Coastal Shrimp Aquaculture in Thailand: Key Issues for Research

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!quality problem over the past 3 years (Q16_4B). It was possible to identify the farmsin Songkhla Province from the total farms in the survey with an accuracy of 92.28%using only those eight variables (see Appendix).Surat Thani ProvinceThe key variables for Surat Thani were: source of salt water; number of farmingyears at the site (Q1_1); major problem (Q2_3); depth of production ponds (Q6_4);four variables relating to measures taken to reduce the impact on the farm; i.e. environmentalimpact assessment during planning (Q8_1), site selection to avoid impactson other users (Q8_2), design of separate water supply/drainage system (Q8_4) andnumber of aquaculture farms sharing the water supply (Q7_9); the existence of wateror sediment related problem (Q14PROB); flooding problem over the past 3 years(Q14_10B); and other problems which have resulted in diminished harvest, loss ofcrop or missed crop (Q16OTH). It was possible to identify the farms in Surat ThaniProvince from the total farms in the survey with an accuracy of 89.58% using onlythose 10 variables (see Appendix).Summary of logistic regression analysisIn summary, the results of the analyses to find the variables that were able toidentify the 10 provinces are shown in Table 1. A total of 29 variables were identified,a high proportion of which related to environmental aspects of shrimp farming.This is an important and interesting finding because it provides strong evidence tosupport the hypothesis that environmental problems are the cause of reduced productivityand incidence of disease in shrimp farms. In this survey, the initial analysis hasshown that the provinces differ significantly in their productivity. The deeper analysishas now identified the key variables that characterise the differences among theprovinces. We conclude that some or all of those identified variables are contributingto the variation in farm productivity among the provinces.Application of GIS to Logistic Regression AnalysisThe results of the logistic regression analysis in Table 1 are also shown using GIS inFigures 1 to 4. Any key variable that defined a particular province is indicated by avalue = 1 on the column graphs, while a non-significant variable has a value = 0. InFigure 1 there are three key fixed or descriptive variables (i.e. type of soil, number offarming years and sourcs of salt water). In Figure 2 there are 10 key environmentalvariables: number of farms within 3 km; number of farms sharing water supply;number of farms discharging into water supply; environmental impact assessment;site selection to avoid impact on others; site selection to avoid impact of others; separatewater supply/drainage system; retention of mangrove buffer; effluent treatmentpond; and other environmental measure.&#
!Table 1.A summary of the key variables that characterise the 10 shrimp farming provincesin Thailand (see text and Appendix for explanation of codes for variables). Anasterisk (*) indicates a variable that identifies that province.VariableChanthaburiTratChumphonKrabiNakhon SriThamaratSOIL * * *Q1_1 * * *Q2_3 * * * *WSALT * * *Q6_4 * * *Q7_8 * * * *Q7_9 * * *Q7_10 * * * * *Q8_1 * * * * *Q8_2 * * * * *Q8_3 * *Q8_4 * * * *Q8_5 * * * *Q8_6 * *Q8_7 *Q14PROB * * *Q14_1B *Q14_2B *Q14_3BQ14_10B *Q15PROBQ15_1D *Q16OTH * * * *Q16_2B *Q16_3B *Q16_4B * *Q16_5BQ16_6B *Q16_7B *PhetchaburiSamut SakhonSatunSongkhlaSurat ThaniThere are three key problem variables in Figure 3 (i.e. major problem, water orsediment related problem, and other problems). The other nine key variables forproblem analysis are shown in Figure 4 and they were: salinity problem; temperatureproblem; flooding problem; bacteria problem; seed quality; feed quantity; feedquality problem; poaching problem; and power-cut problem.&$
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!The 1994 Workshop had a working hy
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!The Workshop at Hat Yai, SongkhlaA
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!ReferenceSmith, P.T., ed.. 1999. T
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!Shrimp Farming in ThailandOver the
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! !Table 1. World production (# 1,0
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!Domestic and world demandJapan is
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!Intensification of farms has been
Page 24 and 25: !no significant difference between
Page 26 and 27: !Production Practices and Technique
Page 28 and 29: !use water which is almost fresh (F
Page 30 and 31: !mended quantities for application
Page 32 and 33: !the biological processes in sedime
Page 34 and 35: !factors (Fegan 1994). Environmenta
Page 36 and 37: !Acid sulphate soils occur in most
Page 38 and 39: during the rainy season, some deter
Page 40 and 41: Land subsidenceCoastal land subside
Page 42 and 43: first when an area becomes environm
Page 44 and 45: !Administration. Their work has inv
Page 46 and 47: agriculture, selects coastal land p
Page 48 and 49: Sustainability, Sustainable Develop
Page 50 and 51: cultural and environmental asset va
Page 52 and 53: $+Table 12. (cont’d) Impediments
Page 54 and 55: $#Table 12. (cont’d) Impediments
Page 56 and 57: $%Table 12. (cont’d) Impediments
Page 58 and 59: capacities of alternative organisms
Page 60 and 61: sources. This is one of the greates
Page 62 and 63: Table 13. (cont’d) Summary of the
Page 64 and 65: Boonyapiwat, S. 1989. Species of ph
Page 66 and 67: Katesombun, B. 1992. Aquaculture pr
Page 68 and 69: Sirisup, S. 1988 Socio-economic cha
Page 70 and 71: !In the preliminary analysis, the s
Page 72 and 73: !until all variables had been teste
Page 76 and 77: !Figure 1. Characterisation of prov
Page 78 and 79: !Variables Contributing to Shrimp P
Page 80 and 81: !Figure 5. Relationship between ave
Page 82 and 83: !southern). The linear regression m
Page 84 and 85: !Appendix. Results of logistic regr
Page 86 and 87: !2) Trat ProvinceClassification tab
Page 88 and 89: !Chi-square df significanceModel Ch
Page 90 and 91: !8) Satun ProvinceClassification ta
Page 92 and 93: !Variables in the equationConstant
Page 94 and 95: !A total of 49 participants were in
Page 96 and 97: ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! ! !Fi
Page 98 and 99: !All issues are listed in Appendice
Page 100 and 101: !!were short term and one issue was
Page 102 and 103: !reared broodstock (issue 71). They
Page 104 and 105: !!Two participants commented that i
Page 106 and 107: mation transfer and training. This
Page 108 and 109: 108Appendix 1. (cont’d) Descripti
Page 112 and 113: 112Appendix 2. Descriptive statisti
Page 122 and 123: Appendix 5. (cont’d) Descriptive
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Appendix 7. Descriptive statistics
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Appendix 7. (cont’d) Descriptive
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128Appendix 8. (cont’d) Descripti
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!ParticipantsAustraliaDr Paul T. Sm
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