HighlightsA discrete element simulation model was used to improve the performance of a corn silage crushing and throwing device.Feed rate, crushing speed, and dial speed were used as the test factors, and the average cutting force and average energy loss were used as the evaluation indexes in orthogonal testing.The order of significance of the factors was crushing speed > feed rate > dial speed for average cutting force and crushing speed > dial speed > feed rate for average energy loss.Abstract. To improve the performance of a corn silage crushing and throwing device and address the problems of low crushing quality and high power consumption, a discrete element simulation model of a corn silage crushing and throwing device and granular straw was established based on discrete element theory using EDEM, a general-purpose CAE software program designed with modern discrete element model technology to simulate and analyze particle processing and production operations. The average cutting force and average energy loss of the particles were the evaluation indexes, and the influence of feed rate, crushing speed, and dial speed on the evaluation indexes was analyzed using single-factor simulation tests. The order of significance was crushing speed > feed rate > dial speed for the average cutting force and crushing speed > dial speed > feed rate for the average energy loss. Using multi-objective optimization, the optimal combination of feed rate, crushing speed, and dial speed was 3.52 kg s-1, 892.06 rpm, and 1502 rpm, respectively. With the optimal parameters, the average cutting force was 58.20 N and the average energy loss was 0.85 J. To verify the feasibility of the EDEM simulation, field tests were conducted using a trial-produced device, with the acceptability of straw crushing and power consumption as the test indicators. During the field tests, the feed rate, crushing speed, and dial speed were set to 3.52 kg s-1, 890 rpm, and 1500 rpm, respectively. The field tests showed that the acceptability of straw crushing and the power consumption were 93.60% and 6.73 kW·h, respectively, with the optimal parameters, which satisfied the corn silage crushing standard and provides a theoretical and scientific basis for the design and optimization of the device. Keywords: Corn silage, Crushing and throwing device, Discrete element simulation, Motion simulation, Multi-objective optimization method.
采用离散元仿真模型对玉米青贮粉碎抛撒装置的性能进行了改进。以进给速度、破碎速度和拨盘速度为试验因素，以平均切削力和平均能量损失为正交试验的评价指标。各因素对平均切削力的影响程度依次为破碎速度>进给速度>拨盘速度，对平均能量损失的影响程度依次为破碎速度>拨盘速度>进给速度。为了提高玉米青贮破碎抛撒装置的性能，解决玉米青贮破碎抛撒装置破碎质量低、能耗大的问题，基于离散元理论，利用现代离散元模型技术设计的通用CAE软件EDEM，建立了玉米青贮破碎抛撒装置和颗粒秸秆的离散元仿真模型。以颗粒的平均切削力和平均能量损失为评价指标，通过单因素模拟试验分析了进给量、破碎速度和拨盘速度对评价指标的影响。平均切削力的显著性顺序为破碎速度>进给速度>拨盘速度，平均能量损失的显著性顺序为破碎速度>拨盘速度>进给速度。采用多目标优化方法，得到给料速度、破碎速度和拨盘速度的最优组合分别为3.52 kg s-1、892.06 rpm和1502 rpm。在优化参数下，平均切割力为58.20 N，平均能量损失为0.85 j。为了验证EDEM模拟的可行性，利用试制装置进行了现场试验，以秸秆破碎可接受性和能耗为试验指标。在现场试验中，给料速度、破碎速度和拨盘速度分别设置为3.52 kg s-1、890 rpm和1500 rpm。现场试验结果表明，秸秆破碎接受度为93.60%，能耗为6.73 kW·h，优化参数满足玉米青贮破碎标准，为装置的设计和优化提供了理论和科学依据。关键词:玉米青贮，破碎抛掷装置，离散元仿真，运动仿真，多目标优化方法
{"title":"Discrete Element-Based Optimization Parameters of an Experimental Corn Silage Crushing and Throwing Device","authors":"Shenghe Bai, Qizhi Yang, K. Niu, Zhao Bo, Liming Zhou, Yanwei Yuan","doi":"10.13031/trans.14463","DOIUrl":"https://doi.org/10.13031/trans.14463","url":null,"abstract":"HighlightsA discrete element simulation model was used to improve the performance of a corn silage crushing and throwing device.Feed rate, crushing speed, and dial speed were used as the test factors, and the average cutting force and average energy loss were used as the evaluation indexes in orthogonal testing.The order of significance of the factors was crushing speed > feed rate > dial speed for average cutting force and crushing speed > dial speed > feed rate for average energy loss.Abstract. To improve the performance of a corn silage crushing and throwing device and address the problems of low crushing quality and high power consumption, a discrete element simulation model of a corn silage crushing and throwing device and granular straw was established based on discrete element theory using EDEM, a general-purpose CAE software program designed with modern discrete element model technology to simulate and analyze particle processing and production operations. The average cutting force and average energy loss of the particles were the evaluation indexes, and the influence of feed rate, crushing speed, and dial speed on the evaluation indexes was analyzed using single-factor simulation tests. The order of significance was crushing speed > feed rate > dial speed for the average cutting force and crushing speed > dial speed > feed rate for the average energy loss. Using multi-objective optimization, the optimal combination of feed rate, crushing speed, and dial speed was 3.52 kg s-1, 892.06 rpm, and 1502 rpm, respectively. With the optimal parameters, the average cutting force was 58.20 N and the average energy loss was 0.85 J. To verify the feasibility of the EDEM simulation, field tests were conducted using a trial-produced device, with the acceptability of straw crushing and power consumption as the test indicators. During the field tests, the feed rate, crushing speed, and dial speed were set to 3.52 kg s-1, 890 rpm, and 1500 rpm, respectively. The field tests showed that the acceptability of straw crushing and the power consumption were 93.60% and 6.73 kW·h, respectively, with the optimal parameters, which satisfied the corn silage crushing standard and provides a theoretical and scientific basis for the design and optimization of the device. Keywords: Corn silage, Crushing and throwing device, Discrete element simulation, Motion simulation, Multi-objective optimization method.","PeriodicalId":23120,"journal":{"name":"Transactions of the ASABE","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91228957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Highlights To improve the performance for precision planting of soybean at high speeds, a guiding groove precision metering device was developed. The seed feeding and clearing processes were analyzed to determine the critical design and operational factors. The effects of critical metering parameters on the meter performance were simulated using the DEM. The metering performance was evaluated using bench tests. ABSTRACT Precision planting is the inevitable trend of agricultural development, and the promotion of precision planting technology is the key to increase crop yield. To improve the performance of precision planting at high speeds, a mechanical-type precision metering device was developed for soybean. The innovative feature of the device was the guiding-groove (GG) that provided “waiting areas” for seeds to form a line and subsequently enter the seed cells in an orderly and rapid fashion. By the force analysis, mechanical model of seed feeding stage was set up. Relationships between design parameters of the meter and the metering performance (multiple index, miss index, quality index and feeding efficiency index) were obtained through simulations using a discrete element model (DEM). The simulations conducted in this study were based on the central composite design (CCD). Then, the relationships were used to determine the design parameters to achieve the best metering performance. With these design parameters, the GG meter was fabricated and evaluated through bench tests. Results showed that the critical design parameters were the width of inner groove-wheel (L), cone angle of the shell (δ), the width of guiding-groove (L1), and the angle of the groove bottom surface to the horizontal plane (ɳ). The relationship between these parameters and metering performance could be described by a second-order polynomial equation, and the best metering performance occurred when L=25.3 mm, δ=23.6°, L1=8.1 mm and ɳ=8.3°. The bench test results showed that the GG meter designed with these optimal design parameters had metering performance values (quality index) of 93% and higher over planter travel speeds of 8 to 15 km h-1. In addition, the coefficients of variation of metering performance over the range of planter travel speeds were lower than 30%.
为提高大豆高速精密种植的性能，研制了一种导向槽精密计量装置。分析了种子的饲喂和清除过程，确定了关键的设计和操作因素。利用DEM模拟了关键计量参数对电表性能的影响。通过台架试验对计量性能进行了评价。精准种植是农业发展的必然趋势，推广精准种植技术是提高作物产量的关键。为提高大豆高速精密种植的性能，研制了一种机械式大豆精密计量装置。该装置的创新之处在于导向槽(GG)，它为种子形成一条线提供了“等待区”，随后以有序和快速的方式进入种子细胞。通过受力分析，建立了种子输送阶段的力学模型。采用离散元模型(DEM)进行仿真，得到了计量仪表设计参数与计量性能(多重指标、脱靶指标、质量指标和进料效率指标)之间的关系。本研究的模拟基于中心复合设计(CCD)。然后，利用这些关系来确定设计参数，以获得最佳的计量性能。根据这些设计参数，制作了GG仪表，并通过台架试验对其进行了评价。结果表明:内槽轮宽度(L)、壳体锥角(δ)、导向槽宽度(L1)和导向槽底面与水平面的夹角(%)是关键设计参数。当L=25.3 mm， δ=23.6°，L1=8.1 mm, n =8.3°时，测光性能最佳。台架试验结果表明，在播种机8 ~ 15 km h-1的速度下，以这些优化设计参数设计的GG仪的计量性能值(质量指数)达到93%以上。此外，计量性能在播种机行驶速度范围内的变异系数均小于30%。
{"title":"Development of a Guiding-Groove Precision Metering Device for High-Speed Planting of Soybean","authors":"Hao Shen, Zhang Junjie, X. Chen, Jian-Xin Dong, Yuxiang Huang, Jiangtao Shi","doi":"10.13031/TRANS.14307","DOIUrl":"https://doi.org/10.13031/TRANS.14307","url":null,"abstract":"Highlights To improve the performance for precision planting of soybean at high speeds, a guiding groove precision metering device was developed. The seed feeding and clearing processes were analyzed to determine the critical design and operational factors. The effects of critical metering parameters on the meter performance were simulated using the DEM. The metering performance was evaluated using bench tests. ABSTRACT Precision planting is the inevitable trend of agricultural development, and the promotion of precision planting technology is the key to increase crop yield. To improve the performance of precision planting at high speeds, a mechanical-type precision metering device was developed for soybean. The innovative feature of the device was the guiding-groove (GG) that provided “waiting areas” for seeds to form a line and subsequently enter the seed cells in an orderly and rapid fashion. By the force analysis, mechanical model of seed feeding stage was set up. Relationships between design parameters of the meter and the metering performance (multiple index, miss index, quality index and feeding efficiency index) were obtained through simulations using a discrete element model (DEM). The simulations conducted in this study were based on the central composite design (CCD). Then, the relationships were used to determine the design parameters to achieve the best metering performance. With these design parameters, the GG meter was fabricated and evaluated through bench tests. Results showed that the critical design parameters were the width of inner groove-wheel (L), cone angle of the shell (δ), the width of guiding-groove (L1), and the angle of the groove bottom surface to the horizontal plane (ɳ). The relationship between these parameters and metering performance could be described by a second-order polynomial equation, and the best metering performance occurred when L=25.3 mm, δ=23.6°, L1=8.1 mm and ɳ=8.3°. The bench test results showed that the GG meter designed with these optimal design parameters had metering performance values (quality index) of 93% and higher over planter travel speeds of 8 to 15 km h-1. In addition, the coefficients of variation of metering performance over the range of planter travel speeds were lower than 30%.","PeriodicalId":23120,"journal":{"name":"Transactions of the ASABE","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88733908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chao Zheng, Yang Xiaofei, Ke-xing Liu, Yongxiang Huang
HighlightsThe effects of potash fertilizer and straw returning on a banana orchard were studied by field experiment.Fertilizer with straw was more conductive to potassium nutrient balance and improved banana yield and quality.The economic benefits of straw replacing different amounts of potassium fertilizer were compared.Abstract. To explore the effects of potash fertilizer and straw returning in banana production, a field experiment was carried out, and four treatments were set up: NP fertilizer (NP), NP fertilizer and banana straw (NP+St), NPK fertilizer (NPK), and NPK fertilizer and banana straw (NPK+St). Through the soil potassium balance, the effects of potash fertilizer and straw returning on the yield, quality, and economic benefits of bananas were studied. The results showed that the application of potash fertilizer and straw could improve banana yields. Compared with the NP treatment, the banana yields of the NP+St, NPK, and NPK+St treatments increased by 17.5%, 50.5%, and 71.6%, respectively. The order of banana yield, potassium balance coefficient, and nutrient accumulation was NPK+St > NP+St > NPK > NP. The NPK+St treatment also improved the recovery rate and agronomic utilization rate of potash fertilizer, which were higher than that of potassium application without straw (NPK) and straw application without potassium (NP+St). Potassium application with straw improved the banana yield, increased the total accumulation of nitrogen, phosphorus, and potassium, and improved the efficiency of potash fertilizer uptake by the crop. Therefore, this study demonstrates the importance of straw for maintaining the soil potassium balance in banana production. The input cost of potassium fertilizer was reduced, and the resource utilization of banana straw was realized by straw returning, which can be promoted in local agricultural production. Keywords: Banana, Potassium application, Potassium balance, Straw returning, Yield.
{"title":"Effects of Potassium Application and Straw Returning on Potassium Management and Benefit of Banana","authors":"Chao Zheng, Yang Xiaofei, Ke-xing Liu, Yongxiang Huang","doi":"10.13031/TRANS.14653","DOIUrl":"https://doi.org/10.13031/TRANS.14653","url":null,"abstract":"HighlightsThe effects of potash fertilizer and straw returning on a banana orchard were studied by field experiment.Fertilizer with straw was more conductive to potassium nutrient balance and improved banana yield and quality.The economic benefits of straw replacing different amounts of potassium fertilizer were compared.Abstract. To explore the effects of potash fertilizer and straw returning in banana production, a field experiment was carried out, and four treatments were set up: NP fertilizer (NP), NP fertilizer and banana straw (NP+St), NPK fertilizer (NPK), and NPK fertilizer and banana straw (NPK+St). Through the soil potassium balance, the effects of potash fertilizer and straw returning on the yield, quality, and economic benefits of bananas were studied. The results showed that the application of potash fertilizer and straw could improve banana yields. Compared with the NP treatment, the banana yields of the NP+St, NPK, and NPK+St treatments increased by 17.5%, 50.5%, and 71.6%, respectively. The order of banana yield, potassium balance coefficient, and nutrient accumulation was NPK+St > NP+St > NPK > NP. The NPK+St treatment also improved the recovery rate and agronomic utilization rate of potash fertilizer, which were higher than that of potassium application without straw (NPK) and straw application without potassium (NP+St). Potassium application with straw improved the banana yield, increased the total accumulation of nitrogen, phosphorus, and potassium, and improved the efficiency of potash fertilizer uptake by the crop. Therefore, this study demonstrates the importance of straw for maintaining the soil potassium balance in banana production. The input cost of potassium fertilizer was reduced, and the resource utilization of banana straw was realized by straw returning, which can be promoted in local agricultural production. Keywords: Banana, Potassium application, Potassium balance, Straw returning, Yield.","PeriodicalId":23120,"journal":{"name":"Transactions of the ASABE","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90492913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
HighlightsFour crop growth modules in RZWQM2 were calibrated for four sugarbeet rotation sequences.Sugarbeet following wheat had a slightly higher yield (3% to 6.5%).Moldboard plow increased sugarbeet yield by 1% to 2%.The difference in N losses under different crop rotations and tillage operations was negligible.Abstract. Sugarbeet (Beta vulgaris) is considered to be one of the most viable alternatives to corn for biofuel production as it may be qualified as the feedstock for advanced biofuels (reducing greenhouse gas emission by 50%) under the Energy Independence and Security Act (EISA) of 2007. Because sugarbeet production is affected by crop rotation and tillage through optimal use of soil water and nutrients, simulation of these effects will help in making proper management decisions. In this study, the CSM-CERES-Beet, CSM-CERES-Maize, CROPSIM-Wheat, and CROPGRO-Soybean models included in the RZWQM2 were calibrated against experimental field data of crop yield, soil water, and soil nitrate from the North Dakota State University Carrington Research Extension Center from 2014 to 2016. The models performed reasonably well in simulating crop yield, soil water, and nitrate (rRMSE = 0.055 to 2.773, d = 0.541 to 0.997). Simulation results identified a non-significant effect of crop rotation on sugarbeet yield, although sugarbeets following wheat resulted in 3% to 6.5% higher yields compared to other crops. Net mineralization and N uptake rates were slightly higher when sugarbeets followed wheat compared to the other crops. Seasonal N and water mass balances also showed lower N and water stresses when sugarbeets followed wheat. The effects of tillage operations on sugarbeet yield were also non-significant. The difference in the N losses to runoff and drainage from the sugarbeet fields under different crop rotations and tillage operations was negligible. As sugarbeet production may be expanded into nontraditional planting areas in the Red River Valley due to potential demand for biofuel production, our findings will help to assess the associated environmental impacts and identify suitable crop rotations and management scenarios in the region. Keywords: Biofuel, Crop rotation, RZWQM2, Sugarbeet, Tillage.
{"title":"Modeling the Effects of Crop Rotation and Tillage on Sugarbeet Yield and Soil Nitrate Using RZWQM2","authors":"M. J. Anar, Zhulu Lin, Liwang Ma, A. Chatterjee","doi":"10.13031/TRANS.13752","DOIUrl":"https://doi.org/10.13031/TRANS.13752","url":null,"abstract":"HighlightsFour crop growth modules in RZWQM2 were calibrated for four sugarbeet rotation sequences.Sugarbeet following wheat had a slightly higher yield (3% to 6.5%).Moldboard plow increased sugarbeet yield by 1% to 2%.The difference in N losses under different crop rotations and tillage operations was negligible.Abstract. Sugarbeet (Beta vulgaris) is considered to be one of the most viable alternatives to corn for biofuel production as it may be qualified as the feedstock for advanced biofuels (reducing greenhouse gas emission by 50%) under the Energy Independence and Security Act (EISA) of 2007. Because sugarbeet production is affected by crop rotation and tillage through optimal use of soil water and nutrients, simulation of these effects will help in making proper management decisions. In this study, the CSM-CERES-Beet, CSM-CERES-Maize, CROPSIM-Wheat, and CROPGRO-Soybean models included in the RZWQM2 were calibrated against experimental field data of crop yield, soil water, and soil nitrate from the North Dakota State University Carrington Research Extension Center from 2014 to 2016. The models performed reasonably well in simulating crop yield, soil water, and nitrate (rRMSE = 0.055 to 2.773, d = 0.541 to 0.997). Simulation results identified a non-significant effect of crop rotation on sugarbeet yield, although sugarbeets following wheat resulted in 3% to 6.5% higher yields compared to other crops. Net mineralization and N uptake rates were slightly higher when sugarbeets followed wheat compared to the other crops. Seasonal N and water mass balances also showed lower N and water stresses when sugarbeets followed wheat. The effects of tillage operations on sugarbeet yield were also non-significant. The difference in the N losses to runoff and drainage from the sugarbeet fields under different crop rotations and tillage operations was negligible. As sugarbeet production may be expanded into nontraditional planting areas in the Red River Valley due to potential demand for biofuel production, our findings will help to assess the associated environmental impacts and identify suitable crop rotations and management scenarios in the region. Keywords: Biofuel, Crop rotation, RZWQM2, Sugarbeet, Tillage.","PeriodicalId":23120,"journal":{"name":"Transactions of the ASABE","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87288146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
HighlightsThis study focused on the base cutting energy consumption for sugarcane stools instead of single stalks, thus being more consistent with actual field harvesting.The energy consumption increased with increasing rotational speed (RS) and stool diameter (SD), while it decreased with increasing tilt angle (TA) and feed rate (FR).Each pair of levels of each factor was compared using Duncan’s multiple range test. Three factors (RS, SD, and FR) had significant effects on energy consumption at 95% confidence level, while one factor (TA) had no significant effect.The order of influence and the optimal combination of the four factors to minimize the energy consumed during base cutting were determined.Abstract. Previous studies on contra-rotating basecutter designs based on supported cutting have mainly focused on the base cutting energy consumption for single sugarcane stalks instead of sugarcane stools. However, in the actual base cutting process, a basecutter typically cuts multiple sugarcane stalks (in one sugarcane stool) simultaneously. Therefore, this study investigated how the rotational speed (RS) and tilt angle (TA) of the cutting discs, the sugarcane stool diameter (SD), and the feed rate (FR) affected the energy consumed when cutting cane stools using a contra-rotating cutting platform. Four single-factor experiments and an orthogonal experiment were performed using a Taguchi orthogonal experimental design, and each group was replicated five times. The results of the single-factor experiments showed that the energy consumption was proportional to RS and SD, while it was negatively correlated with TA and FR. The significance of the difference between each pair of levels of each factor was investigated using Duncan’s multiple range test. According to the results of the orthogonal experiment, RS, SD, and FR had significant influences on the base cutting energy consumption at the 95% confidence level; however, TA had no significant influence. The order of influence of the four factors was SD > FR > RS > TA (18.45 > 18.39 > 12.91 > 9.06), and the optimal factor-level combination for minimizing the cutting energy was RS2, TA4, SD1, and FR3 (200 rpm disc RS, 20° disc TA, 60 mm SD, and 1.0 m s-1 FR). An understanding of the relationships between energy consumption and its influencing factors can serve as a valuable reference for researchers seeking to optimize the design of contra-rotating basecutters, which could lead to increased energy efficiency and a reduction in energy consumption during sugarcane harvesting. Keywords: Contra-rotating basecutter, Energy consumption, Orthogonal experiment, Single-factor experiment, Sugarcane stools, Supported cutting.
{"title":"Base Cutting Energy Consumption for Sugarcane Stools Using Contra-Rotating Basecutters","authors":"Fenglei Wang, Shaochun Ma, Haonan Xing, Jing Bai, Jinzhi Ma, Yezhen Yang, Jiwei Hu","doi":"10.13031/TRANS.13997","DOIUrl":"https://doi.org/10.13031/TRANS.13997","url":null,"abstract":"HighlightsThis study focused on the base cutting energy consumption for sugarcane stools instead of single stalks, thus being more consistent with actual field harvesting.The energy consumption increased with increasing rotational speed (RS) and stool diameter (SD), while it decreased with increasing tilt angle (TA) and feed rate (FR).Each pair of levels of each factor was compared using Duncan’s multiple range test. Three factors (RS, SD, and FR) had significant effects on energy consumption at 95% confidence level, while one factor (TA) had no significant effect.The order of influence and the optimal combination of the four factors to minimize the energy consumed during base cutting were determined.Abstract. Previous studies on contra-rotating basecutter designs based on supported cutting have mainly focused on the base cutting energy consumption for single sugarcane stalks instead of sugarcane stools. However, in the actual base cutting process, a basecutter typically cuts multiple sugarcane stalks (in one sugarcane stool) simultaneously. Therefore, this study investigated how the rotational speed (RS) and tilt angle (TA) of the cutting discs, the sugarcane stool diameter (SD), and the feed rate (FR) affected the energy consumed when cutting cane stools using a contra-rotating cutting platform. Four single-factor experiments and an orthogonal experiment were performed using a Taguchi orthogonal experimental design, and each group was replicated five times. The results of the single-factor experiments showed that the energy consumption was proportional to RS and SD, while it was negatively correlated with TA and FR. The significance of the difference between each pair of levels of each factor was investigated using Duncan’s multiple range test. According to the results of the orthogonal experiment, RS, SD, and FR had significant influences on the base cutting energy consumption at the 95% confidence level; however, TA had no significant influence. The order of influence of the four factors was SD > FR > RS > TA (18.45 > 18.39 > 12.91 > 9.06), and the optimal factor-level combination for minimizing the cutting energy was RS2, TA4, SD1, and FR3 (200 rpm disc RS, 20° disc TA, 60 mm SD, and 1.0 m s-1 FR). An understanding of the relationships between energy consumption and its influencing factors can serve as a valuable reference for researchers seeking to optimize the design of contra-rotating basecutters, which could lead to increased energy efficiency and a reduction in energy consumption during sugarcane harvesting. Keywords: Contra-rotating basecutter, Energy consumption, Orthogonal experiment, Single-factor experiment, Sugarcane stools, Supported cutting.","PeriodicalId":23120,"journal":{"name":"Transactions of the ASABE","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88024654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hao Wang, Songming Zhu, H. Ramaswamy, L. Ting, Yong Yu
HighlightsFTC-4 treated brown rice showed better protein digestibility.BR-AAS was used to evaluate the nutritive value of released free amino acids.G24P treated brown rice is recommended to supplement amino acid intake in a daily diet.Abstract. The objective of this study was to evaluate the in vitro protein digestibility of brown rice (BR) after high-pressure (HP), freeze-thaw cycle (FTC), and germination-parboiling (GP) treatments. Four-cycle freeze-thaw (FTC-4) treatment enhanced digestibility, and all treated BR released more essential and total amino acids after digestion. To evaluate the nutritive value of free amino acids released after digestion (on the premise of the same intake of BR products), a BR amino acid score (BR-AAS) was used, based on the patterns of protein digestibility-corrected amino acid scores with modifications. Results suggested that BR treated with 24 h of germination followed by 10 min of parboiling (G24P) was a better choice for supplementing amino acid intake in a daily diet. All treatments resulted in decreased protein solubility, which was negatively correlated with surface hydrophobicity and disulfide bond contents. The HP, FTC, and GP treatments affected certain protein properties, which was helpful in explaining the differences in protein digestibility of BR. Changes in other constituents were considered important with respect to the treatment influence on protein digestibility. Keywords: Brown rice, Freeze-thaw cycles, Germination-parboiling, High-pressure, Protein in vitro digestibility.
经ftc -4处理的糙米具有较好的蛋白质消化率。采用br -原子吸收光谱法评价释放的游离氨基酸的营养价值。建议用G24P处理糙米补充日粮中氨基酸的摄入量。本研究旨在评价高压(HP)、冻融循环(FTC)和萌发-沸腾(GP)处理后糙米(BR)的体外蛋白质消化率。四循环冻融(FTC-4)处理提高了BR的消化率，所有处理的BR消化后释放的必需氨基酸和总氨基酸都更多。为了评估消化后释放的游离氨基酸的营养价值(在相同BR产品摄入量的前提下)，采用BR氨基酸评分(BR- aas)，基于修正后的蛋白质消化率修正氨基酸评分模式。结果表明，BR萌发24 h后再煮10 min (G24P)是补充日粮氨基酸摄入量的较好选择。所有处理均导致蛋白质溶解度降低，并与表面疏水性和二硫键含量呈负相关。HP、FTC和GP处理影响了某些蛋白质特性，这有助于解释BR蛋白质消化率的差异。对于处理对蛋白质消化率的影响，其他成分的变化被认为是重要的。关键词:糙米，冻融循环，萌发-半煮，高压，蛋白质体外消化率
{"title":"In Vitro Protein Digestibility of Brown Rice after High-Pressure Freeze-Thaw Cycles and Germination-Parboiling Treatments","authors":"Hao Wang, Songming Zhu, H. Ramaswamy, L. Ting, Yong Yu","doi":"10.13031/trans.14314","DOIUrl":"https://doi.org/10.13031/trans.14314","url":null,"abstract":"HighlightsFTC-4 treated brown rice showed better protein digestibility.BR-AAS was used to evaluate the nutritive value of released free amino acids.G24P treated brown rice is recommended to supplement amino acid intake in a daily diet.Abstract. The objective of this study was to evaluate the in vitro protein digestibility of brown rice (BR) after high-pressure (HP), freeze-thaw cycle (FTC), and germination-parboiling (GP) treatments. Four-cycle freeze-thaw (FTC-4) treatment enhanced digestibility, and all treated BR released more essential and total amino acids after digestion. To evaluate the nutritive value of free amino acids released after digestion (on the premise of the same intake of BR products), a BR amino acid score (BR-AAS) was used, based on the patterns of protein digestibility-corrected amino acid scores with modifications. Results suggested that BR treated with 24 h of germination followed by 10 min of parboiling (G24P) was a better choice for supplementing amino acid intake in a daily diet. All treatments resulted in decreased protein solubility, which was negatively correlated with surface hydrophobicity and disulfide bond contents. The HP, FTC, and GP treatments affected certain protein properties, which was helpful in explaining the differences in protein digestibility of BR. Changes in other constituents were considered important with respect to the treatment influence on protein digestibility. Keywords: Brown rice, Freeze-thaw cycles, Germination-parboiling, High-pressure, Protein in vitro digestibility.","PeriodicalId":23120,"journal":{"name":"Transactions of the ASABE","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86528388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Leonard, H. Xin, B. Ramirez, John P. Stinn, Somak Dutta, Kai Liu, T. Brown-Brandl
HighlightsA calibration procedure was conducted using a Kinect V2 to convert image pixels to physical measurements.A total of 61 sows were observed, and their static and dynamic space usage was measured from depth images.Equations were developed to predict the length, width, and height of sow space usage.Abstract. The amount of space provided to individually housed sows has both financial and animal welfare implications. Many U.S. swine producers use stall dimensions based on recommendations published in the 1980s (length × width × height: 2.13 m × 0.61 m × 1.00 m). Limited empirical data are available concerning the space allocation needed to accommodate modern sows housed in stalls during breeding, gestation, or farrowing. This study used a time-of-flight depth sensor to quantify static and dynamic space usage of 61 modern sows in late gestation. A calibration equation was developed to convert image pixels to physical dimensions. Statistical models were developed to relate the length, width, and height of sow space usage to body weight. The dimensions of sow space usage were then predicted. Results showed that free choice space usage of average (228 kg) sows was 1.96 m × 1.15 m × 0.93 m (length × width × height). For 95th percentile (267 kg) sows, space usage was 2.04 m × 1.12 m × 0.95 m. The width of space usage was primarily attributed to sow body depth when lying recumbent and the dynamic space used for transitioning between postures. These results help to inform future gestating and farrowing sow housing designs. Further work is needed to understand how restrictions on sow space usage may impact sow welfare and production performance, as well as the space needed to perform behaviors such as defecating, feeding, and turning around. Keywords: Animal welfare, Computer vision, Farrowing stall, Gestation stall, Kinect V2, Space allowance.
HighlightsA校准程序使用Kinect V2将图像像素转换为物理测量值。共观察了61头母猪，并通过深度图像测量了它们的静态和动态空间使用情况。建立了预测母猪空间利用的长度、宽度和高度的方程。为单独饲养的母猪提供的空间数量涉及经济和动物福利。许多美国养猪户使用的猪舍尺寸是基于20世纪80年代发布的建议(长×宽×高:2.13米× 0.61米× 1.00米)。关于在繁殖、妊娠或分娩期间容纳现代母猪所需的空间分配，现有的经验数据有限。本研究使用飞行时间深度传感器对61头现代母猪妊娠后期的静态和动态空间利用进行了量化。建立了将图像像素转换为物理尺寸的校准方程。建立了母猪空间利用的长度、宽度和高度与体重之间的统计模型。然后对母猪空间利用的各维度进行了预测。结果表明，平均(228 kg)母猪自由选择空间利用率为1.96 m × 1.15 m × 0.93 m(长×宽×高)。对于第95百分位(267 kg)母猪，空间利用率为2.04 m × 1.12 m × 0.95 m。空间使用的宽度主要归因于母猪平卧时的身体深度和姿势之间转换所使用的动态空间。这些结果有助于为未来妊娠和分娩母猪的住房设计提供信息。对母猪空间使用的限制如何影响母猪的福利和生产性能，以及进行排便、进食和转身等行为所需的空间，需要进一步的工作来了解。关键词:动物福利，计算机视觉，产房，孕房，Kinect V2，空间补贴
{"title":"Static and Dynamic Space Usage of Late-Gestation Sows","authors":"S. Leonard, H. Xin, B. Ramirez, John P. Stinn, Somak Dutta, Kai Liu, T. Brown-Brandl","doi":"10.13031/TRANS.14002","DOIUrl":"https://doi.org/10.13031/TRANS.14002","url":null,"abstract":"HighlightsA calibration procedure was conducted using a Kinect V2 to convert image pixels to physical measurements.A total of 61 sows were observed, and their static and dynamic space usage was measured from depth images.Equations were developed to predict the length, width, and height of sow space usage.Abstract. The amount of space provided to individually housed sows has both financial and animal welfare implications. Many U.S. swine producers use stall dimensions based on recommendations published in the 1980s (length × width × height: 2.13 m × 0.61 m × 1.00 m). Limited empirical data are available concerning the space allocation needed to accommodate modern sows housed in stalls during breeding, gestation, or farrowing. This study used a time-of-flight depth sensor to quantify static and dynamic space usage of 61 modern sows in late gestation. A calibration equation was developed to convert image pixels to physical dimensions. Statistical models were developed to relate the length, width, and height of sow space usage to body weight. The dimensions of sow space usage were then predicted. Results showed that free choice space usage of average (228 kg) sows was 1.96 m × 1.15 m × 0.93 m (length × width × height). For 95th percentile (267 kg) sows, space usage was 2.04 m × 1.12 m × 0.95 m. The width of space usage was primarily attributed to sow body depth when lying recumbent and the dynamic space used for transitioning between postures. These results help to inform future gestating and farrowing sow housing designs. Further work is needed to understand how restrictions on sow space usage may impact sow welfare and production performance, as well as the space needed to perform behaviors such as defecating, feeding, and turning around. Keywords: Animal welfare, Computer vision, Farrowing stall, Gestation stall, Kinect V2, Space allowance.","PeriodicalId":23120,"journal":{"name":"Transactions of the ASABE","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85693941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Liao Juan, Wang Yao, Yin Junnan, Bi Lingling, Zhang Shun, Huiyu Zhou, Zhu Dequan
Highlights A GPS/INS/VNS integrated navigation system to improve navigation accuracy. An adaptive federal Kalman filter with the adaptive information distribution factor to fuse navigation information. Detection of seedling row lines based on sub-regional feature points clustering. A modified rice transplanter as an automatic navigation experimental platform. In this study, a global positioning system (GPS)/inertial navigation system (INS)/visual navigation system (VNS)-integrated navigation method based on an adaptive federal Kalman filter (KF) was presented to improve positioning accuracy for rice transplanter operating in paddy field. The proposed method used GPS/VNS to aid INS and reduce the influence of the accumulated error of the INS on navigation accuracy. An adaptive federal KF algorithm was designed to fuse navigation information from different sensors. The information distribution factor of each local filter was obtained adaptively on the basis of its own error covariance matrix. Computer simulation and the transplanter test were conducted to verify the proposed method. Results showed that the proposed method could provide accurate and reliable navigation information outputs, and achieve better navigation performance compared with that of single GPS navigation and integrated method based traditional federal KF.
{"title":"An Integrated Navigation Method Based on an Adaptive Federal Kalman Filter for a Rice Transplanter","authors":"Liao Juan, Wang Yao, Yin Junnan, Bi Lingling, Zhang Shun, Huiyu Zhou, Zhu Dequan","doi":"10.13031/TRANS.13682","DOIUrl":"https://doi.org/10.13031/TRANS.13682","url":null,"abstract":"Highlights A GPS/INS/VNS integrated navigation system to improve navigation accuracy. An adaptive federal Kalman filter with the adaptive information distribution factor to fuse navigation information. Detection of seedling row lines based on sub-regional feature points clustering. A modified rice transplanter as an automatic navigation experimental platform. In this study, a global positioning system (GPS)/inertial navigation system (INS)/visual navigation system (VNS)-integrated navigation method based on an adaptive federal Kalman filter (KF) was presented to improve positioning accuracy for rice transplanter operating in paddy field. The proposed method used GPS/VNS to aid INS and reduce the influence of the accumulated error of the INS on navigation accuracy. An adaptive federal KF algorithm was designed to fuse navigation information from different sensors. The information distribution factor of each local filter was obtained adaptively on the basis of its own error covariance matrix. Computer simulation and the transplanter test were conducted to verify the proposed method. Results showed that the proposed method could provide accurate and reliable navigation information outputs, and achieve better navigation performance compared with that of single GPS navigation and integrated method based traditional federal KF.","PeriodicalId":23120,"journal":{"name":"Transactions of the ASABE","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85768286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
HighlightsLeaf nitrogen percentage in corn was estimated using various vegetation indices derived from UAVs.Eight machine learning methods were compared to find the most accurate model for nitrogen estimation.The most influential vegetation indices were determined for estimation of leaf nitrogen.Abstract. Nitrogen (N) is the most critical component of healthy plants. It has a significant impact on photosynthesis and plant reproduction. Physicochemical characteristics of plants such as leaf N content can be estimated spatially and temporally because of the latest developments in multispectral sensing technology and machine learning (ML) methods. The objective of this study was to use spectral data for leaf N estimation in corn to compare different ML models and find the best-fitted model. Moreover, the performance of vegetation indices (VIs) and spectral wavelengths were compared individually and collectively to determine if combinations of VIs substantially improved the results as compared to the original spectral data. This study was conducted at a Mississippi State University corn field that was divided into 16 plots with four different N treatments (0, 90, 180, and 270 kg ha-1). The bare soil pixels were removed from the multispectral images, and 26 VIs were calculated based on five spectral bands: blue, green, red, red-edge, and near-infrared (NIR). The 26 VIs and five spectral bands obtained from a red-edge multispectral sensor mounted on an unmanned aerial vehicle (UAV) were analyzed to develop ML models for leaf %N estimation of corn. The input variables used in these models had the most impact on chlorophyll and N content and high correlation with leaf N content. Eight ML algorithms (random forest, gradient boosting, support vector machine, multi-layer perceptron, ridge regression, lasso regression, and elastic net) were applied to three different categories of variables. The results show that gradient boosting and random forest were the best-fitted models to estimate leaf %N, with about an 80% coefficient of determination for the different categories of variables. Moreover, adding VIs to the spectral bands improved the results. The combination of SCCCI, NDRE, and red-edge had the largest coefficient of determination (R2) in comparison to the other categories of variables used to predict leaf %N content in corn. Keywords: Corn, Gradient boosting, Machine learning, Multispectral imagery, Nitrogen estimation, Random forest, UAV, Vegetation index.
利用无人机获取的各种植被指数估算了玉米叶片氮含量。比较了八种机器学习方法，找到了最准确的氮估计模型。确定了对叶片氮估算影响最大的植被指数。氮(N)是健康植物最关键的成分。它对光合作用和植物繁殖有重要影响。由于多光谱传感技术和机器学习技术的最新发展，植物的理化特征如叶片氮含量可以在空间和时间上进行估计。本研究的目的是利用光谱数据进行玉米叶片氮估计，比较不同的ML模型，找到最适合的模型。此外，对植被指数(VIs)和光谱波长的性能进行了单独和集体比较，以确定VIs组合是否比原始光谱数据显著改善了结果。本研究在密西西比州立大学玉米田进行，将玉米田分为16块，施氮量分别为0、90、180和270 kg hm -1。从多光谱图像中去除裸露土壤像元，并基于蓝、绿、红、红边和近红外5个光谱波段计算26 VIs。利用安装在无人机上的红边多光谱传感器获取的26个VIs和5个光谱波段进行分析，建立了玉米叶片%N估算的ML模型。各模型输入变量对叶绿素和氮含量影响最大，且与叶片氮含量相关性较高。八种机器学习算法(随机森林、梯度增强、支持向量机、多层感知器、脊回归、lasso回归和弹性网)应用于三种不同类别的变量。结果表明，梯度增强和随机森林是估计叶片%N的最佳拟合模型，对于不同类别的变量，其决定系数约为80%。此外，在光谱带中加入VIs改善了结果。与其他预测玉米叶片%N含量的变量相比，SCCCI、NDRE和红边组合的决定系数(R2)最大。关键词:玉米，梯度增强，机器学习，多光谱图像，氮估计，随机森林，无人机，植被指数
{"title":"Comparison of Machine Learning Methods for Leaf Nitrogen Estimation in Corn Using Multispectral UAV Images","authors":"Razieh Barzin, G. Bora","doi":"10.13031/TRANS.14305","DOIUrl":"https://doi.org/10.13031/TRANS.14305","url":null,"abstract":"HighlightsLeaf nitrogen percentage in corn was estimated using various vegetation indices derived from UAVs.Eight machine learning methods were compared to find the most accurate model for nitrogen estimation.The most influential vegetation indices were determined for estimation of leaf nitrogen.Abstract. Nitrogen (N) is the most critical component of healthy plants. It has a significant impact on photosynthesis and plant reproduction. Physicochemical characteristics of plants such as leaf N content can be estimated spatially and temporally because of the latest developments in multispectral sensing technology and machine learning (ML) methods. The objective of this study was to use spectral data for leaf N estimation in corn to compare different ML models and find the best-fitted model. Moreover, the performance of vegetation indices (VIs) and spectral wavelengths were compared individually and collectively to determine if combinations of VIs substantially improved the results as compared to the original spectral data. This study was conducted at a Mississippi State University corn field that was divided into 16 plots with four different N treatments (0, 90, 180, and 270 kg ha-1). The bare soil pixels were removed from the multispectral images, and 26 VIs were calculated based on five spectral bands: blue, green, red, red-edge, and near-infrared (NIR). The 26 VIs and five spectral bands obtained from a red-edge multispectral sensor mounted on an unmanned aerial vehicle (UAV) were analyzed to develop ML models for leaf %N estimation of corn. The input variables used in these models had the most impact on chlorophyll and N content and high correlation with leaf N content. Eight ML algorithms (random forest, gradient boosting, support vector machine, multi-layer perceptron, ridge regression, lasso regression, and elastic net) were applied to three different categories of variables. The results show that gradient boosting and random forest were the best-fitted models to estimate leaf %N, with about an 80% coefficient of determination for the different categories of variables. Moreover, adding VIs to the spectral bands improved the results. The combination of SCCCI, NDRE, and red-edge had the largest coefficient of determination (R2) in comparison to the other categories of variables used to predict leaf %N content in corn. Keywords: Corn, Gradient boosting, Machine learning, Multispectral imagery, Nitrogen estimation, Random forest, UAV, Vegetation index.","PeriodicalId":23120,"journal":{"name":"Transactions of the ASABE","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81368931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
HighlightsNitrous oxide and methane emissions were measured from a commercial beef feedyard following large rainfall events.Nitrous oxide emissions dropped below detection levels for ten days following a 77 mm rainfall event.Daily N2O and CH4 emissions followed a diel pattern, peaking at manure temperatures of 36°C to 38°C.Results will be used to refine empirical models for predicting GHG emissions from open-lot feedyards.Abstract. More than six million beef cattle are fed annually in feedyards on the semiarid Southern Great Plains (SGP). Manure deposited on the open-lot pen surfaces contributes to greenhouse gas (GHG) emissions. Nitrous oxide (N2O) and methane (CH4) are GHGs linked to climate change, and both have global warming potentials greater than carbon dioxide (CO2). Two sampling campaigns were conducted in 2019 to quantify N2O and CH4 emissions from open-lot pen surfaces. The occurrence of large, unforecast rainfall events during both campaigns provided an opportunity to compare GHG emissions from the dry manure before rainfall and from the wetted pen surface for one to two weeks following precipitation. Temporal variability was quantified by continuous sampling using six to eight automated flux chambers, a multiplexer system, and real-time analyzers. Spatial variability was quantified using a recirculating portable chamber on a 5 × 8 grid. Nitrous oxide emissions dropped below detection levels for ten days after the precipitation event. Nitrous oxide emissions were related to nitrification or other aerobic processes. Methane emissions dropped below detection levels for five days after the precipitation event and then increased to pre-rainfall levels by day 8. When present, N2O and CH4 emissions followed a diel pattern, with the highest emissions occurring during the afternoon when manure pack temperatures at the 25 mm depth were 36°C to 38°C and ambient temperatures were 31°C to 32°C. Average CH4 emissions from the feedyard pen surface were 96-fold lower than estimated enteric CH4 emissions. The results of this field research will be used to refine empirical models for predicting annual N2O and CH4 emissions from open-lot beef cattle feedyards on the semiarid SGP. Keywords: Beef cattle, Flux chamber, Greenhouse gas, Manure, Nitrous oxide, Rainfall.
{"title":"Nitrous Oxide and Methane Emissions from Beef Cattle Feedyard Pens Following Large Rainfall Events","authors":"D. Parker, K. Casey, Will Willis, B. Meyer","doi":"10.13031/trans.14480","DOIUrl":"https://doi.org/10.13031/trans.14480","url":null,"abstract":"HighlightsNitrous oxide and methane emissions were measured from a commercial beef feedyard following large rainfall events.Nitrous oxide emissions dropped below detection levels for ten days following a 77 mm rainfall event.Daily N2O and CH4 emissions followed a diel pattern, peaking at manure temperatures of 36°C to 38°C.Results will be used to refine empirical models for predicting GHG emissions from open-lot feedyards.Abstract. More than six million beef cattle are fed annually in feedyards on the semiarid Southern Great Plains (SGP). Manure deposited on the open-lot pen surfaces contributes to greenhouse gas (GHG) emissions. Nitrous oxide (N2O) and methane (CH4) are GHGs linked to climate change, and both have global warming potentials greater than carbon dioxide (CO2). Two sampling campaigns were conducted in 2019 to quantify N2O and CH4 emissions from open-lot pen surfaces. The occurrence of large, unforecast rainfall events during both campaigns provided an opportunity to compare GHG emissions from the dry manure before rainfall and from the wetted pen surface for one to two weeks following precipitation. Temporal variability was quantified by continuous sampling using six to eight automated flux chambers, a multiplexer system, and real-time analyzers. Spatial variability was quantified using a recirculating portable chamber on a 5 × 8 grid. Nitrous oxide emissions dropped below detection levels for ten days after the precipitation event. Nitrous oxide emissions were related to nitrification or other aerobic processes. Methane emissions dropped below detection levels for five days after the precipitation event and then increased to pre-rainfall levels by day 8. When present, N2O and CH4 emissions followed a diel pattern, with the highest emissions occurring during the afternoon when manure pack temperatures at the 25 mm depth were 36°C to 38°C and ambient temperatures were 31°C to 32°C. Average CH4 emissions from the feedyard pen surface were 96-fold lower than estimated enteric CH4 emissions. The results of this field research will be used to refine empirical models for predicting annual N2O and CH4 emissions from open-lot beef cattle feedyards on the semiarid SGP. Keywords: Beef cattle, Flux chamber, Greenhouse gas, Manure, Nitrous oxide, Rainfall.","PeriodicalId":23120,"journal":{"name":"Transactions of the ASABE","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90368727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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