Global agriculture data

Spatial distribution of potential yield (crops/y) of carp fed at 75% satiation and harvested at 600g. Yield estimations for carp were based on water temperature which in turn were predicted from air temperature. Mean maximum and minimum monthly air temperatures grids were provide by FAO's Agrometeorology group. In order to explore a range of commercial aquaculture possibilities by the use of the growth model, simulations were conducted for carp, assuming two different feeding levels and two harvesting weights (600 and 1,500 g). Feed application rates corresponding to 50% and 75% satiation were assumed to represent commercial aquaculture operations with low and high feeding rates, respectively. **Data publication:** 1997-01-01 **Supplemental Information:** This map derives from a wider study that evaluates inland fish farming potential at a continental-level, published by J.M. Kapetsky and S. S. Nath with the title "A strategic assessment of the potential for freshwater fish farming in Latin America " . COPESCAL Technical Paper. No. 10. Rome, FAO. 1997. 128p. Resolution 9 km x 9 km. **Contact points:** Metadata Contact: [Jose Aguilar-Manjarrez](mailto:Jose.AguilarManjarrez@fao.org) **Data lineage:** Conversion from ASCII files to Arc/Info GRID **Online resources:** Online resource Online resource Potential yield of Carp (GRID format)

Areas in which supplementary food such as agricultural by-products could contribute to the establishment of pond culture systems such as integrated agriculture-aquaculture farming. Data on crops were derived from USGS's Global Land Cover Characteristics database. **Data publication:** 1998-01-01 **Supplemental Information:** This map derives from a wider study that evaluates inland fish farming potential at a continental-level, published by J.Aguilar-Manjarrez and S. S. Nath with the title "A strategic reassessment of fish farming potential in Africa" . CIFA Technical Paper. No. 32. Rome, FAO. 1998. 170p. This study is a follow-up of an earlier assessment of warm-water fish farming potential in Africa by Kapetsky (1994) with the objective to assess locations and areal expanses that have potential for warm-water and temperate-water fish farming in continental Africa. Resolution 5 km x 5 km. **Contact points:** Metadata Contact: [Jose Aguilar-Manjarrez](mailto:Jose.AguilarManjarrez@fao.org) **Data lineage:** Conversion from ASCII files to Arc/Info GRID **Online resources:** Online resource Cropland areas (GRID format)

Areas with market potential for commercial fish farming in Latin America. Map based on urban locations and motorable roads. It identifies the extent and the location of areas influenced by a certain marketing centre taking into account the travel time and the importance of the urban market (in terms of number of people living in the urban area). Locations or urban centres were provided by ArcWorld at a 1:3 million scale. The road network was derived from the Digital Chart of the World (DCW). **Data publication:** 1997-01-01 **Supplemental Information:** This map derives from a wider study that evaluates inland fish farming potential at a continental-level, published by J.M. Kapetsky and S. S. Nath with the title "A strategic assessment of the potential for freshwater fish farming in Latin America " . COPESCAL Technical Paper. No. 10. Rome, FAO. 1997. 128p. Resolution 9 km x 9 km. **Contact points:** Metadata Contact: [Jose Aguilar-Manjarrez](mailto:Jose.AguilarManjarrez@fao.org) **Data lineage:** Conversion from ASCII files to Arc/Info GRID **Online resources:** Online resource Online resource Potential market demand: commercial fish farming (GRID format)

Engineering capability for pond construction obtained by combining soil texture and slope. Soil texture was interpreted in terms of suitability for pond construction. Soil texture data was obtained from the FAO-UNESCO Soil Map of Africa at 1:5 million scale. This dataset includes three soil texture classes: coarse, medium and fine and these are given for the dominant soils of each association and refer only to the upper 30cm. Slope was interpreted in terms of suitability of topography for pond construction. Slope data was also obtained from the FAO-UNESCO Soil Map of Africa at 1:5 million scale. This dataset includes three slopes classes which reflect the topography in which the soil association occurs. **Data publication:** 1994-01-01 **Supplemental Information:** This map derives from a wider study that evaluates inland fish farming potential at a continental-level, published by J.M. Kapetsky with the title "A strategic assessment of warm-water fish farming potential in Africa " . CIFA Technical Paper. No. 27. Rome, FAO. 1994. 67p. Resolution 18 km x 18 km. **Contact points:** Metadata Contact: [Jose Aguilar-Manjarrez](mailto:Jose.AguilarManjarrez@fao.org) **Data lineage:** Conversion from ASCII files to Arc/Info GRID **Online resources:** Engineering capability (GRID format) CIFA Tecnical Paper 27 CIFA Tecnical Paper 27 - 8. APPENDIX Online resource

Spatial distribution of water availability for fish ponds. Map derived from the combined suitability of annual rainfall with density of perennial streams and rivers. Annual rainfall interpreted as rainfall runoff to fill fishponds. Mean annual precipitation data for Africa were obtained from UNEP/GRID in Nairobi and was developed for the Global Assessment of Soil Degradation (GLASOD) using data from 1951-1980. A digital map of perennial streams and rivers was generated by ESRI (1984). **Data publication:** 1994-01-01 **Supplemental Information:** This map derives from a wider study that evaluates inland fish farming potential at a continental-level, published by J.M. Kapetsky with the title "A strategic assessment of warm-water fish farming potential in Africa ". CIFA Technical Paper. No. 27. Rome, FAO. 1994. 67p. Resolution 18 km x 18 km. **Contact points:** Metadata Contact: [Jose Aguilar-Manjarrez](mailto:Jose.AguilarManjarrez@fao.org) **Data lineage:** Conversion from ASCII files to Arc/Info GRID **Online resources:** Water availability (GRID format) CIFA Tecnical Paper 27 CIFA Tecnical Paper 27 - 8. APPENDIX

Slope interpreted in terms of suitability of topography for pond construction. Slope data was obtained from the FAO-UNESCO Soil Map of Africa at 1:5 million scale. This dataset includes three slopes classes which reflect the topography in which the soil association occurs. **Data publication:** 1994-01-01 **Supplemental Information:** This map derives from a wider study that evaluates inland fish farming potential at a continental-level, published by J.M. Kapetsky with the title "A strategic assessment of warm-water fish farming potential in Africa ". CIFA Technical Paper. No. 27. Rome, FAO. 1994. 67p. Original map is available in raster format at a 18 km x 18 km pixel size resolution. **Contact points:** Metadata Contact: [Jose Aguilar-Manjarrez](mailto:Jose.AguilarManjarrez@fao.org) **Data lineage:** Conversion from ASCII files to Arc/Info GRID **Online resources:** Slope (GRID format) CIFA Tecnical Paper 27 CIFA Tecnical Paper 27 - 8. APPENDIX Online resource

Soil texture interpreted in terms of suitability for pond construction. Soil texture data was obtained from the FAO-UNESCO Soil Map of Africa at 1:5 million scale. This dataset includes three soil texture classes: coarse, medium and fine and these are given for the dominant soils of each association and refer only to the upper 30cm. **Data publication:** 1994-01-01 **Supplemental Information:** This map derives from a wider study that evaluates inland fish farming potential at a continental-level, published by J.M. Kapetsky with the title "A strategic assessment of warm-water fish farming potential in Africa " . CIFA Technical Paper. No. 27. Rome, FAO. 1994. 67p. Resolution 18 km x 18 km. **Contact points:** Metadata Contact: [Jose Aguilar-Manjarrez](mailto:Jose.AguilarManjarrez@fao.org) Resource Contact: National Center for Geographic Information and Analysis (NCGIA) - University of California **Data lineage:** Conversion from ASCII files to Arc/Info GRID **Online resources:** Soil texture (GRID format) CIFA Tecnical Paper 27 CIFA Tecnical Paper 27 - 8. APPENDIX Online resource

Availability of road infrastructure for the development of commercial fish farming interpreted as density of paved and motorable. The density of paved and motorable roads was used as a surrogate for the general level of infrastructure. It was reasoned that were there are main, all weather roads, there are possibilities to transport inputs and supplies to fish farms and to send products to markets than in areas without such road infrastructure. It was also reasoned that with that where all-weather roads exist, the probability of other kinds of infrastructure in close proximity, such as electricity and communication, is enhanced. Roads data was derived from the US Defense Mapping Agency (DMA) Topographic Maps in Africa and from the DMA Global Navigation and Planning Charts of Africa. **Data publication:** 1994-01-01 **Supplemental Information:** This map derives from a wider study that evaluates inland fish farming potential at a continental-level, published by J.M. Kapetsky with the title "A strategic assessment of warm-water fish farming potential in Africa " . CIFA Technical Paper. No. 27. Rome, FAO. 1994. 67p. Resolution 18 km x 18 km. **Contact points:** Metadata Contact: [Jose Aguilar-Manjarrez](mailto:Jose.AguilarManjarrez@fao.org) **Data lineage:** Conversion from ASCII files to Arc/Info GRID **Online resources:** Roads of Africa (GRID format) CIFA Tecnical Paper 27 CIFA Tecnical Paper 27 - 8. APPENDIX iiasa

Areas with suitability for small-scale fish farming development and operation. Map derived from the combined suitability of four land-quality factors important for fish farming development and operation; net annual water loss from ponds through evaporation and seepage; soil and terrain suitability for ponds; potential for agriculture by-products and fertiliser inputs; and potential for farm gate sales. **Data publication:** 1997-01-01 **Supplemental Information:** This map derives from a wider study that evaluates inland fish farming potential at a continental-level, published by J.M. Kapetsky and S. S. Nath with the title "A strategic assessment of the potential for freshwater fish farming in Latin America " . COPESCAL Technical Paper. No. 10. Rome, FAO. 1997. 128p. Resolution 9 km x 9 km. **Contact points:** Metadata Contact: [Jose Aguilar-Manjarrez](mailto:Jose.AguilarManjarrez@fao.org) **Data lineage:** Conversion from ASCII files to Arc/Info GRID **Online resources:** Online resource Online resource Suitability for small scale fish farming (GRID format)

Areas with potential for subsistence fish farming under three temperature regimes providing for one crop/year (1C) with fair to good growth (FGG), two crops/year with fair growth (FG) and with good growth (GG) in combination with four land-quality factors important for fish farming development and operation; water availability, engineering capability, and economic considerations ranging from all suitable to one suitable and two optimum (S-NO), or all optimum (all 0). **Data publication:** 1994-01-01 **Supplemental Information:** This map derives from a wider study that evaluates inland fish farming potential at a continental-level, published by J.M. Kapetsky with the title "A strategic assessment of warm-water fish farming potential in Africa " . CIFA Technical Paper. No. 27. Rome, FAO. 1994. 67p. Resolution 18 km x 18 km. **Contact points:** Metadata Contact: [Jose Aguilar-Manjarrez](mailto:Jose.AguilarManjarrez@fao.org) **Data lineage:** Conversion from ASCII files to Arc/Info GRID **Online resources:** Subsistence fish farming potential (GRID format) CIFA Tecnical Paper 27 CIFA Tecnical Paper 27 - 8. APPENDIX Online resource

Spatial distribution of annual precipitation interpreted as rainfall runoff to fill fish ponds. Mean annual precipitation data for Africa were obtained from UNEP/GRID in Nairobi and was developed for the Global Assessment of Soil Degradation (GLASOD) using data from 1951-1980. **Data publication:** 1994-01-01 **Supplemental Information:** This map derives from a wider study that evaluates inland fish farming potential at a continental-level, published by J.M. Kapetsky with the title "A strategic assessment of warm-water fish farming potential in Africa " . CIFA Technical Paper. No. 27. Rome, FAO. 1994. 67p. Resolution 18 km x 18 km. **Contact points:** Metadata Contact: [Jose Aguilar-Manjarrez](mailto:Jose.AguilarManjarrez@fao.org) **Data lineage:** Conversion from ASCII files to Arc/Info GRID **Online resources:** Annual rainfall (GRID format) CIFA Tecnical Paper 27 CIFA Tecnical Paper 27 - 8. APPENDIX IIASA

Population density (inhabitants/km2) represented in terms of local market demand for fish farming at the commercial level. Population density as inhabitants/km2 for the year 1988 was obtained from UNEP/GRID Nairobi. **Data publication:** 1994-01-01 **Supplemental Information:** This map derives from a wider study that evaluates inland fish farming potential at a continental-level, published by J.M. Kapetsky with the title "A strategic assessment of warm-water fish farming potential in Africa " . CIFA Technical Paper. No. 27. Rome, FAO. 1994. 67p. Resolution 18 km x 18 km. **Contact points:** Metadata Contact: [Jose Aguilar-Manjarrez](mailto:Jose.AguilarManjarrez@fao.org) **Data lineage:** Conversion from ASCII files to Arc/Info GRID **Online resources:** Local market demand (commercial) (GRID format) CIFA Tecnical Paper 27 CIFA Tecnical Paper 27 - 8. APPENDIX IIASA

Spatial distribution of water temperature regimes most favourable for warm water fish farming given one and two crops per year. Using the Nile tilapia as a model, fish farming was assessed for three water temperature regimes corresponding to two crops/year with good growth (temperature) **Data publication:** 1994-01-01 **Supplemental Information:** This map derives from a wider study that evaluates inland fish farming potential at a continental-level, published by J.M. Kapetsky with the title "A strategic assessment of warm-water fish farming potential in Africa ". CIFA Technical Paper. No. 27. Rome, FAO. 1994. 67p. Resolution 18 km x 18 km. **Contact points:** Metadata Contact: [Jose Aguilar-Manjarrez](mailto:Jose.AguilarManjarrez@fao.org) **Data lineage:** Conversion from ASCII files to Arc/Info GRID **Online resources:** Water temperature (GRID format) CIFA Tecnical Paper 27 CIFA Tecnical Paper 27 - 8. APPENDIX iiasa

Map derived from the combined suitability of areas apt for small-scale fish farming development and operation with areas with potential yield (crops/y) of African catfish for small-scale farming. Areas with suitability for small-scale fish farming were derived from the combined suitability of four land-quality factors important for fish farming development and operation; net annual water requirement for shallow ponds, soil and terrain suitability for the construction of fish ponds, potential livestock wastes and agriculture by-products as feed and fertilizer inputs, and potential for farm-gate sales. Predictions of growth were based on water temperature, in turn predicted from air temperature and wind velocity data. Gridded values of mean monthly daily minimum and maximum temperatures were obtained from the Centre for Resource and Environmental Studies (CRES) at the Australian National University. Mean annual wind velocity was obtained from UNEP/DEIA/GRID-Geneva. **Data publication:** 1998-01-01 **Supplemental Information:** This map derives from a wider study that evaluates inland fish farming potential at a continental-level, published by J.Aguilar-Manjarrez and S. S. Nath with the title "A strategic reassessment of fish farming potential in Africa" . CIFA Technical Paper. No. 32. Rome, FAO. 1998. 170p. This study is a follow-up of an earlier assessment of warm-water fish farming potential in Africa by Kapetsky (1994) with the objective to assess locations and areal expanses that have potential for warm-water and temperate-water fish farming in continental Africa. Resolution 5 km x 5 km. **Contact points:** Metadata Contact: [Jose Aguilar-Manjarrez](mailto:Jose.AguilarManjarrez@fao.org) **Data lineage:** Conversion from ASCII files to Arc/Info GRID **Online resources:** Online resource Online resource

Areas with potential for commercial fish farming under three temperature regimes providing for one crop/year (1C) with fair to good growth (FGG), two crops/year with fair growth (FG) and with good growth (GG) in combination with four land-quality factors important for fish farming development and operation; water availability, engineering capability, and economic considerations ranging from all suitable to one suitable and two optimum (S-NO), or all optimum (all 0). **Data publication:** 1994-01-01 **Supplemental Information:** This map derives from a wider study that evaluates inland fish farming potential at a continental-level, published by J.M. Kapetsky with the title "A strategic assessment of warm-water fish farming potential in Africa " . CIFA Technical Paper. No. 27. Rome, FAO. 1994. 67p. Resolution 18 km x 18 km. **Contact points:** Metadata Contact: [Jose Aguilar-Manjarrez](mailto:Jose.AguilarManjarrez@fao.org) **Data lineage:** Conversion from ASCII files to Arc/Info GRID **Online resources:** Commercial fish farming potential (GRID format) CIFA Tecnical Paper 27 CIFA Tecnical Paper 27 - 8. APPENDIX IIASA

Population density (inhabitants/km2) interpreted in terms of local market demand for fish farming at the subsistence level. Population density as inhabitants/km2 for the year 1988 was obtained from UNEP/GRID Nairobi. **Data publication:** 1994-01-01 **Supplemental Information:** This map derives from a wider study that evaluates inland fish farming potential at a continental-level, published by J.M. Kapetsky with the title "A strategic assessment of warm-water fish farming potential in Africa " . CIFA Technical Paper. No. 27. Rome, FAO. 1994. 67p. Resolution 18 km x 18 km. **Contact points:** Metadata Contact: [Jose Aguilar-Manjarrez](mailto:Jose.AguilarManjarrez@fao.org) **Data lineage:** Conversion from ASCII files to Arc/Info GRID **Online resources:** Local market demand (subsistence) (GRID format) CIFA Tecnical Paper 27 CIFA Tecnical Paper 27 - 8. APPENDIX IIASA

Economic considerations for subsistence fish farming obtained by combining local market demand with availability of agricultural by-products as inputs. Population density (inhabitants/km2) was used to represent local market demand for fish farming at the commercial level. Productivity and variety of crops were interpreted as availability of agricultural by products as inputs for fish farming. Population density as inhabitants/km2 for the year 1988 was obtained from UNEP/GRID Nairobi. Agro-ecological zones for 11 crops were defined for Africa by FAO (1978). LGP was calculated from data collected from 730 stations and plotted at 30-day intervals at 1:5 million scale. **Data publication:** 1994-01-01 **Supplemental Information:** This map derives from a wider study that evaluates inland fish farming potential at a continental-level, published by J.M. Kapetsky with the title "A strategic assessment of warm-water fish farming potential in Africa " . CIFA Technical Paper. No. 27. Rome, FAO. 1994. 67p. Resolution 18 km x 18 km. **Contact points:** Metadata Contact: [Jose Aguilar-Manjarrez](mailto:Jose.AguilarManjarrez@fao.org) **Data lineage:** Conversion from ASCII files to Arc/Info GRID **Online resources:** Economics (subsistence) (GRID format) CIFA Tecnical Paper 27 CIFA Tecnical Paper 27 - 8. APPENDIX Online resource

Spatial distribution of potential yield (crops/y) of Nile tilapia for small-scale farming. Predictions of growth were based on water temperature, in turn predicted from air temperature and wind velocity data. Gridded values of mean monthly daily minimum and maximum air temperatures were obtained from the Centre for Resource and Environmental Studies (CRES) at the Australian National University. Mean annual wind velocity was obtained from UNEP/DEIA/GRID-Geneva. **Data publication:** 1998-01-01 **Supplemental Information:** This map derives from a wider study that evaluates inland fish farming potential at a continental-level, published by J.Aguilar-Manjarrez and S. S. Nath with the title "A strategic reassessment of fish farming potential in Africa". CIFA Technical Paper. No. 32. Rome, FAO. 1998. 170p. This study is a follow-up of an earlier assessment of warm-water fish farming potential in Africa by Kapetsky (1994) with the objective to assess locations and areal expanses that have potential for warm-water and temperate-water fish farming in continental Africa. Resolution 5 km x 5 km. **Contact points:** Resource Contact: UNEP/DEIA/GRID-Geneva and (CRES) Centre for Resource and Environmental Studies (Australian National University Institute of Advanced Studies) Metadata Contact: [Jose Aguilar-Manjarrez](mailto:Jose.AguilarManjarrez@fao.org) **Data lineage:** Conversion from ASCII files to Arc/Info GRID **Online resources:** CIFA Tecnical Paper 32 CIFA Tecnical Paper 32 - 8. APPENDIX IIASA Australian National University

Spatial distribution of potential yield (crops/y) of African catfish for small-scale farming. Predictions of growth were based on water temperature, in turn predicted from air temperature and wind velocity data. Gridded values of mean monthly daily minimum and maximum air temperatures were obtained from the Centre for Resource and Environmental Studies (CRES) at the Australian National University. Mean annual wind velocity was obtained from UNEP/DEIA/GRID-Geneva. **Data publication:** 1998-01-01 **Supplemental Information:** This map derives from a wider study that evaluates inland fish farming potential at a continental-level, published by J.Aguilar-Manjarrez and S. S. Nath with the title "A strategic reassessment of fish farming potential in Africa". CIFA Technical Paper. No. 32. Rome, FAO. 1998. 170p. This study is a follow-up of an earlier assessment of warm-water fish farming potential in Africa by Kapetsky (1994) with the objective to assess locations and areal expanses that have potential for warm-water and temperate-water fish farming in continental Africa. Resolution 5 km x 5 km. **Contact points:** Metadata Contact: [Jose Aguilar-Manjarrez](mailto:Jose.AguilarManjarrez@fao.org) **Data lineage:** Conversion from ASCII files to Arc/Info GRID **Online resources:** CIFA Tecnical Paper 32 CIFA Tecnical Paper 32 - 8. APPENDIX IIASA

Suitable areas for the construction of fish ponds based on a soils evaluation. Soils evaluation was based on FAO's Digital Soil Map of the World (DSMW) and included: texture, effective soil depth, pH, salinity, and the presence of catclays and gypsum. **Data publication:** 1998-01-01 **Supplemental Information:** This map derives from a wider study that evaluates inland fish farming potential at a continental-level, published by J.Aguilar-Manjarrez and S. S. Nath with the title "A strategic reassessment of fish farming potential in Africa" . CIFA Technical Paper. No. 32. Rome, FAO. 1998. 170p. This study is a follow-up of an earlier assessment of warm-water fish farming potential in Africa by Kapetsky (1994) with the objective to assess locations and areal expanses that have potential for warm-water and temperate-water fish farming in continental Africa. Resolution 5 km x 5 km. **Contact points:** Metadata Contact: [Jose Aguilar-Manjarrez](mailto:Jose.AguilarManjarrez@fao.org) **Data lineage:** Conversion from ASCII files to Arc/Info GRID **Online resources:** Soil suitability for ponds (GRID format) CIFA Tecnical Paper 32 CIFA Tecnical Paper 32 - 8. APPENDIX

Productivity and variety of crops interpreted as availability of agricultural by products as inputs for fish farming. The objective of this map is to indicate the locations, availability and variety of agricultural by-products that could be used as inputs for fish farming. These can be related to agronomic suitability as measured by the length of growing period (LGP). Agro-ecological zones for 11 crops were defined for Africa by FAO (1978). LGP was calculated from data collected from 730 stations and plotted at 30-day intervals at 1:5 million scale. **Data publication:** 1994-01-01 **Supplemental Information:** This map derives from a wider study that evaluates inland fish farming potential at a continental-level, published by J.M. Kapetsky with the title "A strategic assessment of warm-water fish farming potential in Africa ". CIFA Technical Paper. No. 27. Rome, FAO. 1994. 67p. Resolution 18 km x 18 km. **Contact points:** Metadata Contact: [Jose Aguilar-Manjarrez](mailto:Jose.AguilarManjarrez@fao.org) **Data lineage:** Conversion from ASCII files to Arc/Info GRID **Online resources:** Inputs (GRID format) CIFA Tecnical Paper 27 CIFA Tecnical Paper 27 - 8. APPENDIX IIASA

Map derived from the combined suitability of areas apt for small-scale fish farming development and operation with areas with potential yield (crops/y) of Nile tilapia for small-scale farming. Areas with suitability for small-scale fish farming were derived from the combined suitability of four land-quality factors important for fish farming development and operation; net annual water requirement for shallow ponds, soil and terrain suitability for the construction of fish ponds, potential livestock wastes and agriculture by-products as feed and fertilizer inputs, and potential for farm-gate sales. Predictions of growth were based on water temperature, in turn predicted from air temperature and wind velocity data. Gridded values of mean monthly daily minimum and maximum temperatures were obtained from the Centre for Resource and Environmental Studies (CRES) at the Australian National University. Mean annual wind velocity was obtained from UNEP/DEIA/GRID-Geneva. **Data publication:** 1998-01-01 **Supplemental Information:** This map derives from a wider study that evaluates inland fish farming potential at a continental-level, published by J.Aguilar-Manjarrez and S. S. Nath with the title "A strategic reassessment of fish farming potential in Africa" . CIFA Technical Paper. No. 32. Rome, FAO. 1998. 170p. This study is a follow-up of an earlier assessment of warm-water fish farming potential in Africa by Kapetsky (1994) with the objective to assess locations and areal expanses that have potential for warm-water and temperate-water fish farming in continental Africa. Resolution 5 km x 5 km. **Contact points:** Metadata Contact: [Jose Aguilar-Manjarrez](mailto:Jose.AguilarManjarrez@fao.org) **Data lineage:** Conversion from ASCII files to Arc/Info GRID **Online resources:** CIFA Tecnical Paper 32 - 8. APPENDIX IIASA Suitability for small-scale farming and potential yield (crops/y) of Nile tilapia - GRID format CIFA Tecnical Paper 32

__GSOCseq - SOC Stocks Uncertainties__ The Initial SOC stocks (stocks at time 0, t0) and the final SOC stocks under the BAU and three SSM scenarios are accompanied by three uncertainty maps. Uncertainties in GSOCseq v1.1 represent the forward propagation of plausible uncertainty ranges of input layers (see the Technical Report, Section 5.6). These uncertainties do not include the model structural uncertainties. The uncertainties maps of the SOC stocks include the following products: T0: uncertainty of initial SOC stocks (stocks at time 0; T0).The uncertainties (in percent) in the estimate of initial soil organic carbon stocks in 2020, due to the uncertainties of the input layers. BAU: uncertainty of final SOC stocks under business as usual scenario. The uncertainties (in percent) in the estimate of the projected soil organic carbon stocks after 20 years of business as usual (BAU) management, due to the uncertainties of the input layers. SSM: uncertainty of final SOC stocks under sustainable soil management scenarios This map represents the uncertainties (in percent) in the estimate of the projected soil organic carbon stocks after 20 years of sustainable soil management practices, due to the uncertainties of the input layers **Contact points:** **Data lineage:** Data quality information not available **Online resources:** Download the data from Google Cloud Storage

__GSOCseq - SOC Sequestration Potential__ These maps are expressed as the annual average sequestration rates compared to BAU management (relative sequestration rates RSR). Relative SOC sequestration rates under sustainable soil management scenario 1 (SSM1) The average annual rate of soil organic carbon stock change (in t C ha-1 yr-1) after 20 years of implementation of sustainable soil management (SSM) practices that generate a 5 percent increase in carbon inputs (SSM Scenario 1) compared to business as usual (BAU) management, at a soil depth of 0-30 cm. Relative SOC sequestration rates under sustainable soil management scenario 2 (SSM2) The average annual rate of soil organic carbon stock change (in t C ha-1 yr-1) after 20 years of implementation of sustainable soil management (SSM) practices that generate a 10 percent increase in carbon inputs (SSM Scenario 2) compared to business as usual (BAU) management, at a soil depth of 0-30 cm. Relative SOC sequestration rates under sustainable soil management scenario 3 (SSM3) The average annual rate of soil organic carbon stock change (in t C ha-1 yr-1) after 20 years of implementation of sustainable soil management (SSM) practices that generate a 20 percent increase in carbon inputs (SSM Scenario 3) compared to business as usual (BAU) management, at a soil depth of 0-30 cm. **Contact points:** **Data lineage:** Data quality information not available **Online resources:** Download the data from Google Cloud Storage

The Relative SOC sequestration rates are accompanied by their corresponding uncertainty maps (%). Uncertainties in GSOCseq v1.1 were estimated by forward propagation of plausible uncertainty ranges of input layers (see the Technical Report, Section 5.6), and do not include the model structural uncertainties. The uncertainties maps include: SSM1: uncertainty of relative sequestration rates under sustainable soil management scenario 1 This map represents the uncertainties (in percent) in the estimate of the average annual rate of soil organic carbon stock change after 20 years of implementation of sustainable soil management practices (Scenario 1) compared to business as usual practices, due to the uncertainties of the input layers. SSM2: uncertainty of relative sequestration rates under sustainable soil management scenario 2 This map represents the uncertainties (in percent) in the estimate of the average annual rate of soil organic carbon stock change after 20 years of implementation of sustainable soil management practices (Scenario 2) compared to business as usual practices, due to the uncertainties of the input layers. SSM3: uncertainty of relative sequestration rates under sustainable soil management scenario 3 This map represents the uncertainties (in percent) in the estimate of the average annual rate of soil organic carbon stock change after 20 years of implementation of sustainable soil management practices (Scenario 3) compared to business as usual practices, due to the uncertainties of the input layers. **Contact points:** **Data lineage:** Data quality information not available **Online resources:** Download the data from Google Cloud Storage

GSOCseq is a country-driven global product which depicts the projected Soil Organic Carbon (SOC) stocks (in t C ha-1) 20 years into the future, in current agricultural lands under different soil management scenarios that vary in the degree of carbon inputs to the soil, at 0-30 depth and 1 x 1 km spatial resolution. __Initial SOC stocks (GSOCseq_T0_Map030)__ This map represents an estimate of current soil organic carbon stocks (in t C ha-1) in 2020 (stocks at time 0; t0), at a soil depth of 0-30 cm. The best available national datasets collected over the years from the different countries were used to derive the 2020 baseline stocks __Final SOC stocks under business as usual scenario(GSOCseq_finalSOC_BAU_Map030)__ This map represents the projected soil organic carbon stocks (in t C ha-1) in 2040 (t20), after 20 years of business as usual (BAU) management, at a soil depth of 0-30 cm. __Final SOC stocks under sustainable soil management scenario 1 (GSOCseq_finalSOC_SSM1_Map030)__ This map represents the projected soil organic carbon stocks (in t C ha-1) in 2040, after 20 years of implementation of sustainable soil management (SSM) practices that generate a 5 percent increase in carbon inputs (SSM Scenario 1), at a soil depth of 0-30 cm. __Final SOC stocks under sustainable soil management scenario 2 (GSOCseq_finalSOC_SSM2_Map030)__ This map represents the projected soil organic carbon stocks (in t C ha-1) in 2040, after 20 years of implementation of sustainable soil management (SSM) practices that generate a 10 percent increase in carbon inputs (Scenario 2), at a soil depth of 0-30 cm. __Final SOC stocks under sustainable soil management scenario 3 (GSOCseq_finalSOC_SSM3_Map030)__ This map represents the projected soil organic carbon stocks (in t C ha-1) in 2040, after 20 years of implementation of sustainable soil management (SSM) practices that generate a 20 percent increase in carbon inputs (Scenario 3), at a soil depth of 0-30 centimeters. **Contact points:** **Data lineage:** Data quality information not available **Online resources:** Download the data from Google Cloud Storage

This map builds upon work done by Dixon (Dixon et al., 2001) in an attempt to propose a general classification of major farming systems. Based on a combination of global dataset, the map proposes a harmonized classification of major agricultural systems used as the basis for the analysis of SOLAW's Systems at risk. The map is based on an interpretation of global land cover data, combined with thematic datasets showing irrigated land and paddy rice extent. The map is published in SOLAW Report: The state of the world's land and water resources for food and agriculture - Managing systems at risk (2011) and SOLAW Thematic Report 15: Sustainable options for addressing land and water problems - a problem tree and case studies (links in the online resources section). **Data publication:** 2010-11-02 **Contact points:** Metadata Contact: [AQUASTAT](mailto:aquastat@fao.org) Resource Contact: [Jippe Hoogeveen](mailto:Jippe.Hoogeveen@fao.org) Resource Contact: Hubert George FAO-UN Land and Water Division **Data lineage:** The spatial datasets used for the production of this map consist of: - GLOBCOVER, global land-cover map for the year 2005 at http://www.gofc-gold.uni-jena.de/sites/globcover.php - Paddy rice systems (Ellis and Ramankutty, 2008) - Median altitude and thermal climates (both datasets are part of the GAEZ v3.0 (IIASA/FAO, 2010) - Global Map of Irrigation Areas v4.0.1 at 5 arc-min (Siebert at al., 2007) - Global Map of Aridity at 10 arc-min (FAO) - Global Administrative Unit Layers (GAUL 2008) - FAO’s Global Livestock Production and Health Atlas (GLiPHA, available at: http://www.fao.org/ag/againfo/home/en/news_archive/AGA_in_action/glipha.html) - FAO’s Agro-MAPS, available at (http://kids.fao.org/agromaps/) **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International licence (CC-BY-4.0) **Online resources:** SOLAW website Download - Agricultural systems (ESRI GRID) Download - Agricultural systems - (ASCII grid with Vat table and class codes) Download - Agricultural systems (GeoTiff) The state of the world's land and water resources for food and agriculture - Managing systems at risk. Sustainable options for addressing land and water problems - a problem tree and case studies.

This map shows the extent of land area equipped for irrigation -expressed as percentage- around the turn of the 20th century according to the Global Map of Irrigation Areas (version 4.0.1), together with areas of rainfed agriculture. Data are available from AQUASTAT - programme of the Land and Water Division of the Food and Agriculture Organization of the United Nations. **Data publication:** 2010-10-28 **Contact points:** Metadata Contact: [AQUASTAT](mailto:aquastat@fao.org) **Data lineage:** The spatial datasets used to produce this map are: - The global map of irrigation areas at 5 arc-min (Siebert et al., 2007) - The cropland categories of the global agricultural systems map (SOLAW report "Managing Systems at Risk", map 1.3). **Resource constraints:** copyright **Online resources:** SOLAW website Download- Percentage of area equipped for irrigation (ESRI GRID) GMIA page on AQUASTAT

Total Biomass Production (TBP) is defined as the sum of the above-ground dry matter produced during the course of the growing season. The seasonal value represents the total accumulated biomass during one growing season, from start of season (SOS) to end of season (EOS). A pixel value of -9997 denotes \"out of season\". The data is provided from January 2009 to 2022. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: -9999 Unit: kg/ha Scale Factor: 10 Map code: L2-TBP-S Conversion factor: the pixel value in the downloaded data must be multiplied by 10 New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. This implies that other temporal aggregations (monthly, seasonal, annual), and layers that depend on those, are updated as well. Practically this means that a final annual aggregation of the most recent full calendar year can only be produced after the end of February. Likewise, the final monthly aggregation of the most recent calendar months can only be produced 2 full months later. **Citation:** FAO 2018. WaPOR Database Methodology: Level 2. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [WaPOR](mailto:wapor@fao.org) Resource Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** To derive the accumulation in biomass production over or during a growing season, first the start and the end of the growing season need to be identified using the phenology data component. TBP is then calculated as the sum of NPP, converted into dry matter productivity (DMP) units (kg/ha), between the start of the season (SOS) and the end of the season (EOS). At the end of the season, when the crops for the area can be assessed, the dekadal and seasonal TBP values are adjusted using an additional root-shoot correction factor data layer that allows the user to correct the TBP using the land cover specific root-shoot values. A limitation for the derivation of TBP is the dependency on phenological information, meaning that TBP can only be derived for areas where seasonality is detected. For ecosystems, such as tropical forests or deserts, that experience almost no seasonality, the start of season is theoretically set at January 1st and end of season is set at December 31st. Data component developed through collaboration with the FRAME Consortium. More information can be found at: http://www.fao.org/in-action/remote-sensing-for-water-productivity/en/. Until December 2019 the base input layers (NDVI, albedo, and fAPAR) for the Level 2 (100m) products were derived from the Proba-V satellite. Proba-V was decommissioned in June 2020. From January 2020 onwards the base input layers of NDVI, albedo and fAPAR for level 2 are derived from the Copernicus Sentinel-2 mission. **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) data product provides cropland extent data over Europe, Central Asia, Russia and the Middle East for nominal year 2015 at 30 meter resolution (GFSAD30EUCEARUMECE). The monitoring of global cropland extent is critical for policymaking and provides important baseline data that are used in many agricultural cropland studies pertaining to water sustainability and food security. The GFSAD30EUCEARUMECE product uses a pixel-based supervised random forest machine learning algorithm to retrieve cropland extent from a combination of Landsat 7 Enhanced Thematic Mapper (ETM+), Landsat 8 Operational Land Imager (OLI) data, and elevation derived from the Shuttle Radar Topography Mission (SRTM) Version 3 data products. Each GFSAD30EUCEARUMECE GeoTIFF file contains a cropland extent layer that defines areas of cropland, non-cropland, and water bodies over a 10° by 10° area. Known Issues * Known issues, including constraints and limitations, are provided on page 20 of the ATBD.

Agricultural raw materials comprise section 2 of SITC Rev. 3 (crude materials, inedible, except fuels) excluding divisions 22 (oil-seeds and oleaginous fruits), 27 (crude fertilizers and minerals excluding coal, petroleum, and precious stones), and 28 (metalliferous ores and scrap). This indicator is expressed as a percentage of merchandise exports which is comprised of goods whose economic ownership is changed between a resident and a non-resident and that are not included in the following specific categories: goods under merchanting, non-monetary gold, and parts of travel, construction, and government goods and services n.i.e.

Phenology indicates the cycle or season of a crop and, in this case, is defined by the dekad (D) corresponding to the start, maximum and end of the growing season. This information can be derived from satellite-based vegetation index time series. Phenology is delivered for a maximum of two growing seasons annually. The phenology for one growing season is therefore delivered as three raster files. The first raster indicates the Start of Season (SOS), the second the Maximum of Season (MOS) and the third represents the End of Season (EOS). With a maximum of 2 growing seasons annually, a full year is described by 6 raster files. The pixel values of the phenology data components are expressed in dekad numbers. The data is provided from January 2009 to 2022. **Data publication:** 2020-01-01 **Supplemental Information:** No data value: 255 Unit: D (dekad number) Map code: L2-PHE-S New dekadal data layers are released approximately 5 days after the end of a dekad. A higher quality version of the same data layer is uploaded after 6 dekads have passed. This final version of the dekadal dataset has a higher quality because gap filling and interpolation processes, where needed, have been based on more data observations. This implies that other temporal aggregations (monthly, seasonal, annual), and layers that depend on those, are updated as well. Practically this means that a final annual aggregation of the most recent full calendar year can only be produced after the end of February. Likewise, the final monthly aggregation of the most recent calendar months can only be produced 2 full months later. **Citation:** FAO 2018. WaPOR Database Methodology: Level 2. Remote Sensing for Water Productivity Technical Report: Methodology Series. Rome, FAO. 72 pages. Licence: CC-BY-4.0 **Contact points:** Resource Contact: [WaPOR](mailto:wapor@fao.org) Metadata Contact: [WaPOR](mailto:wapor@fao.org) **Data lineage:** Phenological information is derived from a time series of dekadal vegetation index composites (NDVI) applying the methodology described by Van Hoolst et al. (see methodology document for references). The input dekadal NDVI time series covers exactly three calendar years (3 x 36 = 108 dekads), with the target year in the middle (1-36 for the first year, 37-72 for the target year, 73-108 for the next year). A growing season is attributed to a calendar year if the EOS occurs in it or in the first 3 dekads of the following calendar year. Thus, EOS has a value between 37 and 75 whilst SOS pixel values must be smaller than 72. 251 denotes "no season" (if no growing season can be distinguished, which is true for perennial land cover and fallow cropland). The following data is used to calculate Phenology: NDVI Data component developed through collaboration with the FRAME Consortium. More information can be found at: http://www.fao.org/in-action/remote-sensing-for-water-productivity/en/ Until December 2019 the base input layers (NDVI, albedo, and fAPAR) for the Level 2 (100m) products were derived from the Proba-V satellite. Proba-V was decommissioned in June 2020. From January 2020 onwards the base input layers of NDVI, albedo and fAPAR for level 2 are derived from the Copernicus Sentinel-2 mission. **Resource constraints:** Creative Commons Attribution 4.0 International (CC-BY-4.0) **Online resources:** Download the data from Google Cloud Storage

Livestock production index includes meat and milk from all sources, dairy products such as cheese, and eggs, honey, raw silk, wool, and hides and skins.

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission measures the temperature of plants to better understand how much water plants need and how they respond to stress. ECOSTRESS is attached to the International Space Station (ISS) and collects data globally between 52° N and 52° S latitudes. The ECOSTRESS Tiled Downscaled Soil Moisture Instantaneous L3 Global 70 m (ECO_L3T_SM) Version 2 data product provides instantaneous soil moisture (SM) estimates downscaled using linear regression. The linear regression uses up-sampled surface temperature (ST), normalized difference vegetation index (NDVI), and albedo as predictor variables and SM from Goddard Earth Observing System Version 5 (GEOS-5) Forward Processing (FP) as response variables for their relative outputs. Once the regression coefficients have been determined, they are applied to the 70 meter (m) ST, NDVI, and albedo as a first pass, which is then bias corrected using a GEOS-5 FP image. The downscaled soil moisture estimates are recorded into the ECO_L3T_SM data product and tiled using a modified version of the Military Grid Reference System (MGRS), which divides Universal Transverse Mercator (UTM) zones into square tiles that are 109.8 km by 109.8 km with a 70 m spatial resolution. The ECO_L3T_SM Version 2 data product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate COG. This product contains three layers including SM, cloud mask, and water mask. Known Issues * Data acquisition gap: ECOSTRESS was launched on June 29, 2018, and moved to autonomous science operations on August 20, 2018, following a successful in-orbit checkout period. On September 29, 2018, ECOSTRESS experienced an anomaly with its primary mass storage unit (MSU). ECOSTRESS has a primary and secondary MSU (A and B). On December 5, 2018, the instrument was switched to the secondary MSU, and science operations resumed. On March 14, 2019, the secondary MSU experienced a similar anomaly, temporarily halting science acquisitions. On May 15, 2019, a new data acquisition approach was implemented, and science acquisitions resumed. To optimize the new acquisition approach, only Thermal Infrared (TIR) bands 2, 4, and 5 are being downloaded. The data products are the same as before, but the bands not downloaded contain fill values (L1 radiance and L2 emissivity). This approach was implemented from May 15, 2019, through April 28, 2023. * Data acquisition gap: From February 8 to February 16, 2020, an ECOSTRESS instrument issue resulted in a data anomaly that created striping in band 4 (10.5 micron). These data products have been reprocessed and are available for download. No ECOSTRESS data were acquired on February 17, 2020, due to the instrument being in SAFEHOLD. Data acquired following the anomaly have not been affected. * Data acquisition: ECOSTRESS has now successfully returned to 5-band mode after being in 3-band mode since 2019. This feature was successfully enabled following a Data Processing Unit firmware update (version 4.1) to the payload on April 28, 2023. To better balance contiguous science data scene variables, 3-band collection is currently being interleaved with 5-band acquisitions over the orbital day/night periods. * Missing Cloud Layer Alert: All users of ECOSTRESS Tiled and Gridded L3 Soil Moisture and Surface Energy Balance v002 products (ECO_L3T_SM, ECO_L3G_SM, ECO_L3T_SEB and ECO_L3G_SEB) should be aware that the ‘cloud mask’ layer may be unavailable for a select number of granules for the year 2023. Users are encouraged to get that information from the corresponding Level 2 Standard Cloud Mask products (ECO_L2_CLOUD and ECO_L2G_CLOUD) to assess if a pixel is clear or cloudy (see section 3 of the User Guide). * Solar Array Obstruction: Some ECOSTRESS scenes may be affected by solar array obstructions from the International Space Station (ISS), potentially impacting data quality of obstructed pixels. The 'FieldOfViewObstruction' metadata field is included in all Version 2 products to indicate possible obstructions: * Before October 24, 2024 (orbits prior to 35724): The field is present but was not populated and does not reliably identify affected scenes. * On or after October 24, 2024 (starting with orbit 35724): The field is populated and generally accurate, except for late December 2024, when a temporary processing error may have caused false positives. * A list of scenes confirmed to be affected by obstructions is available and is recommended for verifying historical data (before October 24, 2024) and scenes from late December 2024. * The ISS native pointing information is coarse relative to ECOSTRESS pixels, so ECOSTRESS geolocation is improved through image matching with a basemap. Metadata in the L1B_GEO file shows the success of this geolocation improvement, using categorizations "best", "good", "suspect", and "poor". We recommend that users use only "best" and "good" scenes for evaluations where geolocation is important (e.g., comparison to field sites). For some scenes, this metadata is not reflected in the higher-level products (e.g., land surface temperature, evapotranspiration, etc.). While this metadata is always available in the geolocation product, to save users additional download, we have produced a summary text file that includes the geolocation quality flags for all scenes from launch to present. At a later date, all higher-level products will reflect the geolocation quality flag correctly (the field name is GeolocationAccuracyQA). * Coarse Resolution Artifacts: Users may sometimes observe coarse resolution (~500m - 1km) artifacts in the L3 and L4 gridded and tiled products. These artifacts are caused by corresponding artifacts within the ECOSTRESS Tiled Ancillary NDVI and Albedo L2 Global 70 m (ECO_L2T_STARS) Version 2 data product. The ECO_L2T_STARS product is produced through a data fusion of Suomi NPP VIIRS (~500 m - 1 km resolution) and Harmonized Landsat Sentinel-2 (HLS, 30 m) products. When there is significant temporal lag between HLS acquisitions, the VIIRS data dominates, which can produce coarse resolution artifacts in the ECO_L2T_STARS product. This typically corresponds to relatively large uncertainty values.

Cereal yield, measured as kilograms per hectare of harvested land, includes wheat, rice, maize, barley, oats, rye, millet, sorghum, buckwheat, and mixed grains. Production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed, or silage and those used for grazing are excluded. The FAO allocates production data to the calendar year in which the bulk of the harvest took place. Most of a crop harvested near the end of a year will be used in the following year.

The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security Support Analysis Data (GFSAD) Crop Mask Global 1 kilometer (km) dataset was created using multiple input data including: remote sensing such as Landsat, Advanced Very High Resolution Radiometer (AVHRR), Satellite Probatoire d'Observation de la Terre (SPOT) vegetation and Moderate Resolution Imaging Spectrometer (MODIS); secondary elevation data; climate 50-year precipitation and 20-year temperature data; reference submeter to 5 meter resolution ground data and country statistics data. The GFSAD1KCM provides spatial distribution of a disaggregated five class global cropland extent map derived for nominal 2010 at 1 km based on four major studies: Thenkabail et al. (2009a, 2011), Pittman et al. (2010), Yu et al. (2013), and Friedl et al. (2010). The GFSAD1KCM nominal 2010 product is based on data ranging from years 2007 through 2012. Known Issues * See Section 3.0 of the GFSAD 1 km User Guide.