Open AccessInnovative trend analysis of rainfall in relation to soybean productivity over western Maharashtra
Author(s) -
R.N. Singh,
Sonam Sah,
Gaurav Chaturvedi,
Bappa Das,
Himanshu Pathak
Publication year - 2021
Publication title -
journal of agrometeorology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 11
eISSN - 2583-2980
pISSN - 0972-1665
DOI - 10.54386/jam.v23i2.73
Subject(s) - trend analysis , spearman's rank correlation coefficient , rank correlation , productivity , linear regression , monsoon , series (stratigraphy) , environmental science , time series , lag , regression analysis , mathematics , statistics , physical geography , climatology , geography , meteorology , biology , geology , paleontology , computer network , computer science , economics , macroeconomics
This study examined and compared the new innovative trend analysis (ITA) of monthly, seasonal and annual rainfall with traditional trend analysis methods in relation to soybean productivity in western Maharashtra. Spearman’s rank correlation, Mann-Kendall and its 6 different modifications were used to analyze the trends of rainfall, whereas Spearman’s rho, simple linear regression and Sen’s slope with two different modifications were employed to quantify the magnitude of trends at 1%, 5% and 10% level of significance. Autocorrelation coefficient was calculated at lag-1 and tested at 5% level of significance. Rainfall variability of the region is very high (CV>30) in all the months and seasons with positively skewed rainfall distribution. Our results revealed that out of 34-time series data analyzed, ITA was able to ide ntify all the significant trends (11 -time series) that can be detected by traditional methods. Meanwhile, ITA also identified trends in 17-time series which cannot be detected by any of the traditional methods. The study revealed significant increase in monsoon and annual rainfall values, which is helpful in sustaining soybean productivity in the western parts of the Maharashtra.