基于统计模型和GIS制图的印度马哈拉施特拉邦气候变化对棉花生产的影响分析

Q3 Agricultural and Biological Sciences
Anirup Sengupta, M. Thangavel
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引用次数: 1

摘要

棉花是一种重要的经济作物,用于生产纤维、食用油和油饼。全球环境问题,如气候变化,改变了棉花植物健康生长和发育所必需的天气参数,影响了纤维质量和经济产量。该研究旨在说明马哈拉施特拉邦气候变化的证据,并评估其对该地区棉花生产的影响。这项研究是在印度马哈拉施特拉邦进行的。基于矢量数据(马哈拉施特拉邦及其地区的地缘政治边界)和相应的栅格属性(气象数据)建立了基于地理信息系统(GIS)的模型,以研究1990 - 2015年期间温度、降雨和干旱严重程度(标准化降水指数- spi)的分布模式变化。此外,利用国际半干旱热带作物研究所(ICRISAT)的地区产量和气候参数数据,建立了统计多元线性回归模型,以估计因变量(棉花产量)和自变量(年降雨量和年平均温度)之间的关系。GIS建模和制图提供了降雨和温度空间分布变化的证据。虽然回归分析看起来很弱,但对于自然系统来说是可以接受的,因为自然系统是复杂的,而且往往是高度可变的,很难创建一个完美的模型。多元线性回归模型表明,气候参数的变化对棉花经济产量有显著的负向影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis of the Effects of Climate Change on Cotton Production in Maharashtra State of India Using Statistical Model and GIS Mapping
Cotton is a prominent cash crop cultivated for fiber, edible oil and oil cake. A global environmental issue, like climate change, alters weather parameters necessary for the healthy growth and development of cotton plants, affecting fiber quality and economic yield. The study aims to illustrate the evidence of climate change in Maharashtra and assess its impact on the production of cotton in this region. The study was conducted in the state of Maharashtra, India. Geographic information system (GIS)-based models were created based on the vector data (geopolitical boundaries of the state of Maharashtra and its districts) and the corresponding raster attributes (meteorological data) to examine the changes in the patterns of distribution of temperature, rainfall and severity of drought (Standardized Precipitation Index-SPI) over the study period (1990 to 2015). Further, a statistical multiple linear regression model was developed using district-wise data on yield and climatic parameters obtained from International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) to estimate the relationship between the dependent variable (yield of cotton) and the independent variables (annual rainfall and annual mean temperature). GIS modeling and mapping provide evidence of changes in the spatial distribution of rainfall and temperature. Although the regression analysis seems weak, it is acceptable for natural systems because natural systems are complex and often highly variable, making it difficult to create a perfect model. The multiple linear regression model shows that such changes in climatic parameters have a significant negative impact on the economic yield of cotton.
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