3.1. The Soaring Labor Cost in Grain Production
In 2009–2019, the yield per unit area, the price, and the revenue of grain increased, while the cost of grain production increased more intensively (
Table 4). The yield per unit area, the selling price, and the revenue of rice, wheat, and maize increased by 5.85–49.41%. However, the total cost of rice, wheat, and maize increased more (by 81.47–91.56%). As a result, the net profit of rice, wheat, and maize decreased largely. It can be concluded that the rapid increasing cost became the major characteristic of the change in the input–output of grain plantation in this period, which induced the large decrease in net profit of grain plantation.
In different provinces, the cost and benefit increased variously (
Figure 2). From 2009 to 2019, the net profit of rice, wheat, and maize production in most provinces changed from positive to negative. Till 2019, the highest cost and benefit of rice plantation appeared in Fujian Province, while the highest net profit appeared in Anhui. In terms of wheat plantation, the highest total cost appeared in Ningxia Province, the highest benefit appeared in Inner Mongolia, while the highest net profit appeared in Anhui. In terms of maize plantation, the highest cost and benefit appeared in Gansu Province, while the highest net profit appeared in Xinjiang Province.
In terms of the constitution of the production cost, the rapid increasing trend of labor cost, and the slight increases in land and material input costs were clear (
Figure 3). During 2009–2019, the labor cost of rice production increased by 137.83%. The labor cost for wheat plantation increased by 134.03%. For maize production, the labor cost increased by 137.83%. Till 2019, the proportion of labor cost in the total cost of rice, wheat and maize reached 36.89%, 33.13%, and 42.99%, respectively. From the long-term change in the constitution of the cost, the soaring labor cost became a considerable cause of the increasing cost of grain plantation.
3.2. The Spatiotemporal Change in Farmers’ Livelihood Transformation
In 2009–2019, the farmers’ annual income in China increased by 183.65%, among which the wage income, agricultural income, property income, and transfer income increased by 190.90%, 112.06%, 104.61%, and 633.89%, respectively (
Figure 4). Compared with the income, the farmers’ annual expenditure increased more (by 203.6%), among which the expenditure on food, clothes, accommodation, home equipment, transportation, education and entertainment, health, and others increased by 122.83%, 178.58%, 230.51%, 251.79%, 319.45%, 282.19%, 331.28%, and 159.57%, respectively. It can be seen that most of the farmers’ expenditures increased faster than the incomes, which formed a continuous income–expenditure gap for farmers’ livelihood. Among various income and expenditure types, the increase in transfer income and the expenditure on health were the most prominent.
In total, the farmers’ average LSI in China increased from 0.593 to 0.659 in 2009–2019, indicating that the source of farmers’ income became more abundant, which increased the farmers’ livelihood diversities. The farmers’ average RLR in China experienced a fluctuated decrease from 0.225 to 0.168 (
Figure 5), indicating that the farmers’ expenditure increased faster than the income, which formed increasingly severe living pressure in farmers’ daily life.
For different provinces, the change in RLR varies over time (
Figure 6). From 2009 to 2019, the provinces in central and eastern China experienced a general decrease in RLR. In 2009, the farmers in Tianjin, one of the most developed municipalities in China, showed the highest RLR, with an average annual income of
$1259.25 and expenditure of
$619.39. The farmers in Shanxi showed the lowest RLR, with an average annual income of
$498.27 and expenditure of
$485.46. In 2019, the highest RLR appeared in one of the undeveloped areas in China, Xizang, with an average annual income of
$1877.23 and expenditure of
$1220.16. The lowest RLR appeared in Gansu, with an average annual income of
$1395.70 and expenditure of
$1405.13. It can be seen that the balance between farmers’ income and expenditure is broken in addition to the growth of farmers’ living standards, which can appear in both the economically undeveloped and developed areas. As a result, the farmers’ livelihood risk sometimes occurs due to the “double-high” of income and consumption, which meant that some developed provinces in eastern China showed an even lower RLR than the inland provinces.
The LSI showed a different spatial distribution from the RLR (
Figure 7). In 2009, the lowest LSI appeared in Xinjiang Province, the major reason for which is the high proportion of farmers’ agricultural income (79% of the total income). The high proportion of agricultural income in farmers’ total income in Xinjiang formed a specialized livelihood mode, and made the diversification of farmers’ livelihood relatively low in this area. The highest LSI appeared in Qinghai Province, the reason for which is the relatively balanced ratios among different income types (the salary income, agricultural income, property income, and transfer income occupy 32%, 50%, 3%, and 14%, respectively). In 2019, the lowest LSI appeared in Beijing, though the farmers’ yearly income in Beijing ranked second in China (as high as
$3839.72), the farmers’ income source was too specialized (75% of the farmers’ income in Beijing comes from salaries), which formed a low LSI in this area. The highest LSI was still located in Qinghai Province, but compared with the year 2009, the ratios among different income types for farmers in Qinghai became more balanced in 2019 (the salary income, agricultural income, property income, and transfer income occupy 31%, 37%, 4%, and 28%, respectively).
Using the median value of LSI (0.598) and RLR (0.21) among the 31 provinces through 2009–2019 as the dividing points, the livelihood types of farmers in different areas are shown in
Figure 8. Moreover, it is worth mentioning that the changes in farmers’ livelihood types are inherently dynamic. Therefore, the cut-off point of dividing standards of farmers’ livelihood types may not be an absolute value. In future study, if the study time span is extended to other years, a new median can also be used as a new cut-off point.
In terms of livelihood type transformation (
Figure 8), six provinces experienced a farmers’ livelihood transformation from “low-level livelihood” to “livelihood type with potential to improve”, namely, NeiMengol, Gansu, Ningxia, Yunnan, Hunan, and Anhui.
Six provinces and a municipality experienced a farmers’ livelihood transformation from “specialized livelihood” to “livelihood type with potential to improve”, namely, Hebei, Hubei, Chongqing (a municipality in China), Fujian, Guangdong, and Hainan. Specifically, in 2009, farmers in these provinces mainly lived on agricultural income (more than 47% of the farmers’ income come from agriculture), whereas in 2019, farmers tended to live on salary income (at least 35% of the farmers’ income comes from salaries), and the agricultural income decreased (only 26% to 42% of the farmers’ income comes from agriculture). Synchronously, the RLR of these provinces decreased by 0.06–0.17.
Six provinces experienced a farmers’ livelihood transformation from “specialized livelihood” to “progressive livelihood”, namely, Jilin, Jiangsu, Shandong, Henan, Jiangxi, and Xinjiang. Specifically, farmers in these provinces relied even more highly on agricultural income. For instance, 72% of farmers’ income in Hainan and 79% of farmers’ income in Xinjiang come from agriculture, respectively. By 2019, the proportion of agricultural income in total income decreased to 35–54%. As a result, the farmers’ low-level livelihood has been improved, which has led to a higher potential to improve and progress livelihood types.
In all, the provinces with farmers’ low-level livelihood decreased largely, while provinces with farmers’ progressive livelihood and livelihood type with potential to improve increased significantly, which indicated an overall improvement trend in the farmers’ livelihood transformation process.
3.3. The Impact of Farmers’ Livelihood Transformation on the Labor Cost of Grain Production
To test possible multi-collinearity problems, this study performed collinearity diagnosis among variables using the SPSS software. Specifically, if the variance expansibility factor (VIF) of a variable is lower than 10, and the tolerance is higher than 0.1, the variable does not have a strong collinearity problem in the regression. The results show that X
4, X
6, X
8, X
12, and X
13 for rice; X
4, X
6, X
11, X
12, and X
14 for wheat; and X
6, X
9, and X
11 for maize did not pass the collinearity diagnosis (
Table 5), and they were excluded from the regression as a result.
The results of the multiple linear regression show that the indicators of farmers’ livelihood transformation have a significant impact on the labor cost in grain production (
Table 6). Specifically, the Residual Livelihood Ratio (X
1) showed a significantly negative relationship with the labor cost of grain production at the 0.05 level of significance. A possible reason for this is that a larger farmers’ Residual Livelihood Ratio will form a higher capital surplus in the balance of income and expenditure, which is helpful for them to invest in more alternative elements of labor (such as agro-machinery and technologies), and helps to spare more labor cost in gain production. The Livelihood Simpson Index (X
2) is significantly positively related with the labor cost of grain production at the 0.05 level of significance. This may be due to the fact that when the farmers’ livelihood types are more varied, farmers often tend to obtain income from non-agricultural wages, transferred income, and property income, etc., which may form a deficit of agricultural labor and higher cost in grain plantation.
In terms of the control variables, the mechanization input into per unit arable land (X7) is significantly negatively related to the labor cost of grain production (for rice, wheat, and maize). This means that the higher the mechanization level is, the stronger the factor substitution of agro-machinery to laborers will be, which helps to reduce the labor cost in grain production. The grain yield per unit area (X9) is significantly negatively related to the labor cost in grain production (for rice and wheat). The major reason for this is that the higher grain yield per unit area means higher local grain productivity, which is often a reflection of high production efficiency and lower reliance on agricultural labor. Engel’s coefficient of local farmers (X10) is significantly positively related with the labor cost (for rice, wheat, and maize). This is due to the fact that greater poverty often occurs with a high proportion of expenditure on food in farmers’ total expenditure, which indirectly reflects lower local economic development levels and lower prices of labor for grain production. The non-grain plantation degree (X11) is significantly negatively related to the labor cost in grain production (for rice). This means that the higher the proportion of the non-grain crop plantation area, the lower the enthusiasm for grain plantation is, which often pushes more farmers to rely on non-grain crop plantation and spend less on grain plantation labor costs. Due to this transformation, labor costs may increase in non-grain crop production but decrease in grain plantation. The non-agricultural land use degree (X12) is significantly negatively related to the labor cost in grain production (for maize). The major reason for this is that the higher the proportion of local non-agricultural land use is, the higher the proportion of the non-agricultural economy is in the local economic structure, which is often related to the phenomenon that the grain labor force shifts to the non-agricultural industry, and forms a lower grain plantation scale and lower labor cost.