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Value chain analysis of malt barley in North western part of Ethiopia

COGENT SOCIAL SCIENCES 2021年第1期7卷

作者： Kassaw, Metadel Teshome, Akalu Chanie, Essa Addis, Yonnas Wolkite Univ Dept Agribusiness & Value Chain Management POB 07 Wolkite Ethiopia Univ Gondar Dept Agr Econ Gondar Ethiopia Bilateral Ethiopia Netherlands Partnership Food I Adama Ethiopia

This research attempted to analyze value chain of Malt barley in North Western part of Ethiopia in Estie District. The primary data were collected from 121 malt barley producer farmers selected through two stage sampling technique and from other respective actors. To analyze the data, both descriptive and econometric analytical tools were applied. From Malt barley value chain actors, the chain is governed mainly by malt factory with the assistance of primary cooperatives and cooperative union. From three market channels, channel three product passes from produces to malt factory through primary cooperatives and union is the major marketing channel for malt barley product. The highest gross marketing margin (62.9%) was shared by malt factory, but the profit margin was highest for producers (53.4%). multiple linear regression model results show that six variables such as education level of the household, farming experiences in malt barley production, choice of contract agreement, availability of active labor force, credit access and land allocated to malt barley production significantly and positively affect the quantity supply of malt barley product. The results of the study, therefore, suggested that policy implications drawn from the study findings include the need to improve the input supply such as improved seed, improving farmers' knowledge and experience on malt barley production, encouraging adult education, expanding accessibility of credit and improve market infrastructure.

关键词： Value chain analysis malt barley margin value share marketing channel multiple linear regression model

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The spatial characteristics and relationships between landscape pattern and ecosystem service value along an urban-rural gradient in Xi'an city, China

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ECOLOGICAL INDICATORS 2020年 108卷

作者： Hou, Lei Wu, Faqi Xie, Xinli Northwest A&F Univ Inst Soil & Water Conservat Yangling 712100 Shaanxi Peoples R China Northwest A&F Univ Coll Resources & Environm Yangling 712100 Shaanxi Peoples R China

The spatial structure and function of Xi'an city is significantly affected by urbanization, a factor which can be considered as the main driver of landscape pattern and ecosystem service change. Due to these changes are a response to urban land use and land cover (LULC), remote sensing images are interpreted by the method of supervised classification and visual interpretation to obtain the LULC data for research on landscape pattern index (LPI) and ecosystem service value (ESV) of Xi'an city, China. Combined with urban planning theory, concentric buffer zones were used to explore the characteristics and relationships between LPIs and ESVs along an urban-rural gradient. Ten landscape indices and nine ecosystem service types were selected to analyze the correlation and construct multiple linear regression models, and principal component analysis (PCA) was used to eliminate the problem of multi-collinearity in the process of model construction. Results indicate that the highest landscape fragmentation was mainly distributed in the urban-rural fringe, 20-35 km from the urban center, and patch density (PD), edge density (ED), Shannon's diversity index (SHDI), and landscape shape index (LSI) recorded the highest values. Total ESV of Xi'an city was 20493.65 x 10(6) CNY in 2016, and the mean value of ESV increased from 0.06 x 10(6) CNY to 2.60 x 10(6) CNY along an urban-rural gradient. This finding indicates that a higher ESV was recorded for the natural landscape. Results also indicated that SHAPE_MN, FRAC_MN, PLADJ, and AI recorded a positive effect on total ESV whilst ED and PD have a negative effect on total ESV. Results from the regression models showed quantitative relationships between ESVs and LPIs which revealed how ecosystem service values were affected by the landscape pattern. This study will improve the quantitative assessment method on landscape ecology and provide a basis for further research on city's landscape pattern and ecosystem balance, especially for d

关键词： Gradient analysis Landscape metrics Concentric buffer zone PCA multiple linear regression model

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Evaluation of the Performance of Low-Cost Air Quality Sensors at a High Mountain Station with Complex Meteorological Conditions

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ATMOSPHERE 2020年第2期11卷

作者： Li, Hongyong Zhu, Yujiao Zhao, Yong Chen, Tianshu Jiang, Ying Shan, Ye Liu, Yuhong Mu, Jiangshan Yin, Xiangkun Wu, Di Zhang, Cheng Si, Shuchun Wang, Xinfeng Wang, Wenxing Xue, Likun Shandong Univ Environm Res Inst Qingdao 266237 Peoples R China Taishan Natl Reference Climatol Stn Tai An 271000 Shandong Peoples R China Taian Ecol Environm Command & Control Ctr Tai An 271000 Shandong Peoples R China Shandong Univ Sch Phys Jinan 250100 Peoples R China

Low-cost sensors have become an increasingly important supplement to air quality monitoring networks at the ground level, yet their performances have not been evaluated at high-elevation areas, where the weather conditions are complex and characterized by low air pressure, low temperatures, and high wind speed. To address this research gap, a seven-month-long inter-comparison campaign was carried out at Mt. Tai (1534 m a.s.l.) from 20 April to 30 November 2018, covering a wide range of air temperatures, relative humidities (RHs), and wind speeds. The performance of three commonly used sensors for carbon monoxide (CO), ozone (O-3), and particulate matter (PM2.5) was evaluated against the reference instruments. Strong positive linear relationships between sensors and the reference data were found for CO (r = 0.83) and O-3 (r = 0.79), while the PM2.5 sensor tended to overestimate PM2.5 under high RH conditions. When the data at RH >95% were removed, a strong non-linear relationship could be well fitted for PM2.5 between the sensor and reference data (r = 0.91). The impacts of temperature, RH, wind speed, and pressure on the sensor measurements were comprehensively assessed. Temperature showed a positive effect on the CO and O-3 sensors, RH showed a positive effect on the PM sensor, and the influence of wind speed and air pressure on all three sensors was relatively minor. Two methods, namely a multiple linear regression model and a random forest model, were adopted to minimize the influence of meteorological factors on the sensor data. The multi-linear regression (MLR) model showed a better performance than the random forest (RF) model in correcting the sensors' data, especially for O-3 and PM2.5. Our results demonstrate the capability and potential of the low-cost sensors for the measurement of trace gases and aerosols at high mountain sites with complex weather conditions.

关键词： air quality monitoring low-cost sensors evaluation and correction random forest model multiple linear regression model

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Prediction of urban expressway total traffic accident duration based on multiple linear regression and artificial neural network 5

Prediction of urban expressway total traffic accident durati...

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5th International Conference on Transportation Information and Safety (ICTIS)

作者： Zhang, Jie Wang, Junhua Fang, Shouen Tongji Univ Minist Educ Key Lab Rd & Traff Engn Shanghai Peoples R China Tongji Univ Minist Educ Engn Res Ctr Rd Traff Safety & Environmet Shanghai Peoples R China

ISBN: (纸本)9781728104898

Traffic accident duration prediction provides an important basis for traffic mitigation measures after accidents. An accident duration identification method based on velocity thermogram has been established in order to obtain the traffic recovery time after the accident vehicles were removed from the scene. The AIC (Akaike Information Criterion) and the BIC (Bayesian Information Criterion) were applied to fit the probability distribution of the accident duration, and the results showed that the lognormal distribution fitted best. Two multiple linear regression models predicting total duration and clearance time respectively have been constructed based on 9 variables including temporal, spatial, environmental, traffic and accident detail variables. Results showed that traffic condition, location type, accident type and police presence significantly affected total accident duration, while response time and accident type played a significant role in predicting clearance time. After deleting the samples with too short duration, the performance of the two models were both significantly improved, and MAPE were 27.1% and 49.8% respectively. In order to test the performance of the multiple linear regression model, two artificial neural network (ANN) models were also established for comparison. The results of ANN showed more prediction errors and less stability. In general, the multiple linear regression models performed better than ANN.

关键词： accident duration duration prediction multiple linear regression model velocity thermogram artificial neural network

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Sorption Capacity of Pesticides on Soil in a Predominant Apple Cultivation Area

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SOIL & SEDIMENT CONTAMINATION 2020年第1期29卷 107-119页

作者： Kumari, Dev John, Siby Punjab Engn Coll Civil Engn Dept Sect 12 Chandigarh India

Laboratory-based batch sorption experiments were performed using six pesticides and 12 soil samples collected from the predominant apple cultivation areas of Kinnaur district, Himachal Pradesh (India). Freundlich isotherm model was used to determine the sorption coefficients. Soil properties such as texture, pH, organic matter content (OM), cation exchange capacity (CEC), total dissolved solids (TDS), and clay content were measured for different soil samples. The principal component analysis showed that the sorption of pesticides was considerably affected by organic matter in the soil. multiple linear regression models were proposed correlating the sorption coefficients and soil properties. The models and maps developed in this study could be used to estimate the mobility risk of pesticides and to evolve pesticide pollution management plans.

关键词： Pesticides sorption soil properties GIS mapping multiple linear regression model

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Comparison of Canopy Closure Estimation of Plantations Using Parametric, Semi-Parametric, and Non-Parametric models Based on GF-1 Remote Sensing Images

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FORESTS 2020年第5期11卷

作者： Li, Jiarui Mao, Xuegang Northeast Forestry Univ Sch Forestry Harbin 150040 Peoples R China Northeast Forestry Univ Minist Educ Key Lab Sustainable Forest Ecosyst Management Harbin 150040 Peoples R China

Canopy closure (CC) is an important parameter in forest ecosystems and has diverse applications in a wide variety of fields. Canopy closure estimation models, using a combination of measured data and remote sensing data, can largely replace traditional survey methods for CC. However, it is difficult to estimate the forest CC based on high spatial resolution remote sensing images. This study used China Gaofen-1 satellite (GF-1) images, and selected China's north temperate Wangyedian Forest Farm (WYD) and subtropical Gaofeng Forest Farm (GF) as experimental areas. A parametric model (multiple linear regression (MLR)), non-parametric model (random forest (RF)), and semi-parametric model (generalized additive model (GAM)) were developed. The ability of the three models to estimate the CC of plantations based on high spatial resolution remote sensing GF-1 images and their performance in the two experimental areas was analyzed and compared. The results showed that the decision coefficient (R-2), root mean square error (RMSE), and relative root mean square error (rRMSE) values of the parametric model (MLR), semi-parametric model (GAM), and non-parametric model (RF) for the WYD forest ranged from 0.45 to 0.69, 0.0632 to 0.0953, and 9.98% to 15.05%, respectively, and in the GF forest theR(2), RMSE, and rRMSE values ranged from 0.40 to 0.59, 0.0967 to 0.1152, and 16.73% to 19.93%, respectively. The best model in the two study areas was the GAM and the worst was the RF. The accuracy of the three models established in the WYD was higher than that in the GF area. The RMSE and rRMSE values for the MLR, GAM, and RF established using high spatial resolution GF-1 remote sensing images in the two test areas were within the scope of existing studies, indicating the three CC estimation models achieved satisfactory results.

关键词： canopy closure multiple linear regression model random forest generalized addictive model GF-1

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Design and monitoring of a hybrid energy system: performance analysis and modelling

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COGENT ENGINEERING 2021年第1期8卷

作者： Tangwe, Stephen Sikhonza, Mandlenkosi Univ Ft Hare Inst Technol Dept Sci & Agr Alice South Africa Univ Ft Hare Fac Sci & Agr Dept Phys Alice South Africa

The utilization of a hybrid energy system (combined solar water heater (SWH) and an air source heat pump (ASHP) water heater) can result in over 80% reduction in the electrical energy consumed as the system is capable to operate with an energy factor of above 4.0. A major challenge is to develop credible methodology or mathematical model to predict energy savings. The research focused on the design and installation of a hybrid energy system and a data acquisition system to monitor its performance. The average weekday volume of hot water consumed, thermal energy gained by water in the tank of the air source heat pump (ASHP) water heater, electrical energy consumed, and the COP were 225.03 L, 5.25 kWh, 1.52 kWh, and 3.50. The average weekday global solar radiation, ambient temperature, solar fraction of the solar water heater (SWH) and the energy factor of the hybrid energy system were 579.67 W/m(2), 23.58 degrees C, 0.52, and 4.02, respectively. A multiple linear regression model was developed to predict the energy factor of the hybrid energy system. Both the modelled and validated results showed very good determination coefficients of 0.952 and 0.935, with the trained and validated dataset. Hence, by employing both multiple linear regression model and a multiple 2D contour plot simulation, the energy factor and the variation of the input parameters can be accurately determined. The developed model can help homeowners, energy service companies, and policy makers to appreciate and confidently support the rollout of the technology for sanitary water heating.

关键词： Hybrid solar water heater and air source heat pump water heater solar fraction coefficient of performance energy factor data acquisition system multiple linear regression model

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Development of Pedo-Transfer Functions for the Saturated Hydraulic Conductivity of Forest Soil in South Korea Considering Forest Stand and Site Characteristics

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WATER 2020年第8期12卷

作者： Lim, Honggeun Yang, Hyunje Chun, Kun Woo Choi, Hyung Tae Natl Inst Forest Sci Forest Conservat Dept Seoul 02455 South Korea Kangwon Natl Univ Dept Forest Resources Chunchon 24341 South Korea

The saturated hydraulic conductivity (Ks) is one of the most important soil properties for many hydrological simulation models. Especially in South Korea, analyzing theKsof the forest soil is essential for understanding the water cycle throughout the country, because forests cover almost two-thirds of the whole country. However, few studies have focused on the forest soil in the temperate climate zone on a nationwide scale. In this study, 1456 forest soil samples were collected throughout South Korea and pedo-transfer functions employed to predict theKswere developed. The non-linearities of the soil and topographic features were considered with the pretreatment of variables, and the variance inflation factor was used for treating the multicollinearity problem. The forest stand and site characteristics were also categorized by an ANOVA and post hoc test due to their diversity. As a result, theKsvalues were different for various forest stands and site characteristics, which was statistically significant. Additionally, the model performance was higher when both soil properties and topographic features were considered. The sensitivity analysis showed that theKswas highly affected by the bulk density, sand fraction, slope, and upper catchment area. Therefore, the topographic features were as important in predicting theKsas the soil properties of the forest soil.

关键词： pedo-transfer function saturated hydraulic conductivity forest soil forest stand soil properties topographic features multiple linear regression model sensitivity analysis

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Research on Estimation Method of Surface Soil Moisture Content Based on Multi-star Fusion 1

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10th China Satellite Navigation Conference (CSNC)

作者： Huang, Huasheng Liang, Yueji Yuan, Mingli Qiu, Yifan Huang, He Ren, Chao Guilin Univ Technol Coll Geomat & Geoinformat Guilin Peoples R China Guangxi Key Lab Spatial Informat & Geomat Res Ctr Precise Engn Surveying Guilin Peoples R China

ISBN: (数字)9789811377518

ISBN: (纸本)9789811377518;9789811377501

Global Positioning System Interferometric Reflectance (GPS-IR) is a new remote sensing technique, which can be used to estimate soil moisture content near the surface. From the view of multi-satellite fusion, an estimating method of surface soil water content based on multi-satellite fusion is proposed. Firstly, the direct and reflected signals of multiple satellites are separated by low order polynomial fitting, and then the sinusoidal fitting model of the reflected signals is established to obtain the relative delay phase. Secondly, the multiple linear regression inversion model of soil moisture is established, and the input variable set of the model is determined by the correlation coefficient of each satellite. Finally, the advantage of multi-satellite fusion is brought into full play to retrieve soil moisture. The feasibility and effectiveness of single satellite and multiple satellite fusion inversion are compared and analyzed through the monitoring data provided by the Plate Boundary Observation (PBO). The experimental results show that the multiple linear regression model can realize the effective fusion of multiple satellites. Compared with the single satellite, the inversion accuracy is higher and the inversion error is more stable.

关键词： GPS-IR Soil moisture Multi-satellite fusion multiple linear regression model

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Research on the Influence of China's Dry Bulk Importation on International Dry Bulk Shipping Demand

Frontiers in Economics and Management

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Frontiers in Economics and Management 2020年第11期1卷 149-168页

作者： Liu Chen

In recent years, with the rapid development of China's economy, China has received extensive attention from all over the world. As a major importer of the dry bulk market, China's development status and policy measures affect market demand. First, this article analyzes the demand for dry bulk shipping and its impact on freight rates. Secondly, this article conducts a detailed analysis of the dry bulk market demand and finds that the overall market demand shows an increasing trend, and the seaborne import of three main dry bulks still accounts for a large proportion. Aiming at the three main dry bulk, namely iron ore, coal (coking coal, thermal coal), and grain, this paper uses an improved grey correlation model to calculate the degree of influence of the world's major importing countries on the total shipping demand of each cargo. The calculation results show that China's influence on the total demand for iron ore, coking coal, and grain by sea ranks first in the world. It can be said that the trend of China's dry bulk imports will have a significant impact on the international dry bulk market demand. Finally, this article predicts the import trend of China's three major dry bulk cargoes and analyzes their impact on the future international dry bulk market demand. Unified, this article also provides reference for relevant market participants and their decision-making.

关键词： Global trade Improved grey relational model multiple linear regression model

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