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Farm &••••••••••• ̂••••••••••• LI*••••••••••• ̂

CDC/5C/5

Vol.3, No.1, March 1996

The Journal of the

Agro-EconomicSociety

EDITOR-IN-CHIEF

CARLISLE A. PEMBERTON, Senior Lecturer, Department of Agricultural Economics & Farm Management, The University of the West Indies, St. Augustine, The Republic of Trinidad & Tobago

EDITORIAL ADVISORY BOARD

Compton Bourne, UWI. St Augustine, The Republic of Trinidad & Tobago Carlton G. Davis, University of Florida, Gainesville, Florida, USA L. Harlan Davis, University of Georgia, Athens, Georgia, USA Vernon Eidman. University of Minnesota, St. Paul, USA Calixte George, St. Lucia Bishnodath Persaud, UWICED, UWI, Mona, Jamaica William Phillips, University of Alberta, Edmonton, Canada Reginald Pierre, IICA, Washington, D.C., USA Dunstan Spencer, Dunstan Spencer & Associates Ltd., Sierra Leone Karl Wellington, ALCAN, Mandeville, Jamaica Holman Williams, UWI, St Augustine, The Republic of Trinidad & Tobago George Wilson, Kingston, Jamaica Lawrence Wiison, FAO, Bridgetown, Barbados

EDITORIAL STAFF

Editor-in-Chief Carlisle A. Pemberton

Associate Editor Curtis Mclntosh CFNI, UWI, St. Augustine

Cover Design: Karen Yorke

PRELIMINARY EVALUATION OF

ALTERNATIVE MODELS FOR BANANA PRODUCTION

FORECASTING IN THE WINDWARD ISLANDS

LUVETTE THOMAS-LOUISY (Agricultural Services Analyst, Geest, West Indies}

THERESA ALEXANDER-LOUIS I'Production Economist, WIN BAN, St. Lucia)

ABSTRACT

The issue of inaccurate forecasting of oanana production has severe adverse financial and trade implications for the Windward Islands' (Wl) Banana Industry. In the past, a number of unsuccessful attempts have been made to address this problem The Wl's were therefore forced to seek innovative and effective methods to deal with this threat During 1992-1993, a predictive model based on the growth of the banana fruit was tested in St Lucia An alternative, the Lag Model (LM) was later developed and tested in that Island in parallel with the Fruit Growth (FG) model This paper outlines the methodology used m both models as well as the analysis and interpretation of data collected Results obtained indicate that they have served to improve the forecasting performance of the Sf Lucia banana industry A comparison of the predictive abilities of the models suggests that the Lag model was superior with 'espect to statistical criteria It is

concluded that although the Lag model may seem a more cost-effective tool for forecasting banana production at the island level, it may be premature to make a definitive conclusion about the models given the relatively short period of time over which they were tested. Also, the FG model has the advantage of generating valuable agronomic information which could be potentially useful for improved management of the industry

INTRODUCTION

The problem of inaccurate forecasting of banana production volumes continues to be of serious financial and economic implications to the banana industry of the Windward Islands. Poor forecasting (both over or underestimation) has resulted in high costs due to wasted contracted shipping space and "left-back" fruit on the wharves In addition the marketing effort has been severely handicapped due to the large differences between forecasted and

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actual volumes delivered. With the implementation of the new EEC

banana regime in July 1993, the islands are faced with additional problems associated with forecasting. In light of these challenges, the St. Lucia Banana Growers' Association (SLBGA), the largest banana producer of the Windward Islands, decided to implement a Banana Forecasting System (BFS) on a trial basis. The main objective of the BFS was to generate more reliable and accurate estimates of banana production, i.e. improve forecasting in general

THE BANANA INDUSTRY OF THE WINDWARD ISLANDS

The banana industry plays a significant role in the economy of the Windward Islands, though less so in the case of Grenada. Bananas account for approximately 75% of agricultural exports, 50 % of total domestic exports and almost 15% of the islands' Gross Domestic Product. The industry is also one of the leading sectors for generation of employment, with almost 30% of the labour force being directly or indirectly engaged in banana production.

Banana production in the Windwards is characterised by a prevalence of a large number (24,600) of farmers, 65% of whom operate farms of less than five acres mainly on difficult terrain. Total area under production is approximately 40,000 acres. Average yields are low, about 6-7 tonnes per acre, which is typically 30-50% of that achievable in Latin America and Martinique, respectively. Unit cost of production is also higher and labour accounts for almost 60% of total variable cost.

Production tends to follow a seasonal

pattern reaching a peak around May-June, drops, peaking again around November. This pattern is partly influenced by weather; mainly rainfall and temperature. Production volumes over the past five years averaged 256,615 tonnes, with St Lucia producing approximately 50%, Dominica and St. Vincent approximately 24% each and Grenada, 2%. All fruit of marketable quality is purchased by Geest Industries, the sole shipping and marketing company. Over 90% of the fruit is exported to the UK, the traditional dominant market for Windwards production. In July 1993, the European Community introduced new trade regulations resulting in the imposition of two new requirement for the importation of bananas. Under this new regime, a deposit of approximately EC$60 per tonne of fruit must now be made on application for licences to import fruit during the following quarter. This deposit is forfeited if actual exports are less than that forecasted. Loss of deposits for the period July 1993 to March 1994, was estimated at EC$665,000. Additionally, each island's export is restricted to an individual annual non-tariff quota, with quarterly tranches limited to 30% of the annual quota. A tariff level of approximately EC$2,500 per tonne is applied to exports over this annual quota. The negative financial impact of these new requirements further underscores the need for improved production forecasting.

LITERATURE REVIEW

Estimates of crop production are essential statistics for scientists, administrators and managers concerned with the planning and evaluation of agricultural investments or enterprises. A number of methods and/or techniques for

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measuring crop production have been presented in the literature, for example FAO (1982) and Casley and Kumar (1988). Some of the methods utilized by various countries includes:

(1) Sample surveys with objective measurements of the yield (crop cutting over samples of sub-plots).

(2) Complete enumeration (harvesting and measuring) of a sample survey t h r o u g h m a i l e d questionnaires or reports.

(3) Complete enumeration (harvesting and measuring) of a sample survey by enumerations and declarations of the holders.

(4) Eye or judgement estimation by agricultural agents.

(5) Interviewing farmers to obtain estimates.

The first three above techniques have been deemed unsuitable for forecasting and are also not recommended for use with crops with multiple harvests such as bananas as they involve the use of actual measurements which can only be carried out at harvest time.

Eye or Judgement Estimation

Eye or judgement estimation of the total production or the variables used to determine total production, when used in conjunction with regular reporting, appears to have a number of advantages. These include low cost, simplicity of data collection and processing as well as timeliness of information. The main disadvantage reported was that the method, being subjective, has no means of evaluating, the quality of the data which could be inaccurate and biased. It was concluded, that subjective estimation of crop production and/ot parameters can

only be reliable when it is carried out by specialized personnel with wide experience in the subject.

Farmer Estimates

In certain well-defined cropping situations, carefully obtained estimates by farmers can provide valid indications of holding production. The idea of asking a peasant producer to estimate his or her crop output, however, is commonly dismissed as being unscientific and prone to distortion but according to Poate (1987), this method may be no more biased than other popular methods, albeit the limited evidence available. Overall, this method does not require laborious objective measurement and hence can be cheap, quick and applicable on a large scale, compared with the above-mentioned procedures. The merit in the technique depends on the level of bias between the farmers' reported figure and the true production; unfortunately scientific evidence is limited here.

Banana Production Forecasting

The factors central to the derivation of production estimates for bananas are area under production, hanging stem counts (inventory), fruit growth rate (maturation period, recovery rate, harvest management, fruit specifications and market demands, stem weight, historical production and climatic data, as well as factors such as incidence of pest and diseases and natural disasters. With such a myriad of factors influencing the final production volume, it has been rather difficult to develop reliable forecasts or forecast ing systems for bananas,

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particularly for small farming systems. Velez (1992) described two methods for

deriving short-term banana forecasts, the Percentage and the Fruit Growth (FG) approach. The former predicts production in terms of the portion of a stem inventory or ribbon count that will be available for harvesting at a given time, expressed as a percentage. In this approach the fruit available for harvest usually includes three or more different inventories and would therefore require the prediction of three or more numbers. These numbers are primarily based on the previous week's harvesting pattern, historical weekly production trends and anticipated fruit growth trends which are largely influenced by future changes in the climatic pattern.

A major weakness of this method is that it relies on historical production records, which have harvest management biases and other errors from past occurrences such as unusual weather conditions, pests and diseases, strikes, etc. Also, since three or more numbers are to be predicted, the error risk is increased. The main advantage is that it is inexpensive to operate and does not requires substantial initial investment.

The second method, the FG approach, is largely based on data from regular field surveys that allow for the modelling of fruit development. This methodology, which is currently being tested in St Lucia, is discussed in more detail in the following section. Suffice it to say, the advantages reported are many, including, the fact that the data used are continuously up-dated. In addition, production estimation error is minimised because the method predicts only one • number. The approach also incorporates weather variables, has no historical harvest management biases or errors from past occurrences and offers

fruit management alternatives. The main disadvantage is that it requires significant initial investment and is relatively expensive to operate.

Fielding (1993), on the other hand, focused on a long-term forecasting method for bananas. He reported that any long term forecasting method can probably only aim to predict the underlying production pattern, which, therefore, requires the use of harvest data. Further, this data show a large degree of disturbance and traditional statistical techniques (e.g. time series analysis) are difficult to apply. In his study, historical data was used to develop the production pattern for a single banana estate by establishing production cycles. Production was then estimated by a method of summation after synchronizing these cycles.

The main advantages of the system were that forecasts could be derived by hand once the production cycles were known, and that estimates could be updated to take into account latest yields, thereby making the predictions more dynamic. The disadvantages included the likelihood of generating biased predictions of the production pattern (e.g. over estimating following adverse weather conditions) and the fact that the effects of management practices, particularly the effects of harvest management, and the influence of temperature, a key factor affecting the rate of fruit development and maturation are not incorporated.

THE FORECASTING MODELS

Fruit Growth Model

The Fruit Growth (FG) Model presented to the W.l.'s by Dr Julian Velez is used to

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estimate weekly banana production volumes on a short term basis i.e. a 13-week interval. The procedure automatically calculates the percentage of a particular stem inventory (count of hanging banana bunches) which will be available for harvesting at a given time. Calculations are made for as many weeks as stem inventories are available. The model is based on the premise that the amount of fruit available from a particular stem inventory, at any given time, is mainly dependent on calliper grade increases which are in turn affected by climatic changes until the fruit enters the harvest cycle. Thereafter, harvest management becomes the most important determinant of the volume of fruit available.

The term stem inventory is synonymous with fruit age because it denotes all hanging stems shot in the same week. In most production areas of the world, a coloured ribbon is used to identify such stems. The count of all ribbons of the same colour placed on stems in a given week becomes the weekly stem inventory as illustrated in Figure I. Different inventories, therefore, will have different colours and different ages.

Predictions of the weekly average fruit growth for all inventories are obtained through a regression model which correlates climatic variables, mainly temperature and rainfall, with calliper grade increases from one week to the next. Upon establishment of the regression model, calculations of the average fruit growth for every week in the forecast can be undertaken using historical weather data. The growth is then partitioned-into its components, resulting in a growth factor for each stem inventory (age or ribbon). The procedure is repeated for every week in the forecast and each

growth factor is added up to estimate the accumulation of calliper grade for a given inventory. This accumulation is in turn summed to the calliper field measurement for each member of the calliper grade distribution belonging to the stem inventory under estimation. This process creates a forecasted harvest-week calliper grade distribution which is used to provide projections of fruit availability for different harvest management alternatives, together with indicators to analyse the effects of a particular harvest decision, for example, changes in the harvest grade and/or fruit age. The accuracy of the prediction of harvest-week calliper grade distributions improves over time due to the fact that the information for a given inventory is up-dated continually through on-going weekly surveys.

Implementation of the Model in St Lucia

Calliper grade measurement, bunch shooting counts and plant population density were obtained from randomly selected forecasting plots in various geographic locations or zones in St Lucia. The four zones (A, B, C & D) were selected, mainly based on the similarity of agro-climatic conditions, elevation and concentration of banana producing areas forming a zone in the Island. Zone D was selected using elevation as the only criterion; an elevation level of over 750 feet being used as representative of the effect of higher altitude on fruit growth.

Based on the estimation of the statistical variation of calliper grade measurements made in different banana producing areas around the world, Velez (1992) stated that the number of observations required to estimate calliper grade with a 3% error at a confidence level of 95% is 22, implying

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that 22 forecasting plots were required per zone and 88 per island divided into 4 zones. Plot size was based on the number of stems produced (shot) on a weekly basis per unit area. On average, 40 - 50 stems or bunches are produced weekly per hectares of bananas. Since only one stem per plot needs to be surveyed, approximately 200 m2 250 m2 of banana cultivation are required. However, to allow for irregularities in shooting behaviour and other factors involved, an area of 929 m2

(i.e. 10,000 ft2) was used. The 88 forecasting plots were selected

by means of a grid sampling design which divides zones into sampling grids. This sampling design forces the inclusion of measurements from a wide variety of growing conditions, cultural practices and farm management skills creating the variation necessary to develop a representative calliper grade distribution for each inventory. Undesired statistical variation is controlled by grouping the data into zones and, within a zone, into stem inventories (fruit age).

The actual location of plots within the grids is established by randomly selecting grid coordinates. Four survey teams of two members were recruited to collect the data on predesigned forms.

The Lag Model

Recognising the limitations of support to the FG model, particularly the lack of certain parameters such as area under production, the number of boxes packed per harvested stem, fruit age, etc., an attempt was made to develop a simplified empirical model using the readily available stem inventory data from the plots. Based on the fact that the number of hanging stems in the field determines the short term

production trends, the stem inventory was chosen as the backbone of this forecasting model. Various correlations were estimated for stem inventories and corresponding production lagged 9, 10, 11 12, and 13 weeks ie the stem inventory in week, was correlated with production in week,+9, week,t1, ... week,^. Usually the lag, or interval from shooting to harvest, depends on the rate of fruit development. Thus, the lag periods used in the regression model were selected based on the harvest window (i.e. different ages of fruit being harvested in a particular week) as indicated by the data. The best correlative relationship was obtained with the nine week lag with an r2 of 0.58 and a correlation coefficient (r) of 0.76, as shown in Figure 2.

The predictive equation or regression line for estimating the dependent variable, production, based on independent variable, stem inventory, was then determined. The empirical equation derived from this data was:

Yt+9 = 863 + 95.83X, where X, = stem inventory in week, and Yt+9 = production in week,^. This model is used to forecast production up to a period of 9 weeks and is updated weekly as new data becomes available.

COMPARATIVE ANALYSIS OF THE MODELS

Although the system had been established in August 1992, the initial weeks were mainly concerned with resolving teething logistics problems. Therefore, the analysis focuses on the period starting January 1993 and ending June, 1994 (from week 1, 1993 throughout week 26,1994) representing a period of 78 weeks for which useful forecasts were

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available. It should be noted that outliers (for example, strike periods etc.) were removed from the data set to eliminate noise in the data.

For the forecast to be useful to the SLBGA, one of the critical conditions that must be satisfied is that it must be provided forecast for week, must be provided by the beginning of week,.5 when it becomes a contractual production estimate between WINBAN and Geest.

The following criteria were used to evaluate the performance of the models:

(a) Absolute average deviation of forecasted from actual production

(b) Frequency of forecast being within plus or minus 5%, 10%, 15% and 20% of actual production

(c) Frequency of over and underestimation

(d) Absolute maximum deviation of forecasted from actual production.

From Table 1, it can be observed that in terms of achievement of the major objective of the Banana Forecasting System (BFS) - i.e. forecasted production being within plus or minus 5% of actual production - the FG model forecasted at the desired level with a frequency of 32.3%, while the Lag model performed better with a frequency of 48.4%. With respect to production being within an acceptable level of plus or minus 10% of actual production, the FG model forecasted at the desired level with a frequency of 54.8%, while the Lag model performed better with a frequency of 62.9%. Additionally, at all other error levels, i.e. plus or minus 15%, and 20%. the performance of the Lag model was better. The maximum deviation was lower (19.3%) for the Lag than the FG model (35.5%). Thus the Lag model performed better with respect to this criterion. However, the FG

model can be considered 'better1 in terms of absolute average deviation, the value being 8.5% for the FG model and 11.7% for the Lag model. The FG model tended to underestimate, i.e. the forecasted volume were less than the actual more often whereas the Lag model provided estimates that were as often below as above the actual estimates. This is illustrated in Figures 1 and 2.

In the past overestimation (i.e. forecasted greater than the act level has always been preferable to under-estimation. This is because all four islands pay on a pro-rata basis for the excess shipping cost incurred as compared with under-estimation ( left-backs or outshipments) whose individual cost must be fully met by the respective island.

The analysis indicates that given'the set of data which is available, the Lag model performed generally better than the FG model in terms of the criteria used in the evaluation and for the purpose of producing a number to comply with a contractual demand. However, the FG model provided vital information necessary to understand the process of fruit availability and to establish and maintain the principles of fruit management which is the key factor influencing shipping volumes and fruit quality. In addition, the FG model revealed the weaknesses associated with not having vital industry information to adequately manage banana production a, the absence of which proved to be a severe handicap to the performance of the FG model.

Expenditures Incurred Versus Benefits Derived

Expenditures incurred by the SLBGA on

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Table 1. Comparative Analysis of The Banana Forecasting Models

Criteria Fruit Growth Model

Lag Model

Frequency: Within + or - 5% of Actual Prodn 32.3 48.4

Within + or 10% of Actual Prodn. 54.8 62.9 Within + or 15% of Actual Prodn. 69.4 79.0 Within + or 20% of Actual Production 85.5 90.3 Over-estimation 34.0 50.0

Under-estimation 66.0 50.0 Absolute Deviation

Average Maximum

8.5 35.5

11.7 19.3

production forecasting during the period January, 1992 to June 1994 totalled close to EC$800,000, salaries and allowances accounting for almost 93%. With regard to financial benefits derived, it is still a bit premature to determine the exact extent. Left-backs (outshipments) of fruit due to poor forecasting by the SLBGA, totalled 190 tonnes valued at $230,000 for the period under review. This relatively small volume was partly attributed to improved forecasting by the SLBGA. It is difficult to determine precisely the cost of excess shipping space for individual islands, as space is hired on an aggregate Windward Islands and shipping cost apportioned on a production level basis. It is estimated however, that the SLBGA, accounts for approximately 50% of total Windward shipping costs and that cost for unused shipping space for 1993 for St.

Lucia was approximately EC$5.35 million, 30% less than the average of EC$7.02 million for the period 1990-1992.

In terms of deposits for licences, St. Lucia incurred approximately 10% of Windwards total loss (to the end of 1993) compared with its 50% contribution to total export volumes. This relatively small cost incurred by St. Lucia is largely attributable to improved forecasting vis a vis the other Windward Islands.

SUMMARY AND CONCLUSIONS

Based on the criteria used to evaluate the performance of the models, the Lag model performed generally better. The sub-optimal performance of the FG model, however, may be attributable to a number of limitations, the most crucial being the lack of precise data used to compute

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parameters such as the box stem ratio and banana acreages. The model assumed that the harvesting pattern of farmers on the forecasting plots simulates that of all farmers at the island level. However there are likely to be a number of socio-economic factors which occur at short notice and impinge upon farmers' harvesting pattern and adversely affect the forecasts.

Considering the limited period over which the models have been tested, it is somewhat premature to make a definitive conclusion. While the Lag model may be relatively inexpensive (by an estimated 50%), to implement and may seem an attractive alternative, it simply provides a number. The FG model on the other hand, is able to generate additional information which allows management to explore different scenarios (what-if situations). The information can also be utilised to issue fruit cut directives in order to achieve forecasted volumes, maximise farm pro-ductivity, optimise fruit quality and minimise post-harvest risks such as ship-ripes.

It has been increasingly suggested (CDB, 1993) that forecasting at the indivdual farmer level is perhaps the most appropriate method of estimating production at the island level. However, the prevailing production structure of the industry tends to limit the simple and rapid implementation of this method.

Furthermore, the experience in St Lucia has confirmed the fact that to pick a number five weeks in advance is relatively easy, but to actually achieve that number at harvest time is much more difficult due to the absence of harvest management procedures and information at an industry rather than a farmer level. Also, though harvesting is a farmer's practice, shipping and marketing are industry operations

involving all farmers. Nevertheless, the process initiated with the forecasting plots and the development of the models has brought invaluable benefits to the industry in St. Lucia, and it has paved the way for the introduction of the ribbon tagging system and age-grade control at the farm level, thereby providing a means for improving production forecasting.

There is still a need, however, for further research on and development of the concept of age-grade control in the W.l.'s. Also, continued efforts need to be made to incorporate systems for long term forecasting, given the urgency to respond to the challenges imposed by a new and changing international environment.

REFERENCES

CASLEY D. J., & KUMARK (1988): The Collection, Analysis and Use of Monitoring and Evaluation data.

CDB (1993): Development of a Time-Phased Action Plan to Improve the Competitiveness of the Windward Islands Banana Industry, CDB.

FAO (1982): The Estimation of Crop Areas and Yields in Agricultural Statistics. Economic and Social Development Paper No.22, Rome, Italy: FAO.

FIELDING W.I. (1993): A Simple Method of Estimating Production at St. Mary's Banana Estates. Jagrist Bulletin of Jamaican Society for Agricultural Sciences, Vol.5, No.1, pp. 17-21.

POATE, D. (1987): A Review of Methods for Measuring Crop Production from Small Holder Producers. Experimental Agriculture, Vol. 24, pp. 1-14.

VELEZ J. (1992): Proposed Forecasting System for the Windward Islands. Geest Caribbean Americas.

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Thomas-Loutsv. L. and AtexanOer Louis. Farm & Business. Sept.. 1995

Figure 1: Fruit Growth Model vs Actual Prodution

Figure 2: Lag Model vs Actual Production

w&r •

Number

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