Deforestation in the Brazilian Amazon: A review of estimates at the municipal level. By Pablo Pacheco Draft for comments

Transcription

1 Deforestation in the Brazilian Amazon: A review of estimates at the municipal level By Pablo Pacheco p.pacheco@cgiar.org Draft for comments Belém, Pará June 2002 This paper constitutes part of a broader research initiative sponsored by The World Bank Group, Office in Brazil, to analyze the economics of cattle ranching and deforestation in the Brazilian Amazon. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author. They do not necessarily represent the view of The World Bank. The author thanks Sergio Margulis, Sven Wunder, and Diógenes Alves for their useful comments and suggestions.

2 Deforestation in the Brazilian Amazon: A review of estimates at the municipal level By Pablo Pacheco 1. Introduction This paper constitutes part of a broader research process aimed at analyzing the economics of cattle ranching, and the role of different social agents on driving deforestation in the Brazilian Amazon. Some research conducted in the region has argued that the most important proximate cause of deforestation in this regions constitute forest clearing for pasture expansion, particularly driven by large-scale ranching operations (Hecht 1993, Geist and Lambin 2001). There is still an ongoing debate about the underlying causes prompting the expansion of livestock (Kaimowitz 2001), and it has been suggested that additional research is required to better understand the economics of cattle ranching prompting pasture expansion (Margulis 2001). The current debate about the spatial patterns and temporal trends of forest removal, however, is constrained by the lack of detailed information about the location, magnitude and pace of deforestation. The official estimates of deforestation in the Brazilian Amazon are too aggregated, and the more detailed assessments of land-use/cover change undertaken since some years ago are spatially and temporally fragmented. The two factors mentioned amplify the uncertainties embedded in land-use change analysis, which tend to perpetuate because the different estimates are difficult to be compared. Furthermore, the increasing interest within the land-use/cover change research community to look for a more detailed scale of analysis has lead to privilege the municipalities as the main unit of observation of such dynamics. This paper has two main goals: 1) to assess critically the estimates of deforestation currently available for the Brazilian Amazon at the municipal level, as well as to make explicit some of their underlying assumptions, 2) to discuss some of the most recent dynamics of deforestation, and the contribution of small- vs. large landholders in the light of such estimates. Two are the methods often employed to estimate magnitude and rates of deforestation. The first consists in the use of remote sensing information, which is often done through the use of wall-to-wall techniques consisting in the analysis of the scenes covering a specific study area. The second type of estimates comes from land use surveys and agricultural census data. Each of the two methods mentioned has both advantages and shortcomings. The first offers a direct 1

3 measure of actual forest cover, although it is often subject to classification uncertainties. The second method allows gathering very rich land-use information, though the temporal resolution of the data is bounded to the frequency of the census survey, and census data is subject to inaccuracies due to reportage or underreporting problems (McConnell and Moran 2001). There are only a few estimates of land-use change in the Brazilian Legal Amazon (BLA) 1 at the municipal level. Three are the public institutions conducting land-use classification from remote sensing data that will be considered here due to them constitute by far the most relevant initiatives: INPE (National Institute of Spatial Research); IBAMA/CSR (Remote Sensing Center of the Brazilian Institute of Environment), and FEMA Mato Grosso (the state agency of environment of Mato Grosso). In turn, since 1970s the IBGE (Brazilian Institute of Geography and Statistics) conducts agricultural census every five years, excepting the one cancelled in Each of the sources mentioned provides different figures of forest removal depending of factor such as the definition of deforestation employed, the methods used to interpret remote sensing data, as well as the spatial and temporal scales used for the estimation of forest clearing. Although this paper s main interest is to assess deforestation data at the municipal level, some brief references to other well-known estimates of deforestation at both national and regional level will also be provided. The official data of deforestation adopted for the Brazilian government coming from INPE- is provided at the federal state level, for the all the states belonging to the BLA, and for the region as a whole. A more detailed assessment of those figures is available elsewhere (e.g., Machado and Pasquis 2001, Faminow 1998). This paper contains five parts including this introduction. The second part describes the main sources of deforestation available at both the national and state level. The third presents the deforestation estimates at the municipal level, and discusses their main methodological assumptions and outcomes. The fourth compares the relative contribution to deforestation from small- vs. large landholders based on information generated from INPE, CSR/IBAMA, and IBGE. The conclusion summarizes our major arguments regarding the different datasets. 1 The Brazilian Legal Amazon (BLA) covers approximately 5 million square km. The BLA constitutes a political definition created by government decree in It covers the six North states (Acre, Amapá, Amazonas, Pará, Roraima and Rondônia), plus part of three others (Tocantins, north of the 130 parallel; Mato Grosso, north of the 160- parallel; and Maranhão, west of the 440 meridian) (Alves 2001a, Faminow 1998). The main reason for creation of the Legal Amazon was to define an area for the administration of economic development, rather than to describe a region according to a uniform ecosystem. That is the reason why the BLA includes, besides forested land, extensive areas of natural savanna (cerrado), and open forest in the transition zone between closed forest and cerrado (Faminow 1998:88). 2

4 2. The deforestation estimates for the BLA Brazil holds the largest continuous tropical forest in the world, but also loses the highest amount of tropical forest among all tropical countries (Skole et al. 1994). The estimates regarding the amount of original forest cover in the country, as well as the rates of which they are being converted to other land-uses, are still subject of controversy (Machado and Pasquis 2001). FAO s Forest Resources Assessment (FRA) estimates the forest cover 2 for the entire country by 2000 at 543 million ha, and the rate of forest cover change at 2.3 million ha/year between , equivalent to a loss of 0.41 %/year 3. This value is a little higher to the average observed in the other Amazonian countries (0.37%), and equivalent to the average for Latin American countries as a whole (0.4 percent) (FAO 2001:157, Table 3). The total forest cover estimates in the BLA vary among different sources. FAO estimates it at 356 million ha (FAO 1981), whereas IBGE at 379 million ha (IBGE 1988). The INPE estimate of the forest area in the BLA is about 419 million ha (quoted in Faminow 1988:88), while the evaluation of Skole and Tucker (1993) is somewhat lower at 409 million ha. The latter two performed an assessment of deforestation between 1978 and While INPE found that the annual deforestation for the period was equivalent to 2.1 million ha/year, Skole and Tucker (1993) place it at 1.5 million ha/year for the same period. Faminow (1998:88), argues that such difference originates from INPE s assumption that areas obscured by cloud cover were in the same proportion as the measured areas of forest, deforested land and water, while Skole and Tucker (1993) excluded those areas from the analysis. 2 FAO s definition of forest includes natural forests and forest plantations. Forest corresponds to land with a tree canopy cover of more than 10 percent and area of more than 0.5 ha. Forests are determined both by the presence of trees and the absence of other predominant land uses. The trees should be able to reach a minimum height of 5 meters (FAO 2001:137). 3 The FAO s FRA employs a mixed of methods to estimate deforestation. While data for 1990 data is based in model predictions, data for the year 2000 is based on both expert guesses and limited sample of satellite observations. Different studies discuss the shortcomings of FAO s model to estimate the country s rates of deforestation (Faminow 1998, Rudel and Roper 1996, 1997). In turn, Tucker and Townshend (1999) have suggested that the use of samples of satellite images fails to estimate the distribution of deforested areas. As result, main critiques of FRA 2001 assessment are: 1) its results are distorted because of changes in methodology and/or base line data; 2) forest data for many countries is weak and reported in odd ways; and 3) differences between net and gross rates of change are not understood (Matthews 2001). 4 Both studies are based upon Landsat TM 1988 and Landsat MSS for 1978, though cloud cover forced to use some images taken either after or before such years. The data used covered the entire forested portion of the Brazilian Amazon Basin. Skole and Tucker (1993) digitized the deforested area with visual deforestation interpretation and standard vector GIS techniques. Then the digitized scenes were projected into equal-area geographic coordinates, edge matched, and merged in the computer to form a single database for the entire Brazilian Amazon. 3

5 Since 1988 to date, INPE has been the single source providing annual estimates of deforestation at both state and national levels (excepting 1993), conducted by a project labeled PRODES, whose outcomes are adopted as the official estimate of deforestation by the Brazilian government. INPE considers deforestation as the conversion of areas of primary forest by anthropogenic activity for the development of agriculture and cattle raising, detected from orbital platforms (INPE 2000:2). Under this approach, forest regrowth or areas in the process of secondary succession are considered as deforested to the extent they were accounted as such in the year they are first detected, and hence are included in gross deforestation estimation 5. The latter entails that: 1) if areas in secondary succession are re-cleared, they are not accounted for in gross deforestation during the period they are re-cleared, and 2) if they remain abandoned, they are not subtracted from gross deforestation to estimate net deforestation. INPE estimates included a portion of a so-called old deforestation (prior to 1960) in 1978, its baseline period, comprising 9.1 million ha (Faminow 1998:99). A major part of this took place in the states of Maranhão and Pará. The exclusion of secondary succession from forest stocks is problematic in the sense that it overestimates net deforestation taking place in the BLA. The area in secondary forest succession is significant. Skole and associates (1994) reported 30% of the deforested area in Amazonia to be regenerating forest, a figure supported by Lucas and colleagues (2000) who argue that one-third of the deforested area supports forest regrowth, with more than a half of this forest estimated to be less than five years of age. Much of the secondary succession, however, may be temporary fallows, and is not properly forest. Furthermore, secondary forest succession differs significantly throughout the region (Moran et al. 1994). Therefore, the magnitude of such trends depends of the definition of forest employed. According to INPE, the total deforested area in the BLA grew from 15.2 million ha in 1978 to 41.5 million ha in 1990, and was about 58.7 million ha in 2000 (INPE 2000). That figure would be equal to 60.5 million ha in 2001 according to estimates from a linear projection based on a sample from scenes located in the so-called critical areas 6 (see Figure 1). 5 INPE uses Thematic Mapper (Landsat TM) which is processed to a scale of 1:250,000, which only allows for the identification of changes in forest cover areas larger than 6.25 ha (that size in ha corresponds to 1 mm2 in images at a scale of 1:250,000). Each scene is 184 X 185 km, and 229 scenes are required to give complete coverage of the BLA (INPE 2000). Once images are selected, they are printed to the mentioned scale. Then transparent overlays are prepared for each image and analysts mark all deforested areas by hand. The deforestation data are then converted to a digitalized map format by a scanner and introduced to a GIS. Yet, cloud cover creates many spaces without data. INPE, in such cases, assumes that areas under clouds are deforested at the same rate as the non-clouded part of the scene (Faminow 1998). 6 The critical area comprises a relatively small fraction of Landsat scenes of the region (some 44 scenes representing 20% of the total 229 scenes covering the region). Since 1996/97, based on the evidence that a high proportion of deforestation takes place on those scenes, the critical scenes are used to generate 4

6 There is not a clear temporal pattern of deforestation because it follows an oscillatory trend over time. So far, there has not been provided any explanation able to capture such complex dynamic (Kaimowitz 2001). INPE estimates place the highest annual amount of deforestation (excluding a dramatic increase in 1995) in the period (2.1 million ha/year or 0.54% year). The annual area of forest clearing was decreasing from the mid-1980s to the early 1990s. It remained below 1.5 million ha/year during a major part of the 1990s (except for the spike in 1995), and it tends to increase in the late 1990s, though at level inferior to that of the early 1980s. Deforestation was close to 1.8 million ha/year in Figure 1. Deforestation in the BLA, Gross deforestation (thousand ha) 70,000 60,000 50,000 40,000 30,000 20,000 10,000 3,500 3,000 2,500 2,000 1,500 1, Annual deforestation (thousand ha/year) Before 1960 After 1960 Annual loss Source: Adapted by the author, based on INPE (2000). There is some debate about why deforestation rate grew so much in While some argue that technical problems of cloud cover could have produced some effect of unregistered deforestation in previous years was only captured in This, however, constitutes a dubious argument supporting the idea that such series would have produced with some methodological inconsistencies. In contrast, others consider that such increase could in fact be reflecting a real growth in the forest cle aring rates as a result of the policy shift in the mid-1990s (Plan Real) aimed at economic stabilization (Lele et al. 2000). Nevertheless, none of the two arguments can provisional estimates of gross deforestation for the entire Brazilian Legal Amazon. The interpolated deforestation for 2000/01 is equivalent to 1.7 million ha (INPE 2002). 5

7 be disregarded, and additional elements should also be taken into account to improve our understanding of the size of land-use/cover change taking place at a regional level. As shown in figure 2, annual deforestation started to decrease in all states from 1995 to In 1997, it approached a similar level to the early 1990s. In 1998, the annual deforested area increased in all states relative to The trends of deforestation by state are surprisingly similar among all states, though they tend to show some differences during the last three years. There is no indication from such trends that deforestation will tend to slow down in the future. Figure 2. Annual deforestation according to state (thousand ha/year), ,200 1, Amazonas Maranhos Mato Grosso Para Rondonia Others Source: Adapted by the author, based on INPE (2000). The unequal spatial distribution of deforestation is relatively well explored due to an explosion of remote sensing analysis and the use of GIS. The latter has enormously contributed to improve our knowledge about the spatial distribution and patterns of frontier deforestation. Since the early 1990s, several studies have reported that deforestation is a phenomenon relatively concentrated in a few geographical areas (Alves 2002a, Skole and Tucker 1993). INPE (2000:20) ratifies such view by mentioning that 76% of the mean gross deforestation took place in the BLA 6

8 is located in only 49 Landsat scenes. The area where a major part of the deforestation is taking place has due to its east - west going shape been labeled arc of deforestation 7. The studies dealing with deforestation at larger scales support the quite obvious lesson that it is problematic to generalize the processes and patterns of deforestation to the state level because the dynamics of deforestation is highly heterogeneous when it is assessed at the municipal level. Hence, though the concept of arc of deforestation may be instrumentally useful to identify where the deforestation takes place, it constitutes a large area, comprising a third of the entire BLA, where various processes of land-use change are interacting simultaneously. Based on INPE estimates, the studies focus on typifying the spatial occurrence of deforestation reinforce two main conclusions: 1) forest clearing occurs mostly around areas of previous deforestation and near main roads (i.e., 87% of deforestation is located within 25 km of pioneer occupation areas and almost half of it takes place within 25 km of the three major road networks) (Alves 2002a: 102); and 2) deforestation is concentrated in a small number of states, particularly in Mato Grosso, Pará and Rondonia, which together account for 76% of the total gross deforestation in 1998, and 85% of the annual deforestation in 2000 (INPE 2001). 3. The assessments of deforestation at the municipal level As was already mentioned, four institutions provide some estimates of deforestation at the municipal level (INPE, IBAMA/CSR, FEMA-MT and IBGE). This section analyses the procedures employed and assumptions made by each of them. Table 1 below summarizes the temporal scale and vegetation types included in the four assessments, as well as the geographic coverage and methods for them employed to perform land-use classification. FEMA-MT provides the longest temporal series but its spatial extent is restricted to the state of Mato Grosso. The IBAMA/CSR has produced series from 1996 to present but just for some 197 municipalities, mostly located in the arc of deforestation 8. INPE has been processing some data at the municipal level, derived from its state estimates, but it is constrained to the first half of the 1990s; and more recent estimates have not yet been released. In turn, the most updated IBGE s estimates correspond to the land survey carried out in The so-called arch of deforestation is constituted by a total of 249 municipalities embracing an area of about 170 million ha. A large proportion of the critical area earlier described is located within this portion of the BLA where there are higher pressures of land use change. 8 The CSR/IBAMA data covers 197 municipalities, or about 198 million ha, of which almost 75% are located in the states of Mato Gross, Pará and Rondônia. Hence, approximately 40% of the BLA total area is being monitored annually to detect forest conversion. Additionally, the CRS s dataset covers approximately 85% of the arc of deforestation. 7

9 Table 1. Assessments of deforestation in the BLA at the municipal level Source Period Type of estimation INPE Assessment of Annual estimates deforestation for the of land cover periods , change considering , and different various 95. forest cover types (dense, open and CSR / IBAMA FEMA MT IBGE Annual estimates from 1996 to 2000 (2001 in process) Annual estimates from 1992 to 1995, and bi-annual from 1995 to 2001 Estimates for the years 1970, 1975, 1980, 1985, 1995/96 deciduous) Annual estimates of deforestation including only forest cover Annual estimates of land cover change (including forest, cerrado and transition areas) Estimates of cleared area within agricultural establishments including all types of existing vegetations Geographic coverage Embraces the whole BLA region comprising 500 million ha Covers an area from 170 to 190 million ha, mostly within the arc of deforestation Covers the entire state of Mato Grosso (90 million ha) Covers an area of 120 million ha in the agricultural year of 1995/96 Source: Adapted by the author from FEMA (2001), INPE (2000), and Teixe ira (1999). Method employed Color composite satellite images are processed to a scale of 1: 250,000. Deforested areas are scanned and integrated into a GIS Deforested areas are digitalized based on visual interpretation of color composite satellite images Deforested areas are digitalized based on visual interpretation of color composite satellite images Census to establishments producing any plant or animal output Fearnside (1993) notes that part of the confusion surrounding deforestation numbers is the treatment of cerrado, or the way in which the estimates separate it from forest. Whereas INPE and IBAMA/CSR estimates deal exclusively with forest cover types (including dense and open forest, pioneer formations, deciduous forest, and mixed forest covers in transition zones, among the most relevant types), FEMA does not differentiate land cover types in its municipal level dataset, only for the state as a whole. The land survey, in turn, does not differentiate cleared areas taking place either on forest or cerrado areas (Andersen et al. 2001). The INPE dataset of deforestation To the extent that the analysis undertaken at the state level does not reveal much about the spatial dynamics of deforestation, increased attention has been paid to the analysis of landcover change in the municipal realm for the land-use/cover change research community. To date, most research has employed, no matter its limitations, the dataset from INPE generated to analyze land-cover change at a regional scale. INPE has made some efforts to disaggregate its dataset to 8

10 the municipal level. Nevertheless, a factor limiting more progress is that researchers have only limited access to such information because it is not open access information. A pioneer work (Alves et al. 1997) made explicit some of the spatial patterns of deforestation in the BLA based on data produced from INPE for two periods of time ( and ). This study found generally that deforestation was expanding at higher rates in municipalities located in southern and eastern flanks of the BLA, as well as in the western portion of Pará and Roraima. Furthermore, this work found that the likelihood of municipalities with high rates of deforestation to continue deforesting at the same rate during a following period is very high 9. This study support the argument that deforestation is an inertial process by which the areas most likely to be deforested are those located closer to the forest areas already intervened. Figure 3. Accumulated deforested area in BLA by size of municipality, (in %) Source: Adapted by the author, based on Alves (2000). 9 This work analyses 624 municipalities (22 in Acre, 15 in Amapá, 62 in Amazonas, 109 in Maranhão, 117 in Mato Grosso, 128 in Pará, 40 in Rondônia, 8 in Roraima and 123 in Tocantins). The municipalities total area is equivalent to 500 million ha of which four/fifth parts are forests. This study found that 90% of deforestation was concentrated in only 191 municipalities during the first period of analysis ( ), falling to 159 in the second one ( ). A total of 140 municipalities were present in the two periods. The clouded areas were excluded from the analysis, 6.6 million ha in the first period, and 7.2 million ha in the second one (Alves et al. 1997). 9

11 Alves (2000), using a similar approach, estimates the amount of forest removal at the municipal level but looking at the accumulated deforested area during the period This purely descriptive study provides only a one-period view of the relative contribution of each municipality to total deforestation during the mentioned period. To a high extent, it reiterates the outcomes of the previous work about the location of deforestation (see Figure 3). An important issue that arises from this work is that it is difficult to estimate annual estimates at the municipal level because of varying imagery dates and corresponding periods of observation. Though images are normalized for dates to allow comparison, such process tends to introduce some errors, mainly for small areas, which tend to be averaged further at the municipal level. Menezes (2001: ), based on INPE estimates, offers a static picture of the accumulated deforestation per municipa lity by According to this source, 47 of 227 municipalities were responsible for 50% of deforestation in the states of Mato Grosso, Rondonia and Pará. Moreover, 139 municipalities (covering an area of 123 million ha) contain 90% of deforestation in those three same states or 77.4% of the total deforestation in the BLA. Though the accuracy of these results may be questionable due to the coarse resolution of the data used, this approach improves our knowledge about the spatial distribution of deforestation. Yet, the main shortcoming of that it does not show the evolving trend of deforestation over time due to the existence of just fragmented series. The CSR/IBAMA data for the arc of deforestation The CSR, an institution part of IBAMA, is developing another dataset at the rural property level, as part of a larger system of environmental surveillance, licensing and monitoring. A detailed description of this system can be found elsewhere (i.e., SCA 2001, Teixeira et al. 1999). The CSR determines annual deforested areas by a visual interpretation of Landsat +ETM images. The CSR/IBAMA did not make explicit the definition of forest they use as part of its classification procedures. They only perform a binary classification of forest and no forest from which any vegetation like type is classified as forest Menezes et al. (2001), though based on INPE estimates of land cover change, uses a somewhat different estimate of deforestation. They took an INPE s map of deforestation for 1996/97, and overlaid it to a map of municipal boundaries from IBGE. The result is a map of deforestation for each municipality by 1996/97. Yet, due to the problems of areas without data and cloud cover areas the sample is incomplete. 11 Areas with comparatively low reflectance values in the visible bands of the wavelength spectrum (band 1, 2, and 3), and high values in the near-infrared (band 4) area typically considered vegetation. 10

12 This data set consists of annual measurements of deforestation starting in 1996 for areas greater than one ha. In 1996, forest secondary succession at advanced stages was considered as forested land, and since then forest regrowth is no longer considered. In this regard, it is obvious that for CSR/IBAMA the deforested area in 1996 will be lower than INPE s one because it includes intermediate and late stages of forest regrowth excluded from INPE analysis. The treatment of secondary succession during the following years is similar to the one from INPE. IBAMA/CSR covers a large part of the BLA, 80% of the municipalities monitored are part of the arc of deforestation, while the rest is located in the southwest portions of the states of Mato Grosso, Rondonia and Acre. This dataset should in theory capture a high proportion of the deforestation in the BLA due to the fact that it covers over one third of the region, the portion where most of the forest clearing is currently taking place. The major limitation of those data is the missing information for some municipalities for some specific years due to the lack of satellite images. Hence, in some scenarios of no deforestation it exists the doubt either if in practice there was no deforestation on some municipality at some specific year or if merely CSR/IBAMA did not perform an analysis of such area. The CSR/IBAMA is not explicit about it. Figure 4. Accumulated deforestation by size of municipality (by 2000) Source: Adapted by the author based on IBAMA/CSR. 11

13 While spatial patterns reflecting how deforestation is distributed along places is easier to determine with available data, it is more difficult to trace the evolution of deforestation over time to more detailed scales (i.e., municipal level). Three situations of deforestation growth can be identified from CSR/IBAMA data between 1996 to 1999: 1) municipalities with declining rates of growth of deforestation, 2) municipalities with steady rates of growth, and 3) the dominant situation is that where deforestation is growing at accelerated rates (see Figure 5). Figure 5. Annual deforestation: percentage change and Source: Adapted by the author, based on CSR/IBAMA. Percentage of change calculations corresponds to the period and for Mato Grosso. Deforestation data from FEMA- Mato Grosso Another source of deforestation data at the municipal level is the dataset being developed by FEMA-MT, with the technical support of a consultancy company specialized in geoprocessing services. FEMA-MT has implemented a system as part of a larger pilot program of environmental control and licensing supported by the PPG7 aimed at monitoring land-use conversion at the rural property level (de Moura 2001). This system began to operate in 14 municipalities of the State of Mato Grosso, those with the highest rates of deforestation, and 12

14 limited to properties greater than 200 ha. Beginning 2002, the monitoring system has expanded to the entire area of the state, and will include rural properties independently of size. FEMA-MT has produced a series of vegetation cover changes including forest, cerrado, and transition zones between forest and cerrado since 1992 to present for all the municipalities of Mato Grosso (see Figure 6). The limitation is that information of annual cleared areas does not differentiate it by type of vegetation being. Cleared areas disaggregated by vegetation types are available only for the accumulated cleared area by 2000/01 (see Figure 7). Some 46% of original cover removal took place in the cerrado, 39% in forests and 15% in transition zones. Figure 6. Mato Grosso: Rate of total cleared area by size of municipality (by 2000/01) Figure 7. Mato Grosso: Total cleared area by type of land-cover (by 2000/01) 14,000 Total cleared area (thousand ha) 12,000 10,000 8,000 6,000 4,000 2,000 46% 39% 15% - Cerrado Forest Transition Source: Adapted by the author, based on FEMA-MT. The IBGE estimates from the agricultural census The IBGE, as mentioned earlier, produced an estimate of cleared original cover based on a land survey. The last agricultural census for which data is published is from the agricultural year 1995/ This census provides detailed information on private land uses and, in theory, 12 The agricultural census is conducted every five years. The 1990 census was cancelled, and by 1995 IBGE decided to change the reference period from the calendar year to the agricultural year (August 1, 1995 to July 31, 1996). The change in reference period implied a change in the period in which the data 13

15 includes all agricultural establishments in the BLA. The unit of analysis is the agricultural establishment producing any plant or animal output during the time span under analysis, be it a household or a farm, or any kind of rural residence (for a more detailed discussion about the IBGE land survey see Andersen et al. 2001). The agricultural census groups all land into private land and public land. There is no data for the use of public land. Figure 8. Total cleared area within agricultural establishments by 1995/96 Source: Adapted by the author based on IBGE, Agricultural Census 1995/96. In this dataset, it is not possible to estimate accurately deforestation because it is unknown how much of the cleared area was originally forested. Yet, the IBGE estimates of cleared area provide a good estimate about the intervened (or altered) areas for each of the municipalities comprising the BLA (see Figure 8). The cleared area comprises all the areas under either permanent or annual crops, areas in fallow, planted pasture, planted forest and unutilized productive land (see Andersen et al. 2001, Menezes et al. 2001). was gathered. Instead of collecting it in January the following year, as had been done for the 1970, 1975, 1980, and 1985 censuses, the gathering of data for the 1995/96 census began in August of Some researchers have suggested that this would produce a drop in agricultural establishments and agricultural workers in Brazil between 1985 and 1985, because a large portion of temporal establishments would have not been counted (Andersen 2001:53). 14

16 4. How much and why do the estimates differ? This section seeks to compare the different estimates of land-use change described above, but it is a difficult task due to different spatial and temporal scales, and the implicit definitions of deforestation employed as part of the different analysis of deforestation. The latter leads to some researchers doubt whether a comparison should be attempted. The difference between the INPE and IBAMA/CSR figures is notorious. Due to the fact that the CSR estimates cover most of the municipalities with high rates of deforestation, so it should be possible to assume that those data capture a large portion of the BLA deforestation. In practice the CSR numbers range between 33% and 44% of the INPE figures (see Table 2). That difference is not just a matter arising from the different geographical coverage, but largely a methodological issue. Nevertheless, a full comparison can be made in the case of Rondônia exclusively, as the two sources cover the state s entire area. Looking at both sources one can infer that either INPE overestimates deforestation or IBAMA/CSR underestimates it, or both. Table 2. Annual deforestation in BLA by state (thousand ha), 1996/ /99 States 1996/ / / /00 (d) INPE (entire states) Mato Grosso Rondônia Pará Others (a) Total 1, , , ,822.6 CSR (197 municipalities) (b) Mato Grosso N/A Rondônia Pará Others (c) Total Source: Adapted by the author based on INPE (2000) and data provided by IBAMA/CSR. Notes: a) Include the states of Acre, Amapa, Amazonas, Maranhao, Roraima and Tocantins; b) Some municipalities do not have information for some years. The number of municipalities including for monitoring are: 46 in Mato Grosso, 49 in Para, and 52 in Rondonia covering the whole state; c) Include all the states mentioned in note a. except Amapa and Roraima; d) By the moment this report was elaborated had not yet been released the INPE estimates of deforestation for 1999/00. The coarse resolution employed by INPE lead it to both underestimate deforested areas in cases where forest clearing occurs in small plots, and to overestimate deforestation in landscapes with small forest patches. A more conclusive assessment can only be possible by looking at specific situations. Indeed, a more detailed comparison undertaken for CSR/IBAMA shows that 15

17 issues of scale can be relevant to explain such differences between the two datasets, at least that seems to be the case for Rondônia 13. Issues of forest definition and uncertainties linked to classification, however, also constitute important factors to explain the datasets discrepancies. Table 3 compares the datasets of CSR/IBAMA and the SCA (Secretary of the Amazon of the Ministry of Environment) results based on INPE s land-use map for It suggests that CSR/IBAMA figures are lower respect to INPE because they classified secondary forest as forest. Hence, whether CSR/IBAMA underestimate deforestation or INPE overestimate it depends largely of what is defined as forest. Nevertheless, the latter argument is just valid for the accumulated deforestation until 1996 (the CSR/IBAMA base line period), in reason to both sources treat similarly forest regrowth during the following years. That ratifies that differences lie on definitions of forest but also is an issue of scale. It is difficult separate both effects. Table 3. Total deforestation by 1996/97 in municipalities selected by CSR (thousand ha) States No. FEMA (a) INPE (b) CSR/IBAMA Rondônia 52 4,498 4,585 Acre Amazonas Para 49 8, Tocantins 13 1,186 5 Maranhao Mato Grosso 46 4,834 (c) 6,695 4,858 Total ,780 10,756 Source: Adapted by the author based on data provided by FEMA, IBAMA/CSR and SCA/MMA. Note: a) Assumes that clearing is proportional to the distribution of land-cover types within the state; b) Corresponds to SCA/MMA estimates of accumulated deforestation at the municipal level for selected municipalities of the BLA based on INPE land-use map of This data is just referential because it was calculated based just in the 51.5% of the municipalities forest area, then it is unknown the portion of deforested areas that were left out of the analysis; (c) represents the total cleared area by 1996/97 multiplied by the proportion of forest cover existing in the state of Mato Grosso. Let us review the IBGE s census data to complete our comparison. It has been suggested that a method to estimate the portion of cleared land that would have been forest originally consists in multiplying the amount of cleared land with the share of naturally forested land in each municipality under the assumption that clearing is randomly distributed across the municipality (Andersen et al. 2001). This assumption is still likely to lead to an overestimation of deforestation since people would tend to clear the most open areas first. 13 Nilson C. Ferreira, CSR/IBAMA, personal communication, April

18 Table 4 shows the outcome of such estimation. While INPE calculates that accumulated deforestation represented about 10% of the total area of the BLA, it would be equivalent to approximately 6% according to IBGE s estimate of forest clearing. The two mentioned datasets do not account for forest regrowth, though some land-use categories of the agricultural census (such as areas in fallow or unutilized land could possibly be under some type of secondary succession). The reasons underlying the differences seems, among others: the census outcomes reflect only land-use within occupied (private) areas by establishments (which could be undercounted), deforestation taking place elsewhere is not considered, and abandoned deforested areas (though small in theory) are not part of the calculations because they are considered as forested (Andersen et al. 2001). Table 4. Total deforestation by federal state (thousand ha), 1995/96 States INPE (1995/96) Census data (1995/96) Deforested Cleared Deforested (a) Mato Grosso 11,914 20,214 11,906 Rondônia 4,865 3,358 2,985 Pará 17,614 8,681 7,822 Others 17,314 15,506 6,977 Total BLA 51,707 47,760 29,690 % of total area of BLA Source: Adapted by author based on Andersen et al. (2001), INPE (2000), IBGE (1998). Note: a. Under the assumption that clearing is randomly distributed across space. The information provided by the land survey may constitute a good proxy of deforestation at the municipal level under the assumptions noted above. For the reasons already noted, the agriculture census outcomes will underestimate the amount of total deforestation. Figure 9 provides a comparison of accumulated deforestation until 1996 in the state of Rondônia for both IBGE and IBAMA/CSR datasets. The IBGE data distinguish deforested areas from other types of land cover (such as transition zones or some areas of savanna) following the same assumptions made above. As can be seen from the figure, even taking the total cleared area, the IBGE estimates are systematically below to the ones from IBAMA/CSR. 17

19 Figure 9. Accumulated forest clearing in Rondônia to 1996 (in thousand ha): Comparing municipal data from IBGE and CSR/IBAMA Santa Luzia d'oeste Rolim de Moura Rio Crespo Presidente Medici Porto Velho Pimenta Bueno Ouro Preto do Oeste Nova Brasilandia d'oeste Machadinho d'oeste Ji-parana Jaru Guajara-mirim Espigao d'oeste Costa Marques Corumbiara Colorado do Oeste Cerejeiras Cacoal Cabixi Ariquemes AltaFloresta d'oeste Sao Felipe d'oeste Primavera de Rondonia Pimenteiras do Oeste Parecis NovaUniao Monte Negro Mirante da Serra Ministro Andreazza Jamari Governador Jorge Teixeira Cujubim Chupinguaia Castanheiras Candeiasdo Jamari Campo Novo de Rondonia Cacaulandia Novo Horizonte do Oeste Buritis Alto Paraiso Alto Alegre dosparecis Alvorada d'oeste Nova Mamore Sao Miguel do Guapore Vilhena Urupa Theobroma Teixeiropolis Seringueiras Sao Francisco do Guapore Vale do Paraiso Vale do Anari Forest removal (IBGE) Removal other cover types or transition zones (IBGE) Deforested (IBAMA/CSR) Table 5. Summary of datasets strengths and shortcomings Source INPE CSR / IBAMA FEMA MT IBGE Definition of forest Not explicit. Deforestation is considered as all conversion of primary forest by anthropogenic activity to other land-uses Not explicit. Intermediate and advanced stages of secondary succession classified as forest Not explicit. Likely the same as CSR/IBAMA Census data allows deriving cleared areas to different land uses. Main strengths Methodological consistency to produce estimates at a coarser resolution (federal state level) Visual interpretation at a more detailed scale of analysis makes of its classification results more reliable. Visual interpretation at a more detailed scale of analysis makes of its classification results more reliable. Detailed identification of land uses within establishments from which it is likely to derive cleared areas from original vegetation. Main shortcomings Overestimates net deforestation since not consider forest regrowth, and probably overestimate gross deforestation. Limited geographical coverage. Underestimate net deforestation at its base line period due to the fact does not differentiate forest from different stages of regrowth. Does not differentiate cover change by type of vegetation intervened as part of its annual estimates of deforestation. Census may underestimate establishments, and deforestation taking place somewhere else. 18

20 The data here discussed show that INPE overestimate deforestation and CSR/IBAMA underestimate it, and IBGE estimates are lower than the ones obtained from remote sensing analysis. The latter is in part a result of resolution analysis (though coarser resolution analysis can both underestimate deforestation taking place in small plots, and overestimate deforestation in areas with remaining small patches of forest), as well as an issue of definition of forest with has decisive influence on the classification outcomes. The INPE definition of forest, by which any forest can regenerate, leads to overestimate net deforestation. By comparing it with CSR/IBAMA, it is possible to argue that INPE may be also overestimating gross deforestation, though it remains uncertain. In turn, CSR/IBAMA underestimate the accumulated deforestation by considering intermediate and advanced states of forest regrowth in its definition of forest. Hence, whether a source under or overestimate deforestation is a relative issue linked to its definition of forest. The IBGE s methodology is very consistent, though it can often undercount agricultural establishments (particularly due to the change of time period that lead to not include many temporal establishments), and probably there is some deforestation outside of occupied areas (i.e., some abandoned areas) that is not captured by the agricultural census. A major limitation of the agricultural census data is that it does not allows measuring deforestation directly, and auxiliary methods have to be employed by making some assumptions affecting the final outcome. The data discussed here shows that deforestation has not followed a linear trend. It has tended to increase systematically since Three states concentrate most of fourth/fifth parts of deforestation (Para, Rondônia and Mato Grosso), and nothing makes to think that this trend will revert, but Mato Grosso. According to FEMA the latter show some slow down in the deforestation dynamics in 2000/01 respect to 1998/99 in about 32%. INPE suggest that deforestation in the same state decreased in 9% from 1999 to Deforestation in the other two states, though with slight oscillations, tended to rise in the last two years. The deforestation rate in a large part of municipalities has increased in a large part of municipalities, particularly of those located at the core of the deforestation arch. Nevertheless, lack of information about the amount of original forest remaining in such municipalities makes difficult to address if such trends will continue to the same rate during the future. 5. What does the size of deforested plots suggest about agents contribution? To date, there are no reliable data about the contribution to deforestation made by large farmers and ranchers as opposed to smallholder farmers, though large ranchers play a significant role (Cattaneo 2000, Faminow 1998, Walker et al. 2000). Fearnside (1993) suggests that 70% of 19

21 deforestation is attributable to large-scale ranching operations, but Homma and colleagues (1995) mention that half of deforestation in the Amazon is due to small slash-and-burn farmers. Chomitz and Thomas (2000) claim that large establishments (those larger than 2,000 ha) account for about half of all land converted from forest or cerrado to agricultural use. Walker and associates (2000), conclude from an evaluation undertaken in three areas that there is much regional variation due to different settlement history, and development interventions. Since 1995 INPE has provided data on forest clearing by size of the deforested plots. Although not explicitly linked to parcel boundaries, in the way in which this information is processed, those data provide referential information of the contribution to deforestation by different agents. The main constraint of INPE data, however, is the fact that it is not possible to distinguish the contribution to forest clearing from areas smaller than 6.5 ha. This data has to be taken cautiously because, for instance, some portion of deforestation mainly those of state sponsored settlements can take place on adjacent plots. Conversely, medium and large landholders can clear different plots in different parts of their farms. Table 6. Average forest clearing by size of the deforested plot (in %), Hectares INPE (in %) (comprises the whole BLA) Less than More than Total CSR (in %) (179 municipalities) (a) Less than More than Total Source: Adapted by author based on INPE (2000), and IBAMA/CSR ( Notes: a) 18 municipalities of the original CSR dataset have missing data for size of deforested plot. 20

22 INPE s figures show that about one sixth of the total deforestation take place in plots less than 15 ha in size, and that a larger proportion falls between 50 and 500 ha. The IBAMA/CSR numbers tell us a slight different history because they record the size of smaller deforested plots. The latter source indicates that the contribution of plots from one to three ha represent around one percent of the total forest clearing of the 179 municipalities with higher deforestation rates in the BLA. In contrast, this same source suggests that the contribution of larger deforested plots (more than 500 ha) would explain almost a half of the total deforestation (see Table 6). The data here presented confirms the common notion in the debate about land-use/cover change in the BLA that a major part of deforestation is driven by large-scale operations. In this regard, what really matters is their relative contribution to forest removal. Though the data of size of deforested plot is a proxy to identify the patterns of deforestation, they say little about the real contribution of different agents to deforestation due to the reason earlier mentioned. More research is needed linking deforestation analysis to land tenure in specific locations. Neither remote sensing analysis nor census data interpretation census can resolve that issue by its own. Therefore, the debate about the contribution of different agents to deforestation continues to be relevant, and no definitive conclusions can be drawn based on the available data. Table 7. Size of the annual deforested plots by state (in %), average Hectares Rondonia Para Mato Grosso Others (a) Total Less than More than Total No. of municipalities Source: Adapted by author based on IBAMA/CSR (www2.ibama.gov.br) Notes: a) Includes the states of Acre, Amazonas, Tocantins and Maranhão. Many of the agents drive for deforestation is place-specific related, as result of factors such as the settlement history, land prices, profitability, infrastructure, and the evolution of the land tenure. Hence, the proportion of total deforestation of small-size plots is higher in states such as Rondonia or Acre, in opposition to Mato Grosso, where has a much higher concentration of 21