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Agricultural Systems 64 (2000) 137±149
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Use of a land cover model to identify farm types
in the Misiones agrarian frontier (Argentina)
I. Duvernoy
INRA/SAD, BP 27, 31326 Castanet-Tolosan cedex, France
Received 29 January 1999; received in revised form 25 November 1999; accepted 31 March 2000

Abstract
So far there exists no adequate method to identify quickly the farms of a given area and
the type to which they belong in order to assess the respective proportion and distribution of
each farm type. This study was undertaken to determine whether the land cover of a farm
can be an indicator of its type. In a pioneer settlement area in the Misiones Province
(Argentina), four farm types were identi®ed. The land cover characteristics of each farm were
assessed by intersecting the classi®ed SPOT images with the cadastral maps. A ®rst sample of
77 farms was used to build a model which predicts the more probable type of farm knowing
the cleared and the grassland areas of the farm. The model accuracy was tested on a second
sample (81 farms). In 64% of cases, it correctly identi®es the type of farm. In 79% of the
remaining cases, confusion occurs between highly similar types. The model was then used to
classify 949 farms in the four types in this pioneer settlement. # 2000 Elsevier Science Ltd.

All rights reserved.
Keywords: Land use; Land cover; Farm diversity; Farm typology; Agrarian frontier

1. Introduction
1.1. Diversity of farming, diversity of issues
Farming diversity is a crucial aspect of several issues in rural development and
land management. Not all farms in an area produce the same crops, nor do they
apply the same practices. They do not generate the same income levels nor do
they have the same life expectancy. This diversity of farming has long been identi®ed
as a problem for conceiving and implementing development intervention by agricultural organisations and extension services (see for instance the ``recommendation
domains'' method, Collinson, 1987). Farming is one of the main activities which use
0308-521X/00/$ - see front matter # 2000 Elsevier Science Ltd. All rights reserved.
PII: S0308-521X(00)00019-6

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I. Duvernoy / Agricultural Systems 64 (2000) 137±149

and shape landscapes. As such, farming is often the focus of environmental concern,
both for its negative and its positive by-products: water pollution, soil erosion or soil

protection, land degradation or landscape conservation, rainforest disappearance,
etc. The impact of farming on the environment varies greatly, however, with the
kind of agricultural production, and the farming practices, as often claimed by
organic farmers.
1.2. Assessing the diversity of farming systems: farm typologies, land cover maps,
and beyond
There are at present two common ways to assess the diversity of farming systems
in an area: farm typologies and land cover characterisation (e.g. Capillon, 1986;
Dwivedi and Ravi Sankar, 1991).
Farm typologies are a means of categorisation, which enables us to organise reality
from a point of view relevant to the objectives of the study being undertaken. The
approach seeks a coherence within farm data in order to study and represent farming
complexity from this particular point of view. There is no universal formula for elaborating multiple-end farm typologies, as far as the selection of variables and determination of their hierarchy is concerned, as these should be adapted to the questions
guiding the researcher or the agricultural extension expert. Nevertheless, some general methodological principles apply. First, one must delimit the area for which the
typology is valid. The typology, thus, will represent the diversity of farms in that area.
Next, a typology is based on a sample of farms. The sampling may be statistical, based
on geographical strati®cation or, on the contrary, on known farms which are selected
because they are assumed to be representative of the farming diversity of the area
considered. Data on these sample farms are collected through surveys or direct interviewing of the farmers. Farm types are inferred from the sample farms' characteristics, generally by multivariate analysis and clustering techniques.
But how does one assess whether the proportion of each farm type is the same in

the whole population as in the sample? Or whether the farm types are homogeneously distributed in space? And how does one identify farm types over large
areas and locate them? Typologies are usually based on diverse and precise data
obtained by in-depth interviews. Such precision cannot be achieved for large
regions. National farm surveys are sometimes used to generalise farm typologies
(Capillon et al., 1975), but they are scarce, lack precision in terms of farm location,
and do not always contain the relevant variables for the speci®c study.
Monitoring land cover Ð ``the layer of soil and biomass, in particular vegetation,
which covers the surface of the earth'' (Fresco et al., 1994, p. 3) Ð is often used in a
complementary way, as its main features ®ll these gaps: it is exhaustive and its
results are georeferenced. But although land cover in rural areas is clearly a result of
human land use (de®ned as ``the combined human action a€ecting land cover'',
Fresco et al., 1994, p. 3), the same kind of land cover can be produced by di€erent
kinds of farms, with di€erent dynamics, inducing sometimes wide di€erences in their
relations with the environment, and in their resistance to change. As a result, this
severely limits the use which can be made of such land cover maps.

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139

In practice, once an issue (such as cereal over-production or the adverse e€ects of
shifting cultivation on soil) has been identi®ed, land cover maps are drawn in order
to quantify the problem and locate its main areas of occurrence, and follow its
evolution through time (e.g. Jewell, 1989; Dwivedi and Ravi Sankar, 1991). But as
such maps do not provide the necessary information on the kind of human activities
and systems which have an impact on the landscape, that information usually must
be collected separately and then combined with the land cover information. And
information on human activities is mainly available only at larger scales, such as
district surveys.
This suggests that it is worthwhile trying a third novel approach, which combines
the two above ones and in which the land cover information is used to classify all the
farms of a given area, according to an existing farm typology. The rest of this paper
is devoted to an attempt to do so.

2. From land cover types to farm types
2.1. A translation process in three steps
Land cover could help identify farm types only if a correspondence between both
of them is found (De€ontaines et al., 1995). We propose to represent the search for
such correspondence as a three-step translation process. The ®rst and second steps
aim to check whether each farm type presents a speci®c land use, and whether land

cover monitoring is able to identify it. In order to use land cover as an indicator of
farm types, the portion of space used by each farm needs to be identi®ed (third step).
The major step is the ®rst one: to establish whether each type of farm practises a
distinct kind of land use. Clearly, if the answer is no, there is no need to go beyond
this ®rst step. Even though farm classi®cations are speci®cally designed for the problem they study or help resolve, the majority of them take the production system
into consideration, as it has a clear in¯uence on the farm's economy and its impact
on the environment. Hence, except in the case of o€-soil production systems, there is
a good chance that aspects of land use practices di€er from one type of farm to
another, consequent upon di€erences in the size of the farms or the plots, variations
in plot con®guration, di€erent cropping or grazing systems, etc. But the correspondence will not be perfect, as di€erent types of farms can also share land use characteristics (e.g. because they produce the same crops, are of the same size, or found
in the same kind of places).
The second step involves the use of a land cover observation tool such as remote
sensing imagery or aerial photography, in order to get exhaustive information on the
land cover generated by the farms of the whole area. The objective of this step consists of checking the ability of these tools to discriminate the land use or the combination of land uses speci®c of each type of farm. Depending on the spatial resolution
and spectral characteristics of these tools, the objects they will discriminate are different. This discrimination is based on the objects' radiometric behaviour (absorption and re¯ection of electromagnetic radiation), which may vary greatly for the

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same land use, for example according to slope and solar radiation received, and may
be quite similar for very distinct land uses. For instance, a plot of land considered by
a farmer as a pasture may look like shrubland if he allowed some forest regrowth in
order to procure shade for his animals.
The third step is to know how to identify the portion of space used by a speci®c
farm in order to identify its type by land cover identi®cation. In most cases, there is
no easy solution to this problem, as cadastral maps only record land ownership and
this may di€er greatly from the parcels used by a farm. Also, the parcelling of
farmlands may be so great that its identi®cation will be too time consuming. Land
cover will be accessible only at an aggregate level, corresponding to several farms.
The overall model of correspondence between farm types and farm land cover will
thus involve a change of scale as well as a change of observed phenomena: from
classi®ed farm characteristics in term of activities, production, etc., to regional land
cover characteristics produced by combining several farms belonging to distinct
types. This implies taking into account several problems linked with changes of
scale: a change of variance in the data (Meentemeyer, 1989), a change in the link
between phenomena (Veldkamp and Fresco, 1997), and the complexity of disaggregation procedures.
To demonstrate that land cover can be used as a tool for farm type identi®cation, we restricted the study to a case where the land cover of each farm had been
identi®ed.
2.2. Testing the land-cover indicator

The validity domain of the indicator (i.e. the domain, both in space and time,
where the indicator could be used to identify farm types) will depend on both the
validity domain of the typology and the extent of the domain used to construct
the indicator.
As typologies are comparative procedures of categorisation, their validity does not
extend beyond the domain in which they have been constructed, unless their generality is assessed by other procedures. For instance, a typology of farms of a region
A, may be of little use to describe properly the farms of a region B, unless the two
regions are known to share the same characteristics from the point of view chosen to
classify the farms. If the farm typology should be irrelevant beyond its own domain of
construction, then its indicators will lack relevance as well, because they will be unable
to identify new types of farms or, worse, because they will falsely classify farms.
The validity domain of the indicator will also depend on the extent of the domain
used to construct it, i.e. the variability of the farms' land cover taken into account
when constructing it. As farm types are generally broad classes, incorporating a
degree of variation, the land cover of the farms belonging to the type will also
experience some variation. If it is to separate farm types, the extremes of intra-class
variation should be taken into account when building the indicator, otherwise it
may only be able to distinguish the farms used to construct it. That is a good reason
to test the discriminatory power of the indicator on a set of farms other than the
sample used to build it.

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141

Evaluation of the accuracy of the land cover indicator could be of two distinct
kinds: a causal validation and a statistical one. The causal validation is the evaluation of the theoretical consistency between the indicator and the type of farm it is
meant to indicate. This is necessary to avoid constructing a mimetic model (Bourdieu et al., 1983; see also Hard, 1988, for some examples of mimetic models) but it is
insucient, as such coherence usually re¯ects the choices that prevailed in its construction. The statistical procedure of validation consists of checking its ability to
discriminate between types without error.

3. A pioneer settlement area as case study: farming issues, farming types and
methodological features
The study area is an agrarian frontier in the San Pedro district of the Misiones
Province (northeast of Argentina). It is a recent settlement, dating from a few to 20
years back. In this rainforest, a thousand small farmers have settled, encroaching
onto public land. Some of these farmers are descended from the European-born
immigrants who were settled at the beginning of the century in the colonisation
schemes in the south of the province. Others are landless farmers coming from the
neighbouring Brazilian States. After 1983 (the return of democracy), the Provincial

Government progressively regularised this settlement, by way of occupation allowances and property titles. The occupation allowance is an ocial recognition of the
right of a family to use a delimited portion of public land. This was only possible
after farm limits were recorded in the cadastral register, still in progress for several
localities of the region in 1991/1992 (the date of the ®eld study). In most of the other
cases, a draft of the cadastral map existed. Most of the farms consisted of a single
plot of an average size of 25 ha, the limits of which appear on the cadastral maps.
The ability to become land-owner on these fertile soils, then attracted the richer
farmers from the south of the Province, who favoured perennial cash-cropping (tea,
tung and mate), and thus contrasted with the former occupants, who mainly practised self-subsistence agriculture, except for the growing of some tobacco. The
change in land tenure rules created, therefore, a turn-over in occupation: unable to
a€ord the price of the occupation allowances, the former occupants sold their plots
to the new-comers. The added value of the land was then used to pay for a new
settlement, in better conditions, further away. This turn-over is a general pattern in
agrarian frontiers and is one of the main features in frontier extension and evolution. It is not in itself a sign of the unsustainability of farming, but of the evolution
of farms in space as well as in time, using land speculation at some moment of their
trajectory to gain capital for a better farm (LeÂna, 1992). It supposes a di€erential in
the pioneer area, in terms of land tenure legislation and infrastructure, with areas
free of colonisation, and areas where the frontier is already consolidated, where an
informal market in farmland can develop.
The heterogeneity of the San Pedro pioneer area was described according to an

existing model of agrarian frontier dynamic (Reboratti, 1979; Coy, 1996). On the
basis of land tenure regulations, infrastructure development and intensity of wood

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clearing, three stages in the evolution of this pioneer settlement were outlined:
expansionary, intermediate and consolidated stages. Far from the main road, the
frontier is still expanding, the colonisation is as recent as its legislation, and the
forest still covers the majority of the area. Close to the main road and the villages,
the frontier is consolidated; colonisation began in the 1970s, free settlement was
allowed for several years, the percentage of occupation is high, and the forest area is
small. In between, the frontier is at an intermediary stage. This frontier is formed by
distinct localities, which have been identi®ed and spatially circumscribed by social
inquiries on place names, social networks and associations for land registration.
Each locality of the San Pedro agrarian frontier was then classi®ed according to the
above three stages.
In Misiones, the encroachment on private land has now started. This illustrates
the scarcity of unoccupied public forest. A key issue will then be the ability of the

farmers to switch from an illegal, mining occupation to a legal, perennial crop-based
agriculture. The sustainability of farming in this area depends on this ability. The
typology was built on the basis of a comparison of 120 farms in several localities,
chosen to re¯ect the three stages of the frontier. One of these areas has been under
investigation for several years (Albaladejo, 1987). Ten non-redundant variables were
chosen for their contribution to farm stability (the ability to resist a perturbation)
and to the farm's ability to evolve (e.g. to change its production system). These
variables re¯ected the di€erent productions and also the diversity in incomes, in land
holdings and in the labour force of the farm. Factor analysis, followed by hierarchical classi®cation, disclosed four main types of farms. Type 1 farms are small,
with a limited crop area and almost no animal husbandry or cash crops. This type
groups newly settled farms but also farms of the poorest families. Type 2 farms differ from the ®rst ones by their cropping area, the more common presence of cash
crops, mainly tobacco, and the occurrence of occupation allowance. In type 3 farms,
the production system is more diversi®ed, annual as well as perennial cash crops, and
animal husbandry are well developed, and the total farm area is large. Type 4 farms
di€er from the latter in the extent of perennial cropping and of animal husbandry.
Half of the farmers own their farm, and the plot area is sucient to allow the necessary rotation of crops and the settlement of the farmers' children as farmers. (For a
more detailed description of this typology see Albaladejo and Duvernoy, 1997.)
Land use di€ers greatly on these farms in terms of the cropping system and the
number of hectares under cultivation (Step 1). Type 1 farms are either recent settlements, with little cleared forest, or poor farms, where lack of tools, labour force (no
animals for land clearing or ploughing), and illegal occupancy heavily compromise
the ability to extend the cultivated area. Type 4 farms have generally been owned for
several years. Their cropping system is diversi®ed, involving several perennial cash
crops, as well as annuals and a large number of livestock (draught animals and dairy
cows). Cattle is a common way of accumulating wealth and is a symbol of status on
this agrarian frontier (Albaladejo, 1987). Farm size increases from Type 1 to Type 4
(Table 1).
The objective of the work presented in this paper was therefore to use land-cover
maps of the area to recognise these four farms types, in a situation where potentially

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Table 1
Average value of cropping areas, grassland area and number of cattle according to farm typea
Farm type

Cleared land (ha)
Grassland (ha)
Perennial cash-crops (ha)
Tobacco (ha)
Cattle (number)

1

2

3

4

7.6
0.2
0.3
0.0
0.1

10.3
0.6
1.5
0.3
2.3

13.3
1.9
3.0
0.9
5.5

26.0
2.7
6.7
0.3
5.8

a
Calculated on the sample of 120 farms used to build the typology. The cleared land represents the
overall surface of forest clearance for cultivation. It is the sum of the cropping area plus the bush area
(fallow land) plus the grassland area. The perennial cash-crop area is the group of parcels cultivated with
tea, mate (Ilex paraguensis) or tung (Aleuritis fordii ). The number of cattle is the number of adult animals,
including draught oxen.

all farms boundaries were known. In this region censuses are scarce and experts,
such as development agents, know only the more accessible farmers, located close to
roads, who are usually also the richer ones. The ability to depict all the farms in such
an area could therefore be useful to extend the ``clientele'' of the development service. (Olivier de Sardan, 1995).

4. Materials and methods
In order to classify the 949 farms of the study area into the above four farm types,
we constructed two control samples of, respectively, 77 and 81 farms. The farm
types in the control samples were known: most of them formed part of the 120 farms
used to construct the farm typology; the remainder were later classi®ed by projection as extra individuals in the factor analysis. The control samples were selected
according to two principles: (1) the presence of a signi®cant representation of each
farm type; and (2) the representativeness of the farms in terms of the agrarian frontier's evolutionary stages. The ®rst control sample was used to examine links
between farm type and land cover. This sample contained 13 Type 1 farms, 18 Type
2 farms, 23 Type 3 farms, and 23 Type 4 farms. The second control sample was used
to estimate the correspondence accuracy of this farm typology/land cover association. It contained 14 Type 1 farms, 21 Type 2 farms, 22 Type 3 farms, and 24 Type 4
farms.
A land cover map of the whole area was drawn up by supervised classi®cation of
P+XS SPOT images of winter 1991 and summer 1992 (Bonn and Rochon, 1993).
(Step 2). The principal land cover classes were: natural rainforest, pine forest,
grassland, bushes (two classes), perennial crops (two classes: tea and tung), annuals
and mate crop and wood clearing. The accuracy of this classi®cation was assessed
following Congalton's (1991) method. The overall accuracy of the classi®cation was
81%. Omission and commission errors di€ered greatly from one class to another:

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bushes and perennial crops were poorly identi®ed. The omission and commission
errors were inferior to 30%, and generally less than 20%, for the natural rainforest,
pine forest, grassland and annual and mate crop classes.
The land cover of each farm was identi®ed by intersecting the land cover map with
the cadastral map of the area which registered the farm boundaries (Step 3). For
each farm, eight variables were used to describe its land cover: farm size, forest area,
wood-cleared area, cultivated area (in hectares and in percentage of the total farm
size), tea and tung area, bush area, annual and mate crop area and grassland area.
The wood-cleared area represented all land which had been cleared for cultivation.
It was calculated by summing the cultivated area and the bush area (fallow land).
The correspondence model between farm types and land cover was obtained by
tree-based modelling of the ®rst control data. This classi®cation and regression tree
(CART) was implemented in S+ (Clark and Pregibon, 1991). This is an exploratory
technique for uncovering structure within data. By providing a set of predictor
variables xi and a variable to be predicted y (which could be quantitative or qualitative), the model builds a dichotomic classi®cation tree, i.e. a successive partition of
the data into homogeneous subsets for y. At each node of the tree, the predictor
providing the best subset of the data is selected. At the end of the tree-growing
process, this technique provides a set of ordered rules to determine the most probable y value, conditional to xi values. In this case study, the xi predictor variables
were the variables describing the farm land-cover, and the y variable to be predicted
was the farm type. This technique is strongly dependent on the data set. In order to
increase its robustness, two criteria were applied: (1) for a similar error rate, selecting the xi variables most representative of the type of farm it was supposed to predict; and (2) using only the ®rst nodes of the tree since the last ones are based on few
data, and are more like individual ®tting than class ®tting.
The accuracy rate for the ®rst data set was calculated and expressed as the percentage of well-classi®ed farms.
The accuracy of the tree-based prediction was tested on the second control sample. For each of the 81 farms, the land cover characteristics were used to predict the
most probable farm type. The latter was then compared with the known farm type,
and the accuracy rate calculated for this new data set. The correlation between the
farm types and the predicted farm types was calculated using Spearman's rank correlation coecient (Snedecor and Cochran, 1971).
This model was then used to predict the probable farm type of all the farms in 15
localities of the study area. The results were expressed as the proportion of the four
types of farm in each locality.

5. Results
5.1. The correspondence model
The tree-based prediction model was based on a few simple rules, involving only
two land cover classes: the wood-cleared area and the grassland area of the farm

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145

(Fig. 1). These two classes were consistently well di€erentiated by remote sensing
techniques.
This tree delineated a hierarchy of areas from Type 1 farms to Type 4 farms,
according to which, Type 1 farms were the smallest in terms of both cleared area and
pasture land. At the opposite end of the scale, Type 4 farms had a large area under
cultivation and sizeable pasture. Type 2 and Type 3 farms were intermediate stages
in terms of cultivated area and pasture land. These discrimination rules were coherent with the characteristics of the farm typology (Table 1).
The overall discrimination accuracy of the model was low: only 66% of the farms
in the ®rst control sample (used to build the tree-based model for prediction) were
correctly identi®ed.
5.2. The accuracy estimation
Using the second control sample, the tree-based prediction classi®ed 64% of the
farms correctly (Table 2). Seventy-nine per cent of the errors in farm type prediction
occurred between closely similar types. Spearman's coecient of correlation
between the predicted type and the known type was highly signi®cant with 0.78.
5.3. The generalisation: identi®cation of all the farms' type
This tree-based model for prediction was used to identify the type of each farm in
15 localities of the agrarian frontier studied (Fig. 2). Not all the localities were used,
as a cadastral map or cadastral draft did not exist for some of the localities, and in
some other cases no peasant families had permanently settled down (e.g. very steep
areas, or areas where the farming lots were bought for forest speculation).

Fig. 1. Tree-based model for prediction of the farm type conditional upon knowing two of its land cover
variables. Clear, cleared area; grass, grassland area; 1, 2, 3 or 4, most probable type of farm.

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Table 2
Evaluation of the accuracy of farm type prediction using the tree in Fig. 1a
Predicted farm types

Known farm types

Total

1

2

3

4

1
2
3
4

9
2
2
1

1
12
5
3

0
4
10
8

0
0
3
21

10
18
20
33

Total

14

21

22

24

81

a

Results expressed in number of farms.

Fig. 2. The proportion of each farm type in 15 localities of the agrarian frontier of San Pedro. Farm types
were identi®ed by the tree-based model illustrated in Fig. 1. The boundaries of the main localities are also
represented.

6. Discussion
In this case study, the tree-based model used to predict farm types was very simple. It relied only on two land cover variables, easily and accurately recognisable by
remote sensing techniques. These two land cover variables were also strongly related to farm evolution in this agrarian frontier. The accuracy of the prediction was

I. Duvernoy / Agricultural Systems 64 (2000) 137±149

147

quite good. It was similar in the two samples of farms; the sample used to build the
prediction model and the sample used to test it. However, its reliability was not
very good: only two out of three farms were correctly assigned to their proper type.
This could be due to the coarseness of the land cover indicators used, and to the
internal variability of land use characteristics in each farm type. Since misclassi®cation occurred mostly between neighbouring types, we believe that this
prediction model could, nevertheless, be useful for the recognition of farm types in
this frontier zone. The underlying assumption is that the farm typology described a
potential farm evolution process. A Type 3 farm could be considered to be an
intermediate stage between a Type 2 farm and a Type 4 farm. As the transition
from one stage to another is progressive, we could have observed intermediate
stages. This assumption was con®rmed by interviews with the farmers, which discussed family biography and farm history. Nevertheless, the ability of a farm to
change from one stage to another varied from farm to farm, with settlement
conditions (i.e. agrarian legislation, infrastructure) being a particularly important
contributing factor.
The pro®le of the farm types was consistent with the agrarian frontier characteristics of the localities. Type 1 and 2 farms were predominant in localities where
pioneer settlement was the youngest or where land tenure and infrastructure had
been delayed for a long time. Type 3 and 4 farms were in general predominant in the
oldest localities, situated close to the main road, where settlement had been authorised for several years.
Interestingly, there were di€erences between localities where the agrarian frontier
was supposed to be at the same stage of evolution. These di€erences could be related
to the history of the localities, which suggested that the farms had not experienced
the same evolutionary trajectory. For instance, the di€erence in farm pro®les
between the locality of San Jorge and that of el Paraje Lujan could be related to the
land tenure legalisation, which was concurrently implemented in the two localities
but did not take the same form.
The results of this research could be used to focus development projects on speci®c
localities according to the funding priorities. This tool is quite unique since farm
surveys in Misiones are scarce, and are not available at a geographic scale ®ner than
the departmental one, even though the settlement rate is high.
Apart from the relevance of this work for describing pioneer farm types, it suggests that land cover might provide an useful indicator of farm types. As stated in
the Sections 1 and 2, and illustrated in the Section 3, this assumption strongly
depends on the relationship between farm typology and land use characteristics and
on the land use contrast between farm types.
In this case study, it was possible to depict the land cover of each farm; this
enabled us to build the predictive model at the scale of the farm, even if the results
were expressed at a more aggregate level, that of the locality. In order to construct
such an indicator when the farms' land cover cannot be easily described, the corresponding model should be partly constructed at an aggregate level. Its reliability should then be statistically evaluated at that level, which would involve a large
data set.

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Acknowledgements
The author wishes to thank the French Ministry of Research and Technology for
®nancial support, Eileen O'Rourke, Sander van der Leeuw, Christophe Albaladejo,
Alain Langlet, Laurence de Bonneval for helpful comments on the manuscript draft
and Robert Faivre and Annick Moisan for their assistance in statistical methods.
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