JOURNAL OF AGRICULTURE & SOCIAL SCIENCES 1813–2235/2005/01–1–20–24 http://www.ijabjass.org Forecasting and Growth Trends of Production and Export of Kinnow from Pakistan BURHAN AHMAD, ABDUL GHAFOOR AND HAMMAD BADAR Department of Marketing and Agribusiness, University of Agriculture, Faisalabad–38040, Pakistan ABSTRACT The present study was undertaken to estimate the past growth trend in production and export of Kinnow and to forecast the production and export of Kinnow. The log lin model was applied to estimate the past trend in production and export of Kinnow. ARIMA model was used to forecast the production and export of Kinnow for next 20 years. The forecast value of production and export of Kinnow for 2022-23 is 2617.45 thousand tons and 1.11081 x 10 6 tons, respectively. Key Words: Production; Export; Kinnow; Pakistan INTRODUCTION Pakistan is producing and exporting a large variety of fruits that include Mango, Apple, Dates, Pine nuts, Banana, Grapes and Guava etc. Among all the fruits, citrus has got a supreme position with respect to area, production and export (Table I). The major markets for Pakistani kinnow include Bahrain, Dubai, Saudi Arabia, Kuwait, Qatar, Oman, UK, Netherlands, Indonesia, Malaysia and Singapore (GOP, 2003). The major export markets for Pakistani kinnow are mostly developing countries and only 2.6% of total kinnow exports from Pakistan enter in the markets of developed countries (Anonymous, 2002). Present study was planned to (i) estimate the growth in Production and Export of Kinnow, (ii) forecast the production and export of Kinnow for next 20 years, and (iii) suggest the policy guidelines to boost production and export of Kinnow and improve its marketing system. METHODOLOGY Twenty-two years time series data of citrus (Kinnow) production and export were used for the present study. Thus, the secondary data were obtained from various government publications and institutions such as Federal Bureau of Statistics, Ministry of Agriculture and Commerce etc. The secondary data collected, were processed and analysed by using appropriate statistical techniques as follows: Growth trend. The growth trend in export (Production) of Kinnow was estimated through log-lin model. Suppose: X t = Export (Production) of citrus in 2002, X o = Initial value of export (1982) A well known compound interest formula can be written as: X t = X (1 + r ) t Where r is the compound (i.e., over time) rate of growth of X. Taking the natural logrithm of above equation we can write: Now letting β 0 = ln Xo, β 1 = ln (1+r) We can write above equation as LnXt = β 0 + β 1 t Adding the disturbance term to above equation we obtain Log X I = β 0 + β 1 t + u t This equation is known as log-line model. It is a linear regression model like other linear regression models because the parameters β 1 and β 2 are linear. The only difference is that regressand is the logarithm of X and the regressor is “time”. This model is also called semi log model because only one variable (in this case the regressand) appears in the logarithmic form. For descriptive purposes a model in which the regressand is logarithmic will be called a log lin model (Gujarati, 2003) The growth rate was estimated by taking the anti-log of X t , i.e., X t = antilog (β 0 + β 1 t). Forecast. Forecasts can be made by various methods like purely judgmental approaches, structural economic models, univariate time series models, multivariate time series models and econometric models. Economic models require detailed information to specify functional relations among Table I. Area, Production and Export of Citrus Year Area (000’ hectares) % Age change Production (000’ tons) % Age change Export (000’ tons) Export Value (million Rs.) 1990-91 173.3 - 1609 - 112 935 1991-92 176.2 1.673 1630 1.305 125 966 1992-93 176.2 - 1665 2.147 121 1179 1993-94 185.0 4.994 1849 11.0151 127 1324 1994-95 190.7 3.081 1933 4.543 139 1256 1995-96 193.6 1.1521 1960 1.394 135 1487 1996-97 194.4 0.413 2003 2.194 219 2776 1997-98 196.1 0.874 2037 1.697 202 2793 1998-99 197.0 0.458 1862 -8.591 181 2773 1999-00 197.7 0.355 1943 4.35 240 4130 2000-01 198.7 0.506 1865 -2.316 260 4586 2001-02 194.2 - 1830 0.052 216 3958 Source: Federal Bureau of Statistics (2001-02)