Off-Farm Employment, Outsourced Machinery Services, and Farmers’ Ratoon Rice Production Behavior: Evidence from Rice Farmers in Central China

Abstract

Promoting ratoon rice is a critical measure for ensuring food security in China. Understanding the mechanism underlying farmers’ decision-making involving ratoon rice production may contribute to the design and implementation of extension policies. This study examined the impact of off-farm employment and outsourced machinery services on farmers’ ratoon rice production behavior. We used a representative household survey of 1752 rice farmers in Hubei province in central China and a multinomial endogenous treatment effect model to address potential self-selection biases from both observable and unobservable factors. Our estimates suggest that the probability of ratoon rice production decreases by 17.2% for farmers with off-farm employment, while the probability of ratoon rice production increases by 78.9% and 57% for farmers with outsourced machinery services and those with simultaneous off-farm employment and outsourced machinery services, respectively. Moreover, we found that outsourced machinery services can mitigate the negative impact of agricultural labor aging and feminization on ratoon rice production to some extent. Outsourced machinery services have regional heterogeneity effects as farmers in non-plain areas are more likely to engage in ratoon rice production.

1. Introduction

Rice is the most important food crop in the world, and more than half of the global population consumes rice as their primary source of caloric intake. The world’s population is projected to reach 9 billion by 2050, and the demand for rice is expected to increase by 28% during that time span [1,2]. China is the world’s largest rice producer and consumer, and the continuous increase in rice production is important to ensure national food security in China. There is increasing interest in identifying effective options for sustainable rice production and food security.

Ratoon rice production provides an opportunity for productivity increases and food security in China [3]. It refers to the practices of harvesting the first crop (main rice) and further obtaining a second crop that sprouts from the stem nodes on the previously harvested crop in a particular year [4]. Ratoon rice achieves a yield increase with minimal agricultural inputs as the second crop does not require the activities of tillage, sowing, and transplanting [5]. It increases the potential for additional revenue from a second crop on the same land base [6]. Despite these benefits, the promotion of ratoon rice production remains in the preliminary stages. Yet, limited empirical evidence regarding farmers’ ratoon rice production behavior is available.

It has been estimated that the potential total paddy area suitable for ratoon rice in China is 18.69 million hectares [7]. The Chinese government is expected to promote the cultivated areas of ratoon rice to 1 million hectares by 2025, especially in the middle and lower reaches of the Yangtze River. In recent decades, China’s rapid urbanization and continuous industrialization have created off-farm employment opportunities for agricultural households [8,9]. The number of rural off-farm laborers reached 171.7 million in 2021, accounting for 58.7% of the total number of migrant workers [10]. Almost 85% of rural households in China have at least one family member who works in the non-agricultural sector [11]. Some studies reported that off-farm employment insignificantly increases or decreases rice production [12,13], while others demonstrated that off-farm activities increase rice production [14].

The empirical evidence of the relationship between off-farm employment and rice production is mixed. The intra-household division of labor and the socialized division of labor are considered to be popular strategies adopted by agricultural households working in off-farm activities [15,16,17]. The intra-household division of labor is manifested by the gender-based division of labor within the household. This has led to the feminization of agricultural labor as men have migrated at a higher rate than women. Thus, women have become increasingly responsible for household farming [18,19]. The socialized division of labor includes agricultural socialization services represented by outsourced machinery services [20]. The substitution of labor with agricultural machines may maintain or increase farm production [21]. Nevertheless, the impact of the above strategies on farmers’ crop adjustment behavior has not reached a consistent conclusion. Some studies reported that off-farm employment reduces the labor available for agricultural production, thus decreasing rice production and increasing the production of other crops [12,13]. Farmers increase mechanical input to replace the shortage in the labor force, especially in grain production, which can alleviate the pressure from off-farm employment. Other studies found that rural labor transfer did not lead to a reduction in rice-planting areas from 1996 to 2016 in China [14]. Furthermore, Zhong et al. [22] found that the substitution of agricultural machines for labor is relatively difficult in non-plain regions, which inhibits the mitigating impacts of off-farm employment on food crop production.

However, it is noteworthy that existing studies mostly focus on how farmers have adjusted their behavior within traditional rice production. Off-farm employment has contributed to the reduction in multiple crop indexes and led to the shift from double-season to single-season rice [12,23]. As agricultural social services have developed rapidly in recent years, it is possible to mitigate the impacts of off-farm employment [24]. Ratoon rice is increasingly attractive to farmers due to its relatively low labor intensity and high profit [25]. Rice production is a complex process, especially for different production systems where there are differences in the intensity of labor and mechanical input. Thus, the conclusion obtained using traditional rice production behavior may not apply to ratoon rice production behavior decisions.

To our knowledge, this study is the first attempt to explore the impact of off-farm employment and outsourced machinery services on farmers’ ratoon rice production behavior in China. The main contributions of this study are reflected in the following three aspects: First, ratoon rice has been a promising alternative for single- and double-season rice to increase grain production and ensure food security [26]. This study is the first attempt to explore farmers’ ratoon rice production behavior. Second, using a multinomial endogenous treatment effect model (METE) to control the impact of endogenous problems, we analyzed the impact of outsourced machinery services on farmers’ green production behavior. Third, we empirically examined the joint impact of off-farm employment and outsourced machinery services on rice production, in an attempt to provide improved policy suggestions for China to promote farmers’ ratoon rice production in the context of the rapid development of labor migration and outsourced machinery.

The rest of this study is organized as follows: Section 2 presents the theoretical framework of this study. Section 3 introduces the empirical model, data source, and variables used in the analysis. Section 4 presents the main result, robustness check, and heterogeneity results, and Section 5 draws conclusions and presents policy implications.

2. Theoretical Framework

2.1. Ratoon Rice Production in Central China

Ratoon rice is an old rice production system that has been widely practiced since 1950 in China [23]. It was almost practiced in every province in central China in the 1950s [27]. For example, the planted area of ratoon rice reached 73,000 ha in 1994 in Hubei province. Thereafter, due to unstable rice yields and high labor intensity, this area quickly declined to 7000 ha in 2010 in Hubei province. However, ratoon rice has received renewed attention recently, as a mechanized ratoon rice cultivar with a high ratooning ability has been developed together with improved crop production and water management.

There are obvious differences in the production process among single rice, ratoon rice, and double-season rice (

Table 1. Differences in production processes among single rice, ratoon rice, and double-season rice.

Rice Production	Production Process
Single rice	Land preparation→seeding→field management→harvesting
Ratoon rice	Land preparation→seeding→field management→harvesting (main crop)→field management→harvesting (ratooning crop)
Double-season rice	Land preparation→seeding→field management→harvesting (early rice)→land preparation→seeding→field management→harvesting (late rice)

). Specifically, single rice includes only one cycle; double-season rice consists of early and late-season crops; and ratoon rice involves the main crop and a second crop. Compared with single rice, ratoon rice can increase grain yield with minimal agricultural input. Yield increases of 26.10–71.43% were reported in ratoon rice production [23]. Compared with double-season rice, ratoon rice does not require additional labor for land preparation or transplanting of the second rice crop. In recent decades, large areas of double-season rice production were converted into single rice to decrease labor costs. The proportion of planted areas for single rice increased from 44.18% in 1992 to 66.66% in 2018. From 1990 to 2015, the conversion from double-season rice to single rice reduced the cultivated area of rice by 253.16 thousand hectares and the total rice yield by 6.2%.

Thus, ratoon rice has been considered as a mechanized and simplified planting pattern to improve the multiple crop index of rice in a limited area. It is one way to increase rice yield in areas where the annual thermal energy is higher than that required for single rice but is insufficient for double-season rice, and in areas where the annual thermal energy is suitable for double-season rice, but single rice is actually planted [26]. In these regions, mechanized ratoon rice production can effectively reduce labor intensity, and it requires relatively less agricultural inputs including fertilizer, pesticides, and irrigation.

2.2. The Theoretical Logic Underlying Farmers’ Ratoon Rice Production Behavior

If off-farm employment and outsourced machinery services are unavailable for rural households, agricultural income is the important source in the absence of property income and transferred income. In order to maximize household income, farmers have two options, in theory. One is to increase land productivity with the adoption of the crop multiple index or harvesting frequency [28,29], such as ratoon rice or double-season rice production. In this case, the remaining products can be converted into crop income with market exchange, except those used for family consumption. The other is to increase labor productivity with the adoption of labor-intensive farming. The dominant strategy for farmers is to adopt intensification practices to increase crop outputs [30,31]. In other words, farmers tend to increase grain outputs to accumulate household income, thereby promoting their ratoon rice or double-season rice production behaviors.

Once higher earnings can be achieved in the non-agricultural sector than in the agricultural sector, farmers will tend to carry out off-farm work [9]. The increase in the opportunity cost of off-farm employment may reduce farmers’ attention to agricultural production [32]. Agriculture has become a supplementary source of household income or a strategy to mitigate the risk of off-farm employment. Farmers can maximize their household income with the optimal allocation of household labor between on-farm activities and off-farm activities. In the absence of agricultural socialization services, off-farm employment will reduce household labor allocated to on-farm activities. Farmers will adjust their crop arrangement to realize the optimal allocation of household labor. When household labor flows to the non-agricultural sector for higher-paid work, they will likely favor a rice production system with lower labor intensity and a simplified practice. This leads to the first hypothesis of this study:

H1. 

Off-farm employment reduces the possibility of ratoon rice production behaviors of farmers.

Outsourced machinery services can replace traditional labor of farmers, liberate agricultural labor, and ultimately promote farmers’ rice production behavior. Off-farm employment provides the prerequisites for large-scale farming by boosting the supply in the land market [33]. Large-scale farmers can also purchase outsourced agricultural services from the market to solve issues with insufficient labor input [34]. Outsourced machinery services can replace labor with capital elements to expand the scale of farmland operations and create the potential for positive scale economies [35]. Moreover, the labor saved when using outsourced machinery services may be used for off-farm work to maximize the benefits. Farmers can also purchase agricultural socialization services to improve the multiple crop index of rice to earn more on-farm income. This can be stated as the second testable hypothesis:

H2. 

Outsourced machinery services promote farmers’ ratoon rice production behaviors.

The division between the labor market and service network is formed once a number of farmers adopt the same rice production system within a certain region. Farmers will adjust their rice production behaviors to be consistent with the dominant production system, thereby obtaining more convenient and cheap services. Ultimately, to share the economic benefits of the external division of labor, it is necessary to cultivate the market of outsourced machinery services and entice the farmers involved to divide the rice production process. The negative impact of off-farm employment on rice production can be alleviated with outsourced machinery services, thus incentivizing farmers to adopt rice systems with relatively high mechanization and multiple crop index. This leads to the third hypothesis:

H3. 

Outsourced machinery services promote farmers’ ratoon rice production behaviors by promoting off-farm employment.

3. Empirical Methodology

Farmers consider potential combinations when making decisions regarding off-farm employment and outsourced machinery services. The combination of decisions made by farmers is not random, and they endogenously self-select which decisions regarding off-farm employment and outsourced machinery services they will make. These decisions are likely to be influenced by unobservable characteristics that are also related to outcome variables of interest (farmers’ rice production behavior); in this case, endogeneity problems may arise [36]. For instance, farmers may make a decision about off-farm employment and outsourced machinery services based on unobservable factors such as their ability and motivation, and failure to account for this may overestimate or miscalculate the true impact of decisions concerning ratoon rice production.

An effective method to examine treatment effects without randomization in terms of multiple treatments is the multinomial endogenous treatment effect (METE) model [37]. This model allows us to evaluate different combinations of decisions between off-farm employment and outsourced machinery services. Moreover, this model accounts for both the interdependence among the combination of decisions and selection bias as a result of observed and unobserved characteristics.

METE is modeled simultaneously in two stages. In the first stage, a farmer makes a combination of decisions between off-farm employment and outsourced machinery services. Following the work of Deb and Trivedi [37], the first stage is estimated using a mixed multinomial logit (MMNL). Therefore, the utility of the $\mathrm{j}$ ($\mathrm{j}$ = 1,2,3,4) combination of decisions can be described as follows:

$${\mathrm{V}}_{\mathrm{i}\mathrm{j}}^{\mathrm{*}}={\mathrm{X}}_{\mathrm{i}}{\mathsf{\alpha }}_{\mathrm{j}}+{\sum }_{\mathrm{k}=1}^{\mathrm{J}}{\mathsf{\delta }}_{\mathrm{j}\mathrm{k}}{\mathsf{\eta }}_{\mathrm{i}\mathrm{k}}+{\mathrm{n}}_{\mathrm{i}\mathrm{j}}$$

(1)

where ${\mathrm{X}}_{\mathrm{i}}$ is a vector of observed exogenous covariates that represents household head characteristics, family characteristics, farmland characteristics, and socioeconomic characteristics; ${\mathsf{\eta }}_{\mathrm{i}\mathrm{k}}$ is a vector of unobserved covariates such as a farmer’s risk preference and technical abilities; ${\mathsf{\alpha }}_{\mathrm{j}}$ and ${\mathsf{\delta }}_{\mathrm{j}\mathrm{k}}$ are vectors of parameters to be estimated; and ${\mathrm{n}}_{\mathrm{i}\mathrm{j}}$ is the random error term. While ${\mathrm{V}}_{\mathrm{i}\mathrm{j}}^{\mathrm{*}}$ is not observed, we observed the combination of decisions in the form of a set of binary variables ${\mathrm{d}}_{\mathrm{j}}$, and these are collected in a vector, ${\mathrm{d}}_{\mathrm{i}\mathrm{j}}$ = ${\mathrm{d}}_{\mathrm{i}1},{\mathrm{d}}_{\mathrm{i}2},\dots ,{\mathrm{d}}_{\mathrm{i}\mathrm{J}}$. Similarly, let ${\mathsf{\eta }}_{\mathrm{i}\mathrm{k}}={\mathsf{\eta }}_{\mathrm{i}1},{\mathsf{\eta }}_{\mathrm{i}2},\dots ,{\mathsf{\eta }}_{\mathrm{i}\mathrm{J}}$. Let j = 1 denote farmers with off-farm employment and outsourced machinery services and ${\mathrm{V}}_{\mathrm{i}1}^{\mathrm{*}}=0$. Then, the probability of treatment can be written as:

$$\mathrm{Pr}\left({\mathrm{d}}_{\mathrm{i}}|{\mathrm{X}}_{\mathrm{i}},{\mathsf{\eta }}_{\mathrm{i}}\right)=\mathrm{g}({\mathrm{X}}_{\mathrm{i}}{\mathsf{\alpha }}_{\mathrm{i}}+{\sum }_{\mathrm{k}=1}^{\mathrm{J}}{\mathsf{\delta }}_{1\mathrm{k}}{\mathsf{\eta }}_{\mathrm{i}\mathrm{k}}+{\mathrm{X}}_{\mathrm{i}}{\mathsf{\alpha }}_2+{\sum }_{\mathrm{k}=1}^{\mathrm{J}}{\mathsf{\delta }}_{2\mathrm{k}}{\mathsf{\eta }}_{\mathrm{i}\mathrm{k}}+\dots +{\mathrm{X}}_{\mathrm{i}}{\mathsf{\alpha }}_{\mathrm{J}}+{\sum }_{\mathrm{k}=1}^{\mathrm{J}}{\mathsf{\delta }}_{\mathrm{J}\mathrm{k}}{\mathsf{\eta }}_{\mathrm{i}\mathrm{k}})$$

(2)

where $\mathrm{g}(·)$ is an appropriate multinomial probability distribution, which can be estimated using the MMNL model as follows:

$$\mathrm{Pr}\left({\mathrm{d}}_{\mathrm{i}}|{\mathrm{X}}_{\mathrm{i}},{\mathsf{\eta }}_{\mathrm{i}}\right)=\frac{\mathrm{e}\mathrm{x}\mathrm{p}({\mathrm{X}}_{\mathrm{i}}{\mathsf{\alpha }}_{\mathrm{j}}+{\mathsf{\delta }}_{\mathrm{j}}{\mathsf{\eta }}_{\mathrm{i}\mathrm{j}})}{1+{\sum }_{\mathrm{k}=1}^{\mathrm{J}}\mathrm{e}\mathrm{x}\mathrm{p}({\mathrm{X}}_{\mathrm{i}}{\mathsf{\alpha }}_{\mathrm{k}}+{\mathsf{\delta }}_{\mathrm{k}}{\mathsf{\eta }}_{\mathrm{i}\mathrm{k}})}$$

(3)

In the second stage of METE, we estimate the effect of the combination of decisions on outcome variables. The expected outcome equation is formulated as:

$$\mathrm{E}\left({\mathrm{y}}_{\mathrm{i}}|{\mathrm{d}}_{\mathrm{i}},{\mathrm{x}}_{\mathrm{i}},{\mathsf{\eta }}_{\mathrm{i}}\right)={\mathrm{x}}_{\mathrm{i}}\mathsf{\beta }+{\sum }_{\mathrm{j}=1}^{\mathrm{J}}{\mathsf{\gamma }}_{\mathrm{j}}{\mathrm{d}}_{\mathrm{i}\mathrm{j}}+{\sum }_{\mathrm{j}=1}^{\mathrm{J}}{\mathsf{\lambda }}_{\mathrm{j}}{\mathsf{\eta }}_{\mathrm{i}\mathrm{j}}$$

(4)

where ${\mathrm{y}}_{\mathrm{i}}$ is ratoon rice adoption for farmer i, and ${\mathrm{y}}_{\mathrm{i}}$ = 1 if farmer i adopts ratoon rice. Specifically, coefficient ${\mathrm{y}}_{\mathrm{i}}$ indicates the effect of the combination of decisions on ratoon rice adoption. Since $\mathrm{E}\left({\mathrm{y}}_{\mathrm{i}}|{\mathrm{d}}_{\mathrm{i}},{\mathrm{x}}_{\mathrm{i}},{\mathsf{\eta }}_{\mathrm{i}}\right)$ is a function of the latent factors ${\mathsf{\eta }}_{\mathrm{i}}$, the outcome is affected by unobserved characteristics that also affect selection into treatment. When ${\mathsf{\lambda }}_{\mathrm{j}}$ is positive (negative), the treatment is positive (negative), but the outcomes are negatively (positively) correlated through unobserved characteristics. The resulting model was estimated using a maximum simulated likelihood (MSL) approach.

4. Data and Variables

4.1. Data Collection

The empirical data used in this study were from the representative household survey for rice farmers conducted between June and September 2018 in Hubei province, central China. The province is a major producer of rice in the country, and it is one of the hotspots of ratoon rice production. The 14th Five-Year Plan for National Planting Industry Development proposes the expansion of ratoon rice cultivated area in the middle and lower reaches of the Yangtze River. Hubei province is expected to experience progressive increases in the cultivated area for ratoon rice, with >220 thousand hectares of ratoon rice being harvested in 2018 in this province [5].

To obtain a sample with a representative geographic coverage, 9 counties within this province, in which ratoon rice production was popularized, were purposively selected. These counties are distributed across three main paddy regions in Hubei, namely, Japonica rice in the northeast region (Xiaogan), single-season rice in the central region (Shayang and Yingcheng), and single- and double-crop rice fields in the Jianghan plain (Jianglin, Jianli, Honghu, Xianning, Wuxue, and Qichun). Within these regions, we used a multi-stage random sampling approach for data collection. Firstly, 2–3 towns were randomly selected from each county. In Xiaogan, Shayang, and Yicheng counties, we randomly selected three towns from each country. Among the remaining counties, we randomly selected two towns from each county. Then, two villages that were identified based on personal interviews with local agricultural agencies with knowledge regarding rice production were selected from each town. Finally, approximately 45 farmers were randomly selected from each village. Data were collected using field surveys and structured questionnaires at the farm level. Detailed information was provided regarding farmers’ ratoon rice production behavior, demographic characteristics, and agricultural production. The total number of household observations in the questionnaire survey was 1816. Out of these, 64 participating households were excluded from the study sample because of missing data and stark outliers for key outcome variables. Hence, 1752 household observations were used for the econometric analysis, for which, all the key variables were available.

4.2. Variable Selection

4.2.1. Dependent Variable

In this study, we used binary variables to measure farmers’ ratoon rice production behavior. Specifically, the dependent variable was assigned 1 if the respondents answered “yes” to the question “do you cultivate ratoon rice in 2018?” and 0 otherwise. Further, we used the proportion of ratoon rice cultivated area to the total rice cultivated area in a household as an indicator for the robustness test.

4.2.2. Independent Variable

In this paper, off-farm employment refers to all economic activities that take place outside the agricultural household; hence, this includes off-farm salaried and casual wage employment and off-farm self-employment. Outsourced machinery services refer to the process that machinery service organizations or mechanical operation services provide to producers without agricultural machinery equipment in the form of compensation, so as to realize mechanized production. The combination of decisions between off-farm employment and outsourced machinery services can be divided into four types: (1) farmers without off-farm employment and outsourced machinery services (NOEOMS); (2) farmers with off-farm employment (OE); (3) farmers with outsourced machinery services (OMS); (4) farmers with off-farm employment and outsourced machinery services (OEOMS). Finally, 43.4%, 11.4%, 39.9%, and 5.4% of farmers in our samples were classified into the NOEOMS, OE, OMS, and OEOMS groups, respectively.

We used four binary variables to measure the above types of farmers. Specifically, the NOEOMS variable was assigned 1 if a farmer neither participated in off-farm employment nor purchased outsourced machinery services for rice production, and 0 otherwise. Similarly, the OE variable was assigned 1 if a farmer only participated in off-farm employment, and 0 otherwise. The OMS variable was assigned 1 if a farmer purchased outsourced machinery services for rice production, and 0 otherwise. The OEOMS variable was assigned 1 if a farmer participated in off-farm employment and purchased outsourced machinery services for rice production, and 0 otherwise.

As is listed in

Table 2. The mean difference in the combination of decisions and farmers’ ratoon rice production behavior.

	(1) NOEOMS	(2) OE	(3) OMS	(4) OEOMS	Diff.
					(2) − (1)	(3) − (1)	(4) − (1)
Ratoon rice production	0.243	0.201	0.759	0.742	−0.042	0.515 ***	0.498 ***

Note: NOEOMS refers to farmers without off-farm employment and outsourced machinery services; OE refers to farmers with off-farm employment; OMS refers to farmers with outsourced machinery services; OEOMS refers to farmers with off-farm employment and outsourced machinery services (OEOMS). The base category is NOEOMS, that is, farmers without off-farm employment and outsourced machinery services. *** indicates significance at the 1% level with standard errors in parentheses.

, there was a difference among the four groups with OEOMS as the base category. This shows that off-farm employment can inhibit farmers’ ratoon rice production behavior, but outsourced machinery services can effectively promote farmers’ ratoon rice production behavior and offset the negative impact of off-farm employment.

4.2.3. Control Variable

We included a set of control variables that are likely to affect farmers’ ratoon rice production behavior. Our control variables included household head characteristics (i.e., age, gender, years of education, and health status), household characteristics (i.e., income, agricultural labor force, number of children, and number of elderly people), plot characteristics (i.e., number of plots, plot area, soil fertility, distance to plots, soil texture, land property rights, rice system in adjacent plots, access to irrigation, and farmland road condition), and socioeconomic characteristics (i.e., cooperative services, rice selling price, commercialization rate, technical training, subsidies, and distance to the market).

4.2.4. Instrument Variable

In order to identify the equation for the METE model, an instrument variable is required. Specifically, the instrument needs to be both highly correlated with the combination of decisions and have no direct effect on the variables of interest (i.e., farmers’ ratoon rice production behavior).

In this study, we used the ratio of farmers with off-farm employment and farmers with outsourced machinery services to the total farmers in a village as the instrumental variable. Theoretically, villages with a higher ratio of off-farm employment will affect an individual farmer’s decision regarding off-farm employment through their social capital. Social capital can be regarded as an important informal institution [38]. Under imperfect market conditions, social capital plays a critical role in reducing information asymmetry in the labor market and securing the off-farm work of farmers [39]. In villages with higher rates of outsourced machinery services, the agricultural socialized service market is relatively mature within a certain region, which makes it convenient for farmers to purchase the service. These variables are likely to be correlated with the decision concerning off-farm employment and outsourced machinery services but are unlikely to have any direct effect on their adoption. Thus, the instrument is correlated and exogenous with the farmers’ ratoon rice production behavior.

4.3. Descriptive Statistics

Table 3. Variable definitions and descriptive statistics.

Variable	Definition	Mean	SD
Ratoon rice production	Coded 1 if farmers adopted ratoon rice in 2017; 0 otherwise	49.50%	—
The proportion of ratoon rice cultivated area to the total rice cultivated area	Proportion of ratoon rice cultivated area to the total rice cultivated area for a household (%)	47.10%	—
Age	Age of the household head in 2017 (years)	58.25	9.934
Gender	Coded 1 if the household head was male; 0 otherwise	89.90%	—
Years of education	Years of education completed by the household head (years)	6.298	3.492
Health status	Coded 1 if the household head was healthy; 0 otherwise	47.10%	—
Income	Annual total household income in 2017 (CNY)	66,990	84,560
Agricultural labor force	Family agricultural labor force (number of people)	1.993	0.867
Number of children	Number of children under 6 years old in the household; 0 if none	0.469	0.754
Number of elderly people	Number of elderly people over 60 years old in the household; 0 if none	1.123	0.905
Number of plots	Number of rice plots (blocks)	11.78	41.95
Plot area	Area of the largest field (ha)	0.479	1.578
Soil fertility	Poor soil fertility = 1; medium fertility = 2; good fertility = 3	2.180	0.684
Distance to plots	Average distance between field and residence (m)	726.3	1247
Soil texture
Sandy	Coded 1 if the plot was sandy; 0 otherwise	26.70%	—
Loam	Coded 1 if the plot was loam; 0 otherwise	21.20%	—
Clay	Coded 1 if the plot was clay; 0 otherwise	52.10%	—
Land property rights
Land allocated by collectives	Coded 1 if the land was allocated by collectives; 0 otherwise	81.70%	—
Land transferred in	Coded 1 if the land was transferred from other farmers; 0 otherwise	11.50%	—
Land allocated by collectives and transferred in	Coded 1 if the land was both allocated by collectives and transferred from other farmers; 0 otherwise	6.80%	—
Rice system in adjacent plots	Coded 1 if the rice system in adjacent plots was the same; 0 otherwise	93.60%	—
Access to irrigation	Coded 1 if the paddy field had access to irrigation; 0 otherwise	64.80%	—
Farmland road condition	Coded 1 if the paddy field road was passable for agricultural machinery; 0 otherwise	89.70%	—
Cooperative member	Coded 1 if the family was a cooperative member; 0 otherwise	17.90%	—
Rice selling price	Rice sales market price (CNY/kg)	2.426	4.402
Commercialization rate	Proportion of rice sales volume to total output (%)	80.40%	—
Technical training	Coded 1 if household members participate in agricultural technology training; 0 otherwise	45.30%	—
Subsidies	Total amount of government subsides received from rice production (CNY)	1469	9897
Distance to the main market	The time it takes to travel from the household to the nearest main market (minutes)	22.37	19.88

Note: 1 ha = 667 m².

presents the definitions and descriptive statistics of the variables. The dataset contains 1752 farm households and, of these, approximately 50% adopted RR production. The average age of the sample household head was 58.25 and approximately 90% were male. The average years of education for the household head was approximately 6 years. Nearly half of the sample household heads were healthy. The average household income was CNY 66.99 thousand, and the average amount of household agricultural labor was 2.0. The average number of children and elderly was 0.469 and 1.123, respectively.

In terms of plot characteristics, for each farmer, the average number of plots was 11.78, and the average area of the largest plot was 0.479 ha. For most farmers, the soil fertility of their rice paddies was classified as medium fertility. A total of 26.72%, 21.2%, and 52.1% of the paddy soil texture was sandy, loam, and clay, respectively. Additionally, 81.7% and 11.5% of land property rights were from collectives and transferred in, respectively. Only 6.8% was allocated by both collectives and transferred in. For 93.6% of farmers, the rice system in adjacent plots was the same. A total of 64.8% of the farmers’ rice plots had access to irrigation, and 89.7% of farmers had a paddy field road that was passable for agricultural machinery.

With respect to other socio-economic characteristics, 17.9% of the total sample were cooperative members. On average, the rice selling price was CNY 2.426 per kilogram, and the commercialization rate of rice was 80.4%. A total of 45.3% of the sample households participated in technical training. The average government subsidy received from rice production was CNY 1469 for each farmer. The average distance to the main market was 22.37 min from the residence.

5. Results

5.1. The Impacts on Farmers’ Ratoon Rice Production Behavior

Table 4. The impacts of combinations of decisions on farmers’ ratoon rice production behavior.

Variable	OLS	METE
	Ratoon Rice Production	OE	OMS	OEOMS	Ratoon Rice Production
	Model (1)	Model (2)	Model (3)	Model (4)	Model (5)
OE	0.004				−0.172 ***
	(0.031)				(0.027)
OMS	0.569 ***				0.789 ***
	(0.020)				(0.019)
OEOMS	0.588 ***				0.570 ***
	(0.042)				(0.040)
Age	0.001	−0.091 ***	0.011	−0.068 ***	−0.001
	(0.001)	(0.012)	(0.009)	(0.016)	(0.001)
Gender	−0.008	0.713 *	−0.338	2.734 **	0.035
	(0.032)	(0.383)	(0.218)	(1.072)	(0.023)
Years of education	−0.002	0.087 ***	−0.003	0.081	−0.002
	(0.003)	(0.032)	(0.021)	(0.045)	(0.002)
Health status	0.068 ***	0.236	−0.390 ***	0.030	0.088 ***
	(0.019)	(0.199)	(0.136)	(0.272)	(0.041)
Income (log)	0.052 ***	0.051	−0.063	0.107	0.062 ***
	(0.010)	(0.116)	(0.074)	(0.170)	(0.009)
Agricultural labor force	0.003	−0.291 **	−0.131 *	−0.323 *	0.018
	(0.011)	(0.120)	(0.077)	(0.180)	(0.008)
Number of children	−0.012	−0.036	0.100	−0.082	−0.005
	(0.012)	(0.139)	(0.084)	(0.193)	(0.010)
Number of elderly people	−0.018	0.121	0.098	−0.027	−0.010
	(0.011)	(0.114)	(0.084)	(0.155)	(0.010)
Number of plots	−0.001 **	−0.003	0.002	−0.017	−0.001 ***
	(0.000)	(0.003)	(0.001)	(0.016)	(0.000)
Soil fertility	0.033 **	0.164	−0.109	−0.017	0.051 ***
	(0.014)	(0.153)	(0.103)	(0.206)	(0.011)
Distance to plots (log)	0.001	0.055	0.050	−0.252 *	−0.004
	(0.009)	(0.097)	(0.066)	(0.137)	(0.006)
Plot size (log)	−0.149 ***	−0.357	0.220	−0.194	−0.143 ***
	(0.031)	(0.349)	(0.213)	(0.461)	(0.021)
Loam	−0.064 **	0.466 **	0.452 **	0.274	−0.070 ***
	(0.027)	(0.282)	(0.198)	(0.385)	(0.019)
Clay	0.016	0.153	0.097	0.112	0.053 ***
	(0.243)	(0.235)	(0.165)	(0.317)	(0.016)
Land transferred in	−0.022	−0.834	−0.517 **	−1.192 **	0.017
	(0.031)	(0.331)	(0.227)	(0.534)	(0.020)
Land allocated by collectives and transferred in	−0.024	−0.729 **	−0.325	0.180	−0.051 *
	(0.038)	(0.416)	(0.272)	(0.468)	(0.028)
Rice system in adjacent plots	0.113 ***	−0.114 *	−0.566 **	0.277	0.101 ***
	(0.038)	(0.397)	(0.276)	(0.633)	(0.028)
Access to irrigation	0.071 ***	0.604 ***	0.167	0.274	0.086 ***
	(0.020)	(0.224)	(0.147)	(0.308)	(0.016)
Farmland road conditions	0.020	−0.310	0.084	−0.158	−0.010
	(0.031)	(0.305)	(0.233)	(0.450)	(0.021)
Rice selling price	0.001	−0.002	−0.141 *	−1.539 ***	0.002
	(0.002)	(0.019)	(0.074)	(0.438)	(0.001)
Cooperative member	0.148 ***	−0.020	−0.117	−0.483	0.160 ***
	(0.025)	(0.272)	(0.184)	(0.370)	(0.019)
Commercialization rate	0.307 ***	−0.666 **	−0.152	0.523	0.260 ***
	(0.027)	(0.267)	(0.213)	(0.482)	(0.020)
Technical training	0.015	−0.278	0.622 ***	0.153	−0.024
	(0.019)	(0.209)	(0.142)	(0.284)	(0.016)
Subsidies (log)	0.000	0.027	0.066	0.039	−0.007 ***
	(0.003)	(0.031)	(0.022)	(0.046)	(0.002)
Distance to the market (log)	0.006	0.081	−0.240 *	−0.826 ***	0.013
	(0.017)	(0.168)	(0.126)	(0.252)	(0.012)
Ratio of farmers with off-farm employment at the village level		4.441 **	3.213 **	11.070 ***
		(1.891)	(1.427)	(3.458)
Ratio of farmers with outsourced machinery services at the village level		−0.170	5.750 ***	6.099 ***
		(0.649)	(0.396)	(0.887)
Constants	−0.883 ***	0.856	−2.016	−0.403	−0.965 ***
	(0.156)	(1.764)	(1.230)	(2.946)	(0.125)
$\mathrm{l}\mathrm{n}\mathsf{\sigma }$					−2.719 ***
					(0.125)
${\mathsf{\lambda }}_1$					0.178 ***
					(0.008)
${\mathsf{\lambda }}_2$					−0.353 ***
					(0.007)
${\mathsf{\lambda }}_3$					−0.071 ***
					(0.011)
Observations	1752

Note: The base category is NOEOMS, that is, farmers without off-farm employment and outsourced machinery services. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively, with standard errors in parentheses.

reports the estimated impacts of off-farm employment and outsourced machinery services on farmers’ ratoon rice production behavior. For comparison purposes, the outcome variables are estimated under the assumption of exogenously using OLS estimation (column (1) in Table 4) and endogenously using METE estimation (columns (2)–(5) in Table 4). The factor loading of ${\mathsf{\lambda }}_1$ shows evidence of positive selection bias, suggesting that unobserved factors that increase the likelihood of OE are associated with a higher probability of farmers’ ratoon rice production behavior. Negative selection bias is significant in the estimation of OMS and OEOMS decisions, suggesting that unobserved variables that increase the likelihood of OMS and OEOMS are associated with a lower probability of ratoon rice production. Thus, the results are analyzed based on METE estimation under the endogenous assumption.

From model (5) in Table 4, it can be seen that the coefficient of OE is significantly negative at the 1% level, while the coefficients of OMS and OEOMS are significantly positive at the 1% level. The estimated coefficient from the METE model can be interpreted as the change in the mean outcomes in comparison with those of the base category. The results indicate that, compared with farmers without off-farm employment and outsourced machinery services, the probability of ratoon rice production decreases by 17.2% for farmers with off-farm employment. Based on the above theoretical analysis, this indicates that off-farm employment reduces farmers’ adoption rate of ratoon rice production, thus verifying hypothesis 1. The probability of ratoon rice production increases by 78.9% for farmers with outsourced machinery services, and 57.0% for farmers with off-farm employment and outsourced machinery services, compared with farmers without off-farm employment and outsourced machinery services. It can be concluded that outsourced machinery services promote the realization of farmers’ ratoon rice production behavior, thus verifying hypotheses 2 and 3.

5.2. Robustness Test

In this study, the variable substitution method was used to test the robustness of the above results. Following the research of Chen et al. [40], we replaced the dependent variable “farmers’ ratoon rice production behavior” with the proportion of ratoon rice cultivated area to the total rice cultivated area. According to the results reported in

Table 5. Robustness test 1: using the ratio of area for ratoon rice to total rice cultivated area as the alternative dependent variable.

Variable	OLS	METE
	Model (1)	Model (2)
OE	−0.016	−0.089 ***
	(0.031)	(0.289)
OMS	0.500 ***	0.753 ***
	(0.020)	(0.019)
OEOMS	0.483 ***	0.463 ***
	(0.042)	(0.033)
Control variables	Yes	Yes
Constants	−0.277 *	−0.145
	(0.154)	(0.161)
$\mathrm{l}\mathrm{n}\mathsf{\sigma }$	0.992 ***	−2.615 ***
	(0.017)	(0.286)
${\mathsf{\lambda }}_1$		0.136 ***
		(0.014)
${\mathsf{\lambda }}_2$		−0.355 ***
		(0.007)
${\mathsf{\lambda }}_3$		−0.042 ***
		(0.013)
Observations	1752

Note: The case category is NOEOMS, that is, farmers without off-farm employment and outsourced machinery services. *** and * indicate significance at the 1% and 10% levels, respectively, with the standard errors in parentheses.

, the proportion of ratoon rice cultivated area to the total rice cultivated area significantly decreased by 8.9% in the OE group. Conversely, it significantly increased by 75.3% in the OMS group and 46.3% in the OEOMS group. Thus, it can be concluded that the results obtained by the study are robust.

We further replaced the core independent variable with income from off-farm employment and the cost of outsourced machinery services. These two variables reflect farmers’ decision regarding off-farm employment and outsourced machinery services and the extent of off-farm employment and outsourced machinery services.

Table 6. Robustness test 2: using income from off-farm employment and outsourced machinery services cost as the alternative independent variables.

Variable	Probit		IV-Probit
	Model (1)	Model (2)	Model (3)	Model (4)
Income from off-farm employment (log)	−0.016 *		−0.180 ***
	(0.009)		(0.045)
Cost of outsourced machinery services (log)		0.134 ***		0.825 ***
		(0.032)		(0.145)
Control variables	Yes	Yes	Yes	Yes
Constants	−5.553 ***	−6.327 ***	−1.418	−4.139 ***
	(0.654)	(0.685)	(0.894)	(0.453)
Observations	1752

Note: *** and * indicate significance at the 1%, 5%, and 10% levels, respectively, with standard errors in parentheses.

reports the estimation results using the probit model. As is indicated, income from off-farm employment has a significant negative impact on farmers’ ratoon rice production behavior. This implies that the opportunity cost for rice production for farmers increases with the increase in off-farm income, so the possibility of ratoon rice adoption is smaller. However, the cost of outsourced machinery services has a significantly positive impact on farmers’ ratoon rice production behavior. Further, the IV-probit method was used to overcome the potential endogenous problems, and the above results remained robust.

Another robustness check involved using a recursive bivariate probit (RBP) for the estimation. A RBP model is used to evaluate the impact of a binary variable on a binary outcome [41]. This study used the RBP model to jointly estimate the combination of decisions and its impact on ratoon rice adoption. The estimation results are listed in

Table 7. Robustness test 3: a recursive bivariate probit model.

Variables	Model (1)	Model (2)	Model (3)
OE	−0.439 ***
	(0.069)
OMS		0.950 ***
		(0.057)
OEOMS			0.514 ***
			(0.093)
Control variables	Yes	Yes	Yes
Constants	−0.312	−0.764	−0.488
	(0.790)	(0.581)	(1.242)
Observations	1752

Note: The base category is NOEOMS, that is, farmers without off-farm employment and outsourced machinery services. *** indicate significance at the 1%, 5%, and 10% levels, respectively, with standard errors in parentheses.

, which shows that off-farm employment significantly negatively impacts the possibility of ratoon rice adoption. However, outsourced machinery services significantly positively impact the possibility of ratoon rice adoption and can promote farmers’ ratoon rice production behavior. These results further verify the robustness of the previous results.

5.3. Heterogeneity Analysis

The overall average effect of the impact is analyzed above, which does not reflect the possible heterogeneous effect. It is necessary to further explore the potential heterogeneity impact on farmers’ ratoon rice production behavior. Thus, we grouped farmers in terms of age, gender, and terrain to obtain more in-depth conclusions. The results are presented in

Table 8. Heterogeneity analysis results.

Variable	Age						Gender						Terrace
	Elderly Group			Non-Elderly Group			Male Group			Female Group			Plain Group			Non-Plain Group
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)	(14)	(15)	(16)	(17)	(18)
OE	−0.420 ***			−0.422 ***			−0.438 ***			−4.915			−0.392 ***			−0.613 ***
	(0.136)			(0.084)			(0.071)			(4.923)			(0.082)			(0.155)
OMS		0.975 ***			0.943 ***			0.944 ***			1.373 ***			0.926 ***			1.377 ***
		(0.081)			(0.083)			(0.059)			(0.404)			(0.063)			(0.187)
OEOMS			0.555 ***			0.564 ***			0.514 ***			14.072			0.595 ***			0.397 **
			(0.163)			(0.116)			(0.093)			(19.444)			(0.120)			(0.165)
Control variables	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Constants	−2.361 **	−1.849 *	−2.413 **	−0.895	0.465	−2.188	−2.946 ***	−2.760 ***	−2.972 ***	−6.459 **	−5.202 **	−5.785 **	−3.250 ***	−3.009 ***	−3.246 ***	−0.898 **	−1.065 **	−0.945 **
	(0.940)	(1.001)	(0.939)	(1.029)	(0.959)	(1.600)	(0.574)	(0.566)	(0.575)	(2.541)	(2.356)	(2.420)	(0.643)	(0.632)	(0.643)	(1.350)	(1.355)	(1.343)
Observations	851			901			1575			177			1320			432

Note: The base category is NOEOMS, that is, farmers without off-farm employment and outsourced machinery services. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively, with standard errors in parentheses.

.

We divided the samples from household heads over 60 years of age into the elderly group, and the remainder were added to the non-elderly group. From model (1) to model (6), it can be seen that OE has a significantly negative impact on farmers’ ratoon rice production behavior, both in the elderly and non-elderly groups. The effect in the elderly group was more evident than in the non-elderly group. However, OMS and OEOMS had significantly positive impacts on farmers’ ratoon rice production behavior in both groups. The effect of outsourced machinery services was greater in the elderly group. However, a greater effect was observed in the non-elderly group when the decision regarding off-farm employment and outsourced machinery services was made simultaneously by farmers. A relatively reasonable explanation for this result is that the lack of physical strength of the elderly cannot support them in managing the relatively complex crop production, and outsourced machinery services can reduce their labor effort [40].

Further, the sample was divided into male and female groups according to the gender of the household head. As can be seen in model (7) to model (12), we found that in the male group, only OE has a significant negative effect on ratoon rice production. OMS has a significantly negative impact on farmers’ ratoon rice production behavior both in the elderly and non-elderly groups. The effect in the female group was greater than that in the male group. The possible reason for this is that it is very common for female laborers to lack sufficient labor force in terms of the complex process of crop production [42], which can be replaced by outsourced machinery services to avoid adverse effects on ratoon rice production behavior.

Finally, according to the differences in terrain, we divided farmers into the plain group and the non-plain group. The results of model (13) to model (18) show that in both in the plain and non-plain areas, OE has a significant negative impact on farmers’ ratoon rice production behavior. The coefficient value of the impact was smaller in the plain group. For farmers with outsourced machinery services, a significantly positive impact was observed in both groups, and a smaller effect was seen in the plain area. However, the positive impact of OEOMS was stronger at the 1% significance level in the plain area. A possible explanation may be that production conditions such as irrigation and mechanization are poor in non-plain areas, and crops with more complex production processes will inevitably increase the difficulty of crop management for farmers [43]. As machinery services are developed to replace other inputs and reduce technical barriers in non-plain areas, there is sufficient motivation for ratoon rice production.

6. Discussion

Ratoon rice production is an effective way to improve productivity and ensure food security in China [44]. As Chinese rural labor continues to migrate to urban areas for off-farm employment, agricultural machinery has been increasingly used to alleviate the adverse effects of labor shortages in recent years [45]. In this context, understanding the relationship between factor allocation and ratoon rice production behavior has been of great importance for policymakers in the promotion of ratoon rice.

The results of this study show that outsourced machinery services to enhance rice production play an important role in alleviating the lack of manual labor and ultimately increasing the adoption rate of ratoon rice production. This is generally consistent with the findings of Ji et al. [46] and Yang and Wei [47]. In general, off-farm activities reduce household labor, which incentivizes farmers to switch to rice production with a lower labor intensity [24,48]. However, outsourced machinery services can act as an effective substitute for insufficient physical and production skills, thus encouraging farmers’ ratoon rice production behavior [21]. Additionally, machinery services can reduce the technical barriers for farmers in ratoon rice production [42]. Specifically, the professional skills required for ratoon rice production include the main crop harvest cutting height and the time and amount of fertilizer for the second crop [4,5]. Outsourced machinery services help to introduce this technology into ratoon rice production to stabilize grain yields and avoid risks to farmers during the process of learning this production method. This means that it is feasible to promote farmers’ ratoon rice behavior by inducing the agricultural factor flows of labor and mechanization.

There are group differences due to the effect of off-farm employment and outsourced machinery services. Due to physical strength and traditional gender roles, the young and male laborers will allocate more time to off-farm employment to achieve higher earnings [15,21]. The complex process of ratoon rice production requires a certain time input for farmers. The reduction in their time devoted to agricultural production results in a significantly lower probability of ratoon rice production relative to elderly female and female farmers. In other words, the elders and females are more likely to adopt ratoon rice production. Agricultural machinery services can be used as a modern production factor, which can effectively supplement the shortage of agricultural labor [41]. Socialized services have provided a potential approach to replace traditional, under-qualified human capital with specialized services [49,50]. The elderly and females tend to purchase machinery services to cope with complex crop production, thus incentivizing them to adopt ratoon rice production. However, income from off-farm employment effectively relieves the liquidity constraints on the purchase of machinery services [51]. Further, young people and males are more well-educated and skilled in agricultural production [40,42]. When they have access to both off-farm employment and socialized services, they are more likely to adopt ratoon rice production. In terms of plain and sloping areas, flat land is suitable for farming, and sloping areas provide a geographical problem for the application of agricultural machinery services [52]. Based on this advantage, the probability of ratoon rice adoption by farmers in plain areas is significantly higher than that in non-plain areas. Overall, this suggests that farmers in different categories should be rationally guided to adopt ratoon rice production.

Our results can also be applied in a wider context, especially in tropical and sub-tropical areas where ratoon rice is suitable for cultivation. The production system has the potential to be replicated in developing countries, allowing food production to feed the entire population and achieve sustainable food security [6]. As is the case in China, farmers in Southeast Asia are mostly smallholders, and off-farm employment has been an important way to reduce household livelihood vulnerability [53]. Machinery services are needed to enhance the adopted technologies and ensure food security [17]. For instance, in central Vietnam, ratoon rice production is more economically efficient, and this allows farmers to allocate more time to other off-farm activities for income generation [54]. Meanwhile, increasing the use of socialized services resulted in an increase in the probability of ratoon rice production [44]. Therefore, our research is helpful in providing a promotion strategy for ratoon rice production in these regions, which are mainly smallholder-based, with an adjustment to household labor and capital.

Although this study attempted to grasp the relationship between off-farm employment, outsourced machinery services, and farmers’ ratoon rice production, there are still some uncertainties. For instance, cross-sectional data may lose precision because ratoon rice production is an innovative agronomic practice in China and farmers’ growing experience will increase over time. Long-term investigation is still needed in future studies; in particular, studies should attempt to capture the dynamics of farmers’ ratoon rice adoption. In addition, the heterogeneity in off-farm employment arising from farmers’ self-selection was neglected. Further research should be conducted on the impact of gender and the intergenerational division of labor in household farmers. The research conclusions thus obtained may be more policy-oriented.

7. Conclusions and Implications

Using a representative household survey of 1752 rice farmers in Hubei province, this study empirically analyzed the impact of off-farm employment and outsourced machinery services on farmers’ ratoon rice production behavior. The main results can be summarized as follows:

(1) Off-farm employment has a significant and negative impact on farmers’ ratoon rice production. Our estimates suggest that the probability of ratoon rice production decreases by 17.2% for farmers with off-farm employment.

(2) Outsourced machinery services significantly encourage farmers’ behavior in ratoon rice production. Specifically, the probability of ratoon rice production increases by 78.9% for farmers with outsourced machinery services. This result suggests that outsourced machinery services can mitigate the negative impact of off-farm employment on farmers’ ratoon rice production behavior.

(3) Farmers with simultaneous off-farm employment and outsourced machinery services are more likely to adopt ratoon rice production. The probability of ratoon rice production increases by 57.0% for farmers with simultaneous off-farm employment and outsourced machinery services.

(4) We also found that outsourced machinery services have a regional heterogeneity effect. The substitution of mechanical machinery for labor is relatively easy in plain areas; therefore, the impact of off-farm employment on farmers’ ratoon rice production behavior is relatively weak. Outsourced machinery services can significantly promote ratoon rice production in non-plain areas.

In the dual context of off-farm employment and outsourced machinery services, ratoon rice production is important to increase grain productivity and ensure food security in China. Some important policy implications can be drawn from the results.

First, given the role of outsourced machinery services in promoting the multiple crop index or crop harvest frequency, the development of ratoon rice production should be promoted to ensure food security. On the one hand, farmers should be encouraged to participate in contiguous regional production. This can expand the market capacity of outsourced machinery services and promote the division of labor to achieve economies of scale. On the other hand, a larger market capacity can help farmers save on production costs and transaction costs. Thus, it is necessary to increase assistance and support to outsourced machinery service providers and actively promote the construction of their basic service conditions and capabilities.

Second, regional heterogeneity effects exist in terms of the impact of mobility factors such as labor and outsourced services on farmers’ ratoon rice production behavior. Specifically, efforts should be made to develop and apply small- and medium-sized agricultural machinery in non-plain areas since it is difficult to substitute outsourced services for labor these areas. Heavy-duty farm machinery should be promoted in plain areas. It is easy to promote agricultural machinery operation services across the region and share the economics of the division. Outsourced machinery services represent a feasible strategy to relieve the negative impact of off-farm employment on rice production.