Farm typology for planning targeted farming systems interventions for smallholders in Indo-Gangetic Plains of India

Jashanjot Kaur,M L Pasha,Kohima Noopur,A S Panwar,N Ravisankar,Poonam Kashyap,S Chatterjee,Roos Adelhart Toorop,S S Walia,Santiago López-Ridaura,Luis Barba-Escoto,Jeroen C J Groot,Subhash Babu,M Shamim,A K Prusty,M L Jat

doi:10.1038/s41598-021-00372-w

Abstract

Due to complexity of smallholder farms, many times technologies with great potential fail to achieve the desired impact in leveraging productivity and profitability of the farming community. In the Indo-Gangetic Plains there is an urgent need to understand the diversity of farm households, identifying the main drivers deciding their system thus, classifying them into homogenous groups. In the present study, the diversity of smallholder farms was assessed using crop, livestock and income related characteristics and associated farm mechanization. Using principal component analysis and cluster analysis for 252 farm households, 4 farm types were identified i.e. Type 1. Small Farm households with cereal-based cropping system and subsistence livestock (39%), Type 2. Small Farm households with diversified cropping system dominated by cereal and fodder crops with only cattle herd (9%), Type 3. Marginal Farm household with diversified cropping system dominated by cash crop and herd comprising of only cattle (39%), Type 4. Marginal Farm household with diversified cropping system dominated by cereal crops and herd dominated by small ruminants (12%). Based on the constraints identified for different components of farming systems, low-cost interventions were planned for each farm type. These interventions have resulted in 84.8–103.2 per cent increase in the income of the farm HH under study suggesting usefulness of typology-based intervention planning in increasing income of small farm holders.

Highlights

Due to complexity of smallholder farms, many times technologies with great potential fail to achieve the desired impact in leveraging productivity and profitability of the farming community
After correlation studies of the survey data, 7 variables were chosen for principal component analysis (PCA)
PCA resulted in extraction of seven principal components, out of which 3 principal components were retained with eigenvalue more than one, explaining a total of 71.5% variance (Fig. 2A)

Summary

Introduction

Due to complexity of smallholder farms, many times technologies with great potential fail to achieve the desired impact in leveraging productivity and profitability of the farming community. Agriculture is the core of Indian economy, small and marginal farmers being major stakeholders (85% of the farming community)[1] The heterogeneity of these farmers in terms of agroecology and resource endowments calls for the careful targeting towards the transfer of appropriate technology. It helps in understanding the factors that explain the adoption and/or rejection of new technologies[13,14] This exercise can be made by the researchers, end-users and policy makers through developing farm typologies as a major tool for dealing farming system h eterogeneity[15]. This study assumes that classification of farms based on contribution of farm enterprises, together with other related non-economic factors, will provide meaningful insights into the farm type identification and planning targeted technology intervention for improved farm income. We explored homogeneity in the farming system along an agroecological gradient of the IGP and integrate this understanding in exploring possible interventions for improving farm income

Methods

Results

Discussion

Conclusion