Nikhilesh Bagade, Bakul Rao, S. Kedare, Mukul Yambal, Shraddha Vekhande
{"title":"Parameters Influencing the Farm Net Income in an Agrarian Distressed Area of India","authors":"Nikhilesh Bagade, Bakul Rao, S. Kedare, Mukul Yambal, Shraddha Vekhande","doi":"10.5296/jsr.v13i2.20191","DOIUrl":null,"url":null,"abstract":"Rural poverty in India is linked with agricultural earnings, which can be assessed with Farm Net Income (FNI). This study identified the parameters significantly affecting the FNI for Yavatmal, India, during 2016-17 and 2017-18. The R 2 values computed by Multivariable Linear Regression Analysis for explaining FNI are 73% and 79% for 2016-17 and 2017-18. In 2017-18, the FNI was influenced by household (HH) head education, working population in HH, operating farmland, agriculture machine usage, and livestock sale. This study can help farmers in their farm-decisions for improving FNI and policy makers for designing and implementation of farm policies and schemes.","PeriodicalId":239220,"journal":{"name":"Journal of Sociological Research","volume":"172 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Sociological Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5296/jsr.v13i2.20191","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Rural poverty in India is linked with agricultural earnings, which can be assessed with Farm Net Income (FNI). This study identified the parameters significantly affecting the FNI for Yavatmal, India, during 2016-17 and 2017-18. The R 2 values computed by Multivariable Linear Regression Analysis for explaining FNI are 73% and 79% for 2016-17 and 2017-18. In 2017-18, the FNI was influenced by household (HH) head education, working population in HH, operating farmland, agriculture machine usage, and livestock sale. This study can help farmers in their farm-decisions for improving FNI and policy makers for designing and implementation of farm policies and schemes.