An Economic Analysis Of Peach Production In Pakistan

Pakistan is an agrarian country.The agriculture sector has traditionally sustained a satisfactory growth to ensure food security for the growing population.Within agriculture,horticulture is an important sub-sector of the country.Pakistan horticulture sector produces approximately 12 million tonnes per year production of fruits,vegetables and spices.Peach is the second most important stone fruits.Pakistan and especially in northern parts of the country,there is huge scope for promotion of peaches which can be aimed at international marketing by promoting processing industry for value addition and export.Thus,it has a huge potential for the home market as well as for the exports.However,Pakistan has experienced ups and downs in peach production.Moreover,farmers do not know the future prospect of peach production and prices while deciding to cultivate this and other crops.Therefore,this research was planned to find the key determinants which have influences on peach production.This research study is based on an economic evaluation of peach production in Pakistan by using modern econometric techniques.To achieved the objective this study is comprised on five approaches and results are as follow:First,this study analyzed the long-run relationship between agricultural gross domestic product(GDP)and fruits production of Pakistan's economy over a period of 1961-2015 by employing Johansen and autoregressive distributed lag(ARDL)modern econometric technique.Three fruits were selected namely mango,apple and peach in this study.Augmented Dickey-Fuller and Phillips-Perron tests were used to check the data stationarity and conclude that the series are integrated of order one.The Johansen approach was applied to check the joint evolution of all the variables for co-integration.The Johansen test suggests that a long-run co-integration exists between agricultural GDP and fruits production.Results of the ARDL model(Bound test)detects the existence of long-run relationship between agricultural GDP and fruits production.The coefficient of the short-run form of ARDL model reveals that mango,apple,and peach production have a positive impact on agricultural GDP.Moreover,the coefficients of the long-run form of ARDL model have a positive and significant influence on agricultural GDP of Pakistan.These results suggest that a 1% increase in mango,apple and peach production will increase agricultural GDP by 0.06%,0.03%,and 0.03% respectively.Finally,forecast error variance decomposition and impulse response function results depict that mango,apple and peach production significantly contributes to agricultural GDP in the case of Pakistan.Second,this chapter of the study assessed the determinants of the intensity of adoption of improved cultivars(ICs)and best management practices(BMPs)in peach production,in Pakistan.Binary choice logit and Poisson estimators to model socio-economic,institutional,farm resources,and environmental factors influencing the adoption of ICs and BMPs were used respectively.The results demonstrate that estimates of factors influencing adoption were reasonably consistent between these two models but some differences were found.The variables that positively influenced the intensity of technology adoption include farmer's age,experience,education,household size,off-farm income,membership,cell phone,farm size,machinery and livestock ownership,and credit access.However,farmer's off-farm income and access to extension services were found in contrast to BMPs adoption.While climatic factors related variables have a positive and significant effect on the adoption of ICs and BMPs.Third,this chapter of the study employed translog stochastic production frontier model to examine the technical efficiency and its determinants of peach farmers in Khyber Pakhtunkhwa province of Pakistan.Results of the study reveal that peach farmers displayed much variability in technical efficiency ranging from 64% to 95% with a mean technical efficiency of 81%,which suggested a substantial 19% of potential output can be recovered by removing inefficiency.For an agriculture-based country like Pakistan,this gain could help increase income and ensure a better livelihood for the farmers.The results suggest that farmers' age,experience,education,membership,cell phone usage,livestock ownership,credit access,extension services,the role of non-government organization and soil quality perception have a positive influence on technical efficiency.In contrast,off-farm income,household size,price volatility perception,natural disaster risk perception,and weather shocks awareness have a negative influence on technical efficiency.Hence,the study proposed that technical efficiency of peach farmers can be increased by an appropriate choice of input combinations,improving the present level of inefficiency determinants and elimination of errors in the production process through efficient management practices.Forth,this chapter of the study aimed to forecast peach area and production in Pakistan using time series data for the period 1997-98 to 2014-15.Peach area and production were forecasted over a period of time 2015-2016 to 2025-26.The Box-Jenkins(1976)approach was applied to forecast area and production of peach.This study found ARIMA(1,1,0)as an appropriate model to forecast both area and production of peach.The bestforecasted model was determined based on the lowest values of Akaike information criterion,Bayesian information criterion,and Hannan-Quinn criterion.However,the predictability power,performance,and quality of the model was measured based on the lowest error value of the root mean square error,mean absolute error and mean absolute prediction error.The forecasted value of area and production of peach for the year 2025-26 were worked out as 11.05 thousand hectares and 65.05 thousand tonnes respectively.The minimum projection trend indicated declining area and production of peach in Pakistan.Fifth,this study investigated to forecast peach producer price index(PPI)for Pakistan using time series data for the period 2001 to 2014.To forecast peach PPI an autoregressive integrated moving average(ARIMA)model,the Box-Jenkins(1976)method was applied.Both the Augmented Dickey-Fuller and Phillips-Perron unit root tests revealed that the series is integrated of order I(1).After conducting the Jarque-Bera test,it was concluded that the residuals in terms of the model normally distributed.The Ljung-Box Q test was used to test for autocorrelation of the residuals.This test confirms that the series is stationary at first difference.The best-forecasted model determined by using the lowest values of Akaike Information Criterion(AIC),Bayesian Information Criterion(BIC)and Hannan-Quinn Criterion(HQ).The most an appropriate model is ARIMA(1,1,0)to forecast peach PPI.However,the predictability power,performance,and quality of the model was measured based on the lowest error value of the root mean square error,mean absolute error and mean absolute prediction error.The Theil's inequality coefficient value(0.05)lies between 0 and 1 and is close to zero,predicts best-fit model.Finally,peach PPI was forecasted for the period 2015 to 2027 varies from 186.54 to 250.02 respectively.Hence,it was concluded that Peach PPI is expected to continue to rise every year over the period 2015-2027.