HighlightsCracking and subsequent breakage of rice kernels reduces the marketability and profitability of rice.Pre-milling cracks in rice kernels cause breakage during milling, thereby reducing consumer acceptability.Three types of post-milling cracks reported are: surface, internal, and Hanasaki cracks.Post-milling cracks can be minimized throughout the supply chain.Abstract. Rice is consumed as intact grain, and any broken grains are discounted from the main marketable product. Breakage of rice mainly arises from cracks formed in the endosperm before or after milling. The cracks are formed by stress gradients that arise due to moisture absorption or desorption by grains. As a result of such stress, cracks mostly develop in a direction perpendicular to the length of the grain, making it less physically resistant to the stresses of milling, handling, and soaking processes. Until now, research into rice cracking has mainly focused on minimizing breakage during milling, and no significant knowledge is available on the impact and mechanisms of post-milling cracking and/or breakage and its effect on the downstream quality of rice. This article aims to review the existing information on the causes of rice cracking before and after milling. Keywords: Breakage, Crack, Drying, Glass transition temperature, Hanasaki, Head rice yield, Rice, Tempering.
{"title":"Understanding Pre- and Post-Milling Crack Formation in Rice Grain","authors":"P. Oli, Mark Talbot, P. Snell","doi":"10.13031/trans.14649","DOIUrl":"https://doi.org/10.13031/trans.14649","url":null,"abstract":"HighlightsCracking and subsequent breakage of rice kernels reduces the marketability and profitability of rice.Pre-milling cracks in rice kernels cause breakage during milling, thereby reducing consumer acceptability.Three types of post-milling cracks reported are: surface, internal, and Hanasaki cracks.Post-milling cracks can be minimized throughout the supply chain.Abstract. Rice is consumed as intact grain, and any broken grains are discounted from the main marketable product. Breakage of rice mainly arises from cracks formed in the endosperm before or after milling. The cracks are formed by stress gradients that arise due to moisture absorption or desorption by grains. As a result of such stress, cracks mostly develop in a direction perpendicular to the length of the grain, making it less physically resistant to the stresses of milling, handling, and soaking processes. Until now, research into rice cracking has mainly focused on minimizing breakage during milling, and no significant knowledge is available on the impact and mechanisms of post-milling cracking and/or breakage and its effect on the downstream quality of rice. This article aims to review the existing information on the causes of rice cracking before and after milling. Keywords: Breakage, Crack, Drying, Glass transition temperature, Hanasaki, Head rice yield, Rice, Tempering.","PeriodicalId":23120,"journal":{"name":"Transactions of the ASABE","volume":"8 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73530060","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}
HighlightsThe placement and operation of exhaust fans was assessed using CFD simulation.The effective temperature was used to evaluate the indoor thermal environment.The placement and operation of the exhaust fans mainly affected the airflow patterns in the part of the layer house closest to the fans.Abstract. The thermal environment inside a layer house significantly affects the growth, production, and health of the hens. Tunnel ventilation systems have been widely applied to control the indoor climate and air quality for large-scale poultry facilities around the world. Generally, only a few of the exhaust fans operate during mild seasons (spring and fall) in a tunnel-ventilated layer house depending on the outside air temperature. The decision about which exhaust fans to turn on affects the indoor airflow pattern and temperature distribution. However, little research has been reported that investigated the effects of the locations of exhaust fans on ventilation performance. In this study, a computational fluid dynamics (CFD) model was built and validated using field-measured data. The CFD model was then used to evaluate different ventilation strategies (combinations of exhaust fans) in a typical tunnel-ventilated layer house during the fall. The effective temperature was used to assess the performance of different ventilation strategies. Results showed that the locations of the exhaust fans significantly affected the indoor thermal environment, especially in the part of the house closest to the fans, because different locations of operating fans can generate different airflow patterns and affect the airflow through the animal-occupied zone. Based on the simulations, we conclude that the placement and operation of the exhaust fans can be optimized. Turning on the fans that are lower to the ground or near the sidewalls will result in more air bypassing the animal-occupied zones. Our results can help select the best ventilation strategy during the spring and fall in layer houses with tunnel ventilation systems. Keywords: Airflow distribution, Effective temperature distribution, Indoor thermal environments, Ventilation strategy.
{"title":"Numerical Simulation of the Placement of Exhaust Fans in a Tunnel-Ventilated Layer House During the Fall","authors":"Xiaoshuai Wang, Jiangong Li, Jiegang Wu, Qianying Yi, Xin-lei Wang, Kaiying Wang","doi":"10.13031/TRANS.14657","DOIUrl":"https://doi.org/10.13031/TRANS.14657","url":null,"abstract":"HighlightsThe placement and operation of exhaust fans was assessed using CFD simulation.The effective temperature was used to evaluate the indoor thermal environment.The placement and operation of the exhaust fans mainly affected the airflow patterns in the part of the layer house closest to the fans.Abstract. The thermal environment inside a layer house significantly affects the growth, production, and health of the hens. Tunnel ventilation systems have been widely applied to control the indoor climate and air quality for large-scale poultry facilities around the world. Generally, only a few of the exhaust fans operate during mild seasons (spring and fall) in a tunnel-ventilated layer house depending on the outside air temperature. The decision about which exhaust fans to turn on affects the indoor airflow pattern and temperature distribution. However, little research has been reported that investigated the effects of the locations of exhaust fans on ventilation performance. In this study, a computational fluid dynamics (CFD) model was built and validated using field-measured data. The CFD model was then used to evaluate different ventilation strategies (combinations of exhaust fans) in a typical tunnel-ventilated layer house during the fall. The effective temperature was used to assess the performance of different ventilation strategies. Results showed that the locations of the exhaust fans significantly affected the indoor thermal environment, especially in the part of the house closest to the fans, because different locations of operating fans can generate different airflow patterns and affect the airflow through the animal-occupied zone. Based on the simulations, we conclude that the placement and operation of the exhaust fans can be optimized. Turning on the fans that are lower to the ground or near the sidewalls will result in more air bypassing the animal-occupied zones. Our results can help select the best ventilation strategy during the spring and fall in layer houses with tunnel ventilation systems. Keywords: Airflow distribution, Effective temperature distribution, Indoor thermal environments, Ventilation strategy.","PeriodicalId":23120,"journal":{"name":"Transactions of the ASABE","volume":"30 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73283127","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. T. Sheffield, J. Dvorak, Bolling W. Smith, Cynthia Arnold, Cameron Minch
HighlightsModels using LiDAR measurements and field observations as predictors can accurately predict alfalfa canopy height.The most efficient model used only the 95th percentile of LiDAR-measured height to estimate canopy height.Adding field observations of weed, insect, and disease pressure only marginally improved the predictive models.Abstract. Alfalfa is a popular crop that is grown worldwide because it is a nutritious feed for livestock and fixes nitrogen in the soil. Profitable alfalfa production greatly relies on monitoring the status of the alfalfa crop. Traditionally, producers have used crop assessment techniques that rely on manual measurements of alfalfa plant height, which can be used to predict nutritive quality and yield. These manual measurements are often labor-intensive and provide low-resolution data that is not acceptable for field-scale monitoring. The goal of this study was to assess the capability of a simple LiDAR setup to accurately estimate the average canopy height of an alfalfa crop. To achieve this goal, we first developed predictive models of alfalfa canopy height using LiDAR-derived measurements as predictor variables. Second, we assessed the accuracies of the models and compared the properties of each model. Third, we determined the optimal LiDAR-derived measurements to use to accurately predict average alfalfa canopy height. The data used in our models were collected in two separate fields planted with two different cultivars of alfalfa. Data collection was performed on five dates spanning one entire growth cycle during the summer of 2019. A simple single-beam LiDAR sensor was used to scan the canopy of sample plots within the fields. Manual measurements of plant height and field observations of insect, disease, and weed pressure were also recorded. Of the data used in the predictive models, the 95th percentile of LiDAR-measured height was found to be the optimal predictor for estimating alfalfa canopy height. Using the 95th percentile as a single predictor in a linear regression model of measured average canopy height resulted in an R2 of 0.90 and RMSE of 4.5 cm. Two other linear regression models using a combination of LiDAR measurements and LiDAR measurements with alfalfa health observations, respectfully, were developed for comparison. These models exhibited marginally better accuracies but required more inputs than the model only using the 95th percentile. This study shows how simple LiDAR configurations can be used for timely collection of accurate alfalfa canopy height data. From our findings, we suggest using the 95th percentile of LiDAR-derived canopy height to estimate alfalfa canopy height. This study lays the groundwork for research into more advanced LiDAR configurations for alfalfa applications, such as LiDAR-equipped UAVs. Keywords: Alfalfa, Canopy height, LiDAR.
{"title":"Using LiDAR to Measure Alfalfa Canopy Height","authors":"S. T. Sheffield, J. Dvorak, Bolling W. Smith, Cynthia Arnold, Cameron Minch","doi":"10.13031/trans.14492","DOIUrl":"https://doi.org/10.13031/trans.14492","url":null,"abstract":"HighlightsModels using LiDAR measurements and field observations as predictors can accurately predict alfalfa canopy height.The most efficient model used only the 95th percentile of LiDAR-measured height to estimate canopy height.Adding field observations of weed, insect, and disease pressure only marginally improved the predictive models.Abstract. Alfalfa is a popular crop that is grown worldwide because it is a nutritious feed for livestock and fixes nitrogen in the soil. Profitable alfalfa production greatly relies on monitoring the status of the alfalfa crop. Traditionally, producers have used crop assessment techniques that rely on manual measurements of alfalfa plant height, which can be used to predict nutritive quality and yield. These manual measurements are often labor-intensive and provide low-resolution data that is not acceptable for field-scale monitoring. The goal of this study was to assess the capability of a simple LiDAR setup to accurately estimate the average canopy height of an alfalfa crop. To achieve this goal, we first developed predictive models of alfalfa canopy height using LiDAR-derived measurements as predictor variables. Second, we assessed the accuracies of the models and compared the properties of each model. Third, we determined the optimal LiDAR-derived measurements to use to accurately predict average alfalfa canopy height. The data used in our models were collected in two separate fields planted with two different cultivars of alfalfa. Data collection was performed on five dates spanning one entire growth cycle during the summer of 2019. A simple single-beam LiDAR sensor was used to scan the canopy of sample plots within the fields. Manual measurements of plant height and field observations of insect, disease, and weed pressure were also recorded. Of the data used in the predictive models, the 95th percentile of LiDAR-measured height was found to be the optimal predictor for estimating alfalfa canopy height. Using the 95th percentile as a single predictor in a linear regression model of measured average canopy height resulted in an R2 of 0.90 and RMSE of 4.5 cm. Two other linear regression models using a combination of LiDAR measurements and LiDAR measurements with alfalfa health observations, respectfully, were developed for comparison. These models exhibited marginally better accuracies but required more inputs than the model only using the 95th percentile. This study shows how simple LiDAR configurations can be used for timely collection of accurate alfalfa canopy height data. From our findings, we suggest using the 95th percentile of LiDAR-derived canopy height to estimate alfalfa canopy height. This study lays the groundwork for research into more advanced LiDAR configurations for alfalfa applications, such as LiDAR-equipped UAVs. Keywords: Alfalfa, Canopy height, LiDAR.","PeriodicalId":23120,"journal":{"name":"Transactions of the ASABE","volume":"12 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74131015","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":"30 1","pages":""},"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}
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":"118 1","pages":""},"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}
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":"17 1","pages":"0"},"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}
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":"32 1","pages":"461-474"},"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":"59 1","pages":"221-230"},"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}
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":"27 1","pages":""},"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}
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":"7 1","pages":""},"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}
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