Agriculture最新文献

{"title":"Optimization and Prediction of Operational Parameters for Enhanced Efficiency of a Chickpea Peeling Machine","authors":"Khaled Abdeen Mousa Ali, Sheng Tao Li, Changyou Li, E. A. Darwish, Han Wang, T. Abdelwahab, A. E. M. Fodah, Y. F. Elsaadawi","doi":"10.3390/agriculture14050780","DOIUrl":"https://doi.org/10.3390/agriculture14050780","url":null,"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 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.","PeriodicalId":503580,"journal":{"name":"Agriculture","volume":"119 50","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141125200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research and Experiment on Airflow Field Control Technology of Harvester Cleaning System Based on Load Distribution","authors":"Duanxin Li, Qinghao He, Dong Yue, Duanyang Geng, Jianning Yin, Pengxuan Guan, Zehao Zha","doi":"10.3390/agriculture14050779","DOIUrl":"https://doi.org/10.3390/agriculture14050779","url":null,"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 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.","PeriodicalId":503580,"journal":{"name":"Agriculture","volume":"104 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141124872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of the Club Convergence and Driving Factors of China’s Green Agricultural Development Levels","authors":"Silin Chen, Xiangyu Guo","doi":"10.3390/agriculture14040553","DOIUrl":"https://doi.org/10.3390/agriculture14040553","url":null,"abstract":"Green agricultural development opens up a feasible way for China to construct sustainable communities and promote rural revitalization strategies. However, a clear gap on the subject sets apart various regions in China, resulting in off-balance development. This paper, based on the panel data from 31 provinces (municipalities and autonomous regions) in China from 2013 to 2020, established an evaluation index system for China’s green agricultural development levels, measured the green agricultural development in different regions, and applied the log(t) model, the Ordered Probit model, and the Ordered Logit model to identify convergence trends and causes of China’s green agricultural development level club convergence, respectively. Our research found the following: (1) The green agricultural development levels in various regions in China are on the rise, and regional gaps are still significant. The output of green agricultural products is the focus of various regions in promoting green agricultural development. (2) The green agricultural development levels in China’s 31 provinces (municipalities and autonomous regions) have converged to four clubs. China is gradually forming a convergence pattern of green agricultural development levels, with the main agricultural production areas as the core. (3) The regional gap at the level of environmental regulation for green agricultural development first narrows, then expands, and then narrows. The intensity of environmental regulation in most areas is in the first stage. This indicates that China should continue to maintain the current intensity and promote green agricultural development levels to converge towards high-level clubs. In addition, the GDP, grain production area, fiscal support for agriculture, and rural human capital can narrow the gaps in green agricultural development levels between regions.","PeriodicalId":503580,"journal":{"name":"Agriculture","volume":"115 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140359977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smart Pig Farming—A Journey Ahead of Vietnam","authors":"Md Sharifuzzaman, H. Mun, K. M. Ampode, Eddiemar B. Lagua, Hae-Rang Park, Young-Hwa Kim, Md Kamrul Hasan, Chul-Ju Yang","doi":"10.3390/agriculture14040555","DOIUrl":"https://doi.org/10.3390/agriculture14040555","url":null,"abstract":"Vietnam heavily relies on pork as its primary source of animal protein. Traditional farming methods, characterized by small-scale operations, dominate the industry. However, challenges such as rising feed costs, disease outbreaks, and market volatility are prompting many farmers to abandon their businesses. Recognizing the pivotal role of the swine sector in both economic development and nutrition, authorities must intervene to prevent its collapse. In developed nations, smart pig farming, utilizing technologies like sensors and cameras for data collection and real-time decision-making, has significantly improved health and productivity. These technologies can detect subtle indicators of animal well-being, enabling prompt intervention. This review aims to analyze the drivers of Vietnam’s swine farming, identify existing production system flaws, and explore innovative precision farming methods worldwide. Embracing precision farming promises to enhance Vietnam’s competitiveness in export markets and bolster consumer confidence. However, reliance solely on expensive foreign technologies may benefit large-scale farms, leaving smaller ones behind. Therefore, fostering local innovation and validating cost-effective solutions will be crucial for the sustainable growth of small- and medium-scale pig farming in Vietnam.","PeriodicalId":503580,"journal":{"name":"Agriculture","volume":"15 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140359576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Quality of Winter Wheat Grain by Different Sowing Strategies and Nitrogen Fertilizer Rates: A Case Study in Northeastern Poland","authors":"Krzysztof Lachutta, K. J. Jankowski","doi":"10.3390/agriculture14040552","DOIUrl":"https://doi.org/10.3390/agriculture14040552","url":null,"abstract":"The present study was undertaken to determine the effect of different sowing strategies and spring nitrogen (N) fertilizer rates on the technological quality of winter wheat (Triticum aestivum L.) grain in terms of its milling quality, protein complex quality, and enzyme activity (falling number). Winter wheat grain for laboratory analyses was produced in a small-area field experiment conducted between 2018 and 2021 in the AES in Bałcyny (53°35′46.4″ N, 19°51′19.5″ E, NE Poland). The experimental variables were (i) sowing date (early: 6 September 2018, 5 September 2019, and 3 September 2020; delayed by 14 days: 17–20 September; and delayed by 28 days: 1–4 October), (ii) sowing density (200, 300, and 400 live grains m−2), and (iii) split application of N fertilizer in spring (40 + 100, 70 + 70, and 100 + 40 kg ha−1) at BBCH stages 22–25 and 30–31, respectively. A sowing delay of 14 and 28 days increased the bulk density (by 1 and 1.5 percent points (%p), respectively), vitreousness (by 3 and 6%p, respectively), and total protein content of grain (by 1% an 2%, respectively). A sowing delay of 14 days increased grain hardness (by 5%), the flour extraction rate (by 1.4%p), and the falling number (by 3%) while also decreasing grain uniformity (by 1.9%p). In turn, a sowing delay of 28 days increased the wet gluten content of grain (+0.5–0.6%p) and improved the quality of the protein complex in the Zeleny sedimentation test (+1.5%). An increase in sowing density from 200 to 300 live grains m−2 led to a decrease in grain uniformity (by 2.6%p), the total protein content (by 1.5%), and the wet gluten content of grain (by 0.7%p). A further increase in sowing density decreased grain vitreousness (by 1.4%p). The grain of winter wheat fertilized with 40 and 100 kg N ha−1 in BBCH stages 22–25 and 30–31, respectively, was characterized by the highest hardness (64.7), vitreousness (93%), flour extraction rate (73.9%), total protein content (134 g kg−1 DM), wet gluten content (36%), and Zeleny sedimentation index (69 mL).","PeriodicalId":503580,"journal":{"name":"Agriculture","volume":"5 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140362759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decomposition and Driving Factors of Total Factor Productivity of Food Crops in the Yellow River Basin, China","authors":"Jianxu Liu, Xiaoqing Li, Yansong Li, Jirakom Sirisrisakulchai, Xuefei Kang, Jiande Cui","doi":"10.3390/agriculture14040547","DOIUrl":"https://doi.org/10.3390/agriculture14040547","url":null,"abstract":"The urgency of enhancing agricultural productivity within the Yellow River Basin cannot be overstated, given its critical role in ensuring food security amidst the challenges posed by climate change, natural disasters, and the increasing demand for food crops. Utilizing panel data from nine provinces within the Yellow River Basin for the period 2001 to 2020, this study examines the temporal characteristics and spatial distribution of Total Factor Productivity (TFP) for key grain crops—namely wheat, corn, and soybean—through the application of the Malmquist index which can be decomposed through the DEA-Malmquist index methodology. The empirical results demonstrate that TFP growth rates for these crops have exhibited significant phase variations, with soybean recording the highest TFP growth rate in the basin. Additionally, this study underscores marked regional disparities in soybean productivity. TFP decomposition reveals that the primary drivers of TFP improvement across these crops are attributed to technical progress, with gains in overall technical efficiency largely due to scale efficiency enhancements, whereas pure technical efficiency has shown limited progress. Regional analysis indicates that Inner Mongolia leads in TFP growth for all crops, while Ningxia, Sichuan, and Shaanxi lag behind in wheat, corn, and soybean. Additionally, our analysis delineates natural disasters as a significant barrier to Total Factor Productivity (TFP), notably obstructing technological advancements in wheat cultivation. The investigation further reveals a positive relationship between regional per capita income and the growth of wheat TFP, in contrast to a negative relationship with the TFP growth of corn and soybeans. Moreover, investing in agriculture, forestry, water management, and road infrastructure supports the growth of wheat TFP, while urbanization levels pose constraints. Conclusively, an uptick in annual rural electricity usage, along with improved per capita postal and telecommunication services, exerts a favorable influence on TFP for corn and soybeans.","PeriodicalId":503580,"journal":{"name":"Agriculture","volume":"55 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140365592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Review of Applications of Remote Sensing towards Sustainable Agriculture in the Northern Savannah Regions of Ghana","authors":"A. Moomen, L. L. Yevugah, Louvis Boakye, Jeff Dacosta Osei, Francis Muthoni","doi":"10.3390/agriculture14040546","DOIUrl":"https://doi.org/10.3390/agriculture14040546","url":null,"abstract":"This paper assesses evidence-based applications of Remote Sensing for Sustainable and Precision Agriculture in the Northern Savanna Regions of Ghana for three decades (1990–2023). During this period, there have been several government policy intervention schemes and pragmatic support actions from development agencies towards improving agriculture in this area with differing level of success. Over the same period, there have been dramatic advances in remote sensing (RS) technologies with tailored applications to sustainable agriculture globally. However, the extent to which intervention schemes have harnessed the incipient potential of RS for achieving sustainable agriculture in the study area is unknown. To the best of our knowledge, no previous study has investigated the synergy between agriculture policy interventions and applications of RS towards optimizing results. Thus, this study used systematic literature review and desk analysis to identify previous and current projects and studies that have applied RS tools and techniques to all aspects of agriculture in the study area. Databases searched include Web of Science, Google Scholar, Scopus, AoJ, and PubMed. To consolidate the gaps identified in the literature, ground-truthing was carried out. From the 26 focused publications found on the subject, only 13 (54%) were found employing RS in various aspects of agriculture observations in the study area. Out of the 13, 5 studies focused on mapping the extents of irrigation areas; 2 mapped the size of crop and pasturelands; 1 focused on soil water and nutrient retention; 1 study focused on crop health monitoring; and another focused on weeds/pest infestations and yield estimation in the study area. On the type of data, only 1 (7%) study used MODIS, 2 (15%) used ASTER image, 1 used Sentinel-2 data, 1 used Planetscope, 1 used IKONOS, 5 used Landsat images, 1 used Unmanned Aerial Vehicles (UAVs) and another 1 used RADAR for mapping and monitoring agriculture activities in the study area. There is no evidence of the use of LiDAR data in the area. These results validate the hypothesis that failing agriculture in the study area is due to a paucity of high-quality spatial data and monitoring to support informed farm decision-making.","PeriodicalId":503580,"journal":{"name":"Agriculture","volume":"18 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140365816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a Modified Method for Measuring the Actual Draft Force Using a Tractor-Attached Dynamometer","authors":"Hyo-Geol Kim, J. Lee, Su-Chul Kim, Jooseon Oh, Sung-Bo Shim","doi":"10.3390/agriculture14040544","DOIUrl":"https://doi.org/10.3390/agriculture14040544","url":null,"abstract":"In this study, crank-locker kinematic equations were used to analyze the three-point hitch behavior when the dynamometer was connected to the work machine. The dynamometer was statically tested with a hydraulic actuator, and the accuracy of the three-way force and the moment was confirmed to be 96–99%. The calibrated dynamometer was put to the test on a real farm field, and data were collected using a data acquisition system. Using the transport pitch correction equation, the collected data can be transformed into more realistic data. International standards were used to determine the point of connection between the tractor, dynamometer, and implement. The results of this study made it possible to accurately measure force and moment, which will have an important role in future agricultural technologies such as autonomous agricultural operation.","PeriodicalId":503580,"journal":{"name":"Agriculture","volume":"55 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140365905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spike Device with Worm Gear Unit for Driving Wheels to Improve the Traction Performance of Compact Tractors on Grass Plots","authors":"R. Abrahám, R. Majdan, K. Kollárová, Z. Tkáč, Eva Matejková, S. Masarovičová, R. Drlička","doi":"10.3390/agriculture14040545","DOIUrl":"https://doi.org/10.3390/agriculture14040545","url":null,"abstract":"In general, energy loss reduction via the interaction of tires with the ground improves tractor traction performance when a drawbar pull is generated. This paper is examines the driving wheels with steel spikes for a tractor equipped with modern radial tires. An improved design of the spike device that allows for the change between an active and inactive position of the spikes is presented. The traction performance of a compact articulated tractor with the spike device was tested on a grass plot with two soil moisture contents (SMC). The highest difference in the drawbar pull in the range from 14.2% to 40.5% and from 17.1% to 36.8% was reached by the spikes in the active position in comparison with the tires without spikes, which were at the slip range from 45% to 5% in the case of the low SMC when the test tractor was in the 3rd and 1st gear. The motion resistance difference between the spikes in the active position and the tires without spikes was 11.8% and 2.5% at the low and medium SMC, respectively. At the low and medium SMC, the highest tractive efficiency of 0.765 (0.721) and 0.757 (0.731) was reached by the spikes in the active position when the test tractor was in the 1st (3rd) gear in comparison with 0.736 (0.7) and 0.723 (0.708) in the case of the tires without spikes. The results indicated that the spike device allowed for the improvement of tractor tractive performance.","PeriodicalId":503580,"journal":{"name":"Agriculture","volume":"47 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140366670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Crop Classification Combining Object-Oriented Method and Random Forest Model Using Unmanned Aerial Vehicle (UAV) Multispectral Image","authors":"Hui Deng, Wenjiang Zhang, Xiaoqian Zheng, Houxi Zhang","doi":"10.3390/agriculture14040548","DOIUrl":"https://doi.org/10.3390/agriculture14040548","url":null,"abstract":"The accurate and timely identification of crops holds paramount significance for effective crop management and yield estimation. Unmanned aerial vehicle (UAV), with their superior spatial and temporal resolution compared to satellite-based remote sensing, offer a novel solution for precise crop identification. In this study, we evaluated a methodology that integrates object-oriented method and random forest (RF) algorithm for crop identification using multispectral UAV images. The process involved a multiscale segmentation algorithm, utilizing the optimal segmentation scale determined by Estimation of Scale Parameter 2 (ESP2). Eight classification schemes (S1–S8) were then developed by incorporating index (INDE), textural (GLCM), and geometric (GEOM) features based on the spectrum (SPEC) features of segmented objects. The best-trained RF model was established through three steps: feature selection, parameter tuning, and model training. Subsequently, we determined the feature importance for different classification schemes and generated a prediction map of vegetation for the entire study area based on the best-trained RF model. Our results revealed that S5 (SPEC + GLCM + INDE) outperformed others, achieving an impressive overall accuracy (OA) and kappa coefficient of 92.76% and 0.92, respectively, whereas S4 (SPEC + GEOM) exhibited the lowest performance. Notably, geometric features negatively impacted classification accuracy, while the other three feature types positively contributed. The accuracy of ginger, luffa, and sweet potato was consistently lower across most schemes, likely due to their unique colors and shapes, posing challenges for effective discrimination based solely on spectrum, index, and texture features. Furthermore, our findings highlighted that the most crucial feature was the INDE feature, followed by SPEC and GLCM, with GEOM being the least significant. For the optimal scheme (S5), the top 20 most important features comprised 10 SPEC, 7 INDE, and 3 GLCM features. In summary, our proposed method, combining object-oriented and RF algorithms based on multispectral UAV images, demonstrated high classification accuracy for crops. This research provides valuable insights for the accurate identification of various crops, serving as a reference for future advancements in agricultural technology and crop management strategies.","PeriodicalId":503580,"journal":{"name":"Agriculture","volume":"72 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140368737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}