View
216
Download
0
Category
Preview:
Citation preview
8/8/2019 Working File_Tilapia Training Need
1/41
i
ABBRIVATION
Cons = Constrain for Tilapia culture and Tilapia farming business
DOF = Department of Fisheries, ThailandHH = Tilapia fish farmer household
Tr = Training content
8/8/2019 Working File_Tilapia Training Need
2/41
1
INTRODUCTION
Tilapia is becoming the important culture freshwater specie with the large amount of production and
still demand is increasing1. Many strategies have been developed under the national policy
2in
promoting culture, marketing and quality control for certification of this specie. Farm registration,
Trader and farmer meeting, typical training, advice and service providing to fish farmer, etc areconsidered activities help expanding good quality Tilapia production.
Culture practice for Tilapia in Thailand has very long development started around 1960/1970. In early
stage of aquaculture promotion, Tilapia was the specie of interest and it was totally promoted as
extensive system to stock fish without any management for production. Later on the practice on semi-
intensive was carried out from various extension programmes to promote better production of food
fish and alternative income of household in the rural area around late 1980. Many training
programmes and typical training materials for Tilapia culture had been developed ad discrimination to
the fish farmer and the production of Tilapia showed increased significantly.
In aquaculture development process, training activities and program offer to improve experience and
knowledge in culture are crucial. Although the knowledge in tilapia culture in Thailand has long-term
promoted and the fish farmer also have good experience. The development of training programs thatenable appropriate technology to transfer to the fish farmers is still needed. In the extension point of
view, the wish of giving sound advice to fish farmers has to understand the needs of information and
knowledge in order to provide proper advises and beneficially training programme.
This training needs survey continued from the Tilapia Stakeholder Workshop which carried out in
November 2008 to broadly collect the problem and potential in improve production system. Results
summarized of four main headings highlighted of new technology, fish disease, better access to
information and fish farmer institution had been considered in developing the training materials in
order to help providing knowledge and experience in improving Tilapia culture and farming business.
The aims of this survey are to find out the need to improve Tilapia culture and Tilapia farming
business and also reduce constraints in culture through proper training process. The survey designed
according to information derived from Tilapia Stakeholder Workshops. Few specific objectives
developed in order to better understanding current situation of Tilapia culture practices, determining
the obstacles to Tilapia production process and Tilapia farming business and finding out the
requirement of training regarding the contents on Tilapia culture techniques and information included
other related topics.
1 National statisticreport of Fishery Information Technology Centre, Department of Fisheries, Thailand(Thai version)
2 Report of Tilapia workshop organized by the DOF, Thailand at Chaingrai Province(Thai version)
8/8/2019 Working File_Tilapia Training Need
3/41
2
SURVEY DESIGN
The finding of training needs carried out by the use of interview face to face application. Simply
design of semi-structural questionnaire (appendix 1) applied to elicit information and perception of
Tilapia culture practice, constrains of tilapia farming and training needs for fish farmers. The survey
conducted in April 2009. A total of 47 fish farmers in Ang-thong Province which mainly the cage
culturist had joined this survey. Overall, the survey contains three parts of general information of
respondent and culture practice, constrains and difficulty encountered in Tilapia farming and the last
part of training needs as;
General information
Background information of respondents included age, gender, experience, culture practice and broad
detail information related to production and farm management.
Constrains to Tilapia farming
The survey conducted interview on situation that is presently limiting Tilapia culture and generate the
difficulty in raising the production. Fish farmer had been asked to present their perceptions on any
limitation in the production process and factors that affected yield. List bellowed of eight constrains
are from the workshops organized in November 2008 and the site visited in Mar 2009.1. Environment (Cons 1) is the major concern to the successfulness of crop. Fish farming
system is strongly needed the good environment. In addition, environmental issue had been
arisen in the Tilapia stakeholder workshops as the uncontrollable physical forces of nature.
2. Fish disease (Cons 2) for this survey is specific to Tilapia disease.
3. Information orientation and access to information of tilapia culture (Cons 3)
4. Access to information of tilapia marketing (Cons 4) is limitation in business in term of losing
ability to influence profitability of the farm enterprise.
5. Fish price (Cons 5) of both access to price information and ability to negotiate for price is
obviously one of limitation in business perspective.
6. Fish seed (Cons 6) refers to the quality and availability of good quality Tilapia seed for fish
farmer. Good quality is concerning fish health, high survival rate after stock and efficiency of
sex-reversed or percent of male in each batch.
7. Farmer organisation and connection among the farmer groups (Cons 7). As a management
point of view, fish farmer institution considers as weak management to promote Tilapia
culture and business.
8. Cost of investment for crop which mostly refers to feed (Cons 8). For cage culturist, cost of
feed found directly affect to farm enterprise as it contains of around 70 percent of total cost.
Investigating of difficulty encountered is for current situation which covers the period of three - five
years. All constrains raised to the respondents to provide the personal opinion on those are facing to
culture practices, production and farming business. Factors accepted affected their farming activities,
then, again asked for further ranked for the degree of serious affected. Scaling to degree started from
1 as the most important factors.
Training needs
Training needs is a part consider tremendous important to help increasing Tilapia production and
strengthening Tilapia farming business. The study exposes the needs of Tilapia fish farmer in
particular four main area which have been explored from Tilapia fish farmer during conducted Tilapia
Stakeholder Workshops. Training contents that has been taken into consideration for training needs
survey refers to four main headings derived from the Tilapia Stakeholders Workshops below;
1 Tilapia culture content refers to intensive culture techniques for tilapia. The content given
diverse of typical practice and also more specific to new techniques which can provide high
8/8/2019 Working File_Tilapia Training Need
4/41
3
yield and/or reduce disrupting of the production process. There are seven contents
considered proper knowledge as:
1.1 Tilapia hatchery and quality Tilapia seed production process - (Tr 11).
1.2 Intensive and semi-intensive culture practice (Tr 12).
1.3 Tilapia feed and feeding - (Tr 13).
1.4 Harvesting technique - (Tr 14).
1.5 Tilapia farm management - (Tr 15).1.6 Tilapia farming in closed system - (Tr 16).
1.7 Tilapia hybrid culture (Tr 17).
2 Tilapia disease has the content concerning disease control, disease management and
chemical available and use to control disease. Fish disease training context considers the
knowledge help reducing disrupting of the production process and managing on risk of
uncontrollable physical forces of nature that affect fish health and farm hygiene. Total 6
contents in Tilapia disease can be provided as;
2.1 Tilapia fish disease management - (Tr 21).
2.2 Environmental and the affect to Tilapia disease - (Tr 22).
2.3 Caused of fish disease - (Tr 23).
2.4 Knowledge various symptoms of Tilapia disease - (Tr 24).
2.5 Chemical and medicine used for Tilapia disease - (Tr 25).
2.6 Tilapia disease management for small-scale farm - (Tr 26).
3 Information is concerning access to tilapia culture and marketing. There are 7 contents of
information required to get better in accessing as;
3.1 Tilapia culture technique - (Tr 31).
3.2 Environmental factor affect to Tilapia culture and farming - (Tr 32).
3.3 Source of information on chemical and medicine for Tilapia disease - (Tr 33).
3.4 Source of information of Tilapia price at difference market levels - (Tr 34).
3.5 Negotiate for fish price - (Tr 35).
3.6 Source of information on farming certification and farming certification system -
(Tr 36).
4 Fish farmer institution and farmer organization refer to the need of farmer organisation
establishment, conflict management and etc to strengthen tilapia farmer group and buildingfish farmer network.
4.1 Fish farmer organisation - (Tr 41).
4.2 Technique to develop proposal for funding and loan from financial institute -
(Tr 42).
4.3 Technique in straitening farmer group and build up linkage among farmer groups
- (Tr 43).
4.4 Conflict management - (Tr 44).
4.5 Building up leadership - (Tr 45).
4.6 Technique to contract and communicate with government agencies - (Tr 46).
5 Others contents
5.1 Culture technique of other species - (Tr 51).
5.2 Knowledge for Tilapia crop/production insurance - (Tr 52).5.3 Others - (Tr 53).
The dependent variable is the training information needs for Tilapia fish farmers. Result summarised
measured by subjecting the respondents to 5 major heading and total 28 contents on culture and
management practices place on scaling method. The analysis determines by ascertaining the
important and needed requirement by respondents. Any content to illustrate the important and need
for training was placed on 4 points where extremely need = 3, moderately need = 2,
marginally need = 1 and not important and no requirement for training = 0.
8/8/2019 Working File_Tilapia Training Need
5/41
4
Data obtained were analysed using the statistic package JMP versio 8 (Trail version) and MS Office
Excel 2007. Results summarised for descriptive involved frequencies, percentage and mean of
perception of training material requirement. Training needs score was computes by cumulating the
total respondents score and frequency shared for all training contents. Independent variables of
socio-economic like age and gender were introduced for analysis to present difference needs among
different groups of respondents.
For this report,
Training needrefer mainly to training information and content to produce the training material support
the implement training programs.
Perceptionrefers to the opinion or view of Tilapia fish farmers on training topics with reference to the
three levels of scales (very useful/strongly required = 1, moderately = 2, marginally useful/strongly
required = 1 and not useful = 0).
Interviewing Tilapia fish farmer
8/8/2019 Working File_Tilapia Training Need
6/41
5
RE
ULT
The
ey
ied
t t
ill
t
te the li
it
ti
i
Til
i
lt
e
ti
e i
A
thong inordertohel
determining
reaof interest fortheneedson trainingand
ilding
etterexperience inTilapia
farming.
esult from thesurveydivided intopartsofgeneral information, constrains inordifficulty in
Tilapiacultureand farming, trainingneeds to improvecultureandorganisation forTilapia fish farmer.
GENERAL INFORMATION OF INTERVIEW GROUP
Agegroupofrespondents
erevaries fromyoung tohighageandmostly inagroupofmid-age
eteen
and
years (figure
. It isalsonoted thatomenhashighly involved in tilapiaculture.
f total
!
"respondents,
#
respondents
ere female fish farmer(
! !
."
%
. Theobservationand
informal discussionamong fish farmers foundomen fish farmerappreciated to tilapiaculture,
especially forcageculture. It isacceptedasasuitableeconomicactivity foroman fish farmer.
$any
of themshared that cageculturecouldmade thembetter liveandhelpgeneratedgood income.
%ish farmershavegoodexperiencealthough theyhave just engaged inTilapiacultureas the
informationofculture techni&ues ispassing farm to farm included the farmervisiting fromvarious
groupsofprivateandgovernment sectors.Therespondentsexperience in tilapiaculturesummarised
ofaverage
years.'
ataobtainedshowed therangeofexperiencewasbetweenyearandup to
more than
years.(ewTilapia farmerhave just engaged inTilapiaculturebusiness influencedby
perceptionongoodopportunity forincomegenerateat the timeofhighdemandofdomestic fish
market. Interestingpoint raisedabout basicknowledgeofTilapiaculture techni&ues isavailablebut
theriskofcropsuccessfulness isstill beingabigconstrain forTilapiacultureandbusiness.
The twomajorculturepracticesofTilapiaarepondandcage.)ultureperiod fortilapia inearthen
pond isaroundoneyear.Tilapia farm ismainlysmall-scale, especiallycageculture. Thenumberofcageownedofaverage
"cageswith therange from
to
0cagesper
1 1.Thecycleofcrop forcage
culture foundhigh frequency.)ageculturist normallycarrieson
#
-3cropsperyear(0
% of total cage
culturist respondents).2enerally, cropperiod takearound
!
months.)agesi
3evaries from3x3m to
x5m. Themost commonlyused in Angthong Province is3x3m. Stockingrate foundhighof
average0
ind/m2
(n4
32). Therangeofstockingratewas from5
ind/m2up tohighstockingrateof
more than20 ind/m
2. Si
3eat first stocked isnormallyaround2 inchoraround20-25gram.
%igure2
showeddistributionofstockingrateclass for tilapiacageculture. Themost commonstockingratewas
between006
20 ind/m
2.
%igure
'
istributionofagegroupofrespondents
7
8
9 9
and5missingdata
CDF Plot
Note: 2 = 21-30 years
3 = 31-40 years
4 = 41-50 years
5 = 51-60 years
6 = more than 60 year
8/8/2019 Working File_Tilapia Training Need
7/41
6
Theproduction fromcageculturereportedof fluctuationdue tomany factorofuncontrollable factors
likeenvironment andpollutionand farmmanagement. Dataobtainedofproductionvaried from500-
1,000percagepercropof themarketablesi@e from500-1,000gram.
Figure2Distributionof Stockingrate forTilapiacageculturepractice
A
B
32
CDF Plot
Note: 1 = 60 80 ind./m 2
2 = 81 100 ind./m2
3 = 101 120 ind./m2
4 = > 120 ind./m2
8/8/2019 Working File_Tilapia Training Need
8/41
7
CONSTRAINS IN TILAPIA CULTURE AND FARMING BUSINESS
Thedeterminationondifficultyorconstrains inTilapiacultureandTilapia farmingbusinesshas
summrised fromallC
D
respondents. Personal perceptionwasalmost focusingon therisk toraise
successfulnesscrop.Figure2showedattitudeonobstacle factorsencountered toTilapia fish farmer
whichreferred toenvironment and farmmanagement includedorganisationand information. The
summarised frequencypresentedscorerankingofeachaspect. ClearlyshowedriskofTilapia farmingcausedbyenvironment anddiseaseand these two factorsarewidelyacceptedas themajor
constrains, especially forTilapiacageculturist.
Pricewasalsoprioritisedalthough therankof important limitation factortoTilapia farmingwasbelow
environmental and fishdisease factors.E
fa total respondent, 19 fish farmersperformed theopinion
that lowpriceofproductioncancauseof the failure toattainprofitability.FanyTilapia fish farmerhas
lessability innegotiate tobetterpriceofproduct. Additionally, most of the famersunderstand that fish
farmerorganisationcouldperhapshelpbuildingupabilityinpricenegotiation.
Duringconducted thesurvey, fish farmersprovidedaddition information that manyof them joined
informal fish farmergroup inorder to increasepower inmarketingsystemandsupport eachotherfor
loan, material, information, etc.Thepersonal attitudeofTilapia fish farmerfoundquitepositive to the
needofestablishment ofTilapia farmergroup. Therefore, low frequencyonconstrain forfarmer
organisation (Cons7)performed from thissurvey. By theway, the limitationraisedby fish farmerthe
issueof fish farmerinstitutionwas thesustainof thegroupandconnectionamongdifference fish
farmergroups toshare, knowledge, experience and informationrelated toTilapiacultureand
farming.
GimitationofTilapiamarket (Cons4)wasalsonot highprioritisedbyrespondents. Thesituationof
demandgradually increaseddue to thepotential ofexportationanddomesticdemand increasing
whereassupply isstill limited is forcingmarket expansion.Fost of fish farmersshared thepersonal
opinions forgoodopportunity toexpansescaleofproduction toservemarket demand. The limitation
raised fortilapiamarket was the lackofexperience inbuildupmarket, especially forexportation.
Tilapia fish farmersaremainlysmall-scaleand theirability inexplorenewmarket isvery limited.
Access to information (Cons3) founddoesnot beinghighprioritisedaswell asmarket issue. F ost of
the fish farmersattitudewascommunicationamong fish farmersand theconnectionbetween fish
farmerandprivatesectorand/orgovernment agency isgood. Farm isnormallyvisitedregularlyby the
middle-man, salemanagerofbigagro-industrial cooperationnamedCharoen PokphangroupCo.ltd
orknownas CP ofotherfish feedcompanyand fisheriesextensionofficer. The informationrelated
toculture technique, marketingandmarket price included farmcertification foundofnoobstacle. The
limitation in termof informationwasmainly fortechnical support toprevent andmanage forfish
disease.
Figure3Frequencychart ofattitudeonconstrains forTilapiacultureand farmingbusiness
Const1_Environment
Const2_Disease
Const3_AccInformation
Const4_TilapiaMkt
Const5_Price
ConstH
_FishSeed
Const7_FForganisation
Const I _Gover
8/8/2019 Working File_Tilapia Training Need
9/41
8/8/2019 Working File_Tilapia Training Need
10/41
9
Table 2 Frequency of respondents responded to constrain items and cumulative percent acceptation
of respondents by gender group
f = 21 and m =28
Freq Share ofConstrains
Gender Number of respondents response by score ranked Cumulativepercent ofacceptation
of
respondents
Notranked(0)
1 2 3 4 5 6
rank_Cons1 f 10 6 3 0 0 0 38.78 2
m 17 6 1 2 0 0 53.06 2
rank_Cons2 f 10 7 0 2 0 0 38.78 2
m 12 11 1 1 1 0 53.06 2
rank_Cons3 f 0 0 1 2 0 0 6.12 18
m 0 2 3 0 1 0 12.24 22
rank_Cons4 f 0 1 1 0 1 0 6.12 18
m 0 1 2 1 2 0 12.24 22
rank_Cons5 f 1 3 6 1 0 0 22.44 10
m 0 6 7 2 1 1 34.69 11
rank_Cons6 f 1 2 0 2 0 0 10.20 16
m 1 2 2 3 0 1 18.36 19
rank_Cons7 f 0 0 0 0 0 0 0 21m 0 0 0 1 0 0 2.04 27
rank_Cons8 f 1 0 0 0 0 0 2.04 18
m 0 1 0 0 0 0 2.04 22
8/8/2019 Working File_Tilapia Training Need
11/41
10
RESULT OF TRAINING NEEDS
TRAINING NEEDS CONTENTS
Fourmainheadingofculture technique, fishdisease, informationand fish farmerorganisationwhich
consist of25 topics/contentsandadditional 3other topicshavebeensummarised inorder to illustrate
theneedof trainingmaterial forTilapia fish farmers. Simpleanalysisof trainingneeds iscomputing in
frequencyof49 tilapia fish farmerresponsesandpercent byscoreranked to training topics.Data
obtainedon theusefulnessandrequirement ofeachof the topic tohelpTilapia fish farmerto improve
cultureandbusiness.Figure4showed theperceptionon trainingneeds from frequencyofscorerated
foreachof the training topic fromall respondents.
From theabove figuresummarised frequencyof the training topics, thecontent of fishdisease
showedhighperception to theknowledgeand informationneeded.Thecontext ofTilapiaculture
techniques, many fish farmers interested in fourcontentsofseedproductionprocessorTilapia
hatcheries, Intensiveculture techniques, feedandfeedingandharvestingmanagement. Additionally
forfishharvesting, it wasraisedas the issue tomaintaingoodproductionandpricewhich the fish
farmerscouldgainbetterprofitability.
The fishdiseaseheading forall topicshasgot highresponses.Table2showed three topicsof the
causeofTilapiadisease, diseasemanagement, anddiseasesymptomrespondedofhighscoreand
accounted forP5.7,
P3.7and
P1.
Qpercent of total respondents. The interestingpoint found from the
survey isTilapia fish farmerhadnegativeattitudeon farmmanagement toprevent from fishdisease.
N = 49
Tr11_SeedTr12_InntensiveCul
Tr13_FeedTr14_Harvest
Tr15_FarmingTechniqueTr16_CloseSyst
Tr17_StrainTr21_DiseaseMgt
Tr22_Env&DisTr23_CauseDisTr24_DisDetail
Tr25_ChemcalUsedTr26_FMgtPrev
Tr31_AccCulTechTr32_AccEnvInfTr33_AccChemTr34_AccPriceTr35_AccNigoTr36_AccCert
Tr37_AccIntMktTr41_FFInstituteTr42_ProposalD
Tr43_StrenGroupTr44_ConflictMgtTr45_LeadershipTr46_DrilGovm
Tr51_OthSppCulTr52_Insurance
Tr53_Others
Figure4Frequencychart ofperceptionofall respondentsonrequirement to trainingcontents
Tilapia
cultu R e
tecS T
ique
Fis S
disease
Access to
information
Fis S
farmerinstitution
Other
8/8/2019 Working File_Tilapia Training Need
12/41
11
Many of them mentioned of the effort spent for management on fish health and farming system. This
might be because there is an uncontrollable physical factor for cage culture practice.
Access t information is strong required for Tilapia fish farmer. Although fish farmers have quite good
experience in Tilapia culture and well communication with the outsiders, the issue of information on
new technology and knowledge on culture aspects, disease and chemical are still needed. In
consideration to the topics of information required, technical aspects ((Tr 31) found high frequency of
respondents compared to information on price and certification. Many fish farmer informed the detail
information about certification system is well delivery to fish farmer through various channels such as
the middle man, field extension officer, sale manager from feed and chemical company, etc. The
regularly farm visiting by the field extension office to inspect production process can build up
understand the certification system of food fish.
The attitude on farmer institution is interesting that many fish farmers concern of establishment the
proper organisation and build up connection among the famer groups (Tr 41). From table 2, high
frequency of response fish farmers of 87.7 percent of total samples agreed upon the need to
understand on good functioning and networking of Tilapia fish farmer. The activities under farmer
organisation included developing proposal for credit and loan (Tr 44), conflict management (Tr 44),
strengthening leadership of the member(Tr 45) have high response of 77.5, 73.5 and 69.4 percent of
total respondents. In comparison with the need on technical aspects like culture and diseasemanagement, fish farmer institution had been scored more of the moderately requirement of training
material.
About other three topics which had been raised by some fish farmers during conducted interview
survey were another species culture technique, crop insurance seed quality control. These training
topics were the interest of few fish farmers and the farmer responded to these topic had strong
required for the training material
The determination on some socio-economic dependent variables with the training needs. Chi-square
test on percent of training topic scored and gender was applied. The result clearly indicates gender
role didnt generate difference perception on information need regarding training topics. Age group is
significantly affected to the need of training contents.
8/8/2019 Working File_Tilapia Training Need
13/41
12
Table 2 Perception of training needs regarding content of aquaculture and management summarised
in percent of respondents and mean score.
N = 49
Responses content Percent of respondent by score Number and
% response
need
training
%
response
not need
0
Mean
3 2 1
TUc
V W Xc
Yl
Ysp
Uc
s
a WT
Xl
Yp
X Ycul
ur
U
Tr11_Seed 53.06 14.29 10.20 (32) 77.55 22.45 1.98
Tr12_InntensiveCul 53.06 16.33 8.16 (32) 77.55 22.45 2.00
Tr13_Feed 46.94 12.24 20.41 (39) 79.59 20.41 1.86
Tr14_Harvest 24.49 10.20 12.24 (23) 46.93 53.06 1.06
Tr15_Farming Technique 36.73 22.45 8.16 (33) 67.34 32.65 1.63
Tr16_CloseSyst 0.00 2.04 0.00 (1) 2.04 97.96 0.04
Tr17_Strain 2.04 0.00 0.00 (1) 2.04 97.96 0.06
Fbs
cd
bs
d es
d
Tr21_DiseaseMgt 67.35 8.16 6.12 (40) 81.63 18.37 2.24
Tr22_Env&Dis 59.18 10.20 8.16 (38) 77.54 22.45 2.06
Tr23_CauseDis 69.39 12.24 4.08 (42) 85.71
14.29 2.37Tr24_DisDetail 61.22 18.37 4.08 (41) 83.67 16.33 2.24
Tr25_Chemcal f sed 59.18 16.33 2.04 (38) 77.55 22.45 2.12
Tr26_FMgtPrev 53.06 18.37 2.04 (36) 73.47 26.53 1.98
Access to information
Tr31_AccCulTech 32.65 28.57 12.24 (36) 73.46 26.53 1.67
Tr32_AccEnvInf 51.02 22.45 2.04 (37) 75.51 24.49 2.00
Tr33_AccChem 57.14 14.29 2.04 (36) 73.47 26.53 2.02
Tr34_AccPrice 24.49 26.53 10.20 (30) 61.22 38.78 1.37
Tr35_AccNigo 32.65 16.33 16.33 (32)65.31 34.69 1.47
Tr36_AccCert 36.73 16.33 14.29 (33) 67.35 32.65 1.57
Tr37_AccIntMkt 18.37 8.16 24.49 (25) 51.02 48.98 0.96
Fish farmer institution
Tr41_FFInstitute 40.82 30.61 16.33 (43) 87.76 12.24 2.00
Tr42_ProposalD 55.10 14.29 8.16 (38) 77.55 22.45 2.02
Tr43_StrenGroup 30.61 20.41 22.45 (36) 73.47 26.53 1.55
Tr44_ConflictMgt 18.37 22.45 28.57 (34) 69.39 30.61 1.29
Tr45_Leadership 26.53 22.45 14.29 (31) 63.27 36.73 1.39
Tr46_DrilGovm 0.00 2.04 2.04 (2) 4.08 95.92 0.06
Og h i
rcp q g i q g
s
Sc trr s q
51 2.04 0.00 0.00 (1) 2.04 97.96 0.06
Sc tr t u v 52 2.04 0.00 0.00 (1) 2.04 97.96 0.06
Sc trw x y
53 2.04 0.00 0.00 (1) 2.04 97.96 0.06
Note: level of score 3 = extremely needed, 2 = moderately needed, 1 = marginally needed = 1 and
0 = not important and no requirement for training
8/8/2019 Working File_Tilapia Training Need
14/41
13
The analysis Data obtained indicated age of respondents and topics of training need is significantly
difference among different age groups. Gender role
8/8/2019 Working File_Tilapia Training Need
15/41
14
DISCUSS ION
Requirement on training material is subjecting to culture process and management to increase
production and help reducing risk for Tilapia farming business of fish farmer. The survey obtained
personal attitude on the important/usefulness of 29 training topics from 49 Tilapia fish farmers to
reveal perception on important training context
The following recommendations have been made according to the results from field survey:
1. The perception of The fish farmers, especially the cage culturist mentioned that tilapia
disease is seriously affect the croup during these few years and make them feel of
uncertainty in continue the culture activities.
2. In the view of promoting gender policy to put women to boost rural economy, women found
as important group respond for culture and the information derived this activity is necessarily
to women compared to other field works like paddy, etc. Cage culture is good as it not very
hard work and they can work nearby their house which easy for them to earn money as well
as carry on their chore work. They want the problem to be solved properly so that they can
continue Tilapia culture. Most of the women are willing to get to know new technologies
included disease control practice.
3. Result clearly determined age affect to the response on training needs. The high age group
seemed to have low interest to all training programme and information. Therefore, the
development of training material must concern to the issue of age and especially the
material will be develop should consider of age.
8/8/2019 Working File_Tilapia Training Need
16/41
15
APPENDIX 1
Interview form
8/8/2019 Working File_Tilapia Training Need
17/41
16
APPENDIX 2
Gender By train11
CountTotal %Col %
Row %
0 1
f 510.20
45.4523.81
1632.65
42.1176.19
2142.86
m 612.2454.5521.43
2244.9057.8978.57
2857.14
1122.45
3877.55
49
Gender By train12
CountTotal %
Col %Row %
0 1
f 510.2045.4523.81
1632.6542.1176.19
2142.86
m 612.2454.5521.43
2244.9057.8978.57
2857.14
1122.45
3877.55
49
Gender By train13
CountTotal %Col %Row %
0 1
f 510.20
50.0023.81
1632.65
41.0376.19
2142.86
m 5
10.2050.0017.86
23
46.9458.9782.14
28
57.14
1020.41
3979.59
49
Gender By train14
CountTotal %
Col %Row %
0 1
f 11
22.4542.3152.38
10
20.4143.4847.62
21
42.86
m 1530.6157.69
53.57
1326.5356.52
46.43
2857.14
26
53.06
23
46.94
49
TestsN DF -LogLike RSquare (U)
49 1 0.01947497 0.0007
Test ChiSquare Prob>ChiSq
Likelihood Ratio 0.039 0.8435Pearson 0.039 0.8433
Fisher's ExactTest
Prob Alternative Hypothesis
Left 0.7082 Prob(train11=1) is greater for Gender=f than mRight 0.5549 Prob(train11=1) is greater for Gender=m than f2-Tail 1.0000 Prob(train11=1) is dif ferent across Gender
TestsN DF -LogLike RSquare (U)
49 1 0.01947497 0.0007
Test ChiSquare Prob>ChiSq
Likelihood Ratio 0.039 0.8435Pearson 0.039 0.8433
Fisher's ExactTest
Prob Alternative Hypothesis
Left 0.7082 Prob(train12=1) is greater for Gender=f than mRight 0.5549 Prob(train12=1) is greater for Gender=m than f2-Tail 1.0000 Prob(train12=1) is different across Gender
TestsN DF -LogLike RSquare (U)
49 1 0.12990765 0.0052
Test ChiSquare Prob>ChiSqLikelihood Ratio 0.260 0.6102Pearson 0.262 0.6089
Fisher's ExactTest
Prob Alternative Hypothesis
Left 0.8080 Prob(train13=1) is greater for Gender=f than mRight 0.4353 Prob(train13=1) is greater for Gender=m than f2-Tail 0.7256 Prob(train13=1) is different across Gender
TestsN DF -LogLike RSquare (U)
49 1 0.00341377 0.0001
Test ChiSquare Prob>ChiSqLikelihood Ratio 0.007 0.9341Pearson 0.007 0.9341
Fisher's ExactTest
Prob Alternative Hypothesis
Left 0.5813 Prob(train14=1) is greater for Gender=f than mRight 0.6450 Prob(train14=1) is greater for Gender=m than f
2-Tail 1.0000 Prob(train14=1) is different across Gender
8/8/2019 Working File_Tilapia Training Need
18/41
17
CountTotal %
Col %Row %
0 1 Gender Bytrain15
f 4
8.1625.0019.05
17
34.6951.5280.95
21
42.86
m 12
24.4975.0042.86
16
32.6548.4857.14
28
57.14
1632.65
3367.35
49
CountTotal %Col %Row %
0 1 Gender Bytrain16
f 2142.8643.75
100.00
00.000.000.00
2142.86
m 2755.10
56.2596.43
12.04
100.003.57
2857.14
4897.96 12.04 49
CountTotal %Col %Row %
0 1 Gender Bytrain17
f 2142.8643.75
100.00
00.000.000.00
2142.86
m 2755.1056.25
96.43
12.04
100.00
3.57
2857.14
48
97.96
1
2.04
49
CountTotal %
Col %Row %
0 1 Gender Bytrain21
f 4
8.1644.4419.05
17
34.6942.5080.95
21
42.86
m 510.2055.56
17.86
2346.9457.50
82.14
2857.14
918.37
4081.63
49
CountTotal %
Col %Row %
0 1 Gender Bytrain22
f 5
10.2045.4523.81
16
32.6542.1176.19
21
42.86
m 612.2454.55
21.43
2244.9057.89
78.57
2857.14
1122.45
3877.55
49
TestsN DF -LogLike RSquare (U)
49 1 0.56741518 0.1162
Test ChiSquare Prob>ChiSq
Likelihood Ratio 1.135 0.2867Pearson 0.766 0.3816
Fisher's ExactTest
Prob Alternative Hypothesis
Left 1.0000 Prob(train16=1) is greater for Gender=f than m
Right 0.5714 Prob(train16=1) is greater for Gender=m than f
2-Tail 1.0000 Prob(train16=1) is different across Gender
TestsN DF -LogLike RSquare (U)
49 1 0.56741518 0.1162
Test ChiSquare Prob>ChiSqLikelihood Ratio 1.135 0.2867Pearson 0.766 0.3816
Fisher's ExactTest
Prob Alternative Hypothesis
Left 1.0000 Prob(train17=1) is greater for Gender=f than mRight 0.5714 Prob(train17=1) is greater for Gender=m than f2-Tail 1.0000 Prob(train17=1) is different across Gender
Tests
N DF -LogLike RSquare (U)49 1 0.00565859 0.0002
Test ChiSquare Prob>ChiSqLikelihood Ratio 0.011 0.9153Pearson 0.011 0.9152
Fisher's ExactTest
Prob Alternative Hypothesis
Left 0.6864 Prob(train21=1) is greater for Gender=f than m
Right 0.5999 Prob(train21=1) is greater for Gender=m than f2-Tail 1.0000 Prob(train21=1) is different across Gender
TestsN DF -LogLike RSquare (U)
49 1 0.01947497 0.0007
Test ChiSquare Prob>ChiSq
Likelihood Ratio 0.039 0.8435Pearson 0.039 0.8433
Fisher's ExactTest
Prob Alternative Hypothesis
Left 0.7082 Prob(train22=1) is greater for Gender=f than m
Right 0.5549 Prob(train22=1) is greater for Gender=m than f2-Tail 1.0000 Prob(train22=1) is different across Gender
TestsN DF -LogLike RSquare (U)
49 1 1.6064317 0.0519
Test ChiSquare Prob>ChiSqLikelihood Ratio 3.213 0.0731
Pearson 3.093 0.0786
Fisher's ExactTest Prob Alternative Hypothesis
Left 0.0720 Prob(train15=1) is greater for Gender=f than mRight 0.9824 Prob(train15=1) is greater for Gender=m than f2-Tail 0.1241 Prob(train15=1) is different across Gender
8/8/2019 Working File_Tilapia Training Need
19/41
18
CountTotal %
Col %Row %
0 1 Gender Bytrain23
f 4
8.1657.1419.05
17
34.6940.4880.95
21
42.86
m 36.12
42.86
10.71
2551.0259.52
89.29
2857.14
714.29
4285.71
49
CountTotal %Col %Row %
0 1 Gender Bytrain24
f 48.16
50.0019.05
1734.6941.4680.95
2142.86
m 48.16
50.00
14.29
2448.9858.54
85.71
2857.14
816.33
4183.67
49
CountTotal %
Col %Row %
0 1 Gender Bytrain25
f 5
10.2045.4523.81
16
32.6542.1176.19
21
42.86
m 612.2454.55
21.43
2244.9057.89
78.57
2857.14
1122.45
3877.55
49
CountTotal %
Col %Row %
0 1 Gender Bytrain26
f 5
10.2038.4623.81
16
32.6544.4476.19
21
42.86
m 816.3361.54
28.57
2040.8255.56
71.43
2857.14
1326.53
3673.47
49
CountTotal %Col %
Row %
0 1 Gender Bytrain31
f 48.16
30.7719.05
1734.6947.2280.95
2142.86
m 918.3769.2332.14
1938.7852.7867.86
2857.14
1326.53
3673.47
49
TestsN DF -LogLike RSquare (U)
49 1 0.09878373 0.0045
Test ChiSquare Prob>ChiSqLikelihood Ratio 0.198 0.6567
Pearson 0.199 0.6554
Fisher's Exact
Test
Prob Alternative Hypothesis
Left 0.7990 Prob(train24=1) is greater for Gender=f than m
Right 0.4727 Prob(train24=1) is greater for Gender=m than f2-Tail 0.7102 Prob(train24=1) is different across Gender
TestsN DF -LogLike RSquare (U)
49 1 0.54063979 0.0191
Test ChiSquare Prob>ChiSq
Likelihood Ratio 1.081 0.2984Pearson 1.056 0.3042
Fisher's ExactTest
Prob Alternative Hypothesis
Left 0.2436 Prob(train31=1) is greater for Gender=f than mRight 0.9138 Prob(train31=1) is greater for Gender=m than f2-Tail 0.3477 Prob(train31=1) is different across Gender
TestsN DF -LogLike RSquare (U)
49 1 0.33653890 0.0167
Test ChiSquare Prob>ChiSqLikelihood Ratio 0.673 0.4120Pearson 0.681 0.4094
Fisher's ExactTest
Prob Alternative Hypothesis
Left 0.8914 Prob(train23=1) is greater for Gender=f than mRight 0.3368 Prob(train23=1) is greater for Gender=m than f2-Tail 0.4427 Prob(train23=1) is different across Gender
TestsN DF -LogLike RSquare (U)
49 1 0.01947497 0.0007
Test ChiSquare Prob>ChiSqLikelihood Ratio 0.039 0.8435Pearson 0.039 0.8433
Fisher's ExactTest
Prob Alternative Hypothesis
Left 0.7082 Prob(train25=1) is greater for Gender=f than mRight 0.5549 Prob(train25=1) is greater for Gender=m than f2-Tail 1.0000 Prob(train25=1) is different across Gender
TestsN DF -LogLike RSquare (U)
49 1 0.07026062 0.0025
Test ChiSquare Prob>ChiSqLikelihood Ratio 0.141 0.7078
Pearson 0.140 0.7087
Fisher's Exact
Test
Prob Alternative Hypothesis
Left 0.4845 Prob(train26=1) is greater for Gender=f than mRight 0.7564 Prob(train26=1) is greater for Gender=m than f
2-Tail 0.7553 Prob train26=1 is different across Gender
8/8/2019 Working File_Tilapia Training Need
20/41
19
CountTotal %
Col %Row %
0 1 Gender Bytrain32
f 3
6.1225.0014.29
18
36.7348.6585.71
21
42.86
m 9
18.3775.0032.14
19
38.7851.3567.86
28
57.14
1224.49
3775.51
49
CountTotal %Col %Row %
0 1 Gender Bytrain33
f 48.16
30.7719.05
1734.6947.2280.95
2142.86
m 918.37
69.2332.14
1938.78
52.7867.86
2857.14
1326.53 3673.47 49
CountTotal %Col %Row %
0 1 Gender Bytrain34
f 918.3747.3742.86
1224.4940.0057.14
2142.86
m 1020.4152.63
35.71
1836.7360.00
64.29
2857.14
19
38.78
30
61.22
49
CountTotal %
Col %Row %
0 1 Gender Bytrain35
f 10
20.4158.8247.62
11
22.4534.3852.38
21
42.86
m 714.2941.18
25.00
2142.8665.63
75.00
2857.14
1734.69
3265.31
49
CountTotal %
Col %Row %
0 1 Gender Bytrain36
f 8
16.3350.0038.10
13
26.5339.3961.90
21
42.86
m 816.3350.00
28.57
2040.8260.61
71.43
2857.14
1632.65
3367.35
49
TestsN DF -LogLike RSquare (U)
49 1 0.24637983 0.0080
Test ChiSquare Prob>ChiSq
Likelihood Ratio 0.493 0.4827Pearson 0.495 0.4817
Fisher's ExactTest
Prob Alternative Hypothesis
Left 0.8440 Prob(train36=1) is greater for Gender=f than m
Right 0.3449 Prob(train36=1) is greater for Gender=m than f2-Tail 0.5474 Prob(train36=1) is different across Gender
TestsN DF -LogLike RSquare (U)
49 1 1.0815449 0.0397
Test ChiSquare Prob>ChiSqLikelihood Ratio 2.163 0.1414Pearson 2.069 0.1503
Fisher's ExactTest Prob Alternative Hypothesis
Left 0.1347 Prob(train32=1) is greater for Gender=f than mRight 0.9649 Prob(train32=1) is greater for Gender=m than f2-Tail 0.1918 Prob(train32=1) is different across Gender
TestsN DF -LogLike RSquare (U)
49 1 0.54063979 0.0191
Test ChiSquare Prob>ChiSqLikelihood Ratio 1.081 0.2984
Pearson 1.056 0.3042
Fisher's Exact
Test
Prob Alternative Hypothesis
Left 0.2436 Prob(train33=1) is greater for Gender=f than mRight 0.9138 Prob(train33=1) is greater for Gender=m than f
2-Tail 0.3477 Prob(train33=1) is different across Gender
TestsN DF -LogLike RSquare (U)
49 1 0.12867864 0.0039
Test ChiSquare Prob>ChiSqLikelihood Ratio 0.257 0.6119Pearson 0.258 0.6116
Fisher's ExactTest
Prob Alternative Hypothesis
Left 0.7894 Prob(train34=1) is greater for Gender=f than mRight 0.4152 Prob(train34=1) is greater for Gender=m than f2-Tail 0.7682 Prob(train34=1) is different across Gender
Tests
N DF -LogLike RSquare (U)49 1 1.3533626 0.0428
Test ChiSquare Prob>ChiSqLikelihood Ratio 2.707 0.0999Pearson 2.710 0.0997
Fisher's ExactTest
Prob Alternative Hypothesis
Left 0.9744 Prob(train35=1) is greater for Gender=f than m
Right 0.0899 Prob(train35=1) is greater for Gender=m than f2-Tail 0.1339 Prob(train35=1) is dif ferent across Gender
8/8/2019 Working File_Tilapia Training Need
21/41
20
CountTotal %
Col %Row %
0 1 Gender Bytrain37
f 10
20.4141.6747.62
11
22.4544.0052.38
21
42.86
m 14
28.5758.3350.00
14
28.5756.0050.00
28
57.14
2448.98
2551.02
49
CountTotal %Col %Row %
0 1 Gender Bytrain41
f 24.08
33.339.52
1938.7844.1990.48
2142.86
m 48.16
66.6714.29
2448.98
55.8185.71
2857.14
612.24 4387.76 49
CountTotal %Col %Row %
0 1 Gender Bytrain42
f 48.16
36.3619.05
1734.6944.7480.95
2142.86
m 714.2963.64
25.00
2142.8655.26
75.00
2857.14
11
22.45
38
77.55
49
CountTotal %
Col %Row %
0 1 Gender Bytrain43
f 5
10.2038.4623.81
16
32.6544.4476.19
21
42.86
m 816.3361.54
28.57
2040.8255.56
71.43
2857.14
1326.53
3673.47
49
CountTotal %
Col %Row %
0 1 Gender Bytrain44
f 5
10.2033.3323.81
16
32.6547.0676.19
21
42.86
m 1020.4166.67
35.71
1836.7352.94
64.29
2857.14
1530.61
3469.39
49
TestsN DF -LogLike RSquare (U)
49 1 0.40663797 0.0135
Test ChiSquare Prob>ChiSq
Likelihood Ratio 0.813 0.3672Pearson 0.801 0.3709
Fisher's Exact
Test
Prob Alternative Hypothesis
Left 0.2823 Prob(train44=1) is greater for Gender=f than mRight 0.8872 Prob(train44=1) is greater for Gender=m than f
2-Tail 0.5327 Prob(train44=1) is different across Gender
TestsN DF -LogLike RSquare (U)
49 1 0.01361374 0.0004
Test ChiSquare Prob>ChiSqLikelihood Ratio 0.027 0.8689Pearson 0.027 0.8690
Fisher's ExactTest
Prob Alternative Hypothesis
Left 0.5493 Prob(train37=1) is greater for Gender=f than mRight 0.6746 Prob(train37=1) is greater for Gender=m than f2-Tail 1.0000 Prob(train37=1) is dif ferent across Gender
TestsN DF -LogLike RSquare (U)
49 1 0.12943968 0.0071
Test ChiSquare Prob>ChiSqLikelihood Ratio 0.259 0.6109
Pearson 0.253 0.6148
Fisher's Exact
Test
Prob Alternative Hypothesis
Left 0.4820 Prob(train41=1) is greater for Gender=f than mRight 0.8255 Prob(train41=1) is greater for Gender=m than f
2-Tail 0.6884 Prob(train41=1) is different across Gender
TestsN DF -LogLike RSquare (U)
49 1 0.12352159 0.0047
Test ChiSquare Prob>ChiSqLikelihood Ratio 0.247 0.6192Pearson 0.244 0.6212
Fisher's ExactTest
Prob Alternative Hypothesis
Left 0.4451 Prob(train42=1) is greater for Gender=f than mRight 0.7981 Prob(train42=1) is greater for Gender=m than f2-Tail 0.7369 Prob train42=1 is different across Gender
TestsN DF -LogLike RSquare (U)
49 1 0.07026062 0.0025
Test ChiSquare Prob>ChiSqLikelihood Ratio 0.141 0.7078
Pearson 0.140 0.7087
Fisher's Exact
Test
Prob Alternative Hypothesis
Left 0.4845 Prob(train43=1) is greater for Gender=f than mRight 0.7564 Prob(train43=1) is greater for Gender=m than f
2-Tail 0.7553 Prob(train43=1) is different across Gender
8/8/2019 Working File_Tilapia Training Need
22/41
21
CountTotal %
Col %Row %
0 1 Gender Bytrain45
f 7
14.2938.8933.33
14
28.5745.1666.67
21
42.86
m 11
22.4561.1139.29
17
34.6954.8460.71
28
57.14
1836.73
3163.27
49
CountTotal %Col %Row %
0 1 Gender Bytrain46
f 2142.8644.68
100.00
00.000.000.00
2142.86
m 2653.06
55.3292.86
24.08
100.007.14
2857.14
4795.92 24.08 49
CountTotal %Col %Row %
0 1 Gender Bytrain51
f 2142.8643.75
100.00
00.000.000.00
2142.86
m 2755.1056.25
96.43
12.04
100.00
3.57
2857.14
48
97.96
1
2.04
49
CountTotal %
Col %Row %
0 1 Gender Bytrain52
f 20
40.8241.6795.24
1
2.04100.00
4.76
21
42.86
m 2857.1458.33
100.00
00.000.00
0.00
2857.14
4897.96
12.04
49
CountTotal %
Col %Row %
0 1 Gender Bytrain53
f 20
40.8241.6795.24
1
2.04100.00
4.76
21
42.86
m 2857.1458.33
100.00
00.000.00
0.00
2857.14
4897.96
12.04
49
TestsN DF -LogLike RSquare (U)
49 1 1.1510410 0.1378
Test ChiSquare Prob>ChiSqLikelihood Ratio 2.302 0.1292Pearson 1.564 0.2111
Fisher's ExactTest
Prob Alternative Hypothesis
Left 1.0000 Prob(train46=1) is greater for Gender=f than mRight 0.3214 Prob(train46=1) is greater for Gender=m than f2-Tail 0.5000 Prob(train46=1) is different across Gender
TestsN DF -LogLike RSquare (U)
49 1 0.86122036 0.1764
Test ChiSquare Prob>ChiSqLikelihood Ratio 1.722 0.1894Pearson 1.361 0.2433
Fisher's ExactTest
Prob Alternative Hypothesis
Left 0.4286 Prob(train53=1) is greater for Gender=f than mRight 1.0000 Prob(train53=1) is greater for Gender=m than f2-Tail 0.4286 Prob(train53=1) is different across Gender
TestsN DF -LogLike RSquare (U)
49 1 0.01361374 0.0004
Test ChiSquare Prob>ChiSqLikelihood Ratio 0.027 0.8689Pearson 0.027 0.8690
Fisher's ExactTest
Prob Alternative Hypothesis
Left 0.5493 Prob(train37=1) is greater for Gender=f than mRight 0.6746 Prob(train37=1) is greater for Gender=m than f
2-Tail 1.0000 Prob(train37=1) is different across Gender
TestsN DF -LogLike RSquare (U)
49 1 0.56741518 0.1162
Test ChiSquare Prob>ChiSqLikelihood Ratio 1.135 0.2867
Pearson 0.766 0.3816
Fisher's Exact
Test
Prob Alternative Hypothesis
Left 1.0000 Prob(train51=1) is greater for Gender=f than mRight 0.5714 Prob(train51=1) is greater for Gender=m than f
2-Tail 1.0000 Prob(train51=1) is different across Gender
Tests
N DF -LogLike RSquare (U)49 1 0.86122036 0.1764
Test ChiSquare Prob>ChiSq
Likelihood Ratio 1.722 0.1894Pearson 1.361 0.2433
Fisher's ExactTest
Prob Alternative Hypothesis
Left 0.4286 Prob(train52=1) is greater for Gender=f than m
Right 1.0000 Prob(train52=1) is greater for Gender=m than f2-Tail 0.4286 Prob(train52=1) is different across Gender
8/8/2019 Working File_Tilapia Training Need
23/41
22
8/8/2019 Working File_Tilapia Training Need
24/41
23
A
EN
2
Analysison training topicandagegroup
Logi
i
i
of
i
ge
Logi
i
i
of
i
ge
Logi
i
i
of
i
ge
W
ole Model
estModel LogLi
eli
ood
iS j
k
le P
lob>ChiS
Difference 3.959187 1 7.918373 0.0049*Full 19.623048
meduced 23.582235
m Square (U) 0.1679Observations (orSum
n
gts) 44ConvergedbyGradient
P
meterEstim
tesoerm Estim
k
te Std Error ChiS j
k
re Prob>ChiS
Intercept[0] -5.7729953 1.9686493 8.60 0.0034*Age 1.08540925 0.4367036 6.18 0.0129*
Whole Model
estModel LogLi
elihood
ChiS
re Prob>ChiS
Difference 1.516613 1 3.033226 0.0816Full 20.775528
educed 22.292141
Square (U) 0.0680
Observations (orSum
gts) 44ConvergedbyGradient
Pz
rameterEstimates{erm Estimate Std Error ChiS
| }are Prob>ChiS
|
Intercept[0] -4.0089305 1.7033624 5.54 0.0186*
Age 0.64551907 0.3869057 2.78 0.0952
Whole Model~
estModel LogLi
elihood
ChiS
are Prob>ChiS
Difference 2.239210 1 4.47842 0.0343*Full 20.052930
educed 22.292141
Square (U) 0.1004
Observations (orSum
gts) 44
ConvergedbyGradient
ParameterEstimateserm Estimate Std Error ChiS
are Prob>ChiS
Intercept[0] -4.6860702 1.8113457 6.69 0.0097*Age 0.8021305 0.4055424 3.91 0.0479*
8/8/2019 Working File_Tilapia Training Need
25/41
24
Logisti
itoftrai
ge
Whole Model
est
Model
LogLi
elihood
ChiS
are Prob>ChiS
Difference 2.137892 1 4.275783 0.0387*Full 28.360584
educed 30.498476
Square (U) 0.0701
Observations (orSum
gts) 44
ConvergedbyGradient
ParameterEstimateserm Estimate Std Error ChiS
are Prob>ChiS
Intercept[0] -2.4805461 1.305989 3.61 0.0575Age 0.63556503 0.3255596 3.81 0.0509
Logisti
itoftrai
ge
Whole Model
estModel
LogLi
elihood
ChiS
are Prob>ChiS
Difference 1.417237 1 2.834474 0.0923Full 23.325509
educed 24.742746
Whole Model
estModel
LogLi
elihood
ChiS
are Prob>ChiS
Difference 2.239210 1 4.47842 0.0343*Full 20.052930
educed 22.292141
Square (U) 0.1004
Observations (orSum
gts) 44ConvergedbyGradient
ParameterEstimates
erm Estimate Std Error ChiS
are Prob>ChiS
Intercept[0] -4.6860702 1.8113457 6.69 0.0097*Age 0.8021305 0.4055424 3.91 0.0479*
8/8/2019 Working File_Tilapia Training Need
26/41
8/8/2019 Working File_Tilapia Training Need
27/41
26
Whole odel Test
odel -LogLikelihood DF ChiSquare Prob>ChiSq
Difference 0.0038874 1 0.007775 0.9297Full 4.7688515Reduced 4.7727389
RSquare (U) 0.0008Observations (or Sum Wgts) 44
Converged by Gradient
Parameter EstimatesTerm Estimate Std Error ChiSquare Prob>ChiSqIntercept[0] 4.09859896 3.992727 1.05 0.3046 Age -0.0853443 0.9661295 0.01 0.9296
Logistic Fit of train21 By Age
Whole odel Test
odel -LogLikelihood DF ChiSquare Prob>ChiSq
Difference 1.878078 1 3.756156 0.0526Full 15.647435Reduced 17.525513
RSquare (U) 0.1072Observations (or Sum Wgts) 44
Converged by Gradient
Parameter EstimatesTerm Estimate Std Error ChiSquare Prob>ChiSqIntercept[0] -5.4881529 2.2004627 6.22 0.0126* Age 0.86077498 0.4762209 3.27 0.0707
8/8/2019 Working File_Tilapia Training Need
28/41
27
Logistic Fit of train22 By Age
Whole
odel Test
odel -LogLikelihood DF ChiSquare Prob>ChiSqDifference 1.712992 1 3.425984 0.0642
Full 17.566018Reduced 19.279010
RSquare (U) 0.0889Observations (or Sum Wgts) 44
Converged by Gradient
Parameter EstimatesTerm Estimate Std Error ChiSquare Prob>ChiSqIntercept[0] -4.8555573 1.977452 6.03 0.0141*
Age 0.76360091 0.4365379 3.06 0.0803
Logistic Fit of train23 By Age
Whole
odel Test
odel -LogLikelihood DF ChiSquare Prob>ChiSqDifference 2.858559 1 5.717119 0.0168*Full 14.666954
Reduced 17.525513
RSquare (U) 0.1631Observations (or Sum Wgts) 44
8/8/2019 Working File_Tilapia Training Need
29/41
28
Converged by Gradient
Parameter EstimatesTerm Estimate Std Error ChiSquare Prob>ChiSqIntercept[0] -6.6181659 2.4595716 7.24 0.0071* Age 1.10741563 0.5200045 4.54 0.0332*
Logistic Fit of train24 By Age
Whole odel Test
odel -LogLikelihood DF ChiSquare Prob>ChiSq
Difference 2.858559 1 5.717119 0.0168*Full 14.666954Reduced 17.525513
RSquare (U) 0.1631Observations (or Sum Wgts) 44
Converged by Gradient
Parameter EstimatesTerm Estimate Std Error ChiSquare Prob>ChiSqIntercept[0] -6.6181659 2.4595716 7.24 0.0071* Age 1.10741563 0.5200045 4.54 0.0332*
Logistic Fit of train25 By Age
8/8/2019 Working File_Tilapia Training Need
30/41
29
Whole odel Test
odel -LogLikelihood DF ChiSquare Prob>ChiSqDifference 1.598238 1 3.196476 0.0738
Full 19.263892Reduced 20.862130
RSquare (U) 0.0766
Observations (or Sum Wgts) 44
Converged by Gradient
Parameter EstimatesTerm Estimate Std Error ChiSquare Prob>ChiSqIntercept[0] -4.3818448 1.820375 5.79 0.0161* Age 0.69541571 0.4082112 2.90 0.0885
Logistic Fit of train26 By Age
Whole
odel Test
odel -LogLikelihood DF ChiSquare Prob>ChiSqDifference 1.516613 1 3.033226 0.0816
Full 20.775528Reduced 22.292141
RSquare (U) 0.0680Observations (or Sum Wgts) 44
Converged by Gradient
Parameter EstimatesTerm Estimate Std Error ChiSquare Prob>ChiSqIntercept[0] -4.0089305 1.7033624 5.54 0.0186* Age 0.64551907 0.3869057 2.78 0.0952
8/8/2019 Working File_Tilapia Training Need
31/41
30
Logistic Fit of train31 By Age
Whole
odel Test
odel -LogLikelihood DF ChiSquare Prob>ChiSqDifference 0.523604 1 1.047209 0.3062
Full 20.338525Reduced 20.862130
RSquare (U) 0.0251Observations (or Sum Wgts) 44
Converged by Gradient
Parameter EstimatesTerm Estimate Std Error ChiSquare Prob>ChiSqIntercept[0] -3.0640419 1.6401071 3.49 0.0617
Age 0.3860903 0.3818788 1.02 0.3120
Logistic Fit of train32 By Age
Whole
odel Test
odel -LogLikelihood DF ChiSquare Prob>ChiSqDifference 2.575939 1 5.151879 0.0232*Full 16.703071
Reduced 19.279010
RSquare (U) 0.1336Observations (or Sum Wgts) 44
8/8/2019 Working File_Tilapia Training Need
32/41
31
Converged by Gradient
Parameter EstimatesTerm Estimate Std Error ChiSquare Prob>ChiSqIntercept[0] -5.7648405 2.1576598 7.14 0.0075* Age 0.96686056 0.4669906 4.29 0.0384*
Logistic Fit of train33 By Age
Whole odel Test-
odel -LogLikelihood DF ChiSquare Prob>ChiSq
Difference 2.239210 1 4.47842 0.0343*Full 20.052930Reduced 22.292141
RSquare (U) 0.1004Observations (or Sum Wgts) 44
Converged by Gradient
Parameter EstimatesTerm Estimate Std Error ChiSquare Prob>ChiSqIntercept[0] -4.6860702 1.8113457 6.69 0.0097* Age 0.8021305 0.4055424 3.91 0.0479*
Logistic Fit of train34 By Age
8/8/2019 Working File_Tilapia Training Need
33/41
32
Whole odel Test
odel -LogLikelihood DF ChiSquare Prob>ChiSqDifference 1.365353 1 2.730706 0.0984
Full 26.866659Reduced 28.232012
RSquare (U) 0.0484
Observations (or Sum Wgts) 44
Converged by Gradient
Parameter EstimatesTerm Estimate Std Error ChiSquare Prob>ChiSqIntercept[0] -2.7356587 1.364353 4.02 0.0450* Age 0.52008167 0.3255824 2.55 0.1102
Logistic Fit of train35 By Age
Whole
odel Test
odel -LogLikelihood DF ChiSquare Prob>ChiSqDifference 1.372804 1 2.745608 0.0975
Full 25.333595Reduced 26.706399
RSquare (U) 0.0514Observations (or Sum Wgts) 44
Converged by Gradient
Parameter EstimatesTerm Estimate Std Error ChiSquare Prob>ChiSqIntercept[0] -3.0455794 1.4355534 4.50 0.0339* Age 0.54113402 0.3380266 2.56 0.1094
8/8/2019 Working File_Tilapia Training Need
34/41
8/8/2019 Working File_Tilapia Training Need
35/41
34
Converged by Gradient
Parameter EstimatesTerm Estimate Std Error ChiSquare Prob>ChiSqIntercept[0] -1.9149099 1.2536766 2.33 0.1267 Age 0.4167422 0.3066846 1.85 0.1742
Logistic Fit of train41 By Age
Whole odel Test
odel -LogLikelihood DF ChiSquare Prob>ChiSq
Difference 4.834419 1 9.668839 0.0019*Full 10.743831Reduced 15.578250
RSquare (U) 0.3103Observations (or Sum Wgts) 44
Converged by Gradient
Parameter EstimatesTerm Estimate Std Error ChiSquare Prob>ChiSqIntercept[0] -10.11716 3.7034779 7.46 0.0063* Age 1.78109552 0.7409897 5.78 0.0162*
Logistic Fit of train42 By Age
8/8/2019 Working File_Tilapia Training Need
36/41
35
Whole odel Test
odel -LogLikelihood DF ChiSquare Prob>ChiSqDifference 5.986305 1 11.97261 0.0005*
Full 14.875825Reduced 20.862130
RSquare (U) 0.2869
Observations (or Sum Wgts) 44
Converged by Gradient
Parameter EstimatesTerm Estimate Std Error ChiSquare Prob>ChiSqIntercept[0] -8.5243748 2.7904912 9.33 0.0023* Age 1.60955407 0.5891656 7.46 0.0063*
Logistic Fit of train43 By Age
Whole
odel Test
odel -LogLikelihood DF ChiSquare Prob>ChiSqDifference 3.959187 1 7.918373 0.0049*
Full 19.623048Reduced 23.582235
RSquare (U) 0.1679Observations (or Sum Wgts) 44
Converged by Gradient
Parameter EstimatesTerm Estimate Std Error ChiSquare Prob>ChiSqIntercept[0] -5.7729953 1.9686493 8.60 0.0034* Age 1.08540925 0.4367036 6.18 0.0129*
8/8/2019 Working File_Tilapia Training Need
37/41
36
Logistic Fit of train44 By Age
Whole
odel Test
odel -LogLikelihood DF ChiSquare Prob>ChiSqDifference 4.886321 1 9.772643 0.0018*
Full 19.856425Reduced 24.742746
RSquare (U) 0.1975Observations (or Sum Wgts) 44
Converged by Gradient
Parameter EstimatesTerm Estimate Std Error ChiSquare Prob>ChiSqIntercept[0] -6.1269673 2.0029817 9.36 0.0022*
Age 1.20141476 0.4469618 7.23 0.0072*
Logistic Fit of train45 By Age
Whole
odel Test
odel -LogLikelihood DF ChiSquare Prob>ChiSqDifference 0.897374 1 1.794747 0.1803Full 27.334638
Reduced 28.232012
RSquare (U) 0.0318Observations (or Sum Wgts) 44
8/8/2019 Working File_Tilapia Training Need
38/41
37
Converged by Gradient
Parameter EstimatesTerm Estimate Std Error ChiSquare Prob>ChiSqIntercept[0] -2.3165907 1.3244035 3.06 0.0803 Age 0.41671135 0.317793 1.72 0.1898
Logistic Fit of train46 By Age
Whole odel Test
odel -LogLikelihood DF ChiSquare Prob>ChiSq
Difference 0.3348112 1 0.669622 0.4132Full 7.8011144Reduced 8.1359256
RSquare (U) 0.0412Observations (or Sum Wgts) 44
Converged by Gradient
Parameter EstimatesTerm Estimate Std Error ChiSquare Prob>ChiSqIntercept[0] 5.43725673 3.2615502 2.78 0.0955 Age -0.5709193 0.7116786 0.64 0.4224
Logistic Fit of train51 By Age
8/8/2019 Working File_Tilapia Training Need
39/41
38
Whole odel Test
odel -LogLikelihood DF ChiSquare Prob>ChiSqDifference 0.4357961 1 0.871592 0.3505
Full 4.3369428Reduced 4.7727389
RSquare (U) 0.0913
Observations (or Sum Wgts) 44
Converged by Gradient
Parameter EstimatesTerm Estimate Std Error ChiSquare Prob>ChiSqIntercept[0] 0.18028267 3.8867788 0.00 0.9630 Age 1.04416779 1.2395651 0.71 0.3996
Logistic Fit of train52 By Age
Whole
odel Test
odel -LogLikelihood DF ChiSquare Prob>ChiSqDifference 0.0038874 1 0.007775 0.9297
Full 4.7688515Reduced 4.7727389
RSquare (U) 0.0008Observations (or Sum Wgts) 44
Converged by Gradient
Parameter EstimatesTerm Estimate Std Error ChiSquare Prob>ChiSqIntercept[0] 4.09859896 3.992727 1.05 0.3046 Age -0.0853443 0.9661295 0.01 0.9296
8/8/2019 Working File_Tilapia Training Need
40/41
39
Logistic Fit of train53 By Age
Whole
odel Test
odel -LogLikelihood DF ChiSquare Prob>ChiSqDifference 0.0038874 1 0.007775 0.9297
Full 4.7688515Reduced 4.7727389
RSquare (U) 0.0008Observations (or Sum Wgts) 44
Converged by Gradient
Parameter EstimatesTerm Estimate Std Error ChiSquare Prob>ChiSqIntercept[0] 4.09859896 3.992727 1.05 0.3046
Age -0.0853443 0.9661295 0.01 0.9296
8/8/2019 Working File_Tilapia Training Need
41/41
REFERENCE
F.A.O(1992) Planning for effective training: A guide to curriculum development. Rome, F.A.Opublisher.
Fishery Information Technology Centre . (2007) Fisheries statistics of Thailand 2005. Department
of Fisheries, Technical Report No 6/2005, Bangkok, Thailand. 91 pages.
Recommended