Impacts of digital agricultural extension on allocation inefficiency costs:Evidence from cotton farmers in China

ABSTRACT

Enhancing allocation efficiency is vital for maintaining China’s position as a leading textile producer. The adoption of information and communication technology (ICT) can improve the efficiency of farmers’ input allocation. Nonetheless, limited evidence exists regarding the impact and extent of farmers’ access to digital agricultural extension through subscribing to agricultural WeChat public accounts (WPAs) on allocation inefficiency costs. Using a 2019 farm household survey of cotton farmers in Xinjiang, this paper employs a primal system model to calculate farmers’ allocation inefficiency costs and a two-stage residual inclusion approach model (2SRI) to estimate the effects of agricultural WPAs subscription on allocation inefficiency costs and allocation inefficiency. The empirical findings indicate that the average allocation inefficiency cost is 3.264 Yuan per kilogram, constituting 45.8% of cotton production cost. Our results demonstrate that subscribing to agricultural WPAs can significantly reduce farmers’ allocation inefficiency costs by 19.5% and decrease allocation inefficiency by 0.079. Consequently, promoting smallholder farmers’ access to digital agricultural extension serves as an effective strategy to enhance allocation efficiency and lower costs.

KEYWORDS:

• Digital economics
• WeChat
• international competitiveness
• sustainable production
• cotton farmers

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. This data is from the China Agricultural Product Cost-Benefit Compilation, and the yield is the production quantity of cotton lint without cotton seeds. However, the statistic in our empirical data is the production quantity of cotton seed which is higher than that of cotton lint.

2. The data is from this website: https://baijiahao.baidu.com/s?id=1704615713684373064&wfr=spider&for=pc.

Additional information

Funding

The work was supported by the National Natural Science Foundation of China [72003074;72103115]; Natural Science Foundation of Xinjiang [2021D01A8081]; Xinjiang Tianshan Talent Training Program [2022TSYCCX0093].