Some publications on ZT and DSR adoption determinants, service economy, etc.
Thanks to Andrew McDonald (Unlicensed) for the share
Social inclusion increases with time for zero-tillage wheat in the Eastern Indo-Gangetic Plains
In this study, we investigated the dynamics of farmers’ use of ZT wheat in Bihar and explored how determinants of farmers’ knowl- edge and use of the technology changed over a three-year period. In 2012, when ZT was in its nascent stage of adoption in Bihar, better-educated and higher-caste farmers with larger landholdings were clearly more likely to know about and use ZT. Use rates among farmers in the largest landholding tercile exceeded that of farmers in the smallest tercile by 152%, corroborating the critique that it is mostly the better-off farmers who benefit from SI initia- tives, as argued by Rasmussen et al. (2018). However, over the sub- sequent three-year period, awareness and use of the technology increased more than proportionately among less-educated farmers with smaller landholdings, narrowing the gap in ZT use rates between the largest and smallest terciles to 41%. Hence, the initial scale bias declined substantially over time. Education and caste did not significantly affect recent ZT adoption, and land fragmentation rather than total landholding size became a significant influencing factor.
Beyond the household level, physical proximity of ZT SPs remained an important determinant of ZT use among recent adopters as most farmers access the technology via custom-hiring services. For ZT SPs, apart from the total area serviced per customer, area fragmentation influences transaction costs and, hence, the relative attractiveness of a customer. Our analysis suggests that land fragmentation also affects the quality of ZT services, with poorer quality being delivered on smaller plots. In conclusion, farmers with small but contiguous landholdings may have adequate access to quality ZT services. Smallholders with fragmented landholdings, however, may be disadvantaged both with respect to access to ZT services in general, but also with respect to the quality of the service received. Poor quality – be it in terms of delayed service or poor machine calibration or both – may lead to adverse outcomes, such as terminal heat stress caused by delayed establishment or a poor crop stand due to improper machine calibration. Such experiences may easily discourage farmers from continuing to use the practice.
Our descriptive analysis of ZT use across social network members (Table 4) illustrates the growing social inclusiveness of the practice over time, and the econometric models corroborate the important role of networks in the adoption process. Network effects are even stronger with respect to recent than early adoption and apply to all landholding terciles. This is plausible as, with greater diffusion of the technology, there is greater scope for farm- ers to learn from existing ZT users’ experience. Of course, this means that also negative experiences due to poor-quality services will be easily shared and can have significant ripple effects, which highlights the importance of proper training of ZT SPs. Our analysis indicates significant variation in the quality of ZT services and suggests that technical training does enhance quality.
Furthermore, our analysis corroborates previous research that found social networks to be relatively homophilous, with limited social interaction across socioeconomic strata within a village. Hence, if agricultural extension messages are primarily diffused through ‘progressive’ farmers, who usually belong to the better- off, better-educated, and higher-caste stratum, they have limited scope of reaching the poorer segments in a village. To further boost farmers’ awareness of the technology, extension messages should therefore be targeted to farmers representing different social strata.
