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sustainability
Article
Sustainability of Smallholder Agriculture in
Semi-Arid Areas under Land Set-aside Programs:
A Case Study from China’s Loess Plateau
Qirui Li 1, 2, *, T. S. Amjath-Babu 1, Peter Zander 1, Zhen Liu 3and Klaus Müller 1
1Institute of Socio-Economics, Leibniz Centre for Agricultural Landscape Research (ZALF),
Eberswalder Straße 84, 15374 Müncheberg, Germany; Amjath.Babu@zalf.de (T.S.A.-B.);
Peter.Zander@zalf.de (P.Z.); kmueller@zalf.de (K.M.)
2Faculty of Agriculture and Horticulture, Humboldt University of Berlin, 12489 Berlin, Germany
3School of Business, Nanjing Normal University, 210046 Nanjing, China; 54194@njnu.edu.cn
*Correspondence: leolee8612@gmail.com; Tel.: +49-176-8153-1576
Academic Editor: Marc A. Rosen
Received: 30 November 2015; Accepted: 19 April 2016; Published: 22 April 2016
Abstract:
This article analyzes agricultural sustainability in the context of land degradation, rural
poverty and social inequality, taking China’s Loess Hills as an example. The analysis attempts to
understand the multi-dimensionality of sustainability at the farm level and its relationship with
physical-socio-economic-infrastructural-technological framework conditions in the context of the
land set-aside program viz. the Grain for Green Project (GGP). We developed composite indices of
sustainability and its environmental, economic and social dimensions using a principal component
analysis (PCA)-based weighting scheme. Regression analyses were conducted to examine the
relationship between the estimated sustainability indicators and the variables representing framework
conditions of knowledge, demographics, resource endowment and production techniques. The stated
analysis was conducted on a dataset collected by means of household surveys in 2014 in valleys and
flood plain areas in Yanhe Township. Findings reveal hidden correlations among the indicators of
environmental, economic, and social pillars of sustainability. The ratio of land under the conservation
program to actual farmland emerged as a key determinant of overall agricultural sustainability and
its social dimension, which reaches the maximum when the ratio is around 0.56 and 0.64, respectively.
The results also show that there is need to balance off-farm and on-farm income diversification as
well as highlight the role of women in ensuring the sustainability of farming households. The core
achievement of the article is the definition of the thresholds for the land set-aside program and the
identification of major determinants of agricultural sustainability in the rural Chinese context in
particular and in rural farming communities in general.
Keywords:
sustainable agriculture; framework conditions; smallholder; Grain for Green Project;
composite indicators; principal component analysis
1. Introduction
The Chinese government has invested large sums of money in environmental restoration
with multiple goals of alleviating land degradation, reducing poverty, improving livelihoods and
restructuring the rural economy [
1
]. As one of the largest environmental restoration projects, the Grain
for Green Project (GGP), also known as the Sloping Land Conversion Program (SLCP), was initiated in
1999 and continued up to 2010 with a total budget of RMB 430 billion (US$ 63 billion; US$ 1 was equal
to RMB 6.83 in 2009). It intended to convert 14.67 million ha of farmland into forest and grassland
(4.4 million of which is land with slopes exceeding 25
˝
) and 17.3 million ha of wasteland (including
Sustainability 2016,8, 395; doi:10.3390/su8040395 www.mdpi.com/journal/sustainability
Sustainability 2016,8, 395 2 of 17
abandoned farmland) into forest [
2
–
4
] in 25 provinces in western China. On the Loess Plateau, the
GGP attributed highest priority to controlling soil erosion and reducing rural poverty by providing
an annual
compensation of RMB 1350 per hectare of sloping farmland (with a gradient exceeding 25
˝
)
set aside under the program [
5
]. However, it is not clear whether the GGP succeeded in improving
rural livelihood [
6
–
8
], as many farmers have been unable to restructure their remaining farm land or
find alternative income sources after joining the scheme. In 2007, the Chinese government decided
to continue to pay the subsidies even after the initial contract period, but the compensation level
was cut in half after the first round [
9
,
10
]. Under these conditions, the current article investigates the
sustainability of agriculture of the farmlands involved in the GGP scheme in the Loess Plateau area.
Agricultural systems can be considered to be sustainable if they are economically viable over
the long term with minimal impacts to the environment while being socially equitable [
11
,
12
].
In China
, sustainable agriculture generally refers to a type of farming that ensures a balance among
production (self-sufficiency), employment and income generation, natural resource management
and environmental protection [
13
]. In previous studies with regard to sustainable agriculture on
the Loess Plateau, a holistic “variation-selection-replication-retention” model was built to analyze
the transformation of agricultural development from an evolutionary perspective [
10
]: the spatial
variability of soil total nitrogen (STN) and soil total phosphorus (STP) levels as well as water use
efficiencies and long-term crop rotation systems were analyzed to reflect sustainable agriculture
development [
14
,
15
]; land suitability was evaluated and land use scenarios were simulated to explore
sustainable agriculture [
16
]; and runoff and sediment loss as well as soil organic matter and soil
nutrients were measured [
17
,
18
]. Although these studies shed light on agricultural sustainability, little
attention has been paid to evaluating the environmental benefits, economic viability and social equity
of agricultural systems in an integrative way [
19
–
21
]. Taking the case of China’s Loess Hills, this study
analyzes the environmental, economic and social sustainability of farming systems under the GGP
environmental program.
The current study attempts to answer the following questions: (1) How can we measure the
sustainability of farming systems in China’s Loess Hills? (2) How is the sustainability of farming
systems influenced by conservation programs such as the GGP? (3) Apart from the conservation
program, how do the framework conditions affect the sustainability of farm households? As the major
aim of the current work is to understand how participation in GGP programs affects farm-level
sustainability, it is important to devise ways to integrate economic, social and environmental
dimensions of sustainability and subsequently to analyze how GGP participation along with other
critical variables such as wealth, farming skills, farm size, population, land use patterns and other
social-economic conditions affect it and the constituent dimensions. In previous studies, similar sets
of variables are taken into account in the analysis of technical efficiency, nitrogen losses and farm
productivity [22–25]. This study advances the analysis into the aspect of sustainability of agriculture.
The following sections are organized as follows. The statistical analysis of agricultural sustainability
and farm-level determinants is laid out in Section 2. Empirical results and implications are reported in
Section 3. Conclusions are presented in Section 4.
2. Methodology
2.1. Study Area
Fifteen of 28 villages in Yanhe Township in Ansai County in Shaanxi Province were involved
in this study. These villages are located in the hilly area and the middle part of the Loess Plateau in
the north of China, with almost all farming households participating in the GGP. These 15 villages
are home to 10,400 people (62% of the population of the township), with a population density of
73 inhabitants
per km
2
; this is typical of the Loess Hills region [
26
]. Yanhe Township is a good
snapshot of the geographic and economic conditions prevailing in the Loess Hills. It lies between
urban areas, namely 16 km south of the county seat and 25 km northwest of Yanan City. The township
Sustainability 2016,8, 395 3 of 17
covers 210.7 square kilometres (0.1%) of China’s Loess Hills, which is a semi-arid area interlaced with
hills, ravines and plains, and with an average gully density of 4.7 km/km
2
[
27
]. Annual precipitation
ranges from
296.6 mm
to 645.0 mm (mean 505.3 mm) with a short season from July to September;
annual temperature varies from
´
23.6
˝
C to 36.8
˝
C (mean 8.8
˝
C) [
5
,
28
]. Additionally, 64.4% of the
population take up farming as their primary economic activity [
29
], on land with soils containing 60%
to 70% silt, around 15% clay and 30% sand [
28
]. Per capita arable land is approximately 0.1 ha, which
is close to the average area in China [
30
]; 44.3% of the arable land is used for open-field crops (e.g.,
potato, soybean, millet, corn, oilseed rape and sorghum), 30% for orchard crops (e.g., apples), 4.1%
for horticulture crops (e.g., chilli, cabbage, cucumber, eggplant and carrot), and 0.6% for melons (e.g.,
watermelon and honey melon). Per capita agricultural output of the region is RMB 8339, 40% below
the national average, of which cropping accounts for 88% and livestock production (e.g., pork and
lamb) accounts for 7.5% [29,31].
2.2. Sampling and Data Collection
In Yanhe Township where 3390 households settle, 247 households in the 15 villages were randomly
selected with a confidence interval of 6 at a 95% confidence level. The villages surveyed cover
both V-shaped valley areas (Houjiagou, Siyaoxian, Yanta, Yujiahe and Zhaiziwan in the highlands;
Gaojiamao and Yayao in the middle elevations; and Zhuanyaogou and Fangjiahe in the lowlands)
as well as floodplain areas (Chafang and Yunping upstream from floodplain areas; Hougoumen and
Yanjiawan midstream; and Lijiawan and Yangjiagou downstream), which can be distinguished by
their elevation and their distance to markets. Heads of households that account for 20% of total
households in each village were personally interviewed from February to May 2014 using a standard
semi-structured questionnaire. The dataset consists of household and farm characteristics as well as
information from 2013 on agro-environmental and socio-economic conditions and various farm-level
actions under the influence of the GGP. Additional farm-level data was generated by locating Global
Positioning System coordinates of surveyed farms on the soil map from the Soil Testing and Formulated
Fertilisation system, which uses Geographic Information System data and 4314 soil samples from
Ansai County in 2009 [32].
2.3. Sustainability Assessment
2.3.1. Selection of Sustainability Indicators
Given our focus on the integrated sustainability assessment at the farm household scale, the
present paper adopts an ex post assessment of the sustainability performance of farms using indicators
and develops an indicator-based sustainability assessment schema [
33
]. According to literature,
indicators representing multiple dimensions of sustainability are needed for a comprehensive
assessment [
34
]. In order to identify the indicators capturing the essence of agricultural sustainability
at the farm level, we applied a framework similar to the sustainability assessment of farming and
environment (SAFE) by Van Cauwenbergh and others [
35
], considering indicators from environmental,
social, and economic pillars and followed the procedure by Gómez-Limón and Sanchez-Fernandez [
36
]
viz. data gathering, normalization of indicators, weighting indicators, and aggregation of indicators.
The indicators related to the three pillars were selected based on two main factors viz. the specificity
and relevance of the indicator to the sustainability of farming in the study area and the availability
of data [
37
]. In the present paper, sustainability of farming systems is interpreted as the ability
of farm households to ensure their balanced livelihoods in terms of economic viability [
38
,
39
],
environmental friendliness [
40
,
41
] and physical well-being [
35
]. Here the three dimensions of
agricultural sustainability were represented by a set of eight key indicators demonstrated below.
The current approach makes sure that all dimensions of sustainability at the farm level are adequately
represented with a minimum number of indicators.
Sustainability 2016,8, 395 4 of 17
2.3.2. Index of Farm-Household Sustainability
Using the eight indicators, a composite index of “Farm-Household Sustainability” (FHS) was
constructed. The environmental dimension is described by natural vegetation (i.e., ratio of area under
forest and grass vegetation to total land area), soil organic matter (i.e., soil organic carbon at a depth
0–20 cm, in g/kg), and crop diversity [42–44]. The crop diversity index (CDI) was calculated as
CDI “1´
n
ÿ
j“1ˆaj
A˙2
(1)
where
aj
is the area of the
jth
crop and
A
is the total area planted under all crops for a specific household.
The index is zero for a household that grows no crops or only one crop [45].
We assumed that higher vegetation coverage, crop diversity and soil organic matter content
could reduce soil and water erosion, ameliorate land degradation and restore ecosystem services
over longer time periods [
46
–
48
]. The economic dimension is characterized by liquidity (i.e., current
ratio = current assets/current liabilities) and solvency (i.e., equity-to-asset ratio) to measure the
long-term financial condition of farmers’ operations [
49
,
50
]. A current ratio higher than 1 means
that farmers can meet short-term debt payment obligations, whereas the higher equity-to-asset ratio
indicates a less risky financial condition, as it shows how many RMB of net worth a farm has for
every RMB of assets [
51
]. These two indicators thus can reveal the long-term financial viability of
farming households. Indicators used in the social dimension are protein consumption (i.e., percentage
of household protein consumption compared to what is adequate for nutrition) [
52
,
53
], education
affordability (i.e., percentage of education expenditure to total household expenditure) and healthcare
affordability (i.e., percentage of expenditure for health care to total household expenditure) [
54
,
55
].
This is expected to capture the long-term quality of life and health of households.
The FHS indicators are intended as a statistical construct that can support decision-making of farm
households by revealing their relationships to the GGP program as well as to other spheres of influence,
such as knowledge levels, demographics, economics, technology, settlement pattern, land use and
social participation. The insights from the analysis are expected to generate policy suggestions [56].
2.4. Statistical Analysis
2.4.1. Measurement of Farm-Household Sustainability
The FHS index measures sustainability by aggregating the selected indicators using a weighting
scheme derived from a principal component analysis (PCA). Using SPSS 13.0 [
57
], we performed
a Kaiser-Meyer-Olkin
test and Barlett’s sphericity test to test the appropriateness of the PCA.
For easier
interpretation of the PCA results, varimax rotation was applied [
58
]. To obtain composite scores,
Equations (2) and (3) are assigned as follows:
Cik “ÿ
l
al
kpXl
iq(2)
Si“ÿ
k
vkpCikq(3)
where
Si
is the composite score of sustainability of the
ith
household,
Cik
is the
kth
principal component
for the
ith
household,
al
k
is the loading of the
kth
component for the
lth
indicator,
vk
is the
%
of variance
accounted by the
kth
principal component,
Xl
i
is the standardized value of the
ith
household for the
lth
indicator [59].
Sustainability 2016,8, 395 5 of 17
2.4.2. Impact of GGP and Framework Conditions on Sustainability: Multiple Regression Models
In addition to key variables representing the GGP program, a broad set of indicators concerning
knowledge level, demography, economics, technology, settlement pattern, land use and social
participation were selected to understand how the sustainability of farm households is driven by these
factors (Table 1). It is hypothesized that the FHS depends on seven broad categories of determinants:
knowledge, demographics, economics, technology, settlement type, land use, and level of social
participation, in addition to GGP participation.
(a)
Knowledge-related variables include experience of the household in agricultural production (
H1
)
and the head of household’s education level (H2).
(b) Demographics-related variables include the age (H3) and gender (H4) of the head of household.
(c)
Economic variables, which explore the role of non-farming activity in farm-household
sustainability, consist of the ratio of off-farm income and subsidy to total household income (
H5
).
(d)
For technology, farming equipment (
H6
), irrigation (
H7
), electronic communication (
H8
) and
mulching (H9) are used to represent households’ application of farming technologies.
(e)
The type of settlement, which may be decisive in access to information, cropping patterns, product
prices and marketing cost, is defined by altitude (H10) and distance to market (H11 ).
(f)
For land use drivers, four representative variables are used: Firstly, the ratio of land area to family
work force (
H12
), followed by the amount of rental land by area (
H13
) and intensification regarding
intercropped areas and continuously cropped areas (
H14
). Finally, farmland fragmentation (
H15
)
is calculated using Equation (4) as follows:
FI “1´«řpa2
p
přpapq2ff(4)
where
FI
is the Simpson Index to quantify farmland fragmentation and
ap
is the area of the
pth
plot of farmland for a specific household. An index of zero means that a household farms a single,
contiguous plot of land fragment and that all farmland is completely consolidated [60].
(g)
In order to capture the influence of the GGP program, the financial side of the program is
represented by the GGP subsidy as a share of total household income (
H16
) and the physical
aspect is covered by the ratio of GGP land to cultivated farmland (H17).
(h)
Finally, social participation is indicated by the frequency of household members going to markets
(H18) and access for females to training and official meetings (H19).
The paper develops multiple regression models to understand the relationship of the GGP
program and identified broad categories of framework conditions to agricultural sustainability.
This paper
puts forth the hypothesis that there exists a threshold level of area under GGP that
maximizes the farm-level sustainability. The following regression model examines how the standardized
value of the composite FHS index and its three dimensions is determined by the selected indicators on
participation in the GGP program and farm-level framework conditions:
Ss1
i“ÿ
j
βjpHi
jq ` β0i“1, 2, 3, . . . , I; j “1, 2, 3, . . . , J (5)
where
Ss1
i
is the estimated value of the standardized composite indicator of sustainability of the
ith
household in terms of the environmental dimension (EnD), economic dimension (EcD), social
dimension (SoD) and the composite FHS (CFHS),
β0
is the constant coefficient, and
βj
is the coefficient
of
Hi
j
, which is the standardized value of the
jth
indicators of farm-level framework conditions
and GGP-related variables (including a squared term to find the threshold level if it exists) of the
ith household.
Sustainability 2016,8, 395 6 of 17
Table 1. Indicators of GGP and farm-level framework conditions.
Framework Conditions and
GGP Variables Variables Description Descriptive
Mean Std. Deviation
Knowledge Experience (H1)
Number of years of experience in production concerning horticulture, orchard, livestock, off-farm, in years
13.4 6.7
Education level (H2) Number of years of head of household at school, in years 5.9 3.4
Demographics Age (H3) Age of head of household, in years 47.9 9.4
Gender (H4) Gender of head of household is female or male, 0/1 0.8 0.4
Economics Non-farming income (H5) * Ratio of off-farm income and subsidy to total household income 0.6 0.3
Technology
Farming equipment (H6) Current value of farming machines that household had, in RMB
10,483.2
27,343.3
Irrigation (H7) Current value of irrigation infrastructure that household had, in RMB
465.894
572.904
Electronic communication (H8) Expenditures of phone call and Internet, in RMB 103.7 275.9
Mulching (H9) Current value of mulches used on field and bags used for wrapping fruits, in RMB 449.6 946.1
Settlement Altitude (H10) Altitude of the household, in meters 1079.1 64.2
Distance to market (H11) Distance from household place to the closest market, in km 8.1 4.0
Land use
Land/labour (H12) * Ratio of household’s land area to the work force of family labor 0.7 0.5
Fragmentation (H13)0–1, 0 means that a household farms a single, contiguous plot of land fragment and that all farmland is
completely consolidated 0.6 0.4
Intensification (H14)#Total area intercropped and continuously cropped, in ha 0.2 0.5
Land rental (H15) Total area of land rental including the rented and rented out land, in ha 0.1 0.2
GGP
Share of GGP in income (H16) * Ratio of GGP subsidy in aggregate household income 0.03 0.04
GGP ratio (H17) Percentage of GGP land to the farmland cultivated by the household 3.4 4.9
Squared GGP ratio (H2
17)Sared GGP ratio 35.3 132.0
Social participation Frequency to markets (H18) Frequency of household members going to markets for shopping or selling, in days 14.2 15.2
Access for females (H19) Females participate in training and official meetings, representing the household or not: 1/0 0.8 0.4
* See appendix for information about how to calculate the man-day equivalent and income;
#
Intensification of land use means more than one crops are simultaneously and sequentially
planted on the same plot of farmland in one year.
Sustainability 2016,8, 395 7 of 17
3. Results and Discussion
3.1. Components of Sustainable Agriculture
A dataset of 246 samples was used for statistical analysis after reviewing the data to check for
missing values, potential errors, outliers and correlations. Eight indicators of FHS were included in
the PCA, of which the Kaiser-Meyer-Olkin test and Barlett’s sphericity test (see footnote for Table 2)
indicate that the PCA analysis was appropriate. Four principal components were obtained, all of which
contain eigenvalues greater than 1 and explain 71.6% of the variance (Table 2). The correlation between
a component and indicators is called a loading, which reveals the contribution of the indicators to the
variation accounted for by this component [
61
]. The highest loading for each variable is highlighted in
bold letters; this is used as a weighting factor in constructing the index [62].
The first component (C1), which explains 24.7% of the variance, is correlated with the variables
(
i.e., liquidity
and solvency) of the economic dimension. Thus, C1 represents economic viability, which
is included as one of the pillars in the SAFE framework of Van Cauwenbergh and others [
35
] as well
as the FAO/IBSRAM (International Board for Soil Research and Management) framework for the
Evaluation of Sustainable Land Management (FELSM) [
38
]. The second component (C2), explaining
17.4% of the variance, is correlated with the environmental aspect (i.e., natural vegetation, crop diversity,
soil organic matter) and hence implies the environmental dimension. The third component (C3),
explaining 16.2% of the variance, is correlated with a subset of the social dimension (i.e., education
affordance and healthcare affordance), while the fourth component (C4), explaining 13.2% of the
variance, correlates with the variable protein consumption. Hence, C3 indicates that a household’s
ability to afford education and health care are correlated, whereas C4 depicts farming households’
nutritional security. This reveals hidden correlations in social development. Although the PCA results
provide insights into correlations among the three dimensions of FHS, the key objective here is to
generate aggregating weights for the composite score of the FHS index.
Sustainability 2016,8, 395 8 of 17
Table 2. PCA component loadings for the farm-household sustainability (FHS) index.
Indicator Description Descriptive Principal Component
Mean Std. Deviation 1 2 3 4
Environmental
Natural vegetation Ratio of area under forest and grass vegetation to total land area 0.65 0.28 ´0.038 ´0.503 0.189 0.328
Crop diversity Crop diversity index: 0–1 0.44 0.29 ´0.092 0.792 ´0.171 0.211
Soil organic matter Soil organic carbon at a depth 0–20 cm, in g/kg 9.01 0.27 0.012 0.688 0.224 ´0.123
Economic
Liquidity Ratio of current assets to current liabilities 3.53 2.43 0.989 ´0.039 ´0.071 0.025
Solvency Ratio of equity to asset 2.33 2.47 0.987 ´0.001 ´0.088 0.014
Social
Protein consumption Percentage of household protein consumption to the adequate amount 1.31 0.36 0.045 ´0.040 0.011 0.942
Education affordability Percentage of education expenditure to total household expenditure 0.10 0.14 ´0.123 0.103 0.788 0.060
Health affordability
Percentage of expenditure on health care to total household expenditure
0.04 0.05 0.019 0.147 ´0.742 0.013
Bold values indicate that the component loading is greater than 0.5, Kaiser-Meyer-Olkin measure of sampling adequacy: 0.502, Bartlett’s test of sphericity: Chi-square: 788.019
(p< 0.001).
Sustainability 2016,8, 395 9 of 17
3.2. Determinants of Sustainable Agriculture
Multiple regression models significantly fit the data because the p-value of the F-test is less than
0.0001 (Table 3). The primary aim of the analysis was to examine the relationship of the GGP program
with the sustainability of farm households. The regression results show that the level of physical and
economic participation in the GGP exerts significant influences. The composite score of agricultural
sustainability showed an inverted U-shaped relationship with the physical share (area) of the GGP,
and a negative relationship with the income share of GGP subsidies. This means that it appears that
households with lower proportions of GGP subsidies as part of their annual income gain a higher level
of sustainability through the economic dimension. With regard to the three dimensions, the economic
(EcD) and social dimension (SoD) followed the inverted U-shaped relation with the physical share
(area) of the GGP, but the environmental dimension (EnD) had a U-shaped relation. Threshold levels
of the physical share (area) of the GGP under each dimension and for the composite indicator (CFHS)
can be found by the derivative of Equations (6)–(9) of the estimated relations as follows:
CFHSs1“0.410H17 ´0.366H2
17 `ÿ
j‰17
βj‰17pHj‰17 qj“1, 2, 3, . . . , 19 (6)
dpCFHSs1q
dpH17q“0.410 ´0.366 ˆ2ˆH17
EcDs1“0.194H17 ´0.183H2
17 `ÿ
j‰17
βj‰17pHj‰17 qj“1, 2, 3, . . . , 19 (7)
dpEcDs1q
dpH17q“0.194 ´0.183 ˆ2ˆH17
EnDs1“ ´0.202H17 `0.111H2
17 `ÿ
j‰17
βj‰17pHj‰17 qj“1, 2, 3, . . . , 19 (8)
dpEnDs1q
dpH17q“ ´0.202 `0.111 ˆ2ˆH17
SoDs1“0.659H17 ´0.518H2
17 `ÿ
j‰17
βj‰17pHj‰17 qj“1, 2, 3, . . . , 19 (9)
dpSoDs1q
dpH17q“0.659 ´0.518 ˆ2ˆH17
Sustainability 2016,8, 395 10 of 17
Table 3. Multiple regression estimates of the determinants of farm household sustainability (FHS) and three dimensions.
Framework Conditions
and GGP Variables Variables Composite FHS (CFHS) Economic Dimension (EcD) Environmental Dimension (EnD) Social Dimension (SoD)
Coef. (β) SE t Coef. (β) SE t Coef. (β) SE t Coef. (β) SE t
Knowledge Experience (H1) 0.143 0.064 2.216 ** 0.054 0.066 0.820 0.055 0.051 1.071 0.140 0.066 2.126 **
Education level (H2) 0.008 0.063 0.130 0.046 0.064 0.718 ´0.010 0.050 ´0.199 ´0.032 0.064 ´0.496
Demographics Age (H3) 0.027 0.068 0.392 0.180 0.070 2.558 ** 0.121 0.055 2.202 ** ´0.265 0.070 ´3.778 **
Gender (H4)´0.007 0.062 ´0.111 ´0.032 0.064 ´0.503 0.014 0.050 0.284 0.014 0.064 0.219
Economics Non-farming income (H5) * 0.087 0.071 1.215 0.269 0.073 3.669 ** ´0.012 0.057 ´0.207 ´0.155 0.073 ´2.121 **
Technology
Farming equipment (H6)´0.043 0.068 ´0.630 ´0.006 0.070 ´0.086 ´0.005 0.055 ´0.090 ´0.064 0.070 ´0.916
Irrigation (H7)´0.062 0.068 ´0.912 ´0.035 0.070 ´0.494 0.045 0.055 0.820 ´0.105 0.070 ´1.507
Electronic communication (H8)´0.055 0.059 ´0.943 ´0.088 0.060 ´1.464 ´0.033 0.047 ´0.711 0.035 0.060 0.578
Mulching (H9) 0.364 0.071 5.134 ** 0.247 0.073 3.390 ** 0.165 0.057 2.902 ** 0.210 0.073 2.894 **
Settlement Altitude (H10) 0.089 0.124 0.723 ´0.006 0.127 ´0.051 0.150 0.099 1.508 0.040 0.127 0.318
Distance to market (H11)´0.146 0.106 ´1.381 ´0.296 0.109 ´2.729 ** 0.110 0.085 1.294 0.003 0.108 0.023
Land use
Land/labour (H12) * ´0.145 0.083 ´1.747 * 0.055 0.085 0.643 ´0.121 0.067 ´1.806 * ´0.220 0.085 ´2.578 **
Fragmentation (H13) 0.183 076 2.400 ** 0.040 0.078 0.515 0.323 0.061 5.286 ** 0.006 0.078 0.071
Intensification (H14)#0.084 0.067 1.258 ´0.051 0.068 ´0.741 0.154 0.053 2.886 ** 0.079 0.068 1.157
Land rental (H15) 0.055 0.061 0.896 0.061 0.063 0.973 0.036 0.049 0.732 ´0.006 0.063 ´0.091
GGP
Share of GGP in income (H16) * ´0.255 0.074 ´3.460 ** ´0.271 0.076 ´3.578 ** ´0.004 0.059 ´0.072 ´0.124 0.076 ´1.642
GGP ratio (H17) 0.410 0.137 2.999 ** 0.194 0.141 1.380 ´0.202 0.110 ´1.839* 0.659 0.140 4.690 **
Squared GGP ratio (H2
17)´0.366 0.129 ´2.835 ** ´0.183 0.133 ´1.383 0.111 0.104 1.068 ´0.518 0.133 ´3.907 **
Social participation Frequency to markets (H18) 0.018 0.069 0.258 0.116 0.071 1.633 0.046 0.056 0.831 ´0.144 0.071 ´2.026 **
Access for females (H19) 0.110 0.061 1.810 * 0.067 0.063 1.071 0.119 0.049 2.446 ** 0.015 0.062 0.245
Constant 0.000 0.056 0.000 0.000 0.057 0.000 0.000 0.045 0.000 0.000 0.057 0.000
*, ** = 0.1 and 0.05 significance levels, respectively; observation: 246; Model of FHS: F = 4.733; prob > F = 0.0001; R
2
= 0.296; Model of Economic Dimension: F = 3.903; prob > F = 0.0001;
R
2
= 0.258; Model of Environmental Dimension: F = 13.571; prob > F = 0.0001; R
2
= 0.547; Model of Social Dimension: F = 3.920; prob > F = 0.0001; R
2
= 0.258; * See appendix for
information about how to calculate the man-day equivalent and income;
#
Intensification of land use means more than one crop are simultaneously and sequentially planted on the
same plot of farmland in one year. Model validity is proved by the supplementary materials.
Sustainability 2016,8, 395 11 of 17
Setting the derivatives equal to zero, the turning points of the CFHS and its three dimensions were
calculated for GGP variables. Thus, agricultural sustainability would be at its maximum if the ratio of
the GGP land to farmland were to remain at 0.56, while its social dimension would be the best if it were
to remain at 0.64. The thresholds levels of economic and environmental dimensions are insignificant,
but they show that the economic dimension requires a ratio of 0.53 and the environmental dimension
demands a ratio below 0.91. However, the current GGP ratio is close to 3.38, as an average of set-aside
(GGP) land is around 1.79 ha while mean farmland area is only 0.53 ha per household.
This emphasizes
the need to adjust the proportion of GGP land to farmland for enhancing the sustainability of farm
households under the set-aside program. Our analysis suggests limiting the physical share of GGP
to around 0.6. It is to be noted that the GGP in Northern China has already been criticized for the
retirement of productive and low-sloped land [4].
In the case of the economic dimension, non-farming income (economic variable) has no
significant influence on the CFHS and EnD, but positively affects the EcD and negatively affects
the SoD at a significant level. This serves as a warning that non-farm production (contract jobs
and casual employment in non-farm sectors in peri-urban or urban areas) is not a sufficient
condition for sustainable agriculture in the Loess Hills, but it does nevertheless improve households’
economic situation. Non-farming activity is recommended for GGP-participating households to raise
production and income [
7
]. A labor shift away from agriculture to other economic sectors has been
promoted to diversify income sources and increase farm size in rural China [
63
].
Nevertheless, the
non-farming activity undermines households’ social situation as farmers suffer from a lack of assets
and capacity [
7
,
64
]. The high fixed costs involved in non-farming production shrink households’
investments in food, education and health care. A combination of credit constraints and high
transaction costs of information-sharing also discourages farmers from taking up non-farming
activities [65,66].
In the case of demographics and settlement-related variables, the CFHS is not significantly
influenced by them. However, the age of the head of household has significant positive effects on the
EcD and EnD, but a negative effect on the SoD. This may be due to the fact that households with an
older head possess more assets and social networks for information and credit to improve production
and environment, but they are also more risky in terms of long-term health. In addition, the distance of
settlements to markets has a significant negative effect on the EcD. This proves that providing access to
markets would confer substantial economic benefits on households [
67
]. The analysis does not suggest
any significant difference between male- and female-headed households as well as households settled
in various altitudes regarding sustainability. It should be noted that female-headed households and
high-land settlements may have less access to natural and financial resources, but the result shows that
women and highlands are on a par (or superior) in developing household sustainability compared to
men and lowlands under existing conditions.
With regard to knowledge and technology, farmers’ experience and their use of mulches have
significant effects. This means that households with more experience in production perform better
in household sustainability, particularly in the social situation. This appreciates the fact that farmers’
experience is often associated with the adoption of sustainable techniques and practices [
68
] which
can help households improve the quality of their life and the sustainability. Households using
techniques such as mulches and fruit bags (for apples) perform better in sustainability in all three
dimensions. Mulching is an effective way to increase carbon sequestration of soils, to improve weed
control, to conserve soil and to enhance crop yield in northwestern China [
69
–
72
]. Fruit bagging
can improve fruit coloring and flesh quality [
73
], raising economic returns. In addition, the analysis
does not suggest any significant effects from the education level, farming equipment, irrigation and
electronic communication. This may be due to their homogenous levels in the Loess Hills (Table 3).
Additionally, the
use of large amounts of equipment, fertilizers, pesticides and irrigation may have the
potential to make agricultural systems dependent on high energy consumption and unsustainable [
74
].
Sustainability 2016,8, 395 12 of 17
In the case of land use, all variables have significant influences except the total area of rental land.
This can be explained by the low incidence of land rental transactions in rural China [
75
,
76
]. The ratio
of land to labor has significant negative effects on the CFHS, EnD and SoD; farmland fragmentation has
positive effects on the CFHS and the EnD; and farmland intensification has positive effects on the EnD.
Such information implies that the small fragmented farm (as shown in Table 1, means of Land/labor
and Fragmentation Index are higher than 0.60) does not significantly contribute to the economic
dimension, but benefits household sustainability through environmental and social dimensions.
From a
sustainability perspective, a small efficient farm is the most sustainable (at lower income
levels) pattern due to its fewer off-farm inputs and higher biotic diversity [
77
]. A high land-to-labor
ratio can push down the marginal benefit from labor and thus decrease income per hectare [
78
] while
intercropping and continuous cropping in plots can ensure vegetation coverage of farmland for soil
and water conservation [74] as well as increase economic returns within a farming year.
With respect to social participation, access for females to training has significant effects on the
CFHS and EnD. It shows that female participation plays a great role in environmental protection
with regard to building the sustainability of agricultural systems. Another study also argued that
farm production could improve if women learn more about farming practices [
79
]. In addition, the
frequency of farmers going to markets has no significant effect on the CFHS, but a negative one on
the SoD. This indicates that the current system of markets could neither significantly trigger income
increase nor improve the economic situation. On the contrary, frequent trips to the market seem to
be
a burden
on households, deteriorating the quality of their life. The benefits of physical access to
markets for producers may be offset by the low selling price of agricultural products or high costs
of transport.
3.3. Summary and Implications
The study was intended to understand the sustainability of farm households under the GGP
conservation program in the Loess Hills in China. In order to operationalize the concept of farm
household sustainability, a composite index was formulated regarding economic, environmental
and social dimensions. The principal component analysis was applied to reveal the latent structure
and internal correlations among indicators and generate a weighting scheme for the composite
index. The generated PCA components explain sustainable agriculture in terms of environmental
conservation, economic viability, physical well-being (i.e., affordability of education and health care),
and food security [
35
]. Thus, the composite scores of farm household sustainability and its dimensions
can reveal the expected relation of agricultural sustainability of farming households to GGP and
other determinants.
The regression models highlight that the ratio of GGP land to farmland is a major determinant of
agricultural sustainability in the study area and that the ability of farm households to diversify into
non-farm enterprises can substantially enhance their economic viability. It is necessary to adjust the
proportion of GGP land and reallocate labor between “on-farm” to “off-farm” work. This may promote
agricultural production, diversify income sources, and hence improve the sustainability of agricultural
households. There is also a need for an improved system of markets and cheaper transport to markets,
schools and hospital facilities in order to support the household’s attempts to increase its sustainability.
Increased access to information and credit for farmers can empower farmers to be more active in
production, processing and marketing. Skills training and life-long education [
80
], especially female
participation, are necessary for the dissemination of knowledge and the accumulation of experience in
enhancing production and building sustainable farm households.
The study also reveals a positive relationship between farm fragmentation and socio-environmental
sustainability [
81
]. This result can be related to the European experiences that land consolidation
as an isolated tool may trigger unfavorable side-effects on rural development in transitional
environments [
82
]. Given the existing conditions of imperfect land markets, sustainably intensifying
cultivation of remaining farming land (e.g., intercropping of apple trees and beans, continuous cropping
Sustainability 2016,8, 395 13 of 17
of vegetables and melons) by applying agronomic techniques (e.g., plastic mulching, fruit bagging)
and a labor shift toward non-farm production can help foster the sustainable agriculture of China’s
Loess Hills in the medium term.
4. Conclusions
Using the techniques of dimensionality reduction, the multi-dimensional concept of farm-level
sustainability is captured, adopting an indicator-based sustainability assessment schema, and the
relevance of the conservation program (land set-aside) on the sustainability of farm households in the
Loess Hills of China is revealed through a follow-up regression of relevant variables.
The share
of the area of set-aside (GGP) land as a proportion of the total farmland proved to be the key
determinant of agricultural sustainability. Inverted U-shaped relationships between the share of
GGP and the composite index of sustainability and its social dimension were revealed. Moreover,
relationships between the share of GGP land and its economic and environmental dimensions
were also probed. This led to the conclusion that there is a threshold of the physical (area)
share of the GGP that eventually determines agricultural sustainability in terms of economic
viability, environmental benefits and social welfare. Further analysis revealed how the selected
physical-socio-economic-infrastructural-technological framework variables exerted different levels of
influence on various dimensions of sustainability and hence highlighted the need to understand the
individual pillars of sustainability along with the composite one to find out about trade-offs.
In the
study area, a small farm size, land fragmentation and imperfect land markets restrain households’
agro-economic production and their adaptation of socially and environmentally sound practices.
Therefore, increasing the share of farmland relative to set-aside land towards the threshold level,
training female farmers in environmentally friendly farming practices, providing financial services to
diversify the enterprises of farmers, encouraging agronomic practices such as mulching, and providing
better market facilities can lead to a path towards more sustainable agricultural systems in the study
area. Our study thus provides a number of insights on land restoration and set-aside programs for
further research. Some limitations also need to be considered. While the currently built index can
measure sustainability encompassing three dimensions, more profound insights could be brought
with a broader set of indicators and different scenarios concerning land and labor allocation, technical
innovation and others.
Supplementary Materials:
The following are available online at www.mdpi.com/2071-1050/8/4/395/s1,
Figure S1: Histogram of residual normality test. (
a
) Sustainability (CFHS); (
b
) Economic dimension (EcD);
(c) Environmental
dimension (EnD); (
d
) Social dimension (SoD). Figure S2: Residual normality test. (
a
) Sustainability
(CFHS); (
b
) Economic dimension (EcD); (
c
) Environmental dimension (EnD); (
d
) Social dimension (SoD).
Table S1
:
Residuals statistics for residual normality test of the regression models. Table S2: Robustness test of the
regression models.
Acknowledgments:
The authors acknowledge the work of the research assistants in data collection and analysis.
We are also grateful to all of the farmers who cooperated in the household surveys and provided data.
Author Contributions:
Qirui Li prepared the data and wrote the paper. T.S. Amjath-Babu and Peter Zander
contributed to the research design and statistical analysis. Zhen Liu and Klaus Müller assisted with the research.
All the authors revised the manuscript and approved the final version.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix
In our study, the total active work force in man-day equivalents and family labor in per-capita
equivalents were calculated by applying conversion factors to male and female household members in
different age groups as follows: 1.0 for males aged between 16 and 60 years; 0.75 for females between
16 and 60 years; 0.75 for males above 60 years; 0.5 for females above 60 years. Household members
below 16 and above 70 years of age were not considered. In addition, income gained from off-farm
work, open-field crop, horticulture and GGP compensation was considered per family labor workforce
Sustainability 2016,8, 395 14 of 17
and by farm price rather than market price. For the sake of current value, in addition, annuity was
used for orchard income and livestock income by calculating inputs and outputs over their lifetime.
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