Directory UMM :Data Elmu:jurnal:A:Agricultural Systems:Vol64.Issue3.Jun2000:

(1)

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 agri-cultural organisations and extension services (see for instance the ``recommendation domains'' method, Collinson, 1987). Farming is one of the main activities which use

(2)

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 ela-borating multiple-end farm typologies, as far as the selection of variables and deter-mination of their hierarchy is concerned, as these should be adapted to the questions guiding the researcher or the agricultural extension expert. Nevertheless, some gen-eral 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 inter-viewing of the farmers. Farm types are inferred from the sample farms' character-istics, 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 homo-geneously 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.

(3)

In practice, once an issue (such as cereal over-production or the adverse eects 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 (Deontaines 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 pro-blem 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 dier from one type of farm to another, consequent upon dierences in the size of the farms or the plots, variations in plot con®guration, dierent cropping or grazing systems, etc. But the correspon-dence will not be perfect, as dierent types of farms can also share land use char-acteristics (e.g. because they produce the same crops, are of the same size, or found in the same kind of places).

(4)

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 dier 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 dis-aggregation procedures.

To demonstrate that land cover can be used as a tool for farm type identi®ca-tion, 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 gen-erality 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.

(5)

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 evalua-tion 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 (Bour-dieu et al., 1983; see also Hard, 1988, for some examples of mimetic models) but it is insucient, as such coherence usually re¯ects the choices that prevailed in its con-struction. 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 allow-ances and property titles. The occupation allowance is an ocial 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 prac-tised 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 aord 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 evolu-tion. 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 dierential 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.

(6)

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 dierent productions and also the diversity in incomes, in land holdings and in the labour force of the farm. Factor analysis, followed by hier-archical 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 dif-fer 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 dier 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 sucient to allow the neces-sary 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 diers 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 settle-ments, 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).

(7)

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 ser-vice. (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 projec-tion 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 fron-tier'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 associa-tion. 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 diered greatly from one class to another: Table 1

Average value of cropping areas, grassland area and number of cattle according to farm typea

Farm type

1 2 3 4

Cleared land (ha) 7.6 10.3 13.3 26.0

Grassland (ha) 0.2 0.6 1.9 2.7

Perennial cash-crops (ha) 0.3 1.5 3.0 6.7

Tobacco (ha) 0.0 0.3 0.9 0.3

Cattle (number) 0.1 2.3 5.5 5.8

a _{Calculated on the sample of 120 farms used to build the typology. The cleared land represents the}

(8)

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 variablesxiand a variable to be predicted y(which could be quantitative or quali-tative), 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 prob-able y value, conditional toxi values. In this case study, the xi predictor variables were the variables describing the farm land-cover, and theyvariable 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, select-ing thexivariables most representative of the type of farm it was supposed to pre-dict; 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 per-centage of well-classi®ed farms.

The accuracy of the tree-based prediction was tested on the second control sam-ple. 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 cor-relation coecient (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

(9)

(Fig. 1). These two classes were consistently well dierentiated 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 coher-ent 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 coecient 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).

(10)

6. Discussion

In this case study, the tree-based model used to predict farm types was very sim-ple. It relied only on two land cover variables, easily and accurately recognisable by remote sensing techniques. These two land cover variables were also strongly rela-ted to farm evolution in this agrarian frontier. The accuracy of the prediction was 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 9 1 0 0 10

2 2 12 4 0 18

3 2 5 10 3 20

4 1 3 8 21 33

Total 14 21 22 24 81

a _{Results expressed in number of farms.}

(11)

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 mis-classi®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 dis-cussed 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 character-istics 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 author-ised for several years.

Interestingly, there were dierences between localities where the agrarian frontier was supposed to be at the same stage of evolution. These dierences could be related to the history of the localities, which suggested that the farms had not experienced the same evolutionary trajectory. For instance, the dierence 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 sug-gests 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.

(12)

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.

References

Albaladejo, C., 1987. AmeÂnagement de l'espace rural et activiteÂs d'eÂlevage dans des reÂgions de petites exploitations agricoles. L'exemple des CeÂvennes-Sud en France et de la Province de Misiones en Agentine, PhD thesis, UniversiteÂ de Grenoble I, France.

Albaladejo, C., Duvernoy, I., 1997. La durabiliteÂ des exploitations agricoles de fronts pionniers vue comme une capaciteÂ d'eÂvolution. In: JourneÂes du Programme Environnement-Vie-SocieÂteÂ `les Temps de l'Environnement'. Toulouse, France, 5±7 November, pp. 203±210.

Bonn, F., Rochon, G., 1993. PreÂcis de TeÂleÂdeÂtection. Principes et MeÂthodes. Presses de l'UniversiteÂ du QueÂbec, QueÂbec.

Bourdieu, P., Chamboredon, J.-C., Passeron, J.-C., 1983. Le MeÂtier de Sociologue. Mouton, Paris, France.

Capillon, A., 1986. A classi®cation of farming systems, preliminary to an extension program. In: Butler, Flora C., Tomecek, M. (Eds.), Farming Systems Research and extension; Management and Metho-dology. Kansas State University, USA, pp. 219±235.

Capillon, A., SeÂbillotte, M., Thierry, J., 1975. Evolution des Exploitations d'une Petite ReÂgion Elabora-tion d'une MeÂthode d'Etude. Institut NaElabora-tional Agronomique, Paris, France.

Clark, L.A., Pregibon, D., 1991. Tree-based models.. In: Chambers, J.M., Hastie, T.J. (Eds.), Statistical Models in S. Wadsworth and Brooks/Cole, Paci®c Grove, USA, pp. 377±419.

Collinson, M.P., 1987. Farming systems research: procedures for technology development. Experimental Agriculture 23, 365±386.

Congalton, R.G., 1991. A review of assessing the accuracy of classi®cations of remotely sensed data. Remote Sensing of Environment 37, 35±46.

Coy, M., 1996. DieÂrenciation et transformation de l'espace au nord du Mato Grosso. Contribution aÁ un modeÁle dynamique des fronts pionniers en Amazonie breÂsilienne. In: Albaladejo, C., Tulet, J.C. (Eds.), Les Fronts Pionniers de l'Amazonie BreÂsilienne. L'Harmattan, Paris, France, pp. 103±128.

Deontaines, J.P., Thenail, C., Baudry, J., 1995. Agricultural systems and landscape patterns: how can we build a relationship? Landscape and Urban Planning 31, 3±10.

Dwivedi, R.S., Ravi Sankar, T., 1991. Monitoring shifting cultivation using space-borne multispectral and multitemporal data. International Journal of Remote Sensing 12 (3), 427±433.

Fresco, L.O., Stroosnijder, L., Bouma, J., van Keulen, H. (Eds.), 1994. The Future of the Land. Mobi-lising and Integrating Knowledge for Land Use Options. Wiley, Chichester, UK.

Hard, G., 1988. Storks and Children, Orchids and the Sun. Walter de Gruyter and Co, Berlin.

Jewell, N., 1989. An evaluation of multi-date SPOT data for agriculture and land use mapping in United Kingdom. International Journal of Remote Sensing 10 (6), 939±951.

LeÂna, P., 1992. Trajectoires sociales, mobiliteÂ patiale et accumulation paysanne en Amazonie breÂsilienne: un exemple en RondoÃnia. Les Cahiers des Sciences Humaines 28 (2), 209±234.

Meentemeyer, V., 1989. Geographical perspectives of space, time and scale. Landscape Ecology 3 (3/4), 163±173.

Olivier de Sardan, J.P., 1995. Anthropologie et deÂveloppement. Essai en socio-anthropologie du change-ment social. Karthala, Paris, France.

(13)

Snedecor, G.W., Cochran, W.G., 1971. MeÂthodes statistiques. Acta, Paris, France (translation of the 6th English edition).