Abstract  

Jayalaxmi Agro Tech (JAT), a company based out of Bellary in Karnataka and co-founded by Anand Babu, strives to keep the Indian farmer informed about the modern best practices, thereby boosting the agricultural yield. The company’s flagship product is a suite of crop-specific mobile apps in several regional languages with heavy emphasis on audio-visual content to break the language and literacy barrier prevalent in rural areas. The farmer is empowered with the right information at the right time to make agriculture sustainable and more profitable.

JAT intended to collect data on sericulture (rearing of silkworms for producing raw silk) to improve income of silk producers. Karnataka is one of the largest producers of raw silk in India. Sericulture requires less investment but offers high returns if done correctly. Sericulture also involves cultivation of mulberry trees, the leaves of which are used to feed the silkworms. The yield of sericulture is heavily dependent on the quality of inputs such as the type of silkworm breed used, quality of the mulberry leaves and environmental conditions of the silkworm rearing house.

Jayalaxmi Agro Tech collected farmer level data on sericulture practices in the districts of Belagavi, Bellary, Chikballapur, Mandya, and Tumakuru in the state of Karnataka. The company wanted to analyze the data collected to gain insights so that they could make grassroot level impact by fine tuning sericulture as an occupation. These insights could possibly help towards building better policy interventions to improve the welfare of sericulture farmers.  

Learning Objective 

 The case contains instructional material on how to perform various hypothesis testing procedures like the Z-test, single sample t-test, two sample t-test, comparison of proportions, comparison of variances, chi-square test of independence, ANOVA and non-parametric tests like Kruskal-Wallis test. It also contains preliminary material on performing multiple linear regression analysis, stepwise regression, and post-regression diagnostics. Finally, imputation of a dataset can be demonstrated using the kNN imputation algorithm investigated in this case. The case can be used to introduce basic concepts in data science and business analytics courses.