Household perceptions of factors that affect food consumption in grassland areas: a case study in the Xilin Gol Grassland, China

The Xilingol League lies in the central part of China's Inner Mongolia Autonomous Region. It belongs to the Mongolian Plateau, with an elevation that ranges from 800 to 1800 m asl. Annual precipitation totals 288 mm, and annual pan evaporation ranges between 1700 and 2600 mm. The annual average temperature is 3.60^°C, with mean monthly temperatures ranging from −20^°C in January to 21^°C in July. The league comprises two county-level cities, one county, nine banners, and two districts. The total registered population was 1.036×10⁶ at the end of 2016, of which rural people accounted for 35.5%. The population's ancestry is 64.2% Han Chinese and 31.3% Mongolian. The Xilingol League covers 200 000 km², including 180 000 km² of grassland and 5860 km² of forest, which amount to 90.0% and 2.9% of the total area, respectively. Cows and sheep are the main livestock raised in this region, but camels and horses are also raised because of their high economic value, especially in Taibus Banner. In 2016, the per capita disposable income of residents was 25 554 yuan per year, of which the per capita disposable income of urban residents was 32 903 yuan per year, versus 13 188 yuan per year for rural residents [48].

Most of the Xilin Gol Grassland is located within the Xilingol League. The grassland cover can be divided into typical grassland, desert grassland, cultivated land, and sparse grassland (figure 1). The carrying capacity of these grassland types gradually decreases from south to north.

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In this study, we selected three typical areas of the Xilin Gol Grassland for investigation: the West Ujimqin Banner, which is a typical pastoral grassland area; the Zhenglan Banner, which is a pastoral area of the Hunshandake Sandy Land; and the Taibus Banner, which is an agro-pastoral transitional zone (figure 1). The three areas represent a north to south transect through changing environments and ecosystems. The West Ujimqin Banner is in the eastern part of the Xilingol League, whereas the Zhenglan Banner and Taibus Banner are located in the southern part of the league. All three banners are in an arid to semi-arid transitional zone, with annual precipitation around 400 mm, and the annual pan evaporation is very high ($\gt$1800 mm). The ecosystems in these study areas are very vulnerable to degradation (table 1).

2.2.1. Interviews and data collection

2.2.2. Basic information about the interviewees

Our 67 interviews revealed 28 different factors that directly affected food consumption (table 3). We classified the factors into four dimensions: ecological factors (10), social factors (5), economic factors (5) and personal factors (8). Here our classification referred to the usual sustainable development indicator and dimension [3], while the factors relative to the personal characteristic and personal emotion were sorted as the personal dimension. Four dimensions consisted of all types of the impact factors.

Each interviewee scored the strengths of the relationships among the factors in the fuzzy cognitive maps. These strengths (V) were divided into four degrees: very weak ($0 < {\mkern 1mu} {\textrm{V}}{\mkern 1mu} < {\mkern 1mu} {\textrm{1}}$), weak ($1 \le V < 2$), important ($2\leq V {<}3$), and very important ($3\leq V {<} 4$). Although we expected the scores to be between −5 and 5, in practice the interviewees only assigned values between −4 and 4.

According to the interviewees, 28 factors affected the food consumption in their daily life (table 3). However, the factors differed among the study areas. In the pastoral areas (Zhenglan and West Ujimqin), interviewees identified more direct and indirect factors than in the agro-pastoral transitional zone (Taibus) (figure 2(a)). However, the numbers did not differ significantly between the three areas, with an average of 7 direct impact factors and 4 indirect intermediate impact factors in each area.

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Among the four dimensions of the impact factors, personal and ecological factors were perceived as the most important factors in all three banners, with both a greater number of mentions (figure 2(b)) and a greater cumulative score (figure 2(c)) than the other factors. The key impact factors analysis also indicated that interviewees showed stronger cognition in personal and ecological factors, which we will discuss in section 3.2.

From the view point of each banner, the factors in four dimension also showed the differences. In Taibus Banner, ecological (8 times per interviewee) and personal factors (7 times) were mentioned most often and social factors were mentioned least often (3 times). In Zhenglan Banner, personal factors were mentioned most frequently (10 times), and economic factors were mentioned least frequently (4 times), although economic factors were ranked 3rd instead of 4th in the cumulative scores. In West Ujimqin Banner, the pattern was similar to that in Zhenglan, which means that even though residents did not mention economic factors frequently (5 times), they ranked the economic factors highly. Although Taibus residents did not mention ecological factors and personal factors as often as residents in Zhenglan and West Ujimqin did, they gave both dimensions higher scores. Residents in Taibus considered the social factors to be least important, with the fewest mentions (3 times) and lowest score (1.8 in total). This may be because socioeconomic development in the pastoral areas (Zhenglan and West Ujimqin) has been very rapid since 2008. We will discuss this in more detail later in the paper. The residents of these areas strongly perceived the changes and the impacts on their lives, which would lead them to focus more on this dimension.

Our analysis of the structural and functional demographic variables showed that residents in Taibus, Zhenglan, and West Ujimqin banners differed significantly in their perceptions of impact factors, family size, rangeland size, cropland size, number of sheep, number of cows, years of education, household income, and subsidies (table 4).

Table 4. Overview of the fuzzy cognitive mapping results for the number of factors per dimension and additional indicators for the three study areas. Values are means±SE.

	p-Value	West Ujimqin (n = 21)X ± SE	Zhenglan (n = 21) X ± SE	Taibus (n = 25) X ± SE
Impact factors (no. for the 4 dimensions)	0.617 ns	6.95 ± 2.18	6.9 ± 2.43	7.44 ± 1.56
Rangeland size (ha)	$\lt$0.001***	2515.71 ± 353.74	1289.64 ± 219.63	4.87 ± 1.30
Cropland size (ha)	0.0106*	0 ± 0	0 ± 0	42.41 ± 17.55
Family size (no. of people)	0.010*	3.62 ± 0.20	3.62 ± 0.29	2.72 ± 0.22
Education (no. of years)	0.003**	7.71 ± 0.70	9.00 ± 0.69	5.4 ± 0.79
Number of cows (no.)	$\lt$0.001***	34.57 ± 9.28	41.80 ± 7.04	2.44 ± 0.74
Number of sheep (no.)	$\lt$0.001***	317.14 ± 49.81	34.90 ± 11.92	5.84 ± 3.02
Household income (104 yuan per year)	$\lt$0.001***	18.62 ± 2.14	13.83 ± 1.95	2.40 ± 0.31

Significance (ANOVA): ns, not significant; *$p\lt0.05$, **$p\lt0.01$, ***$p\lt0.001$.

Livestock numbers, household income, regional economic development, consumption habits, age, and infrastructure were the most important impact factors mentioned by the interviewees and showed the most interactions with other factors (figure 3). In all study areas,the high scores that interviewees gave goes to livestock numbers, household income, regional economic development, consumption habits, which were all showing $V\gt2.0$ (important degree). Especially, age was thought as an important factor affected household daily food consumption in Taibus, where V = 2.6, showing important degree compared to other two areas). Besides, though the social factors were ranked lower than other factors, infrastructure were scored highest among all the social factors in West Ujimqin (V = 1.8) and Zhenglan (V = 1.2), which also showed distinguishing difference between pastoral areas and agro-pastroal transitional zone (V = 0.1 in Taibus). Thus, in this paper we focus on these important factors and analyzed their characteristic.

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Additionally, through the key impact factors we found that interviewees paid more concerns on the ecological factors and economic factors, which could also find the clue s in figure 2—interviewees showed high frequency and high cumulative score in ecological factors, while relative low frequency and high cumulative score in economic factors. It suggested that ecological factors likes livestock numbers are so important that interviewees all mentioned them frequently as well as gave high score; though interviewees mentioned economic factors not so often, they tended to gave high score as long as they gave the economic factors a nomination.

Among all these key factors, interviewees were more concerned about ecological factors (livestock number) in the northern areas (Zhenglan and West Ujimqin) than in the southern area (Taibus) (figure 3). In addition, personal factors (especially consumption habits) and economic factors (especially household income and regional economic development) were ranked as the second and third-most important groups of factors after the ecological factors in the pastoral areas (Zhenglan and West Ujimqin), whereas in the agro-pastoral transitional area (Taibus), ecological factor (livestock number) was also ranked first, and economic factors (especially household income) were ranked as the second-most important factors. These results suggest that as we moved from the north to the south, residents became more concerned about the economic and personal factors and less concerned about the ecological factors.

3.2.1. Ecological factor: number of livestock

3.2.2. Personal factors: consumption habits and age

3.2.3. Economic factors: regional economic development and household income

3.2.4. Social factor: infrastructure

Based on changes in the socioeconomic characteristics of the study areas from 2000 to 2017 and the basic information on farmers and herders that we obtained in the 2019 field survey, we analyzed the social, economic, and ecological dimensions of the study areas to reveal causes of the differences in the number of livestock, household income, regional economic development, consumption habits, and age in the three typical areas based on the fuzzy cognitive mapping (figure 3).

The number of livestock differed greatly among the three study areas. The number of livestock in the study area also decreased (in response to a government policy to prevent overgrazing) at different rates from 2000 to 2017. In 2017, the number of livestock in West Ujimqin was 930 900, versus 365 600 in Zhenglan and 191 200 in Taibus (figure 4(c)). The residents of West Ujimqin tended to herd more sheep, whereas residents of Zhenglan tended to herd more cattle, and residents of Taibus tended to herd a small number of animals with a high economic value, such as camels and horses. In addition, the size of rangeland decreased moving from north to south (table 4), and this may have affected the number and type of livestock owned by a family.

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With economic development, the per capita GDP and household income of interviewees have grown rapidly since 2000. The per capita GDP of West Ujimqin increased from 9680 yuan (2000) to 153 971 yuan (2017), an increase to 15.9 times the original value; the per capita GDP of Zhenglan increased from 5005 yuan (2000) to 85 583 yuan (2017), an increase to 17.1 times the original value; and the per capita GDP of Taibus increased from 3035 yuan (2000) to 26 767 yuan (2017), an increase to 8.8 times the original value (figure 4(a)). The household disposable income of residents in West Ujimqin (23 662 yuan) was always highest, and the income decreased from north to south (figure 4(b)). Moreover, the GDP differences between the three areas widened during the study period, which suggests there were significant regional differences in the economic conditions. The number of livestock decreased from 2000 to 2017 in West Ujumqin and Zhenglan, but remained relatively constant (despite some fluctuation) in Taibus. Besides, residents in Xilingol League witnessed a significant wealth growth in the past decades, on one hand it was mainly contributed by the resource exploitation (i.e. mining) [6], which also posed challenges to regional sustainable development; on the other hand, the rapid economic development would change residents' daily food budget as well as daily food consumption structure [16] , which raised new demand for regional food supply.

Consumption habits and age also differed significantly between the three regions. Previous research on our study areas showed that the food consumption by residents of the three study areas differed greatly in quantity, structure, and preference. In West Ujimqin, residents tended to consume meat, vegetables, and fruits; in Zhenglan, residents tended to consume meat, grains, vegetables, and fruits; and in Taibus, residents tended to consume grains, vegetables and fruits (table 1). This suggests changes in the diet from primarily pastoral to primarily agricultural areas.

The mean age of residents of the three study areas increased from north to south, from 45 years in West Ujimqin to 51 in Zhenglan and 63 in Taibus. However, the total population of each region remained stable from 2000 to 2017 (figure 4(d)). However, the age structure in Taibus represents a potential problem. On the one hand, the banner had a much higher population than the other two regions (figure 4d) as well as a high population density (62 persons per km²; table 1, figure 4d); on the other hand, large numbers of rural people have moved into the banner's cities to find jobs, leaving a growing proportion of elderly people in rural areas. Many of the elderly people are unable to find jobs in the city or preferred to remain in rural areas due to the low cost of living.

As GDP increased, infrastructure development also increased, especially in terms of the construction of roads. In Taibus, the small land area (table 1) means that construction of roads has not caused problems related to access to food supplies; that is, a relatively small number of roads can meet the needs of residents. However, in West Ujimqin and Zhenglan, which are larger, governments have had more serious problems building sufficient roads.

In the Xilin Gol Grassland, there is a natural gradient of ecosystems and environmental conditions from north to south. Our results and previous research suggest that this gradient has affected the local culture, customs, and perceptions of local residents [14, 26, 49]. The socioeconomic characteristics of the residents differed among the three areas, as did their perceptions of the grassland system that sustained them, with both direct and indirect impacts on human systems and activities in the Xilin Gol Grassland. Human activities have adversely affected the region's vulnerable environment; on the one hand, overgrazing and unsustainable use of local natural resources (i.e. grassland, water) led to the environmental issues [6, 7]; on the other hand, since the implementation of key ecological projects (i.e. Grassland Ecological Compensation Policy, Grain for Green) by the government, the local ecosystem has recovered since 1999 [50, 51]. As we moved from north to south along our transect, residents relied less on grasslands for their living and more on agriculture. Thus, their perceptions of the importance of the grassland weakened, and they showed less concern about the ecology of their local grasslands. Such transitions along a grassland transect have been described previously [14, 52]. Furthermore, in the other grassland areas, people's food consumption behaviors were found to be affected not only by infrastructure, consumption habits, household income, local food supplies, and family size [18, 53], but also market accessibility, eco-friendliness, local production, and nutrition [53–55].

Moreover, household perceptions between Han people and Mongolian people showed some differences in the impact factor scale and score of factors (appendix D1). We took Zhenglan as example to divide the interviewees into two group by nationality of the family members, in which Han family group was consist of 11 interviewees and Mongolian family group was consist of 10 interviewees. The results showed that Mongolian interviewees perceived more factors (26) than Han interviewees (18). What's more, Mongolian showed special focus on infrastructure (V = 2.0 ) while Han interviewees especially concerned the season (V = 2.7).It suggests that the infrastructure (e.g. road) did affect household food consumption a lot through the food accessibility; while the Han family tend to plant some vegetable in the yard which are one of most important sources of food, and the planting of vegetables is directly relative to the season in this area.

The largest limitation on our study related to budget and time constraints, which limited the number of residents we could interview. In order to test the effectiveness of our results in limited sample scale, we divided all the family into two group randomly to compare the results in three banners. The results showed that there were not significantly difference between two groups in all three banners (appendix D). On one hand, no matter in West Ujimqin (appendix D2), Zhenglan (appendix D3) or Taibus (appendix D4), the scale of food consumption impact factors revealed by the interviewees were close; on the other hand, the high scored factors and high frequency factors in different groups within the banner were similar and kept consistency to the results of the whole banner.

We nonetheless found interesting differences among the three banners that suggest a need for additional research to clarify these differences. Fuzzy cognitive mapping provided an effective way to analyze household perceptions of the factors that affected food consumption and to provide a quantitative (though subjective) description of the study area. However, there are limitations of this approach, including the subjectivity of the assessments of the strength of a given factor that affects food consumption. In addition, our approach cannot assess the impacts of unknown factors. Because we did not provide interviewees with a predetermined list of factors, it is possible that many of them were unaware of some of the factors that other interviewees identified or failed to remember those factors during the interviews. However, this weakness of our research is also a strength, since the interviewees had a chance to identify factors that they considered to be important rather than being biased by our prior choices of factors. Future research should focus on the complete set of factors that we identified in the present study.

Our results are also influenced by various subjective biases. For example, residents tended to remember the impact of recent or extreme climate events, and this may have exaggerated the weight they assigned to such factors. This shortcoming could be mitigated by conducting surveys with a time lag in future research to see whether perceptions change.