Pub Date : 2024-03-30DOI: 10.3390/agriculture14040551
V. Španić, Zvonimir Lalic, Ivica Beraković, Goran Jukić, Ivan Varnica
The wheat grain yields increased in EU from 4.98 t ha−1 to 5.45 t ha−1 in the periods from 2006 to 2014 to from 2015 to 2023. It is hypothesized that changes in specific morphological traits over the years resulted in grain yield increase due to the utilization of new wheat varieties in production. To highlight the current status and changes over time, we evaluated a comprehensive panel of 1322 wheat varieties that included testing of morphological traits of varieties recognized from period from 2006 till 2023. Positive relation of registration year with traits such as seed color, glaucosity of neck of culm, plant height, ear length, scurs and awns length, ear color, and shape of the beak of the lower glume were obtained. The most significant changes over time resulted in a darker color of the seed, decreased area of hairiness of the convex surface of the apical rachis segment, enhanced glaucosity of the neck of the culm and decreased frequency of the plants with recurved flag leaves. It was shown that traits such as the frequency of plants with recurved flag leaves, time of emergence, glaucosity of flag leaves, existence of scurs and awns, and area of the hairiness of the convex surface of the apical rachis segment had significant decreases over time. This research demonstrated the importance of twelve morphological traits in the varietal improvement of grain yield over the time from 2006 to 2023.
{"title":"Morphological Characterization of 1322 Winter Wheat (Triticum aestivum L.) Varieties from EU Referent Collection","authors":"V. Španić, Zvonimir Lalic, Ivica Beraković, Goran Jukić, Ivan Varnica","doi":"10.3390/agriculture14040551","DOIUrl":"https://doi.org/10.3390/agriculture14040551","url":null,"abstract":"The wheat grain yields increased in EU from 4.98 t ha−1 to 5.45 t ha−1 in the periods from 2006 to 2014 to from 2015 to 2023. It is hypothesized that changes in specific morphological traits over the years resulted in grain yield increase due to the utilization of new wheat varieties in production. To highlight the current status and changes over time, we evaluated a comprehensive panel of 1322 wheat varieties that included testing of morphological traits of varieties recognized from period from 2006 till 2023. Positive relation of registration year with traits such as seed color, glaucosity of neck of culm, plant height, ear length, scurs and awns length, ear color, and shape of the beak of the lower glume were obtained. The most significant changes over time resulted in a darker color of the seed, decreased area of hairiness of the convex surface of the apical rachis segment, enhanced glaucosity of the neck of the culm and decreased frequency of the plants with recurved flag leaves. It was shown that traits such as the frequency of plants with recurved flag leaves, time of emergence, glaucosity of flag leaves, existence of scurs and awns, and area of the hairiness of the convex surface of the apical rachis segment had significant decreases over time. This research demonstrated the importance of twelve morphological traits in the varietal improvement of grain yield over the time from 2006 to 2023.","PeriodicalId":503580,"journal":{"name":"Agriculture","volume":"4 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140364555","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}
Pub Date : 2024-03-30DOI: 10.3390/agriculture14040549
M. Iatrou, M. Tziouvalekas, Alexandros Tsitouras, Elefterios Evangelou, C. Noulas, D. Vlachostergios, V. Aschonitis, Georgios Arampatzis, Irene Metaxa, Christos Karydas, P. Tziachris
Storm ‘Daniel’ caused the most severe flood phenomenon that Greece has ever experienced, with thousands of hectares of farmland submerged for days. This led to sediment deposition in the inundated areas, which significantly altered the chemical properties of the soil, as revealed by extensive soil sampling and laboratory analysis. The causal relationships between the soil chemical properties and sediment deposition were extracted using the DirectLiNGAM algorithm. The results of the causality analysis showed that the sediment deposition affected the CaCO3 concentration in the soil. Also, causal relationships were identified between CaCO3 and the available phosphorus (P-Olsen), as well as those between the sediment deposit depth and available manganese. The quantified relationships between the soil variables were then used to generate data using a Multiple Linear Perceptron (MLP) regressor for various levels of deposit depth (0, 5, 10, 15, 20, 25, and 30 cm). Then, linear regression equations were fitted across the different levels of deposit depth to determine the effect of the deposit depth on CaCO3, P, and Mn. The results revealed quadratic equations for CaCO3, P, and Mn as follows: 0.001XCaCO32 + 0.08XCaCO3 + 6.42, 0.004XP2 − 0.26XP + 12.29, and 0.003XMn2 − 0.08XMn + 22.47, respectively. The statistical analysis indicated that corn growing in soils with a sediment over 10 cm requires a 31.8% increase in the P rate to prevent yield decline. Additional notifications regarding cropping strategies in the near future are also discussed.
{"title":"Analyzing the Impact of Storm ‘Daniel’ and Subsequent Flooding on Thessaly’s Soil Chemistry through Causal Inference","authors":"M. Iatrou, M. Tziouvalekas, Alexandros Tsitouras, Elefterios Evangelou, C. Noulas, D. Vlachostergios, V. Aschonitis, Georgios Arampatzis, Irene Metaxa, Christos Karydas, P. Tziachris","doi":"10.3390/agriculture14040549","DOIUrl":"https://doi.org/10.3390/agriculture14040549","url":null,"abstract":"Storm ‘Daniel’ caused the most severe flood phenomenon that Greece has ever experienced, with thousands of hectares of farmland submerged for days. This led to sediment deposition in the inundated areas, which significantly altered the chemical properties of the soil, as revealed by extensive soil sampling and laboratory analysis. The causal relationships between the soil chemical properties and sediment deposition were extracted using the DirectLiNGAM algorithm. The results of the causality analysis showed that the sediment deposition affected the CaCO3 concentration in the soil. Also, causal relationships were identified between CaCO3 and the available phosphorus (P-Olsen), as well as those between the sediment deposit depth and available manganese. The quantified relationships between the soil variables were then used to generate data using a Multiple Linear Perceptron (MLP) regressor for various levels of deposit depth (0, 5, 10, 15, 20, 25, and 30 cm). Then, linear regression equations were fitted across the different levels of deposit depth to determine the effect of the deposit depth on CaCO3, P, and Mn. The results revealed quadratic equations for CaCO3, P, and Mn as follows: 0.001XCaCO32 + 0.08XCaCO3 + 6.42, 0.004XP2 − 0.26XP + 12.29, and 0.003XMn2 − 0.08XMn + 22.47, respectively. The statistical analysis indicated that corn growing in soils with a sediment over 10 cm requires a 31.8% increase in the P rate to prevent yield decline. Additional notifications regarding cropping strategies in the near future are also discussed.","PeriodicalId":503580,"journal":{"name":"Agriculture","volume":"53 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140363599","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}
Pub Date : 2024-03-30DOI: 10.3390/agriculture14040552
Krzysztof Lachutta, K. J. Jankowski
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).
{"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}
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.
{"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}
Pub Date : 2024-03-29DOI: 10.3390/agriculture14040546
A. Moomen, L. L. Yevugah, Louvis Boakye, Jeff Dacosta Osei, Francis Muthoni
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.
{"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}
Pub Date : 2024-03-29DOI: 10.3390/agriculture14040544
Hyo-Geol Kim, J. Lee, Su-Chul Kim, Jooseon Oh, Sung-Bo Shim
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.
{"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}
The technology of plastic film mulching is widely applied in Xinjiang, but it also brings about serious issues of residual film pollution. Currently, the 1MSF-2.0 residual film recovery machine can effectively address the problem. However, it faces challenges such as high overall machine weight and noticeable frame vibrations, which affect the stability of the entire machine operation. The frame, as the installation foundation, needs to bear loads and impact. Therefore, the reliability of the frame is crucial for the stability of the entire machine. Improving the frame’s vibration is of great importance. In response to the significant vibration issues during the operation of the 1MSF-2.0 residual film recovery machine, this paper utilized Workbench 2020 R2 to establish a finite element model of the machine frame and conducted static analysis to obtain strength information, thereby initially understanding the optimization space of the frame. Building upon this, Mechanical was employed to solve the first 14 natural frequencies and mode shapes of the frame, and the accuracy of the theoretical analysis was verified through modal testing. After analyzing the frequency characteristics of external excitation forces, it was found that the fourth-order natural frequency of the frame fell within the frequency range of the excitation force of the shaft of the straw grinder, causing resonance in the frame and necessitating structural optimization. The optimal results indicated that the optimized frame increased in mass by 4.41%, reduced the maximum stress value by 2.56 MPa, and increased the fourth-order natural frequency to 22.7 Hz, avoiding the frequency range of the excitation force of the shaft of the straw grinder, thus improving the resonance issue. This paper provides a reference for optimizing the design of the frame of the residual film recovery machine.
{"title":"Optimized Design for Vibration Reduction in a Residual Film Recovery Machine Frame Based on Modal Analysis","authors":"Xinzhong Wang, Tianyu Hong, Weiquan Fang, Xingye Chen","doi":"10.3390/agriculture14040543","DOIUrl":"https://doi.org/10.3390/agriculture14040543","url":null,"abstract":"The technology of plastic film mulching is widely applied in Xinjiang, but it also brings about serious issues of residual film pollution. Currently, the 1MSF-2.0 residual film recovery machine can effectively address the problem. However, it faces challenges such as high overall machine weight and noticeable frame vibrations, which affect the stability of the entire machine operation. The frame, as the installation foundation, needs to bear loads and impact. Therefore, the reliability of the frame is crucial for the stability of the entire machine. Improving the frame’s vibration is of great importance. In response to the significant vibration issues during the operation of the 1MSF-2.0 residual film recovery machine, this paper utilized Workbench 2020 R2 to establish a finite element model of the machine frame and conducted static analysis to obtain strength information, thereby initially understanding the optimization space of the frame. Building upon this, Mechanical was employed to solve the first 14 natural frequencies and mode shapes of the frame, and the accuracy of the theoretical analysis was verified through modal testing. After analyzing the frequency characteristics of external excitation forces, it was found that the fourth-order natural frequency of the frame fell within the frequency range of the excitation force of the shaft of the straw grinder, causing resonance in the frame and necessitating structural optimization. The optimal results indicated that the optimized frame increased in mass by 4.41%, reduced the maximum stress value by 2.56 MPa, and increased the fourth-order natural frequency to 22.7 Hz, avoiding the frequency range of the excitation force of the shaft of the straw grinder, thus improving the resonance issue. This paper provides a reference for optimizing the design of the frame of the residual film recovery machine.","PeriodicalId":503580,"journal":{"name":"Agriculture","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140365966","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}
Pub Date : 2024-03-29DOI: 10.3390/agriculture14040545
R. Abrahám, R. Majdan, K. Kollárová, Z. Tkáč, Eva Matejková, S. Masarovičová, R. Drlička
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.
{"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}
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.
{"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}
Apiculture is an important industry closely related to the national economy and people’s livelihoods. Beekeepers’ behavior is an important factor affecting the yield, quality, and benefits of apiculture. However, there is a lack of a systematic understanding of the long-term changes in beekeeping decisions made by beekeepers. Using panel data, we analyzed the dynamic trends and related influencing factors of decisions made by beekeeping models, honey source plant selection, and the migration flow space of beekeepers from 2009 to 2020. The results showed that the proportion of the LMB model decreased, while the PAB and SMB models continued to increase, the frequency of utilization of the main nectar source plants for honey collection decreased, and the concentration of migratory flow of beekeeping increased. Behavior of beekeepers from 2009 to 2020 showed a certain degree of spatial contraction, which seriously restricted the effective use of nectar plant resources. Family attributes, economic status, beekeeping models, and disaster conditions directly or indirectly affected beekeepers’ decisions. We propose a series of recommendations to facilitate the transformation and advancement of the Chinese bee industry. This study promotes an understanding of sustainable development of the bee industry in China and other countries worldwide.
{"title":"Beekeeping Behavior of Chinese Beekeepers Shows Spatial Contraction","authors":"Yulu Hou, Zhijun Zhao, Haibin Dong, Jiliang Ma, Yun Gao","doi":"10.3390/agriculture14040540","DOIUrl":"https://doi.org/10.3390/agriculture14040540","url":null,"abstract":"Apiculture is an important industry closely related to the national economy and people’s livelihoods. Beekeepers’ behavior is an important factor affecting the yield, quality, and benefits of apiculture. However, there is a lack of a systematic understanding of the long-term changes in beekeeping decisions made by beekeepers. Using panel data, we analyzed the dynamic trends and related influencing factors of decisions made by beekeeping models, honey source plant selection, and the migration flow space of beekeepers from 2009 to 2020. The results showed that the proportion of the LMB model decreased, while the PAB and SMB models continued to increase, the frequency of utilization of the main nectar source plants for honey collection decreased, and the concentration of migratory flow of beekeeping increased. Behavior of beekeepers from 2009 to 2020 showed a certain degree of spatial contraction, which seriously restricted the effective use of nectar plant resources. Family attributes, economic status, beekeeping models, and disaster conditions directly or indirectly affected beekeepers’ decisions. We propose a series of recommendations to facilitate the transformation and advancement of the Chinese bee industry. This study promotes an understanding of sustainable development of the bee industry in China and other countries worldwide.","PeriodicalId":503580,"journal":{"name":"Agriculture","volume":"48 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140373228","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}
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