Factors Influencing the Adoption of Agricultural Machinery by Chinese Maize Farmers

Abstract

As the major labor force has shifted from rural areas to cities, labor shortages in agricultural production have resulted. In the context of technical progress impact, and depending on farm resource endowments, farmers will choose effective labor saving technology such as machinery to substitute for the missing manual labor. The reasons behind farmers’ adoption of machinery technology are worth exploring. Therefore, this study uses 4165 Chinese maize farmers as the target group. Multivariate probit models were performed to identify the factors that affect maize farmers’ adoption of four machinery technologies as well as the interrelation between these adoption decisions. The empirical results indicate that maize sowing area, arable land area, crop diversity, family labor, subsidy, technical assistance, and economies of scale have positive effects on machinery adoption, while the number of discrete fields in the farm has a negative impact. Maize farmers in the Northeast and North have higher machinery adoption odds than other regions. The adoption of these four machinery technologies are interrelated and complementary. Finally, moderate scale production, crop diversification, subsidizing agricultural machinery and its extension education, and land consolidation, are given as recommendations for promoting the adoption of agricultural machinery by Chinese maize farmers.

The role of agricultural mechanization services in reducing pesticide input: promoting sustainable agriculture and public health

“…In contrast, in other provinces with higher pesticide expenditure, the AMS rate is relatively low. Similarly, the results of Quan and Doluschitz indicate that the maize farmers in the northeast and northern regions of China are more likely to use agricultural machinery than in other areas ( 21 ).…”

Promoting the Transition towards Agriculture 4.0: A Systematic Literature Review on Drivers and Barriers

“…Therefore, it is essential for entrepreneurs and farmers to consider the long-term benefits of any technology investment [68]. Moreover, economies of scale can also positively influence the adoption of digital technologies [58]. There is a positive relationship between yearly income and the use of Agriculture 4.0 technologies [63].…”

“…Farmers with larger farms exhibit greater enthusiasm towards the adoption of smart technologies, as they can afford to bear the entire costs and relative risks [39,40]. In contrast, small-scale farmers often struggle to adopt innovative technologies due to a scarcity of investment capital or knowledge [43], including those from family farms [58]. Small and medium farmers are likely to manage farm operations in a traditional manner and may not require the utilisation of 4.0 technologies for farm management [62].…”

“…Kernecker et al (2019) [48] suggest that arable and orchard growers have the same level of adoption but adopt different types of technologies. Furthermore, Quan and Doluschitz (2021) [58] find that increasing crop variety on farms may incentivise farmers to implement more sophisticated technology across a range of crops, thereby enhancing their efficiency. Giua et al (2022) [66] reported that the adoption of multiple Agriculture 4.0 tools is positively influenced by specialisation within the arable sector, whereas non-adopters appear unaffected by sectoral specialisation.…”

China's Rural Industrialization and Agricultural Mechanization

“…The empirical literature on agricultural mechanization mainly focuses on the factors that aff ect agricultural mechanization and its socio-economic impacts. Studies have shown that household-level influencing factors of agricultural mechanization include age and education , total household assets (Mottaleb et al, 2016), technical training (Nakano et al, 2018), and off -farm employment opportunities of rural householders , as well as the availability of arable land (Quan and Doluschitz, 2021). Regional-level influencing factors include physical geography (Ullah and Anad, 2007), credit availability , road density (Mottaleb et al, 2016), labor costs (Zheng and Xu, 2017), labor migration (De Brauw and Rozelle, 2008), and the price of agricultural technology (Mottaleb, 2018).…”