Farm machinery use and maize yields in China: an analysis accounting for selection bias and heterogeneity

Zhou, Xiaoshi; Ma, Wanglin; Li, Gucheng; Qiu, Huanguang

doi:10.1111/1467-8489.12395

Cited by 56 publications

(34 citation statements)

References 61 publications

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“…We use Cochran’s formula to determine the sample size due to a lack of information on the population of wheat farmers in the survey regions (Cochran, 1977; Zhou et al ., 2020). Cochran’s formula is expressed as

n_{0} = p q Z^{2} / e^{2}

, where we assume a P ‐value of 0.5, a confidence level q of 95%, a Z ‐value of 1.96 and margin of error e of 5%.…”

Section: Data Variables and Descriptive Statisticsmentioning

confidence: 99%

Adoption of organic soil amendments and its impact on farm performance: evidence from wheat farmers in China*

Zheng

2020

Aus J Agri & Res Econ

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This study examines the determinants of adoption of organic soil amendments (OSAs) such as organic fertiliser and farmyard manure and its impact on crop yields and net returns, using household survey data of 558 wheat farmers in China. We employ an endogenous switching regression model to account for selection bias stemming from both observable and unobservable factors. The empirical results show that household size, dependency ratio, machine ownership and non-paid labour are main factors that determine farmers' decision to adopt OSA, and the OSA adoption has a positive and statistically significant impact on wheat yields and net returns. In particular, the treatment effects of OSA adoption are to increase wheat yields and net returns by appropriately 22 and 24 per cent, respectively. Moreover, disaggregated analysis by farm size reveals that large-scale households tend to obtain higher wheat yields and net returns than their small-scale counterparts.

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n_{0} = p q Z^{2} / e^{2}

, where we assume a P ‐value of 0.5, a confidence level q of 95%, a Z ‐value of 1.96 and margin of error e of 5%.…”

Section: Data Variables and Descriptive Statisticsmentioning

confidence: 99%

Adoption of organic soil amendments and its impact on farm performance: evidence from wheat farmers in China*

Zheng

2020

Aus J Agri & Res Econ

Self Cite

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“…Another limitation in previous marginal land studies is the lack of understanding about the drivers of farmers' heterogeneous perceptions of marginal land availability. Numerous studies have demonstrated the existence of heterogeneous behavior rules in farmers' land‐use decisions (Fewell et al, 2016; Garrod et al, 2012; Zhou et al, 2020). However, little is known about the heterogeneity of farmers' perceptions on marginal land availability, especially how the dominant causes of land marginality vary among farmers.…”

Section: Introductionmentioning

confidence: 99%

Farmers' heterogeneous perceptions of marginal land for biofuel crops in US Midwestern states considering biophysical and socioeconomic factors

2021

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Planting bioenergy crops on marginal land is critical for avoiding competition with food crop production. While many studies have estimated marginal land availability using various methods, only a few studies have considered the role of socioeconomic factors in affecting perceptions about the availability of marginal land. This study analyzes land‐use survey data to examine the determinants of farmers' perceptions of marginal land availability on their farms. We find that farmers' perceptions are affected by a combination of unfavorable biophysical (e.g., soil water capacity, temperature variability, and slope) and socioeconomic factors, of which farm size appears to be significant. Interestingly, we identify different determinants of perceptions among farmers that claim to have marginal land and those that do not; the former are determined mainly by unfavorable biophysical factors, while the latter are mainly explained by small farm size. We further apply a prediction model that is trained by a machine learning algorithm to Midwestern states, and derive maps of marginal land likelihood and associated dominant influencing factors. The results suggest that marginal land is primarily under pastureland and grassland cover and in the Dakotas and Nebraska; there is also some marginal land under crop production in the Corn Belt. Our findings contribute to improving understanding of the complex determinants of heterogeneous perceptions of marginal land and can inform the design of more targeted policies for bioenergy crop adoption.

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“…Several studies have analyzed the factors influencing the adoption of agricultural machinery by Chinese maize farmers [4,[7][8][9][10][11] (Table 1). These factors mainly include three aspects: farmer features (e.g., age, gender, education level, farming experience, off-farm employment, etc.…”

Section: Introductionmentioning

confidence: 99%

“…"+" means a positive effect and "−" means a negative effect. Specifically, Zhou et al [11] estimated the impacts of farm machinery use on maize yields by using a control function approach. In the first stage, smartphone use was employed as an instrumental variable in the farm machinery adoption equation; in the second stage, the inverse mills ratio estimated from the first stage was added to the maize production function as an extra regressor to correct the endogeneity issue caused by selection bias in farm machinery adoption.…”

Section: Introductionmentioning

confidence: 99%

Factors Influencing the Adoption of Agricultural Machinery by Chinese Maize Farmers

Quan

Doluschitz

2021

Agriculture

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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.

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Farm machinery use and maize yields in China: an analysis accounting for selection bias and heterogeneity

Cited by 56 publications

References 61 publications

Adoption of organic soil amendments and its impact on farm performance: evidence from wheat farmers in China*

Adoption of organic soil amendments and its impact on farm performance: evidence from wheat farmers in China*

Farmers' heterogeneous perceptions of marginal land for biofuel crops in US Midwestern states considering biophysical and socioeconomic factors

Factors Influencing the Adoption of Agricultural Machinery by Chinese Maize Farmers

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