Journal Description
Agriculture
Agriculture
is an international, scientific peer-reviewed open access journal published monthly 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 - Q2 (Plant Science)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.7 days after submission; acceptance to publication is undertaken in 2.4 days (median values for papers published in this journal in the second half of 2023).
- 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 (2022);
5-Year Impact Factor:
3.6 (2022)
Latest Articles
The Residue Chemistry Transformation Linked to the Fungi Keystone Taxa during Different Residue Tissues Incorporation into Mollisols in Northeast China
Agriculture 2024, 14(6), 792; https://doi.org/10.3390/agriculture14060792 (registering DOI) - 21 May 2024
Abstract
Managing carbon input from crop straw in cropland ecosystems could increase soil organic carbon (SOC) sequestration to achieve C neutrality and mitigate climate change. The complexity of the chemical structures of crop residue largely affects SOC sequestration. Fungi communities play an important role
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Managing carbon input from crop straw in cropland ecosystems could increase soil organic carbon (SOC) sequestration to achieve C neutrality and mitigate climate change. The complexity of the chemical structures of crop residue largely affects SOC sequestration. Fungi communities play an important role in the degradation of crop residues. However, the relationship between the fungal community composition and the chemical structures of crop residues remains unclear and requires further investigation. Therefore, a 120-day incubation experiment was conducted in Mollisols in Northeast China to investigate the decomposition processes and dynamics of maize straw stem (ST), leaf (LE) and sheath (SH) residues using 13C-NMR spectroscopy. Additionally, the microbiomes associated with these residues were analyzed through high-throughput sequencing to explore their relationship. Our results showed that the alkyl C contents in all treatments exhibited increases ranging from 15.1% to 49.1%, while the O-alkyl C contents decreased, ranging from 0.02% to 11.2%, with the incubation time. The A/OA ratios of ST, LE and SH treatments were increased by 23.7%, 43.4% and 49.3% with incubation time, respectively. During the early stages of straw decomposition, Ascomycota dominated, and in the later stage, Basidiomycota were predominant. The class of Sordariomycetes played a key role in the chemistry transformation of straw tissues during decomposition. The keystone taxa abundances, Fusarium_kyushuense, and Striatibotrys_eucylindrospora, showed strong negative correlations with di-O-alkyl C and carbonyl-C content and positive correlations with the β-glucosidase and peroxidase enzyme activity, respectively. In conclusion, our study demonstrated that the keystone taxa play a significant role in regulating the chemical structures of straw tissues, providing a better understanding of the influence of residue quality on SOC sequestration.
Full article
(This article belongs to the Special Issue Monitoring, Modelling and Management of Agricultural Air Pollutants and Greenhouse Gases)
Open AccessArticle
A Study of Farmers’ Behavior in Classifying Domestic Waste Based on the Participants Intellectual Decision Model
by
Jing Wang, Nan Zhao, Dongjian Li and Shiping Li
Agriculture 2024, 14(6), 791; https://doi.org/10.3390/agriculture14060791 - 21 May 2024
Abstract
The farmers’ deep participation in the classification of domestic waste plays a crucial role in reducing the amount of waste out of the village from the source, lowering the cost of waste treatment, and realizing the sustainable development of rural waste resocialization, reduction,
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The farmers’ deep participation in the classification of domestic waste plays a crucial role in reducing the amount of waste out of the village from the source, lowering the cost of waste treatment, and realizing the sustainable development of rural waste resocialization, reduction, and harmlessness. This paper aims to identify the key factors and logical structure that influence the farmers’ behavior in classifying domestic waste and provide recommendations for improving it. Based on the Participants’ Intellectual Decision (PID) Model, we constructed a theoretical analysis framework for farmers’ decision-making on domestic waste classification, and the PID model was further extended by combining with the practice of rural domestic waste management in China and proposing the research hypothesis that factors, such as community attributes, rules of operation, the status of the participants, and the situation of external actions, have a significant impact on the farmers’ behavior in classifying domestic waste. Empirical analyses were carried out with the help of the ordered logistic model and the DEMATEL-ISM using 939 research data of farmers in Jiangsu and Gansu provinces of China. The results show the following: (1) classification of domestic waste by farmers in the sample area was predominantly unclassified (34.40%) and two-classified (40.58%); (2) 17 factors, including regional disparity, Party affiliation, organizational support perception, environmental emotions, conscious governance attitudes, trust in village cadres, social reference norms, and expected outcomes, have a significant impact on the farmers’ behavior in classifying domestic waste; (3) trust in village cadres, organizational support perception, and environmental emotion are superficial direct factors; incentive measures, fee level, waste transport situation, difficulty perception, self-consciousness perception, social reference norms, and expected outcomes are middle indirect factors; whether or not it is a demonstration village, Party membership and regional disparity are deep root factors affecting farmers to classify their domestic waste.
Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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Open AccessArticle
Unstable State of Hydrologic Regime and Grain Yields in Northern Kazakhstan Estimated with Tree-Ring Proxies
by
Irina P. Panyushkina, Altyn Shayakhmetova, Sergey Pashkov and Leonid I. Agafonov
Agriculture 2024, 14(6), 790; https://doi.org/10.3390/agriculture14060790 - 21 May 2024
Abstract
Changes in the hydrologic regime impose great challenges for grain production. We investigated the impact of dry and wet extremes on the recent losses of crops in Severo-Kazakhstanskaya Oblast (SKO), where 25% of Kazakhstan’s wheat is produced. We reconstructed the Palmer Drought Severity
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Changes in the hydrologic regime impose great challenges for grain production. We investigated the impact of dry and wet extremes on the recent losses of crops in Severo-Kazakhstanskaya Oblast (SKO), where 25% of Kazakhstan’s wheat is produced. We reconstructed the Palmer Drought Severity Index (June–August PDSI) and average grain yields (with an explained variance of 48% and 44%, respectively) using five tree ring width chronologies. The extended history of the moisture variability and yields of spring wheat, oats, and barley shows the strong impact of hydrology, rather than the heat, on the grain production. We defined three distinctive hydrologic regimes in SKO: (1) 1886–1942, (2) 1943–1977, (3) 1978–2023. The early regime had fewer drought events, including some that covered a single year. Their duration increased up to 3 years in the second period. The latest regime is an extreme mode of hydrologic variability with events abruptly switching from extremely dry to extremely wet conditions (called “whiplash”). The 21st century regime signifies that the intensified and prolonged decade-long drought transitioned into pluvial condition. The new regime created sizable instability for grain producers. This crop yield reconstruction denotes the potential of the tree-ring proxy for understanding the impact of climate change on the agriculture and food security of Central Asia.
Full article
(This article belongs to the Section Crop Production)
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Open AccessArticle
Assessment of Resistance of Barley Varieties to Diseases in Polish Organic Field Trials
by
Tomasz Lenartowicz, Henryk Bujak, Marcin Przystalski, Inna Mashevska, Kamila Nowosad, Krzysztof Jończyk and Beata Feledyn-Szewczyk
Agriculture 2024, 14(5), 789; https://doi.org/10.3390/agriculture14050789 - 20 May 2024
Abstract
Leaf rust and net blotch are two important fungal diseases of barley. Leaf rust is the most important rust disease of barley, whereas net blotch can result in significant yield losses and cause the deterioration of crop quality. The best and the most
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Leaf rust and net blotch are two important fungal diseases of barley. Leaf rust is the most important rust disease of barley, whereas net blotch can result in significant yield losses and cause the deterioration of crop quality. The best and the most environmentally friendly method to control diseases is to cultivate resistant varieties. The aim of the current study was to identify barley varieties with an improved resistance to leaf rust and net blotch in Polish organic post-registration trials conducted in the years 2020–2022. For this purpose, the cumulative link mixed model with several variance components was applied to model resistance to leaf rust and net blotch. It was found that the reference variety Radek was the most resistant to leaf rust, whereas variety Avatar outperformed the reference variety in terms of resistance to net blotch, although the difference between the two varieties was non-significant. In the present study, the use of the cumulative link mixed model framework made it possible to calculate cumulative probabilities or the probability of a given score for each variety and disease, which might be useful for plant breeders and crop experts. Both, the method of analysis and resistant varieties may be used in the breeding process to derive new resistant varieties suitable for the organic farming system.
Full article
(This article belongs to the Section Crop Protection, Diseases, Pests and Weeds)
Open AccessArticle
A Standardized Treatment Model for Head Loss of Farmland Filters Based on Interaction Factors
by
Zhenji Liu, Chenyu Lei, Jie Li, Yangjuan Long and Chen Lu
Agriculture 2024, 14(5), 788; https://doi.org/10.3390/agriculture14050788 - 20 May 2024
Abstract
A head loss model for pressureless mesh filters used in farmland irrigation was developed by integrating the four basic test factors: irrigation flow, filter cartridge speed, self-cleaning flow, and initial sand content. The model’s coefficient of determination was found to be 98.61%. Among
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A head loss model for pressureless mesh filters used in farmland irrigation was developed by integrating the four basic test factors: irrigation flow, filter cartridge speed, self-cleaning flow, and initial sand content. The model’s coefficient of determination was found to be 98.61%. Among the basic factors, the total irrigation flow accounted for only 17.20% of the relatively small self-cleaning flow. The contribution of initial sand content was found to be the smallest, with a coefficient of only 0.0166. Furthermore, the contribution rate of the flow term was significantly higher than that of the initial sand content, with a value of 159.73%. In terms of quadratic interaction, the difference between the interaction term of flushing flow and filter cartridge speed, and the interaction term of filter cartridge speed and self-cleaning flow was 38.42%. On the other hand, the difference within this level for the interaction term between initial sand content and filter cartridge speed, as well as the interaction term between irrigation flow and self-cleaning flow, was 2.82%. Finally, through joint optimization of the response surface and model, the optimal values for the irrigation flow rate, filter cartridge speed, self-cleaning flow rate, and initial sand content were determined to be 121.687 m3·h−1, 1.331 r·min−1, 19.980 m3·h−1, and 0.261 g·L−1; the measured minimum head loss was found to be 21.671 kPa. These research findings can serve as a reference for enhancing the design of farmland filters and optimizing irrigation systems.
Full article
(This article belongs to the Special Issue Sustainable Water-Resource Strategies in Agriculture for Climate Change Adaptation)
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Open AccessArticle
Online Detection of Dry Matter in Potatoes Based on Visible Near-Infrared Transmission Spectroscopy Combined with 1D-CNN
by
Yalin Guo, Lina Zhang, Zhenlong Li, Yakai He, Chengxu Lv, Yongnan Chen, Huangzhen Lv and Zhilong Du
Agriculture 2024, 14(5), 787; https://doi.org/10.3390/agriculture14050787 - 20 May 2024
Abstract
More efficient resource utilization and increased crop utilization rate are needed to address the growing demand for food. The efficient quality testing of key agricultural products such as potatoes, especially the rapid testing of key nutritional indicators, has become an important strategy for
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More efficient resource utilization and increased crop utilization rate are needed to address the growing demand for food. The efficient quality testing of key agricultural products such as potatoes, especially the rapid testing of key nutritional indicators, has become an important strategy for ensuring their quality and safety. In this study, visible and near infrared (Vis/NIR) transmittance spectroscopy (600–900 nm) was used for the online analysis of multiple quality parameters in potatoes. The study concentrated on comparing three one-dimensional convolutional neural network (1D-CNN) models, specifically, the fine-tuned DeepSpectra, the fine-tuned 1D-AlexNet, and classic CNN, with UVE-PLS (uninformative variable elimination–partial least squares) models. These models utilized spectral data for the real-time detection of dry matter (DM) content in potatoes. To address the challenges posed by limited data from Vis/NIR, this study strategically implemented data augmentation techniques. This approach significantly enhanced the robustness and generalization capabilities of the models. The 1D-AlexNet and DeepSpectra models achieved 0.934 and 0.913 R2P and 0.0603 and 0.0695 g/100 g RMSEP for DM, respectively. Compared to UVE-PLS, the R2P value improved by 21.31% (0.770 to 0.934) for the 1D-AlexNet model and 18.64% (0.770 to 0.913) for the DeepSpectra model. The RMSEP value was reduced by 47.31% (0.114 to 0.0603) for 1D-AlexNet, and 39.30% (0.114 to 0.0695) for the DeepSpectra model. As a result, this study would be helpful for researching the online Vis/NIR transmission determination of potato DM using deep learning. These results highlighted the immense potential of employing specific spectral features in deep-learning models for a more precise and efficient online assessment of agricultural quality. This advancement provided some insight and reference for further contributing to the evolution of more targeted and efficient quality assessment methods in agricultural products.
Full article
(This article belongs to the Special Issue Application of Spectroscopy and Sensor Technology in Agricultural Products—Series II)
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Open AccessArticle
Optimizing Active Disturbance Rejection Control for a Stubble Breaking and Obstacle Avoiding Control System
by
Huibin Zhu, Tao Huang, Lizhen Bai and Wenkai Zhang
Agriculture 2024, 14(5), 786; https://doi.org/10.3390/agriculture14050786 - 20 May 2024
Abstract
In order to improve the obstacle avoidance control performance and anti-interference ability of a stubble breaking device of a no-tillage planter, a back-propagation neural network (BPNN)-optimized fuzzy active disturbance rejection control (ADRC) controller was designed to optimize the control performance of a servo
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In order to improve the obstacle avoidance control performance and anti-interference ability of a stubble breaking device of a no-tillage planter, a back-propagation neural network (BPNN)-optimized fuzzy active disturbance rejection control (ADRC) controller was designed to optimize the control performance of a servo motor. Firstly, a negative feedback mathematical model was established for the obstacle avoidance control system. Then, the nonlinear state error feedback (NLSEF) parameters in the fuzzy ADRC were intelligently optimized by the BPNN algorithm. In this way, a fuzzy ADRC controller based on BPNN optimization was formed to optimize the control process of a servo motor. Matlab/Simulink (R2022b) was used to complete the simulation model design and parameter adjustment. Consequently, the response time was 0.089 s using the BPNN fuzzy ADRC controller, which was shorter than the 0.303 s of the ADRC controller and the 0.100 s of the fuzzy ADRC controller. The overshoot was 0.1% using a BPNN fuzzy ADRC controller, which was less than the 2% of the ADRC controller and the 1% of the fuzzy ADRC controller. After noise signal interference was introduced into the control system, the regression steady state time of the BPNN fuzzy ADRC controller was 0.22 s, which was shorter than the 0.56 s of the ADRC controller and the 0.45 s of the fuzzy ADRC controller. A hardware-in-the-loop simulation experimental platform of the obstacle avoidance control system was constructed. The experiment results show that the servo motor control system has a fast dynamic response, small steady-state error and strong anti-interference ability for obstacle avoidance at the target height. Then, the control system error was within the allowable range. The servo motor control effect of the BPNN fuzzy ADRC was better than the ADRC and fuzzy ADRC. This optimized servo motor control method can provide a reference for improving the obstacle avoidance control effect problem of no-tillage seeders in stubble breaking operations on rocky desertification areas.
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(This article belongs to the Section Agricultural Technology)
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The Impact of Agricultural Socialized Service on Grain Production: Evidence from Rural China
by
Ruisheng Li, Jiaoyan Chen and Dingde Xu
Agriculture 2024, 14(5), 785; https://doi.org/10.3390/agriculture14050785 - 20 May 2024
Abstract
Although China’s grain production has reached nineteen consecutive harvests, the uncertainty of the current domestic and international environment has put more pressure on further increasing grain production in the future. For the past few years, agricultural socialization services have been crucial in boosting
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Although China’s grain production has reached nineteen consecutive harvests, the uncertainty of the current domestic and international environment has put more pressure on further increasing grain production in the future. For the past few years, agricultural socialization services have been crucial in boosting grain production and farmers’ revenue by addressing the issue of land cultivation and farming methods. In this regard, the question of whether and how agricultural socialized services may resolve the present grain production conundrum is extremely practical. Therefore, the study employs the China Rural Revitalization Survey data of 3709 households. Based on the 2SLS model, stepwise regression method, and moderated effects model, it creatively takes into account a variety of agricultural production segments, investigates the mechanism of services on grain production from the standpoint of improved production efficiency and plot concentration, and further examines the effects of aging populations and regional variations in grain production areas. The study found the following: (1) The average proportion of grain production area of farmers in the sample is 49%, and 42% of farmers have purchased agricultural socialization services. (2) Agricultural socialization services can significantly promote farmers’ grain cultivation behavior by facilitating connected transfers in and inhibiting connected transfers out to take advantage of plot concentration, and boosting the use of agricultural machines to enhance output efficiency. (3) The aging of the agricultural population will, to a certain extent, strengthen the promoting effect of agricultural socialization services on grain cultivation. Agricultural socialization services affect grain cultivation more in main grain-producing areas. Therefore, emphasizing the role of agricultural socialization services in accelerating the shift to moderate-scale operations, decreasing the non-grain component of the planting structure, and promoting the implementation of policies tailored to actual production needs are important steps to safeguard the production capacity of grain in different regions.
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(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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Simultaneous Localization and Mapping System for Agricultural Yield Estimation Based on Improved VINS-RGBD: A Case Study of a Strawberry Field
by
Quanbo Yuan, Penggang Wang, Wei Luo, Yongxu Zhou, Hongce Chen and Zhaopeng Meng
Agriculture 2024, 14(5), 784; https://doi.org/10.3390/agriculture14050784 - 19 May 2024
Abstract
Crop yield estimation plays a crucial role in agricultural production planning and risk management. Utilizing simultaneous localization and mapping (SLAM) technology for the three-dimensional reconstruction of crops allows for an intuitive understanding of their growth status and facilitates yield estimation. Therefore, this paper
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Crop yield estimation plays a crucial role in agricultural production planning and risk management. Utilizing simultaneous localization and mapping (SLAM) technology for the three-dimensional reconstruction of crops allows for an intuitive understanding of their growth status and facilitates yield estimation. Therefore, this paper proposes a VINS-RGBD system incorporating a semantic segmentation module to enrich the information representation of a 3D reconstruction map. Additionally, image matching using L_SuperPoint feature points is employed to achieve higher localization accuracy and obtain better map quality. Moreover, Voxblox is proposed for storing and representing the maps, which facilitates the storage of large-scale maps. Furthermore, yield estimation is conducted using conditional filtering and RANSAC spherical fitting. The results show that the proposed system achieves an average relative error of 10.87% in yield estimation. The semantic segmentation accuracy of the system reaches 73.2% mIoU, and it can save an average of 96.91% memory for point cloud map storage. Localization accuracy tests on public datasets demonstrate that, compared to Shi–Tomasi corner points, using L_SuperPoint feature points reduces the average ATE by 1.933 and the average RPE by 0.042. Through field experiments and evaluations in a strawberry field, the proposed system demonstrates reliability in yield estimation, providing guidance and support for agricultural production planning and risk management.
Full article
(This article belongs to the Topic Current Research on Intelligent Equipment for Agriculture)
Open AccessArticle
Research on Rapeseed Seedling Counting Based on an Improved Density Estimation Method
by
Qi Wang, Chunpeng Li, Lili Huang, Liqing Chen, Quan Zheng and Lichao Liu
Agriculture 2024, 14(5), 783; https://doi.org/10.3390/agriculture14050783 - 19 May 2024
Abstract
The identification of seedling numbers is directly related to the acquisition of seedling information, such as survival rate and emergence rate. It indirectly affects detection efficiency and yield evaluation. Manual counting methods are time-consuming and laborious, and the accuracy is not high in
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The identification of seedling numbers is directly related to the acquisition of seedling information, such as survival rate and emergence rate. It indirectly affects detection efficiency and yield evaluation. Manual counting methods are time-consuming and laborious, and the accuracy is not high in complex backgrounds or high-density environments. It is challenging to achieve improved results using traditional target detection methods and improved methods. Therefore, this paper adopted the density estimation method and improved the population density counting network to obtain the rapeseed seedling counting network named BCNet. BCNet uses spatial attention and channel attention modules and enhances feature information and concatenation to improve the expressiveness of the entire feature map. In addition, BCNet uses a 1 × 1 convolutional layer for additional feature extraction and introduces the torch.abs function at the network output port. In this study, distribution experiments and seedling prediction were conducted. The results indicate that BCNet exhibits the smallest counting error compared to the CSRNet and the Bayesian algorithm. The MAE and MSE reach 3.40 and 4.99, respectively, with the highest counting accuracy. The distribution experiment and seedling prediction showed that, compared with the other density maps, the density response points corresponding to the characteristics of the seedling region were more prominent. The predicted number of the BCNet algorithm was closer to the actual number, verifying the feasibility of the improved method. This could provide a reference for the identification and counting of rapeseed seedlings.
Full article
(This article belongs to the Section Agricultural Technology)
Open AccessArticle
Unraveling the Major Determinants behind Price Changes in Four Selected Representative Agricultural Products
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Nisa Sansel Tandogan Aktepe and İhsan Erdem Kayral
Agriculture 2024, 14(5), 782; https://doi.org/10.3390/agriculture14050782 - 19 May 2024
Abstract
This study aims to analyze the drivers behind price changes in agricultural products in Türkiye from 2002 to 2021, considering the impacts of three crises of different causes which are the global food crisis, the Russia–Türkiye aircraft crisis, and the COVID-19 pandemic. The
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This study aims to analyze the drivers behind price changes in agricultural products in Türkiye from 2002 to 2021, considering the impacts of three crises of different causes which are the global food crisis, the Russia–Türkiye aircraft crisis, and the COVID-19 pandemic. The potential factors are categorized into four subgroups: governmental effects, agricultural inputs, macroeconomic indicators, and climatic conditions. The selected agricultural goods for price change measurement include wheat and maize representing subsistence goods, and olive oil and cotton as marketing goods. The autoregressive distributed lag (ARDL) model is applied to observe both the short- and long-term impacts of the variables on price developments. The results suggest that government effectiveness, regulatory quality, nitrogen use, water price, money supply, exchange rate, and GDP under the related categories are the most effective factors in price changes. Among the variables under the category of climatic conditions, significant values are obtained only in the analysis of the temperature impact on olive oil. The analysis also reveals the variable impact of crises on the prices of the chosen products, depending on the goods involved. The maize and wheat analyses yield particularly noteworthy results. In the long run, nitrogen use demonstrates a substantial positive impact, registering at 29% for wheat and 19.47% for maize, respectively. Conversely, GDP exhibits a significant negative impact, with 26.15% and 20.08%. Short-term observations reveal that a unit increase in the governmental effect leads to a reduction in inflation for these products by 17.01% and 21.42%. However, changes in regulatory quality result in an increase in inflation by 25.45% and 20.77% for these products, respectively.
Full article
(This article belongs to the Special Issue Globalisation, Regionalisation, Market Integration and Price Analysis of Agricultural Products)
Open AccessArticle
Coupling Coordination between Agricultural Eco-Efficiency and Urbanization in China Considering Food Security
by
Xiuli He and Wenxin Liu
Agriculture 2024, 14(5), 781; https://doi.org/10.3390/agriculture14050781 - 18 May 2024
Abstract
When studying the coupling coordination relationship between agricultural eco-efficiency and urbanization, it is crucial to consider food security, especially in a populous country like China. This paper focuses on 31 provinces in China as the research units, covering the time period from 2000
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When studying the coupling coordination relationship between agricultural eco-efficiency and urbanization, it is crucial to consider food security, especially in a populous country like China. This paper focuses on 31 provinces in China as the research units, covering the time period from 2000 to 2020. Based on the concept of agricultural eco-efficiency, an evaluation index system was developed to include undesirable outputs (carbon emissions), and agricultural eco-efficiency scores were calculated using the SBM–DEA model. An urbanization evaluation index system, covering six dimensions and twelve indexes, was constructed. A comprehensive index of urbanization is measured using the entropy method. On this basis, a coupling coordination model was applied to quantify the relationship between agricultural eco-efficiency and urbanization at the provincial scale in China. The results showed that the agricultural eco-efficiency of all provincial units in China exhibited an overall trend of improvement. Average efficiency followed a spatial pattern of majority grain-consuming areas > grain production–consumption balance areas > majority grain-producing areas. The level of coupling between agricultural eco-efficiency and urbanization is generally low. Currently, no regions have reached the stage of synergy or high-level coupling. Most regions are currently in an antagonistic stage with a coupling degree of 0.3 < C ≤ 0.5. The classification of coupling coordination levels changed from four levels of “severe imbalance”, “moderate imbalance”, “mild imbalance”, and “primary coordination” to “moderate imbalance”, “mild imbalance”, “primary coordination”, and “intermediate coordination”. The level of “severe imbalance” disappeared, the level of “intermediate coordination” appeared, and the level of “mild imbalance” became the largest scale level. From the perspective of food security, the proportion of grain production in the categories of “primary coordination” and “intermediate coordination” was less than 10%, and these provinces never achieved self-sufficiency in food production. The proportion of grain production at the “mild imbalance” level reached 62.4%, while the per capita grain production at the “moderate imbalance” level reached 846.7 kg. Provinces with lower levels of coupling coordination have stronger food security capabilities. It can be observed that the weaker the coupling coordination between agricultural eco-efficiency and urbanization, the higher the food self-sufficiency. Based on the research results above, we discussed strategies to enhance agricultural eco-efficiency in majority grain-producing regions by focusing on technological progress and technical efficiency. Additionally, we analyzed approaches to achieve grain self-sufficiency in regions characterized by a high level of coordination between agricultural eco-efficiency and urbanization, considering both production and trade dimensions.
Full article
(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
Open AccessArticle
Optimization and Prediction of Operational Parameters for Enhanced Efficiency of a Chickpea Peeling Machine
by
Khaled Abdeen Mousa Ali, Sheng Tao Li, Changyou Li, Elwan Ali Darwish, Han Wang, Taha Abdelfattah Mohammed Abdelwahab, Ahmed Elsayed Mahmoud Fodah and Youssef Fayez Elsaadawi
Agriculture 2024, 14(5), 780; https://doi.org/10.3390/agriculture14050780 - 18 May 2024
Abstract
Chickpeas hold significant nutritional and cultural importance, being a rich source of protein, fiber, and essential vitamins and minerals. They are a staple ingredient in various cuisines worldwide. Peeling chickpeas is considered a crucial pre-consumption operation due to the undesirability of peels for
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Chickpeas hold significant nutritional and cultural importance, being a rich source of protein, fiber, and essential vitamins and minerals. They are a staple ingredient in various cuisines worldwide. Peeling chickpeas is considered a crucial pre-consumption operation due to the undesirability of peels for some uses. This study aimed to design, test, and evaluate a small chickpea seed peeling machine. The peeling prototype was designed in accordance with the chickpeas’ measured properties; the seeds’ moisture content was determined to be 6.96% (d.b.). The prototype was examined under four different levels of drum revolving speeds (100, 200, 300, and 400 rpm), and three different numbers of brush peeling rows. The prototype was tested with rotors of four, eight, and twelve rows of brushes. The evaluation of the chickpea peeling machine encompassed several parameters, including the machine’s throughput (kg/h), energy consumption (kW), broken seeds percentage (%), unpeeled seeds percentage (%), and peeling efficiency (%). The obtained results revealed that the peeling machine throughput (kg/h) exhibited an upward trend with increases in the rotation speed of the peeling drum. Meanwhile, the throughput decreased as the number of peeling brushes installed on the roller increased. The highest recorded productivity of 71.29 kg/h was achieved under the operational condition of 400 rpm and four peeling brush rows. At the same time, the peeling efficiency increased with the increase in both of peeling drum rotational speed and number of peeling brush rows. The highest peeling efficiency (97.2%) was recorded at the rotational speed of 400 rpm and twelve peeling brush rows. On the other hand, the lowest peeling efficiency (92.85%) was recorded at the lowest drum rotational speed (100 rpm) and number of peeling brush rows (4 rows). In the optimal operational condition, the machines achieved a throughput of 71.29 kg/h, resulting in a peeling cost of 0.001 USD per kilogram. This small-scale chickpea peeling machine is a suitable selection for small and medium producers.
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(This article belongs to the Section Agricultural Technology)
Open AccessArticle
Research and Experiment on Airflow Field Control Technology of Harvester Cleaning System Based on Load Distribution
by
Duanxin Li, Qinghao He, Dong Yue, Duanyang Geng, Jianning Yin, Pengxuan Guan and Zehao Zha
Agriculture 2024, 14(5), 779; https://doi.org/10.3390/agriculture14050779 - 18 May 2024
Abstract
The wind sieve cleaner is widely used in the screening system of combine harvesters due to its compact structure and efficient screening capability. In order to study more deeply the feeding load distribution of the combine harvester and the influence of the airflow
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The wind sieve cleaner is widely used in the screening system of combine harvesters due to its compact structure and efficient screening capability. In order to study more deeply the feeding load distribution of the combine harvester and the influence of the airflow field on the clearing effect, a mechanical analysis method was adopted to analyze the dynamics of the material in the inclined airflow, and a kinetic model was established. At the same time, the motion state of the material in the airflow field was explored, and combined with the actual orthogonal test, the response surface model of factors and indicators was established. Experimental validation was carried out. It provides an important research foundation and theoretical basis for optimizing the structural parameters of the screening system and improving its operational performance.
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(This article belongs to the Section Agricultural Technology)
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Dust and Bacterial Air Contamination in a Broiler House in Summer and Winter
by
Ivica Ravić, Mario Ostović, Anamaria Ekert Kabalin, Matija Kovačić, Kristina Matković, Željko Gottstein and Danijela Horvatek Tomić
Agriculture 2024, 14(5), 778; https://doi.org/10.3390/agriculture14050778 - 18 May 2024
Abstract
This study aimed to investigate dust and bacterial air contamination in a broiler house during different seasons. The study was carried out in commercial housing conditions during five weeks of the rearing cycle in summer and winter. The total dust concentration ranged from
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This study aimed to investigate dust and bacterial air contamination in a broiler house during different seasons. The study was carried out in commercial housing conditions during five weeks of the rearing cycle in summer and winter. The total dust concentration ranged from 1.90 to 4.50 mg/m3 in summer and from 2.80 to 5.10 mg/m3 in winter. The total bacterial count ranged from 2.85 × 104 to 1.03 × 105 CFU/m3 in summer and from 2.12 × 104 to 2.28 × 105 CFU/m3 in winter. The study results showed the dust concentration to be increased in winter as compared to summer, yielding a significant correlation (r = 0.602, p < 0.05) with a significantly higher airborne bacterial count in winter (p < 0.001). Furthermore, dust concentration showed significant correlations (p < 0.05) with air temperature (r = −0.418), relative humidity (r = 0.673), and broiler activity (r = 0.709), while bacterial count yielded significant correlations (p < 0.05) with air temperature (r = −0.756), relative humidity (r = 0.831), and airflow rate (r = 0.511). The results obtained in the study can prove useful in the field. Seasonal variability in dust and bacterial air contamination should be considered in the development of guidelines or standards of air quality in broiler housing and evaluation of the effectiveness of remedial strategies.
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(This article belongs to the Special Issue The Influence of Environmental Factors on Farming Animals)
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Open AccessArticle
Effects of Unbalanced Incentives on Threshing Drum Stability during Rice Threshing
by
Kexin Que, Zhong Tang, Ting Wang, Zhan Su and Zhao Ding
Agriculture 2024, 14(5), 777; https://doi.org/10.3390/agriculture14050777 - 17 May 2024
Abstract
As a result of the uneven growth of rice, unbalanced vibration of threshing drum caused by stalk entanglement in combine harvester is more and more severe. In order to reveal the influence of unbalanced excitation on the roller axis locus during rice threshing,
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As a result of the uneven growth of rice, unbalanced vibration of threshing drum caused by stalk entanglement in combine harvester is more and more severe. In order to reveal the influence of unbalanced excitation on the roller axis locus during rice threshing, the stability of threshing drum was studied. The dynamic signal test and analysis system are used to test the axial trajectory of threshing drum. At the same time, the influence of the unbalanced excitation caused by the axis winding on the axis trajectory is analyzed by the experimental results. Axis locus rules under no-load and threshing conditions are obtained. In order to simulate the axial and radial distribution of unbalanced excitation along the threshing drum, the counterweight was distributed on the threshing drum instead of the entangled stalk. Then, the definite effect of unbalanced excitation on the rotating stability of threshing drum is analyzed. Results show that the amplitude of stem winding along the grain drum is larger in the vertical direction and smaller in the horizontal direction when compared with the unloaded state under 200 g weight. It was found that the amplitude in both horizontal and vertical directions decreased after 400 g and 600 g counterweights were added, respectively, to simulate the radial distribution of stalk winding along the grain barrel. Finally, it can be seen that with the increase in the weight of the counterweight, the characteristics of the trajectory misalignment of the threshing cylinder axis become more and more obvious. This study can provide reference for reducing the unbalanced excitation signal of threshing drum and improving driving comfort.
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(This article belongs to the Section Agricultural Technology)
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Open AccessArticle
Preliminary Results of the Impact of Beneficial Soil Microorganisms on Okra Plants and Their Polyphenol Components
by
Alaa Abdulkadhim A. Almuslimawi, Lívia László, Alhassani Leith Sahad, Ahmed Ibrahim Alrashid Yousif, György Turóczi and Katalin Posta
Agriculture 2024, 14(5), 776; https://doi.org/10.3390/agriculture14050776 - 17 May 2024
Abstract
Okra (Abelmoschus esculentus L.) is a highly nutritious vegetable rich in vitamins, minerals, and bioactive compounds, including polyphenols, offering numerous health benefits. Despite its nutritional value, okra remains underutilized in Europe; however, its cultivation and popularity may rise in the future with
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Okra (Abelmoschus esculentus L.) is a highly nutritious vegetable rich in vitamins, minerals, and bioactive compounds, including polyphenols, offering numerous health benefits. Despite its nutritional value, okra remains underutilized in Europe; however, its cultivation and popularity may rise in the future with increasing awareness of its advantages. In agricultural practices, beneficial soil microorganisms, such as arbuscular mycorrhizal fungi (AMF), Trichoderma spp., Streptomyces spp., and Aureobasidium spp., play crucial roles in promoting plant health, enhancing agricultural productivity together with improved crop nutritional value. This study aimed to investigate the effects of individual and combined inoculation on the polyphenol content of okra fruits, as analyzed by HPLC. Moreover, growth parameters and glutathione-S-transferase enzyme (GST) activities of okra leaves were also estimated. Tested microorganisms significantly increased the yield of okra plants except for A. pullulans strain DSM 14950 applied individually. All microorganisms led to increased GST enzyme activity of leaves, suggesting a general response to biotic impacts, with individual inoculation showing higher enzyme activity globally compared to combined treatments. According to the polyphenol compound analysis, the application of tested microorganisms held various but generally positive effects on it. Only the combined treatment of F. mosseae and Streptomyces strain K61 significantly increased the coumaric acid content, and the application of Aureobasidium strain DSM 14950 had a positive influence on the levels of quercetin and quercetin-3-diglucoside. Our preliminary results show how distinct polyphenolic compound contents can be selectively altered via precise inoculation with different beneficial microorganisms.
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(This article belongs to the Special Issue Beneficial Microbes for Sustainable Crop Production)
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The Influence and Mechanism of Digital Village Construction on the Urban–Rural Income Gap under the Goal of Common Prosperity
by
Muziyun Liu and Hui Liu
Agriculture 2024, 14(5), 775; https://doi.org/10.3390/agriculture14050775 - 17 May 2024
Abstract
Digital village construction is not only a vital component of the digital China strategy but also a crucial measure by which to realize common prosperity. This study theoretically elaborates the influence of digital village construction on the urban–rural income gap (URIG) and its
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Digital village construction is not only a vital component of the digital China strategy but also a crucial measure by which to realize common prosperity. This study theoretically elaborates the influence of digital village construction on the urban–rural income gap (URIG) and its mechanism and empirically tests it by using a panel fixed-effect model, a mediating-effect model, and a moderating-effect model based on the provincial data of major producing areas from 2011 to 2020. The results show that digital village construction can significantly narrow the URIG, and rural industry revitalization is a vital channel for digital village construction in driving the decline of the URIG. The construction of transportation infrastructure can significantly enhance the inhibition effect of digital village construction on the URIG. Moreover, there is a human capital threshold for the impact of digital village construction on the URIG; after crossing the threshold, digital village construction better suppresses the URIG. So, the government should increase the financial support and technical support for digital village construction, improving the rural production conditions and industrial development environment and establishing a rural digital talent cultivation mechanism so as to achieve the goal of common prosperity.
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(This article belongs to the Section Agricultural Economics, Policies and Rural Management)
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Lightweight-Improved YOLOv5s Model for Grape Fruit and Stem Recognition
by
Junhong Zhao, Xingzhi Yao, Yu Wang, Zhenfeng Yi, Yuming Xie and Xingxing Zhou
Agriculture 2024, 14(5), 774; https://doi.org/10.3390/agriculture14050774 - 17 May 2024
Abstract
Mechanized harvesting is the key technology to solving the high cost and low efficiency of manual harvesting, and the key to realizing mechanized harvesting lies in the accurate and fast identification and localization of targets. In this paper, a lightweight YOLOv5s model is
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Mechanized harvesting is the key technology to solving the high cost and low efficiency of manual harvesting, and the key to realizing mechanized harvesting lies in the accurate and fast identification and localization of targets. In this paper, a lightweight YOLOv5s model is improved for efficiently identifying grape fruits and stems. On the one hand, it improves the CSP module in YOLOv5s using the Ghost module, reducing model parameters through ghost feature maps and cost-effective linear operations. On the other hand, it replaces traditional convolutions with deep convolutions to further reduce the model’s computational load. The model is trained on datasets under different environments (normal light, low light, strong light, noise) to enhance the model’s generalization and robustness. The model is applied to the recognition of grape fruits and stems, and the experimental results show that the overall accuracy, recall rate, mAP, and F1 score of the model are 96.8%, 97.7%, 98.6%, and 97.2% respectively. The average detection time on a GPU is 4.5 ms, with a frame rate of 221 FPS, and the weight size generated during training is 5.8 MB. Compared to the original YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x models under the specific orchard environment of a grape greenhouse, the proposed model improves accuracy by 1%, decreases the recall rate by 0.2%, increases the F1 score by 0.4%, and maintains the same mAP. In terms of weight size, it is reduced by 61.1% compared to the original model, and is only 1.8% and 5.5% of the Faster-RCNN and SSD models, respectively. The FPS is increased by 43.5% compared to the original model, and is 11.05 times and 8.84 times that of the Faster-RCNN and SSD models, respectively. On a CPU, the average detection time is 23.9 ms, with a frame rate of 41.9 FPS, representing a 31% improvement over the original model. The test results demonstrate that the lightweight-improved YOLOv5s model proposed in the study, while maintaining accuracy, significantly reduces the model size, enhances recognition speed, and can provide fast and accurate identification and localization for robotic harvesting.
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(This article belongs to the Section Agricultural Technology)
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Soil-Specific Calibration Using Plate Compression Filling Technique and Monitoring Soil Biomass Degradation Based on Dielectric Properties
by
Hongjun Chen, Muhammad Awais, Linze Li, Wei Zhang, Mukhtar Iderawumi Abdulraheem, Yani Xiong, Vijaya Raghavan and Jiandong Hu
Agriculture 2024, 14(5), 773; https://doi.org/10.3390/agriculture14050773 - 17 May 2024
Abstract
Accurate estimation of soil water content (SWC) is crucial for effective irrigation management and maximizing crop yields. Although dielectric property-based SWC measurements are widely used, their accuracy is still affected by soil variability, soil–sensor contact, and other factors, making the development of convenient
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Accurate estimation of soil water content (SWC) is crucial for effective irrigation management and maximizing crop yields. Although dielectric property-based SWC measurements are widely used, their accuracy is still affected by soil variability, soil–sensor contact, and other factors, making the development of convenient and accurate soil-specific calibration methods a major challenge. This study aims to propose a plate compression filling technique for soil-specific calibrations and to monitor the extent of soil biomass degradation using dielectric properties. Before and after biodegradation, dielectric measurements of quartz sand and silt loam were made at seven different water contents with three different filling techniques. A third-order polynomial fitting equation explaining the dependence of the dielectric constant on the volumetric water content was obtained using the least-squares method. The suggested plate compression filling method has a maximum mean bias error (MBE) of less than 0.5%, according to experimental results. Depending on the water content, silt loam’s dielectric characteristics change significantly before and after biodegradation. The best water content, measured in gravimetric units, to encourage the decomposition of biomass was discovered to be 24%. It has been demonstrated that the plate compression filling method serves as a simple, convenient, and accurate alternative to the uniform compaction method, while the dielectric method is a reliable indicator for evaluating biomass degradation. This exploration provides valuable insights into the complex relationship between SWC, biomass degradation, and soil dielectric properties.
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(This article belongs to the Section Agricultural Soils)
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