by
Fabio Grita
FAO GIS Consultant
The study was carried out on a SUN SparkStation 10, using a geographical information system (ARC, Version 7.0.3, ESRI, Redlands, CA, USA) which has both raster and vector capabilities. This system is able to efficiently store geographically referenced information in a database which includes both digital maps and their attribute files. Different themes are kept separate in the database and recalled when required. Layers are combined using logical conditions and/or mathematical operations according to the criteria of the models.
Base grid
The study is based on a 5' × 5' resolution grid covering Central and South America. Themes originally stored in vector format, or having a different resolution, were converted to the base grid.
Basic themes
The basic layers used for the assessment of fish farming potential are:
Models
Two models based on multiple criteria analysis were developed for the fish farming potential assessment:
Inputs for the commercial fish farming model are:
Inputs for the subsistence fish farming model are:
Input layers are evaluated in terms of suitability by assigning scores to the pixel values. A standard classification was applied to the layers used by the two models:
4 = Very Suitable (VS)
3 = Suitable (S)
2 = Marginally Suitable (MS)
1 = Unsuitable (U)
Pixel values are therefore re-classified to these codes.
The database was entirely developed under the directory /disk4/faogis4/ffp-pond of the SUN-2. Each theme (precipitation, temperature, soils, etc.) is stored in a seperate directory:
Directory | GIS layer |
ffp_pond/rfl | Rainfall |
ffp_pond/sol | Soil suitability for ponds |
ffp_pond/newtmp | Min. and max. mean monthly temperature |
ffp_pond/pop | Population density |
ffp_pond/rds | Roads |
ffp_pond/pro | Protected areas |
ffp_pond/wbodies | Water bodies |
ffp_pond/wtp | Water temperature |
ffp_pond/fish | Suitability for fish species |
ffp_pond/pop_rds2/weight2 | Urban market potential and proximity |
ffp_pond/cpt | Potential for agricultural by-products |
ffp_pond/srd | Solar radiation |
ffp_pond/wbl | Cumulated water loss |
ffp_pond/fgs | Farm-gate sales for aquaculture |
ffp_pond/evp | Mean monthly evaporation |
ffp_pond/commercial | Commercial model |
ffp_pond/subsist | Small-scale model |
ffp_pond/statist | Statistics |
ffp_pond/tiff | Tiff files |
The GRID /disk4/faogis4/ffp-pond/SCAMASK was used as a template to standardize grid extensions (GRID command: SETWINDOW /disk4/faogis4/ffp-pond/SCAMASK). All grids consist of 1092 rows and 1020 columns; cell size is 0.08333 (5' × 5').
Grid boundaries in decimal degrees are:
X min = -119.000
X max = -34.003
Y min = -57.000
Y max = 33.996
Vector coverages were used as background to prepare the final output maps:
Directory | Coverage |
ffp_pond/cntbnd | country boundaries |
ffp_pond/graticule | graticule in latitude and longitude |
Other themes, analysed during the study, were subsequently excluded:
In addition to the database, hard copy and TIFF-format maps and statistical tables were produced for the layers listed below.
Constraint maps
Protected areas.
Input factor maps
Output factor maps
Crops per year
Yields
Tilapia (75% feeding level; 600 g harvest weight).
Feed requirements
Tilapia (75% feeding level; 600 g harvest weight).
Models
Suitability potential for commercial fish farming
Suitability potential for small-scale fish farming
Delineation of protected areas in digital format (vector) were available from the International Union for Conservation of Nature and Natural Resources (IUCN) - The World Conservation Union (IUCN, 1992). Those areas were classified as “Wild forest”, “Conservation” and “Additional forest”. They were grouped into a single class and rasterized.
The grid name is /disk4/faogis4/ffp_pond/pro/PROCSA.
They were derived from ARCWORLD 1.3 Million (ESRI, 1992) and rasterized. The grid includes major lakes and rivers which can be detected at 5' resolution.
The grid name is /disk4/faogis4/ffp_pond/wbodies/WATBODIES.
Mean maximum and minimum monthly air temperature images at 5' × 5' resolution were provided by the FAO Agrometeorology Group. Temperature data collected from the meteorological stations, were interpolated to generate a surface representing the estimated temperature values of each cell of the grid. The surface values were derived taking into account the temperature of the closest stations and the elevation.
Temperature files, originally in IDA format, were first converted to ASCII using the convert facility provided by IDA software and then imported to GRID using the ARC command ASCIIGRID (See Appendix A1 for conversion details). Twenty-four GRIDs were prepared.
To correct a small difference between the project template grid and the IDA images, the grids were first expanded by 3 cells and then “clipped” using the project template (SCAMASK).
IDA images can only contain integers from 0 to 255. Temperature data were stored in the IDA images using the following formula:
P = 2 * (T + 60)
where T is the temperature value and P is the pixel value
Therefore to re-calculate T (with a precision of 0.5°C):
T = P * 0.5 – 60
The following AML (/disk4/faogis4/ffp-pond/ida/gridproc.aml) automates these steps:
&s file1 = [open list.txt openerr -read]
&if%openerr% ne 0 &then
&return &warning Error opening file.
&s cc := [read %file1% readerr]
&do &while %readerr% ne 102
%cc%exp = expand(%cc%, 3, file, range.txt)
&s cc := [read %file1% readerr]
&end
&s aa [close -all]
setwindow/disk4/faogis4/ffp_pond/scamask
setmask/disk4/faogis4/ffp_pond/scamask
&s file1 = [open list.txt openerr -read]
&if %openerr% ne 0 &then
&return &warning Error opening file.
&s path/disk4/faogis4/ffp_pond/newtmp
&s cc := [read %file1% readerr]
&do &while %readerr% ne 102
%path%/%cc%t = float(%cc%exp * 0.5 - 60)
&s cc := [read %filel% readerr]
&end
&s aa [close -all]
setcell/disk4/faogis4/ffp_pond/scamask1
setmask off
&s filel = [open list.txt openerr -read]
&if %openerr% ne 0 &then
&return &warning Error opening file.
&s cc := [read %file1% readerr]
&do &while %readerr% ne 102
%path%/%cc% = con(isnull(%path%/%cc%t) == 1, -99, %path%/%cc%t)
&s cc := [read %file1% readerr]
&end
min.txt = sample(/disk4/faogis4/ffp_pond/scamask1, %path%/min01, %path%/min02,
%path%/min03, %path%/min04, %path%/min05, %path%/min06, %path%/min07,
%path%/min08, %path%/min09, %path%/min10, %path%/min11, %path%/min12)
max.txt = sample(/disk4/faogis4/ffp_pond/scamask1, %path%/max01, %path%/max02,
%path%/max03, %path%/max04, %path%/max05, %path%/max06, %path%/max07,
%path%/max08, %path%/max09, %path%/max10, %path%/max11, %path%/max12)
&label end
&s aa [close -all]
&return
Grid names: /disk4/faogis4/ffp_pond//newtmp/MAX01....MAX12 and MIN01....MIN12.
The source data were IDRISI binary raster files from J.D. Corbett, CIMMYT, Mexico, at 5' × 5' cell resolution.
The procedure to import these files into GRID is similar to the one described for the temperature data. IDRISI is, in fact, able to convert binary images to ASCII.
Grid names: /disk4/faogis4/ffp_pond/rfl/RFLCSA_1, RFLCSA_2.....RFLCSA_12.
The road network was derived from the Digital Chart of the World (DCW). Road coverage from ArcWorld, considered at an earlier stage, was excluded because it was incomplete for various countries, such as Argentina, Chile, Paraguay, etc. The DCW road network is an ARC/INFO coverage that had to be rasterized. It appears very dense at the resolution of the study but it was not possible to extract a subset of roads of different quality. DCW roads, in fact, are not properly classified since most of them fall within a single road class. In addition, a sharp (and unrealistic) change of class in the northern part of the South American continent coincides with the edges of map tiles. It seems to be an error in the road classification rather than a real change of road type. Therefore the “road type” parameter was not considered in the study and all roads were assumed to be motorable. A unique identifier was assigned to the pixels containing roads. They were classified as “I” while NODATA was assigned to other land.
Coverage name: /disk4/faogis4/ffp_pond/rds/RDSLINE3_CSA
Grid name: /disk4/faogis4/ffp_pond/rds/RDSGRD
This layer was provided by NCGIA from the University of California at Santa Barbara. It is a GRID produced as a result of town interpolation. The algorithm used to generate the surface also takes into account the total population of the countries and the number of people living in the 2nd level administrative districts. The grid expresses population density in persons/km2.
The grid name is: /disk4/faogis4/ffp_pond/pop/DENSSMOO_CSA
Mean monthly water temperature was calculated using 2 programs (DATAIO.EXE and WATEMP.EXE) written by S. Nath of Oregon State University. The programs require data in ASCII format, and therefore mean monthly (air) temperature grids had to be exported to ASCII files. These files were processed and the output water temperature file was converted to a grid (the procedure is described in Appendix A2).
The grid names are: /disk4/faogis4/ffp_pond/wtp/WATMP1....WATMP12
This was derived from the rasterized Soil Map of the World at 1:5 000 000 scale produced by the FAO Land and Water Development Division (AGLS). This map, originally in IDRISI format, was converted to GRID (grid name: /disk4/faogis4/ffp_pond/sol/SMU_CSA) assigning the soil mapping unit codes to the pixel values.
Suitability values were calculated by F. Nachtergaele (AGLS) for each soil mapping unit and expressed in percentages of S1 (Very suitable), S2 (Suitable) and NS (Not suitable) of the mapping unit area. The evaluation of the soil suitability for ponds of the mapping units was carried out weighting S1 by twice its value. For example, if S1 = 65 and S2 = 23, the final score is: Score = (2 * 65) + 23 = 153; values range from 0 to 200. A grid (grid name: /disk4/faogis4/ffp_pond/sol/SOLSUIT2) was created using the score values. These values were classified as follows:
200 - 141 | = Very Suitable |
140 - 21 | = Suitable |
20 - 1 | = Marginally Suitable |
<1 | = Unsuitable |
The grid name is: /disk4/faogis4/ffp_pond/sol/SOLSUIT2
This layer was produced by linking the digital map of the Agro-ecological Zones (FAO World Soil Resources Report, No. 48, 1978) to 10 land classes defined to estimate the agricultural potential in each zone (FAO, 1995). Those classes were provided by the International Institute for Applied Systems Analysis, Laxenburg (IIASA).
The 10 land classes were defined as described below.
Productive land
AT2: Moist semi-arid (LGP 120–179 days; >50% of the area Very Suitable + Suitable land)
AT3: Sub-humid (LGP 180–269 days; >50% of the area Very Suitable + Suitable land)
AT4: Humid (LGP 270–365 days; >50% of the area Very Suitable + Suitable land)
AT6: Fluvisols and gleysols (Naturally flooded land (NFL)); >50% of the area Very Suitable + Suitable land)
Marginally productive land
AT1: Dry semi-arid (LGP 75–119 days; >50% of the area Very Suitable + Suitable land + Marginally Suitable land)
AT5: Moist semi-arid, sub-humid and humid (LGP 120–365 days; >50% of the area Marginally Suitable land)
AT7: Fluvisols and gleysols (NFL); >50% of the area Marginally suitable land)
Unproductive land
NS1: Partly suitable (LGP 75–365 days or NFL; 20–50% of the area Very Suitable + Suitable land + Marginally Suitable land)
NS2: Mostly Unsuitable (LGP 75–365 days or NFL; 0–20% of the area Very Suitable + Suitable land + Marginally Suitable land)
NS3: Unsuitable
The classes were expressed in percentage of the areas of the agro-ecological zones, and the final classification was based on 2 factors: LGP and productivity. The score system was:
LGP:
4 = 270–365 days; 3 = 180–269 days; 2 = 120–179 days; 1 = <120 days.
Productivity:
4 = predominantly productive land
3 = predominantly marginally productive land
2 = predominantly unproductive land
1 = unsuitable
Class | LGP score | Productivity score | Combination |
---|---|---|---|
AT2 | 2 | 4 | 8 |
AT3 | 3 | 4 | 12 |
AT4 | 4 | 4 | 16 |
AT6 | 2 | 4 | 8 |
AT1 | 3 | 3 | 9 |
AT5 | 3 | 3 | 9 |
AT7 | 2 | 3 | 6 |
NS1 | 2.5 | 2 | 5 |
NS2 | 2.5 | 2 | 5 |
NS3 | 1 | 1 | 1 |
LGP and productivity scores were aggregated into 4 classes as follows:
Combination range | Class | Class meaning |
---|---|---|
16 - 10 | 4 | Very suitable |
9 - 7 | 3 | Suitable |
6 - 3 | 2 | Moderately suitable |
2 - 1 | 1 | Unsuitable |
Those class scores were finally assigned to the cells of the grid.
Grid name: /disk4/faogis4/ffp_pond/cpt/CROPPOT_CSA
Solar radiation (sr) is an estimate of the energy available at the land surface, which is the primary factor influencing evaporation. It can be derived from the sunset hour angle (in radians) sh and the solar declination (in radians) sd.
The sh factor is given by:
sh = arccos(-tan(latitude)*tan(sd))
and sd by:
sd = 0.4093 * sin((2¶ * sJ / 365) - 1.405)
where: J is the Julian day number.
In GRID, the factor sh was calculated for each month as follows:
January: omega1grd = acos(- tan(CSAMLATGRD / DEG) * tan(-0.36573))
February: omega2grd = acos(- tan(CSAMLATGRD / DEG) * tan(-0.2361))
March: omega3grd = acos(- tan(CSAMLATGRD / DEG) * tan(-0.04598))
April: omega4grd = acos(- tan(CSAMLATGRD / DEG) * tan(0.161754))
May: omega5grd = acos(- tan(CSAMLATGRD / DEG) * tan(0.325704))
June: omega6grd = acos(- tan(CSAMLATGRD / DEG) * tan(0.402342))
July: omega7grd = acos(- tan(CSAMLATGRD / DEG) * tan(0.370068))
August: omega8grd = acos(- tan(CSAMLATGRD / DEG) * tan(0.235586))
September: omega9grd = acos(- tan(CSAMLATGRD / DEG) * tan(0.039089))
October: omega10grd = acos(- tan(CSAMLATGRD / DEG) * tan(-0.16799))
November: omega11grd = acos(- tan(CSAMLATGRD / DEG) * tan(-0.33003))
December: omega12grd = acos(- tan(CSAMLATGRD / DEG) * tan(-0.40274))
CSAMLATGRD is a grid containing the latitude value of each cell.
Solar radiation is expressed in mm/day because the formula calculates sr as equivalent potential evaporation. It is given by:
sr = 15.392 * dr (sh * sin(latitude) * sin(sd) + cos(latitude) * cos(sd) * sin(sh))
where: dr is the relative distance between the earth and the sun, obtained from
dr = 1 + 0.033 cos(2p * J/365)
The GRID calculation is as follows:
Jan: so_1grd = 15.392 * 1.031381 * ((omega1grd * sin(CSAMLATGRD / DEG) * [sin -0.36573]) + (cos(CSAMLATGRD / DEG) * [cos -0.36573] * sin(omega1grd)))
Feb: so_2grd = 15.392 * 1.023159 * ((omega2grd * sin(CSAMLATGRD / DEG) * [sin -0.2361]) + (cos(CSAMLATGRD / DEG) * [cos -0.2361] * sin(omega2grd)))
March: so_3grd = 15.392 * 1.009004 * ((omega3grd * sin(CSAMLATGRD / DEG) * [sin -0.04598]) + (cos(CSAMLATGRD / DEG) * [cos -0.04598] * sin(omega3grd)))
April: so_4grd = 15.392 * 0.977291 * ((omega4grd * sin(CSAMLATGRD / DEG) * [sin 0.161754]) + (cos(CSAMLATGRD / DEG) * [cos 0.161754] * sin(omega4grd)))
May: so_5grd = 15.392 * 0.977291 * ((omega5grd * sin(CSAMLATGRD / DEG) * [sin 0.325704]) + (cos(CSAMLATGRD / DEG) * [cos 0.325704] * sin(omega5grd)))
June: so_6grd = 15.392 * 0.968595 * ((omega6grd * sin(CSAMLATGRD / DEG) * [sin 0.402342]) + (cos(CSAMLATGRD / DEG) * [cos 0.402342] * sin(omega6grd)))
July: so_7grd = 15.392 * 0.9684 * ((omega7grd * sin(CSAMLATGRD / DEG) * [sin 0.370068]) + (cos(CSAMLATGRD / DEG) * [cos 0.370068] * sin(omega7grd)))
Aug: so_8grd = 15.392 * 0.976892 * ((omega8grd * sin(CSAMLATGRD / DEG) * [sin 0.235586]) + (cos(CSAMLATGRD / DEG) * [cos 0.235586] * sin(omega8grd)))
Sept: so_9grd = 15.392 * 0.991531 * ((omega9grd * sin(CSAMLATGRD / DEG) * [sin 0.039089]) + (cos(CSAMLATGRD / DEG) * [cos 0.039089] * sin(omega9grd)))
Oct: so_10grd = 15.392 * 1.008463 * ((omega10grd * sin(CSAMLATGRD / DEG) * [sin -0.16799]) + (cos(CSAMLATGRD / DEG) * [cos -0.16799] * sin(omega10grd)))
Nov: so_11grd = 15.392 * 1.023126 * ((omega11grd * sin(CSAMLATGRD / DEG) * [sin -0.33003]) + (cos(CSAMLATGRD / DEG) * [cos -0.33003] * sin(omega11grd)))
Dec: so_12grd = 15.392 * 1.031529 * ((omega12grd * sin(CSAMLATGRD / DEG) * [sin -0.40274]) + (cos(CSAMLATGRD / DEG) * [cos -0.40274] * sin(omega12grd)))
This layer provides a preliminary evaluation of the market potential for aquaculture. It is based on urban locations and motorable roads and it aims to identify the extent and the location of areas influenced by a certain marketing centre, taking into account the travelling time and the importance of the urban market (in terms of the number of people living in the urban area).
Urban centres
Locations of urban centres were provided by Arc World 1:3M and the roads from the DCW (see Section 2.5). ArcWorld's urban centres are represented by points, consequently even large towns do not have an area. Points are assumed to be located at the centre of the towns. Since one of the objectives is to analyse proximity to the potential market, the undefined town extent is not a restriction as the fish market(s) could be located in any part of the town. It was important to classify the towns for their marketing potentials according to their population size. ArcWorld does not provide the actual urban population, but subdivides them into 7 ranges:
1 = > 5 000 000
2 = 1 000 000 – 5 000 000
3 = 500 000 – 1 000 000
4 = 250 000 – 500 000
5 = 100 000 – 250 000
6 = 50 000 – 100 000
7 = < 50 000
Those classes were re-grouped into 4 classes:
4 = Classes 1 and 2
3 = Classes 3 and 4
2 = Classes 5 and 6
1 = Class 7
Towns were rasterized using the GRID command POINTGRID.
Grid name: /disk4/faogis4/ffp_pond/pop_rds2/CITY_ONLY
Roads
The road network was rasterized as described in Section 2.5.
The market potential analysis calculates the least-cost path from any cell location to the closest town in real distances along the roads. Subsequently it considers the importance of the market centres as an additional factor, and modifies the itineraries accordingly; finally, it classifies the areas in the standard 4 classes.
Two assumptions were made:
The travelling time limit was fixed at 6 hours one-way, on the premise that this is the maximum possible travelling time, after which the target market is no longer convenient.
The vehicle speed was fixed to 90 km/h on the road, and at 22.5 km/h outside the mapped road system on an assumed feeder road system. It was assumed, in fact, that, although feeder roads are not included in the ArcWorld database, such unimproved roads must exist and it is possible to travel (at a lower speed) almost anywhere using the feeder road system. The consequence of this is that, although the least-cost paths are strongly conditioned by the roads, sometimes it may be more convenient to take a cross-country “shortcut” than a better quality, but very long, road.
The procedure used to prepare the market potential and proximity analysis is described in Appendix A3.
The limiting factor considered in evaluating the potential of the local market was the density of population. Urban land was considered not suitable because of the lack of space and high land cost for fish farms. The limit of the Urban/Not-Urban land was fixed to 300 persons/km2. The remaining land was classified as follows, on the basis of persons/km2:
299 - 150 | Very Suitable |
149 - 25 | Suitable |
24 - 1 | Marginally Suitable |
0 and >299 | Unsuitable |
The grid name is: /disk4/faogis4/ffp_pond/fgs/FGSCSA
The physical suitability of the considered species is evaluated by a program called FISHGRO.EXE which requires as input the mean monthly temperature file. This program matches water temperature of each cell location with the fish requirement database (SPECIES.DB). The output file contains the following data:
Longitude of the cell
Latitude of the cell
Number of crops per year
Yield in kg/ha/year
Feed requirements in kg/ha/year
Various scenarios can be analysed using this program. The user is in fact asked to specify a number of parameters which will affect the results of the simulation. These parameters are:
Feeding level (%)
Stocking weight (g)
Harvest weight (g)
Stocking density (fish/m2)
The program IMPOINT2.BAS (see Appendix A2) - already used to create water temperature grids - is used to generate grids for the three outputs of the program FISHGRO.EXE (number of crops per year; yield; feed requirements).
Among all possible parameter combinations, the following scenarios were selected for the simulations:
Group 1: Feeding levels 75% and 50%
Species | Stocking weight | Harvest weight | Stocking density |
---|---|---|---|
(g) | (g) | (fish/m2) | |
Tilapia | 50 | 300 | 3 |
50 | 600 | 1.5 | |
Tambaqui | 50 | 600 | 1.5 |
50 | 1 000 | 0.9 | |
Pacu | 50 | 600 | 1.5 |
50 | 1 000 | 0.9 | |
Carp | 100 | 600 | 1.25 |
100 | 1 500 | 0.5 |
Group 2: Small-scale fish farming (no feeding). CSC of 75 g/m2 assumed for both species
Species | Stocking weight | Harvest weight | Stocking density |
---|---|---|---|
(g) | (g) | (fish/m2) | |
Tilapia | 25 | 150 | 2 |
Carp | 50 | 350 | 1 |
The water balance for ponds was calculated by estimating the deficit between precipitation and combined evaporation + seepage.
Evaporation
Evaporation (ET) was calculated using the Hargreaves formula, which expresses ET in mm/day:
ET = 0.00126 * So * √(Tmax - Tmin) * [1.8 * ((Tmax + Tmin) /2) + 32]
where T is the air temperature and So is the solar radiation in mm/day
For the total evaporation by month the formula has to be multiplied by the number of days in the considered month. Thus the GRID calculation for January is:
el_hargr = 31 * 0.00126 * so_lgrd * sqrt(setnull(../newtmp/max01 eq -99,../newtmp/max01) - setnull(../newtmp/min01 eq -99, ../newtmp/min01)) * (1.8 * ((setnull(../newtmp/max01 eq -99, ../newtmp/max01) + setnull(../newtmp/min01 eq -99, ../newtmp/min01)) /2) + 32),
where 31 is the number of days in January; so_lgrd is the solar radiation grid for January (mm/day); and max01 and min01 are the mean monthly maximum and minimum temperature grids of January.
Seepage
Seepage was considered to be a constant of 80 mm/month.
Preparation of the total deficit
Evaporation + Seepage = Total deficit
Water balance = Precipitation - Total deficit
Two correction factors were introduced:
precipitation was multiplied by 1.1 to include the amount of rain that drains into the pond from the from the pond dikes; and
evaporation was multiplied by 1.15 to compensate for the higher evaporation from free surfaces such as small open ponds.
The calculation is the following:
totd_1n = setnull(rflcsa_1 == -99, rflcsa_1 * 1.1) / E1_hargr2 * 1.15) - 80
totdef_1n = abs(con(totd_1n > 0,0,con(totd_1n - int(totd_1n) < .5, int(totd_1n) - 1, int(totd_1n))))
which gives the deficit for January.
Preparation of the total annual deficit
It is obtained by summing up the deficits of each of the 12 months:
totdef_ann2 = totdef_1n + totdef_2n + totdef_3n + totdef_4n + totdef_5n + totdef_6n + totdef_7n + totdef_8n + totdef_9n + totdef_10n + totdef_11n + totdef_12n
The input parameters considered for the two models are listed in Section 1.1.
Commercial
This grid was produced using the following formula:
constrgrd3 = (../pop_rds2/weight2/mktgrd_sl * 0.4944) + (../wbl/wbl_sl * 0.2739) + (../sol/sol_sl * 0.1290) + (../cpt/croppot_csa * 0.0457) + (../fgs/fgsgrd * 0.057)
where: mktgrd_sl is the market size and proximity grid; wbl_sl is the water balance grid; sol_sl is the soil and terrain suitability for ponds grid; fgsgrd is the farm-gate sales grid; and croppot_csa is the agricultural by-products grid.
This grid is combined with the fish yield results (grids contained in the directory /disk4/faogis4/ffp_pond/fish) using /disk4/faogis4/ffp_pond/commercial/model.aml. This AML first checks the maximum value of the yield for fish species grid to be analysed and then subdivides it into 4 equal-interval classes (quarters), assigning the number 1 to the lowest range and 4 to the highest. The program produces a temporary grid storing those classes in the VALUE. This grid is added to the commercial model grid in which the VALUE is multiplied by 10. The VALUE of the output grid is a two-digit integer in which the first digit indicates the class of the commercial model and the second the class of yield in terms of crops/y for each fish species.
Small-scale
The formula is the following:
constrgrd4 = (../wbl/wbl_sl * 0.5095) + (../sol/sol_sl * 0.1571) + (../cpt/croppot_csa * 0.1948) + (../fgs/fgsgrd * 0.1386)
The AML to combine this grid with the physical suitability for fish species grids is /disk4/faogis4/ffp_pond/subsist/model.aml.
The result grids were converted to an equal-area projection (Flat Polar Quartic) to calculate the areas covered by each class in each country of Latin America. Statistics were produced for the grids mentioned in Section 1.3.
The grid COUNTRYGRD_PQ contains the country boundaries. The VALUE is the country code and the relevant country name is contained in the attribute (VAT) file. This grid was overlaid on the various themes in order to produce the statistics by country.
In general, these AMLs project the original grids to Flat Polar Quartic projection according to the parameters specified in the file llpolq.prj; combine the new grid with the one containing the country boundaries; add the item AREA to the attribute file and calculate the areas of each class occurring in the countries by multiplying the number of cells (COUNT) by the square of the cell size in kilometres (9.831174 * 9.831174). The country code, the country name, the class and the area are converted to an ASCII file.
The AMLs dosmld.aml, dospar.aml and dosfsh.aml convert those ASCII files from UNIX to DOS.
The directories that contain the statistical results are:
/disk4/faogis4/ffp_pond/statist/comm | commercial model |
/disk4/faogis4/ffp_pond/statist/subs | small-scale model |
/disk4/faogis4/ffp_pond/statist/fish | yield of fish species |
/disk4/faogis4/ffp_pond/statist/param | input parameters |
/disk4/faogis4/ffp_pond/statist/statdos | ASCII files converted to DOS format (all) |
The following AML (/disk4/faogis4/ffp_pond/statist/statsim.aml) produces the statistics on yield for the considered fish species. This AML first checks the maximum value of the grid to be analysed and then subdivides it into 4 equal-interval classes (quarters) assigning the number 1 to the lowest range and 4 to the highest. The program produces a temporary grid storing those classes in the VALUE. Then the grid is projected to the equal area projection and combined with the country boundaries.
&setvar ww = [close -all]
&setvar file1 = [open fish/gridlist.txt open_ok -read]
&if%open_ok% ne 0 &then
&return &warning Error opening file.
&label loop
&setvar var1 = [read %file1% read_ok]
&if%read_ok% = 102 &then
&goto end
&s pathi ../fish
&s patho fish
&s tt [exists %patho%/%var1%_pq -GRID]
&s tt1 [exists %patho%/st_%var1% -GRID]
&if%tt%eq. TRUE. &then
kill %patho%/%var1%_pq all
&if%tt1%eq. TRUE. &then
kill %patho%/st_%var1% all
&describe %pathi%/%var1%
&s max %grd$zmax%
&type %max%
&s cc 1
&s file3 [open sl1.rem openerr -write]
&if%openerr% <> 0 &then
&return &warning Error opening file.
&label calc
&if%cc%le 4 &then
&do
&s range [calc (%max%/4) * %cc% + 0.05]
&s range [substr %range% l 3]
&if %cc% eq 4 &then
&s range [calc %range% + 1]
&s string [quote %range%: %cc%]
&s ww = [write %file3%%string%]
&s cc [calc %cc% + 1]
&goto calc
&end
&s dd [close %file3%]
grid_sl = slice(../fish/%var1%, table, sl1.rem)
&sys del sl1.rem
%patho%/%var1%_pq = project(grid_sl, llpolq.prj, nearest)
%patho%/st_%var1% = combine(countrygrd_pq, %patho%/%var1%_pq)
kill grid_sl all
&sys arc joinitem %patho%/st_%var1%.vat cntlut.dat %patho%/st_%var1%.vat countrygrd-pq
countrygrd_pq
&sys arc additem %patho%/st_%var1%.vat%patho%/st_%var1%.vat area 10 10 i
q
w %patho%
tables
sel st_%var1%.vat
calculate area = count * (9.831174 * 9.831174)
sort countrygrd_pq%var1%-pq
&s out1 [substr %var1% 1 2]
&s out2 [substr %var1% 4 2]
&s out3 [substr %var1% 7 4]
unload %out1%%out2%%out3%u.fsh countrygrd_pq cntry_name %var1%_pq area delimited
init
q
w..
grid
&goto loop
&label end
&s dd [close -all]
&return
The AML (/disk4/faogis4/ffp_pond/statist/statpar.aml) produces the statistics for the input parameters of the commercial and small-scale models. Because those grids are already classified as 1,2,3 and 4, the program does not need to reclassify the grids.
The AML (/disk4/faogis4/ffp_pond/statist/statmld.aml) produces the statistics for the results of the combination of the commercial and small-scale models with the yield for the fish species. The grids are projected and combined with the country boundaries.
Grid and file names were coded in such a way that names portray the essential information about the contents of the grids.
Grids | ||
Digit | Content | Meaning |
Digits 1 – 3: | md_ | Model |
st_ | Statistics | |
Digits 4 – 5 | cl | Tambaqui (Colossoma) |
cr | Carp | |
pc | Pacu | |
tl | Tilapia | |
Digits 6 | c | Crop/year |
y | Yield | |
f | Feed requirements | |
Digits 7 – 8 | 00 | Low feeding level (CSC 0.075) |
50 | Intermediate feeding level | |
75 | High feeding level | |
Digit 9 | _ | Separator |
Digits 10 – 13 | 0300 | Harvest size (0300, 0600, 1000, 1500) |
DOS files | ||
Digits | Content | Meaning |
Digits 1 – 2 | cl | Tambaqui (Colossoma) |
cr | Carp | |
pc | Pacu | |
tl | Tilapia | |
Digits 3 – 4 | 00 | Low feeding level (CSC 0.075) |
50 | Intermediate feeding level | |
75 | High feeding level | |
Digits 5 – 8 | 0300 | Harvest size (0300, 0600, 1000, 1500) |
Extension | .fsh | Physical suitability for fish species |
.mld | Model results | |
.par | Parameters |
Grids | ||
---|---|---|
Digits | Content | Meaning |
Digits 1 – 2 | cl | Tambaqui (Colossoma) |
cr | Carp | |
pc | Pacu | |
tl | Tilapia | |
Digits 3 | c | Crop/year |
y | Yield | |
f | Feed requirements | |
Digits 4 – 5 | 00 | Low feeding level (CSC 0.075) |
50 | Intermediate feeding level | |
75 | High feeding level | |
Digits 6 | _ | Separator |
Digits 7 – 10 | 0300 | Harvest size (0300, 0600, 1000, 1500) |
IUCN. 1992. Protected Areas of the World. IUCN Publications Services Unit.
ESRI. 1992. ArcWorld 1:3M - Edition l. ESRI
FAO. 1995. World Agriculture: Towards 2010. Rome: FAO; and John Wiley.
FAO. 1978. Report on the Agro-Ecological Zones Project. [FAO] World Soil resources Report No. 48, 1978.