A multidimensional approach to understanding agro-ecosystems. A case study in Hubei Province, China

Agricultural Systems 76 (2003) 207–225 www.elsevier.com/locate/agsy

A multidimensional approach to understanding agro-ecosystems. A case study in Hubei Province, China D. Ottaviania, Li Jib, G. Pastorea,* a

Istituto Nazionale di Ricerca per gli Alimenti e la Nutrizione, Via Ardeatina 546, 00178 Rome, Italy b China Agricultural University, Beijing, PR China Accepted 12 December 2001

Abstract Understanding agro-ecosystems implies acknowledging its complexity and its heterogeneity. Complexity refers to different hierarchical levels at which agriculture can be described; heterogeneity refers to the variability recorded within each hierarchical level considered. When dealing with valuation aimed at sustainability it is essential to assess multi-criteria and multi-scale descriptions and to find a way to bridge them. For this reason and to support the decision processes, it is fundamental to understand the role of households in this system. Here we suggest a multivariate statistical approach to the definition of household types with a case study of five rural villages in Hubei province, China. In each village, 50 random households were sampled and interviewed. Results show five distinct household types, differing in size and composition, economic activities, technical development, cereal productivity, food sufficiency, education and overall income. However, the analysis pointed out a recent and still on-going process of polarisation as equality seems still the main feature of Chinese society. Severe constraints existing in the socio-economic boundary conditions could provide very limited possibilities for rural farmers to emerge from poverty. # 2003 Elsevier Science Ltd. All rights reserved. Keywords: Household types; Multivariate analysis; Agro-ecosystem; China

1. Introduction China is the world’s largest country accounting for more than one fifth of the world population in a combination of huge demographic pressure and massive poverty. * Corresponding author. E-mail address: [email protected] (G. Pastore). 0308-521X/03/$ - see front matter # 2003 Elsevier Science Ltd. All rights reserved. PII: S0308-521X(02)00007-0

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The continued growth of the already numerous population and the rapid modernisation of Chinese society exasperates the challenge of generating sufficient food surplus and creates severe problems for the development of Chinese agriculture which has to achieve three main—but often contrasting—objectives: it must guarantee an adequate supply of food for the entire population, avoid excessive stress on agro-ecosystems to safeguard long-term productivity, stimulate the economic development of rural areas and eliminate rural poverty and unemployment. Historically all arable land was held by few landlords and rich peasants (10% of the rural population), so that most rural households were obliged to earn their livelihoods by share-cropping, paying a rent to their owners which ranged as little as 50% to as much as 80% of their harvest (Rahman, 1995). In 1950, the new Government instituted a sweeping land reform, distributing 46 million hectares among 300 million landless and marginal landholding farmers. At the same time a cooperative organisation was constituted to increase production through collective action. This process was carried out in three stages with a final organisation of ‘‘advanced cooperative’’ in which lands were pooled, farm equipment and draught animals were held collectively, and individual produce and income was proportional to the estimated amount of work put into the cooperative (Kung, 1994). By the end of 1959, 96% of all rural households were members of cooperatives and 2 years later agricultural cooperatives had been united into communes which were political as well as economic entities. The large-scale cooperative commune dominated the agricultural sector for the next two decades. Finally, the 1980s were marked by a major shift in Government policy towards the agricultural sector, promoted by growing dissatisfaction with the commune system. The shift took the form of a radical revision of the productive unit from the collective to the so-called Household Responsibility System (HRS) determining a gradual relaxing of the state control over markets and prices (Rahman, 1995). These historical events showed that great changes have affected the whole agricultural system in a relatively short periods well as rural household consumption (Fan et al., 1994). The most obvious change has been the transition from egalitarianism to polarisation. In fact all these political and socio-economic reforms brought great changes in the pattern of inequality in the distribution of income and wealth between peasants (Khan, 1993). The transformation is however still in progress, so that a survey of the socio-economic conditions at the household level in China would be extremely important and useful in any future planning of agricultural improvement policies. The present paper follows up the results obtained in the EC project entitled ‘‘Impacts of Agricultural Intensification on Resources Use Sustainability and Food Safety and Measures for it’s Solution in Highly Populated Subtropical Rural Areas in China’’ carried out in China from 1994 to 1996. From this study (Giampietro and Pastore, 1999; Pastore and Giampietro, 1999) emerged that agricultural intensification in China and its sustainability must be studied through multicriteria and multiscale analyses, using the idea of ‘‘household type’’ and ‘‘distribution of household types’’ as a tool to bridge descriptions of indicators working on different hierarchical spatial and temporal scales. The controversial problem of ‘‘how’’ household types should be defined (Hailu, 1991) was not widely

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discussed in the previous papers, leaving open a wide range of options going from a simple informed—but subjective—guess to a closed statistical clustering method. The main aim of this paper is to verify the possibility of applying a multivariate procedure to define household types.

2. Materials and methods 2.1. Data source The data presented refer to a EC project (see earlier) aimed at a socio-economic description of highly populated rural areas of southern China, carried out in 1996 on five rural villages (Yuan Qiao, Zhuang Chang, Xun kou, Qun Lian and Lijiang) of Qianjiiang County, in Hubei Province. In each village, 50 households were randomly selected and a questionnaire was administered to the head of each. The interview consisted of 53 questions which sought information on the demographic structure of the household, on skills and education levels of its members, on their time allocation, on land-use pattern and agriculture production, on the presence and consistency of livestock and on cash flows. More details on the sampling and data collection are reported in Pastore and Giampietro (1999). 2.2. Selection of ‘‘relevant indicators’’ Defining household types in an agricultural system is per se an ‘‘ambiguous process’’. It is strictly dependent on the variables that are utilised in the analyses which are, in turn, dependent on the dimensions that are considered. For this reason, when local community participation cannot support the entire process the results obtained could be theoretically correct but, at the same time, could also be totally irrelevant. This paper suggests a way to access the data, through a multivariate statistical path, moving across complexity and exploring existence of household types. This approach aims to have a bottom-up approach relying mainly on data structure. However, local communities should be involved for identifying relevant indicators and also for the validation of the household types found. As previously described, our analyses were mainly focused on the assessment of household living conditions so that we arbitrarily selected the following five macro-variables to frame the analyses: demographic structure, land use, socio-cultural level, technological development, economic welfare. Within these macro-variables 13 indicators were finally included in the analyses. The indicators were selected by trying to assess both their relevance in describing the macro-variables, and their reliability in the questionnaire. 2.3. Description of the indicators Several indicators were expressed per unit of land area (conversion factor 1 ha=15 mu) or by adult equivalent value. The ‘‘adult equivalent value’’ was assessed for each household according to its specific composition and was calculated as the

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ratio between the household food energy requirement and the food energy requirement of the average male adult of the population. The caloric requirement of each member was computed for each household according to the standard procedures (James and Schofield, 1990). The household energy requirement was obviously the sum of that of all its components. The education level was computed, as proposed by Yang (1994), as the average of the years of schooling completed by the adults (over 18) in the household. The subsistence potential ratio (Frankenberger, 1992) was used as an indirect estimator of the self sufficiency of the household to feed itself. It was computed as the ratio between the amount of food energy which a household can produce over a year, and its energy requirement. The energy value of the food produced by the households was calculated by multiplying the yearly production of the major crops (rice, peanuts, soybean, sesame, fababean, vegetable, melon, wheat), by their energy content (Carnovale and Marletta, 1997). Livestock was considered as having a multi-purpose intrinsic value that could not be fully described merely by its commercial value or its biomass. Therefore we assumed that frequency itself could express the multipurpose value (Branca et al., 1993) of livestock. Value of pigs (0.15 cattle equivalent) and hens (0.05 cattle equivalent) were computed as the ratio of their number over the total of cattle (buffaloes and cows). Agricultural productivity was expressed in yuan/ha, which was obtained by multiplying the current price of each crop recorded in the local market times its own total production standardised by the area cultivated. The production expenses, which include the purchase of farm machines and tools, seeds, fertilisers and livestock, as well as wages, were also divided by land owned by the household. 2.4. Multivariate analysis and non parametric statistics to household definition The complete adopted procedure is reported in Table 1. An understanding of the system as defined by the indicators selected was performed by analysing the relationship between indicators through a principal component analysis (PCA). Starting from a numeric matrix of n (cases)=240 and v (variables)=13 the procedure (available in SAS statistical software, PROC PRINCOMP), allows the information expressed by the variables to be compressed in principal components axis. Graphically, observation points form an hypervolume with v dimensions, and ACP analysis seeks the component principal axes which pass through the original cluster points with the least possible deformation (inertia). To reduce this inertia a varimax rotation of the axes was carried out. Each principal component becomes an axis obtained as a linear combination of the original variables; factor weights represent the contribution of each variable to each axis. Each axis expresses a certain percentage (eingenvalues) of the total variance of the initial system. The number of principal components considered included all axes with an eingenvalues=1 (Fabbris, 1983). A screen plot criteria was also checked as confirmatory output. By definition axes are independent of each other, the variance percentage is cumulative so that graphically their intersection generates a different hypervolume with a number of dimensions equal to the principal components

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Table 1 Multivariate procedure applied to household types definition Procedure

Step of the protocol

Methods

Identification of a multidimensional space

Identify five different dimensions: demographic, land use, socio-cultural level, technological development, economic welfare

Selection of ‘‘relevant indicators’’ for different dimensions

Search in the questionnarie for suitable indicators

Insight and understanding of the defined system

Principal component analysis

PRINCOMP procedure SAS-DOS

Identification of household types on PCA results

Cluster (K-means) analysis

FASTCLUST procedure SAS-DOS

Validation of the asymmetric distribution obtained

Comparison with others cluster methods (Average, Complete, Single, Ward) analysis

CLUSTER procedure SAS-DOS

Concordance between different cluster methods results

Concordance between partitions obtained with Complete, K-means, Ward cluster methodsa

Non parametric test K-statistic for agreement of different assignments of n objects to m nominal categories (Siegel and Castellan, 1988)

Test for the largest cluster as baricenter of observations

Significance of difference between median values of variables found in the largest cluster vs. total sample

Non-parametric test Mann–Whitney

Validation of a description of household types with initial selected indicators

Concordance between cluster analysis results (step 3) and K-means cluster analysis performed on the 10 indicators statistically relevant for the characterisation of PCA

Non parametric test K-statistic for agreement of different assignments of n objects to m nominal categories (Siegel and Castellan, 1988)

Unplanned multiple comparison between households

Significance of difference between median values of variables

Non-parametric test Mann–Whitney (Day and Quinn, 1989)

Adjustment of significance level in the unplanned comparison

Hochbelrg algorithm (Hochberg and Benjamini, 1990)

Program Multi-DOS kindly distributed by The University of Texas M.D. Anderson Cancer Center, Houston

a Average and single cluster methods showing extreme aggregation results were not considered sensitive enough in the analysed context.

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considered. The interpretation of these was assessed by checking the discriminatory power of each variable as the absolute value of its factors weight. On the PCA output a Hartigan’s K-means clustering analysis was carried out to identify household types. This method is recommended with large sample sizes and is a useful tool to identify outliers. The classification was obtained by a procedure available in SAS statistical software (PROC FASTCLUS). Observations were allocated to the groups based on the smallest Euclidean distance from the initial seeds of the cluster. Cluster centroids were updated as each observation was assigned. The method maximising homogeneity inside a group and diversity within the groups offered different protocols to decide how many groups would have parted from the initial distribution. For this purpose an index Pseudo F of Fraire (1994) was used. This gives an estimate of the value of the partition obtained. It reaches the maximum value for number of cluster (ncl)=N 1 while the minimum value=0 for ncl=1. It follows that a higher Psuedo F value gives a better separation between clusters, in particular the final number to be considered was identified when the index reached a peak followed by a sharp fall (Fraire, 1994). As K-means clustering method identified a large cluster (including 63% of sample observations), four hierarchical methods (average, complete, single, ward clustering methods) were also applied to confirm the earlier result. As the asymmetric result was a common feature in all the different methods, a test was made to see if the largest cluster could always be considered the baricenter of the observations. The comparative approach supported the initial K-means results and led to a description of household types. As household types were identified on the ACP output, reference to the initial variables needed validation. K-mean cluster was repeated on the 10 variables whose contribution was statistically relevant for the characterisation of PCA; the two partitions were then compared with the Kappa statistic non parametric test (Siegel and Castellan, 1988). To identify household types an unplanned multiple comparison procedure was used. As indicated by Day and Quinn (1989) a significant difference recorded in each pair comparison was assessed with a Mann–Whitney test. The level of significance of the tests were adjusted using the Hochbergh algorithm. This method proved to be more precise than the commonly used Bonferroni and Holmes methods (Hochberg and Benjamini, 1990). The probability was computed using a computer program (Multi) kindly distributed by The University of Texas (M. D. Anderson Cancer Center).

3. Study area The study area is a very highly populated and cultivated Chinese subtropical zone: Jiang-Han Plain (Hubei Province), located between the main stream Yangtze River (Chang Jiang) and its tributary Han River. Alluvial soils constituted of Han River deposits and lake sedimentation are particularly suitable for cultivation. An analysis of the landscape shows that 60% of the area is occupied by cropland, 21% by water bodies (rivers, lakes, ponds), 7% by settlements and 3% by forestry and

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fruit trees (Chunru, 1997). Meteo and geopedological characteristics make Qianjiang territory particularly suitable for rice growing. Double or triple cropping seasons based on rice or other cereals, oil crops and vegetables are common. Cotton is also grown as cash crop.

4. Results A description of the variables gives a synthetic picture of the economy of this rural context (Table 2). Demographic indicators integrate well with each other and supply important historic information. Household size is on average 4.4 individuals. The value is very similar to the average (4.14) found for Yutian County and slightly smaller than the one (average size 5.5) found in a study of Dongyao village (Yutian town, Hebei province; Li, 1993). The adult children ratio reflects the demographic boom of the 1950s, and the politically encouraged decline in birth rate of the 1980s (Pastore and Giampietro, 1999). The lack of arable land (0.16 ha per adult equivalent) describes clearly one of the major constraints of the Chinese agro-ecosystem. As a consequence of the increase in population, pro-capita arable land has passed from 0.23 in 1955, to 0.13 in 1977, Table 2 Descriptive statistics of selected variables Mean

SD

Confidence limits 95%

Median

Demographic structure Size of the family (no.) Children 0–9 years (no.) Elderly >60 years (no.)

4.4 0.7 0.3

1.2 0.8 0.6

4.2–4.6 0.6–0.8 0.2–0.4

4.0 1.0 0.0

Land use Land (ha/ae) Livestock (no. of cattle equivalent)

0.16 1.3

0.65 1.2

0.08–0.24 1.2–1.5

0.10 1.15

Socio-cultural level Years of education (no.)a

6.6

3.0

6.2–7.0

6.5

Technological development Electrical expenditure (yuan/year) Fertiliser (kg/ha) Production expenditure (yuan/ha) Productivity (yuan/ha)

199 1131 71.3 584

131 721 276.3 343

183–216 1040–1223 36.1–106.4 540–627

180 1050 4.8 513

Economic welfare Income (yuan/year/ae) Cereal production (kg/ha) Food sufficiency (kcal prod/kcal requir)

3495 5220 5.2

2686 2778 4.3

3153–3836 4867–5574 4.7–5.8

2989 4862 4.4

a

Sum years of education of the adults in household/number of total adults in household.

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to 0.12 in 1984 and in 0.1 in 1992 (Qianjiang Agricultural Annals in: Li Ji et al., 1999). The demographic pressure forced intensification process through massive use of fertilisers and substitution of traditional cultivar with high yielding varieties so that the region is now responsible for most of the national foodgrain production (Chunru, 1997) As a measurement of land use note that the multiple cropping index of Qianjiang in 1993 was significantly higher than the national average in 1950s (200% vs. 150%; Li Ji et al., 1999). The average cereal production (5220 kg/ha) is very close to the 1993 average value (6000 kg/ha) reported in Qijang Statistical Yearbook (Li Ji et al., 1999). The increase in agricultural productivity recorded since 1949 can be connected to the wide use of fertilisers (1131 kg/ha) higher than the amount recorded in 1990 (860 kg/ha) in Qijang Statistical Yearbook (Li Ji et al., 1999). Cattle, pig and chicken have different values and purposes. Cattle are supposed to facilitate field work, however the relative little variation in the arable land owned by each household (SD 0.65) did not allow a significant correlation between number of cows and land owned. In fact many households share or borrow a cow during the ploughing season. Pigs on a farm have a unique high quality alimentary value but relatively expensive to keep. They can represent a long-term investment for the household, and correlation recorded between their number and income in the household (N=195, r=0.51, P < 0.05) shows how pigs could be considered as potential proxy for wealth (Li Ji Personal Communication). On the contrary, poultry did not correlate with income, as the little space and food needed by this small livestock characterises even most poor subsistence economies. The average income per year found, standardized both per adult equivalent (3945 yuan/ae) or per capita (2665 yuan/ca), was higher than the value (1100 yuan/ca) recorded in Dongyao Village at the end of 1991 (Li, 1993). As suggested by Yang (1994), Khan (1993) and Cook (1996), the average number of years of education was considered a measure of the socio-cultural level. The low average level (females=4,8 years, males 6,8 years) and the difference between sexes (KW= 57, 60 P < 0.000 N=741) takes into account the 21% of illiterates present in the sample (41 males and 116 females). At the same time the larger number of females amongst illiterates could highlight inequalities in the condition of women. However, illiteracy proved to be a by-product of age. In fact, by considering only adults between 18 and 30 years illiteracy value was reduced to 1.4% (no males and 3 females, N=209), and no significant sex difference was recorded (average years of education: males=8.2 and females=7.9 N=209 KW=1.14 P < 0.284). Performing an PCA analysis on the initial 13 input variables five axes were identified which express 58% of the total variance. The principal components were named according to their interpretation and the relative contribution of the initial variables (Table 3). The first component (14.8% of total variance), named ‘‘agricultural production’’ is characterised by the amount of fertilisers used and the overall productivity per hectare. Therefore, methods of cultivation had greater importance than amount of land cultivated. The second axis (11.3% of total variance) representing the ‘‘demographic component’’, is characterised by the household size

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and the number of elders. The third component is named ‘‘day welfare’’, and is mainly determined by food sufficiency and electricity expenses. It reflects living conditions of the household in terms of daily welfare expressing the possibility to minimise the risk in primary needs (self-sufficiency) and satisfy secondary needs (electric power). The fourth component (10.1% of total variance) represents the ‘‘productive investment’’ including yearly production expenses and cattle equivalent owned by the household. The fifth axis (10% of total variance) is a ‘‘socio-cultural component’’ described by the average years of education in adults and number of children. Carrying out a K-mean cluster analysis procedure on these five components, five distinct household types were found (Table 4). Comparing the averages of the variables within the clusters it follows that households of type 1 are characterised by productive investment, type 3 by agricultural production, type 4 by day welfare, type 5 by socio-cultural component, while type 2 always presents intermediate values. The asimmetric segregation of household types was confirmed even when other algorithms were utilised (see Table 5). This comparative approach confirm the reliability of the initial choice of K-means algorithm for the analysis. All cluster analysis results show a large cluster including from 63% (K-means) to 95% (single) of the observations. It could be assumed that the larger cluster represents the baricentre of the entire population in a situation where the process of polarisation has initiated. This assumption is examined in Table 6 where median value of the variables of the largest cluster found in different clustering methods did not result statistically different from the median of the total sample. In a less theoretical context the presence of a big ‘‘central’’ cluster well adapts to the fact that the existing severe boundary (environmental, social) condition do not allow many very different strategies and it is possible to find only finely tuned adaptations that will lead to apparent not emergent economic strategy.

Table 3 Interpretation of principal components axes Variables Size of the family Children Elderly Land Livestock Education Electricity Fertiliser Prod. Expenditure Productivity Income Cereal production Food sufficiency

Factor 1

Factor 2

Factor 3

0.028 0.021 0.009 0.078 0.008 0.056 0.092 0.895 0.049 0.617 0.013 0.842 0.116

0.839 0.089 0.835 0.078 0.123 0.051 0.142 0.019 0.066 0.108 0.304 0.081 0.116

0.091 0.084 0.058 0.365 0.192 0.036 0.672 0.077 0.070 0.531 0.334 0.048 0.646

Factor 4 0.077 0.075 0.024 0.313 0.699 0.062 0.020 0.036 0.767 0.0289 0.256 0.007 0.238

Factor 5 0.234 0.680 0.034 0.078 0.016 0.714 0.229 0.002 0.022 0.118 0.338 0.138 0.246

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Table 4 Household types in relation to principal components analysis Factor 1

Factor 2

Factor 3

Factor 4

Factor 5

Agricultural production

Demographic component

Daily welfare

Productive investment

Socio-cultural component

Cluster 1 (7) M 0.19 SD 1.28 CV 743.10

0.42 1.83 481.52

0.30 0.58 219.60

3.58 0.93 28.93

0.25 0.71 321.57

Cluster 2 (150) M 0.47 SD 0.52 CV 111.31

0.09 0.86 968.89

0.12 0.64 548.50

0.15 0.79 531.52

0.25 0.80 321.53

Cluster 3 (57) M 1.38 SD 0.77 CV 56.17

0.01 0.91 7701.19

0.08 0.72 866.14

0.07 0.82 1174.59

0.03 0.76 2645.10

Cluster 4 (6) M 0.24 SD 0.52 CV 247.24

0.88 1.16 150.89

4.49 1.47 37.24

0.04 0.54 1717.97

0.44 1.08 282.01

Cluster 5 (17) M 0.36 SD 0.73 CV 213.27

0.97 1.36 146.56

0.14 0.71 549.31

0.10 0.58 609.78

2.07 0.97 48.60

The K-means cluster analysis was conservative (n=237; kappa statistic=2.649; P < 0.0001) even when repeated on the 10 variables that contribute mainly to the five PCA components. The characterisation of the household types according to the initial indicators is shown in Table 7. Those of type 1 are a small group of households with living conditions of a high standard, as indicated by highest average income value (11 707 yuan per year) significantly higher than types 2 and 3 (1–2 P < 0.003 and 1–3 P < 0.000) and highest level of education (13.8 years/adults), significantly different from most types (1–2 P < 0.0003; 1–3 P < 0.0008; 1–4 P < 0.004). Mainly, productive activities of households of type 1 are not related to agriculture (see Table 8) The little land area available (0.08 ha) is mainly cultivated with cash crop (cotton) giving a high income rate. Those of type 3 are larger households (average size significantly different from all others types [3–1 P < 0.0055; 3–2 P < 0.0004; 3–4 P < 0.0009; 3–5 P < 0.0000]) while the number of elder is different from type 4 (P < 0.003) type 5 (P < 0.0000) and type 2 (P < 0.0000). In absolute, they are the poorest households with the lowest income value (2556 yuan/year, significantly different from all other types. (3–1 P < 0.0005; 3–2 P < 0.0005; 3–4 P < 0.0015; 3–5 P < 0.0000). They are households with less land compared with type 2 (P < 0.0042), 4 (P < 0.0013), 5 (P < 0.0000). Cereal production

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Table 5 Comparative household types distribution using different cluster analysis methods (no. of households) Cluster methods

Cluster 1

Cluster 2

Cluster 3

Cluster 4

Cluster 5

K-means Complete Ward Average Single

150 121 127 215 228

57 73 58 11 4

17 36 33 4 3

7 4 15 4 1

6 3 4 3 1

Table 6 Significance of comparison between median values of largest cluster found with different clustering methods towards total sample median Variables

K-means

Complete

Ward

Average

Single

Size of largest cluster

Size of the family Children Elderly Land Livestock Education Electricity Fertilizer Prod. Expenditure Productivity Income Cereal production Food sufficiency

150

121

127

215

228

0.000 0.028 0.000 0.601 0.655 0.287 0.288 0.327 0.843 0.503 0.582 0.184 0.604

0.15 0.48 0.80 0.49 0.25 0.32 0.15 0.00 0.49 0.10 0.81 0.10 0.53

0.10 0.93 0.80 0.72 0.34 0.74 0.50 0.01 0.90 0.08 0.78 0.60 0.22

0.70 0.71 0.82 0.70 0.96 0.80 0.84 0.19 0.81 0.47 0.73 0.45 0.80

0.97 0.90 0.97 0.74 0.86 0.86 0.94 0.77 0.79 0.74 0.80 0.73 0.70

Table reports Mann–Whitney test significance values. Total sample size=237 households.

(5069 kg/ha) is low when compared with type 5 (P< 0.0001) as well as food sufficiency (P < 0.0004). They also obtain a lower productive rate from its cultivation (3–5 P < 0.005). Households of type 4 are a small group with an intermediate income significantly different from types 3 and type 5 (4–3 P < 0.001; 4–5 P < 0.000). Together with type 5 have they have more arable land (4–3 P < 0.001) but are characterized by the greatest average value of livestock owned (4.0 cattle-equivalent). Production expenditures are the highest (significantly higher from all types (4–1 P < 0.0066; 4–2 P < 0.0000; 4– 3 P < 0.0000; 4–5 P < 0.0004). Type 5 are small sized families whose income is mainly derived from land cultivation. Agriculture is mainly concentrated on intensive cereal production [significantly higher than types 2 and 3 (5–2 P < 0.0000; 5–3 P < 0.0001)] and resulted in the highest food sufficiency value (5–2 P < 0.0006; 5–3 P< 0.0004). The surplus produced represents the main source of their income as shown by the highest

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Table 7 Characterisation of household types

Size of family (no.) K–W P=0.0000 N. of children K–W P=0.0000 N. of elderly K–W P=0.0000 Land (ha/ae) K–W P=0.0000 Livestock (no. cattle equivalent) K–W p= n.s. No. of years of education K–W P=0.0019 Electricity (yuan/year) K–W P=n.s. Fertiliser (kg/ha) K–W P=0.0000 Prod. expenditure (yuan/ha) K–W P=0.0011 Productivity (kg/ha) K–W P=0.0434 Income (yuan/year) K–W P=0.0000 Cereal prod (kg/ha) K–W P=0.0004 Food sufficiency (kcal prod/kcal req) K–W P=0.0022

Mean SD Median Mean SD Median Mean SD Median Mean SD Median Mean SD Median Mean SD Median Mean SD Median Mean SD Median Mean SD Median Mean SD Median Mean SD Median Mean SD Median Mean SD Median

Cluster 1

Cluster 2

Cluster 3

Cluster 4

Cluster 5

Total

7

150

57

6

17

237

4.7 1.5 4.0 1.6 0.5 2.0 0.4 0.8 0.0 0.08 0.05 0.08 1.2 0.6 1.2 13.8 9.3 9.5 395.6 397.1 300.0 922 164 1009 67.0 112.5 4.8 713.1 479.5 614.7 11 708 9550 7865 5370 2783 5150 4.7

3.9 0.8 4.0 0.5 0.7 0.0 0.1 0.3 0.0 0.12 0.06 0.11 1.2 1.0 1.2 6.2 2.5 6.5 182.2 104.0 156.0 1015 388 1046 30.1 72.6 4.8 545.6 245.8 502.0 3449 1888 3009 4669 2015 4573 5.0

5.8 1.2 6.0 1.0 0.9 1.0 1.0 0.6 1.0 0.09 0.03 0.08 1.4 1.2 1.2 6.7 2.2 6.8 208.1 125.7 190.0 1020 300 993 44.8 143.5 3.3 534.1 183.1 504.3 2556 1282 2337 5069 1814 4988 4.1

4.3 0.5 4.0 0.7 0.8 0.5 0.2 0.4 0.0 0.17 0.05 0.16 4.0 3.3 2.7 6.1 1.1 6.0 212.7 104.8 208.0 985 187 918 1544.9 750.4 1751.9 528.5 114.0 513.9 4379 902 4412 5036 1442 5204 8.5

3.9 0.7 4.0 1.3 0.7 1.0 0.2 0.4 0.0 0.17 0.09 0.16 1.1 0.8 1.2 7.1 2.2 8.0 236.2 125.0 180.0 1964 610 2000 16.3 39.3 2.9 752.4 290.5 711.6 3553 1067 3508 9283 5168 7576 11.0

4.4 1.2 4.0 0.7 0.8 1.0 0.3 0.6 0.0 0.12 0.06 0.10 1.3 1.2 1.2 6.6 3.0 6.5 199.4 131.6 180.0 1080 452 1050 72.1 277.9 4.8 562.2 248.2 508.8 3509 2700 2989 5125 2604 4838 5.3

2.5 5.3

3.8 4.4

2.2 4.0

5.6 9.1

7.6 9.5

4.3 4.5

productivity value significantly different from type 2 (5–2 P < 0.0033) and type 3 (5– 3 P < 0.0050). Once household types were defined, we investigated the assumption that their characteristics are determined by environmental and socio-economic boundary conditions. The external pressures to which households respond by altering their

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D. Ottaviani et al. / Agricultural Systems 76 (2003) 207–225 Table 8 Average return of labour (ARL) by household type Cluster 1

Cluster 2

Cluster 3

Cluster 4

Cluster 5

Total

7

150

57

6

17

237

Total ARL (yuan/h)

Media Median SD

11.2 4.8 13.1

5.6 3.6 7.1

4.0 3.7 2.2

3.3 1.4 1.4

6.8 4.7 4.7

5.4 3.8 6.4

Agricultural ARL (yuan/h)

Media Median SD

3.5 3.5 0.5

5.4 4.5 3.4

4.8 4.7 3.0

3.9 4.4 2.2

8.8 7.5 5.3

5.4 4.6 3.5

composition, activities and strategies can be defined as ‘‘obscure market forces’’ (Wallerstein, and Smith, 1992) which are often intangible and difficult to measure. However as these socio-economic constraints are reflected in the biophysical ones to which the household is submitted, household time allocation pattern was considered an indicator of the width and the elasticity of the on going boundary constraints. As advised by Giampietro and Pastore (1999) and Pastore and Giampietro (1999) land– time–budget analysis when coupled with measures of productivity and labour returns permit a refinement of the measure of labour utilisation and understanding of the labour poverty nexus. The total average return of labour was compared in Table 8 with the agricultural average return of labour (AgrARL).While the former did not record any difference amongst household types (N=237 KW=6,28 P < 0.136), AgrARL did find difference between the households most devoted to intense agriculture (type 5) and other types (5–1 P < 0.0069; 5–2 P < 0.0028; 5–3 P < 0.0012; 5–4 P < 0.011). It is interesting to note that the return of labour in agriculture is inversely correlated to time spent in this activities (r= 0.36). This means that a greater labour force must compensate for little technological development. Fertilisers are likely to fight poverty in a short-term view, obviously disregarding on-going already severe environmental loading and damage. Agricultural activities represent the major component axis of this system. However, within the existing boundary conditions every household looks for slight adjustment of its time allocation and income activities and different mixture of agricultural and non-agricultural activities. This results in a general equality amongst household types of total average return of labour (ARL tot). Moreover, the total ARL relatively narrow distribution shows how boundary conditions are limiting and tight.

5. Discussion A first consideration is on the quality of data presented. The most common bias in socio-economic analysis occurs during the transmission of information between

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people interviewed and fieldworkers on what can be considered ‘‘sensitive information’’ (illegal activities, highly personal subjects, cultural practices; Christensen, 1993; Hazell and Ramasamy, 1991). As the survey was carried out in small communities such as rural villages we consider our data potentially affected by this type of bias. Further studies are needed in this socio-cultural context to assess the importance of this bias, which at present could not be excluded. Bearing in mind these limitations, our study points out that in a complex system where the boundary conditions are very tight (in particular regarding the high demographic pressure) there is no room for the households to significantly differentiate themselves. In this environment the differences among households normally deal with minor choices and few families are able to modify their land–time and economic budget. We can reasonably assume that the main objectives of each household may be resumed into three complementary aims: to increase the economic return of labour, to minimise the risk by increasing food security and to decrease the time allocated to work and, even more, the time allocated to the primary sector of the economy (Giampietro and Pastore, 1999; Pastore and Giampietro, 1999). Of course these goals cannot be achieved all together. Farmers will obviously look for solutions that will include an adequate or acceptable level of each dimension according to their socio-cultural perspectives. On this basis, if we cluster the sample through one of these aims we find differences in the behaviour and in the outcomes in different groups (Giampietro and Pastore, 1999; Pastore and Giampietro, 1999). Each household make different choices on how to organise its own land–time budget and adopt different life strategies to achieve those values which they expect will improve their life quality. Different life strategies are influenced by a great number of variables which, in a complex system, are usually closely linked directly or indirectly. It could happen that differences in a given variable are flattened or hidden by the effect of other related variables. If this is true, when more that one variable is used to cluster a population, a big cluster, even comprising more than half of the population, can be found. The behaviour of the household determine characteristics of higher organisation levels (so called emergent properties, not visible when studying the system at a lower hierarchical level), but at the same time their behaviour is in turn influenced by feedbacks from socio-economic boundary conditions. Therefore, they are actors in a creative process and subject of the created process. One of the most interesting results of our study is the importance of household survey as a bridge to cross different hierarchical levels. Due to the recent process of transformation we can suppose that Chinese agro-ecosystems still conserve memories of its past. It will be interesting to verify if our data analysis shows any traces of this past, and if can identify constraints for the households. In this discussion, we do not intend to create a direct link between our surveyed households and more general considerations expressed by other authors on China, as we are aware that the sample is limited and non representative when compared with huge and diverse conditions affecting China as a nation. Nevertheless, we believe that our analysis capture a sort of ‘‘resonance’’ of the higher hierarchical level represented by rural China, so that we could try hazardously to cross levels and

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put what has emerged from our limited data analysis beside what has been already discussed about transformation in China. Chinese rural households face a constant risk of fluctuation in their livelihoods. Many expenses are regular and can be anticipated, whereas others are completely unpredictable: illness or injury, catastrophic events. Strategies to cope with such fluctuations include accumulation or disaccumulation of cash, grain, livestock, formal and informal credit transactions, claim for transfers on kin. Our data analysis showed that livestock ownership could be considered by our population as a form of household insurance. Livestock value was shown to be a main determinant of the productive investment axis, and in particular pigs, linked less closely to agriculture duties, could buffer emergencies and be a simple indicator of welfare. This agrees also with a survey carried out in a poor rural region (Douglan county) on costs of illness, which shows that selling livestock was a common strategy to face unexpected medical fees (Wilkes, 1993). Livestock was shown to be used as a buffer against fluctuation of the food/cash/ goods availability. However it needs space to grow and live. The problem of land holding is in fact the most critical issue in the whole of China. The land distribution that took place under the Household Responsibility System was assessed on a procapita basis without considering the different productivity of different plots. With time the initial distribution was slightly readjusted because land was too fragmented to be easily cultivated and population increase required further land allocation (Li, 1993). As suggested by Khan (1993), there is an ‘‘intriguing possibility’’ that in China land allocation was assessed as a complement to other productive resources. As a consequence its variability in relation to a collection of other productive factors would be so limited that the observed response to variations in endowment of land across household would be very small. Moreover the successive ‘‘green revolution’’ with the adoption of fertilisers, high yielding varieties, and enhanced irrigation did not record any significant effect on the aggregate distribution of owned land, either. Indeed the possibility of using different crop combinations and technical input gave to the household a change of fine-tuning farming output both for crop production for home use and market sales (Li Ji et al., 1999) Our findings clearly point out that land use more than land size is a determinant for agriculture. Technological development is likely to have caused differences amongst interviewed households in productivity rates, cereal productivity and food sufficiency values as confirmed by differences recorded between poor and traditional farmers (type 3) and richer and modern farmers (type 5). In rural China commonly the amount of fertilizers is often a good proxy for technology development because of the positive effect on agricultural production (Rahman, 1995). An historical review of traditional and ecological agriculture shows how since the 1960s agricultural modernisation in China has meant ‘‘mechanization, electrification, use of chemicals and irrigation’’ (Wu and Wang, 1994) which have enhanced productivity (Li Ji et al., 1999). In fact agricultural return of labour was inversely correlated to time spent in agriculture and was connected to the degree of ‘‘modernisation’’ of the practices. Almost all available arable land is already under intense cultivation, so that productivity probably would not be able to increase because of

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on-going environmental loading. Therefore, although holdings of land may be a determinant of income in our population, as already stated by McKinley and Griffin (1993) for the whole of China, its importance is often overshadowed by fixed productive assets, use of fertilisers, employment in non agricultural occupations. Cereal crops are still the primary goal for our interviewed households. Cash crops, successfully and remunerative crops, were cultivated only by a small cluster of richer farmers who preferred cash productivity rather than minimisation of their food security risk, so these households were totally dependent on the availability and prices of foods and grains in the local markets. One more worthwhile issue is that the concept of ‘‘boundary conditions’’ cannot be limited to environmental or economic constraints (Smith and Wallerstein, 1992). Other sociological and cultural customs can affect household behaviour. In this perspective we can show how the small number of children found in our surveyed villages agrees with the general demographic trend and the birth control campaigns aimed at reducing demographic pressure. In our data the larger households (type 3) were also the poorest, the same as was found on a national scale. Larger families imply in fact higher dependency ratio and Khan (1993) found that the addition of one member to an average household leads to a reduction of 11% in the household capita income. In fact although certain goods (water taps, cooking utensils, clothing and housing) can be shared in families so that the individual cost of a living standard is lower, this is only a minor benefit where wider sharing regards foods (Lanjiouw and Ravallion, 1994). It is obvious that, as household members constitute the labour force and shape the household consumption, the relation between household size and poverty can vary in different socio-economic situations. In China, the emancipation of rural women began in 1980 with a law which gave to women the same status and rights as men. Actually, women’s political participation is still well below men’s, while great steps forward have unquestionably been made in giving women access to education (Rahman, 1995). This social change was shown in our educational level analysis. Sex discrimination in illiteracy resulted linked more to the old generation than to the new one. Traditionally males were favoured in educational opportunities. New evidence come together to show the great importance of child education in rural households. Interviews in Dongyao village showed that education was considered a greater honour than a very high income. Moreover it is strongly believed that the only way out of poverty for daughters is a good marriage, which is possible only with a good education level (Li, 1993). An initial state of polarisation was recorded in our analysis, however the process still seemed very slow and limited, because there is still an egalitarian condition where most of sampled families is struggling against poverty and negotiating daily survival. A socio-economic interpretation of this situation implies a strictly political discussion and lies outside the aims of this paper. However as Griffin and Renwei (1993) call for attention controversial interpretation often arises because of the lack of data and consequently difficulties of empirical analysis at the household level. In particular a careful understanding and description of the social change that is taking place in rural China is much needed.

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With the new household responsibility system the household became the basic unit of production, management and decision making (Li, 1993). The new emphasis on the individual in economic decisions brings the attention back to household perspectives. Acknowledging on one hand the individual power and freedom of working out a life strategy and on the other the constraints that shape this decision space, we come to the possibility of identifying different household types in the society. In fact even if theoretically infinite possible solutions can be imagined, natural resources distribution, social and political conditions make possible few feasible solutions for the households. The present analysis at the household level in Hubei region in China found five different household types differing in size and composition, economic activities performed, degree of technical development, intensification of cereal production, food sufficiency, level of education and overall income. However the analysis of the constraints present in their socio-economic boundary conditions show that very limited options are available for rural farmers to emerge from poverty. In fact the common average return of labour indicates that if little local inequalities arise in land distribution, other assets compensate such differences and lead to a general equality in the distribution of rural wealth (McKinley and Griffin, 1993). Defining household types is therefore a powerful tool to understand agro-ecosystems. In order to make this tool operative and effective in sustaining political and economic decisions of development it is necessary to define household types as close to reality as possible (Pinstrup-Andersen, 1993; Teklu, 1993). This explorative approach can help to provide a reliable and realistic information and give field research a greater applicative and decision-making power.

Acknowledgements The study was supported by EC project Impacts of Agricultural Intensification on Resources Use and Sustainability and Food Safety and Measures for it’s Solution in Highly Populated Subtropical Rural Areas in China from 1994 to 1996 (Grant STD TS3 CT92 0065). The authors are grateful to Dr. F. Galimberti for invaluable assistance in the statistical design of the analysis, to Dr. A. Turrini, Dr. S. Rosati, Dr. M. Vassallo, Dr. M. G. Ottaviani for valuable and generous consultations and suggestions. We have also greatly appreciated important comments related to Chinese socio-economic conditions from Dr. N. Alexandratos, Dr. T. Gomiero during the preparation of the manuscript. Last but not least we are extremely grateful to Ms. S. Sette for all the encouragement and assistance kindly offered.

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