ABSTRAC= T

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Soils data from t= he 1000-km² area covered by the Biological Dynamics of Forest Fragm= ents (BDFFP) reserves (north of Manaus, Amazonas) provides detailed information = of the type used in development planning, such as the economic-ecological zoni= ng of the region now being carried out by the Brazilian government. The soil in the reserves is typica= l of vast areas of Brazilian Amazonia that are likely to be increasingly defores= ted for agriculture and ranching. These soils have low fertility, are acid and have high levels of toxic aluminum i= ons. They also have limitations due to topography, generally very high clay content, and low values of available w= ater capacity. Agricultural potent= ial is low, and the prospects for resilience are not bright given the reality of changing land-use patterns in the region.&= nbsp; The soils under forest also give indications of the environmental im= pact of converting these areas to pasture and other uses, including the potential for release of greenhouse gases (which depends on forest biomass, a feature related to distribution of natural vegetation and favorability of soils for= plant growth), the probable response of forest to fragmentation, and the probable response of forest to climatic change.&nbs= p; Our soils research indicates that areas like this would produce litt= le if converted to agriculture or ranching, and point to land uses that mainta= in forest cover intact as preferable. The value of the environmental services provided by the standing for= est far exceeds the returns that can be expected from clearing it for agricultu= re or ranching.

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I.) INTRODUCTION

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A.) RELEVANCE OF = SOILS TO PLANNING

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Soil quality is obviously fundamental to the potential of any area to produce and sustain agricultural yields. If plann= ing decisions are made to encourage agriculture in areas with soils that are not suited to this purpose, crops can be expected to fail. Planning decisions opening vast ar= eas to settlement are frequently made in a near vacuum of information on soils. For example, when the POLONOROESTE= Project opened Rondônia to settlement through a World Bank-financed regional development program centered around paving the BR-364 (Cuiabá-Porto Velho) Highway, the soils information available for Rondônia (Brazil, Projeto RADAMBRASIL, 1978: Vol. 16) was based on only 85 soil cores. Such events are recurring today qu= ite near the Biological Dynamics of Forest Fragments Project (BDFFP) reserves: = on 24 June 1997, president Fernando Henrique Cardoso announced in his weekly r= adio program "Palavra do Presidente" that six million hectares (ha) al= ong the BR-174 (Manaus-Caracaraí) Highway would be opened to settlement,= and suggested that the area farmed there would be "so colossal that it wou= ld double the nation's agricultural production" (de Cássia, 1997). Despite almost certain hyperbole in both the expected production and the area likely to be settled= , the intention of initiating a major program on the BR-174 Highway appears to be real. The announcement of the BR-174 settlement program came as a surprise, as the paving of the highway = (in 1996 and 1997) had been presented as a surgical cut through the forest that would allow the city of Manaus to trade with Venezuela and have access to t= hat country's ports.

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Neither studies of soils and agronomic potential nor assessment of environmental impact were d= one prior to the announcement of the BR-174 agricultural project. As is true for most of Brazilian Amazonia, information on soils used in planning is essentially restricted to the results of the RADAMBRASIL Project, which, in the early 1970s, mapped s= oil, vegetation and other features based on side-looking airborne radar (SLAR) imagery (Brazil, Projeto RADAMBRASIL, 1976: Vol. 10, 1978: Vol. 18). Original imagery was at a scale of 1:250,000 and was published at a scale of 1:1,000,000. Areas with the same appearance on = the images were mapped as the same, with field checking to identify vegetation = and soil in many of the units. The BR-174 area has been identified by the Amazonas state government as a prior= ity for economic-ecological zoning (Pinheiro, 1997).

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The portion of the BR-174 to be opened for agriculture lies over the Guianan Shield, and can therefore be expected to be more fertile than those in the BDFFP reserves.<= span style=3D'mso-spacerun:yes'> However, the Landless Rural Worker= s' Movement (MST) has recently invaded some ranches in the SUFRAMA (Manaus Free Trade Zone Superintendency) Agriculture and Ranching District (not those un= der study by the BDFFP), raising the possibility that some of these ranches may= be distributed to small farmers for agriculture (Pacífico, 1997).

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B.) SOIL CLASSIFI= CATION

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The BDFFP reserve= s are located 80 km north of Manaus (2^o30'S, 60^oW), and are spread over a 20 X 50 km (1000 km²) area. The site is approximately 25-50 m = above mean sea level; mean annual temperature at Manaus is 26.7^oC and = mean annual rainfall (30-year average) is 2186 mm with a three-month dry season lasting from July to September (Lovejoy and Bierregaard, 1990). The site of the BDFFP reserves is typical of much of central Amazonia.

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The RADAMBRASIL m= aps classify the BDFFP reserves as allic yellow latosols, which are Allic Haplorthoxes (Oxisols) in the US soil taxonomy and haplic or xanthic Ferral= sols in the FAO/UNESCO system (see Beinroth, 1975). The classification as a latosol(1) relates to the type of clay minerals present. The relative amounts of clay miner= als composed of silicate (kaolinite), iron (goethite) and aluminum (gibbsite) determine the structural stability, the natural fertility and the effect of applying fertilizers (Sombroek, 1966: 73).

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Oxisols are the m= ost common soil in the Amazon Basin, covering 220 million ha or 45.5% of the to= tal (including areas outside of Brazil); most of the remainder is covered by Ultisols (such as the red-yellow podzolic soils of the Brazilian system), covering 142 million ha or 29.4% (Cochrane and Sánchez, 1982: 152).<= span style=3D'mso-spacerun:yes'> Oxisols are distinguished from Ult= isols by lack of higher clay content with depth; Ultisols have at least a 20% increase in clay content in the lower (B) horizon (United States, Departmen= t of Agriculture, Soil Survey Staff, 1975).&nbs= p; At a high level of generality, Ultisols are considered less appropri= ate than Oxisols for mechanized agriculture due to susceptibility to soil compaction and their frequent occurrence on more steeply sloping terrain (Sánchez, 1977: 539). However, the broad range of characters within either of these great groups (as illustrated, for example, by the results of the present study) indicate the need for caution in applying such generalizations to specific management decisions (see Fearnside, 1984).

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Oxisols and Ultis= ols (i.e., yellow latosols and red-yellow podzolics) often occur in close proximity in Amazonia and frequently intergrade with each other (Sombroek, 1966: 68). Profiles in nearby sites confirm t= his (Chauvel, 1982; Ranzani, 1980). However, since both Oxisols and Ultisols are similar in being acid, infertile soils that are indistinguishable from each other without informat= ion on differences in the B horizon (which is outside the rooting zone of most crops), taxonomic differences between them generally have little relevance = for agriculture as practiced in Amazonia.

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The BDFFP reserve= s are located about 50 km south of the edge of the Guianan Shield (the Balbina hydroelectric dam is located at the edge of the Shield). The reserves are within the basin = bounded by the Brazilian and Guianan Shields. This basin covers approximately 1.2 X 10⁶ km² = in Brazil, or about 25% of Brazil's 5 X 10⁶ km² "Le= gal Amazon" region. It is kn= own as the Alter do Chão Formation (formerly called the Barreiras Formation). The soils are der= ived from sedimentary deposits from the bottom of a shallow sea that occupied the center of the Amazon Basin during the Tertiary (Falesi, 1974; Jordan, 1985). The soils derived from= these sediments have been exposed to heavy rainfall and high temperature over mos= t of the approximately 60 million years since the region was uplifted at the time that the Andes mountains were formed; most of the nutrients have therefore = been leached out of the soil (Sombroek, 1984).&= nbsp; Younger soils, such as those derived from igneous rocks in the Guian= an and Brazilian Shields, have higher fertility than those of the BDFFP area, although they too are far from being classed as fertile for agriculture.

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The general characteristics of the origin of soils are closely associated with their overall fertility. In the cas= e of the BDFFP area soils, this history rules out occurrence of high-fertility soils, with the exception of patches of anthropogenic black soil (terra preta do índio), which was not found in the project area. However, great variation in some characters occurs within any given area, such as the BDFFP reserves, that appears as uniform at the scale considered by the RADAMBRASIL survey (the maximum level of detail that can presently enter land-use planning decision= s); in reality, not even this level of detail is considered when many actual decisions are taken. Perhaps = the clearest example of this was the complete ignoring of existing soil maps wh= en government decisions were taken on the location of settlement areas in agriculturally unpromising areas of Rondônia (Fearnside, 1986a).

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C.) SOIL VARIABIL= ITY

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The BDFFP soil su= rvey offers a unique opportunity to assess fine-scale variability in Amazonian s= oils and the potential significance of this variability for development planning. Fine-scale variabil= ity can be expected to affect agricultural success; it also affects the natural vegetation, both in influencing what trees (and thereby other life forms) o= ccur and the stress placed on those trees when they find themselves isolated in forest fragments.

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Variability in in= itial soil quality, as well as in other factors, is a key determinant of human carrying capacity (Fearnside, 1986b). In the Transamazon Highway colonization area, for example, soils vary from fertility levels similar to those found in the BDFFP area to considera= bly higher levels in terra roxa (Alfisol) areas, as well as in much smal= ler patches of terra preta do índio (anthropogenic black soil) (Fearnside, 1984). The occurr= ence of fertile patches, even if small in area, is important in the success of colonist agriculture, as is the ability of farmers to identify these patche= s by knowledge of the tree species that typically grow on them (Moran, 1981).

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D.) ECONOMIC-ECOL= OGICAL ZONING

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Brazil's 1988 con= stitution calls for zoning of the entire country, after which land-use decisions for = each area would have to be made in accord with the zone assigned to it. This provision was supported by the "environmental caucus" of constitutional delegates, and was viewe= d as a great victory for increasing the amount of protection against excessive environmental impacts of development modes such as those that result in tropical deforestation. Howev= er, the question of zoning soon became a controversial one, with those who implement zoning at the level of state governments often viewing it more as= a means of "opening up" areas to development rather than as a means= of containing development excesses (see Fearnside and Barbosa, 1996, for examp= les from Roraima).

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Following the Oct= ober 1988 constitution, an inter-institutional struggle ensued for control of the zoning at the federal level; the principal contenders were the Brazilian Institute for Geography and Statistics (IBGE), the Brazilian Enterprise for Agriculture and Ranching Development (EMBRAPA), and the Ministry of Science= and Technology (MCT) (Régis, 1989).&nbs= p; The matter was settled by a 1991 presidential decree giving responsibility to the Secretariat for Strategic Affairs (SAE), which had formerly been known as the National Information Service (SNI): a much-feared internal espionage agency. Th= is institutional setting can potentially affect both the priority assigned to environmental concerns and the degree of popular participation in the zoning process.

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The methodology t= o be used in the zoning has been the subject of long controversies. A series of very general maps was prepared at a scale of 1:2.5 million using data from the RADAMBRASIL survey= s; additional data collection was not undertaken for these "diagnostic&qu= ot; maps, but some additional data collection is foreseen in state-level zoning projects. The zoning projects= and the strengthening of the state-level agencies that have begun to carry them= out is being done with assistance from G7-Pilot Program to Conserve the Brazili= an Rainforest (with funds administered by the World Bank).

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Increasing the le= vel of detail of data on characteristics such as vegetation and soils is obviously important in order to increase the degree of confidence in zoning conclusions. It is also impor= tant to achieve a better understanding of how to scale down from general zoning = maps to the fine-scale variation that exists on the ground where development actually takes place. One app= roach proposed to interpreting soils information in conjunction with information = on vegetation, topography and other features is the "land units" approach (Sombroek, 1966; see Sombroek et al., 1999). Land units use naturally co-occurr= ing sets of these characters as the basis for defining the categories into which the landscape is divided for purposes of planning.

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E.) IMPORTANCE OF= SOIL PROPERTIES

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&= nbsp; 1.) Soil texture

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One of the most important characteristics of the soil is its texture: for example, the bala= nce between sand and clay fractions. Very sandy soils are poor for plant growth because they lack sites f= or holding cations (which are consequently leached away, leaving the soil infertile), and because sandy soils do not hold water well, exposing plants= to drought stress during dry periods. Clays have more sites for cations, largely because clay content is positively correlated with organic matter when comparing surface samples ta= ken at different locations (as distinct from comparisons made between different depths in the soil profile). = Clay also holds cations directly, independent of its effect on organic matter. Organic matter is especially impor= tant in contributing to cation exchange capacity (CEC) in soils with clay minera= ls of low activity, such as the Oxisols and Ultisols of Amazonia (Lenthe, 1991= : 121). Cation exchange capacity is a meas= ure of the negatively charged sites on the surfaces of clay and humus molecules; t= hese sites may be occupied either by nutrients needed for plant nutrition (Ca2+, Mg²⁺, K⁺ and Na⁺) or competing ions in acid soils (H⁺, Al³⁺ and Fe²⁺, but only the fir= st two of these are included in the CEC).

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Clays also hold w= ater better in the smaller pores in the soil matrix. However, very heavy clays can have disadvantages because they have a higher proportion of very small micropores from which removing water requires an undue exertion of force by the plant roots (i.e. water requires a tension of more than 15 atmospheres to remove). The large percentage= of water at tensions above 15 atmospheres (bars) in very clayey soils, which c= an be as high as 30% by weight, has a positive role as well: it increases the stability of organic matter, contributing to the positive relationship betw= een clay content and organic matter (Bennema, 1977: 35). The relationship between clay and = plant growth can be complicated by the effect of aluminum ions, which are toxic to most plants in high concentration. The allic (high aluminum) clays that are common in Amazonia lead to a strong positive correlation between clay content and aluminum. Because most of the effects of cla= y are beneficial to plants, one can expect a positive association between aluminum and plant growth even though the effect of aluminum per se is negati= ve.

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Excessively heavy= clays can also pose a physical impediment to the growth of tree roots. Soils for citrus trees, for exampl= e, are considered best if they have a mix of sand and clay rather than being pure = or almost pure clay. For cacao, = the best soils have 30-40% clay, 50% sand, and 10-20% silt (Smyth, 1966 cited by Alvim, 1977: 289). The various effects of clay content on plant growth are summarized in Figure 1. In causal loop diagrams such as th= is, the sign at the head of each arrow indicates the direction of change in the= quantity at the head of the arrow given an increase in the quantity at the ta= il of the arrow.

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&= nbsp; [Figure 1 here]

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Clay content of t= he surface soil is relevant to the susceptibility of soils to erosion, particularly laminar (sheet) erosion (see Fearnside, 1980). Clay, being composed of particles = of the smallest dimensions, is more easily carried away by runoff water than are coarser particles such as sand. Clay can make the soil less permeable, leading to greater amounts of runoff for a given amount of rainfall, but in well-aggregated clay soils, s= uch as those in the BDFFP area, drainage can be good despite high clay contents. In typical yellow latosols (Oxisols) the soil aggregates are not covered with silicate clay s= kins or linings, and the soils are little subject to gully erosion (Sombroek, 19= 66: 77). Oxisols with low iron co= ntent (a feature indicated by the yellow color of the BDFFP reserve soils) are the ones most susceptible to topsoil deterioration when exposed to sun and rain under agriculture, making them more susceptible to erosion when the surface becomes impermeable and runoff consequently increases (Bennema, 1977: 35).<= /p>

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&= nbsp; 2.) Available water capacity

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"Available w= ater capacity," also known as "available water," is a measure of = the amount of water that the soil can hold in a form that can be extracted by p= lant roots. It is calculated as the difference between field capacity (total moisture content of soil after gra= vity water has been allowed to drain away) and wilting point (the moisture conte= nt at which plants wilt and do not recover on re-wetting) (Young, 1976: 40). Water held in fine pores cannot be extracted from the soil at the pressures that plants are able to exert thro= ugh their roots. Field capacity is determined at 0.33 atmospheres (bars) of tension, and wilting point at 15 b= ars.

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The available wat= er capacity calculated by this standard procedure is really only an index of availability for several reasons. One is that the ability of plants to extract water from the soil var= ies among species, which may differ from the sunflower--the plant taken as a standard by soil scientists. A second is that the 0.33 bar pressure used as a standard operational definit= ion of field capacity underestimates water holding capacity: 0.1 bar is believed more appropriate, meaning that the standard method leads to an underestimat= ion of available water capacity by about 35% (Sánchez, 1976: 111). A third is that intact soil cores = taken from the side of a soil pit are the ideal material for the measurements, and results using dried material from traditional soil samples (as in the prese= nt study) provides only an indication of these parameters in the field.

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Some indications = exist that available water capacity may pose a restriction on use of water by the= forest in soils similar to those in the BDFFP reserves. A study of undeformed cores in INP= A's "Model Basin" (30 km south of the BDFFP reserves) indicated that, while pores make up 60% of the soil volume at 10 cm depth, only 17% of the water contained in the pores is available to plants, while 50% is bound wat= er and 33% is lost at a pressure less than 0.33 bar and drains away by gravity (Ferreira, 1997: 168).

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&= nbsp; 3.) Phosphorus

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Phosphorus is a limiting element for agricultural crops and cattle pasture in Brazilian Amazonia. For example, P fertilization is the key to increasing pasture grass growth in technical packages formulated by EMBRAPA (e.g. Koster et al., 1977; Serrão et al., 1979). Phosphorus is low in virtually all soils in Brazilian Amazonia, even= including relatively fertile ones such as the terra roxa (Alfisol) occurrences= in settlement areas along parts of the Transamazon Highway in Pará and = the BR-364 Highway in Rondônia (see Fearnside, 1984, 1986b). Furthermore, the prospects are poo= r for maintaining vast areas of agriculture dependent on phosphate fertilizer in Amazonia because of limited deposits of rock phosphate in Brazil and worldw= ide; virtually all of Brazil's modest supplies of this mineral are located outsi= de of Amazonia (see de Lima, 1976; Fenster and León, 1979; Fearnside, 1997a,b; Beisiegel and de Souza, 1986).

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It is available phosphorus (PO₄, actually the anion H₂PO₄<= sup>-), rather than total P, that is most directly related to plant growth. Phosphorus availability in latosol= s is generally very low because most of it is in highly insoluble Fe and Al compounds (Kamprath, 1973a: 139). Phosp= horus availability in agriculture is usually stated in terms of phosphoric anhydr= ide (P₂O₅); considering analyses made with the North Carolina extractant (0.05 N= HCl and 0.025 N H₂SO₄) that is standard in Brazil, 1 milliequivalent (m.e.) of PO₄^3-/100 g of dry soil is equal to 103 parts per million (ppm) of P or 23.7 mg of P₂O_=
5/100 g of dry soil (e.g., Vieira, 1975: 451-453). While the total soil P obviously s= ets a limit on the amount of available phosphorus that can be present, a variety = of soil characteristics and processes determine the available phosphorus prese= nt within this limit. Values of = pH below 5.5 are generally associated with marked decrease in phosphorus availability (Young, 1976: 299). Organic c= arbon and Fe₂O₃ both are positively related to available phosphorus (P₂O₅) in Brazilian Oxisols (Bennema, 1977: 43). Since both carbon and ir= on are associated with high clay content, the granulometric structure of the soil relates to plant growth through phosphorus, as well as through other nutrie= nts and through water. Micorrhiza= e are important in mobilizing P into available forms (St. John, 1985). Micorrhizal associations have been= found in many, but by no means all, of the few Amazonian trees that have been examined (St. John, 1980).

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&= nbsp; 4.) pH, aluminum and cations

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Little information exists on the response of Amazonian trees to different soil characters. One important exception is the cas= e of cacao, which is a native Amazonian tree that has been the subject of much m= ore research than other species due to its economic importance. It is noteworthy that, while most Amazonian trees appear to be highly tolerant of very acid soils, soil pH (s= oil reaction) is the best predictor of cacao yields (Hardy, 1961; see Fearnside, 1986b). Under the acid condit= ions that prevail in most Amazonian soils, soil pH is the single most important influence on yields for many crops, such as maize (see Fearnside, 1986b). In agricultural settings, soil pH = at the time of planting is dependent not so much on the initial pH of the soil as = on the quality of the burn that the farmer obtains when preparing the land for planting (Fearnside, 1986b). = The relations of soil pH to plant growth are included in Figure 1.

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Cation exchange capacity of soils rich in iron and aluminum oxides (such as those of the BD= FFP reserves) is pH dependent (Young, 1976: 95). In addition, low pH leads to incre= ased concentrations of ions that are toxic or useless to plants (Fe²⁺= , Al³⁺ and H⁺), which occupy some of the reduced number of binding sites that do exist. "Base saturation" is the percentage of the sites that are occupied by useful cations, or exchangeable bases (Ca²⁺, Mg²⁺, K^{+
and Na⁺, the sum of which is "total exchangeable
bases"). Cation exchange
capacity is normally calculated as total exchangeable bases + Al^{3+ +
H⁺, all in units of m.e./100 g of dry soil (Guimarães =
et
al., 1970: 85). Cation ex=
change
capacity, total exchangeable bases and base saturation are, in practice, of=
ten
calculated without data on Na⁺ (e.g., Young, 1976: 426-42=
9);
the results are little altered as sodium ions are normally present in low a=
nd
fairly constant quantities (around 0.01 m.e./100 g of dry soil).}}

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The importance of= low pH to agriculture is often due to its close relationship to toxic aluminum = ions (Al³⁺). Aluminum i= on concentration normally has a negative logarithmic relationship with pH (see Fearnside, 1984; Sánchez, 1976: 225). It is generally believed that alum= inum saturation, rather than the absolute concentration of aluminum ions, is most closely related to plant growth (Primavesi, 1981: 100). Aluminum saturation can be compute= d in two ways, with or without including exchangeable acidity (H⁺). Including H⁺, the sum o= f Al³⁺ + H⁺ is divided by the total exchangeable bases plus Al^{3+
and H⁺, and the result is expressed as a percentage (Sánc=
hez,
1976: 224). This is the same =
as
100% minus the base saturation, so only one or the other need be used in
analyzing relations with plant growth when defined in this way. In Brazil, however, aluminum satur=
ation
is usually calculated without H⁺, it being Al³⁺ divid=
ed
by the exchangeable bases + Al³⁺, expressed as a percentage (Bra=
zil,
SNLCS-EMBRAPA, 1979). Aluminum
saturation calculated in this way of over 25% indicates inhospitable soils =
for
cacao (Alvim, 1977: 291).}

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Crops vary widely= in their sensitivity to aluminum toxicity: in terms of aluminum saturation wit= h H⁺, maize can tolerate up to 60% aluminum saturation, while sorghum is restrict= ed even at very low levels of aluminum saturation (Sánchez, 1976: 231). Aluminum ions also have= a detrimental effect as one factor affecting fixation of phosphorus into unavailable forms (Kamprath, 1973b: 127).&= nbsp; Aluminum toxicity itself acts partly through phosphorus, as aluminum tends to accumulate in the roots and impede uptake and translocation of bot= h P and Ca to the aerial portion of the plant (Sánchez, 1976: 231). Aluminum in the soil solution depe= nds not only on pH but also on organic matter content of the soil: aluminum ions decrease as organic matter increases because organic matter forms strong complexes with aluminum (Sánchez, 1976: 226).

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The difference be= tween pH in potassium chloride (KCl) and water, or delta pH, indicates the charge status of an oxide system (a soil in which entire clay particles consist of iron and aluminum oxides, or allophanes, or a soil in which stable coats of these oxides cover silicate particles)(Sánchez, 1976: 140-141). If pH in KCl is less than that in = water (delta pH is negative: the usual case), then there is a net negative charge (cation exchange capacity), whereas if the reverse is the case there is a n= et positive charge (anion exchange capacity).= The magnitude of the charge affects the soil pH at the isoelectric point, or zero point of charge. This, in turn, determines the cation exchange capacity (CEC) at any given soil pH level.⁽²⁾ The organic matter content of the soil has a strong influence on the= se relationships and resulting CEC (Bennema, 1977: 39; Sánchez, 1976: 146). Delta pH is often used = as an indicator of organic matter. = If pH in KCl is higher than pH in H₂O (delta pH is positive), then org= anic matter is low. Delta pH is no= rmally positively associated with C/N ratio in Amazonian soils, especially when disparate soils are compared with wide differences in delta pH values (Tana= ka et al., 1984: 55).

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&= nbsp; 5.) Nitrogen

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Nitrogen is traditionally considered to be the major nutrient deficiency in tropical agriculture (National Academy of Sciences, 1972: 8; Webster and Wilson, 198= 0: 220). Leguminous trees are ab= le to fix nitrogen with the aid of symbiotic bacteria, which probably gives membe= rs of this superfamily a competitive advantage over species in families that l= ack this capability. This helps e= xplain why legumes are a common group in the BDFFP reserves, but hardly a dominant one: Burseraceae, Sapotaceae and Lecythidaceae are all more common (Rankin-de-Merona et al., 1990: 574). In a model developed by the BIONTE (Biomass and Nutrients) project for INPA's "Model Basin" (located= on the same soil type about 30 km south of the BDFFP reserves), N was assumed = to be limiting for the forest as a whole (Biot et al., 1997: 284). In the BDFFP reserves, total N was= found to be positively correlated with forest biomass (Laurance et al., 19= 99).

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Sollins (1998: 23) reviews literature on relation of soils to lowland rainforest composition, = and suggests that soil factors in decreasing order of importance would be: P availability, Al toxicity, depth to water table, amount and arrangement of pores of different sizes, and availability of base-metal cations, micronutrients (e.g., B, Zn), and N. Nitrogen is listed last because mo= st lowland tropical soils are relatively N-rich. On the other hand, studies in trop= ical montane forests in Venezuela, Jamaica and Hawaii have found relations of tr= ee occurrence and growth with nitrogen (Tanner et al., 1998). Work on tropical montane forests i= s more advanced than that in lowland forests, as some montane forest work includes manipulative experiments rather than relying exclusively on correlation. Tanner et al. (1998) specul= ate that many lowland forests are limited by P and many montane forests by N.

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&= nbsp; 6.) Zinc

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Zinc deficiency c= an be caused by fixation by crystalline sesquioxides (Bennema, 1977: 36). Since sesquioxides (Fe₂= O₃ and Al₂O₃) are the hallmark of latosols (Oxisols), zi= nc deficiency may pose a problem for plant growth. Zinc solubility bears a negative exponential relation to pH (pH values represent an exponent), and increases rapidly at soil pH values below 5 (Coelho and Verlengia, 1972: 58). Critical values for zinc in agricu= lture are between 1 and 2 ppm when extracted with 1 N HCl (Cox, 1973a: 183).

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&= nbsp; 7.) Manganese

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Manganese is a micronutrient required in small quantities by plants (Young, 1976: 291). However, at high levels it is toxi= c and, like aluminum toxicity, can inhibit plant growth. The availability of Mg to plants is closely tied to pH, with highest solubility at low pH. Manganese toxicity is a common pro= blem for legume production in Brazilian agriculture on acid soils (Cox, 1973a: 8= 7).

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&= nbsp; 8.) Other elements

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The above discuss= ion has been restricted to elements believed to be potentially limiting for for= est growth and for agriculture and ranching implanted on soils like those in the BDFFP reserves. The data set = for the BDFFP reserve soils includes information on sulfur and copper. Boron, molybdenum and chloride wer= e not analyzed. Total potassium was= not analyzed: this measure is ordinarily not correlated with plant uptake and t= hus is primarily just of academic interest (Cox, 1973b: 162).

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II.) TH= E BDFFP SOILS SURVEY

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A.) OVERVIEW OF T= HE DATA SET

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The soils data set contains surface samples from 1693 locations under forest in the BDFFP rese= rves (272 of which have additional samples taken at later dates), plus 41 soil profiles and 1693 soil density cores. This allows the effects of "fine scale" variation to be investigated in a way that would not be possible at the scale of soil maps = used for zoning and other planning exercises, such as those based on the RADAMBR= ASIL surveys. The BDFFP soils data indicate substantial variability within an area that is all mapped as having identical soil on the RADAMBRASIL maps.&nb= sp; The differences among sampling locations in the BDFFP reserves are sufficient to have an impact on agriculture were these areas (or areas like them) cleared and planted. The differences are also likely to affect the distribution of tree species with= in the forest, the biomass of the forest, and the susceptibility of the forest= to drought stress if exposed to climatic variability (such as that provoked by= the El Niño phenomenon), to the effects of edge formation through fragmentation and to the effects of probable climatic changes such as global warming and reduced rainfall due to loss of evapotranspiration.

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B.) FIELD METHODS=

&n= bsp;

Surface samples (= 0-20 cm) were taken using a screw-type soil auger, each sample being a composite of = 15 cores taken at haphazardly chosen locations within a 20 m X 20 m quadrat. Quadrats were delimited by permane= nt markers at the corners (PVC pipes with numbered aluminum tags), thereby allowing the same locations to be resampled in additional studies.

&n= bsp;

A soil density sa= mple was taken at the center of each quadrat where surface samples were taken. Volumetric cylinders were used 20 = cm in length and 6.9 cm in diameter. The cylinder fits into the end of a soil corer on the shaft of which a movable weight slides up and down. The weight was raised and dropped repeatedly to pound the cylinder into the gro= und. Once in place, the cylinder was re= moved by digging around it with a digging tool, taking care that the cylinder is removed completely full of soil (which requires that the cylinder not be pu= lled out from above). The soil was removed by hitting the outside of the cylinder with a blunt instrument. Samples were placed in double plas= tic bags for transport to the laboratory and weighing.

&n= bsp;

A profile to 1.5-m depth was taken at the center of a subset of the quadrats using a "Dutch"-type soil auger. Each profile was divided into eight layers, the first seven samples corresponding to layers 20-cm thick (to a total of 1.4 m) and the last samp= le representing a 10-cm layer. T= he samples were laid out on a plastic tarp beside the hole to allow identifica= tion of horizons (by color, texture and plasticity). These descriptions follow the meth= ods of Vieira and Vieira (1983: 60-81). The samples were then placed in separate plastic bags for transport = to the laboratory. The sites of = the profiles were identified by PVC pipes with numbered aluminum tags.

&n= bsp;

C.) LABORATORY ME= THODS

&n= bsp;

&= nbsp; 1.) Sample preparation, soil color, textural appearance

&= nbsp; and consistency

&n= bsp;

When surface and profile samples arrived from the field they were classified in the wet state for color (using a Munsell color chart), wet consistency (stickiness and plasticity), and textural appearance. These descriptions followed the methods of Vieira and Vieira (1983: 60-81).

&n= bsp;

The samples were = then dried in a solar dryer, followed by 24 hours in an electric oven at 1050C. After this drying, charcoal fragme= nts were removed by hand, weighed and stored.&= nbsp; The dry sample was then ground by hand (using a board and rolling pi= n), passed through a 20-mm and then a 2-mm mesh sieve. Stones and lateritic concretions r= emoved in the sieves were weighed and recorded.&n= bsp; A 300-g subsample of the dried soil was stored in a glass container = in the voucher collection ("soloteca"). Additional 300-g subsamples were prepared for the INPA (National Institute for Research in the Amazon, Manau= s, Amazonas) and CENA (Center for Nuclear Energy in Agriculture, Piracicaba, São Paulo) soils laboratories. Analyses of granulometric characters, bulk density, organic carbon, available water capacity and pH in H₂O and KCl were done at INPA; total N, total C, Al³⁺, H⁺, Ca²⁺, Mg^2=
+, K⁺, PO₄^3-, Cu, Fe, Zn, Mn were done at CEN= A. The INPA soil texture results were checked by comparison of 10 blind samples that were also analyzed by the EMBRAPA soils laboratory in Manaus; no significant differences were encountered. The CENA laborat= ory sends 10 samples every two months to one of a group of cooperating Brazilian laboratories to obtain an independent check on its chemical analyses.

&= nbsp; 2.) Soil texture

&n= bsp;

Textural (granulometric) analysis separated material into four fractions: coarse sand (grains 0.2-2.0 mm diameter), fine sand (grains 0.05-0.2 mm diameter), silt (particles 0.002-0.05 mm diameter) and clay (particles < 0.002 mm diamet= er) (Vieira and Vieira, 1983: 68). The particle size limits in these classes observe the United States Department = of Agriculture standards, rather than those of the International Society of So= il Science. The pipette method w= as used (Brazil, SNLCS-EMBRAPA, 1979).

&n= bsp;

The clay fraction= was determined by mixing soil in NaOH solution in an electric agitator. The mixture was then placed in a 1= -liter sedimentation cylinder (graduated cylinder), mixed by successive inversions, and allowed to settle. After a period of time determined from a table depending on the temperature (e.g= ., 3 hours and 33 minutes for 25^oC) a 25-ml sample is drawn at a de= pth of 5 cm, using a pipette. The material was placed in a tared container, dried in an electric oven at 105<= sup>oC, and weighed.

&n= bsp;

Total sand (coars= e + fine) was determined by sieving with a 0.053-mm mesh, followed by washing, = oven drying and weighing the material. This material was then sieved in a 0.2-mm mesh sieve. The fine sand was weighed and the = coarse sand determined by difference from the total sand. Silt was determined by difference = of the total sample weight from the sum of the other three fractions.

&n= bsp;

&= nbsp; 3.) Bulk density

&n= bsp;

The samples were = dried to constant weight in an electric oven at 105^oC. Fine roots and charcoal were remov= ed from these samples immediately after dry weight determination. Roots were then washed, oven dried= , and weighed. Charcoal was stored = after weighing.

&n= bsp;

&= nbsp; 4.) Organic carbon and organic matter

&n= bsp;

The modified Walkley-Black method was used at INPA, in which volumetric measurements are made by potassium bichromate and titration with ferrous sulfate. The method (Walkley and Black, 193= 4) included the modifications employed by EMBRAPA (Brazil, SNLCS-EMBRAPA, 1979). Organic matter was der= ived by multiplying the result by the constant 1.72 (Brazil, SNLCS-EMBRAPA, 1979= ), a common practice (Young, 1976: 102). The C/N ratio is organic carbon (Walkley-Black) divided by total nitrogen.

&n= bsp;

&= nbsp; 5.) Available water capacity

&n= bsp;

Field capacity and wilting point were determined indirectly on a pressure membrane apparatus (= Soil Moisture Equipment Co., Santa Barbara, California), field capacity corresponding to moisture content under a suction of 0.33 atmospheres (bars) and wilting point to the corresponding value at 15 atmospheres. The difference in the amount of wa= ter held by the soil between these two points is the available water capacity. = Moisture content and available water capacity are expressed as percent water by weight on a dry basis (Klar, 198= 4: 71). Note that, following the standard procedure, the soil was dried and sieved before being placed in the pressure plate rings, thereby altering its structure.

&n= bsp;

&= nbsp; 6.) pH in water and KCl

&n= bsp;

Soil reaction (pH= ) in distilled H₂O and in 1-N KCl solution were measured using a pH meter. The ratio of oven-dried soil to water or KCl solution is 1:1 on a volumetric basis (20 ml soil in 20 ml of water or solution). Work in tropical soils elsewhere h= as found soil reaction to increase by about 0.3 pH units in dried samples as compared to field conditions; pH is unaffected by storage once dried (Gillm= an and Murtha, 1983).

&n= bsp;

&= nbsp; 7.) Macro and micronutrients

&n= bsp;

The CENA methodol= ogy for micronutrient determination has been described by Zagatto et al. (1981) and Jorgensen (1977). Samples were digested using the EMBRAPA methodology: 0.0025 N HCl and 0.005 N H₂SO₄ (SNLS-EMBRAPA, 1979). K⁺ was measured by atom= ic emission spectrophotometry in an air-acetylene flame, using a Perkin-Elmer AAS 306; = Al³⁺, Ca²⁺, Cu, Fe, Mg²⁺, Mn²⁺, Na⁺ a= nd Zn⁺ by atomic absorption spectrometry with plasma induced in arg= on, using a computerized spectrometer system (Jarell-Ash Plasma Atomcomp Direct Reading Spectrometer), and S (as barium sulphate) by turbidimetry, using a = flow injection system. PO_{4^3-
was determined in an autoanalyzer using the molybdenum blue method (Jorgens=
en,
1977: 10).}

&n= bsp;

&= nbsp; 8.) Total nitrogen

&n= bsp;

Total nitrogen was determined with Kjeldahl digestion, using a mixture of H₂O₂₌, Li₂SO₄ and concentrated H₂SO₄ to break down the organic matter and transform the nitrogen in the sample into= NH₄⁺ (Parkinson and Allen, 1975). = The distillate was titrated with H₂SO₄.

&n= bsp;

&= nbsp; 9.) Total carbon (%C)

&n= bsp;

Total carbon (%C)= was determined at CENA by the "dry" method, which converts the forms = of soil carbon into CO₂ by combustion at 1100^oC. The gas was then sent to a standard sodium chloride cell, where the difference in electrical conductivity betwe= en this solution and one carbonated with CO₂ was detected, and the result is expressed in milligrams of carbon (Cerri et al., 1990).

&n= bsp;

D.) SUMMARY OF RE= SULTS

&n= bsp;

Results presented= here are restricted to the 54 non-contiguous ha in which tree surveys were carried o= ut in the BDFFP project. The res= erve area lacks patches of the best Amazonian soil types (anthropogenic black so= il "terra preta do índio" and Alfisol "terra r= oxa estruturada"), and of the worst types such as white sand campin= a soils (podzols or Spodosols). Nevertheless, the data indicate a substantial variation in soil qual= ity despite its uniformity at the level of general soil maps. Table 1 presents descriptive statis= tics on the hectare-level means.

&n= bsp;

&= nbsp; [Table 1 here]

&n= bsp;

Clay content (mean value =3D 54.7%) varies greatly from 18.0-68.8%; since clay content is clos= ely tied to several indicators of soil fertility, the general fertility level a= lso varies substantially. The mea= n soil would be classed in the middle of the "clay" category (Vieira and Vieira, 1983: 68). The soil h= as a significant percentage of silt-sized particles (mean value =3D 21.2%), maki= ng the soil differ from some Amazonian soils where particles are concentrated in s= and and clay fractions, with very little in between. However, it is possible that incom= plete dispersion of clay aggregation could result in an apparent abundance of silt-sized particles when, in fact, this size class is present in much smal= ler amounts (Thierry Desjardins, personal communication, 1998). The statistical analyses were ther= efore performed lumping the clay and silt categories.

&n= bsp;

The terrain is undulating, with a mean slope of 10.8%.&nb= sp; A portion of the area with steep slopes would be prone to erosion problems, in addition to not being appropriate for mechanized agriculture.<= span style=3D'mso-spacerun:yes'> Land-use capability classifications consider slopes of 0-2% to be without limitations, 2-8% to be slightly limi= ted, 8-30% to be moderately limited, and >30% to be very limited (Benites, 19= 94: 215). By these criteria, of t= he 373 points with slope measurements in the data set used in the current study, 5= 7.1% had no limitation, 23.6% had slight limitation, 23.1% had moderate limitati= on, and 2.7% had severe limitation from slope.= In addition, the clay/sand content ratio of 2.6 (or 3.1, considering clay+silt) in the BDFFP reserves is considered an impediment to mechanizati= on, the maximum value of this ratio considered appropriate being 2.0 (Vieira and Vieira, 1983: 123). However, = this classification of mechanization limitations based on clay content represent= s an average for all of Brazil; W. Sombroek (personal communication, 1998) belie= ves that yellow latosols in Amazonia, such as those in the BDFFP reserves, soils with higher clay contents could be mechanized.

&n= bsp;

The soil is quite= acid, with a mean pH in water of 4.0 (range 3.4-4.4). However, it should be remembered t= hat what is of interest to agriculture is not the level of nutrients in the soil under forest, but rather the level that will be present when the forest is cleared and burned. Especiall= y in the case of pH, the values are increased by an amount that varies with burn quality (Fearnside, 1986b).

&n= bsp;

The delta pH (mean value =3D -0.3) indicates a net negative charge (a cation exchange capacity). This also confirms= a reasonable level for soil organic matter by the standards of tropical agricultural soils.

&n= bsp;

Aluminum saturati= on (excluding H⁺), with a mean value of 92.4%, is clearly high, whi= le base saturation (without Na⁺) is low, with a mean value of only 7.6%. Al saturation (without = H⁺) < 50% is classified as low and > 50% is classified as high (Vieira and Vieira, 1983: 144).

&n= bsp;

Total carbon had = a mean value of 1.96%. Organic carbo= n, as determined by the modified Walkley-Black method, had a mean value of 1.58%. Organic matter had a m= ean value of 2.72%. While these v= alues may appear low for agricultural soils, they are typical of Amazonia.

&n= bsp;

Total N levels are moderate, with a mean value of 0.16%; values less than 0.1% are considered "low" with a probable response to fertilization in crop plants, w= hile values in the 0.1-0.2% are in the "moderate" range where responses are possible (Young, 1976: 291). The C/N ratio (mean value =3D 9.9) indicates a reasonable, but not i= deal, quantity of nitrogen available to plants (values over 15 indicate little N in availa= ble forms).

&n= bsp;

Phosphate levels = are very low (mean value of PO₄^3-=3D0.030 m.e./100 g of d= ry soil). PO₄^{3-<=
/sup>
is considered "insufficient" for crop plants with levels <0.09=
7 m.e./100
g of dry soil (equivalent to <2.30 m.e. of P₂O₅/10=
0 g
of dry soil); "fair" levels of PO₄^3- are
0.097-0.253 m.e./100 g of dry soil (equivalent to 2.30-6.00 m.e. of P₂₌O₅/100
g of dry soil), while "good" soil has >0.253 m.e. of PO_{4<=
/sub>^3-/100
g of dry soil (>6.00 m.e. of P₂O₅/100 g of dry soi=
l)
(Vieira and Vieira, 1983: 144)}}

&n= bsp;

Exchangeable pota= ssium (K⁺) is low, with a mean value of 0.06 m.e./100 g of soil; values below 0.2 m.e./100 g of dry soil are considered to be low, with fertilizer response probable in crop plants (Young, 1976: 291). A land-use capability rating syste= m used in Brazil (Vieira and Vieira, 1983: 144) classifies K⁺ levels &l= t; 0.11 m.e./100 g of dry soil as "insufficient," 0.11-0.37 as "fair" and > 0.37 as "good." The absolute minimum level require= d by crop plants is considered to be 0.10 m.e./100 g of dry soil (Boyer, 1972: 102). Of the nutrient cations, potassium is the one which is in greatest demand by crops (Webster and Wils= on, 1980: 74). Lack of exchangeab= le potassium may therefore affect plant growth on these soils.

&n= bsp;

Calcium ion concentrations are very low (mean value =3D 0.058 m.e./100 g of dry soil).<= span style=3D'mso-spacerun:yes'> For crops, < 1.50 m.e./100 g of= dry soil is "insufficient," 1.50-3.50 m.e./100 g of dry soil is "fair," and > 3.50 m.e./100 g of dry soil is "good" (Vieira and Vieira, 1983: 144).

&n= bsp;

Magnesium ion concentrations are fair (mean value =3D 0.076 m.e./100 g of dry soil. For crops, < 0.50 m.e./100 g of= dry soil is "insufficient," 0.50-1.00 m.e./100 g of dry soil is "fair" and > 1.00 m.e./100 g of dry soil is "good" (Vieira and Vieira, 1983: 144).

&n= bsp;

The cation exchan= ge capacity (expressed as the CEC of clay) is very low, with a mean value of 1= 4.4 m.e./100 g of clay after correction for carbon. With a correction for carbon (each= 1% of carbon is considered to correspond to 4.5 m.e. of CEC), a CEC of 24 m.e./100 g of clay is the dividing line between "low" and "high" activity clay (Vieira and Vieira, 1983: 38). The value of 24 m.e./100 g of clay= is considered low for agriculture (Benites, 1994: 231).

&n= bsp;

Base saturation &= gt;50% is considered as indicating high fertility, provided that CEC of the clay is > 24 m.e./100 g of clay; base saturation < 50% indicates moderate fertility like these (Benites, 1994: 215; see also Vieira and Vieira, 1983: 46). The mean value of 7.6% (without Na⁺) in the study area is obviously low, as is the maxi= mum value of 12.2%. The Na^{+<=
/sup>
levels found appear to be unusually high (mean =3D 0.052 m.e./100 g dry soi=
l);
combined with very low values for Ca²⁺ (mean =3D 0.058 m.e./100 =
g dry
soil), Na⁺ makes an unusually large contribution to exchangeable
bases. However, due to the mu=
ch
less complete data set for Na⁺, cation measures excluding sodium
were used in the statistical analyses.}

&n= bsp;

The relationships= among soil characters in the BDFFP data set are summarized in Table 2 for total exchangeable bases, organic matter, aluminum ions and available water capacity. Some of the other relationships shown in Figure 1, although known to hold generally, did not emerge as significant regressions in the BDFFP data set. Total exchangeable bases was not predicted significantly better by including pH (or log₁₀ pH) in = the regression, although higher pH values are known to be associated with great= er amounts of cations such as Ca²⁺ and Mg²⁺ (see Fearnsi= de, 1986). Phosphate was not significantly related to pH, Al³⁺ and Fe in the data set.

&n= bsp;

&= nbsp; [Table 2 here]

&n= bsp;

III.) L= ESSONS FOR DEVELOPMENT

&n= bsp;

A.) AGRICULTURAL POTENTIAL

&n= bsp;

Our knowledge of = the BDFFP reserve area greatly exceeds the level of knowledge that can be expec= ted to be available for zoning decisions in the rest of Amazonia. If we can't come up with well-foun= ded recommendations for this area, then we can't expect such suggestions to be obtainable for the vast majority of the rest of the region.

&n= bsp;

Wide differences = of opinion exist as to whether a given fertility level means that an area shou= ld be opened up for agriculture, or that it should be maintained under forest cover. The data make clear th= at soil fertility is low and impediments to agriculture, such as aluminum toxicity, are substantial. Th= is suite of problems leads some authors to conclude that soils such as these should not be used for agriculture. Irion (1978: 519), for example, concludes for soils on the Barreiras= [or Alter do Chão] formation, such as those of the BDFFP reserves, "extensive cultivation .... is impossible, as the quality of the soil = is insufficient. Any such cultiv= ation, aiming at export of agricultural products, would result in an impoverishmen= t of the soil within a few years, thus making the soil agriculturally unusable f= or many decades." Van Wambe= ke (1978: 233) warns of the potential for "irreversible destruction of so= ils and the creation of fertility deserts." On the other hand, Serrão a= nd Homma (1993: 287), look at the same situation and conclude that 70% of the = land in Brazilian Amazonia is "appropriate for crop production," and assert that "regions with low fertility and acidic soils have not been transformed into deserts, as some have foreseen.... On the contrary, such regions have= been very dynamic in terms of agricultural development" (Serrão and Homma, 1993: 288). Whether th= ese regions can be described as "very dynamic" is open to question; c= ertainly the cattle ranches on which the BDFFP reserves are located are less-than-dynamic.

&n= bsp;

Amazonia in gener= al, and particularly the state of Amazonas, has been protected from deforestati= on by impediments that include poor soils, human diseases (particularly malari= a), and difficulty of access from densely populated portions of Brazil. But disease is no longer the barri= er that it once was; for example, if opening areas today required the sacrific= e of lives that opening the Madeira-Mamoré railway in Rondônia did = at the beginning of the 20th century, then the threat of deforestation would be mu= ch less. Poor access is also rap= idly changing: many settlers have gone directly from Rondônia to Roraima, bypassing the state of Amazonas through which they passed, a phenomenon exp= lained both by somewhat better soils on the Guianan Shield in Roraima and by inducements offered by the government of Roraima. The paving of the BR-319 Highway l= inking Rondônia with Manaus, expected in 1998 or 1999, will open the flood g= ates to an influx of prospective settlers to central Amazonia, including the gen= eral area of the BDFFP reserves.

&n= bsp;

What happens when= an area like the BDFFP reserves is cleared depends on what is done with the land. Some options are better= than others from the standpoints of maximizing sustainability and minimizing environmental impact. Agrofor= estry, for example, is better in many respects than either annual crops or cattle pasture. However, severe limitations exist for widespread use of agroforestry in the vast areas of degraded lands that have already been created in the Amazon region, let alo= ne the even larger areas that would exist if more forest were to be cut in the belief that agroforestry can solve the problems of sustaining production to support the human population (Fearnside, 1998).

&n= bsp;

The political dis= course surrounding the announcement of new settlements in Amazonia is invariably permeated with images of permanent prosperity emanating from the agricultur= al systems to be implanted in the cleared areas. However, governmental decisions to= cut the forest and implant agricultural settlements in areas with soils like th= ose of the BDFFP reserves, which are typical of vast expanses of Amazonia, imply one of two things: either the government must be willing to provide or subsidize regular inputs of fertilizers for the indefinite future (a highly improbable scenario), or the decision-maker must accept responsibility for trading the forest for a landscape of degraded cattle pastures and second growth stands. Unfortunately,= this responsibility is frequently avoided by recourse to histrionic devices such= as the platitude that agriculture or ranching will be sustainable with "adequate management," the implication being that, if the project fails at some future date, then it will be the farmers who are to blame for= not having applied "adequate management."

&n= bsp;

Two of the princi= pal crops that are currently in fashion for promotion in Amazonia are not likel= y to be successful in areas like the BDFFP reserves. One is oil palm (e.g., Smit= h et al., 1995). In areas with= a significant dry season (such as the Manaus area) the yield of oil drops qui= ckly in comparison with optimal yields. In Brazilian Amazonia the two locations with optimal climatic condit= ions for oil palm are the area near Belém (Pará) and Tefé (Amazonas). Plantations in the Belém area suffer the effects of shoot rot disease (Fearnside, 1990). Because most of the co= sts of establishing and maintaining plantations are similar on a per-area basis regardless of the yield, plantations located in suboptimal areas are unlike= ly to be competitive.

&n= bsp;

The other crop th= at figures heavily in current discourse on Amazonian development is soybeans.<= span style=3D'mso-spacerun:yes'> Unfortunately, the social benefits= from this crop are small for the local population, and the mechanized agricultur= e it requires is capital and chemical intensive. Although mechanization in areas li= ke the BDFFP reserves is not impossible, the area would be less-than-ideal for the= se methods due to undulating terrain and high clay content of the soil.

&n= bsp;

Low soil fertilit= y can be compensated by application of fertilizers. However, a series of severe constr= aints limits the extent to which agriculture based on a supply of nutrients from fertilizers can be extended to wide areas in Amazonia. The best example of this is the hi= story of the "Yurimaguas technology" developed for continuous cultivati= on in Amazonian Peru (Sánchez et al., 1982; see Fearnside, 1987, 1988; Walker et al., 1987). Despite a long list of subsidies ranging from chemical inputs to free soil analyses and technical advice on a field-by-field basis, this high-inp= ut management package did not gain popular acceptance in the area. The limits of physical resources, = such a phosphate deposits, as well as financial and institutional restraints, make widespread use of such systems unlikely (see Fearnside, 1997a).

&n= bsp;

Application of fertilizers is only one means by which soil fertility limitations can be addressed. One must consider = the extent to which the agricultural prospects of areas like the BDFFP reserves would change if other kinds of technical advances were to occur. For example, recent progress has b= een made on removing aluminum saturation limitations through development of transgenic crop plants (Barinaga, 1997; de la Fuente et al., 1997).<= span style=3D'mso-spacerun:yes'> It is not inconceivable that phosp= horus limitations could be relaxed by development of crop plants with appropriate micorrhizal associations. Nit= rogen limitations of various non-leguminous crops may be relaxed through pseudosymbiotic relationships with a variety of types of nitrogen-fixing bacteria, an area in which significant advances have been achieved in Brazil through the work of Johanna Dbereiner (e.g., D= bereiner, 1992).

&n= bsp;

Today, however, i= t is not viable to sustain agriculture and ranching in vast areas of Amazonia du= e to limits of markets, phosphate deposits, and funds. Therefore one should not count on a "technological fix" to solve the problems of sustainability until such time as the technological advances in question are actually achieved.<= span style=3D'mso-spacerun:yes'> Not "counting one's chickens = before they hatch" is a universal precautionary principle that is a basic rule for avoiding unwise adventures in myriad situations in addition to this one. One should also remember= that the environmental costs of forest loss must be considered, and that these c= osts do not change much if the agriculture implanted is productive or not (see Fearnside, 1997a).

&n= bsp;

The soils in the = BDFFP reserves are clearly infertile: indicators of soil fertility such as pH, ca= tion exchange capacity, total exchangeable bases and PO₄^3-= are low, while aluminum saturation is high.&nb= sp; Under such circumstances, it is logical to maintain these areas under forest rather than converting them to short-lived low-productivity land use= s. But to what extent would the situa= tion be different if the soils were more productive? What level of soil quality would m= ake it worthwhile to sacrifice the forest? There are no simple answers to these questions. Rational decision-making will requ= ire assessment of the value of both the agricultural production that can realistically be expected from the area and the environmental cost of sacrificing the forest.

&n= bsp;

So what is the lesson? These researchers are= of the opinion that the main lesson is that the returns from converting areas = like the BDFFP reserves to agriculture or ranching are minimal when compared the true value of the environmental services of the intact forest. Even though the amount that countr= ies like Brazil may one day be able to collect on the basis of supplying these services is much less than the true value of the services, the returns from agriculture and ranching are also meager when compared with the amounts that might, in fact, be collected (Fearnside, 1997c; see Chapter ____[Laurance])= .

&n= bsp;

B.) PROSPECTS FOR RESILIENCE

&n= bsp;

The relationships= found among the various soil characters follow patterns that are consistent with = what is generally known about tropical soils.&n= bsp; These relationships (Figure 1 and Table 2) give an indication of som= e of the changes that are likely to occur in the face of human interventions suc= h as selective logging, formation of edges through forest fragmentation, and rem= oval of forest for implanting agriculture and ranching.

&n= bsp;

In the case of lo= gging, changes normally found include soil compaction along logging tractor paths (Veríssimo et al., 1992).&nb= sp; Nutrients are exported in the biomass removed (Ferraz et al., 1997), and soil cations are lost through leaching and runoff (Jonkers and Schmidt, 1984).

&n= bsp;

Outright removal = of the forest has major impacts on soil nutrients and structure (see review in Fearnside, 1986b). Soil chara= cters important for plant growth can be expected to change together: as they adju= st to a new equilibrium they will tend to maintain the same relationships among each other that were found in the forest soil. Burning raises pH and provides inp= uts of nutrients to the soil, but cultivation usually leads to losses of organic matter and clay that gradually reduce the ability of the soil to hold catio= ns. Some of the changes, such as loss = of organic matter content and increase in soil compaction, can be reversed thr= ough periods under fallow. How muc= h this capability for recovery of the soil, as well as capability for recovery of = the forest, compensate for the impacts of deforestation is an important area of debate. Unfortunately, the theoretical possibility of recovery if areas are abandoned for many decades= or even centuries has little relevance to the real impacts that are caused by = the continued advance of deforestation in the region.

&n= bsp;

"Resilience&= quot; is a term that is fashionable in discussions of Amazonian development, and refers to the ability of a system to recover its original characteristics if perturbed (e.g., Smith et al., 1995). Are the soils of the BDFFP reserves likely to be "resilient" if they are converted to pasture and degraded? Reasons to doubt th= is include the already low levels of key elements that would be further depleted. Among the changes t= hat normally occur are loss of clay and carbon, together with the cations that = are tied to these components. Car= bon can be regenerated under extended fallows, but, in practice, it is doubtful that fallows would be left for sufficient periods for this to happen (Fearnside, 1996a).

&n= bsp;

Different people = can look at the same set of facts and arrive at radically different conclusions. Serrão et al. (1996: 8), for example, take the regrowth of secondary forest in abandoned pastures to indicate that "the Amazonian upland forest ecosystem is fa= irly resilient to current uses," whereas others would disagree (e.g., Fearnside and Guimarães, 1996).

&n= bsp;

C.) DISTRIBUTION = OF NATURAL VEGETATION

On a broad scale,= a relationship between soil and tree species and biomass is evident, such as = the difference between campina and campinarana vegetation with low biomass that grows on white sand soils in the Manaus area, versus the upland forests with high biomass on more clayey soils like those in the BDFFP reserves. Within more similar soils, such as those in the reserve system, differences are more subtle but= are likely to be present.

&n= bsp;

Studies in various parts of the tropics indicate relationships between tree species occurrence= and soils. In a review by Sollins (1998) of 18 studies in tropical lowland forests, relations between species occurrence and soil drainage regimes were common. Notable is the study by Lescure and Boulet (1985) of 16.8 ha of forest in French Guiana, which found that 69% of the tree species showed response to soil drainage conditions.

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Only three of the= 18 studies reviewed by Sollins (1998) were able to detect relationships of tree species occurrence to chemical properties: Ashton and Hall (1992) found relationships with P and cations in Sarawak and Brunei; Clark et al. (1995) found relationships with P, Al and pH in Costa Rica, and Van Schaik = and Mirmanto (1985) found a relationship with pH in Sumatra. The three studies that reported relationships with chemical properties were those with the largest ranges of values for the chemical indicators of fertility (the independent variables). Sollins (1998) be= lieves that the limited range of soil fertility within the areas in which studies = have been attempted to relate species occurrence to chemical properties is a pri= mary reason for the elusiveness of demonstrating significant relationships. Other confounding factors include seasonal variation in some key fertility indicators, especially available phosphorus, cations and pH. B= oth of these restrictions apply to the BDFFP data set as well.

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Soil fertility and water relations can be expected to be related to seedling survival and to t= he growth rates of trees. Species associations with soils may be related to forest biomass if species characterized by larger individuals are found on certain soils. Any soils effect on the occurrence= of species that become large canopy emergents would have a great effect on bio= mass because a large part of forest biomass is often held in only a few very lar= ge individual trees (Brown et al., 1995; Clark and Clark, 1985). Independent of species effects, ge= neral favorability of the soil for plant growth can be expected to be positively associated with forest biomass. An ordination analysis relating biomass to soils in the BDFFP reserves indicat= es that 53% of the variation in biomass is explained by a gradient in clay con= tent (and positively associated levels of total N, organic C, exchangeable bases= , K⁺, Mg²⁺, Ca²⁺, Al³⁺, H⁺ and CEC)(Laurance et al., 1999).

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D.) POTENTIAL FOR RELEASE OF GREENHOUSE GASES

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Carbon stocks in = soil and in forest biomass in Amazonia are very large due to high per-hectare loading and to the vast areas of Amazonian forest still standing. Release of these stocks is a signi= ficant contributor to global warming at present rates of deforestation (Fearnside, 1996b, 1997d), while the large remaining stocks mean that the potential fut= ure importance of avoiding emission is even greater (Fearnside, 1997c). Converting each hectare of forest = to the equilibrium landscape that replaces it releases approximately 8.5 t of soil carbon to the atmosphere considering soil to 8-m depth over 15 years, or 7.= 4 t C/ha to a depth of 1 m (Fearnside and Barbosa, 1998).

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The fine-scale sp= atial variability in soil carbon distribution indicated by the BDFFP soil survey suggests a substantial amount of uncertainty is inherent in studies of soil carbon change based on "chronosequences," where the effects of la= nd uses (such as pasture) are inferred from comparison of roughly simultaneous samples taken at a series of sites with different land-use histories (see Fearnside and Barbosa, 1998). Substantial differences among these sites could be due to natural spatial variation, rather than to the effect of land use.

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&nb= sp; E.) PROBABLE RESPONSE OF FOREST TO FRAGMENTATION

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The BDFFP has pro= vided undeniable evidence that creation of edges results in greatly increased mortality of trees located near the edges (Laurance et al., 1998; Lovejoy et al., 1984). These edges have drier air than the forest interior (Kapos, 1989; Kapos et al<= /u>., 1993). Tree mortality near the edges leads to a "biomass collapse," releasing carbon to the atmosphere (Laurance et al., 1997).

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Soils are likely = to play a role in mortality of trees under these conditions. Soils with greater amounts of sand= can be expected to retain less water, leading to greater drought stress when conditions are dry due to edge proximity.&= nbsp; However, sandy soils are associated with valley bottoms, which would= be expected to have some additional water as compared to higher locations.

F.) PROBABLE RESP= ONSE OF FOREST TO CLIMATIC CHANGE

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Climatic variatio= ns and changes of several types exist or are predicted. The El Niño-Southern Oscill= ation event, a periodically recurring phenomenon which is not caused by human act= ion, results in droughts that can have significant impact on Amazonian forest. Tree species that are specialized = on wetter sites are particularly vulnerable to drought stress. For example, such species in the f= orest on Barro Colorado Island, Panama, suffered extraordinarily high mortality during the 1982-1983 El Niño event (Hubbell and Foster, 1990: 531).<= /p>

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Archeological evi= dence suggests that catastrophic fires have occurred in Amazonia at the times of major El Niño events four times over the past 2000 years: 1500, 1000, 700 and 400 B.P. (Meggers, 1994). Human action could now turn less-intense El Niño events, such as the 1982-= 1983 and 1997-1998 events, into major catastrophes. Such less-intense events are much = more frequent than major ones, but these would be added to the effects expected = from climatic changes such as reduction in rainfall from lowered evapotranspirat= ion caused by continued deforestation (Salati and Vose, 1984) and the effect of temperature and rainfall changes caused by global warming (see Fearnside, 1995). While neither of these changes is expected to result in radical reductions in rainfall, the effect= is added to that of natural variability such as that caused by El Niño events and disturbances such as logging and edge formation. Logging is rapidly increasing in Amazonian forests, leading to more flammable conditions from accumulation of slash and accidentally killed trees in the logged-over forests (Uhl and Buschbacher, 1985; Uhl and Kauffman, 1990). The continued advance of settlemen= t and deforestation in the region means that many more opportunities exist than in the past for fires to enter adjacent forests. These effects are added to those of climate variability and climatic change, increasing the risk of fire enteri= ng standing forest.

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Soils play a role= in the forest's response to these events.&nbs= p; As in the case of edge-related drought stress, trees on soils with little available water are more likely to succumb to extreme events.

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IV.) CO= NCLUSION

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The soils of the = BDFFP reserves are typical of vast areas of Brazilian Amazonia that are likely to come under increasing pressure for deforestation. These soils have low fertility, ar= e acid and have high levels of toxic aluminum ions. They also have limitations from ro= lling topography, high clay content, and low available water capacity. The soils data indicate that they = would produce little if converted to agriculture or ranching, and point to land u= ses that maintain forest cover intact as preferable. Although the value of the environm= ental services provided by standing forest currently provides no source of cash income, the potential value of these services far exceeds the returns that = can be expected from cutting the forest for agriculture or ranching.

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V.) NOT= ES

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^(1=
) The defining criterion for latosols is that the B horizon must be "latosolic," as opposed to "textural" (i.e., clayey). The criterion for identifying "latosolic" character in the Brazilian soil classification system is that the ratio of silicon and aluminum oxides (SiO= ₂:Al₂O₃) must have a value less than two (see Sombroek, 1966: 69). Values below two generally indicat= e that the silicate clay minerals have a 1:1 lattice structure (Sombroek, 1966: 80). The ratio refers to the = number of sheets of silica tetrahedra to sheets of alumina octahedra, and is one of the primary determinants of the properties of the clay (Young, 1976: 73). The lower amount of silicate in th= ese soils, as compared to those with a 2:1 lattice structure, is a consequence = of removal of silicon from the soil column (along with most weatherable minera= ls, including important nutrients for plants) over millions of years of leaching. In the absence of analytical data on SiO₂ and Al₂O₃, clay mineralogy of subsoil horizons (which are low in organic matter) can be ded= uced from the cation exchange capacity (CEC) of the clay fraction, which is calculated as the CEC of the soil divided by the percent clay, multiplied by 100; values below a cutoff point of 16-20 m.e./100 g clay indicate absence = of 2:1 lattice minerals (Young, 1976: 95-96).

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^(2=
) The pH of the extracts used in laboratory analyses affects the values obtai= ned for the cations that make up the CEC and, since the determinations are not normally made at the natural pH of the original soil, the values only repre= sent an index of the true CEC. In Brazil, the extracts used for the determinations are buffered to pH 7.0 (Brazil, SNLCS-EMBRAPA, 1979). Values for CEC determined at the pH of the soil (effective CEC) are = much lower than those determined either at pH 7.0 or at the pH 8.2 standard sometimes used in the United States (Sánchez, 1976: 150-151).

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VI.) ACKNOWLEDGMENTS

Field collections were done by Irene Tosi Ahmad, Celso Paulo de Azevedo, Ronaldo Gomes Chaves, Fernando Moreira Fernandes, Jorge Gouveia, Marcelo Vilela Gal= o, Michael Keller, Evaldo Moreira Filho, Manuel de Jesus Barros Nogueira, Fern= ando José Alves Rodrigues, Joel Costa Souza, Wilson Roberto Spironello. <= /span>Sample preparation was performed by Félix de Almeida, Raimundo Matos. Soil analyses performed at INPA we= re done by Rosinéia Gomes da Silva, Newton Falcão and Eduardo Wh= ite Martins. Takashi Muroaka coordinated analyses at the Centro de Energia Nuclear na Agricultura (CENA)= , in Piracicaba, São Paulo. The EMBRAPA soils laboratory in Manaus analyzed samples for comparison with the INPA soil texture results. Su= san G. Laurance and William F. Laurance helped with data coding and checking. Theirry Desjardins, Wim Sombroek, = Ed Tanner, Summer Wilson, and two anonymous reviewers commented on the manuscript. Financial support= was provided by World Wildlife Fund-US and the Smithsonian Institution, by the National Institute for Research in the Amazon (INPA PPIs 5-3150 and 1-3160)= and the National Council of Scientific and Technological Development (CNPq AI 350230/97-98). This is contri= bution No. _____ in the BDFFP technical series.

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&n= bsp;

FIGURE = LEGEND

&n= bsp;

Figure 1 -- Relations of soil clay content and pH to plant growt= h.

&n= bsp;

LIST OF= TABLES

&n= bsp;

Table 1 -- Variation in soil properties in the BDFFP rese= rves (0-20 cm).

&n= bsp;

Table 2 -- Relationships among soil parameters.


TABLE 1: VARIATION IN SOIL PROPER= TIES IN THE BDFFP RESERVES (0-20 cm)
	(descriptive statistics of site means)

Category	Soil character		Units	Mean	SD<= /span>	Minimum	Maximum	N
---------	---------	---------	---------	---------	---------	---------	---------	---------

GRANULOMETRIC CHARACTERS

	Clay		%	54.7	13.5	18.0	68.8	54<= /p>
	Silt		%	21.2	4.3=	8.3=	32.5	54<= /p>
	Fine sand		%	5.6=	3.9=	1.3=	18.1	54<= /p>
	Coarse sand		%	18.5	12.5	4.4=	56.6	54<= /p>

AVAILABLE WATER

	Soil moisture content @ 1/3 bar		% H2O by weight	31.8	6.8=	13.0	41.2	45<= /p>
	Soil moisture content @ 15 bars		% H2O by weight	24.3	5.8=	9.1=	31.9	45<= /p>
	Available water capacity		% H2O by weight	7.6=	2.0=	3.3=	12.0	45<= /p>

TOPOGRAPHY

	Slope		%	10.8	8.9=	1.4=	38.7	36<= /p>

CARBON

	Organic matter		%	2.1=	0.7=	0.8=	3.3=	50<= /p>
	Organic C (Walkley Black)		%	1.6=	0.3=	0.8=	2.2=	40<= /p>
	Total C		%	1.96	0.45	1.27	3.07	51<= /p>
	C/N ratio		dimensionless	9.9=	1.6=	8.4=	17.0	38<= /p>
	C stock to 20 cm		t/ha
SOIL REACTION

	pH in H2O		pH units	4.0=	0.3=	3.4=	4.4=	53<= /p>
	pH in KCl		pH units	3.8=	0.2=	3.2=	4.3=	53<= /p>
	Delta pH		pH units	-0.3	0.1=	-0.5	0.0=	53<= /p>

PRIMARY NUTRIENTS

	N (total)		%	0.16	0.03	0.10	0.21	38<= /p>
	PO43-		m.e./100 g dry soil	0.030	0.005	0.022	0.041	38<= /p>
	K+		m.e./100 g dry soil	0.060	0.011	0.032	0.077	38<= /p>

SECONDARY NUTRIENTS

	Ca2+		m.e./100 g dry soil	0.058	0.026	0.015	0.131	38<= /p>
	Mg2+		m.e./100 g dry soil	0.076	0.031	0.013	0.125	38<= /p>
	Na+		m.e./100 g dry soil	0.052	0.018	0.026	0.106	18<= /p>
	S		ppm	13.0	1.4=	10.6	15.0	13<= /p>

MICRONUTRIENTS

	Cu		ppm	0.33	0.12	0.10	0.54	24<= /p>
	Fe		ppm	137=	31<= /p>	77<= /p>	185=	24<= /p>
	Zn		ppm	1.48	0.78	0.61	2.99	24<= /p>
	Mn		ppm	1.81	0.49	0.87	2.49	24<= /p>

OTHER IONS

	Al3+		m.e./100 g dry soil	1.63	0.29	1.03	2.22	38<= /p>
	Al sat. w/ H+, w/ Na+		% of CEC	89.4	1.0=	86.3	90.5	18<= /p>
	Al sat. w/ H+, w/o Na+		% of CEC-Na+	92.4	1.7=	87.8	96.2	38<= /p>
	Al sat. w/o H+, w/ Na+		% of Base sat.+Al3+	85.2	1.2=	82.4	86.5	18<= /p>
	Al sat. w/o H+, w/o Na+		% of Base sat.+Al3+	89.5	2.2=	84.3	94.4	38<= /p>
	H+		m.e./100 g dry soil	0.70	0.12	0.39	0.85	38<= /p>

CATION MEASURES

	Cation exchange capacity (w/ Na+)		m.e./100 g dry soil	2.7=	0.2=	2.5=	3.0=	18<= /p>
	Cation exchange capacity (w/o Na+)		m.e./100 g dry soil	2.5=	0.4=	1.7=	3.3=	38<= /p>
	CEC of clay (w/o Na+)		m.e./100 g dry clay	5.1=	1.6=	3.7=	10.5	38<= /p>
	CEC of clay (w/o Na+) w/ C correction		m.e./100 g dry clay	14.4	1.7=	10.9	18.2	38<= /p>
	Total exchangeable bases (w/ Na+)		m.e./100 g dry soil	0.3=	0.0=	0.2=	0.4=	18<= /p>
	Total exchangeable bases (w/o Na+)		m.e./100 g dry soil	0.2=	0.1=	0.1=	0.3=	38<= /p>
	Base saturation (w/ Na+)		% of CEC	10.6	1.0=	9.5=	13.7	18<= /p>
	Base saturation (w/o Na+)<= /p>		% of CEC	7.6=	1.7=	3.8=	12.2	38<= /p>

TABLE 2: RELATIONSHIPS AMONG SOIL PARAMETER= S

-------------------------------------------= ----------------------

TOTAL EXCHANGEABLE BASES (without Na^+<=
/sup>):

= ; TEB =3D 1.86 X 10^-3 CS + 3.88 X 10^{-2<=
/sup>
OM - 5.20 X 10^-2}

= ; (p < 0.00001, r²=3D0.84, n=3D38)

= ; where: TEB =3D Total exchangeable bases (= without Na⁺)

= ; &n= bsp; (m.e./100g dry soil)

= ; = CS =3D Clay + silt (%)

= ; = OM =3D Organic matter (%)<= /span>

ORGANIC MATTER

= ; OM =3D 2.13 X 10^-2 CS + 1.12

= ; (p < 0.00001, r²=3D0.73, n=3D38)^a

= ; where: OM =3D Organic matter (%)

= ; = CS =3D Clay + silt (%)<= /o:p>

ALUMIN= UM IONS

&= nbsp;

&= nbsp; Al³⁺ =3D 7.66 X 10^-3 CS - 7.70 X 10^-1 log₁₀ pH = + 5.64

&= nbsp;

&= nbsp; (p < 000001, r²=3D0.78, n=3D3= 8)

= ; where: Al³⁺ =3D Al³⁺ (m.e./100g dry soil)

= ; = CS =3D Clay + silt (%)

= ; = pH =3D pH in water (pH units)

AVAILABLE WATER CAPACITY<= /p>

= ; AWC =3D 5.24 X 10^-2 CS= + 3.66

= ; (p < 0.01, r² =3D 0.43, n=3D45)

= ; where: AWC =3D Available water capacity (% H₂O by weight)

= ; = CS =3D Clay + silt (%)

-------------------------------------------= ----------------------

a.) With elimination of one outlier.

Fig. 1

=