Long-term temperature trend analysis associated with agriculture crops

Abstract

Temperature is one of the most significant elements in climate and weather forecasting. There was an increase in the earth’s surface (land and ocean) temperature by 0.6 ± 0.2 °C during 1901–2000 (NOAA, Global Climate Report 2017). In evaluating the effects of climate change, the spatiotemporal variability of temperature was examined in the Chhattisgarh State, India, using monthly data at 16 stations over the period 1901–2016 with a length of 116 years. The standard normal homogeneity test was used to evaluate the homogeneity of temperature data. Linear regression analysis and four altered versions of the Mann-Kendall (MK) method were utilized to analyze the existence of trends in temperature series. These four versions of the MK tests include the conventional Mann-Kendall method (MK1), the removed influence of noteworthy lag-1 autocorrelation (MK2), the removed influence of all noteworthy autocorrelation coefficients (MK3) and the considered Hurst coefficient (MK4). The results of both parametric and non-parametric tests indicated an increase in the annual and seasonal temperature in the Chhattisgarh State over the period 1901–2016. The most likely change year in the state was 1950. There was a decreasing trend at some stations during the period 1901–1950, which reversed in the following period 1951–2016. Overall, annual and seasonal temperature time series showed increasing trends in all stations over the course of the long-term period. Our results confirmed a fact that the agriculture crop production has been decreased due to climate change.