scholarly journals Effect of Environmental Factors on Soil Nutrient Loss under Conditions of Mining Disturbance in a Coalfield

Forests ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1370
Author(s):  
Ziguan Wang ◽  
Guangcai Wang ◽  
Chengshu Wang ◽  
Xiaohui Wang ◽  
Meiling Li ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Water Conservation ◽  
Soil Nutrient ◽  
Soil Samples ◽  
Nutrient Loss ◽  
Available Phosphorus ◽  
Factors Affecting ◽  
Mining Soil

Underground coal mining can result in land deformation (e.g., land subsidence and ground fissures), and may consequently change the soil nutrients. Soil organic matter (SOM), total nitrogen (TN), and available phosphorus (AP) are critical indicators of soil fertility and eco-restoration in mining areas. In this study, soil samples (depth: 0–20 cm) were collected twice from 20 sampling points in pre-mining and post-mining in the No.12 panel of Caojiatan coalfield, in the Loess Plateau of China. SOM, TN, and AP in soil samples were measured, and the nutrient loss was evaluated. Ten environmental factors affecting soil nutrient loss were identified from a 5-m resolution digital elevation map (DEM). The paired t-test was utilized to evaluate the differences between SOM, TN, and AP in pre-mining and post-mining soil. The mechanisms of the effects of environmental factors on soil nutrient loss were revealed based on multiple linear regression, redundancy analysis (RDA), and the random forest algorithm (RF). Ordinary kriging and RF were utilized to predict and optimize the spatial distribution of the soil nutrient loss. The results showed that significant differences existed between the SOM, TN, and AP in the pre-mining and post-mining soil. The model established by RF provided a higher accuracy in terms of fitting the correlation between soil nutrient loss and environmental factors compared to the model established by multiple linear regression, and the feature importance obtained by RF showed that profile curvature, distance to working panel margin, and surface roughness were the most significant factors affecting the loss of SOM, TN, and AP, respectively. This study provides a theoretical reference for eco-restoration, as well as soil and water conservation, in subsided lands in coalfields.

scholarly journals FAKTOR-FAKTOR YANG MEMPENGARUHI PRODUKSI SUSU SAPI PERAH KELOMPOK TANI TERNAK SAPI PERAH (KTTSP) KANIA, KABUPATEN BOGOR

2019 ◽  
Vol 11 (2) ◽  
pp. 148-160
Author(s):  
Adam Adinegoro ◽  
Edmon Daris ◽  
Zulmanery Z
Keyword(s):  
Linear Regression ◽  
Dairy Cattle ◽  
Multiple Linear Regression ◽  
Milk Production ◽  
Regression Models ◽  
Linear Regression Models ◽  
Factors Affecting ◽  
Cattle Ranchers ◽  
Multiple Linear Regression Models

The purpose of this study are: (1) to identify and to analyze the factors that influence milk production of dairy cattles, and (2) to determine the elasticity of milk production. This research was conducted at the Dairy cattle group KANIA, Bogor. Data were obtained from interviews and questionnaires with cattle ranchers. Multiple linear regression models and elasticity calculations were employed to analyze the data with the Excel 2007 and software for Statistical Product and Service Solution (SPSS) version 16. Results of the analysis revealed that the factors affecting milk production is labor, forages, and feed concentrates. The result of the calculation of the elasticity indicated that all production variables are elastic variables.

scholarly journals Factors Affecting Company's Capability in Performing Integrated Reporting: An Empirical Evidence From Indonesian Companies

2018 ◽  
Vol 7 (2) ◽  
pp. 141
Author(s):  
Putu Sukma Kurniawan ◽  
Made Arie Wahyuni
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Institutional Ownership ◽  
Multiple Linear Regression Analysis ◽  
Managerial Ownership ◽  
Contrast Level ◽  
Integrated Reporting ◽  
Factors Affecting

<p>This study examines the factors that affect the company's capability to perform integrated reporting. The analysis used in testing the hypothesis is multiple linear regression analysis. Results show that company’s size has positive and significant connection and stakeholder’s pressure has negative and significant connection with the company’s capability in performing integrated reporting. In contrast, level of company’s profitability, company’s managerial ownership, and company’s institutional ownership did not have enough connection with company’s capability in performing integrated reporting.</p><p> </p>

scholarly journals ANALYSIS OF FACTORS AFFECTING THE PRICE FLUCTUATION OF CAYENNE PEPPER IN MALANG REGENCY

2021 ◽  
Vol 5 (1) ◽  
pp. 19-26
Author(s):  
Nurul Laili ◽  
Sri Hindarti ◽  
Dwi Susilowati
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Research Method ◽  
Commodity Prices ◽  
Secondary Data ◽  
Regression Method ◽  
Linear Regression Method ◽  
Factors Affecting ◽  
Independent Variables ◽  
Multiple Linear Regression Method

 This study aims to 1) Analyze the pattern of changes in commodity prices for spanish pepper in Malang District. 2) Analyzing the factors that influence fluctuations in the price of spanish pepper in Malang District. The research method used is quantitative method that uses secondary data in the form of time series obtained from several related agencies, namely the Central Statistics Agency of Malang District, Department of Industry and Trade, and Department of food crops, horticulture, and plantation in Malang District. Analysis of the data used is multiple linear regression with the dependent variable is the price at the consumer level from 2009-2018, while the independent variables use the data of the price of spanish pepper at the producer level, the amount of production, and the amount of consumption from 2009-2018. The study found that: 1) The development of the price of spanish pepper had a trend that tended to increase during the last 10 years. 2) From the results of data processing using multiple linear regression method with Eviews 9.0 application, it is found that the factor that significantly influences changes in the price of spanish pepper is the price at the producer level, while the amount of production of spanish pepper and the number of requests does not significantly affect the change in spanish pepper prices in Malang District. 

scholarly journals Factors Affecting Relative Growth Rate of Lettuce and Spinach in Hydroponics in a Greenhouse

HortScience ◽  
2017 ◽  
Vol 52 (12) ◽  
pp. 1742-1747
Author(s):  
Martin P.N. Gent
Keyword(s):  
Growth Rate ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Relative Growth Rate ◽  
Relative Increase ◽  
Solution Temperature ◽  
Area Index ◽  
Relative Growth ◽  
Factors Affecting ◽  
Daily Light Integral

Relative growth rate (RGR), the relative increase in weight per day, can analyze the effect of environment and nutrition on growth. I examined which of the parameters responding to plant growth scaled according to RGR for lettuce and spinach grown in heated greenhouses in hydroponics with control of the nutrient solution. The experiments for lettuce in 2006–08 included all times of year, high vs. low temperature, and effect of withdrawal of nitrogen. There were four parameters that were significant in multiple linear regression vs. RGR; irradiance divided by leaf area index if it was greater than one, or normalized daily light integral (NDLI), solution temperature, electrical conductivity (EC), and logarithm solution nitrate when it was between 3 and 55 mg·L−1 N. NDLI had the most significant coefficient, but the other parameters had regression coefficients more than three times se. For experiments on spinach in 2009–10, all the parameters mentioned previously were significant in multiple linear regression vs. RGR, except EC. The coefficient for NDLI in spinach was about half the value in lettuce. The coefficients for solution temperature and low nitrate were two and three times that in lettuce. In a third set of experiments on lettuce in 1996–98, solution temperature was the only significant parameter among those mentioned previously. The coefficient for solution temperature was similar to that for regression of lettuce in 2006–08.

scholarly journals Production Analysis of Green Mussel (Perna viridis) in Lampung Province

2020 ◽  
Vol 8 (2) ◽  
pp. 975
Author(s):  
Sulvina Sulvina ◽  
Zainal Abidin ◽  
Supono Supono
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Influential Factors ◽  
Perna Viridis ◽  
Factors Affecting ◽  
Cultivation Process ◽  
Quantitative Descriptive

This study was conducted to find out factors affecting and level of mussel production, level of efficiency of using the tools and materials in cultivation process and whether the cultivation of mussels in Pasaran. This study was analyzed using Cobb-Douglass. The study were analyzed in quantitative descriptive, multiple linear regression analysis, and analysis of efficiency. The dependent variable (Y) is the result of production of green mussels cultivation and free variables are the number of bamboos (X 1), the amount of strap (X2), grouper (X3) and labor (X4). Mussel fisherman in Pasaran NPM with Px calculated to obtain the level of efficiency of each factors of production in messels cultivation. Studies show that the most influential factors production are variable bamboo, rope and labor. While the results of analysis the level of efficiency of using tools and materials is not efficient. The number of bamboo and labor should be reduced, because it tends to be a waste and not profitable either technically or economically. The value of the return to scale of 1.22 showed cultivation mussels are on increasing return to scale.

scholarly journals Housing Price Prediction Based on Multiple Linear Regression

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Qingqi Zhang
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Housing Prices ◽  
Housing Price ◽  
Multiple Linear Regression Model ◽  
Data Set ◽  
Factors Affecting ◽  
Price Prediction

In this paper, the author first analyzes the major factors affecting housing prices with Spearman correlation coefficient, selects significant factors influencing general housing prices, and conducts a combined analysis algorithm. Then, the author establishes a multiple linear regression model for housing price prediction and applies the data set of real estate prices in Boston to test the method. Through the data analysis and test in this paper, it can be summarized that the multiple linear regression model can effectively predict and analyze the housing price to some extent, while the algorithm can still be improved through more advanced machine learning methods.

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