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Understanding the geographic pattern of diffusion of modern crop varieties in India: a multilevel modeling approach

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Abstract

The diffusion of agricultural technologies is influenced by a number of factors, including the farm-, household- and location-specific characteristics, institutions, infrastructures, and agri-food policies. The empirical literature, however, focuses largely on the household-level factors, ignoring the higher-level factors that simultaneously may influence the technology diffusion process. Employing a multilevel modeling approach this paper analyzes the mutually reinforcing and reciprocal relationships between people (compositional effects) and places (contextual effects) to know the relative importance of different geographical or administrative levels in the diffusion of modern crop varieties in India. The findings show strong contextual effects of states (i.e., policies) and also equally strong compositional effects of the between household differences. These findings suggest the need for a greater policy emphasis on agricultural research and dissemination of its outputs, and redressal of the constraints that farmers face in switching over to new technologies and innovations. Further, the findings also suggest that relaxing credit and information constraints will accelerate the spread of technology diffusion. The contextual effects of the intermediate geographical levels are small, and point towards strengthening coordination between different geographical levels for faster dissemination of technologies and subsequent realization of their economic and social outcomes.

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Data availability

The data and material will be made available upon request.

Notes

  1. Indian society is stratified along religion and caste, but caste is an important indicator of social status. There are four broad caste groups viz., scheduled castes, scheduled tribes, other backward castes and upper castes. Scheduled castes and scheduled tribes are at the bottom of caste hierarchy and are considered to be socially-disadvantaged; other backward castes lie in the middle, and the upper castes at the top.

  2. In our dataset, we find a negative correlation between the age and farming experience of the household-heads (r = −0.18).

  3. In our classification, loamy, sandy-loam and clay represent primarily the alluvial soils and are more fertile. The black and red soils are relatively less fertile. In particular the black soils swell on wetting and shrink on drying, and thus are difficult to manage for crop cultivation. Red soils are poor in nitrogen, phosphorous, potassium and lime. We consider black and red soils as less fertile.

  4. Formal sources include the government extension workers, Krishi Vigyan Kendras (KVKs) i.e. Agriculture Science Centers and Agricultural Universities. The informal sources comprise of the progressive farmers, media and input dealers.

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Acknowledgments

We are grateful to the Indian Council of Agricultural Research (ICAR) for extending financial support to conduct this study. This study was undertaken as a part of ICAR-IFPRI workplan.

Funding

The study was supported by the Indian Council of Agricultural Research.

Author information

Authors and Affiliations

  1. International Food Policy Research Institute (IFPRI), South Asia Regional Office, New Delhi, India

    Anjani Kumar & Gaurav Tripathi

  2. ICAR–National Institute of Agricultural Economics and Policy Research, New Delhi, India

    Jaweriah Hazrana & Pratap S. Birthal

  3. Indira Gandhi Institute of Development Research (IGIDR), Mumbai, Maharashtra, India

    Digvijay S. Negi

Authors
  1. Anjani Kumar
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  2. Jaweriah Hazrana
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  3. Digvijay S. Negi
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  4. Pratap S. Birthal
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  5. Gaurav Tripathi
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Corresponding author

Correspondence to Gaurav Tripathi.

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There is no conflict of interest.

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The codes will be made available upon request.

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Appendix

Appendix

Table 9 Summary statistics
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Table 10 VPCs for varietal adoption with proportion of area under modern varieties as dependent variable
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Fig. 5
figure 5

Distribution of age of crop varieties. Source: Authors’ calculations using data from IFPRI-ICAR survey

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Fig. 6
figure 6

Distribution of area under modern varieties. Source: Authors’ calculations using data from IFPRI-ICAR survey

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Kumar, A., Hazrana, J., Negi, D.S. et al. Understanding the geographic pattern of diffusion of modern crop varieties in India: a multilevel modeling approach. Food Sec. 13, 637–651 (2021). https://doi.org/10.1007/s12571-020-01114-y

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