气候变化与孟加拉国水稻生产:利用多元线性回归建模技术寻找最佳预期因素

Mst. Noorunnahar, Rabbani Rushsa, Keya Rani Das
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摘要

温度、降雨、湿度、二氧化碳和太阳辐射等气候因素对农业生产有显著影响。孟加拉国主要是一个以农业为基础的发展中国家。水稻(Oryza sativa L.)是孟加拉国人民的主要食物,在他们的常规均衡饮食中也占很大比例。已经进行了许多研究,以确定气候变率和变化对孟加拉国水稻生产力的影响。本研究旨在探讨水稻产量与气候变量(即平均温度、降雨量、CO2和湿度)之间的关系,并找出对水稻产量有实际影响的最佳模型。从众多可能的变量中选择“潜在预测因子”来影响预测变量,并利用潜在预测因子子集研究最合适的模型是拟合多元线性回归模型的两个主要困难。采用R软件,采用最佳子集回归和逐步回归对模型进行拟合。结果表明,温度和CO2对水稻产量的影响分别在5%和1%的显著水平上具有统计学意义。从调整后的R2来看,气候参数占水稻产量变化的17.39%。根据模型Cp和AIC值,温度和CO2是最好的预测因子,逐步回归也支持这一发现。已如此成功地拟合的模式被认为是非常重要的,显示出有关的规划人员和决策者在现实中使用它的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Climate Change and Rice Production in Bangladesh: Finding the Best Prospective Factors Using Multiple Linear Regression Modeling Techniques
Climatic factors like temperature, rainfall, humidity, CO2 and solar radiation significantly impact agricultural production. Bangladesh is primarily an agriculture-based developing country. Rice (Oryza sativa L.), the main food of Bangladeshi people also provides a significant percentage of their regular, balanced diet. Many studies have been conducted to determine the effects of climate variability and change on rice productivity in Bangladesh. This study aimed to investigate the relationship between rice crop production and climate variables (namely, average temperature, rainfall, CO2, and humidity) and find out the best model that has an actual impact on rice production. Selecting 'potential predictors' from numerous possible variables to influence the forecast variable and investigating the most appropriate model with a subset of the potential predictors are two major difficulties of fitting the multiple linear regression model. Best subset regression and stepwise regression were used to fit the model using R software. Our results revealed that temperature and CO2 were statistically significant for rice production at 5% and 1% levels of significance respectively. From Adjusted R2, climatic parameters account for 17.39 percent of the variation in rice production. Temperature and CO2 are the best predictors, according to model Cp and AIC values, and stepwise regression also supports this finding. The model that had been so successfully fitted was considered to be highly significant, demonstrating its potential for use in reality by the concerned planners and policymakers.
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