Assessing economic and welfare values of fish in the Lower Mekong Basin Project funded by ACIAR FISHING DEPENDENCY IN CAMBODIA Mapping and methodology report Joshua NASIELSKI, John TRESS, Eric BARAN May 2013 TABLE OF CONTENTS 1. INTRODUCTION 3 2. DATA AND METHODS 3 2.1. Data 3 2.1.1. Population data 3 2.1.2. Shape files used to create maps 4 2.2. Calculations 4 2.2.1. Calculating Fishers Density 4 2.2.2. Calculating Fishing Dependency Score 5 3. RESULTS 6 3.1. Fishers Density 6 3.2. Shape files created 8 3.3. Maps created 9 4. CONCLUSIONS 11 5. ANNEX 1: FISHERMEN DENSITY PER PROVINCE AND DISTRICT 12 6. ANNEX 2: MAPPING METHODOLOGY 15 6.1. Procedure 15 6.2. Classification procedure 16 Citation: Nasielski J., Tress J., Baran E. 2012. Fishing dependency in Cambodia - mapping and methodology report. Report for the project “Assessing economic and welfare values of fish in the Lower Mekong Basin”. WorldFish, Phnom Penh, Cambodia. 16 pp. 2 1. INTRODUCTION In Cambodia fisheries represent a significant source of income and food to rural households. At the national scale, identifying those areas which are most dependent on fisheries is useful for selecting priority zones for development interventions and research. By calculating Fishers Density and Fishing Dependency at the commune level using cross-checked data, we contribute to the visualization of the exceptional fishing dependency in Cambodia and help identify particularly dependent zones and communes. This report details the methodology used to produce the national map of Fishing Dependency per commune in Cambodia. We detail how the Fishers Density and the Fishing Dependency Scores are calculated, and indicate the source and nature of the data used. 2. DATA AND METHODS 2.1. DATA 2.1.1. POPULATION DATA 2008 National Census: Cambodia’s 2008 census surveyed every household in Cambodia for basic demographic information. This data was aggregated at the village level. We were interested in the following census questions: 1 • Commune Classification : Urban or Rural • Total number of persons • Total number of persons whose primary occupation is fishing • Total number of persons whose secondary occupation is fishing 2 2010 Commune Database (CDB) from SEILA: • Total number of families whose primary occupation is fishing • Number of families with row boats used for fishing • Number of families with motor boats used for fishing 1If a commune is classified as urban, all villages within it are also classified as urban, and vice versa. 2The name is somewhat of a misnomer because the data is actually available at the village level as well. 3 2006 Ministry of Planning Poverty Score: The 2006 Ministry of Planning is itself a composite indicator of village-level poverty. It uses a variety of variables such as the number of televisions in the village, literacy rates and the proportion of children in school to create a poverty score for each village in Cambodia. 2.1.2. SHAPE FILES USED TO CREATE MAPS Commune Boundary File: Commune_.shp A shape file showing commune boundaries for all of Cambodia with unique ID codes for each. This file was created by the Geographic Survey Institute of Cambodia and was used in the 2006 Atlas of Cambodia. This shape file is projected using: UTM, Zone 48, Sph.: Everest 1830, Dat.: Indian 1954 " 2.2. CALCULATIONS 2.2.1. CALCULATING FISHERS DENSITY The Fishers Density equals, in each commune, the number of people who engage in fishing compared to the population of these communes. It is a composite indicator comprised of variables from two datasets: the 2008 Census and the 2010 SEILA Commune Database (CDB). 1) Number of self-identified fishers within a commune. We define this as any individual who has been identified as a ‘fisher’ in the 2008 Census by indicating “fishing” as a primary or secondary occupation. 2) Cross-checking #1. In a given commune, the number of individuals who selected fishing as a primary occupation is cross-checked by comparing with the number of families whose primary occupation is fishing in the 2010 SEILA Commune Database (CDB). To avoid double counting we selected whichever of these numbers is greater. 3) Cross-checking #2. When the number of fishing boats in a commune is greater than the number of primary or secondary fishers in that commune, the number of fishing boats is considered to be a better proxy of the total number of fishers (i.e. fishers accounted for in the census plus unaccounted fishers identified by their fishing boat). Again, we select whichever number is greater. Let be the set of individuals in commune that engage in some level of fishing activity according to the 2008 census or 2010 CDB. Thus, 4 In other words we add up the number of unaccounted fishers to the number of primary and secondary fishers, the number of unaccounted fishers being the number of fishing boats A minus the total number of fishers B, if A>B. The fishers density is obtained by dividing the above [number of individuals in a given commune that engage in some level of fishing activity] by the [population of the commune] obtained from the 2008 census. The fishers density can also be interpreted as the probability that a randomly selected individual in commune is dependent on fishing. Figure 1: Illustration of data sources and calculation of fishers density 2.2.2. CALCULATING FISHING DEPENDENCY SCORE The Fishing Dependency Score is the Fishers Density weighted with a score reflecting the poverty of the commune. Each commune is assigned a poverty score calculated by the Ministry of Planning in 2006. The poverty weighting reflects the fact that poorer communes are more dependent on natural resources since they have less economic alternatives. The fishers density is multiplied by the poverty level of the commune . is standardized with a mean of 1, , implying that a village with a score of 1 features the average poverty of villages in Cambodia, while a village with a score of 1.01 is slightly poorer than the average village, etc. Hence, the final fishery dependency score is: 5 Figure 2: Data sources and calculation of the Fishing Dependency Score 3. RESULTS 3.1. FISHERS DENSITY All communes in Cambodia were classified into 5 groups according to their Fishers Density Score. The first group represents those communes with a score of 0 and is named “No Fishers”. The rest of the communes, with scores greater than 0, are classified into quartiles with 25% of the remaining communes in each group. An analysis per province of the number of fishers per 100 people results in the following classification (Table 1) 6 Province Fishers per 1000 persons Koh Kong 130 Stung Treng 67 Kampong Chhnang 66 Preah Sihanouk 51 Kep 40 Kampong Thom 39 Kandal 38 Pursat 35 Kratie 35 Siem Reap 27 Kampong Cham 19 Banteay Meanchey 19 Battambang 18 Takeo 16 Kampot 15 Preah Vihear 12 Ratanak Kiri 11 Prey Veng 10 Mondul Kiri 6 Oddar Meanchey 5 Svay Rieng 4 Phnom Penh 3 Kampong Speu 1 Pailin 0 Table 1: Density of fishers per 1000 inhabitants (Italics: marine provinces) A detailed analysis of the districts of the top 11 inland provinces featuring a high density of fishermen is given in the annex. This analysis highlights the first quartile (top 25%, density higher than 46 fishers per 1000 inhabitants) of the 106 districts of the top 11 provinces. Table 2 shows the districts that feature a fishing population superior to 10% of their total population. Province District Fishers per 1000 persons Stung Treng Siem Bouk 112 Baribour 115 Kampong Chhnang Chol Kiri 253 Kampong Leaeng 149 Kandal Lvea Aem 100 Pursat Kandieng 104 Battambang Aek Phnum 154 Takeo Borei Cholsar 121 Table 2: Density of fishers per 1000 inhabitants in top 8 districts 7 Fishing Dependency All communes in Cambodia were classified into 5 groups according to their Fishing Dependency Score. The first group contains those communes with a score of 0 and is named “No Dependency”. The rest of the communes, with scores greater than 0, are classified into quartiles with 25% of the communes in each group. These groups are named as low dependency, medium dependency, high dependency and very high dependency. 3.2. SHAPE FILES CREATED The Fishing Density Map –no poverty Score (file: Fishers Density Map.mxd) Fishers_PP.shp No_Fishers.shp Missing_Data.shp Protected_Areas.shp For the Fishing Dependency Map-with poverty (Fishing Dependency per Commune.mxd) FDS_PP_pov.shp No_dependency.shp Missing_Data.shp Protected_Areas.shp See Annex 2 for a detailed description of the procedure for creating the maps. 8 3.3. MAPS CREATED 9 10 4. CONCLUSIONS This analysis shows that among the non-marine provinces, Stung Treng and Kampong Chhnang Provinces stand out as having the largest population of people actively involved in fishing (respectively 67 and 66 fishers per 1000 inhabitants), followed by Kampong Thom, Kandal, Pursat, Kratie and Siem Reap (between 27 and 39 fishers per 1000 inhabitants) then by Kampong Cham, Banteay Meanchey, Battambang and Takeo (16 to 19 fishers per 1000 inhabitants). The provinces least involved in fishing are the mountainous, southern and urban provinces. Thus Stung Treng is the second most involved province in fishing in Cambodia, just after Koh Kong; this highlights the importance of the Mekong mainstream and of the 3S Rivers (Sekong, Sesan and Srepok Rivers) to the economy of this province. Figure 3: Density of fishers per 1000 inhabitants (Blue: marine provinces) Within individual provinces, eight districts feature more than 10% of their population involved in fishing; these districts are Siem Bouk in Stung Treng (Mekong banks and islands downstream of Stung Treng city); Baribour, Chol Kiri and Kampong Leaeng in Kampong Chhnang (Tonle Sap River banks just downstream of Snoc Trou), Lvea Aem in Kandal (Mekong banks opposite to Phnom Penh), Kandieng in Pursat (Pursat main landing site in the floodplain), Aek Phnum in Battambang (district of the former Fishing lot #2) and Borei Cholsar in Takeo (floodplain/delta district near the Vietnam border).