Resource integration for multiple benefits Multifunctionality of integrated farming systems in Northeast Thailand

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Resource integration for multiple benefits Multifunctionality of integrated farming systems in Northeast Thailand Powered By Docstoc
					                                                                                                                                 AGRICULTURAL
                                                                                                                                   SYSTEMS

                                                     Agricultural Systems 94 (2007) 694–703
                                                                                                                          www.elsevier.com/locate/agsy




            Resource integration for multiple benefits: Multifunctionality
               of integrated farming systems in Northeast Thailand
                                   a,*
       Prasnee Tipraqsa               , Eric T. Craswell b, Andrew D. Noble c, Dietrich Schmidt-Vogt                                           d

               a
                    Chiang Mai University, Unit for Social and Environmental Research (USER), P.O. Box 144, Chiang Mai 50202, Thailand
                               b
                                 University of Bonn, Global Water System Project, Walter-Flex Strasse 3, 53113 Bonn, Germany
                  c
                    International Water Management Institute (IWMI), c/o WorldFish Center, P.O. Box 500 GPO, 10670 Penang, Malaysia
  d
      Asian Institute of Technology (AIT), School of Environment, Resources and Development, P.O. Box 4, Klong Luang, Pathumthani 12120, Thailand

                              Received 7 July 2006; received in revised form 7 February 2007; accepted 20 February 2007




Abstract

    Resource degradation in rice farming systems in Thailand endangers food security, but the systems may become more sustainable by
combining them with aquaculture and livestock farm enterprises by capitalization of their synergies in resource use and re-use, i.e. by
adopting integrated farming systems. Most empirical studies that assess this potential have focused on a few specific aspects, but not
on the multiple social, economic, and ecological functions of resource integration. This study uses the framework of multifunction agri-
culture to assess the performance of integrated farming systems in Thailand and compares its performance with that of ‘normal-rice’ or
non-integrated farming systems. Surveys were conducted in Khon Kaen province of Northeast Thailand using a combination of quan-
titative and qualitative survey methods.
    Integrated farming systems were found to outperform the normal or commercial farming systems in all four dimensions of a multi-
functional agriculture: food security, environmental functions, economic functions, and social functions. The findings support the notion
that diversification and integration of resources on farms is feasible in both economic and ecological terms. The analyses shows that
integrated farming does not, however, diminish the need for external inputs. High start-up cost might constrain farmers from switching
to integrated farming and from exploiting the benefits of resource integration.
Ó 2007 Elsevier Ltd. All rights reserved.

Keywords: Lowland rainfed agriculture; Northeast Thailand; Sustainability; Resource integration; Biodiversity




1. Introduction                                                              frequently identified as major constraints to crop produc-
                                                                             tivity in Northeast Thailand (Noble et al., 2000; Wijnhoud
   Northeast Thailand is a region where smallholder farm-                    et al., 2001). Several development agencies have stressed
ing prevails and where farmers operate under conditions of                   that this endangers food security at all scales (ADB,
environmental constraints and rapid economic changes.                        1998). Past development efforts have aimed at large
The region’s agricultural productivity and per capita                        increases in food production through the cultivation of a
income is much below the national average (OAE, 2003).                       few high yielding varieties and intensive use of mineral fer-
A declining soil fertility and a low water availability are                  tilizers. Farm households rich in resources did benefit to
                                                                             some degree from these green revolution technologies.
  *
    Corresponding author. Address: Chiang Mai University, Unit for           Yet for resource-poor farmers, the reliance on a few
Social and Environmental Research (USER), P.O. Box 144, Chiang Mai           improved varieties, high levels of external inputs, and the
50202, Thailand. Tel.: +66 83 3012608; fax: +66 53 854347.                   inefficient use of those inputs has made them vulnerable
    E-mail addresses: prasnee@gmail.com (P. Tipraqsa), eric.craswell@        to the vagaries in weather and markets, with increasing
uni-bonn.de (E.T. Craswell), a.noble@cgiar.org (A.D. Noble), schmidt@
ait.ac.th (D. Schmidt-Vogt).
                                                                             debt levels as a result.

0308-521X/$ - see front matter Ó 2007 Elsevier Ltd. All rights reserved.
doi:10.1016/j.agsy.2007.02.009
                                      P. Tipraqsa et al. / Agricultural Systems 94 (2007) 694–703                               695

   To stop soil fertility decline and to regain productivity,         serving biodiversity, and supporting the socio-economic
farmers have organized themselves in groups, some of                  viability of rural areas. Four principles were considered
which have adopted integrated farming as a strategy. The              fundamental to agriculture and the agro-food sector in gen-
basic idea behind integrated farming is that species diversi-         eral: food security, environmental functions, economic
fication and resource integration can contribute to regain-            functions, and social functions (OECD, 2001). Multifunc-
ing productivity on resource-poor farms (Smyth and                    tional agriculture aims to strengthen the mutual synergies
Dumanski, 1993; Konboon et al., 2001; Devendra and                    between these four principles (OECD, 2001).
Thomas, 2002a,b; Prein, 2002; Halwart et al., 2006). This                In OECD countries such as Germany, the UK, Switzer-
move needs to be seen within the larger context of an                 land, Norway, Australia, and Japan the concept has since
emerging policy change which favours diversification over              become a shared policy objective, although it is sometimes
intensification as a strategy to achieve not only productiv-           (mis)used to justify government support to agriculture,
ity increases, but also livelihood and environmental sus-             which has come under pressure in international trade nego-
tainability (Conway and Barbier, 1991; Naegel, 1994). It              tiations (Potter and Burney, 2002). The concept is, how-
has been motivated by concerns over agricultural resources            ever, rarely applied in developing countries, like
decline and the experience of the Thai economic crisis in             Thailand, where the focus is still very much on the primary
1997 (Coxhead and Plangpraphan, 1999). In Thailand, this              function of agriculture as a supplier of food and fibre.
policy change is promoted by institutions at various levels,             This study was designed to address the four principles of
including farmer associations at the grassroots-level in the          multifunctional agriculture in the case of Northeast Thai-
northeast (Ruaysoongnern and Penning de Vries, 2005)                  land by testing the following hypotheses.
and the King’s Policy of Self Sufficiency at the government
level (Suwanraks, 2000).                                              1. Food security functions: Resource integration increases
   This study compares the performance of the integrated                 food availability.
farming system (IFS) with that of the non-integrated farm-            2. Environmental functions: Resource integration improves
ing system, which we denote as the commercial farming                    the quality of resources (soil, water, and trees).
system (CFS). That is not to say that the IFS is not com-             3. Economic functions: Resource integration improves the
mercial, but whereas the CFS has a clear focus on produc-                economic returns of farms.
ing rice for the market, the IFS – through diversification             4. Social functions: Resource integration is a practice well
and resource integration – pursues multiple objectives, such             acceptable to the local community.
as food production for the household, the maintenance of
natural resources for food security and the well-being of
household members, and the support for local                          2. Materials and methods
communities.
   Studies on the performance of IFS have been conducted              2.1. Study area
in many countries and all have shown that synergies
between farm enterprises increase productivity (Talpaz                   The study area is located in the Huai Nong Ian water
and Tsur, 1982; Alsagoff et al., 1990; Dalsgaard and Ofi-               catchment in the Waeng Yai and Chonnabot districts,
cial, 1997; Gomiero et al., 1999; Berg, 2002; Jamu and                Khon Kaen province. The catchment, covering an area of
Piedrahita, 2002; Frei and Becker, 2005; Pant et al.,                 285 km2, was selected because of the presence of a group
2005). Most studies have focused on the sustainability of             of innovative farmers who are practising integrated farm-
the IFS in terms of productivity and economic viability.              ing and have organized themselves in a farmer network.
Yet, farm households practising IFS have a multitude of               The average slope of the catchment is 0–2%. Soils with fine
objectives and these should ideally be assessed integrally            sandy to very fine loamy textures are most prevalent in the
(Paris, 2002). There is a clear need for studies that examine         catchment. The bimodal distribution of rainfall is influ-
in a comprehensive manner the full range of resources that            enced by the southwest monsoon and tropical cyclones
are vital for agricultural systems. Using the concept of mul-         from the South China Sea. Water resources are influenced
tifunctional agriculture widens the focus to include envi-            by the hydrological characteristics of the Chi River basin.
ronmental and social services in addition to crop yields              Floods occur every few years in the lower catchment near
and profits (OECD, 2001). This paper presents an assess-               the Chi River. Drought in the catchment results from the
ment of IFS that considers a wide range of both socio-eco-            uneven distribution of rainfall. The forest type consists of
nomic and biophysical dimensions while using the                      dry dipterocarp, riverine, riparian forests, and plantations.
framework of multifunctional agriculture.
   The concept of multifunctionality was introduced in                2.2. Sample selection
Western countries in the 1980s and originated with enter-
prising farmers and scientists (Vereijken, 1997, 2003). The              A pre-survey of the area showed that 21 farmers in the
role of agriculture was recognized to go beyond the mere              Huai Nong Ian catchment area were practising integrated
supply of food and fibre, and to include its role in shaping           farming. It was therefore decided to use a purposive
the rural landscape, sustaining renewable resources, pre-             (non-probability) sample (Dillon and Hardaker, 1993),
696                                   P. Tipraqsa et al. / Agricultural Systems 94 (2007) 694–703

also because the objective was to gain an in-depth knowl-             been to evaluate the sustainability of the member farms for
edge about the farms, including their vegetation, soils,              which a list of indicators has been developed by the farmers
resource flows, land management, production, and income                themselves. Variables for this study were selected by com-
for which each farm would have to be visited frequently.              bining this list of farmer indicators with other indicators
   The pre-survey also showed that the soil, vegetation,              from literature. Seven proxy variables were selected to
and water resources varied through the catchment. The                 assess the four dimensions of a multifunctional agriculture,
catchment area was therefore divided into three parts:                which are shown in Table 1 and explained below (see Tip-
upper area, middle area, and lower area to control for                raqsa, 2005 for details).
the variation in biophysical resources. Eight integrated                 From the perspective of the farmers practising inte-
farms were selected as being the most representative for              grated farming, food security is achieved when ‘growing
integrated farming in the area. Three of these were selected          everything you eat and eating everything you grow’, which
from the lower area (which had six integrated farms in                reflects the King’s philosophy of self-sufficiency (Suwanraks,
total), two from the middle area (four farms in total),               2000). Following this perspective the following two indica-
and three from the upper area of the catchment (11 farms              tors of food security were identified: (1) Richness of species
in total). This selection was based on a pre-survey among             used for food, which was calculated by counting the number
the integrated farms in the catchment and discussions with            of edible crop and animal species on the farm (Lightfoot
extension agents, members of the farmer network, and field             and Pullin, 1994). This reflects the diversity of available
practitioners from research and NGOs. Where possible,                 food products and because this diversity also relates to cul-
integrated farms were selected that had already several               ture, it can also be taken as an indicator of social functions.
years of experience in integrated farming, for these were             (2) The share of home-produced food, which was calculated
more likely to show changes in the resource base.                     as the value of home-produced food that the household
   Each of the eight integrated farms was paired with an              consumed (valued at market prices) as a percentage of
adjacent commercial farm with similar soil type, natural              the total value of food expenditures, measured over a
vegetation, water resources, farm size, and which had con-            one-year period (Dillon and Hardaker, 1993). This reflects
verted natural forest into agricultural land at about the             the level of self-sufficiency and as such is also an economic
same time. All selected households were cooperative and               indicator.
willing to participate in the research, especially those who             Inherently poor soils and low water availability reduce
had adopted integrated farming showed interest in receiv-             the effectiveness of mineral fertilizers and chemical inputs.
ing feedback from the researcher.                                     In low-input agriculture, the integration of trees and
   Each farm was visited about seven times between                    water reservoirs into the farm has been suggested by some
December 2002 and April 2003 to collect in-depth informa-             scientists as a more suitable alternative to a high reliance
tion using several techniques. First, a general questionnaire         on external inputs (Noble et al., 2000; Viyakorn, 2001;
was designed to investigate the structural characteristics of         Craswell, 2002). To capture these aspects of vegetation,
the households and practices in the farm. This was fol-
lowed by several rounds of informal interviews using
adjusted guideline questions. Socioeconomic data were col-
                                                                      Table 1
lected using direct observation and semi-structured inter-            Selected proxy variables for assessing the four principles of multifunc-
views (Galpin et al., 2000). Data were validated using a              tional agriculture
triangulation strategy (Johnson, 1997), which involves tak-           Indicator                     Principles of multifunctional agriculture
ing data from more than one person in the same household
                                                                                                    Food       Environment    Economic     Social
to improve the accuracy of information and to get a better                                          security
understanding of the context and possibly divergent per-
                                                                      1. Richness of food species   ·                                      ·
spectives. Soil organic matter and soil texture were ana-                (number)
lysed from 45 soil samples taken at each farm (Daly,                  2. Share of home produced     ·                         ·
1992). These samples were taken about one month after                    food (%)
at the end of harvest season (December–January) to avoid              3. Tree growtha                          ·
                                                                      4. Soil organic matter (%)               ·
errors from applying fertilizer or soil amendments. The
                                                                      5. Share of months in dry                ·
farm resources were analyzed using farm mapping and                      season irrigated (%)
farm resource surveys. Finally, vegetation was studied                6. Agricultural                                         ·            ·
using a line-intercept (Bonham, 1989), which was con-                    productivityb
ducted a few months after the rainy season because the veg-           7. Richness of species for                                           ·
                                                                         social purposes
etation has then completed its annual growth cycle.
                                                                         (number)
                                                                       a
                                                                          Includes stem height (m), distribution of tree size and social position,
2.3. Selection of variables
                                                                      stem density (stem haÀ1) and basal area (m2 haÀ1)
                                                                        b
                                                                          Includes agricultural output (USD), land productivity (USD haÀ1) and
   Integrated farms in the study area have organized them-            labour productivity (man-day haÀ1); 1 USD = 43.73 THB (December 2,
selves in a network; one of the activities of the network has         2002).
                                      P. Tipraqsa et al. / Agricultural Systems 94 (2007) 694–703                                697

soils, and water, the following three indicators were                 The study will return to this point in the discussion. It was
selected:                                                             also argued in the above that the diversity of food species
   (3) Growth performance of tree communities, which was              produced on the farm is an indicator of its social functions
assessed from the vertical characteristics of the tree stand          as they relate to the local culture. One additional variable
structure using a line-intercept method (Bonham, 1989).               suggested by the farmers was to count the number of spe-
A sample plot size of 10 · 80 m (800 m2) was used to cover            cies used for social purposes other than food, as this also
the diversity and stand structure of the vegetation in the            relates to the basic idea that diversification contributes to
farm area (Smitinand et al., 1994). The measurement                   the sustainability of the farm. (7) The richness of species
included woody trees, shrubs, and woody perennials with               used for social purposes was calculated as the number of
a minimum height of 1.30 m and with a diameter P5 cm                  species used for medicine, local rituals, the making of tools,
measured at 1.30 m height. The growth performance of                  and the shading of houses.
the tree communities was assessed on the basis of stand                   These seven proxy variables were used to assess the per-
height (m), distribution of tree size and crown social posi-          formance of the integrated farms. Two complementary
tion (following the classification of Dawkins, 1987), stem             methods were used: a comparison of farms based on farm
density (stem haÀ1), and basal area (m2 haÀ1) (Lamprecht              types (IFS vs. CFS) and a comparison between farms based
and Pancel, 1993). The important value index (IVI) was                on the number of synergies between enterprises on the
used in addition to rank the species and to identify the              farm. These synergies are defined as flows of biological
most dominant species in the farm.                                    material between the various enterprises on the farm. These
   (4) Soil organic matter (SOM) was measured at 15 loca-             enterprises included paddy rice, vegetables, cattle, pig,
tions on each farm and at three depths at each location: 0–           poultry, aquaculture, mushrooms, and trees. Synergies,
10 cm, 10–20 cm, and 20–30 cm following Daly (1992) and               for example, include the use of manure and compost to fer-
using the modified Walkley–Black method (Nelson and                    tilize vegetables and rice, rice bran and leaves to feed pigs,
Sommers, 1996). Each sample was analysed separately in                and manure to feed fish. The count of these synergies was
the laboratory. An average value over all 45 samples was              used as a measure of integration level; for each identified
used in the analysis because statistical analyses showed              synergy the integration level was incremented by one. This
no significant differences in SOM at different depths or                 indicator also helped to distinguish an integrated farm
locations on the same farm.                                           from a diversified farm as the latter can comprise many
   (5) The share of months irrigated in the dry season was            enterprises but might have a low integration level if
calculated from the number of months that production                  resources are not recycled on the farm.
activities were irrigated from water reservoirs (farm ponds               The results are presented in two parts. The first part
and wells) in the dry season and expressed as a percentage            gives a general overview of the two farming systems using
of the total number of months in the dry season (Garcia,              descriptive statistics while the second part tests the
1997).                                                                hypotheses.
   The economic function of the farm refers to its ability
to convert inputs such as land and labour into crop and               3. Comparison of commercial with integrated farming
livestock products, which can be expressed as output/                 systems
input ratios, i.e. partial productivity indicators. (6) Agri-
cultural productivity was assessed using two such ratios.                Farm households live close to each other in each village
First, land productivity was calculated as the quotient of            with their fields scattered around at an average distance
total agricultural output (USD) and total farm area                   from the farmstead of about 3 km. Fields were converted
(ha). Second, labour productivity was calculated as the               from natural forest about 30 years ago while the eight
quotient of total agricultural output and the amount of               farms in the IFS converted from commercial to integrated
labour supply (man-days). Agricultural output included                farming about 10 years ago. Fig. 1 illustrates the spatial
both crop and animal production (Dillon and Hardaker,                 characteristics of resource use with an example of farm
1993; Dillon and McConnell, 1997), in which crop output               maps of one integrated and one commercial farm from
was calculated as the sum of total rice, vegetable, and               the upper area of the catchment. Six management units
perennial production and animal output was the sum of                 can be identified: paddy rice fields, vegetable plots, farm
cattle, pig, poultry, and fish production in the year of               ponds, woodlands, farm buildings, and open land. A com-
the survey.                                                           parison of all farm maps between the two systems showed
   Agriculture is widely perceived to be the basis of rural           that the main difference in management units is that all
livelihoods, maintaining the rural communities and their              integrated farms have woodlands and farm ponds, neither
culture. For agriculture to perform this social function, it          of which the commercial farms have.
first of all needs to give an economic return, which is indi-             This is further confirmed by Table 2, which compares
cated by the productivity of the farms. During the inter-             the socio-economic and biophysical characteristics of the
views, farmers stated that one objective of integrated                IFS and the CFS using mean values. Irrigation facilities
farming is to reduce the migration of people from rural               were classified into four types according to structure and
to urban areas by creating more employment on the farm.               location: farm ponds with closed outlets, farm pond with
698                                         P. Tipraqsa et al. / Agricultural Systems 94 (2007) 694–703




Fig. 1. Comparison of spatial characteristics of two representative farms of the IFS (left) and CFS (right). Note: IFS = Integrated farming system.
CFS = Commercial farming system.


Table 2                                                                        Synergies between enterprises in the integrated farms
Comparison of farm resource characteristics by farm type, means             included the feeding of crop residues to pigs, poultry, and
Characteristic                            IFS               CFS             fish; the feeding of solid waste from poultry to carnivorous
Family members (persons)                  5 (2.17)          4 (1.16)
                                                                            fish (catfish); and the application of solid waste from cattle,
Labour force (15–65 years, persons)       4 (1.49)          2 (0.53)        pigs, and poultry to vegetable and tree plots. The direct use
Persons employed on the farm              3 (1.51)          2 (0.74)        of animal waste was widely practised on all integrated
Persons employed off the farm              0 (1.06)          0 (0.00)        farms.
Persons not employed                      1 (1.30)          1 (0.52)           In the second part of the results, the hypotheses are
Distance to the local market (km)         5.5 (1.0)         5.6 (1.2)
Farm area (ha)                            3.86 (1.40)       2.73 (1.97)
                                                                            tested. Fig. 2 summarizes these results using a radar graph.
Sandy fraction in soil (proportion)       0.44 (0.22)       0.43 (0.21)     The figure compares the performance of IFS with that of
Cars (number)                             0.50 (0.53)       0.00 (0.52)     CFS according to the proxies introduced in Section 2. Each
Two-wheeled tractors (number)             0.38 (0.52)       0.88 (0.35)     value shows the performance of one system as a fraction of
Small water pumps (number)                0.88 (0.35)       0.25 (0.46)     the maximum performance of both systems. The relatively
Irrigation facilities (number)*           1.50 (0.53)       0.38 (0.52)
                                                                            large area between the two lines shows that the difference
Notes: Standard deviation in brackets.                                      between the farming systems is substantial. The graph
  *
    Includes farm ponds, wells, and irrigation canals. IFS = integrated
farming system. CFS = commercial farming system.



open outlets, wells, and off-farm water sources associated
with irrigation projects. All eight integrated farms together
had 16 ponds with closed outlets, but none of the commer-
cial farms had a pond of this type.
   The table also shows that the average farm size of the
integrated farms (3.86 ha) was larger than that of the com-
mercial farms (2.73 ha) and integrated farms also used
more labour than the commercial farms. There was no sig-
nificant difference in soil texture between the two farming
systems as this was most strongly determined by the loca-
tion in the catchment: the upper catchment was dominated
by light textured sandy soils while the lower catchment was
dominated by heavy textured soils, which may be associ-                     Fig. 2. Radar graph comparing the IFS with the CFS in eight aspects
ated with the redistribution of sediment (silt and clay)                    (Lightfoot et al., 1993; Pitcher and Preikshot, 2001; Dey et al., 2006). Note:
material between upper and lower catchment.                                 IFS = integrated farming system. CFS = commercial farming system.
                                               P. Tipraqsa et al. / Agricultural Systems 94 (2007) 694–703                                           699

Table 3
Descriptive statistics for differences between the integrated and commercial farming systems, with mean and medians, and statistical tests for significance
Variable                                                       Mean                                                   Median
                                                               IFS                  CFS                               IFS             CFS
                                                                                                         **                                            **
1.   Richness of food species (number)                         38 (10)              17 (2)                            41              18
                                                                                                         **                                            **
2.   Share of home produced food (%)                           68 (14)              33 (9)                            71.75           31.67
3.   Stem density (stem haÀ1)                                  463 (320)            35 (17)              **
                                                                                                                      497             34               **
                                                                                                         *
4.   Soil organic matter (%)                                   0.98 (0.73)          0.64 (0.43)                       0.69            0.51
                                                                                                         **                                            **
5.   Share of irrigation months (%)                            39 (4)               14 (19)                           38              0
6.   Land productivity (USD haÀ1)                              1449 (628)           1307 (548)                        1328            1354
7.   Labour productivity (USD man-dayÀ1)                       3.4 (0.52)           2.7 (0.24)                        2.87            2.57
                                                                                                         **                                            *
8.   Richness of species for social purposes (number)          35 (14)              18 (2)                            37              19
The significance level refers to a t-test for difference in means and a rank-sum test for difference in medians. IFS = integrated farming system.
CFS = commercial farming system.
 *
   P < 0.05.
**
   P < 0.01.



clearly shows that the IFS outperform the CFS in every                         average tree density of 35 stems haÀ1. There was no evi-
aspect although not significantly for land and labour pro-                      dence of seedling recruitment in between these scattered
ductivity. Table 3 compares the same indicators using                          trees due to annual cropping activities around the base of
means and medians and each is discussed in the following.                      the trees. The rank-sum and t-tests indicate that the median
                                                                               and mean of tree basal area and stem density were signifi-
3.1. Richness of food species                                                  cant greater for the IFS as compared with the CFS.
                                                                                  Three species are particularly useful for farm households
   Table 3 shows a significantly greater number of food                         as they are used for lopping, fodder, firewood, and
species in the IFS. This is confirmed by a positive correla-                    cutting for timber. The IVI reflects the dominance of
tion between integration level and the log of richness of                      Azadirachta indica (neem) on integrated farms and
food species (r = 0.091, p < 0.01).                                            Flacourtia indica (governor’s plum) on commercial farms.
                                                                               Azadirachta indica, which dominated on integrated farms,
3.2. Share of home-produced food                                               is a multipurpose tree species.
                                                                                  The analysis of the size classes with respect to the diam-
   The food expenditures were significantly greater for the                     eter at 1.3 m height showed that the trees on integrated
IFS (257 USD yearÀ1) than for the CFS (197 USD yearÀ1).                        farms had a higher number of stems but were mainly small
Yet if expressed in per capita terms then the food expendi-                    (73.3% were in the class of 0–10 cm diameter), whereas on
tures are about equal for both farming systems                                 the commercial farms trees were relatively fewer but with
(66 USD capitaÀ1 for the IFS and 73 USD capitaÀ1 for                           larger stems.
the CFS). The rank-sum and t-test are consistent and show                         The analysis of the distribution of tree stems in each
that the share of home-produced food is significantly                           class of crown social position showed that the middle sto-
greater for the IFS than for the CFS (IFS = 68% and                            ries (classes 3 and 4) of crown social position of trees on
CFS = 33%). This is further confirmed by a linear regres-                       integrated farms appeared to be denser. Most trees (60%)
sion between the log of share of home-produced food                            on commercial farms were in a higher social position class
and the integration level, which shows a positive coefficient                    and had a higher average height but lower stem density.
(r = 0.097, p < 0.01). Together with a greater number of
food species in the IFS, these results suggest that the IFS                    3.4. Soil organic matter
is more secure in the supply of food than the CFS.
                                                                                  The rank-sum and t-tests indicate that the median and
3.3. Tree growth                                                               mean values for soil organic matter were significantly
                                                                               greater on integrated farms than on commercial farms.
   The tree communities on integrated farms were com-                          The relationships between tree stem density as affected by
posed of three vertical layers: tree layer, shrub layer, and                   integration level; soil organic matter as affected by tree
ground cover. The height of the tree layer ranged from 4                       stem density; and soil organic matter as affected by integra-
to 7 m with an average density of 463 stems haÀ1 and a                         tion level were further explored. The results showed mutual
basal area of 0.62 m2 haÀ1. Trees were generally more                          correlations between SOM, integration level, and tree stem
evenly distributed on the commercial farms. This resulted                      density. The integration level on a farm showed a clear
in a more open tree cover dominated by relatively more                         positive correlation with stem density (r = 0.698,
mature trees. The tree height ranged from 10 to 13 m with                      p < 0.05). This suggests a greater number of trees available
stands having an average basal area of 0.06 m2 haÀ1 and an                     on the farm with increasing integration level. The analyses
700                                          P. Tipraqsa et al. / Agricultural Systems 94 (2007) 694–703

further showed a positive correlation between soil organic                    farms (rice: 2783 kg haÀ1 for the IFS and 2500 kg haÀ1 for
matter and stem density (r = 0.581, p < 0.05), and soil                       CFS; vegetables: 6617 for IFS and for CFS 2223 kg haÀ1:
organic matter and the integration level (r = 0.500,                          fruits: 1893 for IFS and 30 kg haÀ1 for CFS).
p < 0.05). These results suggest that soil organic matter                        It could be argued that the higher total output and crop
content on the farm is positively associated with an increas-                 yields at the IFS relate to their greater average farm size
ing integration level, though a more detailed analysis                        and labour supply rather than to the integration of
would be needed to test whether this is because of a greater                  resources. Multivariate regression was therefore used to
number of trees or because of other resource flows on the                      assess the impact of integrated farming on productivity
farm.                                                                         while statistically controlling for the effects of farm size,
                                                                              labour supply, and external input use. Four alternative
3.5. Share of months in dry season irrigated                                  regression models were used to check the robustness of
                                                                              the results. One model examines the total output as a func-
   The irrigation facilities provided water for about 39% of                  tion of arable farm land, labour supply, external input use,
the dry season (from October to April) on integrated farms,                   and integration level (Model A). The three other models
but only for 14% at commercial farms. The rank-sum and                        are modifications of the first one in which the total output
t-tests indicated that the median and mean number of irri-                    is either divided by the arable farm land (Model B) or the
gation months was significantly higher on integrated farms                     labour supply (Model C). Arable land and external input
than on commercial farms.                                                     use were separately included in the last model for these
   This difference is due to the establishment of farm ponds                   were highly correlated (Models C1 and C2).
on the IFS, which provided an average of three months of                         Parameter estimates and significance levels are presented
additional irrigation water. Irrigation canals existed on                     in Table 4 and the four models explored are specified below
both farm types and used water from the Chi River, which                      the table. The explained variance is high for all models, all
was distributed evenly over all farms. Wells were predom-                     signs are positive as expected, and all parameters are less
inantly used for domestic purposes, and only occasionally                     than unity – indicating diminishing returns to each input.
to irrigate the surrounding vegetable gardens.                                The most important observation is that the integration
   The number of irrigation months correlated positively                      level is positive and significant in each of the models. This
with the farm gross income and number of food species.                        clearly shows that additional synergies between enterprises
These results suggest that farms with ponds were more pro-                    improve total agricultural output, agricultural land pro-
ductive and had more alternative production activities, e.g.,                 ductivity, and agricultural labour productivity while con-
raising fish, and irrigating vegetable plots.                                  trolling for the effects of farm size, external input use,
                                                                              and labour use.
3.6. Agricultural productivity
                                                                              3.7. Richness of species used for social purposes
   The total output from the integrated farms (3480 USD
per farm) was significantly above that of the commercial                          The farms in the IFS used an average of 35 species for
farms (2006 USD per farm, p < 0.01). Concerning the yield                     social purposes while the farms in the CFS used 19 species
of individual crops, it was found that rice, vegetable and                    on average. The t-test and non-parametric tests (Table 3)
perennial yields were significantly greater on the integrated                  confirmed that this difference was significant.


Table 4
Multivariate regression results estimating the effect of resource integration on farm production and productivity
                                     Agricultural production                 Land productivity                     Labour productivity
                                     Model A                                 Model B                               Model C1              Model C2
Area (ha)                            0.167                                   –                                     0.499**               –
Labour (man-day)                     0.263*                                  0.123*                                –                     –
Inputs (USD)                         –                                       0.886**                               –                     0.873**
Integration level                    0.064**                                 0.033**                               0.043**               0.033**
Constant                             4.900**                                 1.159**                               3.286**               1.198**
R2                                   0.83                                    0.98                                  0.90                  0.99
F-value (significance)                19.26                                   164.98                                58.91                 512.03
                                     (P < 0.001)                             (P < 0.001)                           (P < 0.001)           (P < 0.001)
Model A: ln(Output) = ln(Area) + ln(Labour) + Integration Level.
Model B: ln(Output/area) = ln(Labour/area) + ln(Input/area) + Integration Level.
Model C1: ln(Output/labour) = ln(Area/Labour) + Integration Level.
Model C2: ln(Output/labour) = ln(Inputs/Labour) + Integration Level.
 *
   P < 0.05.
**
   P < 0.01.
                                       P. Tipraqsa et al. / Agricultural Systems 94 (2007) 694–703                                701

4. Discussion                                                          high cost of purchased inputs such as animals and seeds.
                                                                       External inputs are therefore a necessary ingredient in a
    This study suggested that the framework of multifunc-              regenerative agriculture. This finding does not support
tional agriculture is useful as it allows the assessment of            the notion of eliminating external inputs in the IFS.
the performance of both economic returns and ecosystem                    Because of high start-up costs, commercial farms might
services in farming systems. The idea behind integrated                be constrained in their decision to switch to integrated
farming is to provide multiple benefits to the farm house-              farming. Many farms already have high levels of debt
holds and hence only looking at economic returns or crop               and large new loans might not only be difficult to get but
yields would not be enough.                                            also bring substantial risk to the farm households.
    The study relied on a purposive sample of eight inte-                 One of the reasons stated by farmers for adopting inte-
grated farms that were matched with eight adjacent com-                grated farming is to create an opportunity for their chil-
mercial farms. Each farm was visited about seven times                 dren to continue working on the farm; farmers also
while soils, vegetation, and resource flows were studied                suggested this as an economic indicator for the study
intensively. One alternative would have been to collect less           (see Section 2.3). Enterprises such as vegetable growing,
in-depth information but to include all 21 integrated farms            fish ponds and animal herding are relatively labour inten-
in the catchment or to take a large stratified random sam-              sive. This is also one reason why integrated farms do not
ple from a larger area in Northeast Thailand. We believe,              use as much mechanisation as their commercial counter-
however, that the high quality of data provided by the                 parts (Table 2): a tractor would reduce the need for their
small purposive sample outweighs the gains of a large ran-             children to work on the farm. A decline in agricultural
dom sample with unknown data errors, though the optimal                labour – as is commonly reported for Thai agriculture
balance between sample size and data quality is of course              (Lightfoot et al., 1983; Hussain and Doane, 1995; Chalam-
debatable.                                                             wong, 2001) – could, however, prevent farmers from
    One difficulty in applying this conceptual framework is a            exploiting the benefits from resource integration, as some
possible bias in the selection of appropriate indicators for           labour-intensive enterprises, such as animal herding, might
different dimensions. While the study chose to look at                  have to be abandoned when labour becomes scarce. The
enhancing food security in terms of amounts and richness               alternative would be for integrated farms to adopt more
of species used for food and the share of home-produced                mechanisation; although this would contradict the idea
food, it considered neither the nutritional status of individ-         of self-sufficiency, the analysis has shown that integrated
ual household members nor food collected from the vicin-               farms already use as many external inputs as commercial
ity of the farm, such as products from the communal native             farms do.
forest, which also influences food consumption. Neverthe-
less, it may be hypothesised that household members of                 5. Conclusion
IFS are better nourished because of a more diverse diet,
all other things being equal. Similarly, there is little reason            The study showed that the assessment of integrated
to believe that food collected from the vicinity would bias            farming systems can be expanded to include food security,
the results since the farms are located in the same areas              environmental, and social functions in addition to the eco-
and all households have equal access to the communal                   nomic functions that usually get most attention. The
areas.                                                                 framework of multifunctional agriculture offers a useful
    The study considered the practice of resource integra-             tool for studying these multiple functions in a country such
tion as the main indicator distinguishing integrated from              as Thailand.
non-integrated agriculture. For each farm, the level of inte-              Based on a carefully selected sample of eight integrated
gration was calculated by counting the synergies between               and eight commercial farms this study showed that the
enterprises on the farm. This simple count gives equal                 integrated farming system outperforms the commercial
weight to all enterprises, though they are not all equally             farming system in all four dimensions of a multifunctional
important to the farm household. We therefore suggest                  agriculture: it gives a more secure supply of food, it
improving this measure by applying weights to each enter-              improves the resource base, creates higher economic
prise; for instance, by multiplying each count by the enter-           returns, and better matches the social needs of agriculture
prise’s proportion in the on-farm labour use, or by its                as a supplier of materials for food, medicines, local rituals,
proportion in the farm gross margin. This could not be                 tools, and shading.
done in the present study as such detailed data at the enter-              Past development efforts in the Northeast aimed at
prise level had not been collected.                                    increasing food production by introducing high yielding
    The practice of integrated farming enables the farm                varieties of rice in combination with mineral fertilizers.
households in this study area to increase agricultural pro-            The results of this study do not support such policy but
duction while not depleting their natural resource base.               rather suggest a two-pronged strategy of diversification
However, during the interviews farmers explained that                  and resource integration. A reduction in the use of external
the success of the IFS relates to a high start-up cost for             inputs, as suggested by the King’s Policy of Self Sufficiency
the establishment of ponds, landscape levelling, and the               is, however, not supported by our findings.
702                                          P. Tipraqsa et al. / Agricultural Systems 94 (2007) 694–703

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