Paper 5: Forecast of area, yield and production of Thai cassava roots

by
Mrs. Kajonwan Itharattana

Office of Agricultural Economics
Ministry of Agriculture and Cooperatives
Bangkok, Thailand
www.oae.go.th

Summary

Cassava is an important crop of the Thai economy. Its major production is in the Northeast. However, the total production declined during the past two decades due to a decreased in acreage. The demand for Thai cassava products depends on the overseas markets. Their policies will have an effect on the production, trade and prices of cassava in Thailand. Thus, forecast of the production is considered to be useful for policy makers to foresee and prepare some measures to cope with the changing situation.

The model construction was attempted at two levels, i.e national and regional levels. Ordinary Least Square is used to estimate the coefficients in each equation. Cobb-Douglas type is applied in the planted area equation while time series model is used in the yield one. Applying the model for the ex-ante forecast, the total production is expected to be almost the same in 2002 when compared with to the previous year.

Weaknesses in the model still remains in terms of some specification errors. Thus, to make ex-ante forecast more useful, some policy variables should be added to reflect more the real situation.

Résumé

Le manioc constitue une importante plante pour l'Economie thaïlandaise. Sa plus grande production provient de la région du Nord-est du pays. Cependant, la production totale a gravement chuté au cours des deux décennies passes dues a sa réduction a chacune des surfaces données. La demande du manioc thaïlandaise dépend largement des marches extérieures. La nature de son bon fonctionnement aura des répercussions sur la production, le marché et les prix sur le marché thaïlandais. Ensuite, la prévision de la production est considérée comme étant très importante pour les décideurs politiques afin d'entrevoir et planifier certaines mesures d'urgence pour gérer des situations imprévisibles.

L'élaboration du plan d'action s'est fait à deux niveaux; par exemple: Niveau National et Regional. En Moyenne, une petite superficie a ainsi été utilisée pour estimer les coefficients de production dans chaque formule. Le prototype de COBB-DOUGLAS a ainsi été applique dans chaque formule par rapport à toute surface plantée pendant que le vecteur Temps ayant été utilise pour évaluer la production.

En appliquant le model de la prévision ex-ante, la production totale espérée est considérée comme appartenant à la même année a savoir l'an 2002 comparativement a celle récente.

Les limites relevant de ce model demeurent encore du fait de certaines erreurs de spécifications. Ensuite afin de rendre une prévision ex-ante plus intéressante encore, certaines tactiques variables devraient être ajoutées pour refléter au mieux la situation réelle.

Introduction

Cassava or tapioca is a tropical root crop which has played an important role for the Thai economy. It contributed about 7,000 million baht in Gross Domestic Product. It ranks sixth following rice, rubber, vegetables and fruits, sugarcane and maize. Cassava in Thailand has been mainly produced for export in the form of pellets and chips for animal feeds and starch. The production of cassava has been expanded continuously since planting cassava is very simple, requires a minimal tending and grows well even on the soils with poor fertility. It is also drought resistant, having little pest and diseases. The return of the crop to farm investment is good. As the market is quite ready, the area planted to the cassava had been fast expanded and caused low prices due to over-supply. At the same time, Thai cassava demand depends on the overseas market, especially the EU being a major market for the Thai cassava products. However, the EU has launched its Common Agriculture Policy since 1993 which has affected heavily on the Thai cassava production. Therefore, the analysis and forecast of cassava production is very useful in providing information and suggestions for policy makers to adjust the production to cope with its demand and/or preparation of some measures to reduce price problems.

Production Situation

Cassava is believed to have its origin in Brazil and it came to Thailand through Indonesia to the South of Thailand since long time ago. Commercial production of cassava has been started in the East of Thailand and spread the area coverage over the Northeast, North and Central Plains due to need of less attention.

As mentioned previously, the market of Thai cassava being quite ready, the area planted of cassava in Thailand had increased rapidly from 8.780 million rai in 1984 to the record high of 10.136 million rai in 1989. However, the planted area was on gradual decline between 1990–1992 prior to the EU's implementation of its Common Agricultural Policy. The acreage fell by 3.31 percent after launching this policy in the period of 1993 up to 2001.

Considering the distribution of cassava production in the regions of Thailand, it was found that the Northeast and Central regions had been the major producing areas, followed by the North. During the past two decades, the acreage in the Central Plains and the Northeast had declined while it rose at a fast rate of 4.752 percent in the North due the rapid expansion of the area planted in the initial stage of development, after which the area planted in the North declined too.

Marketing Situation

A perishable crop, fresh cassava roots must be processed within two days. Therefore, after rooting up, most of the fresh roots are quickly sold and the rest of the harvest is usually sliced, dried and sold in that form. The main traders buying the fresh roots comprise of the local vendors and traders who forward the produce for pelleting and processing into flour. The cassava products of pellets and starch are mostly shipped overseas while 15-20% only of the total manufactures in the form of flour is for use in many local downstream industries and some pellets as a feed ingredient.

The farm prices received are commonly fluctuating depending on a total quantity reaching the market and the starch content in the roots. A low price is usually received by the cassava farmer in February and March when bulk quantities of the roots are marketed, also in July and August of the rainy season when the starch content becomes low. The export capability also contributes to the price instability. So, a stagnant export flow creates a still buying of the exporter and a further chain effect on farm sale of the fresh roots.

In the period of 1990–2001 the farm prices received for the roots fluctuated within the range of 0.60–0.91 baht/kg. Only in 1995 and 1998 that the prices were abnormally exceptional due to higher export demand.

Fluctuations in the Bangkok wholesale prices for the cassava products followed a similar pattern as a result. The F.O.B. prices for the pellets fell from 3,850 baht/ton in 1992 to 2,736 baht/ton in 1993 due to the EU's implementation of its Common Agriculture Policy. But prices for the flour had an accelerated trend in 1993 and continued to rise to 11,306 baht/ton in 1998 since it was not the main item exported to EU.

Export

A leading exporter of the cassava products, Thailand claims to have a largest market share of approximately 88 percent. However, the export trend of Thailand has been on the decline as a result of the reduced import demand by half of the previous use by of the EU whose past import had been more than 50 percent of the world import., Other importing nations such as the People's Republic of China (PRC), Japan and Indonesia formerly did not consume much of the products but they, PRC in particular, increased their demand after 1999. These caused a major upward shift in the world export. It is notable that the PRC increased its demand for cassava products to compensate the needs for animal feeds in its livestock industry.

Thailand exported 9.09 and 7.33 million tons of the cassava products in 1992 and 1993, and earned the country more than 20,000 million baht. However, the export volume and the value started to drop in 1994. From then on, Thailand experienced the fall in both the export volume and value. The volume declined to 4.12 -5.75 million tons with the value of 20,000-22,000 million baht.

The Thai cassava exports include pellets, chips, flour and sago. As the major export item, pellets had 80-90% share of the total export and EU had been importing from Thailand 60-70% of its total cassava imports. The export reduction impact upon Thailand since 1993 was due to the launch of the Common Agricultural Policy by EU which pulls down their feedgrain prices in a major effort to provide incentive to more use of the domestic grains in place of the usual use of cassava pellets. At the same time, Thailand exercises voluntary restraint of limiting the annual pellet export to 5.5 million tons on the average under the agreement that EU provides assistance for cassava replacement cropping in the bilateral attempt to reduce the cassava planting.

The cassava flour is the second export item following the pellets. Its share ranges from 8-30 percent of the total export. The flour export value continues to show a rising trend of 10 percent with the share of 18 percent in 1992 which increased to 62 percent in 2000. The foreign demand for Thai cassava flour has been expanding and its main importers have been Japan, South Korea and Taiwan. However, competition is stiff with substitutable potato flour and corn flour from sources in Europe and America. Only if the price of the Thai cassava flour is comparatively not quite high, then its consumption potentials seem to become greater. In addition, quality of the flour is another main variable that should be brought into account.

As Thailand's major markets for the cassava products are limiting and subsequently in seeking new market access, it has launched emerging market program with certain policy of granting special export quotas outside the EU market. However, the result is not very fruitful since feeding practices using cassava product as one of the ingredients are not yet widespread and the product has several substitutes.

Methodology

To forecast the cassava root production, the econometric analysis has been applied through the estimation of planted area and yield. The specification of the equations is based on the results of the study carried out by the Office of Agricultural Economics (OAE) and Kasetsart University (KU) in which the planted area function showed good relation to its lagged own price, lagged price of competing crops and its lagged planted area.

In specifying yield function, time series model was also introduced in the OAE and KU study. The moving average yield of cassava roots can be described by a continuous function of time.

In this study, as the specification of the model is based on the OAE and KU study, the estimation of total production is carried through two categories:

1. The planted area and yield at the national level are estimated.

2.The planted area and yield in the major three regions; namely the Central, North and Northeast are estimated. The South is not included due to no production in this region. As such, total production equals the sum of the three regions.

Thus, the basic form of planted area and yield function which is used for estimation and shown below.

1. AC_t = f(PC_t-1, PM _t-1, AC _t-1)
2.YC_t = f(t, t², t³)
3. ACC_t = f (PCC _t-1, PMC _t-1, ACC _t-1)
4. YCC_t = f (t, t², t³)
5. ACN_t = f (PCN _t-1, PMN _t-1, ACN _t-1)
6. YCN_t = f (t, t², t³)
7. ACNE_t = f(PCNE _t-1, PSNE _t-4, ACNE _t-1)
8. YCNE_t = f(t, t², t³)

Where

AC = Total Planted area of Cassava (thousand rai)
ACC= Planted area of Cassava in Central Region (thousand rai)
ACN= Planted area of Cassava in Northern Region (thousand rai)
ACNE= Planted area of Cassava in North-Eastern Region (thousand rai)
MY = 3 Year Moving Average Yield of Cassava (ton/rai)
MYC= 3 Year Moving Average Yield of Cassava in Central Region (ton/rai)
MYN = 3 Year Moving Average Yield of Cassava in Northern Region (ton/rai)
MYNE=3 Year Moving Average Yield of Cassava in North-Eastern Region(ton/rai)
PC = Farm Price of Cassava (baht/ton)
PCC= Farm Price of Cassava in Central Region (baht/ton)
PCN= Farm Price of Cassava in Northern Region (baht/ton)
PCNE= Farm Price of Cassava in North-Eastern Region (baht/ton)
PM = Farm Price of Maize (baht/ton)
PMC = Farm Price of Maize in Central Region (baht/ton)
PMN = Farm Price of Maize in Northern Region (baht/ton)
PSNE = Farm Price of Sugar cane in North-Eastern Region (baht/ton)
QC = Total Production of Cassava (thousand tons)
QCC = Production of Cassava in Central Region (thousand tons)
QCN = Production of Cassava in Northern Region (thousand tons)
QCNE = Production of Cassava in North-Eastern Region (thousand tons)
t = Trend (t=1 to 18)
YC = Yield per rai of Cassava (ton/rai)
YCC = Yield per rai of Cassava in Central Region (ton/rai)
YCN = Yield per rai of Cassava in Northern Region (ton/rai)
YCNE = Yield per rai of Cassava in North-Eastern Region (ton/rai)

Statistical data

The analysis is based on a set of yearly data related to the national level and three regions of cassava production:- planted area, yield per rai, weather condition, cassava prices, and competing crop prices during 1984-2001. These data are obtained from the Office of Agricultural Economics.

Estimated model

The planted area equations at national and regional level are estimated by the ordinary least square applying the Cobb-Douglas type.

The three year moving average yield of cassava roots is assumed to depend on the time trend (polynomial of degree 3).

The results of the statistical analyses of the country and the three regions are presented below. The figures in parentheses are t-statistic. D.W denotes the Durbin-Watson statistic.

Predictive performance of the model

To establish the confidence in the model, it is necessary to test its predictive performance. Thus, the ex-post test is used which means that it is an examination over the sample period (1988-2001) and these figures are also computed in a 90 percent confidence intervals. The results of the predictive power of the variables, namely planted area, yield and production are presented in Table 1 to 6. The success of the prediction can be checked by comparing the actual values of each variable in the sample period with the predicted values of that variable. The statistic, Theil-U is used to measure the accuracy of the predictions of the model. The prediction of each variable is also carried out in three levels, i.e low, medium and high. The medium level is based on the results obtained from the model. A 90 percent confidence interval for low and high projected values is also calculated to estimate error of the forecast. The overall results are fairly satisfactory over the sample period.

Forecast for 2002

The application of the model is the computation of the ex-ante forecasts for year 2002. The results of forecast are shown in Table 1 to 6. It is predicted that the planted area will decrease while the yield is expected to rise about the same rate of 5 percent in 2002. This will cause the total production to be almost constant compared to the previous year.

Summary

Main producing regions of cassava are the Northeast, followed by the Central Plains and the North whereas there is no production in the South. The total production has a declining trend during the past two decades due to decreased acreage. The implementation of CAP reform by EU caused the reduction in cereal prices in the EU countries and consequently set effect of the decline in prices of cassava pellet imports. The situation has gradually reduced the pellet export to EU and affected the price of cassava products in Thailand. This is another reason which caused a decline in the root production. Thus, it is believed that Thai cassava demand depends upon foreign markets and their policies have effect on the production, trade and prices of the cassava in Thailand. The analysis and forecast is considered to be useful information for policy makers to foresee and do some adjustment 0f their policy instruments, to cope with the changing situation in the world market.

The model was constructed at two levels; namely national and regional levels using the econometric analysis. The specification of planted area and yield equations are based on the results of OAE and KU study, i.e the planted area was a function of its lag cassava price, lag price of competing crops and its lag planted area. Time series model was used in the yield equation.

The coefficients in each equation were estimated utilizing OLS. Cobb- Douglas type of function was applied in the planted area equations. The objective of modeling is to forecast the total production of cassava roots which is expected to provide useful information to policy makers. It is hoped that this objective will be achieved. However, there remains some weaknesses in the model and some specification error. In addition, to make ex-ante forecast more useful, the policy variables should be added so that the effect of such a policy can be quantified and the policy makers can select and use one for his purpose.

Table 6 The actual and predicted value of total production

Note: 2002* forecast