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Dixon, Schroeder, and Winjum Global Forest Practices Database
Dixon, Schroeder, and Winjum Global Forest Practices Database
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Dixon, Schroeder, and Winjum Global Forest Practices Database
Explanation of the Global Forest Practices Database Variables
February 25, 1993
The datafile on the attached disk is the Global Forest Practices Database that was assembled in 1991 at the EPA Environmental Research Laboratory, Corvallis, OR, USA. All data is in ascii file format. Variables are delimited with a "~" (tilde) character (1841 observation, 36 variables).
Below are the field numbers, field descriptions, and maximum field lengths (given in parenthesis) for the Global Forest Practices Database.
FIELD # DESCRIPTION
1 3-character World Resources Institute nation code (3).
See FIELD 2.
2 Full name of nation (30).
CODE - NATION NAME
_
ARG - Argentina
CHN - China
MYS - Malaysia
etc.
3 4-character Bailey ecoregion code (4). See FIELD 4.
4 Full name of Bailey ecoregion (33)
CODE - ECOREGION NAME _
L100 - Boreal lowland
L200 - Humid temperate lowland
L300 - Dry tropical and temperate lowland
L400 - Humid tropical lowland
M100 - Boreal upland
M200 - Humid temperate upland
M300 - Dry tropical and temperate upland
M400 - Humid tropical upland
5 2-digit forest practice code (2). See FIELD 6.
6 Full name of forest practice (40)
CODE - FOREST PRACTICE NAME _
10 - Reforestation
20 - Afforestation
30 - Forest reserve
40 - Natural regeneration
60 - Silviculture
70 - Short rotation
80 - Agroforestry
7 Short note on forest practice. For example, an agroforestry practice
may have a short note here describing a specific type of agroforestry such
as "Hedgerow Intercropping" (20).
8 4-character tree species code. Generally, the first two letters from
the genus and the first two letters of the species make up the 4-character
code (4).
9 Wood density of tree species based on dry weight per unit of fresh
volume of wood. Note that some species may have different densities depending
on region, etc... (10)
10 Rotation length in years, minimum (10)
11 Rotation length in years, maximum (10)
12 Rotation length reference number (10).
13 Trees/hectare (10)
14 Trees/hectare reference number (10)
15 Optimistic land available in 10 hectares for practice (20). See
FIELD 16.
16 Pessimistic land available in 10 hectares for practice (20). The
words optimistic and pessimistic reflect qualitative assumptions about
the amount of resources and level of effort that could be expended on reforestation
/ regeneration. Assumptions are based on current condition of vegetation
(from satellite imagery), predisturbance vegetation (from Matthews [1983]
global vegetation map), and allowance for social and economical constraints.
For each nation, mixes of optimistic and pessimistic practices were defined
by Houghton et al. 1991.
17 Optimistic and/or pessimistic land availability reference number
(10)
18 1-character site quality classification (1)
H - High, M - Medium, L - Low.
F - Forested, W - Woodland, G - Grassland.
The F,W,G classifications were used as ecoregion
place- holders, i.e., F equates with the Bailey ecoregion L400 - Humid
tropical lowland, W and G equate with Bailey ecoregion L300 - Dry tropical
and temperate lowland.
19 Site quality reference number (10).
20 Period of growth response. Often given as a range in months (20).
21 Period of growth response reference number (10).
22 Observed growth rate minimum in m/hectare/year (20).
23 Observed growth rate maximum in m/hectare/year (20).
24 Observed growth rate reference number (10).
25 Observed tree biomass growth increment in tons/hectare (20).
26 Observed tree biomass growth increment reference number (10).
27 Cost in US dollars to initiate forest practice, minimum (20). See
FIELD 27.
28 Cost in US dollars to initiate forest practice, maximum. Based on
the year this data was collected, i.e., the costs have not been adjusted
to present value (20).
29 Cost in US dollars reference number (10).
30 Annual cost in US dollars to sustain forest practice on a yearly
bases, minimum (20). See FIELD 30.
31 Annual cost in US dollars to sustain forest practice on a yearly
bases, maximum. Based on the year this data was collected, i.e., the costs
have not been adjusted to present value (20).
32 Annual cost in US dollars reference number (10).
33 Year that costing data was collected (4).
34 Costing multiplier used to adjust costing data from the FIELD 32
US dollars to 1990 US dollars (10). The currency exchange rate for the
reference year was taken from the International Financial Statistics Tables
(IFS) published by the International Monetary Fund (IMF 1990, 1991). Using
the nation's own inflation rate (measured by the Consumer Price Index)
the reference cost data was adjusted to its own 1990 net value. This "current"
cost data was then converted back to 1990 US dollars at the 1990 exchange
rate.
35 End products, such as fuelwood, lumber, poles, etc... (250).
36 General comments (250).
THE GLOBAL FOREST PRACTICES DATABASE SPECIES LISTING
Robert K. Dixon
Paul E. Schroeder
Jack. K. Winjum
July 1, 1992
The 4-character species code corresponds to the 4-character species code given in the Global Forest Practices Database. See EXPLANATION OF THE GLOBAL FOREST PRACTICES DATABASE VARIABLES for a complete listing and description of all variables in the Global Forest Practices Database.
|
|
|
|
AAAN | Araucaria angustifolia | .45 |
|
AACU | Araucaria cunninghamia (use AASP) | .42 | |
AAHU | Araucaria huntseinii (use AASP) | .42 | |
AASP | Araucaria spp. | .42 |
|
ABAL | Abies alba (use Abies concolor) | .37 |
|
ABSP | Abies spp (use Abies concolor) | .37 |
|
ACAU | Acacia auriculiformis | .68( .6- .75) |
|
ACMA | Acacia mangium (use ACSP) | .65 | |
ACME | Acacia mearnsii(syn. mollissima) | .60 |
|
ACNI | Acacia nilotica | .68 |
|
ACSE | Acacia senegal (use ACSP) | .65 | |
ACSP | Acacia spp. | ||
(avg. of all referenced A. species) | .65 | ||
ACTO | Acacia tortilis (use ACSP) | .65 | |
ALFA | Albizia falcataria(syn. falcata) | .32 |
|
ALLE | Albizia lebbek | .51 |
|
ALPR | Albizia procera(use ALSP) | .52 | |
ALRU | Alnus lugosa | .48 | |
ALSP | Albizia spp. | .52( .45- .59) |
|
ANCH | Anthocephalus chinensis(syn. cadamba) | .36( .31- .4) |
|
ARAN | Araucaria angustifolia | .45 | |
ARSA | Acer saccharinum | .44 |
|
ARSP | Acer spp. | .50( .44- .56) |
|
ASGL | Alnus glutinosa (use ASRU) | .37 | |
ASRU | Alnus rubra | .37 |
|
ASSP | Alnus spp. (use ASRU) | .37 | |
AUKL | Aucoumea klaineana | .37 |
|
AZIN | Azadirachta indica | .68 |
|
AZSP | Asadirachta spp. | .52 |
|
BASP | Bambusa spp. (bamboo) (estimate) | .30 | |
BEAL | Betula alleghaniensis | .55 |
|
BELE | Betula lenta | .60 |
|
BEPA | Betula papyrifera | .48 |
|
BESP | Betula spp.: | ||
(avg. of all referenced B. species) | .54 | ||
BOQU | Bombacopsis quinata | .45 |
|
CAEQ | Casuarina equisetfolia | 1.0 ( .8-1.2) |
|
CASP | Casuarina spp. | .83 |
|
CESP | Cedrela spp. | .40( .3- .6) |
|
CLCA | Calliandra calothyrsus (syn. confusa) | .64( .51- .78) |
|
CLSP | Calliandra spp. (use CLCA) | .64 | |
CMSP | Cephalostachyum spp (bamboo)(est.) | .30 | |
CDAL | Cordia alliodora | .48( .44- .52) |
|
CDSP | Cordia spp.: | ||
Alliodora group | .48( .44- .52) |
|
|
Gerascanthus group | .74( .63- .84) |
|
|
COGE | Coniferous genera | .40 |
|
COHA | Conifer-Hardwood mix: | ||
(average of COGE & HAGE) | .44 | ||
CPLU | Cupressus lusitanica | .43 |
|
CPSP | Cupressus spp. (use CPLU) | .43 | |
CRJA | Cryptomeria japonica (use COGE) | .40 | |
CSSA | Cassia siamea | .70( .6- .8) |
|
CSSI | Cassia sianda (use CSSA) | .70 | |
CSSP | Cassia spp. (use CSSA) | .70 | |
CTDE | Castanea dentata | .40 |
|
CTSA | Castanea sativa (use CTDE) | .40 | |
CULA | Cunninghamia lanceolata (use COGE) | .40 | |
CUSP | Cunninghamia spp.(use COGE) | .40 | |
DASI | Dalbergia sissoo(avg. of DANI & DALA) | .78 | |
DANI | Dalbergia nigra | .80 |
|
DALA | Dalbergia latifolia | .75 |
|
DIAL | Dipterocarp alatus (use DISP) | .61 | |
DIGN | Dipterocarp genera (use DISP) | .61 | |
DIPT | Dipterocarp spp. (same as below) | .61 | |
DISP | Dipterocarp spp. | .61( .57- .65) |
|
DRAR | Dryobalanops aromatica (use DRSP) | .61 | |
DRSP | Dryobalanops spp. | .61( .57- .65) |
|
DUSP | Durio spp. | .53( .40- .66) |
|
EUCA | Eucalyptus camaldulensis | .60 |
|
EUCE | ? | .45 | |
EUCI | Eucalyptus citriodora | .93( .75-1 .1) |
|
EUDE | Eucalyptus deglupta | .55( .45- .65) |
|
EUDI | Eucalyptus diversicolor | .82 |
|
EUEX | Eucalyptus exerta (use EUSP) | .58 | |
EUGL | Eucalyptus globulus: | ||
(Forest-grown) | .80 |
|
|
(Plantation-grown) | .67 |
|
|
EUGR | Eucalyptus grandis | .48( .4- .55) |
|
EULE | Eucalyptus leizhou (use EUSP) | .58 | |
EUMA | Eucalyptus marginata | .67 |
|
EUNI | Eucalyptus nitens (use EUSP) | .58 | |
EURO | Eucalyptus robusta | .51 |
|
EUSA | Eucalyptus saligna (use EUSP) | .58 | |
EUSP | Eucalyptus spp. | ||
(avg of all referenced E. species) | .58 | ||
EUTE | Eucalyptus tereticornis (use EUSP) | .58 | |
EUUR | Eucalyptus urophylla (syn. alba) | .48 |
|
EUVI | Eucalyptus viminalis (use EUSP) | .58 | |
FASI | Fagus silvatica (Amer. beech) | .56 |
|
FRSP | Fraxinus spp. | .50( .45- .55) |
|
GLSE | Gliricidia sepium (use HAGE) | .48 | |
GLSP | Gliricidia spp. (use HAGE) | .48 | |
GMAR | Gmelina arborea | .41 |
|
GMSP | Gmelina spp. (use GMAR) | .41 | |
GOSP | Gonystylus spp. | .52 |
|
GUUL | Guazuma ulmifolia | .56 |
|
HAGE | Hardwood genera | .48 |
|
HODU | Hovenia dulcis | .62 |
|
HUCR | Hura crepitans | .36( .33- .38) |
|
JSNI | Juglans nigra | .51 |
|
JUSP | Juniperus spp. | .37( .29- .44) |
|
LADE | Larix decidua (use LALA) | .49 | |
LAGM | Larix gmelini (use LALA) | .49 | |
LALA | Larix laricina | .49 |
|
LALE | Larix leptolepis (use LALA) | .49 | |
LAOC | Larix occidentalis | .48 |
|
LAPO | Larix polonica (use LALA) | .49 | |
LASP | Larix spp. (use LALA) | .49 | |
LEGL | Leucaena glauca (use LELE) | .51 | |
LELE | Leucaena leucocephala | .51 |
|
LESP | Leucaena spp. (use LELE) | .51 | |
LIST | Liquidambar styraciflua | .46 |
|
MASP | Mangifera spp. | .52( .45- .58) |
|
MESP | Melia spp. (Syn. of Azadirachta) | .68 |
|
MGSP | Mangrova spp.: | ||
Rhizophora spp. | .90 |
|
|
Mix | Average for hardwood & conifer mix | .45 | |
NADI | Nauclea diderichii | .63 |
|
NOSP | Nothofagus spp. | .51 |
|
OCPY | Ochroma pyramidale (balsa) | .16 |
|
PASP | Paulownis spp. (use HAGE) | .48 | |
PCEX | Picea excelso (use PCSP) | .41 | |
PCMA | Picea mariana | .38 |
|
PCPU | Picea pungens (use PCSP) | .41 | |
PCSI | Picea sitchensis | .37 |
|
PCSP | Picea spp. | .41( .33-49) |
|
PIAR | Pinus armandi (use PISP) | .48 | |
PIBA | Pinus banksiana | .47 |
|
PICA | Pinus caribaea | .51( .34- .68) |
|
PICO | Pinus contorta | .38 |
|
PICS | Pinus canariensis | .60 |
|
PIEC | Pinus echinata | .47 |
|
PIEL | Pinus elliotti | .54 |
|
PIHA | Pinus halapensis | .71 |
|
PIJE | Pinus jeffreyi | .42 |
|
PIKE | Pinus kesiya (syn. insularis) | .46( .43- .5) |
|
PIKO | Pinus koraiensis (use PISP) | .48 | |
PIMA | Pinus massoniana (use PISP) | .48 | |
PIME | Pinus merkusii | .57 |
|
PIMX | Pinus maximinoi (native) | .46 |
|
PIOO | Pinus oocarpa | .55 |
|
PIPA | Pinus patula | .45( .4- .5) |
|
PIPI | Pinus pinaster (use PISP) | .48 | |
PIPN | Pinus pinea (use PISP) | .48 | |
PIPO | Pinus ponderosa | .38 |
|
PIPS | Pinus palustris | .54 |
|
PIRA | Pinus radiata | .42 |
|
PIRE | Pinus resinosa | .41 |
|
PIRO | Pinus roxburght (use PISP) | .48 | |
PISP | Pinus spp.: | ||
(avg. of all referenced P. species) | .48 | ||
PIST | Pinus strobus | .34 |
|
PISY | Pinus sylvestris (use PISP) | .48 | |
PITA | Pinus taeda | .47 |
|
PITE | Pinus tecunumanii (native) | .50 |
|
PLAC | Platanus acerifolia (use PLOC) | .46 | |
PLOC | Platanus occidentalis | .46 |
|
PMDU | Pithecelobium dulce (use HAGE) | .48 | |
POBA | Populus balsamifera | .31 |
|
POCA | Populus canadensis (use POSP) | .34 | |
PODE | Populus deltoides | .37 |
|
POEU | Populus euramericana (use POSP) | .34 | |
POGR | Populus grandidentata | .36 |
|
POSP | Populus spp. | .34( .31- .37) |
|
POTE | Populus tremuloides | .35 |
|
POTI | Populus trichocarpa | .31 |
|
POTO | Populus tomentosa (use POSP) | .34 | |
PRCH | Prosopis chilensis | .86( .80- .92) |
|
PRJU | Prosopis julifora | .70 |
|
PRSP | Prosopis spp. | ||
(avg. of all referenced P. species) | .78 | ||
PSMA | Pseudotsuga macrocarpa | .45 | |
PSME | Pseudotsuga menziesii | .45 |
|
QUPA | Quercus palustris | .69 |
|
QUSP | Quercus spp. | .66( .52- .80) |
|
ROPS | Robinia pseudoacacia | .66 |
|
ROSP | Robinia spp. (use ROPS) | .66 | |
SASP | Sassafras spp. | .42 |
|
SCAM | Schizolobium amazonicum (use HAGE) | .48 | |
SCSP | Schizolobium spp. (use HAGE) | .48 | |
SESP | Seskania spp. (use HAGE) | .48 | |
SHSP | Shorea spp.: | ||
(avg. of all referenced A. species) | .45 | ||
Balau Group | .70 |
|
|
Dk. Red Meranti-Red Luaun Grp. | .55 |
|
|
Lt. Red Meranti-Lt. Red Lauan Grp. | .40 |
|
|
White Meranti Group | .48( .4- .55) |
|
|
Yellow Meranti Group | .46( .4- .52) |
|
|
SOIN | Solanum inopium | .30 |
|
SWMA | Swietenia macrophylla | .54( .40- .68) |
|
SQSE | Sequoia sempervirens | .41 |
|
SXNI | Salix nigra | .36 |
|
SXSP | Salix spp. (use SXNI) | .36 | |
TAAR | Terminalia arjuna (use TASP) | .44 | |
TAIV | Terminalia ivorensis | .43 |
|
TASP | Terminalia spp.: | ||
(avg. of all referenced T. species) | .44 | ||
TASU | Terminalia superba | .45 |
|
TBRO | Tabebuia rosea | .58 |
|
TEGR | Tectona grandis | .55 |
|
TESP | Tectona spp (use TEGR) | .55 | |
TMDI | Taxodium distichum | .42 |
|
VISU | Virola surinamensis | .42 |
|
Robert K. Dixon
Paul E. Schroeder
Jack K. Winjum
July 1, 1992
The Ref Number corresponds to the "reference number" given in the Global Forest Practices Database. See EXPLANATION OF THE GLOBAL FOREST PRACTICES DATABASE VARIABLES for listing and description of all database variables.
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Each of the 36 fields is described in DSWVAR.TXT (ASCII text), DSWVAR.WP5 (WordPerfect 5.0 format), and in the series of PCX files DSWVAR_1.PCX through DSWVAR_4.PCX, however, the maximum field lengths given are not always accurate.
The entries in the database for field 8 (4-character tree species code) are explained in DSWSPEC.TXT (ASCII text), DSWPEC.WP5 (WordPerfect 5.0 format) and in the series of PCX files DSWSPEC1.PCX through DSWSPEC5.PCX. Furthermore, a number of fields in the database (i.e., fields 12, 14, 17, 19 ,21, 24, 26, 29, and 32) are bibliographic reference numbers which are given in DSWREF.TXT (ASCII text), DSWREF.WP5 (WordPerfect 5.0 format) and in the series of PCX files DSWREF01.PCX through DSWREF44.PCX.
The database file was imported into Microsoft Excel version 4.0 and then exported as a dBase III database file (DSWFOR.DBF). The database was quality checked and fields were converted from text to numeric format whenever possible.
Note that there is not a one-to-one relationship between geographic areas and attribute values. For example, there are three records in the database for humid temperate lowlands in Argentina with a reforestation practice on tree species AAAN (Araucaria angustifolia) with 6500, 2500, and 600 trees per hectare, respectively, and an observed growth rate minimum and maximum of 24.5, 14.5 and 8 m^3/hectare/year, respectively. All other fields for these three records are identical. For this reason the database has been left as is so that each end-user must impose his or her own criteria on the database before any sort of chlorepleth maps can be produced.
The type of each field (text or numeric) was determined, as well as the maximum number of digits in the mantissa for each numeric field (zero indicating an integer field). Each numeric field in the Excel spreadsheet was then reformatted so that all entries had the same number of digits in the mantissa, padding to the right with zeros as necessary.
The next step was to discover if the representation of the database as a spreadsheet in Excel or any of the above manipulations had caused any corruption of the data.
Character histograms were computed for both the source and exported text file and compared. As expected, there was the loss of tilde characters, and a gain in space, decimal point, and zero characters (many entries in floating point fields were originally expressed as integers). However, there was a loss of four percentage characters and a loss of seven plus sign characters.
After inspecting the source file it was discovered that four numbers were originally expressed as percentages but when imported into Excel were converted to decimals (e.g., 13% became 0.13), and these entries were flagged -- see text entries in numeric fields, below. The seven plus signs were lost because seven entries in a numeric field were originally preceded with a plus sign. By default, unsigned numbers are taken as positive, so the seven plus signs were left off.
While this test is not strictly conclusive, it was decided that the Excel spreadsheet was a faithful representation of the original database.
Field 9 -- Wood density of tree species based on dry weight per unit of fresh volume of wood. One record contained the entry "0.25 to 0.." This entry was flagged -2 (minus two).
Fields 12, 14, 17, 24, and 29 -- Bibliographic reference numbers for rotation length, trees per hectare, optimistic and/or pessimistic land availability, observed growth rate, and cost in US dollars, respectively. Multiple records contained the entries "est" "est jl," and "est rd" with varying capitalization's. These entries were flagged -2, -3, and -4, respectively (minus two, three, and four).
Field 13 -- Trees per hectare. Several records contained the entry "varies." These entries were flagged -2.
Field 15 -- Optimistic land available in kilo-hectares for practice. Several records contained the entries "20-30% of country" and ">10000" (greater than ten thousand). These entries were flagged -2 and -3, respectively.
Field 21 -- Period of growth response reference number. One record contained a semi-colon preceding the reference number. This was assumed to be a typographic error and the semicolon was removed.
Fields 22 and 23 -- Observed growth rate minimum and maximum, respectively, in cubic meters per hectare per year. Both fields contained the entries "23/1," "13%," and "23%." These entries were flagged -2, -3, and -4, respectively.
Field 25 -- Observed tree biomass growth increment in tons per hectare. This field contained 26 spreadsheet formulas that were not evaluated but were instead exported verbatim (e.g., "+F22*R22*1.6"). After export these formulas became meaningless since the original row and column ordering was lost, e.g., line 514 in the export file contains the formula "+F22*R22*1.6" (referring to the 22nd record), and calculating "+F22*R22*1.6" would mean multiplying full name of forest practice ("F" column) by one-character site quality classification ("R" column) by 1.6. These entries were flagged -2.
Last, it was generally found that numeric fields would contain both zero values and blank or empty entries. Assuming that zero values should be distinguished from no entry, blank entries were flagged -1 (minus one).