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The Poverty of Farmers in a Main Grain-Producing Area in Northeast China
The Poverty of Farmers in a Main Grain-Producing Area in Northeast China
Ma, Li;Wang, Shijun;Wästfelt, Anders
land Article The Poverty of Farmers in a Main Grain-Producing Area in Northeast China 1 1 , 2 Li Ma , Shijun Wang * and Anders Wästfelt School of Geographical Sciences, Northeast Normal University, Changchun 130024, China; firstname.lastname@example.org Department of Human Geography, Stockholm University, 10691 Stockholm, Sweden; email@example.com * Correspondence: firstname.lastname@example.org; Tel.: +86-0431-85099550 Abstract: Farmers’ poverty has long been of global concern, mainly in poor rather than afﬂuent areas. The goal of this paper is to better understand the range of poverty in the context of regional differentiation and to enrich knowledge on farmers’ poverty in afﬂuent areas and areas with good natural conditions. A questionnaire survey of poor farmers in the major grain-producing area of Changchun, Northeast China was conducted. Farmers’ poverty was studied from income poverty and multidimensional poverty by intertwining qualitative and quantitative methods. The results indicate that low education levels and poor physical health were most prevalent in poor farmers, followed by income poverty and low living standards. Governmental policies and the macroeconomic situation in the agricultural sector, non-agricultural employment, aging, cultivated land, and family size correlated closely with farmers’ poverty. The macro changes in policies and global trade liberalization in the agricultural sector impacted farmers’ income through the prices of agricultural products and subsidies and inﬂuenced the effect of cultivated land. For poor farmers, the effect of employment opportunities in villages was more signiﬁcant than in urban areas. Aging remains a challenge for farmers’ poverty now and in the future. Citation: Ma, L.; Wang, S.; Wästfelt, Keywords: poverty; farmer; major grain-producing areas; geographical differentiation; diversity; A. The Poverty of Farmers in a Main Changchun Grain-Producing Area in Northeast China. Land 2022, 11, 594. https:// doi.org/10.3390/land11050594 1. Introduction Academic Editors: Hossein Azadi and Alberto Matarán Ruiz Rural poverty is a worldwide problem, even in wealthy areas . The effective elimination of rural poverty is a pressing challenge for the international community and Received: 25 February 2022 one of the key sustainable development strategy goals proposed by the United Nations. Accepted: 16 April 2022 Most of the poor are living in rural areas , and farmers form the dominant group of the Published: 19 April 2022 rural poor in most areas. Poverty studies have always highlighted area types as farmers’ Publisher’s Note: MDPI stays neutral poverty is signiﬁcantly related to the geographical context, which differs with changes in with regard to jurisdictional claims in area type [3–7]. To the best of the authors’ knowledge, the widespread concern and efforts published maps and institutional afﬁl- to study farmers’ poverty mainly focused on countries and regions with large numbers of iations. poor people, such as India [8–10] and Bangladesh [11,12]. Another important focus is on areas with poor natural conditions, such as arid areas [13–15], mountainous areas [16,17], and areas with frequent natural disasters . The same holds true for research in China. China’s population base of the rural poor is large and relevant research mainly focused Copyright: © 2022 by the authors. on poor areas. These areas commonly have harsh natural environments, high altitudes, Licensee MDPI, Basel, Switzerland. remote locations, or are ethnic minorities and border areas [19,20]. For areas with poor This article is an open access article natural conditions, typically located in Africa, productivity growth in agriculture always distributed under the terms and effectively reduces farmers’ poverty [21–26], and relevant research focused more on direct conditions of the Creative Commons impacts on farmers’ poverty. Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ Farmers’ poverty in areas with advanced economies or better natural conditions has 4.0/). received relatively little attention from scholars or the public because rural poverty is less Land 2022, 11, 594. https://doi.org/10.3390/land11050594 https://www.mdpi.com/journal/land Land 2022, 11, 594 2 of 16 apparent in developed countries [1,27]. US researchers have published much work on poverty from geographic and spatial perspectives, especially with regard to environmental connections , spatial patterns , and the association between poverty and distance to metropolitan areas [6,28]. However, in Europe, rural poverty has received relatively little attention over recent decades . Existing studies mainly explored the poverty of older people, immigrants, and its connection with social exclusion [29–32]. Farmers’ poverty in these wealthy areas received little research attention. Farmers in wealthy areas, or areas with good agricultural conditions, face a more complex and changeable situation because of their close relationship with their external environment, such as policies, the market, urbanization, and the social context. How these factors affect farmers’ poverty must be explored further . The effects of society and economic factors may be more important in wealthy areas or areas with rich natural conditions compared with areas with poor natural conditions. Furthermore, in areas where absolute poverty has been eliminated or has become rare, relative poverty can be more likely alleviated or the remaining absolute poverty can be overcome, which would otherwise be difﬁcult to achieve. In this sense, studying farmers’ poverty in wealthy areas or areas with less limited natural conditions is of more long-term signiﬁcance. Thus, this is greatly needed in the future stage of the poverty-reduction process when absolute poverty is less common and relative poverty is more common. Based on the concepts of geographical differentiation, this paper explores the poverty of farmers in major grain-producing areas (MGPAs) in a metropolitan area. The speciﬁc questions are: (1) What are the main factors that inﬂuence the poverty of farmers in MGPAs? (2) What are the differences and similarities between farmers’ poverty in MGPAs and that of other areas? These questions were addressed based on a questionnaire survey of poor farmers in Changchun, which is the capital city of Jilin Province and one of the most important MGPAs in China. In Changchun, farmers beneﬁt from more livable conditions compared with other poor areas in China and the rest of the world . This study (1) complements evidence of farmers’ poverty in areas with relatively advanced economies and better natural conditions, enriching focal area types for farm- ers’ poverty, (2) enriches the knowledge exploration of place-related speciﬁcs of farmers’ poverty in the context of different geographies. These achievements help to gain a more comprehensive understanding of poverty and its diversity. The remainder of this paper is structured as follows: Section 2 brieﬂy reviews the methods for measuring poverty and regional differences in farmers’ poverty. Section 3 describes the data source and the methods used to measure poverty and its impact fac- tors. In Section 4, the characteristics and impact factors of farmers’ poverty are analyzed. Sections 5 and 6 offer a discussion and conclusion of the study, respectively. 2. Literature Review 2.1. Poverty Deﬁnition and Measurement Poverty is a complex concept that spans many disciplines and ﬁelds, including soci- ology, economics, geography, and politics. Gilin et al. generally recognized poverty as a living condition, characterized by low income that does not meet the needs of household consumption . Thus, income poverty can be understood as the original interpretation of poverty, and today, this term is widely used around the world. Internationally comparable income poverty is based on the poverty line standard issued by the World Bank. The current standard is USD 1.9 per person per day, but this value varies across different countries according to actual conditions. China’s current rural poverty line is Chinese Yuan (CNY) 2300 per person per year (constant price of 2010, ~USD 1.6 per person per day). With the development of society, economy, and inequality, the connotation of poverty has evolved from a single income aspect to multiple aspects. The ‘feasible ability’ theory has been recognized as an important theoretical basis for multidimensional poverty. It posits that poverty not only refers to low income but also encompasses a lack of basic functions such as adequate nutrition, basic medical conditions, basic housing security, Land 2022, 11, 594 3 of 16 and certain educational opportunities [36,37]. Based on ‘feasible ability,’ the concept of multidimensional poverty has been widely accepted and enriched. The multidimensional poverty index (MPI) is a core concept that has been developed greatly. The most extensively used index was proposed by Alkire and contains 10 indicators covering three key aspects of health, education, and living standards . This index was used by the United Nations Development Programme (UNDP) to measure multidimensional poverty on a global scale. While the indicators adopted by different countries and regions vary, all are mostly based on the above three aspects, which have thus become the main theoretical framework for understanding multidimensional poverty. 2.2. Regional Differentiation of Farmers’ Poverty That poverty has different characteristics and is impacted by different factors in differ- ent areas is a widely accepted concept. The diversity of rural poverty not only enhances available knowledge but also presents a major challenge for rural poverty research, which complicates cross-national comparisons and theoretical conceptualizations . The concept of regional differentiation, including the natural, economic, and social characteristics em- ployed by many comparative studies, may offer an entry point for appropriately addressing this challenge. The particular assemblages of economic, political, social, and cultural factors that are associated with different rural places are important for studying rural poverty . In arid areas, typically located in Africa, low soil fertility and soil degradation are directly linked to low productivity and rural poverty [15,40]. Similarly, in such vulnerable areas that are particularly sensitive to climate change (e.g., heatwaves, sea level rise, de- struction of coastal zones, and water shortages because of drought), farmers faced elevated poverty risks in the past and will remain to be subject to this risk into the future [41,42]. For those poor areas, increasing the agricultural sector is especially important for rural poverty reduction , and promising measures include improving soil fertility [15,43], developing irrigation technology [8,23], and agricultural mechanization . Controlling the prices of agricultural products is also effective [44,45]. In the non-agricultural sector, developing the urban economy, providing non-agricultural employment opportunities which can diversify livelihoods , improving the infrastructure in rural areas , focusing on education , and optimizing credit access  are all helpful to reduce poverty. The accessibility of urban areas, which offers more employment opportunities, a wider market, and better services, is a determinant in mountainous areas [17,19] and other remote areas that are far from cities or metropolitan areas [13,28]. However, this does not mean that farmers’ poverty does not also exist in wealthy areas despite their better economic and natural conditions; merely, the incidence of poverty is lower [5,47]. Except for natural and geographical factors, farmers’ poverty is also affected by numerous socio-economic factors, including governmental policies, social systems, like caste and tribes in India, demographic changes, infrastructures, wars, and prevalence of diseases [9,10,14,27]. In developed countries, farmers have always been an at-risk group in rural areas, and this risk mainly originated from low income, especially affecting small-scale farms [27,48]. For speciﬁc agriculturally developed areas, the processes of globalization and trade liberalization , as well as agricultural product prices are particularly important . The aging of farmers imposes a further challenge both for developed and rapidly developing areas [4,30,32,49,50]. However, aging farmers are not always more likely to be poorer as governments may provide support . The effects of regional differentiation on poverty are not only reﬂected in the large spatial scale between different area types but also the micro-regional perspective both in urban areas and rural areas [52,53]. The local opportunity structure is a related concept that has been used to study rural poverty. However, linking considerations about the structure of local opportunities and rural poverty with the theory of intersectionality has received only a little attention so far . In rural areas, the livelihood strategies of residents highly depend on resources that are closely linked to residents’ speciﬁc place of residence within the rural community, including natural resources and the resources of society, such as enterprises and cooperatives . Land 2022, 11, x FOR PEER REVIEW 4 of 17 structure of local opportunities and rural poverty with the theory of intersectionality has received only a little attention so far . In rural areas, the livelihood strategies of resi- dents highly depend on resources that are closely linked to residents’ specific place of residence within the rural community, including natural resources and the resources of Land 2022, 11, 594 4 of 16 society, such as enterprises and cooperatives . 3. Materials and Methods 3. Materials and Methods 3.1. Study Area 3.1. Study Area As one of the MGPAs in China, Changchun is located in the mid-latitude temperate As one of the MGPAs in China, Changchun is located in the mid-latitude temperate zone of the northern hemisphere, in the hinterland of the Northeastern Chinese Plain (Fig- zone of the northern hemisphere, in the hinterland of the Northeastern Chinese Plain ure 1), This area has a lower altitude but a higher level of cultivated land area per capita (Figure 1), This area has a lower altitude but a higher level of cultivated land area per capita and grain yield than the national average level (Figure 2), This means more fertile soil and and grain yield than the national average level (Figure 2), This means more fertile soil better foundation for the agricultural industry in Changchun compared with other poor and better foundation for the agricultural industry in Changchun compared with other areas of China and the rest of the world. The dominant crop in Changchun is corn. Since poor areas of China and the rest of the world. The dominant crop in Changchun is corn. 2007, China has implemented the minimum guaranteed prices policy for corn to increase Since 2007, China has implemented the minimum guaranteed prices policy for corn to farmers’ planting enthusiasm toward safeguarding the national food security and protect- increase farmers’ planting enthusiasm toward safeguarding the national food security and ing farmers’ income. This resulted in a higher corn price in China than in the rest of the protecting farmers’ income. This resulted in a higher corn price in China than in the rest of world. Driven by economic profit, policy guarantees, and easier planting process, the the world. Driven by economic proﬁt, policy guarantees, and easier planting process, the sown areas and output of corn increased and eventually accounted for about 83% of the sown areas and output of corn increased and eventually accounted for about 83% of the total grain produced in Changchun in 2016, which promoted the simplification trend of total grain produced in Changchun in 2016, which promoted the simpliﬁcation trend of regional crop structure. With the cancelation of this policy in 2016, the corn price entered regional crop structure. With the cancelation of this policy in 2016, the corn price entered a a market-oriented acquisition stage, and thus, experienced clear decrease over recent market-oriented acquisition stage, and thus, experienced clear decrease over recent years. years. Various subsidies from the government, including subsidies for agriculture pro- Various subsidies from the government, including subsidies for agriculture producers, ducers, fertility protection subsidies for arable land, and social security subsidies of min- fertility protection subsidies for arable land, and social security subsidies of minimum imum living standard, disability, and family planning have become important to cushion living standard, disability, and family planning have become important to cushion the the price impacts on farmers’ income. Changchun is the capital city of Jilin Province and price impacts on farmers’ income. Changchun is the capital city of Jilin Province and a a metropolitan area. The residents’ disposable income per capita is very close to the na- metropolitan area. The residents’ disposable income per capita is very close to the national tional average level, and the Gross Domestic Product (GDP) per capita is significantly average level, and the Gross Domestic Product (GDP) per capita is signiﬁcantly higher than higher than the national average level (Figure 2). This means that this area has a higher the national average level (Figure 2). This means that this area has a higher economic level than econmost omic poor level than areas most in China poor a (Figur reas in e 2 ). China (Figure 2). Figure 1. Location and illustration of Changchun City in China. Figure 1. Location and illustration of Changchun City in China. Land 2022, 11, x FOR PEER REVIEW 5 of 17 Land 2022, 11, 594 5 of 16 Figure 2. Summary of the main natural and economic conditions of Changchun compared with Figure 2. Summary of the main natural and economic conditions of Changchun compared with China. China. Since 2015, the Chinese government has exerted an unprecedented effort to reduce Since 2015, the Chinese government has exerted an unprecedented effort to reduce rural poverty and has initiated the strategy of targeted poverty alleviation (TPA). This rural poverty and has initiated the strategy of targeted poverty alleviation (TPA). This strategy tackles rural poverty from income, infrastructure, public services, industrial de- strategy tackles rural poverty from income, infrastructure, public services, industrial de- velopment, and other more comprehensive aspects, thus contributing greatly to reducing velopment, and other more comprehensive aspects, thus contributing greatly to reducing the number of poor farmers . Because of infrastructure, knowledge, technology, and the number of poor farmers . Because of infrastructure, knowledge, technology, and other developmental elements sinking into rural areas, farmers today have more livelihood other developmental elements sinking into rural areas, farmers today have more liveli- choices. However, at the end of 2016, ~30,000 farmers were still identiﬁed as poor by hood choices. However, at the end of 2016, ~30,000 farmers were still identified as poor the government of Changchun. Moreover, farmers who have been lifted out of poverty by the government of Changchun. Moreover, farmers who have been lifted out of poverty are at risk of returning to this state, which has become a problem in China and several are at risk of returning to this state, which has become a problem in China and several other countries . Therefore, further research on farmers’ poverty, based on the local other countries . Therefore, further research on farmers’ poverty, based on the local geographical context, is still urgently needed and has long-term signiﬁcance not only for Changchun geographical but con also texfor t, is similar still urgentl areas. y needed and has long-term significance not only for Changchun but also for similar areas. 3.2. Data 3.2. Data This study presents a case study of farmers’ households living in poverty that were deﬁned by the government in TPA. Data were collected through structured interviews with This study presents a case study of farmers’ households living in poverty that were poor farmers. The ﬁeld survey was carried out between 2017 and 2018, and 1324 households defined by the government in TPA. Data were collected through structured interviews were assessed (Figure 3). To gain comprehensive knowledge of the household situation with poor farmers. The field survey was carried out between 2017 and 2018, and 1324 concerning poverty and its underlying factors, questionnaires were divided into ﬁve parts. households were assessed (Figure 3). To gain comprehensive knowledge of the household They started with the information on the heads of households, including name, gender, situation concerning poverty and its underlying factors, questionnaires were divided into and education level. The second part was about household demographics, such as family five parts. They started with the information on the heads of households, including name, size, age structure, physical health, and educational situation of school-age children. The gender, and education level. The second part was about household demographics, such third part included the condition of assets and living standards of cultivated land areas, as family size, age structure, physical health, and educational situation of school-age chil- housing, diet, and clothing. The fourth part focused on the income and income resources dren. The third part included the condition of assets and living standards of cultivated and these data were used to analyze income poverty and the livelihood strategy. The land areas, housing, diet, and clothing. The fourth part focused on the income and income ﬁfth part assessed policies and social security farmers received. The location of farmers resources and these data were used to analyze income poverty and the livelihood strategy. was collected by GPS and then processed by AMAP to calculate distances to urban areas. The fifth part assessed policies and social security farmers received. The location of farm- Informal interviews with county ofﬁcials and village cadres were also conducted to assess ers was collected by GPS and then processed by AMAP to calculate distances to urban the situation of local development, the enterprises, processing plants, and cooperatives, etc. areas. Informal interviews with county officials and village cadres were also conducted to to analyze the nearby employment opportunities in villages and revise obtained household assess the situation of local development, the enterprises, processing plants, and cooper- data. All data were processed by SPSS into numeric and categorical data. atives, etc. to analyze the nearby employment opportunities in villages and revise ob- tained household data. All data were processed by SPSS into numeric and categorical 3.3. Methods data. The applied income poverty line matches the national rural poverty line of CNY2300 (constant price of 2010, USD ~1.6 per person per day). The FGT (Foster, Greer & Thorbecke) index was used to measure the poverty line, with alpha = 0, reﬂecting the poverty gap (PG). Because the income of speciﬁc households exceeded this poverty line, the formula was adjusted as follows to facilitate calculation. P represents the poverty line and Mi represents Land 2022, 11, 594 6 of 16 the annual per capita income of ith household. PG < 0 indicates that the household is experiencing income poverty, and the smaller the value, the stronger the income poverty. M P PG = (1) The indicator of multidimensional poverty index (MPI) refers to the MPI used by the UNDP and the Oxford Poverty and Human Development Initiative. This is combined with Land 2022, 11, x FOR PEER REVIEW 6 of 17 the TPA practice standards, including seven indicators of health, education, and living standards (Table 1). (a) (b) Figure 3. Field survey of farmers in Changchun: (a) Talking with farmers outside of their house; (b) Figure 3. Field survey of farmers in Changchun: (a) Talking with farmers outside of their house; (b) a a questionnaire survey being conducted with a farmer. questionnaire survey being conducted with a farmer. 3.3. Methods Table 1. Dimensions, indicators, and cutoffs of the multidimensional poverty index (MPI). The applied income poverty line matches the national rural poverty line of CNY2300 Dimensions Indicators Deemed as below the Poverty Line/Living in Poverty If: (constant price of 2010, USD ~1.6 per person per day). The FGT (Foster, Greer & Thor- Physical condition becke) index was us Any ed to family meas member ure thewith povert a chr y onic line, illness, with alph serious a = disease, 0, reflecting or disabili the po ty verty Health Medical insurance Eligible household members do not participate in China’s rural medical system gap (PG). Because the income of specific households exceeded this poverty line, the for- mula was adjusted as follows to facilitate calculation. P represents the poverty line and Years of schooling Head of the household has not completed at least ﬁve years of schooling Education Child enrollment Any school-aged child at the stage of compulsory education is not attending school Mi represents the annual per capita income of ith household. PG < 0 indicates that the household is experiencing income poverty, and the smaller the value, the stronger the Housing No long-term stable and safe housing or dwelling exhibits safety hazards Living income poverty. Diet Lack of staple foods, or protein available less than once per month Standards Clothing No seasonal clothing, shoes, and quilts for daily change or all have been donated M P i− PG = (1) The child mortality rate in China is generally very low . Few families in the The indicator of multidimensional poverty index (MPI) refers to the MPI used by the assessed rural area have newborn children because of aging and emigration. Farmers’ UNDP and the Oxford Poverty and Human Development Initiative. This is combined self-reported health was used as part of the health dimension, which may arguably better with the TPA practice standards, including seven indicators of health, education, and liv- reﬂect their actual health condition . A further factor is farmers’ participation in the ing standards (Table 1). medical insurance system. China’s new rural cooperative medical system is a system of mutual assistance for farmers’ medical treatment that is organized and guided by the Table 1. Dimensions, indicators, and cutoffs of the multidimensional poverty index (MPI). national government and requires voluntary participation of farmers. This system plays an Dimensions Indicators Deemed as below the Poverty Line/Living in Poverty If: important role in reducing medical expenses and ensuring farmers’ access to basic health Physical condition Any family member with a chronic illness, serious disease, or disability services, thus guaranteeing their health. Health Medical insurance Eligible household members do not participate in China’s rural medical system All rural households that were surveyed in this study had electricity. Compared with Years of schooling Head of the household has not completed at least five years of schooling electricity and ﬂoor standards, the comprehensive judgment of safety and stability for Education Child enrollmen walls, t Any schoo roofs, and l-aged ch ﬂoors (in ild a refer t the stag ence to e othe f cor mp ural ulsor housing y educa appraisal tion is no standar t attending d conducted school by the Chinese government) was deemed a more accurate reﬂection of prevalent housing Housing No long-term stable and safe housing or dwelling exhibits safety hazards Living conditions, which is an important aspect in TPA [55,59]. Moreover, diet and clothing data Diet Lack of staple foods, or protein available less than once per month Standards used in the TPA were applied to reﬂect farmers’ living standards. Clothing No seasonal clothing, shoes, and quilts for daily change or all have been donated The Alkire and Forster (A&F) method was used to measure the number of deprived attributes for each household. To facilitate this calculation and analysis, Equation (2) was The child mortality rate in China is generally very low . Few families in the as- adjusted as below. Z represents the cutoff of indicator j, which was set as 1, meaning that sessed rural area have newborn children because of aging and emigration. Farmers’ self- reported health was used as part of the health dimension, which may arguably better re- flect their actual health condition . A further factor is farmers’ participation in the medical insurance system. China’s new rural cooperative medical system is a system of mutual assistance for farmers’ medical treatment that is organized and guided by the na- tional government and requires voluntary participation of farmers. This system plays an important role in reducing medical expenses and ensuring farmers’ access to basic health services, thus guaranteeing their health. All rural households that were surveyed in this study had electricity. Compared with electricity and floor standards, the comprehensive judgment of safety and stability for walls, roofs, and floors (in reference to the rural housing appraisal standard conducted by Land 2022, 11, 594 7 of 16 the indicators were not deprived. Y represents the value of household i’s j indicator. If ij indicator j is deprived, Y is equal to 0; otherwise, it is equal to 1. The MPI value ranges ij from 7 to 0. If MPI = 7, all indicators are deprived; MPI = 0 implies that no deprivation occurs in any indicator. Y Z ij j MPI = (2) Logistic regression, cross-list analysis, statistical description, and qualitative analyses were used to explore the inﬂuencing mechanism of each factor on farmers’ poverty. Thirteen impact factors related to poverty were selected, which matches the approach taken by previous research and ﬁeld surveys. The variance inﬂation factor (VIF) is the statistic for diagnosing collinearity in multiple regression. The larger the VIF, the more serious of collinearity between variables. Generally, the VIF should not exceed 10 . In this study, the VIF of each factor was less than 3, and there was no multicollinearity problem between variables (Table 2). Table 2. Factors impacting poverty. Impact Factors Description Min. Max. Mean Tolerance VIF This includes agricultural subsidies, minimum Amount of government living standard subsidy, disability subsidy, family 0.00 4.00 1.40 0.94 1.07 subsidies provided planning subsidy, and veterans’ allowance Cultivated land area owned by the household 0.01 4.00 0.50 0.82 1.22 Cultivated land area (hm ) 1: none of the family members farming or working; Livelihood strategy 2: all members farming only; 3: any members both 1.00 4.00 2.06 0.71 1.40 farming and working; 4: all members working only 1: no employment opportunities, no enterprises, processing plants, and large cooperatives that can provide employment opportunities for villagers. 2: seasonal employment opportunities: farmers with large-scale farming or breeding work, or Nearby employment cooperatives requiring short-term and temporary 1.00 3.00 1.90 0.96 1.05 opportunities workers. 3: long term employment opportunities: enterprises, processing plants, and cooperatives in the village that require long-term workers, which can provide relatively stable employment opportunities for villagers. Gender of head of household 1: male; 2: female 1.00 2.00 1.20 0.97 1.03 Education level of head of 1: primary school and below; 2: above primary 1.00 2.00 1.23 0.97 1.03 household school Physical condition of family 1: some members disabled and/or sick; 2: nobody 1.00 2.00 1.05 0.98 1.02 members disabled and/or sick Family size Number of family members 1.00 8.00 2.61 0.61 1.63 Proportion of people aged over Number of people over 60/family size100% 0.00 100 48.71 0.77 1.30 60 (%) Proportion of people with Number of people with capacity to work/family 0.00 100 28.72 0.97 1.03 capacity to work (%) size100% Distance from center of The time distances from housing to centers under 0.61 4.99 2.43 0.78 1.28 Changchun (h) driving, calculated by AMAP, which is a widely used road navigation software in China and Distance from county center (h) 0.09 3.39 0.91 0.61 1.65 considers the road conditions in its calculation. Distance from town center (h) 0.02 3.75 0.49 0.70 1.43 N 1324 Land 2022, 11, x FOR PEER REVIEW 8 of 17 Proportion of people aged over Number of people over 60/family size∗100% 0.00 100 48.71 0.77 1.30 60 (%) Proportion of people with capac- Number of people with capacity to work/fam- ity 0.00 100 28.72 0.97 1.03 ily size∗100% to work (%) Distance from center of The time distances from housing to centers un- 0.61 4.99 2.43 0.78 1.28 Changchun (h) der driving, calculated by AMAP, which is a widely used road navigation software in China Distance from county center (h) 0.09 3.39 0.91 0.61 1.65 and considers the road conditions in its calcula- Distance from town center (h) 0.02 3.75 0.49 0.70 1.43 tion. Land 2022, 11, 594 8 of 16 N 1324 4. Results 4. Results 4.1. Income Poverty 4.1. Income Poverty 4.1.1. Characteristics of Income Poverty 4.1.1. Characteristics of Income Poverty Most poor farmers in Changchun had incomes below or near the poverty line. To be Most poor farmers in Changchun had incomes below or near the poverty line. To be specific, 41.09% of poor households presented income poverty. Households with income speciﬁc, 41.09% of poor households presented income poverty. Households with income of less than half of the poverty line, i.e., serious income poverty, accounted for 14.12%. of less than half of the poverty line, i.e., serious income poverty, accounted for 14.12%. Among the 58.91% of households not showing income poverty, the incomes of 27.87% Among the 58.91% of households not showing income poverty, the incomes of 27.87% were 1.25 times lower than the poverty line, which implies that these households were were 1.25 times lower than the poverty line, which implies that these households were very close to the poverty line. The PG mean was 0.376, and the income level of poor house- very close to the poverty line. The PG mean was 0.376, and the income level of poor households holds exceed exceeded ed the po the vert poverty y line line by an by av an era average ge of 37. of60 37.60%. %. Howev However er, a l ,ar a ge lar ge gap gap exiexists sts in the average income level of both groups, i.e., those indicating and not indicating income in the average income level of both groups, i.e., those indicating and not indicating income poverty poverty. . The The PG PG mean meanof ofhhou ouseho seholds lds wwith ith incom income e popoverty verty was was −0.353. 0.353. For th For e group the gr with oup - without out incom income e pov poverty erty, th , e the avaverage erage PG PG va value lue wa was s 0.8 0.885, 85, which which ssubstantially ubstantially excee exceeded ded the the poverty poverty l line ine(Figur (Figure e 44 ).). 30% 1 0.8 24% 0.6 18% 0.4 0.2 12% 6% -0.2 0% -0.4 ≤−0.5 −0.5~0 0~0.5 0.5~1 ≥1 PG＜0 PG≥0 overall Range of PG Figure 4. Proportion of poverty gap (PG) values and PG means of different groups. Figure 4. Proportion of poverty gap (PG) values and PG means of different groups. 4.1.2. 4.1.2. Imp Impact act Factors Factorsof of Income Income Povert Povertyy Logistic Logistic regr regession ressionr res esults ultsshowed showed that that in in Changchun, Changchun, governmental governmental policies, policies, n non- on- agricultur agriculture e employment, employment and , and dem demo ographic graphic characteristics characteristic of s families of famsigniﬁcantly ilies significan impacted tly im- farmers’ pacted fincome armers’ poverty income (T pov able erty 3). (T The ablefactors 3). The r elated factors tore transfer lated toincome transfer wer inco e me complex were and com included plex and the incltypes, uded th quantities, e types, quantiti and standar es, and ds stand of policies, ards of po asliwe cies, ll as as the well number as the num- and ﬁnancial ber and f situation inancial sof itua farmers’ tion of fadult armers’ childr adul en t chi and ldren other and relatives. other relThe atives extern . The ality extern ofality the transfer of the tran income sfer incom may have e may weakened have weak the ened explanatory the explana power tory of po the wer model. of the The mod refor el. Th e,er the e- model was re-constructed after removing transfer income from the total income. The new fore, the model was re-constructed after removing transfer income from the total income. model The new showed model sho that alt wed hough that the alth number ough the numb of major er inﬂuencing of major influe factors ncing decr facto eased rs fr decrea om seven sed to four, the R and prediction accuracy improved signiﬁcantly (Table 3). from seven to four, the R and prediction accuracy improved significantly (Table 3). Farmers who received more governmental subsidies were less likely to suffer from Farmers who received more governmental subsidies were less likely to suffer from income poverty. Transfer income, in which governmental subsidies were dominant, and income poverty. Transfer income, in which governmental subsidies were dominant, and relatives’ ﬁnancial support were minor parts, accounting for an average of 41.10% of the total income. Operating income, wages income, and property income accounted for 25.04%, 16.47%, and 17.39%, respectively. After removing the transfer income, the proportion of households with income poverty increased from 41.09% to 70.02%. The various subsidies that were directly paid to farmers from public ﬁnance expenditure were important for poverty reduction in farmers, which showed the direct effect the government had on farmers’ income. Households that held more cultivated land were less likely to experience income poverty. However, more cultivated land contributed the least to reducing farmers’ income poverty in these two models, and the effect was reduced after removing the transfer income. This is strongly correlated with the falling corn prices and the single cropping structure which were greatly driven by macroeconomic regulation and control policies in trade and markets in the agriculture sector. In Changchun, farmland income was decreased overall Proportion of households The value of MPI Land 2022, 11, 594 9 of 16 by the price drop of corn. Furthermore, more than 90% of households with crop income grew corn only, which disabled them to offset this price impact. Therefore, cultivated land as a factor helping farmers to eliminate income poverty was not advantageous in this MGPA although its agriculture productivity is relativity high. More cultivated land areas implied more agriculture-related subsidies from the government. This may have resulted in the effective reduction of cultivated land in Model 2. Therefore, the effect of cultivated land on farmers’ income poverty was closely related to the policies, trade, and markets in agriculture sectors. Table 3. Results of logistic regression analysis on impact factors of income poverty. Model 1: Model 2: With Transfer Income Without Transfer Income Variables B Exp (B) Sig. B Exp (B) Sig. Amount of government subsidies 0.550 0.577 0.000 — — Cultivated land area 0.102 0.903 0.000 0.082 0.922 0.000 Livelihood strategy 0.000 0.000 Livelihood strategy (1) 2.497 12.147 0.000 3.859 47.431 0.000 Livelihood strategy (2) 1.560 4.757 0.000 1.932 6.903 0.000 Livelihood strategy (3) 0.361 0.697 0.245 0.236 0.790 0.350 Nearby employment opportunities 0.005 — — Nearby employment opportunities (1) 0.498 1.645 0.001 — — Nearby employment opportunities (2) 0.270 1.310 0.106 — — Physical condition of family members 0.900 2.460 0.011 — — Family size 0.501 1.651 0.000 0.349 1.417 0.000 Proportion of people with the capacity to work — — 0.005 0.995 0.015 Distance from center of Changchun 0.005 1.651 0.000 — — Constant 3.542 0.029 0.000 0.754 0.470 0.009 R 0.280 0.410 Prediction accuracy 58.9% 70.0% B = logistic coefﬁcient; Exp (B) = odds ratio; Sig. = signiﬁcance; conﬁdence level = 95%. Factors related to non-agriculture employment, including diversiﬁed livelihood strate- gies and nearby employment opportunities, have contributed to the income poverty allevia- tion of farmers. Households that made their living neither by farming nor by working were always composed of the old and sick who had poor labor capacity and thus face a higher risk of income poverty. For households that made their living both by farming and working, when one income was affected, especially at a time when farmland income was affected, alternative incomes were used to compensate for the effect. Hence, these households were less likely to fall into income poverty. Households living in villages with employment opportunities, especially long-term employment opportunities, were less likely to experi- ence income poverty. Employment opportunities in villages were generally basic skill and basic labor jobs farmers were familiar with or good at. Coupled with advantages of time ﬂexibility and better accessibility in geographic space, nearby employment opportunities were always attractive and effective for farmers. In family demographics, larger families, which had a higher dependency ratio because of more aging members and children, were more likely to show income poverty. The physical condition signiﬁcantly inﬂuenced income poverty, but this was not the case after removing the transfer income. After the removal of transfer income, the number of households in poverty increased signiﬁcantly both in groups with and without the disabled and/or sick. The increasing amount caused a larger proportion change in the latter group because of its small base. Therefore, the difference in poverty incidence between both groups had been narrowed, and the effect of physical condition was not signiﬁcant as before. The proportion of people with the capacity to work followed the opposite trend. The data further showed that the gap in income poverty incidence between household groups with different proportion intervals of labor force widened, and the range increased Land 2022, 11, 594 10 of 16 from 6.35% to 14.67%. This indicates that after losing external support, the effect of the labor force on income poverty was emphasized for farmers. 4.2. Multidimensional Poverty 4.2.1. Characteristics of Multidimensional Poverty The mean MPI was 1.97, and two attributes were found to be deprived in every household, on average. The households with MPI = 2 had the largest proportion and no households had MPI 6. The most severe problem was caused by the physical condition, with a deprivation rate of 96.00%. Among these households, 67.80% had sick members, 7.87% had disabled members, and 24.33% had both. The second most severe problem was caused by the years of schooling, with a deprivation rate of 76.74%, showing that most farmers had little education. Therefore, poor health and low education level were universal problems in rural Changchun. The third most severe problem was that of housing, where 15.42% of households had no long-term stable and safe housing or the dwelling exhibited safety hazards. Medical insurance (3.10%), diet (2.49%), clothing (2.34%), and child enrollment (1.21%) had signiﬁcantly low deprivation rates (Table 4). Table 4. Proportions of MPI and deprivation rates of each indicator. MPI 0 1 2 3 4 5 6 7 Total 0.45% 21.22% 61.78% 14.20% 1.81% 0.53% 0.00% 0.00% Proportion Non-aging family 1.20% 26.62% 58.03% 12.71% 0.96% 0.48% 0.00% 0.00% (%) Semi-aging family 0.00% 20.62% 64.52% 11.97% 2.22% 0.67% 0.00% 0.00% Aging family 0.22% 16.89% 62.50% 17.76% 2.19% 0.44% 0.00% 0.00% Physical Medical Years of Child Mean Indicator Housing Diet Clothing condition insurance schooling enrollment MPI Total 96.00% 3.10% 76.74% 1.21% 15.41% 2.49% 2.34% 1.973 Deprived Non-aging family 94.72% 3.36% 71.70% 1.68% 13.67% 1.20% 0.72% 1.871 rate (%) Semi-aging family 96.23% 3.10% 75.39% 2.00% 14.86% 3.10% 3.10% 1.978 Aging family 96.93% 2.85% 82.68% 0.00% 17.54% 3.07% 3.07% 2.061 Non-aging family: no households members were older than 60 years. Semi-aging family: part of the household members were older than 60 years. Aging family: all members were older than 60 years. 4.2.2. Impact Factors of Multidimensional Poverty Poor health and low education level were strongly related to the backward production and living conditions of rural Changchun in the 20th century, when farmers were born and grew up. The original mode and low level of agricultural mechanization forced farmers to engage in high intensity and low safety manual labor, coupled with diets that were high in oil and salt in this area. This made osteoarthropathy and cardio-cerebrovascular diseases as well as disability by injury relatively common. This situation was also induced by the high demand for labor in agricultural production and to some extent, the neglect of education. The lagging medical and educational public services and poor infrastructure did not allow farmers to receive timely medical treatment and educational opportunities at an affordable cost. Aging caused multidimensional poverty to deepen and to become more widespread. Semi-aging and aging families had signiﬁcantly higher proportions when MPI 2, with more deprived indicators. Except for medical insurance and child enrollment, semi-aging and aging families had higher deprived rates in other indicators, especially aging families. Few non-aging families with better physical health and low medical needs took out medical insurance. In education, the nine-year compulsory education policy implemented by China in 1986 greatly promoted the popularization of school attendance for rural children. Today, families with children that had been removed from school were always those that were unable to attend school because of a physical condition, such as mental disabilities or deaf-mute. Land 2022, 11, x FOR PEER REVIEW 11 of 17 4.2.2. Impact Factors of Multidimensional Poverty Poor health and low education level were strongly related to the backward produc- tion and living conditions of rural Changchun in the 20th century, when farmers were born and grew up. The original mode and low level of agricultural mechanization forced farmers to engage in high intensity and low safety manual labor, coupled with diets that were high in oil and salt in this area. This made osteoarthropathy and cardio-cerebrovas- cular diseases as well as disability by injury relatively common. This situation was also induced by the high demand for labor in agricultural production and to some extent, the neglect of education. The lagging medical and educational public services and poor infra- structure did not allow farmers to receive timely medical treatment and educational op- portunities at an affordable cost. Aging caused multidimensional poverty to deepen and to become more widespread. Semi-aging and aging families had significantly higher proportions when MPI ≤ −2, with more deprived indicators. Except for medical insurance and child enrollment, semi-aging and aging families had higher deprived rates in other indicators, especially aging families. Few non-aging families with better physical health and low medical needs took out med- ical insurance. In education, the nine-year compulsory education policy implemented by China in 1986 greatly promoted the popularization of school attendance for rural children. Today, families with children that had been removed from school were always those that Land 2022, 11, 594 11 of 16 were unable to attend school because of a physical condition, such as mental disabilities or deaf-mute. In response to the rapid urbanization process, the rural aging problem became in- In response to the rapid urbanization process, the rural aging problem became increas- creasingly severe with the population outflowing from rural to urban areas . The out- ingly severe with the population outﬂowing from rural to urban areas . The outﬂow of flow of the younger and better-educated population aggravated farmers’ poverty and the younger and better-educated population aggravated farmers’ poverty and challenged challenged rural poverty alleviation. In addition, another issue worthy of attention was rural poverty alleviation. In addition, another issue worthy of attention was that a number that a number of farmers felt pressured by the increasing education expense because of of farmers felt pressured by the increasing education expense because of the transfer of rural the transfer of rural schools to townships or counties in response to the serious decline of schools to townships or counties in response to the serious decline of the rural population. the rural population. These schools always lie at a certain distance from remote villages, These schools always lie at a certain distance from remote villages, thus causing higher thus causing higher expenses for school buses, accommodation, and meals. expenses for school buses, accommodation, and meals. Income was found to be strongly correlated with multidimensional poverty. As Income was found to be strongly correlated with multidimensional poverty. As shown shown by the results (Figure 5), the smaller the number of deprived attributes in multidi- by the results (Figure 5), the smaller the number of deprived attributes in multidimensional mensional poverty, the smaller the proportion of households trapped in income poverty, poverty, the smaller the proportion of households trapped in income poverty, and the and the lower the degree of income poverty. This correlation becomes more significant lower the degree of income poverty. This correlation becomes more signiﬁcant with with increasing living standards. Households with deprived living standards were increasing living standards. Households with deprived living standards were mainly those mainly those experiencing income poverty, some of which potentially exceeded the pov- experiencing income poverty, some of which potentially exceeded the poverty line while erty line while remaining very close to it. The PG means of households that were deprived remaining very close to it. The PG means of households that were deprived in relation in relation to housing, diet, and clothing were 0.170, −0.299, and −0.374, respectively, to housing, diet, and clothing were 0.170, 0.299, and 0.374, respectively, which were which were clearly lower than the average. The cost of improving housing conditions is clearly lower than the average. The cost of improving housing conditions is relatively relatively high and difficult to afford for certain households whose income is just slightly high and difﬁcult to afford for certain households whose income is just slightly above the above the poverty line. This may be the main reason why the group with deprived hous- poverty line. This may be the main reason why the group with deprived housing was ing was relatively large. relatively large. 1.6 80% 1.2 60% 0.8 40% Propotion of PG<0 0.4 PG mean 20% -0.4 0% -5 -4 -3 -2 -1 0 MPI Figure 5. Correlation between PG and MPI: The gray line shows the correlation between PG < 0 and MPI, while the black line shows the correlation between PG mean and MPI. 5. Discussion In areas with limited natural resources and poor ecological environments, where farmers always face challenges of poor quality and quantity of farmland, and may even experience hunger, increasing agriculture productivity was always an effective means to alleviate poverty [11,15,40]. While agriculture is important, agriculture alone does not reduce poverty [24,26]. This study shows that in MGPAs with a better natural environment and relativity high agriculture productivity, governmental power and economic factors play more important roles in farmers’ income. Better agricultural production conditions do not emphasize the role of cultivated land in poverty reduction, and a higher economic level does not generate comparable income levels for rural residents in Changchun. This means that farmers can hardly share the economic growth and obtain compatible bene- ﬁts in agriculture sectors because of the uneven distribution of economic beneﬁts among sectors [25,49]. However, farmers who are faced with high market risk bear the cost of trade and competition by reducing agricultural income. The reason is that global trade liberalization and market competition in the agricultural sector greatly impacted farmers’ income at the micro-level through agricultural prices [44,46]. Therefore, agriculture and farmers’ income are associated with low beneﬁts but high risks. Combining trade reforms of different countries and macroeconomic regulation of different sectors is necessary . Some measures are the potential to reduce farmers’ poverty in MGPA. These include tilting Mean value of PG Proportion of PG ＜0 Land 2022, 11, 594 12 of 16 the beneﬁts of economic growth to farmers by increasing/stabilizing the prices of agricul- tural products, diversifying planting structure, and transferring the market cost to subjects of enterprises and factories that with higher risk resistance ability in segmented markets. Aging showed different correlations with poverty in farmers. In contrast to the USA, where aging farmers still hold large farms and achieve agricultural modernization, or the EU, where farmers can receive considerable subsidies that sometimes make them face a lower poverty risk [31,51], in China and other developing countries with small scale agriculture operation and relativity low level of agricultural modernization, aging gener- ally implies associated income decrease as well as wider and deeper poverty levels . Moreover, aging results in the deﬁciency and weakening of rural construction bodies, thus restricting rural development . Farmers operate 36.90% of all land area on Earth as cultivated land and are responsible for the food security of humanity , especially those in areas such as MGPAs. Therefore, aging in such areas will aggravate problems of labor force shortage in agriculture and food security. Who will become the farmers to operate agricultural production and whether food security can be guaranteed are key challenges . Aging is an inevitable trend. Improving the human capital of aging farm- ers by strengthening their advantages and weakening their disadvantages is a potential direction. With the extension of the human lifespan, certain farmers over 60 years of age do not lose their labor ability completely, and they are experienced and familiar with local agriculture. Enhancing health education and medical services for farmers to improve their physical quality, combed with improving agricultural technology, mechanization, and vocational training will help reduce the poverty risk and alleviate labor shortage caused by aging, especially for the new generation of older farmers. The education resources in local universities, especially those related to agriculture should be fully utilized by encouraging teachers and students to study on farmland with farmers. This can achieve improvement and updating of farming skills and knowledge of farmers. As a disadvantaged group, poor farmers face inherent obstacles of aging, poor physical health, and little education. This makes it difﬁcult for them to utilize favorable conditions of urban driving forces that enable access to employment opportunities, markets, and ser- vices [28,52,64,65], ultimately causing them to become a poor group in a wealthy area [1,20]. Many transfer payments from public ﬁnance expenditure play an important role in reduc- ing the poverty level of those farmers by providing various direct subsidies. However, in the long run, subsidy-based measures for poverty alleviation impose a heavy burden on governmental ﬁnances and become unsustainable, especially in areas with large pop- ulations. Such measures consume a large share of the government budget and distract from growth-enhancing investments , even making farmers mentally ”relying on” subsidies . Thus, the quality of public spending—the efﬁciency of resource use—is often an even more important issue to address than its level . In MGPAs that have the advantages of metropolitan areas, public ﬁnance expenditure can develop effective poverty reduction measures in more ﬁelds. These include improving land quality as well as supporting the development of agriculture-related enterprises and factories to promote the integrated development of agriculture and other industries. These measures will help farmers, especially those with poor labor ability, get higher asset returns and lower poverty risk in these processes. The concept of unequal spatial opportunity structures offers a potential avenue for a better understanding of the drivers of social disadvantage, and poverty has already been conﬁrmed in different regional and national contexts . Similar to the study by Bernard on the Czech Republic, the present study also conﬁrms that the residential disadvantage was relevant to farmers’ poverty from a micro-regional perspective [52,53]. Poor farmers living in villages that offer employment opportunities were less likely to experience income poverty. Different production and living conditions and socio-economic developments in different regions have created different livelihood environments for farmers, thus affecting their income levels [5,8,11]. Therefore, except as an objective spatial carrier to maintain livelihoods, the location of farmers is also a kind of spatial capital that entails differences Land 2022, 11, 594 13 of 16 in development opportunities that farmers can utilize. Improving the capital value of rural space is a potential measure to improve farmers’ livelihoods and reduce farmers’ poverty. This includes optimizing rural production and living conditions by optimizing infrastructures and facilities to promote the attractiveness of rural areas for enterprises and factories [17,33] as well as developing rural tourism to enhance the added value of rural resources [47,66]. Integrating farmers themselves as the main body in those process enterprises is important. However, spatial opportunity imposes certain requirements on human capital [64,65]. Under a low human capital level, spatial opportunity is not effective for poverty reduction. 6. Conclusions This study is based on a ﬁeld survey of poor farmers in one of China’s MGPAs, i.e., Changchun, which is an area type that was previously overlooked by farmers’ poverty studies. An analysis of income poverty and multidimensional poverty showed that the main issues associated with farmers’ poverty in this area include Governmental policies and the macroeconomic situation in the agricultural sector, non-agricultural employment, aging, cultivated land, and family size. In the MGPA of Changchun, the multidimensional poverty of poor farmers is more widespread than income poverty. The income of poor farmers greatly relied on government subsidies greatly, which have reduced income poverty by more than 30%. The potential of favorable agriculture development in poverty reduction was impacted by governmental policies and the economic development situation behind these policies through market prices and subsidies in agricultural sectors. Non-agriculture and mixed livelihood can reduce income poverty, and such livelihood opportunities are more effective within villages than in urban areas because of the obstacles imposed on poor farmers by their reduced labor capacity. In MGPAs, farmers can be grain self-sufﬁcient, which reduces the depri- vation of living standards and improves income, which also reduces the deprivation of living standards. Aging is a key cause that further deepened farmers’ poverty levels and made farmers’ poverty more widespread, especially for farmers with low education levels and poor physical conditions. These are the most serious and prevalent problems poor farmers face. By examining MGPAs, this paper expands the regional types that rural poverty re- search has tended to overlook. This expansion is conducive to a more comprehensive understanding of poverty and its diversity with changing geographical context. This is also helpful to proceed with poverty alleviation policies in a more targeted way and according to regional differences, highlighting the spatial thinking in poverty governance. This research assesses poor rural households, which provides a basis for the further exploration and anal- ysis of the characteristics and impact factors of poverty from the perspective of the family unit. This perspective is more micro-focused and thus, closer to poor farmers themselves. Farmers’ poverty is a process that dynamically changes with population structure and developments of society and economy. This dynamic process could not be elicited through the cross-sectional household survey data used in this paper. The effect of relevant impact factors on farmers’ poverty, i.e., policies, is sustained and possibly lagging. Although farmers’ poverty has been reduced considerably over the last several years because of the notable effort of TPA, especially regarding medical insurance, child enrollment, housing, diet, and clothing, all of which can be improved in a short time, farmers’ lives have faced and continue to face many objective difﬁculties and risks. Examples are demographic structural problems of serious disease and disability, low educational level, aging, and socio-economic problems of unstable markets, slow economic growth, uneven beneﬁt distribution among sectors, and instabilities associated with the market and the economy. The increasingly complex situation in global trade, the epidemic of Covid-19, war, etc., are exacerbating these problems further. Farmers face a more changeable environment and even higher poverty risks. Therefore, it is necessary to conduct more targeted research in the future. In the research object, taking both poor farmers and non-poor farmers as Land 2022, 11, 594 14 of 16 research objects will help to form a more comprehensive cognition of farmers’ poverty. Regarding methods, follow-up ﬁeld surveys and observations must be conducted, and sequential research must be done through years of data accumulation to understand the dynamics of farmers’ poverty and their underlying mechanism. At the research scale, a macro-scale of farmers’ poverty can be studied by integrating ﬁeld survey data, census data, socio-economic data, spatial and remote sensing data, and by mapping farmers’ poverty at different spatial scales. How socio-economic and land-use changes affect farmers’ poverty are important and valuable future research directions. Author Contributions: Conceptualization, L.M. and S.W.; methodology, L.M.; investigation, S.W. and L.M.; data curation, L.M.; writing—original draft preparation, L.M.; writing—review and editing, S.W. and A.W.; visualization, L.M.; supervision, S.W. and A.W.; project administration, S.W.; funding acquisition, S.W. All authors have read and agreed to the published version of the manuscript. Funding: This research was supported by the National Natural Science Foundation of China (Grant No. 42171198 and No. 42101200), and the Foundation of the Education Department of Jilin Province, China (Grant No. JJKH20211290KJ). 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Multidisciplinary Digital Publishing Institute
The Poverty of Farmers in a Main Grain-Producing Area in Northeast China
, Volume 11 (5) –
Apr 19, 2022
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