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The Comparative Analysis of Technical Efficiency of Jasmine Rice Production in Thailand Using Survey and Measurement Data

AreeAree Wiboonpongse WiboonpongseSongsak SriboonchittaSongsak Sriboonchitta

Chiang Mai University, ThailandChiang Mai University, Thailand

At Fourth International Conference on Agriculture Statistics, At Fourth International Conference on Agriculture Statistics,

October 22-24, 2007, Beijing, ChinaOctober 22-24, 2007, Beijing, China

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OutlineOutline

11 . .Significance of the StudySignificance of the Study

22 . .Key questions and objectivesKey questions and objectives

33 . .Data and Model SpecificationsData and Model Specifications

44 . .Empirical ResultsEmpirical Results

55 . .Policy Implications and ConclusionsPolicy Implications and Conclusions

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• Jasmine rice of Thailand is of premium quality and its export share has increased substantially due to its high price.

• However, its production yield can be regarded as low compared to those of other varieties.

• Questions are naturally asked whether Thai Jasmine rice productivity can be improved.

• Most studies on agricultural production models have used survey rather than measurement data from cropcut samples and record keeping.

• Survey data obtained from interview using questionnaire are subject to errors in recalling of farmers.

• Errors in the yield data could be caused by incorrect recall as well as the loss during harvesting.

1. Significance of the Study1. Significance of the Study

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1. “Analysis of Production Efficiency and the Impact of Blast Disease on Hom Mali rice Output Using Stochastic Frontier Model”

Data crop cut sampling (263 ob.; 168, 25, and 70 from CM, PIT TGR)

Results: Factors with positive impact chemicals, irrigation and labor Factors with negative effect neck blast and severer drought

The related researchesThe related researches

2.“On The Estimation of Stochastic Production Frontiers with self-selectivity: Jasmine and Non-Jasmine Rice in Thailand”

Data survey sampling (489 ob., 112(CM), 176(PIT), 201(TGR) 207(Jasmine rice)

Methodology The probit maximum Likelihood Model with endogenous switching frontier.

Results: Factor affecting Jasmine rice chemical fertilizer, labour, other chemicals, irrigation server drought and the self-selectivity.

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rice field in the north

6blast neck blast

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neck blast

without neck blast

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threshing in NE

carry to threshing in N

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• The present research attempts to compare the efficiency levels of Jasmine rice production given the identical set of environmental factors and production inputs but estimated by two different sets

of output data, cropcut measurement and farmers survey.

• Aim to answer the major question:How has the different type of yield data affected estimates of

stochastic production frontier?

22 . .Key questions and objectivesKey questions and objectives

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• Data on rice yield of crop year 1999/2000 were collected from two different methods:

1. Interviewing farmers (called survey data) 2. Measuring the rice weight from sample plots in the field

(called measurement data)

***The same 130 farmers provided two types of yield data******The same 130 farmers provided two types of yield data***

3. Data and Model Specifications3. Data and Model Specifications

3.1 Data on Rice Farmer Samples3.1 Data on Rice Farmer Samples

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Sampling procedureSampling procedure

1 .Three selected major Jasmine rice production areas in the

upper north, lower north and north east regions (Chiang Mai

Province, Pitsanuloke Province and TungGula Rongha

rice Plane(

2. Districts of most intensive Jasmine rice were selected

3 .For the surveyFor the survey:- Simple random sampling of farm unit of

observations

For the measurementFor the measurement:- three-one square meter plots along the

diagonal of a rice field were randomly selected

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VariableSample Mean

Sample Standard Deviation

MinimumMaximum

Yield (kgs/m2): Survey 10.240.110.020.53

Yield (kgs/m2): Measurement 20.150.140.010.33

Seed (kgs/m2)0.0080.0060.0010.027

Fertiliser (kgs/m2)0.0080.0050.0010.027

Labour(man-hour/m2)0.0150.0100.0010.049

Education (years)4.951.870.0014.00

Age (years)44.729.2725.5071.00

Labour (adult persons)2.531.041.008.00

Labour ratio0.540.180.001.00

Sources: 1. Wiboonpongse, A. and Sriboonchitta, S., 2001.Sources: 1. Wiboonpongse, A. and Sriboonchitta, S., 2001. 2. Gypmanyasiri, P., Kramol, P. and Limniranknl, B. ,20012. Gypmanyasiri, P., Kramol, P. and Limniranknl, B. ,2001.

Table 1: Summary statistics of key variables for the sample Jasmine rice farmersTable 1: Summary statistics of key variables for the sample Jasmine rice farmers

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where the subscript, i , indicates the ith farmer in the sample; yield represents Jasmine yield (in kilograms per square metre);

seed is the amount of seed sown (in kilograms per square metre); fer is the amount of chemical fertiliser applied (in kilograms per square metre); la is the labour used (in man-hours per square metre); Dchem is the chemicals dummy variable; which has value 1 if the sample farmer

applied pesticides or herbicides, and 0, otherwise;

3.2 Model specifications3.2 Model specifications

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3.2 Model specifications3.2 Model specifications (cont.) (cont.)

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3.2 Model specifications3.2 Model specifications (cont.) (cont.)

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4. Empirical Results4. Empirical Results 4.1 Hypotheses testing4.1 Hypotheses testing

Table 2: Table 2: Generalised likelihood-ratio tests of null hypotheses for parameters in the Generalised likelihood-ratio tests of null hypotheses for parameters in the stochastic frontier production function models for survey data and measurement data modelsstochastic frontier production function models for survey data and measurement data models

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Production functionProduction function

For the measurement data model (Model 1):For the measurement data model (Model 1):* The major production variables: Seed(+)* The dummy variables: dummy variables for neck blast(-), severe drought(-) TGR(-), irrigation(+) and chemicals(+)

For the survey data model (Models 2 and 3):For the survey data model (Models 2 and 3):* * Model 2:- Model 2:- There is no single variable affect the management efficiency of farmers* * Model 3:-Model 3:- Generally the significant variables have the expected sign except Generally the significant variables have the expected sign except fertiliser and labour variables fertiliser and labour variables The dummy variables: The dummy variables: dummy variables for neck blast(-), severe drought(-), Phitsanuloke Province(-), and chemicals(+)

4.2 Production frontier estimates4.2 Production frontier estimates

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Table 3: Table 3: Maximum-likelihood estimates for parameters of the preferred stochastic frontier Maximum-likelihood estimates for parameters of the preferred stochastic frontier production models for measurement data and survey data modelsproduction models for measurement data and survey data models

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Inefficiency equationInefficiency equation

For the measurement data model (Model 1):For the measurement data model (Model 1):* Education(-), Age(-) the higher formal education and older farmers

tended to reduce the TI in Jasmine rice

production

For the survey data model (Models 2 and 3):For the survey data model (Models 2 and 3):

* Model 3:-Model 3:- Human resource variables became highly significant Labour Ratio(-) Higher labour force ratio in Jasmine rice

tended to have smaller TI

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Table 3: Table 3: Maximum-likelihood estimates for parameters of the preferred stochastic frontier Maximum-likelihood estimates for parameters of the preferred stochastic frontier production models for measurement data and survey data modelsproduction models for measurement data and survey data models (cont.) (cont.)

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• The predicted technical efficiencies for the sample farmers in the three major areas of Jasmine rice production of Thailand range

between 0.11 and 0.99 for the measurement data model and range between 0.10 and 0.99 for the survey data model.

• The mean technical efficiency of farms over all prefectures is estimated to be 0.62 and 0.65 for the measurement data and survey

data models, respectively.

• A frequency distribution of the predicted technical efficiencies in the decile ranges from less than 0.5 to 1.0 indicates that the

majority of the areas have average technical efficiency of Jasmine rice farms between 0.7-1.0.

4.3. Technical efficiency indexes4.3. Technical efficiency indexes

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IntervalMeasurement data

Model 1

Survey data

Model 2Model 3

<0.5037) 28.5(37) 28.5(33)25.4(

0.50-0.6020) 15.4(17) 13.1(15)11.5(

0.60-0.7026) 20.0(33) 25.4(23)17.7(

0.70-0.8016) 12.3(15) 11.5(18)13.8(

0.80-0.9012) 9.2(12) 9.2(17)13.1(

0.90-1.0019) 14.6(16) 12.3(24)18.5(

Mean technical efficiency0.620.630.65

Table 4: Percentages of technical efficiencies of Jasmine rice farmers in Chiang Mai Table 4: Percentages of technical efficiencies of Jasmine rice farmers in Chiang Mai and and Pitsanuloke provincesPitsanuloke provinces and TungGula Ronghai area and TungGula Ronghai area within decile ranges within decile ranges

Source: From estimation.Note: Number in the parentheses are the percentage.

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• Rice productivity in the study areas was determined primarily by the amount of seeds sown and the responsiveness of the rice

variety to chemical fertilizer input.

• The availability of irrigation would definitely enhance yield.

• Rice productivity can be seriously impaired at the presence of severe drought and neck blast.

• Development of human resource to possess good management practice ability is seen to be crucial for the increase in production efficiency, which can be materialized by learning and experience accumulation processes as well as by increasing the proportion of

male labour input.

5. Policy implications and conclusions5. Policy implications and conclusions

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* The comparative study between the use of survey data and measurement data to estimate the technical efficiency provided the evidence of extremely different conclusions from the use of different dependent variables despite the identical sets of independent parameters.

* Use of measurement data appeared to provide results which are not sensitive to the model specification at the given severity of multicollinearity.

5. Policy implications and conclusions (cont.)5. Policy implications and conclusions (cont.)

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*Apart from the robust variables, the remaining weak determinants would generate different values of estimate and

which are highly sensitive to model specification.

*Agricultural economists and users of survey data, therefore , should be cautioned to pay special attention to survey design so

as to minimize error.

*The test of model specification might be an effective means to detect the errors in estimation due to the choice of variables .

5. Policy implications and conclusions (cont.)5. Policy implications and conclusions (cont.)

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Thank you very muchThank you very much