Towards Sustainable Agricultural Development for Edible Beans in China: Evidence from 848 Households

Abstract

Minor beans other than soybeans or peanuts are edible beans (EBs) that significantly contribute to the Chinese agricultural sector and play a vital role in the sustainability of agricultural production, diversification of food consumption, and income generation for producers. These beans are an important source of protein in a healthy diet, helping to improve national food security. In addition, adjusting and optimizing the industrial structure promotes the sustainable development of agriculture and diversifies staple food crops and introduction of new revenue streams for EB products. The current study examines the responses of mung bean and broad bean producers to environmental and internal input constraints. This study uses the production function with a multilevel mixed-effects method and is based on 848 households from two major EB-producing provinces of China in 2018 and 2019. The results show that local climatic conditions influence planting behavior. These types of beans are considered as a supplement and backup crop to the staple crop. Commercialization encourages cultivation. Producers show variable price responses to output prices, but very strong responses to product costs. Minor bean production is favored by small households because of its low labor intensity. For households growing these beans for consumption, soil fertility and environmental outcomes are improved. Findings from research on planting behavior have strong policy implications for guiding research and development for drought and pest resistance, market monitoring for price stabilization, promoting EB production through low-cost technologies, and encouraging sustainable agriculture.

1. Introduction

It has been noted that the national development goal for the next decade is to achieve harmony and unity between humans and nature through eco-friendly development [1]. This requires a multipronged approach: in addition to increasing the supply of safe and high-quality agricultural products, the focus needs to shift from quantity to quality [2], while increasing agricultural incomes by establishing sustainable and efficient agricultural production structures compatible with existing resources [3]. Although edible beans (EBs) account for a small share of agricultural production in China, EBs play an integral role in the sustainability of agricultural production, diversification of food consumption, and income generation for producers [4]. EBs refer to more than 20 kinds of legume crops such as mung bean (MB), adzuki beans, common beans, broad bean (BB), peas, etc., except soybeans and peanuts. They are an important source of fiber and protein for a healthy diet [5]. At present, the per capita per year consumption of EBs is about 1.7 kg, which is expected to expand rapidly with the increase in income and the improvement of nutrition awareness under the strategy of high-quality agricultural development [5].

EBs are also important crops for ecological protection, utilization of idle land, and reduction in disaster damage, which can continuously promote the development of green, low-carbon, and circular agriculture [6]. Therefore, EBs are widely recommended for intercropping or rotation with cereals, root crops, cotton, and fruit saplings. In addition, they are grown on small marginal plots that are less fertile for other crops. These beans are also heat and drought tolerant and have a short growing period, making them popular in all weather conditions. Consequently, during extreme weather events, farmers are drawn to when they quickly replant them to mitigate income losses. EBs are important tools for optimizing crop structure, developing rural areas, reducing poverty, and revitalizing rural areas. Although EBs are widely grown in most parts of China, their production is concentrated in less-developed provinces with large ethnic minority populations and widespread poverty. The provinces with the most EBs are Yunnan, Inner Mongolia, Heilongjiang, Jilin, Sichuan, Guizhou, Chongqing, Shanxi, and Shaanxi, with a planting area of more than 100,000 hectares. In 2018, together this accounted for 65% of the country’s total arable land.

According to the investigation by China Agriculture Research System, smallholder production remains the main format of EB production and an important source of household income. The central government has been encouraging adjustments and optimizing the agricultural structure and local brand certification and management to increase high-quality agricultural supply. Examples of EBs serving as a major sector for local poverty reduction include MB from Baicheng City, Jilin Province, the BB from Dali Prefecture, Yunnan Province, and adzuki bean from Kelan County, Shanxi Province. In the case of Kelan County, Shanxi Province, a local noncereal production base was established with adzuki as the leading crop. The production base covers 6400 hectares and 141 administrative villages (of which 90 villages are defined as poor), which benefits 20,485 residents in 7923 households. It was estimated that the average household income increased by 244.98 US$ (1 US$ = 6.8985 yuan on 2019) for 3412 poor households (8854 residents). Fresh BB from Dali Prefecture, Yunnan Province also proves to be a feasible approach for erasing poverty among households living at high altitudes. Despite the strategic importance and growing demand, the domestic cultivation area of EBs had more than halved between 2001 and 2018, from 3.8 million hectares to 1.8 million hectares. The share of EBs in crop cultivation areas also dropped from 3.5 to 1.5%. As a result, bean imports climbed sharply and exacerbated the declining competitiveness of domestic production. There is a rich body of literature on the planting behaviors of cereal farmers, but very few on EB farmers.

The main objective of this study was to analyze EB plantations and their impact on China’s sustainable agricultural development (the ratio of EB planted area to total planted area). Based on a survey of 848 households in 2018 and 2019, in two producing areas (MB in Baicheng City, and BB in Dali Prefecture), this study provides an empirical analysis of producers’ planting behavior and its determinants. Results from this analysis will contribute to the understanding of farmers’ planting behavior and provide evidence in policy recommendations on sectoral development to enhance competitiveness and profit under a high-quality agricultural development strategy. The unique market structure of EBs, different from cereal crops, can also shed insights into other low-volume agricultural products.

2. Literature Review

The development of cash crop production (such as mung and broad beans) to improve household welfare has been at the heart of food policy debates in many developing countries [7,8]. Several studies have shown that cash crop production can be an effective way to improve the household’s economic well-being and agricultural development [9,10]. For example, Christiansen et al. [11] studied coffee farmers in Tanzania and found that in the presence of health and drought shocks, coffee farmers remained economically resilient compared to other major crop farmers, suggesting a positive impact on agricultural development and economic well-being of farmers. Similar studies have been found in other developing countries. For example, Kennedy et al. [12] found that participation in a cash crop program resulted in increased household income in six African and Southeast Asian countries (including Gambia, Guatemala, Kenya, Malawi, the Philippines, and Rwanda); later Finnis [13] showed that in southern India, growing cash crops of farmers achieve higher economic and social benefits, and in Malawi, households that choose to grow cash crops also show significantly higher incomes than those that do not use or adopt [14].

Several recognized channels for promoting crop production can improve the economic well-being of households and agricultural development. First, cash crop production can be an effective way to increase family farming income [15,16,17]. Specialization in cash crop production (relative to staple crops) typically results in higher economic returns per unit of land, including land, water, technology, and, to some extent, labor input. Second, boosting cash crop production can help diversify household livelihoods, thereby further improving household resilience to economic shocks (such as market price shocks) and other climate-related shocks (such as droughts, extreme heat, and cold temperatures). For example, several studies have found that crop diversification, such as intercropping and crop rotation, can increase the resilience of family farming production [18,19]. Third, the benefits of cash crop production also benefit other non-cash-crop farmers through the impact on employment because most cash crop production is labor-intensive [20,21]. Increased labor demand for high-value cash crops is likely to increase average wages for non-cash-crop farmers. In addition, the introduction of cash crop opportunities has shown that households can reduce cash constraints and be able to purchase improved crop production inputs [22]. As a result, their ability to adapt to yield enhancement techniques and agronomic practices is enhanced [23]. This cash income ultimately provides farmers with the opportunity to invest and improve farm management, thereby stimulating agricultural innovation and increasing yields [23,24,25].

However, there are reasons to question the positive impact of cash crop production on household economic well-being [14,26]. For example, some recent studies have shown that cash crop production has failed to increase the economic well-being of households in some developing countries, especially the poorest households, due to high barriers to entry [27]. They found that promoting cash crop production did little to improve the living standards of the poorest and that these poorest households were often ignored or barred from participating in the production of these cash crops [28]. In China, previous studies on cash crop production have focused on two aspects. A set of the literature focuses on the conceptual and theoretical basis of farmers’ economic crop production choices and their operating mechanisms [29]. Other research on cash crop production has generally focused on how cash crop production affects household labor distribution, their subsequent household relocation decisions, and other noneconomic outcomes such as environmental and ecological consequences [30,31]. Although there is ample evidence that cash crop production can be an effective approach to improving household economic well-being [28,32], these observations are mostly correlated. Evidence on its causality is rather limited. It is unclear to what extent and under what conditions cash crop production can achieve desirable results at the micro-household level [14,33].

In addition, farmers’ decisions on the production of cash crops (relative to staple crops) are increasingly influenced by perceived risks from climate change [34,35]. In the context of soybean production in China, farmers’ perceptions are enhanced due to repeated changes in climate (such as excessive rainfall and flooding in different regions), which directly affects farmers’ expected soybean yields and their economic benefits [36]. In response to this heightened perception of climate-related risks and the observed adverse effects on soy production, farmers may consider other alternatives and more resilient crops to address this potential negative impact [37]. As hypothesized by Asrat and Simane [38] and Ojo and Baiyegunhi [39], adaptation to climate change involves a multistep process in which strong perceptions (or strongly perceived changes in climate conditions) must be established, and subsequently, appropriate on-site responses may be initiated for these changes. Several studies have examined this relationship [34,35,40,41,42,43] and concluded that household adaptation to climate change behavior is directly related to its perception [44]. However, there is limited research on the combined effects of edible bean (EB) cultivation and its impact on commercial and agriculture development [45] and economic welfare [39]. It can be investigated from the above studies that EBs and crops planting play an important role in increasing farmers’ income and agricultural development in China.

3. Data and Methodology

3.1. Survey Design and Data Collection

Mung bean (MB) in Baicheng City, Jilin Province, and broad bean (BB) in Dali Prefecture, Yunnan Province are selected for this study for their significant role in edible bean (EB) production and representativeness. Both locations are important players in national production. Baicheng City MB is a certified geography signature product with an average annual planting area of 80,000 hectares. Annual MB production in Baicheng City reaches 100,000 tons, accounting for 11% of national total production. Approximately half of Baicheng MB is exported, representing above 30% of total exports. A major noncereal wholesale market is located in Baicheng City, facilitating the commercialization of local MB. Yunnan province is the top producer of EBs in the country, representing 16.5% of the national planting area. In Yunnan province, Dali Prefecture is the largest producer of BB, with more than 20% of provincial production coming from this location.

The household-level of data is obtained from households in two consecutive years, 2018 and 2019. A cluster sampling method was used in the survey [46]. Household-level data was obtained from 5 counties/cities in Baicheng City, Jilin Province, and 4 counties/cities in Dali Prefecture, Yunnan Province, covering 32 townships and 66 villages. Among 938 questionnaires distributed, 848 provide valid responses (90%) (see

Table 1. Samples Location.

Prefecture	Geographical Location	County/City	No. of Households	Share (%)
Dali	Belongs to Yunnan Province, located in the central and western part of China	Dali	106	12.5
		Erdu	103	12.1
		Midu	94	11.1
		Xiangyun	85	10.0
		Subtotal	388	45.8
Baicheng	Belongs to Jilin Province, located in northeast China	Da’an	30	3.5
		Tiaobei	41	4.8
		Tiaonan	146	17.2
		Tongyu	178	21.0
		Zhenglai	65	7.7
		Subtotal	460	54.2
Total			848	100.0

for details). Dali Prefecture represents 45.8% of the total sample, while Baicheng City captures the remainder. The distribution of households reflects the geographic concentration of production, with wide cultivation of BB in Dali Prefecture, but a high concentration of MB in Baicheng City (mainly in Tiaonan and Tongyu counties).

The county-level data on the weather was obtained at the county level from the Chinabric database. The highest and lowest temperature and an average rainfall of Chinabric database at county/city are collected. Weather information in April is collected for MB as they are usually planted in April in Baicheng City and harvested in late September and early October. Dali producers usually plant BB in late September and harvest in April and May of the following year. Hence, weather information in October was collected for Dali Prefecture.

3.2. Empirical Model

This paper uses regression analysis, which is a statistical technique for estimating the relationship that independent variables have on a dependent variable [47]. Regression analysis has many estimation methods to correct for errors. Specifically, a multilevel mixed-effects model was chosen for this study [48,49] because it recognizes the hierarchical nature, clustering, and the structure of the data in this study. Households in the same county tend to be more exposed to the same environmental and social characteristics than households chosen at random from the population. The county difference refers to the resource and environmental endowments difference. Multilevel mixed-effects models accommodate the existence of data hierarchy by introducing separate residual components for each level of the hierarchy. In this study, residuals are assumed to exist at both household and county levels.

The residual variance is partitioned into a between-group component (the variance of county-level residuals) and a within-group component (the variance of the household-level residuals within the same county). Between-group residuals, or county effects, signify unobserved county characteristics that affect household production behavior [50]. County effects are unobservable but can lead to a correlation between EB planting behaviors for households within the same county. Because individual households at the county level are not independently distributed, results from classic Ordinary Least Squares (OLS) are no longer valid [51].

Multilevel mixed-effects models allow both fixed and random effects, especially in the case of non-independence in a hierarchical data structure; they are also called multilevel models. Multilevel mixed-effect models offer several advantages. First, they correct inferences based on observations that are not independent. Ignoring the intercorrelation among households can result in underestimated standard errors of regression coefficients and thus an overstatement of statistical significance. Standard errors for the coefficients of higher-level predictor variables will be the most affected by ignoring the grouping of the county.

Second, multilevel mixed-effects models address individual county-level effects by separating county unobserved conditions from local agronomical practices in obtaining household planting behavior. Moreover, county-level effects are estimated simultaneously with the effects of county-level coefficients. Instead of multiple county dummy variables in a fixed-effects model, this approach allows the estimation of county-level variables of interest (weather indicators in this study), while separating observed (weather) and unobserved (county random effects) county characteristics.

Third, results from the multilevel mixed-effects model can be generalized to a wider population of counties. Unlike a fixed-effects model, inferences are limited to the counties in the sample and cannot be made beyond these groups. By assuming that the random county effects come from a common distribution, planting behaviors obtained from this study can be extrapolated to other counties.

This analysis uses a linear multilevel mixed model with random intercepts. Regression models with one dependent variable and more than one independent variable are called multilinear regression [47]. The regression model can be expressed as

$$y_{ij}={\beta }_{0j}+X_{ij}{\beta }_1+e_{0ij}$$

(1)

$${\beta }_{0j}={\beta }_0+{\mu }_{0j}$$

(2)

where j = 1, 2, …, n refers to county, I = 1, 2, …, m refers to households, y_ij is the share of EB planting area in total planting area for, x_ij are variables affecting EB producer behavior for i-th household of j-th county, ${\beta }_{0j}$ is the sum of fixed intercept ${\beta }_0$ and a random intercept for the i-th county ${\mu }_{0j}$, ${\beta }_1$ is the fixed slope, $e_{0ij}$ is a zero-mean Gaussian error term.

Equation (2) assumes that ${\mu }_{0j}$ and $e_{0ij}$ are unobserved random variables that are independent of each other. Independence of between-group can be measured by ICC (Intra-Class Correlation Coefficient) as the ratio of between-group variance to the total variance. STATA (16th version) software is employed to estimate the results, and Standard Maximum Likelihood (ML) and Restricted Maximum Likelihood (REML) estimation are applied, with the latter providing consistent estimates.

$$\mathrm{ICC}=\frac{ {\sigma }_{{\mu }_0 }^2}{ {\sigma }_{{\mu }_0 }^2+{\sigma }_{e_0 }^2},$$

where ${\sigma }_{{\mu }_0 }^2$ is between-group variance and ${\sigma }_{e_0 }^2$ is within-group variance. ICC takes a value between 0 and 1. ICC is close to 1 when between-group variances are large. If ICC is close to 0, within-group variance is large, or observations are independently distributed. The rule of thumb is that mixed-effects models are appropriate when ICC is greater than 0.1.

4. Results and Discussion

Planting behavior is defined as decisions by producers to maximize profit under certain constraints in resources. The existing literature on agricultural planting behavior covers planting behaviors about crop selection and input. Crop planting behaviors involve the willingness to grow [52,53], and planting area and input decision involves the choice of variety [54], application of chemical fertilizer [55], and pesticide and herbicide [56,57,58,59]. Referring to the literature, the dependent variable is defined as the share of edible beans (EBs) planning area in household total planting area instead of planting area, which controls the variations of land size across households. The explanatory variables address weather, market and household, and individual characteristics.

Weather is proven to have affected crop production [60,61,62], planting system, and crop allocation [60,63], as well as producer decision [64,65], and we noticed that farmer’s willingness to plant maize is negatively related to average temperate during the growing season but positively related to average rainfall. Interviews with EB farmers in our research regions reveal that farmers pay close attention to temperature and rainfall before EB planting. Hence, average rainfall and the highest and lowest temperature at the beginning of the planting season are included in the analysis.

Most research indicated that the higher the market price, the stronger the willingness to plant [66,67]. On the contrary, other researchers pointed out that to a large extent, the planting behavior did not depend on the price level, and the farmers’ response to price fluctuation was more diverse [68]. In addition to prices, the production costs of different crops also affected the behavior of farmers. The higher the production costs, the lower the cost benefit of crops, which directly reduced the production benefits of farmers and the planting behavior [69]. Hence, output prices of last year and production costs are included in the analysis.

In terms of household and individual characteristics, this study selected household size, the share of wage income in household total income, household head age, and education. Larger households are more likely to choose multiple crops to diversify production risks. Wage income is treated as a proxy for non-agricultural income. Household head demographics identify the producer’s access to new technology and information [70,71,72,73].

4.1. Descriptive Statistics of the Sample

There is substantial variation in planting area: the average mung bean (MB) producer reported a planting area of 2.67 hectares (ha), while the average broad bean (BB) producer only reported 0.14 ha. This regional difference is due to the availability of arable land: the average Baicheng household cultivates 9.63 ha, far greater than the average land size of 0.39 ha in Dali Prefecture. BB production is an integral part of livelihood as its average share in total planting area reached 59% (

Table 2. Descriptive statistics of the sample.

Category	Variable	Unit	Mung Bean				Broad Bean
			M	S.E	Min.	Max.	M	S.E	Min.	Max.
Dependent Variable	Share of edible bean planting area	%	0.38	0.25	0.01	1.00	0.59	0.32	0.01	1.00
Level 1: County/City Level
Weather	Lowest temperature	°C	−6.06	1.37	−8.50	−4.50	7.83	2.13	4.50	11.50
	Highest temperature	°C	27.90	0.98	26.70	29.30	24.45	0.59	23.70	25.20
	Average rainfall	mm	0.46	0.50	0	1.70	72.21	50.84	9.60	144.8
Level 2: Household Level
Market	Output price of last year	US$/kg	0.94	0.06	0.82	1.11	0.57	0.11	0.43	0.87
	Average cost	1000 US$/ha	0.63	0.53	0.05	3.83	1.13	0.73	0.02	3.89
Household	Household size		3.46	1.14	1.00	8.00	5.01	1.44	2.00	10.00
	Share of irrigated planting area	%	0.38	0.38	0	1.00	0.78	0.32	0	1.00
	Wage share in total income	%	0.05	0.14	0	0.91	0.46	0.36	0	1.27
Individual	Age	Years	48.27	9.57	21.00	74.00	52.23	9.35	29.00	87.00
	Education	Years	7.96	2.36	0	12.00	8.60	2.31	0	12.00

Note: Weather information in Tiaobei was not available and replaced with Da’an in a similar latitude. Education level: 0 = illiterate, 6 = primary school, 9 = middle high school, 12 = high school graduate and above. Source: Weather data from Chinabric database.

).

Interviews show that farmers adjust their planting decision based on weather conditions. For instance, farmers in Baicheng City will choose to plant maize if the average rainfall is favorable; otherwise, farmers will choose to either delay maize planting or switch to MB for its drought resistance. There was little difference in temperature between 2018 and 2019 in Baicheng, China, with a slightly warmer temperature in July 2019. The highest temperature was 35.9–38.7 °C in 2018 and 36.1–39.7 °C in 2019. Similarly, the lowest temperature was close to −5.2–−4.6 °C in 2018 and −8.5–−6.3 °C in 2019. However, average rainfall differed remarkably across China in Baicheng City.

Market factors include last year’s output prices and average cost. Output prices are the prices sold from the farm gate. The average output price is 0.94 US$/kg for MB and 0.57 US$/kg for BB. The average production cost is 42.40 US$ for MB, and the BB cost almost doubles that of MB at 75.54 US$. Input costs, such as the purchases of seeds, fertilizer, pesticides, and herbicides, accounted for 50.5% of total MB cost, while land rent accounted for another 41.5%. As a result, labor costs only represented 5.1% of the production cost for MB. Input costs were a higher portion of BB production cost at 71.4%. Labor cost is also higher at 12.1%, but land rent was a much smaller part of the total cost at 4.6%. The difference in cost structure can be attributable to the land market and the adoption of mechanization. The land market is very active in Baicheng City, with an average rent of 434.88 US$ per hectare. This leads to a larger land size that supports the use of machines in every stage of MB production, resulting in a much lower dependence on human labor. Dali is the opposite case of smaller plot size from the inactive land market and low adoption of agricultural machinery.

The average size of EB-growing households was four to five persons, with middle school education. The average household head was 48 years old among MB growers. BB growers were older, with the household head at 52 years old. The share of household land that was irrigated was 37.6% of Baicheng because MB is planted in lands without irrigation. BB plots are more likely to be irrigated as 78.3% of household land was irrigated in Dali. MB producers also tend to be more specialized in agriculture with only 5.3% of household income derived from wages. Almost half (46%) of household income came from wages and salaries, indicating that BB-growing households tend to have more diversified income sources and are less dependent on crops for income earning.

Table 3. Estimation results from multilevel models.

Category	Variable	Mung Bean		Broad Bean
		ML	REML	ML	REML
Fixed Effects
Weather	Lowest temperature	0.033 **	0.031 *	0.013	0.005
		(0.016)	(0.018)	(0.041)	(0.042)
	Highest temperature	0.02	0.021	−0.043 *	−0.044 *
		(0.015)	(0.016)	(0.023)	(0.027)
	Average rainfall	−0.070 **	−0.064 *	−0.001	−0.001
		(0.033)	(0.037)	(0.001)	(0.001)
Market	Output price	0.069 **	0.067 *	−0.076	−0.083
		(0.033)	(0.037)	(0.061)	(0.069)
	Average cost	−0.177 ***	−0.178 ***	−0.088 **	−0.089 **
		(0.042)	(0.043)	(0.041)	(0.041)
Household	Household size	−0.041 ***	−0.042 ***	−0.001	−0.002
		(0.010)	(0.010)	(0.010)	(0.010)
	Share of irrigated planting area	−0.048 *	−0.047 *	0.149 ***	0.144 ***
		(0.027)	(0.028)	(0.048)	(0.048)
	Wage share in total income	0.054	0.056	0.156 ***	0.156 ***
		(0.073)	(0.074)	(0.039)	(0.039)
Individual	Age	0.003 **	0.003 **	0.001	0.001
		(0.001)	(0.001)	(0.002)	(0.002)
	Education	−0.002	−0.002	0.001	0.001
		(0.004)	(0.005)	(0.006)	(0.007)
	Constant	−0.755	−0.693	0.695	0.931
		(0.522)	(0.567)	(1.126)	(1.180)
Random Effects
	Level 2 Error	0.046	0.047	0.067	0.069
		(0.003)	(0.003)	(0.005)	(0.005)
	Level 1 Error	0.001	0.002	0.021	0.029
		(0.001)	(.001)	(0.010)	(0.016)
	ICC	0.132	0.142	0.304	0.327
	Observations	460	460	388	388
	Chi-square	118.804 ***	107.193 ***	40.395 ***	38.189 ***
	AIC	−73.258	12.796	106.676	193.105

Note: ***, ** and * indicate 1%, 5% and 10% statistical significance level. Standard errors in parentheses. Estimated parameters are listed above the stand errors in the parentheses.

reports the values of ICC for MB are 0.132 under ML and 0.142 under REML, suggesting that 13.2% of variance comes from between-group components. ICC values are even higher for BB at 0.304 under ML and 0.327 under REML [74]. The results confirm the appropriateness of mixed-effect models in this analysis to avoid inconsistent estimation. The superiority of mixed-effect models is further confirmed by chi-square values when compared with OLS. Akaike information criterion (AIC) values indicate that ML is the preferred model [75]. Coefficients in Table 3 are used to estimate elasticities of planting behaviors in

Table 4. Elasticities of the determinant factors on edible bean planting in China.

Category	Variable	Mung Bean	Broad Bean
Weather	Lowest temperature	1.473	−1.777 *
	Highest temperature	0.528 **	0.172
	Average rainfall	−0.085 **	−0.122
Market	Output price	1.182 **	−0.506
	Average cost	−0.137 ***	−0.078 **
Household	Household size	−0.374 ***	−0.008
	Share of irrigated planting area	−0.048 *	0.197 ***
	Wage share in total income	0.008	0.121 ***
Individual	Household head age	0.382 **	0.088
	Household head education	−0.042	0.015

Note: ***, ** and * indicate 1%, 5% and 10% statistical significance level. Elasticity is an economics concept that measures the responsiveness of one variable to changes in another variable, $\mathrm{Elasticity}=\frac{dy}{dx}\frac{\Delta x}{\Delta y}$, in which y is the independent variable and x is a dependent variable.

, evaluated at the sample mean.

4.2. Impacts of Weather on Farmers’ Planting Behavior

Weather plays an important role in EB producers’ decisions. The highest temperature and average rainfall before planting season could substantially affect planting decisions. A heat wave before planting season is likely to increase MB planting (at a 5% significant level). When the highest temperature in April increases by 1%, the share of MB cultivation would increase by 0.53%. A field study suggests that MB production serves as a strategy to mitigate loss. When it becomes too warm (higher temperature) for maize production, farmers would choose MB as a substitute to decrease crop cultivation loss.

The average rainfall is negatively associated with MB production. A 1% increase in average rainfall in April could lead to a 0.09% drop in MB planting share. This is due to the substitutability between maize and MB. The average profit for MB is about 72.48 US$, slightly higher than that of maize at 65.23 US$. However, farmers prefer maize production because it requires less labor input due to a higher level of mechanization and comes with subsidies from the government. When soil contains more moisture in April, farmers would choose to plant maize for a potential good harvest. However, when average rainfall is low, farmers exhibit a higher propensity for MB, highlighting EBs’ role in loss mitigation. The results of Pataczek et al. [76] show that mung bean (Vigna radiata) is gaining attention as a short-season crop that can tolerate dryland conditions (with less rainfall). MB is such a minor crop that dryland smallholder farmers can use to break the downward spiral and increase the profitability and sustainability of their farms. Integration of mung bean in cropping systems may increase the sustainability of dryland production systems. Diversification of local production systems through the inclusion of mung bean as a catch crop provides additional income to farmers and has the potential to improve soil fertility [77].

The lowest temperature affects the planting decisions of BB planting in Dali. The share of BB planting area would drop by 1.78% if the lowest temperature in October falls by 1% (at a 10% significance level), as short episodes of extreme minimum temperature can essentially impede crop development and thus impact crop yield [78]. This is also associated with the growth pattern of BB. Experienced farmers will cut BB planting in a warm October because it could lead to rapid growth and the flowering season could be susceptible to frost and thus lower crop yield. Average rainfall does not appear to impact the planting behavior of BB, mainly due to reliable and sufficient rainfall patterns. Table 2 indicates that average rainfall was 210.2–473.4 mm in October 2018, and 188.4–541.6 mm in October 2019.

4.3. Impacts of Market on Farmers’ Planting Behavior

MB farmers are very responsive to output price but not BB. Given that the output price is the price sold from the farm gate, the own-price elasticities we estimated represent the derived (farm gate) supply rather than the market supply [79]. If the output price goes up by 1%, the share of the MB planting area will increase by 1.18% (at a 5% significance level). This phenomenon reflects different levels of commercialization between these crops. MB farmers are active participants in markets and thus are more sensitive to price signals. Additionally, MB farms tend to be much larger at 2.6 hectares, allowing large-scale application of machinery to lower production costs and adjustment in the planting area. On the other hand, BB production is more stable and less responsive to market prices because most of the output is still used for own and local consumption. The lower level of machinery further limits farmers’ ability to expand production quickly.

The average cost can negatively impact planting behavior for both EBs. The lower the cost, the higher the planting area. If cost decreases by 1%, the share of the planting area would increase by 0.14 and 0.08% for MB and BB, respectively. Because the majority of the total cost is spent on inputs like seeds, fertilizer, and chemicals, policies targeting lower input costs could promote the production of EBs. In 2018, Yunnan initiated a program to support local green food by providing free fertilizer to lower production costs.

4.4. Impacts of Household and Individual Characteristics on Farmers’ Planting Behavior

Household size is negatively associated with EBs planting share, especially in the case of MB, implying that larger households with more arable labor are less inclined to plant EBs. In Baicheng City, larger households prefer maize for its higher labor productivity from mechanization. In Dali, the BB is less demanding in labor requirement; sometimes farmers choose to skip applying fertilizer or chemicals to cut labor input without sacrificing yield much.

The elasticities of the share of irrigated areas are significant for both MB and BB but of opposite signs due to local agronomical conditions. The share of MB planting area declines when a household has more irrigated plots, with a 1% increase of irrigated land share leading to a 0.37% decrease in MB plant area (at a 10% significance level). The land is largely non-irrigated in Baicheng City, with only 37.6% of the planting area irrigated. Farmers prefer to grow maize on irrigated plots while cultivating MBs in non-irrigated areas. This negative elasticity underscores the key role of MBs in loss mitigation. The impact of irrigation is positive in Dali, partly due to the high availability of irrigated plots (78.3% of the planting area is irrigated).

Wages and salaries are a good proxy of income diversification in rural households. A higher share of wages and salaries in household income indicates a diversified income base and fewer resources dedicated to agricultural production. The elasticity of MB is insignificant but significant for the BB. This is because there are fewer substitute crops in Dali and the flexibility offered by BB production through low labor demand and smaller plots. Diversification from agriculture introduces new income sources for EBs producers. Elasticity associated with household head age is positive and significant for MB with a value of 0.38 at a 5% significance level. Older household heads express a stronger preference for MB for its low input requirement compared to maize.

5. Conclusions and Implications

Few studies have focused on farmers’ behavior in growing noncereal crops such as edible beans (EBs), despite their crucial role in the sustainability of agricultural production, the diversification of food consumption, and farmers’ income. EB production also has its unique characteristics different from cereals: short growth period, low soil requirements, low yield, less machinery usage, but a high degree of commercialization. Therefore, our research will focus on improving the competitiveness of EBs and improving the allocation of agricultural resources under the eco-friendly development strategy. This paper estimates the planting behavior of two representative edible legumes: mung bean (MB) and broad bean (BB) by estimating a multilevel mixed-effects model. The empirical analysis uses the household-level data of Baicheng City in Jilin Province and Dali Prefecture in Yunnan Province in 2018 and 2019, combined with the county-level meteorological observation data in the month of planting.

This study determines whether or not it is a major factor in the decision to grow EBs. In the case of MB, producers choose to grow MB to mitigate the potential loss of income from unfavorable soils and temperatures. BB farmers are also temperature-sensitive when making planting decisions to ensure high yields. Although the commercialization and specialization of the two types of EB are different, and producers may be less responsive to output prices, they are generally responsive to production costs. Smaller households are showing a higher interest in growing edible pulses due to reduced demand for land and income generation. Although BB is grown in small pieces and eaten more often, its role in increasing nitrogen fertilizer cannot be ignored. Based on the above results, it is believed that under the background of high-quality agricultural development, the eco-friendly development and high-quality development level of the bean industry should start by improving the agricultural development capabilities of the bean industry in order to address climate change. To reduce carbon emissions to address climate change, China’s lesser bean producers can improve production efficiency. They can accomplish this by market monitoring and relying on scientific and technological improvements to increase crop productivity per unit input of greenhouse gas emitting inputs (e.g., tractors, processing machinery, etc.).

First, in the process of high-quality agricultural development, to enhance the adaptability of EBs to climate change. It is necessary to increase investment in research and development, carry out the screening of main varieties and key technologies, and enhance stress resistance so that beans are resistant to drought, pests, and other risks. The ecological and cultural value of edible pulses should be further developed. In Dali Prefecture and other key ecological development zones, it is necessary to further explore the ecological service value of beans, improve the organic combination of the beans industry and local culture, and explore new forms of the beans industry. Second, for EBs with a high degree of marketization (such as MB in Baicheng City, Jilin Province), a market price monitoring mechanism should be established, and market supervision of small-scale agricultural products should be strengthened to prevent excessive consumption and reserve as well as stabilize market prices and farmers’ production. At the same time, we should encourage the “Internet + Project” development model of EBs, further standardize and guide the EB sales and circulation market, and provide farmers with services such as prenatal markets. Information, quality control in the process of production and post-purchase, and sales through the internet or Internet of Things technology.

Third, it is important to improve quality and efficiency through science and technology, reduce the cost of bean production, and promote the upgrading of the bean industry. Strengthen the promotion and application of new varieties of high-yield and high-quality EBs suitable for mechanized production, and promote mature, high-yield, efficient, simplified, and integrated production technologies characterized by large-scale, standardized, and mechanized production, reducing the production cost of EBs and increasing farmers. The economic income of farmers has improved their enthusiasm to grow EBs.

Finally, it is recommended to establish a demonstration zone for the integration of the bean product industry, develop and improve the bean product industry chain, and closely integrate the bean product industry with the health industry. After starting with implementation of lesser bean industry integration demonstration areas, China can improve integration of bean production and processing, as well as facilitate marketing to the health industry. Future research can enable breakthroughs in the development of processing edible bean products using more eco-friendly processes.