical ventilation increases operational costs. In this study, the number of [...] Read more.

Heat stress is becoming an increasing concern for dairy farmers due to elevated temperatures and wind shadow caused by rural development. Mechanical ventilation helps mitigate heat stress; however, transitioning from natural to mechanical ventilation increases operational costs. In this study, the number of days with no heat stress, as well as mild, moderate, and severe heat stress, was calculated for Madison, Wisconsin, over the past five years. Monthly milk margins were determined using all milk prices and feed costs from the Dairy Margin Coverage (DMC) program. The goal was to compare the potential reduction in milk margin coverage to the electricity costs of operating ventilation fans. The results indicated that while the five-year average milk margin reduction due to heat stress was USD 20,204 for a 600-head facility, the electricity cost accounted for approximately 42.6% of this amount. However, milk margins fluctuated annually due to volatility in milk and feed markets. For example, in 2021, the reduction in milk margins was estimated at USD 9804, while electricity costs reached USD 8574. It was concluded that in some years, when no severe heat stress occurs, the benefits of ventilation may be close to the expenses. Therefore, adhering to best management practices is critical for minimizing electricity costs while using ventilation fans in dairy operations. Full article

(This article belongs to the Section Farm Animal Production)

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20 pages, 1078 KiB

Open AccessArticle

Mitigating Rural Multidimensional Poverty Through Digital Inclusive Finance: Real Improvement and Psychological Empowerment

by Qiong Liu, Mingwei Wang, Qian Wang and Dawei Wei

Agriculture 2025, 15(9), 954; https://doi.org/10.3390/agriculture15090954 (registering DOI) - 28 Apr 2025

Abstract

Digital inclusive finance (DIF) is regarded as a key instrument in poverty alleviation efforts. However, existing research reveals significant gaps in understanding its poverty-reduction impact: the debate on its inclusivity remains unresolved, its mechanisms of action are unclear, and the psychological empowerment dimension [...] Read more.

Digital inclusive finance (DIF) is regarded as a key instrument in poverty alleviation efforts. However, existing research reveals significant gaps in understanding its poverty-reduction impact: the debate on its inclusivity remains unresolved, its mechanisms of action are unclear, and the psychological empowerment dimension has been largely overlooked. Using micro-level data from seven waves of the China Family Panel Studies (CFPS) from 2010 to 2022, this study employs fixed-effect models, quantile regression models, and mechanism analysis to explore the differentiated impact of digital inclusive finance on rural multidimensional relative poverty and the mechanisms at play. The empirical findings reveal that DIF significantly mitigates multidimensional relative poverty, with more pronounced marginal effects among the poorest households, confirming its pro-poor characteristics. Heterogeneity analysis reveals that, at the regional level, DIF has greater impacts in western regions and remote rural areas farther from county centers; at the individual level, it is particularly effective for women, those with lower education, and individuals with limited digital literacy. Mechanism analysis shows that DIF operates through three channels: promoting employment, encouraging entrepreneurship, and enhancing financial accessibility. Moreover, extended analysis demonstrates that DIF also fosters the endogenous motivation of rural households to escape poverty, as reflected in heightened confidence about the future, increased belief in social mobility and returns of work, and reduced perceived barriers to employment. These findings provide new micro-level evidence to unpack the poverty-alleviation potential of DIF. Full article

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

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