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Modelling Moniliophthora roreri in Costa Rica
Crop Protection 21 (2002) 317–326
Modelling Moniliophthora roreri in Costa Rica A.W. Leacha, J.D. Mumforda,*, U. Kraussb a
Department of Environmental Science and Technology, Imperial College at Silwood Park, Buckhurst Road, Ascot SL5 7PY, UK b CABI Bioscience, c/o CATIE, 7170 Turrialba, Costa Rica Received 31 May 2001; received in revised form 4 September 2001; accepted 5 December 2001
Abstract A model of the field dynamics of Moniliophthora roreri, its management and economics was developed to evaluate net returns of various management strategies to farmers in Central America. The model is primarily based on the cropping patterns and economics of cocoa production in Costa Rica. With minimal inputs the model can be adapted to simulate the crop phenology and economics in any country, where the disease causes significant economic loss. The model is a time-based deterministic spreadsheet model that simulates the production and management of one hectare of cocoa at weekly intervals over a user-selected two-year period. The model demonstrates the need for frequent stripping of infected pods to prevent sporulating pods accumulating in the field, under a broad range of economic scenarios. The model allows the user to evaluate potential extension advice in terms of user-definable ‘‘real world’’ variables including: frequency of harvesting and stripping of infected pods; losses of ripe unharvested pods to rodents; ability to identify infected pods; international cocoa prices; farm-gate prices and premiums; and labour costs and requirements. r 2002 Published by Elsevier Science Ltd. Keywords: Cocoa; Cultural disease control; Economics; Epidemiology; Extension; Modelling; Moniliophthora roreri; Rodent losses; Theobroma cacao
1. Introduction Diseases caused by Moniliophthora roreri (Cif. & Par.) Evans et al. (moniliasis), Crinipellis perniciosa (Stahel) Singer (witches’ broom) and Phytophthora palmivora (Butl.) Butl. (black pod) are the most important factors limiting cocoa production in Central and South America. Moniliasis is a serious fungal disease which, thus far, is confined to the Americas. Its range includes northwestern South America including Ecuador, Colombia, Peru, parts of Venezuela, and large parts of Central America (Panama, Costa Rica, Nicaragua and Honduras). It was first reported in Ecuador around 1914 (Rorer, 1918). Overall cocoa production declined in Ecuador from 50,000 tonnes in 1915–16 following an attack of M. roreri to around 30,000 tonnes in 1922–23 and fell further to around 20,000 tonnes in 1925 due to witches’ broom. The disease can cause losses of up to *Corresponding author. E-mail address: [email protected] (J.D. Mumford), [email protected] (A.W. Leach). 0261-2194/02/$ - see front matter r 2002 Published by Elsevier Science Ltd. PII: S 0 2 6 1 - 2 1 9 4 ( 0 1 ) 0 0 1 4 8 - X
100% (Evans et al., 1998). Landell-Mills (1998) estimated the reduction in cocoa production potential by M. roreri in Latin America at 30,000 tonnes. According to FAO statistics,1 the production of cocoa beans in Costa Rica has fallen from 12,000 tonnes in 1962 to around 4000 tonnes in 2000. This reduction may be attributed to three linked factors: loss of yield; gradual decline in international cocoa price; leading to abandonment of cocoa area. When the reduced international price is combined with considerable losses to M. roreri then, for a large part of the cocoa sector, the production of cocoa without a quality premium/subsidy payment is no longer economically viable. Cultural management is currently considered to be the main practical means of control for the smallholder (Matlick, 1998; Soberanis et al., 1999). The frequency with which infected pods can be removed from the field is essential for the effective control of the disease (Cubillos and Aranzazu, 1979; Porras et al., 1990; Soberanis et al., 1999). Recently, biocontrol has shown 1
FAO website: http://www.fao.org/
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great potential if used in addition to cultural control (Krauss and Soberanis, 2001). The most effective and economical timing of biocontrol application is closely linked to the production cycle of cocoa (Krauss and Soberanis, 2002). Simulation models can help improve disease and pest management both at the practical and strategic level (Teng and Savary, 1992; Rabbinge et al., 1993). They are powerful tools for answering a series of ‘‘what if?’’ scenarios such as how management recommendations will change when the cost of labour increases or the price of cocoa falls (Leach and Mumford, 2001). A simulation model was developed that incorporates pod age structure, epidemiology, management and economic factors to assess best management options for M. roreri by farmers and for answering or anticipating a range of scientific, strategic and policy questions. Currently its potential includes: identifying research priorities, optimising strategies for crop protection that maximise net returns for farmers, training and decision support for extension.
* *
*
The most obvious role for the model, at this stage in its development, is to direct research. The problem in developing a system that matches the field behaviour of M. roreri is that there was little field data available that described the disease’s epidemiology. This paper represents an initial attempt to anticipate the disease dynamics based on a synthesis of available published information and subjective estimates from several experienced field observers. This allows the user to test various harvesting and phytosanitary measures that act at different points in the disease–host interaction. Because the shape of the functions used to simulate the dynamics of the disease are based on expert opinion the simulation of the disease’s epidemiology is no more (or less) than a working, dynamic synthesis of expert opinion that appears to behave like the disease in the field. Further field observations should be directed to determining values for the parameters to validate the processes and conclusions of the model. The management interventions available to farmers are simulated and their associated costs can be applied. This function, given that the disease dynamics and the costs and returns generated appear sensible under a range of economic criteria, has generated interest in the commercial sector. The principal buyer2 of cocoa in Costa Rica is assisting in the development and evaluation of the model so that it could be used as an extension training and decision support tool within the cocoa sector of that country.
2
Organic Commodity Project (OCP).
2. Model components The model was written in an Excel 97TM spreadsheet and runs on any Windows-based PC. The program consists of five interacting sub-models each of which is described below. A flow diagram of these units is shown in Fig. 1 to provide a conceptual framework of the model and its interactions. 2.1. Pod sub-model The principal ‘‘engine’’ of the model is the agestructured pod sub-model that calculates the numbers of pods in weekly age-classes for each week of a two-year period. The data uses historical flowering databases derived from yield and monthly harvest profiles from 1991 to 1997 (Somarriba et al., 1997) (Fig. 2). The user is required to select, from a drop-down menu, a two-year period on which to test management ideas. The user may also enter the average pod count (number of pods per kilo of dry beans) that was obtained for each year of the two years selected. The age, in weeks from flowering, at which pods become harvestable may also be entered externally into the age-structured sub-model to account for differences between regions and varieties. Flowering to harvest takes 5–6 months (McKelvie, 1956; Wood and Lass, 1985; Herna! ndez et al., undated). 2.2. Moniliophthora sub-model The dynamics of the disease are based on two functions. The first relates the number of sporulating pods in the field with the response variable that is the intercept of the second function. This second function or ‘‘infectivity curve’’ determines the proportion of pods that become infected dependent on their age and the output of the first function which shifts the second curve up or down the infectivity axis (y) depending on the number of pods in the field. These two models are currently based on general descriptions of the disease’s epidemiology, mainly from Ecuador (Evans, 1981, 1986), and field perceptions by experienced field observers in Costa Rica (pers. comm. H Evans and U Krauss). The choice of these functions is based on the two main perceptions of disease, namely: *
*
high numbers of sporulating pods in the field increase the probability of an uninfected pod becoming infected (Evans (1981) estimated 44 million spores per cm2, around 7 billion spores from a mature pod), the ability of M. roreri to infect pods declines with increased pod age. Evans (1981) observed that moniliasis has a long (6–10 weeks) incubation period (latent phase) during which it is not normally
A.W. Leach et al. / Crop Protection 21 (2002) 317–326 INPUTS
319
PROCESS
OUTPUTS
Pod sub-model Flowering pattern historical or anticipated
Age to ripeness (weeks) No of ripe pods per kg
Flowering Model
Rodent loss sub-model Rodent losses
Age structured pod model
Management sub-model
Harvesting
No of harvested pods Mass of harvested pods
Moniliophthora sub-mode l
Infected pod stripping
Infected pod model Gross return
Labour costs & requirements Economic sub-model International cocoa price
Net return
Economics Costs & Returns
Proportion of international price received by farmers
Harvest efficiency Harvest frequency
Pod strip efficiency
The sub-model is structured so that the introduction of field-data generated functions will be simple to include when they become available. 2.2.1. Sporulating pod function Evans (1981) observed that sporulation occurs exceedingly rapidly (3–8 d) after lesion development. If not removed, infected pods remain hanging in the canopy. The slightest force, such as a mild breeze, drip or rain, detaches spores. Intermittent winds and convection can carry spores large distances (>1 km from nearest cocoa). Conidia on hanging pods are still infective at up to 7 months of (conidia) age; after that period, there
0.07 0.06 0.05 0.04 0.03 0.02 0.01 Nov
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0 Feb
recognised by an observer. That latent period is agedependent: * pod age at infection o40 d, symptoms easily confused with cherelle wilt, a physiological abortion of young pods, * pod age at infection o60 # d, 40 d to symptoms (brown lesion), * pod age at infection 60–80 d, 60 d to symptoms (brown lesion), although symptoms become clearly visible only at this stage, the seed chamber is completely destroyed by then, * pod age at infection 120–160 d, after 60 d small external brown spot visible to careful observer with trained eye.
Proportion of season's pods contributed by each week's flowering
Fig. 1. Conceptual model of sub-model interactions
Month 1992
1993
Fig. 2. Graph of example flowering data from 1992 and 1993 used by the model to generate age structured pod model. Flowering is shown as a proportion of season’s pods contributed by flowering in each week. These data are derived from Somarriba et al. (1997) and ignores flowering that is lost to cherelle wilt i.e. the flowering data provides numbers of those pods that would normally reach maturity (in the absence of M. roreri and phytosanitary measures).
is a steep decline in infectivity. However, 7 months is long enough to carry over infection between seasons. Conidia on stripped pods on the ground are highly infective after 1 month but declined steeply over several months, with no infectivity in conidia over 3 months old. Herna! ndez et al. (undated) note that if a pod is removed forcefully many conidia are released, thus man
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0.20 Proportion of clean pods becoming infected
Intercept for infectivity function (λo)
1 0.9 0.8 0.7 0.6 0.5 0.4 0.3
0.15
0.10
0.05
0.00
0.2
1
0.1
4
7
10 13 16 19 22 25 28 31 Pod age (weeks)
0 0
10
5
15
20
25
Thousands Sporulating pods per ha (x)
Fig. 3. Gompertz function relating M. roreri infected sporulating pod numbers per hectare with the intercept of the infectivity curve (l0 ).
can be the main disseminator through harvesting or stripping pods. A Gompertz function (Gompertz, 1825) is used to provide a sigmoidal response to sporulating pod numbers in the field (Fig. 3). In medicine and epidemiology monotonic curves (of which Gompertz is a nonlinear example) are important as models for relations which prior knowledge or scientific reasoning dictate should increase or decrease consistently with the predictor value (Royston, 2000). The Gompertz function provides the most appropriate fit to sporulation and has been used extensively for describing plant disease dynamics (Berger, 1981; Luke and Berger, 1982; Nutter, 1997; Fargette et al., 1994). The Gompertz response, in this model, is scaled between zero and one (lower and upper asymptotes). In Fig. 3 the line does not meet at the origin that, from the formula, is specified as the lower asymptote. This is because of the proximity of the point of inflection (m) to the origin. This provides a low level of background infection from sporulating pods external to the field so even if the field is scrupulously clean and devoid of all sporulating pods there will still be spores infecting pods from neighbouring farms (albeit at low levels). The Gompertz model is shown as bðxmÞ
l0 ¼ a þ c expexp
;
ð1Þ
where l0 is the intercept of the infectivity function, a the lower asymptote (0), b the rate of increase of l0 (0.0002), c the upper asymptote (1), m the number of sporulating pods at max rate of increase of l0 (6000), and x the number of sporulating pods per hectare Numbers in brackets refer to values used in the model.
Fig. 4. Example infectivity curve relating pod age to the proportion of uninfected pods becoming infected (this is an example only because this function is dynamic within the model and is dependent upon the value of l0 (the intercept) generated by the Gompertz function).
2.2.2. Infectivity function The infectivity curve is a simple function relating podage to the proportion of pods at age ðPi Þ that become infected. Fig. 4 shows that proportion of uninfected pods that become infected decreases with pod-age (i). The intercept ðl0 Þ is calculated by the Gompertz model described above. The infectivity function is described by the following equation: Pi ¼
l0 1 þ ðigÞh
;
ð2Þ
where l0 is the intercept of the infectivity curve (from Gompertz function), i the pod-age (weeks since flowering), g the coefficient of infectivity with age (0.2) and h the coefficient of infectivity with age (1). Thus the number of infected pods (I) of a certain age class (i) being in the field in each week (t) can be shown as Itþ1;iþ1 ¼ ð1 jÞ It;i þ ðNt;i It;i ÞPi ; ð3Þ where I is the number of infected pods, N the total number of pods and the stripping efficiency (user defined between 0 and 1). The equation above is modified by pod stripping depending on whether the age of the pods or the number of weeks since infection qualifies them for removal at the user-defined efficiency. The user can determine the frequency and efficiency of this phytosanitary activity although the frequency of infected pod stripping, as discussed later, is controlled by the harvesting interval set by the user. The age at which pods may be harvested is also userdetermined to allow for model flexibility to include different maturation rates in regions caused by climatic and/or varietal differences. The number of pods harvested (R) in a harvesting week is a product of the user-defined efficiency of harvest (yFproportion of ripe pods harvested (0 to 1)) and the number of non-infected
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ripe pods present: Rtþ1 ¼
i¼max Xharv
yðNt;i It;i Þ;
ð4Þ
i¼min harv
where min harv is the age in weeks (i) at which pods become harvestable, max harv the age in weeks (i) at which pods cease to be harvestable. In plain terms, this set of models describes how the infectivity of the disease (and thus the number of infected pods in the field) increases when many sporulating pods remain on the trees and decreases as sporulating pods are removed and as pods get older. 2.3. Rodent loss sub-model The user is also able to assess how rodent losses (principally rats and squirrels) may affect harvesting strategies when combined with losses from M. roreri. These are defined in terms of the percentage of ripe pods that are lost to rodents on a daily basis. Mumford (unpublished data from Indonesia) observed rodent losses of up to 3% of ripe pods lost per day from the time of ripening in fields near to rice. In the Talamanca region of Costa Rica, farmer surveys indicated combined losses of 5–60% of the cocoa crop due to squirrels and kinkajous (Potus flavus, Procyonidae) (Guiracocha et al., 2001). 2.4. Management sub-model A number of management strategies may be tested on the model as single or combined strategies though at this stage these are principally restricted to phytosanitary practices. The economic modelling aspect allows the user to evaluate management strategies both in terms of the efficacy of suppressing the disease and more importantly in terms of the net return that this strategy will provide a farmer. The model evaluates two cultural activities capable of affecting disease prevalence: the first is harvesting, which involves the removal of clean ripe pods, and the second, stripping, is the removal of recognisably infected pods. According to research carried out in Colombia (Herna! ndez et al., undated) it is neither necessary nor recommended to remove stripped pods from the plantation. The most convenient option is to leave them at the foot of the tree, covered with leaf litter and/or vegetation. Only in newly afflicted areas where moniliasis is detected for the first time, should pods be buried. Within the model, the frequency at which stripping is conducted is dictated by the user-defined harvest interval, the model dictates that stripping occurs at the same time as the simulated farmer harvests (a reasonable assumption based on farmer practice). The model
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allows the user to explore these two activities as a single operation (harvest only) or in combination where the frequency and efficiency of each action can be evaluated in yield change and net returns. Turning either activity ‘‘on’’ is accomplished by setting the efficiency of action to greater than zero. The efficiency of harvesting and/or stripping is set separately and can be selected between 0% and 100%. Typical values for either activity (when used) are 95% efficient harvesting, in which 95% of ripe pods in the field that week are removed and 85% efficient stripping. It is technically possible, within the model, to evaluate stripping in the absence of harvesting although this is unlikely to occur in the field as only harvesting represents an activity that generates income. The frequencies of harvesting and pod stripping can be changed as independent options that are flexible throughout the course of the season. Farmers usually harvest little (maximum monthly) in the low crop season but weekly during the harvest peak; whereas, removal of diseased pods is needed most during early pod development when these are most susceptible (Evans, 1981) and rapid inoculum build-up can occur. Further investigations will provide an activity profile for farmers in Costa Rica, in the form of a farm diary, which will allow the model to be used with typical or specific harvesting or stripping frequencies. 2.4.1. Harvesting Using the model, it is possible to test how the frequency of harvesting of ripe, clean pods will manage the disease. Harvesting removes sporulating pods from the field and so affects future infection through the feedback in the sporulation sub-model. The efficiency of harvesting, the proportion of ripe pods actually picked at each harvest event, may be varied to evaluate its importance in terms of the net return. The interval between harvests (in weeks) can also be changed between 1 and 52. Thus the return on labour investment for more/less regular harvesting at greater/lower levels of efficiency (% of available ripe pods harvested) can be tested against current practice. 2.4.2. Pod stripping The removal of infected pods from the tree is currently the most effective measure for reducing the impact of the disease (Cubillos and Aranzazu, 1979; Porras et al., 1990, Matlick, 1998; Soberanis et al., 1999). In Peru, pruned diseased material was left on the plantation floor providing some residual source of infection; nevertheless, weekly stripping without burial or other disposal yielded nearly an eight-fold increase in net return (Soberanis et al., 1999). For the moment, the model assumes this approach, leaving the stripped pods within the field, but future versions of the model could evaluate differences in cultural practice such as the removal of infected material (by burial or otherwise).
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Removal is not recommended at present, there is an extra cost of labour and transporting the sporulating pods through cocoa may increase disease spread. A user can explore the effects of phytosanitary pod stripping by making the ‘‘efficiency’’ of stripping (proportion of visibly infected pods removed) greater than zero. Experience in the field suggests that farmers could remove between 85% and 95% of infected pods at each sweep. The number of weeks between sweeps is determined by the harvesting interval to allow for the fact that most farmers would conduct phytosanitation measures as they perform regular harvest. To increase the frequency of phytosanitation in the model one has to shorten the harvesting interval. The age at which infected pods are discernible from cherelle wilts is also user-definable to allow for the fact that it is possible to teach recognition of the disease at an early stage and thus remove infected pods even earlier. When phytosanitary stripping is selected the number of ‘‘visibly’’ infected simulated pods is reduced by the specified amount and the infectivity curve is recalculated for each age classes for the following week. The model calculates the cost of this activity as the product of the number of man-days per hectare required for pod stripping and value of a man-day. The costs of these activities are explained in more detail in the economics section. If the user has specified pod stripping then the extra cost, 1 man-day at US$3 per day (pers. comm. R. Mack) is automatically included by the model. There are a number of unknowns associated with the pod stripping that the model can assist in evaluating: * * *
how often must the farmer strip infected pods? how efficient must the farmer be? does earlier recognition of the disease reduce its impact?
The model provides a testing ground for these sorts of questions and allows the cost of labour and the labour required to be varied to estimate the economic value of these factors. For example it may well be that stripping weekly is more effective in suppressing the disease but if the opportunity cost of labour is high then less regular stripping and harvesting may net a higher return. The fact that the user can define the pod age at which infected pods may be discerned from cherelle wilt allows the user to evaluate how improving extension may assist in reducing losses due to moniliasis by reducing the number of pods that sporulate. 2.5. Economics 2.5.1. Costs The costs of management activities are calculated by the number of man-days per hectare per week that are required to conduct harvesting and pod stripping if this
option is used. The cost of labour may be entered directly and is considered as either the cost of hiring labour or as an opportunity cost for the farmer (who could be earning a wage on someone else’s land if work was available). In Costa Rica, it was estimated that a man-day in the Talamanca region would be in the order of US$6 and that it would be fair to assess the opportunity cost, for labouring on one’s own land, at half this rate (pers. comm. R Mack). The prices, subsidies and labour cost and requirements are listed in Table 1. The values used are approximate and reflect conditions from November 2000 though any value could be entered to reflect current or anticipated changes in economic circumstances. 2.5.2. Returns The gross returns (L) from growing cocoa are calculated by the following equation: L¼
S s q; K 1000
ð5Þ
where S is the number of clean ripe pods harvested per year, K the number of ripe clean pods required to produce 1 kg of dry cocoa, s the international cocoa price $US per tonne and q the proportion of international price paid to farmers at the farm-gate. Costa Rican cocoa is managed following organic guidelines and certified for this niche market and, as such, qualifies for an organic premium, giving a value of q greater than one (Table 1). In most other countries and regions this would typically be less than one (between 0.4 and 0.9). The net return for each year is calculated as the gross return minus the management costs. See Table 2 for a full listing of model parameters and variables.
3. Model output and discussion It is important to stress that the model is in the preliminary stage and as such its outputs have not yet been empirically validated. Nevertheless qualitative verification (Teng, 1981) of the model by a major buyer
Table 1 Economic inputs required by the model. All values can be changed by the user to predict outcomes under specific economic, labour or premium circumstances Description
Units
International cocoa price (US$ per tonne) Proportion of international cocoa price paid to co-operative Harvest labour (man days per hectare) Phytosanitary stripping (man days per hectare) Labour (US$ per day)
$750.00 2 0.50 1.00 $3.00
A.W. Leach et al. / Crop Protection 21 (2002) 317–326 Table 2 Example parameter and variable listing for the model Variable/parameter description
Example value
Trees per hectare Choose years No of pods per kg of cocoa (year 1) No of pods per kg of cocoa in (year 2) Harvest interval (weeks) Harvest efficiency (%) Weeks from infection when Moniliophthora infection is visible Weeks after infection to sporulating Age at which infected pods are discernible from cherelle wilt (weeks after flowering) Efficiency of pod stripping (%) Daily losses of ripe pods to rodents (%) Minimum age for harvesting (weeks after flowering) Maximum age for harvesting (weeks after flowering)
800 1994–1995* 25 25 4* 95* 7
85* 3* 30 38
Gompertz sporulating function Lower asymptote (a) Upper asymptote (c) Point of max growth (m) Growth rate (b)
0 1 6000 0.0002
Infectivity curve Intercept Slope Asymptote
0.2 0.2 1
8 12*
All values listed may be varied to match local circumstances or to explore management outcomes or disease dynamics etc. Those values marked with * will be most changed regularly to explore different management activities or conditions.
and exporter of Costa Rican cocoa and from two CATIE scientists indicate that the model provides a correct framework for analysis and intuitively comprehensible output with sensible assumptions. Each part of the model is on a separate worksheet within the file. The main page for strategy testing is the first worksheet, the ‘‘User Interface’’ (Fig. 5). From here the tree density, fruiting pattern, pod counts and different management and economic criteria can be added. These parameter values are passed directly into the model. The outputs can also be viewed from this page either from tables of economic outputs or graphics of cocoa weights, pods harvested or the percentage of infected pods in the field (Fig. 5). Spreadsheets are very useful tools for modelling because they are very user-friendly and transparent in their working (as opposed to compiled programs). With sympathetic design they can be split into separate organisational units (worksheets) to help the user understand how the various sub-models interact. Cells can be coloured to indicate processes such as pod ageing in the pod sub-model and also to indicate cells to be manipulated to test management ideas. Not only scientists but also managers and trainers in growers’ co-operatives quickly learned to use it and recognised its
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usefulness as a decision-making tool. The model is ! de Pequenos currently being evaluated by the Asociacion * Productores de Talamanca (APPTA) and the Organic Commodity Project. 3.1. Harvesting frequency The model indicates that, in the absence of infected pod stripping, harvesting more frequently than current practice (4–5 weeks) does not offer any relative economic benefits. More regular harvesting does marginally reduce the number of infected pods but the cost involved in more frequent harvests outweighs any benefits (at the current labour and cocoa prices). Harvesting alone is not enough to outweigh the benefits accrued from reduced rodent losses because, without conducting stripping, the net return is not increased because of the paucity of non-infected ripe pods available. 3.2. Pod stripping By contrast, pod stripping, when it is ‘‘switched on’’, does provide a considerable benefit and allows the more frequent harvesting/stripping option to reap considerable benefits (Table 3). The benefits of increased frequency of phytosanitary measures are even more accentuated in the presence of high rodent losses (Table 3). Moniliasis has a latent period of approximately 7 weeks (Evans, 1981). After that period symptoms become recognisable initially only to the trained eye but within a week they are very clear and are soon accompanied by prolific sporulation. The model enables the user to change the age at which Moniliophthorainfected pods can be recognised and be discerned from cherelle wilt and it becomes possible to evaluate the economic impact of an extension programme aimed at the earlier identification of infected pods. In the hypothetical scenario described below it is envisaged that a training programme could get farmers to separate cherelle wilts from infected pods 5 weeks earlier than they are currently able. A policy maker may want to know what effect this may have on the income of farmers trained in this method. The model suggests that earlier recognition (infected pods identified at 10 weeks old rather than 15 weeks after flowering) provides an increase in net return of about 50% over two years when harvesting is performed at 4 week intervals (US$ 931 vs. US$ 613 per hectare over two years). However, the relative benefits were greatly reduced as harvesting, and thus pod stripping, frequency increased to weekly intervals (2% improvement on net return, US$ 1326 vs. US$ 1299). This provides an important insight into the economics of decision-making and provides an extension strategist more options for potential programmes. For example, there may be less resistance
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Fig. 5. Screen-capture image of the User Interface page of the model. In the model itself some cells are coloured blue to denote where the user may make inputs and outputs are denoted by yellow background cells. All of the lightly shaded cells in columns B and D are input cells (blue in colour) and the shaded cells in column G are output cells (yellow).
A.W. Leach et al. / Crop Protection 21 (2002) 317–326 Table 3 Table of model outputs of net returns under various management options and rodent loss burdens Stripping turned on?
Harvest and/or stripping interval (weeks)
Rodent losses?
Net return (2 years) US$/ha
F F F F * * * *
4 1 4 1 4 1 4 1
F F * * F F * *
9 101 37 149 752 1393 314 1117
A * denotes where stripping and/or rat losses were included in the simulation. All harvests were conducted at 95% efficiency. Where stripping is included these are conducted at 85% efficiency. Where rodent losses were specified in the simulation they occurred at 3% loss of ripe pods per day.
from farmers to improving their identification of the disease (at current harvest/stripping intervals) than in encouraging farmers to strip pods more frequently. The model’s most important recommendation is to conduct phytosanitary stripping which, even if conducted at 4-week intervals (at the same time as harvesting), will reap a many-fold increase in net return compared with harvesting alone. If the frequency of harvesting/stripping can then be increased, then benefits increase further still (85% increase in net return when going from 4 to 1 week harvest/stripping intervals). When losses of 3% of ripe pods per day to rodents are included in the model, this difference is even more marked (256% increase in net return between harvest/ stripping at 1-week intervals compared with 4-week intervals). This is because losses to rats will be compounded day by day and week by week so the shorter time between harvests will greatly decrease rodent losses and increase net returns. This model has the potential to evaluate and explore any management permutations so that extension agents and buyers, in conjunction with farmers, can test strategies where the returns on employment could be investigated and evaluated prior to implementation. Apart from providing a skeleton for directing fieldwork the model also provides a tool for regions where the disease is not yet present (such as Bahia, Brazil). Assuming that the entry of the disease is inevitable then the model, when adapted for the region, could provide a tool for extensionists, unfamiliar with the management of M. roreri, to develop some ‘‘field’’ experience and to understand the issues involved in the economic control of the disease. It would also allow for the strategic assessment of potential losses and assist in contingency planning.
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4. Summary A model has been developed that simulates much of the crop phenology of cocoa, the pod age-structure and the disease dynamics within the management and economic context of Costa Rican cocoa. The model demonstrates the value of frequent pod stripping (when harvests are conducted) in terms of the reduction of losses and the associated economic net benefits. The most important output of the model is the recommendation to conduct phytosanitary stripping and harvesting as often as possible since this will reduce losses to M. roreri as well as reduce the compounding losses to rodents that accumulate day by day as ripe clean pods remain on the tree. The model presents great potential value as a training and extension strategy evaluation tool. A number of different management actions can be simulated in the model, which can be tested singly or as integrated strategies. The model allows the user to test permutations of those strategies and gain insight into the dynamics of the disease and its monetary considerations such as cocoa prices, premiums, labour requirements and costs. The model is still in development and the two main assumptions about the disease behaviour require fielddata derived functions to enhance the model’s realism. The functions are currently based on available literature and discussions with research staff in Costa Rica and the United Kingdom. The model could be adapted to the conditions and crop phenology of other countries with the M. roreri problem such as Ecuador, Peru and Colombia. Regions currently unaffected (but likely to succumb to the disease) may find the model of some value. In the latter case the model would be useful for preparing buyers and extension bodies for the likely effects of the disease, in advance, so that appropriate extension strategies and training are developed. The format of the model is such that it could be adapted to other diseases such as Phytophthora in West Africa and Asia or witches’ broom in South America. The user interface renders the model very user-friendly. Acceptance at the major Costa Rican growers’ association is good.
Acknowledgements The authors would like to thank the Biscuit, Cake, Chocolate and Confectionery Alliance and the US Department of Agriculture for co-funding this research. Many thanks to Harry Evans, Serge Savary and Laetitia Willocquet for their perceptions of the disease functions and to Robert Mack, Mario Pierda, Eduardo Somarriba and Philipe Vaast for useful
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discussion. Adrian Leach would also like to thank Ulrike Krauss and all the CATIE staff, for their kind hospitality when he visited CATIE, Turrialba in November 2000.
References Berger, R.D., 1981. Comparison of the Gompertz and logistic equations to describe plant-disease progress. Phytopathology 71, 203. Cubillos, G., Aranzazu, F., 1979. Comparacio´n de tres frecuencias de remocion de frutos enfermos en al control de Monilia roreri Cif. & Par. El Cacaotero Colombiano 8, 27–34. Evans, H.C., 1981. Pod rot of cacao caused by Moniliophthora (Monilia) roreri. Phytopathological Papers, 24. Commonwealth Mycological Institute, Kew, p. 44. Evans, H.C., 1986. A reassessment of Moniliophthora (Monilia) pod rot of Cocoa. Cocoa Growers’ Bull. 37, 34–43. Evans, H.C., Krauss, U., R!ıos, R., Zecevich, T., Ar!evalo, E., 1998. Cocoa in Peru. Cocoa Growers’ Bull. 51, 7–22. Fargette, D., Jeger, M., Fauquet, C., Fishpool, L.D.C., 1994. Analysis of temporal disease progress of African cassava mosaic-virus. Phytopathology 84, 91–98. Gompertz, B., 1825. On the nature of the function expressive of the law of human mortality. Philos. Trans. R. Soc. 115, 513. Guiracocha, G., Harvey, C., Somarriba, E., Krauss, U., Carillo, E., ! de la biodiversidad en sistemas agroforestales 2001. Conservacion con cacao y banano en Talamanca, Costa Rica. Agroforester!ıa en las Am!ericas 8, 7–11. Hern!andez, T., R!ıos, R., Ar!evalo, E., Aranzazu, F. (undated). Monilia ! distribuci!on y control en el Peru. ! UNDCP, del cacao: Deteccion, Project 759, Publicaria Tingo, Lima, Peru. Krauss, U., Soberanis, W., 2001. Biocontrol of cocoa pod diseases with mycoparasite mixtures. Biol. Control 22, 149–158. Krauss, U., Soberanis, W., 2002. Effect of fertilization and biocontrol application frequency on cocoa pod diseases. Biol. Control, in press. Landell-Mills Consulting, 1998, Commodity BulletinFCocoa, May 1998, p. 20. Leach, A.W., Mumford, J.D., 2001. A simulation model of the cocoa pod borer and its management: an extension and policy
tool. Proceedings of the 13th International Cocoa Research Conference. Kota Kinabalu, Sabah, Malaysia, 9–14 October, 2000, pp. 415–424. Luke, H.H., Berger, R.D., 1982. Slow rusting in oats compared with logistic and Gompertz models. Phytopathology 72, 400–402. Matlick, B.K., 1998. Machete technology? What small farmers need! Paper presented at the First International Workshop on Sustainable Cocoa Growing. Smithsonian Migratory Bird Center, Panama City, Republic of Panama, 30 March–02 April, 1998, http://www.si.edu/smbc/matlick.htm. McKelvie, A.D., 1956. Cherelle wilt of cocoa. I. Pod development and its relation to wilt. J. Exp. Bot. 7, 252–263. Nutter, F.W., 1997. Quantifying the temporal dynamics of plant virus epidemics: a review. Crop Protection 16, 603–618. Porras, V.H., Cruz, C.A., Galindo, J.J., 1990. Manejo integrado de la mazorca negra y moniliasis del cacao en el tro´pico hu´medo bajo de Costa Rica. Turrialba 40, 238–245. Rabbinge, R., Rossing, W.A.H., van der Werf, W., 1993. Systems approaches in epidemiology and plant disease management. Neth. J. Plant Pathol. 99, 161–171. Rorer, J.B., 1918. Enfermadades y plagas del cacao en el Ecuador y m!etodos modernos apropriados al cultivo de cacao. Report presented to the Agricultural Society of Ecuador, Guayaquil. Royston, P., 2000. A useful monotonic non-linear model with applications in medicine and epidemiology. Stat. Med. 19, 2053–2066. * Soberanis, W., Rios, R., Ar!evalo, E., Zu! niga, L., Cabezas, O., Krauss, U., 1999. Increased frequency of phytosanitary pod removal in cacao (Theobroma cacao) increases yield economically in eastern Peru. Crop Protection 18, 677–685. Somarriba, E., Mel!endez, L., Campos, W., Lucas, C., Lujon, R., 1997. Cacao bajo sombra de la leguminosas en Talamanca, Costa Rica. Serie T!ecnica. Informe T!ecnico No. 289, Serie Generacio´n y Transferencia de T!ecnologia No. 22, CATIE, Turrialba, Costa Rica. Teng, P.S., 1981. Validation of computer models of plant disease epidemics: a review of philosophy and methodology. Z. Pflanzenkrank. Pflanzenschutz 88, 49–63. Teng, P.S., Savary, S., 1992. Implementing the systems approach in pest management. Agric. Syst. 40, 237–264. Wood, G.A.R., Lass, R.A., 1985. Cocoa. Longmans, London. 620pp, ISBN 0-582-46352-1.
Modelling Moniliophthora roreri in Costa Rica A.W. Leacha, J.D. Mumforda,*, U. Kraussb a
Department of Environmental Science and Technology, Imperial College at Silwood Park, Buckhurst Road, Ascot SL5 7PY, UK b CABI Bioscience, c/o CATIE, 7170 Turrialba, Costa Rica Received 31 May 2001; received in revised form 4 September 2001; accepted 5 December 2001
Abstract A model of the field dynamics of Moniliophthora roreri, its management and economics was developed to evaluate net returns of various management strategies to farmers in Central America. The model is primarily based on the cropping patterns and economics of cocoa production in Costa Rica. With minimal inputs the model can be adapted to simulate the crop phenology and economics in any country, where the disease causes significant economic loss. The model is a time-based deterministic spreadsheet model that simulates the production and management of one hectare of cocoa at weekly intervals over a user-selected two-year period. The model demonstrates the need for frequent stripping of infected pods to prevent sporulating pods accumulating in the field, under a broad range of economic scenarios. The model allows the user to evaluate potential extension advice in terms of user-definable ‘‘real world’’ variables including: frequency of harvesting and stripping of infected pods; losses of ripe unharvested pods to rodents; ability to identify infected pods; international cocoa prices; farm-gate prices and premiums; and labour costs and requirements. r 2002 Published by Elsevier Science Ltd. Keywords: Cocoa; Cultural disease control; Economics; Epidemiology; Extension; Modelling; Moniliophthora roreri; Rodent losses; Theobroma cacao
1. Introduction Diseases caused by Moniliophthora roreri (Cif. & Par.) Evans et al. (moniliasis), Crinipellis perniciosa (Stahel) Singer (witches’ broom) and Phytophthora palmivora (Butl.) Butl. (black pod) are the most important factors limiting cocoa production in Central and South America. Moniliasis is a serious fungal disease which, thus far, is confined to the Americas. Its range includes northwestern South America including Ecuador, Colombia, Peru, parts of Venezuela, and large parts of Central America (Panama, Costa Rica, Nicaragua and Honduras). It was first reported in Ecuador around 1914 (Rorer, 1918). Overall cocoa production declined in Ecuador from 50,000 tonnes in 1915–16 following an attack of M. roreri to around 30,000 tonnes in 1922–23 and fell further to around 20,000 tonnes in 1925 due to witches’ broom. The disease can cause losses of up to *Corresponding author. E-mail address: [email protected] (J.D. Mumford), [email protected] (A.W. Leach). 0261-2194/02/$ - see front matter r 2002 Published by Elsevier Science Ltd. PII: S 0 2 6 1 - 2 1 9 4 ( 0 1 ) 0 0 1 4 8 - X
100% (Evans et al., 1998). Landell-Mills (1998) estimated the reduction in cocoa production potential by M. roreri in Latin America at 30,000 tonnes. According to FAO statistics,1 the production of cocoa beans in Costa Rica has fallen from 12,000 tonnes in 1962 to around 4000 tonnes in 2000. This reduction may be attributed to three linked factors: loss of yield; gradual decline in international cocoa price; leading to abandonment of cocoa area. When the reduced international price is combined with considerable losses to M. roreri then, for a large part of the cocoa sector, the production of cocoa without a quality premium/subsidy payment is no longer economically viable. Cultural management is currently considered to be the main practical means of control for the smallholder (Matlick, 1998; Soberanis et al., 1999). The frequency with which infected pods can be removed from the field is essential for the effective control of the disease (Cubillos and Aranzazu, 1979; Porras et al., 1990; Soberanis et al., 1999). Recently, biocontrol has shown 1
FAO website: http://www.fao.org/
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great potential if used in addition to cultural control (Krauss and Soberanis, 2001). The most effective and economical timing of biocontrol application is closely linked to the production cycle of cocoa (Krauss and Soberanis, 2002). Simulation models can help improve disease and pest management both at the practical and strategic level (Teng and Savary, 1992; Rabbinge et al., 1993). They are powerful tools for answering a series of ‘‘what if?’’ scenarios such as how management recommendations will change when the cost of labour increases or the price of cocoa falls (Leach and Mumford, 2001). A simulation model was developed that incorporates pod age structure, epidemiology, management and economic factors to assess best management options for M. roreri by farmers and for answering or anticipating a range of scientific, strategic and policy questions. Currently its potential includes: identifying research priorities, optimising strategies for crop protection that maximise net returns for farmers, training and decision support for extension.
* *
*
The most obvious role for the model, at this stage in its development, is to direct research. The problem in developing a system that matches the field behaviour of M. roreri is that there was little field data available that described the disease’s epidemiology. This paper represents an initial attempt to anticipate the disease dynamics based on a synthesis of available published information and subjective estimates from several experienced field observers. This allows the user to test various harvesting and phytosanitary measures that act at different points in the disease–host interaction. Because the shape of the functions used to simulate the dynamics of the disease are based on expert opinion the simulation of the disease’s epidemiology is no more (or less) than a working, dynamic synthesis of expert opinion that appears to behave like the disease in the field. Further field observations should be directed to determining values for the parameters to validate the processes and conclusions of the model. The management interventions available to farmers are simulated and their associated costs can be applied. This function, given that the disease dynamics and the costs and returns generated appear sensible under a range of economic criteria, has generated interest in the commercial sector. The principal buyer2 of cocoa in Costa Rica is assisting in the development and evaluation of the model so that it could be used as an extension training and decision support tool within the cocoa sector of that country.
2
Organic Commodity Project (OCP).
2. Model components The model was written in an Excel 97TM spreadsheet and runs on any Windows-based PC. The program consists of five interacting sub-models each of which is described below. A flow diagram of these units is shown in Fig. 1 to provide a conceptual framework of the model and its interactions. 2.1. Pod sub-model The principal ‘‘engine’’ of the model is the agestructured pod sub-model that calculates the numbers of pods in weekly age-classes for each week of a two-year period. The data uses historical flowering databases derived from yield and monthly harvest profiles from 1991 to 1997 (Somarriba et al., 1997) (Fig. 2). The user is required to select, from a drop-down menu, a two-year period on which to test management ideas. The user may also enter the average pod count (number of pods per kilo of dry beans) that was obtained for each year of the two years selected. The age, in weeks from flowering, at which pods become harvestable may also be entered externally into the age-structured sub-model to account for differences between regions and varieties. Flowering to harvest takes 5–6 months (McKelvie, 1956; Wood and Lass, 1985; Herna! ndez et al., undated). 2.2. Moniliophthora sub-model The dynamics of the disease are based on two functions. The first relates the number of sporulating pods in the field with the response variable that is the intercept of the second function. This second function or ‘‘infectivity curve’’ determines the proportion of pods that become infected dependent on their age and the output of the first function which shifts the second curve up or down the infectivity axis (y) depending on the number of pods in the field. These two models are currently based on general descriptions of the disease’s epidemiology, mainly from Ecuador (Evans, 1981, 1986), and field perceptions by experienced field observers in Costa Rica (pers. comm. H Evans and U Krauss). The choice of these functions is based on the two main perceptions of disease, namely: *
*
high numbers of sporulating pods in the field increase the probability of an uninfected pod becoming infected (Evans (1981) estimated 44 million spores per cm2, around 7 billion spores from a mature pod), the ability of M. roreri to infect pods declines with increased pod age. Evans (1981) observed that moniliasis has a long (6–10 weeks) incubation period (latent phase) during which it is not normally
A.W. Leach et al. / Crop Protection 21 (2002) 317–326 INPUTS
319
PROCESS
OUTPUTS
Pod sub-model Flowering pattern historical or anticipated
Age to ripeness (weeks) No of ripe pods per kg
Flowering Model
Rodent loss sub-model Rodent losses
Age structured pod model
Management sub-model
Harvesting
No of harvested pods Mass of harvested pods
Moniliophthora sub-mode l
Infected pod stripping
Infected pod model Gross return
Labour costs & requirements Economic sub-model International cocoa price
Net return
Economics Costs & Returns
Proportion of international price received by farmers
Harvest efficiency Harvest frequency
Pod strip efficiency
The sub-model is structured so that the introduction of field-data generated functions will be simple to include when they become available. 2.2.1. Sporulating pod function Evans (1981) observed that sporulation occurs exceedingly rapidly (3–8 d) after lesion development. If not removed, infected pods remain hanging in the canopy. The slightest force, such as a mild breeze, drip or rain, detaches spores. Intermittent winds and convection can carry spores large distances (>1 km from nearest cocoa). Conidia on hanging pods are still infective at up to 7 months of (conidia) age; after that period, there
0.07 0.06 0.05 0.04 0.03 0.02 0.01 Nov
Dec
Oct
Aug
Sep
Jul
Jun
Apr
May
Mar
Jan
0 Feb
recognised by an observer. That latent period is agedependent: * pod age at infection o40 d, symptoms easily confused with cherelle wilt, a physiological abortion of young pods, * pod age at infection o60 # d, 40 d to symptoms (brown lesion), * pod age at infection 60–80 d, 60 d to symptoms (brown lesion), although symptoms become clearly visible only at this stage, the seed chamber is completely destroyed by then, * pod age at infection 120–160 d, after 60 d small external brown spot visible to careful observer with trained eye.
Proportion of season's pods contributed by each week's flowering
Fig. 1. Conceptual model of sub-model interactions
Month 1992
1993
Fig. 2. Graph of example flowering data from 1992 and 1993 used by the model to generate age structured pod model. Flowering is shown as a proportion of season’s pods contributed by flowering in each week. These data are derived from Somarriba et al. (1997) and ignores flowering that is lost to cherelle wilt i.e. the flowering data provides numbers of those pods that would normally reach maturity (in the absence of M. roreri and phytosanitary measures).
is a steep decline in infectivity. However, 7 months is long enough to carry over infection between seasons. Conidia on stripped pods on the ground are highly infective after 1 month but declined steeply over several months, with no infectivity in conidia over 3 months old. Herna! ndez et al. (undated) note that if a pod is removed forcefully many conidia are released, thus man
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0.20 Proportion of clean pods becoming infected
Intercept for infectivity function (λo)
1 0.9 0.8 0.7 0.6 0.5 0.4 0.3
0.15
0.10
0.05
0.00
0.2
1
0.1
4
7
10 13 16 19 22 25 28 31 Pod age (weeks)
0 0
10
5
15
20
25
Thousands Sporulating pods per ha (x)
Fig. 3. Gompertz function relating M. roreri infected sporulating pod numbers per hectare with the intercept of the infectivity curve (l0 ).
can be the main disseminator through harvesting or stripping pods. A Gompertz function (Gompertz, 1825) is used to provide a sigmoidal response to sporulating pod numbers in the field (Fig. 3). In medicine and epidemiology monotonic curves (of which Gompertz is a nonlinear example) are important as models for relations which prior knowledge or scientific reasoning dictate should increase or decrease consistently with the predictor value (Royston, 2000). The Gompertz function provides the most appropriate fit to sporulation and has been used extensively for describing plant disease dynamics (Berger, 1981; Luke and Berger, 1982; Nutter, 1997; Fargette et al., 1994). The Gompertz response, in this model, is scaled between zero and one (lower and upper asymptotes). In Fig. 3 the line does not meet at the origin that, from the formula, is specified as the lower asymptote. This is because of the proximity of the point of inflection (m) to the origin. This provides a low level of background infection from sporulating pods external to the field so even if the field is scrupulously clean and devoid of all sporulating pods there will still be spores infecting pods from neighbouring farms (albeit at low levels). The Gompertz model is shown as bðxmÞ
l0 ¼ a þ c expexp
;
ð1Þ
where l0 is the intercept of the infectivity function, a the lower asymptote (0), b the rate of increase of l0 (0.0002), c the upper asymptote (1), m the number of sporulating pods at max rate of increase of l0 (6000), and x the number of sporulating pods per hectare Numbers in brackets refer to values used in the model.
Fig. 4. Example infectivity curve relating pod age to the proportion of uninfected pods becoming infected (this is an example only because this function is dynamic within the model and is dependent upon the value of l0 (the intercept) generated by the Gompertz function).
2.2.2. Infectivity function The infectivity curve is a simple function relating podage to the proportion of pods at age ðPi Þ that become infected. Fig. 4 shows that proportion of uninfected pods that become infected decreases with pod-age (i). The intercept ðl0 Þ is calculated by the Gompertz model described above. The infectivity function is described by the following equation: Pi ¼
l0 1 þ ðigÞh
;
ð2Þ
where l0 is the intercept of the infectivity curve (from Gompertz function), i the pod-age (weeks since flowering), g the coefficient of infectivity with age (0.2) and h the coefficient of infectivity with age (1). Thus the number of infected pods (I) of a certain age class (i) being in the field in each week (t) can be shown as Itþ1;iþ1 ¼ ð1 jÞ It;i þ ðNt;i It;i ÞPi ; ð3Þ where I is the number of infected pods, N the total number of pods and the stripping efficiency (user defined between 0 and 1). The equation above is modified by pod stripping depending on whether the age of the pods or the number of weeks since infection qualifies them for removal at the user-defined efficiency. The user can determine the frequency and efficiency of this phytosanitary activity although the frequency of infected pod stripping, as discussed later, is controlled by the harvesting interval set by the user. The age at which pods may be harvested is also userdetermined to allow for model flexibility to include different maturation rates in regions caused by climatic and/or varietal differences. The number of pods harvested (R) in a harvesting week is a product of the user-defined efficiency of harvest (yFproportion of ripe pods harvested (0 to 1)) and the number of non-infected
A.W. Leach et al. / Crop Protection 21 (2002) 317–326
ripe pods present: Rtþ1 ¼
i¼max Xharv
yðNt;i It;i Þ;
ð4Þ
i¼min harv
where min harv is the age in weeks (i) at which pods become harvestable, max harv the age in weeks (i) at which pods cease to be harvestable. In plain terms, this set of models describes how the infectivity of the disease (and thus the number of infected pods in the field) increases when many sporulating pods remain on the trees and decreases as sporulating pods are removed and as pods get older. 2.3. Rodent loss sub-model The user is also able to assess how rodent losses (principally rats and squirrels) may affect harvesting strategies when combined with losses from M. roreri. These are defined in terms of the percentage of ripe pods that are lost to rodents on a daily basis. Mumford (unpublished data from Indonesia) observed rodent losses of up to 3% of ripe pods lost per day from the time of ripening in fields near to rice. In the Talamanca region of Costa Rica, farmer surveys indicated combined losses of 5–60% of the cocoa crop due to squirrels and kinkajous (Potus flavus, Procyonidae) (Guiracocha et al., 2001). 2.4. Management sub-model A number of management strategies may be tested on the model as single or combined strategies though at this stage these are principally restricted to phytosanitary practices. The economic modelling aspect allows the user to evaluate management strategies both in terms of the efficacy of suppressing the disease and more importantly in terms of the net return that this strategy will provide a farmer. The model evaluates two cultural activities capable of affecting disease prevalence: the first is harvesting, which involves the removal of clean ripe pods, and the second, stripping, is the removal of recognisably infected pods. According to research carried out in Colombia (Herna! ndez et al., undated) it is neither necessary nor recommended to remove stripped pods from the plantation. The most convenient option is to leave them at the foot of the tree, covered with leaf litter and/or vegetation. Only in newly afflicted areas where moniliasis is detected for the first time, should pods be buried. Within the model, the frequency at which stripping is conducted is dictated by the user-defined harvest interval, the model dictates that stripping occurs at the same time as the simulated farmer harvests (a reasonable assumption based on farmer practice). The model
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allows the user to explore these two activities as a single operation (harvest only) or in combination where the frequency and efficiency of each action can be evaluated in yield change and net returns. Turning either activity ‘‘on’’ is accomplished by setting the efficiency of action to greater than zero. The efficiency of harvesting and/or stripping is set separately and can be selected between 0% and 100%. Typical values for either activity (when used) are 95% efficient harvesting, in which 95% of ripe pods in the field that week are removed and 85% efficient stripping. It is technically possible, within the model, to evaluate stripping in the absence of harvesting although this is unlikely to occur in the field as only harvesting represents an activity that generates income. The frequencies of harvesting and pod stripping can be changed as independent options that are flexible throughout the course of the season. Farmers usually harvest little (maximum monthly) in the low crop season but weekly during the harvest peak; whereas, removal of diseased pods is needed most during early pod development when these are most susceptible (Evans, 1981) and rapid inoculum build-up can occur. Further investigations will provide an activity profile for farmers in Costa Rica, in the form of a farm diary, which will allow the model to be used with typical or specific harvesting or stripping frequencies. 2.4.1. Harvesting Using the model, it is possible to test how the frequency of harvesting of ripe, clean pods will manage the disease. Harvesting removes sporulating pods from the field and so affects future infection through the feedback in the sporulation sub-model. The efficiency of harvesting, the proportion of ripe pods actually picked at each harvest event, may be varied to evaluate its importance in terms of the net return. The interval between harvests (in weeks) can also be changed between 1 and 52. Thus the return on labour investment for more/less regular harvesting at greater/lower levels of efficiency (% of available ripe pods harvested) can be tested against current practice. 2.4.2. Pod stripping The removal of infected pods from the tree is currently the most effective measure for reducing the impact of the disease (Cubillos and Aranzazu, 1979; Porras et al., 1990, Matlick, 1998; Soberanis et al., 1999). In Peru, pruned diseased material was left on the plantation floor providing some residual source of infection; nevertheless, weekly stripping without burial or other disposal yielded nearly an eight-fold increase in net return (Soberanis et al., 1999). For the moment, the model assumes this approach, leaving the stripped pods within the field, but future versions of the model could evaluate differences in cultural practice such as the removal of infected material (by burial or otherwise).
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Removal is not recommended at present, there is an extra cost of labour and transporting the sporulating pods through cocoa may increase disease spread. A user can explore the effects of phytosanitary pod stripping by making the ‘‘efficiency’’ of stripping (proportion of visibly infected pods removed) greater than zero. Experience in the field suggests that farmers could remove between 85% and 95% of infected pods at each sweep. The number of weeks between sweeps is determined by the harvesting interval to allow for the fact that most farmers would conduct phytosanitation measures as they perform regular harvest. To increase the frequency of phytosanitation in the model one has to shorten the harvesting interval. The age at which infected pods are discernible from cherelle wilts is also user-definable to allow for the fact that it is possible to teach recognition of the disease at an early stage and thus remove infected pods even earlier. When phytosanitary stripping is selected the number of ‘‘visibly’’ infected simulated pods is reduced by the specified amount and the infectivity curve is recalculated for each age classes for the following week. The model calculates the cost of this activity as the product of the number of man-days per hectare required for pod stripping and value of a man-day. The costs of these activities are explained in more detail in the economics section. If the user has specified pod stripping then the extra cost, 1 man-day at US$3 per day (pers. comm. R. Mack) is automatically included by the model. There are a number of unknowns associated with the pod stripping that the model can assist in evaluating: * * *
how often must the farmer strip infected pods? how efficient must the farmer be? does earlier recognition of the disease reduce its impact?
The model provides a testing ground for these sorts of questions and allows the cost of labour and the labour required to be varied to estimate the economic value of these factors. For example it may well be that stripping weekly is more effective in suppressing the disease but if the opportunity cost of labour is high then less regular stripping and harvesting may net a higher return. The fact that the user can define the pod age at which infected pods may be discerned from cherelle wilt allows the user to evaluate how improving extension may assist in reducing losses due to moniliasis by reducing the number of pods that sporulate. 2.5. Economics 2.5.1. Costs The costs of management activities are calculated by the number of man-days per hectare per week that are required to conduct harvesting and pod stripping if this
option is used. The cost of labour may be entered directly and is considered as either the cost of hiring labour or as an opportunity cost for the farmer (who could be earning a wage on someone else’s land if work was available). In Costa Rica, it was estimated that a man-day in the Talamanca region would be in the order of US$6 and that it would be fair to assess the opportunity cost, for labouring on one’s own land, at half this rate (pers. comm. R Mack). The prices, subsidies and labour cost and requirements are listed in Table 1. The values used are approximate and reflect conditions from November 2000 though any value could be entered to reflect current or anticipated changes in economic circumstances. 2.5.2. Returns The gross returns (L) from growing cocoa are calculated by the following equation: L¼
S s q; K 1000
ð5Þ
where S is the number of clean ripe pods harvested per year, K the number of ripe clean pods required to produce 1 kg of dry cocoa, s the international cocoa price $US per tonne and q the proportion of international price paid to farmers at the farm-gate. Costa Rican cocoa is managed following organic guidelines and certified for this niche market and, as such, qualifies for an organic premium, giving a value of q greater than one (Table 1). In most other countries and regions this would typically be less than one (between 0.4 and 0.9). The net return for each year is calculated as the gross return minus the management costs. See Table 2 for a full listing of model parameters and variables.
3. Model output and discussion It is important to stress that the model is in the preliminary stage and as such its outputs have not yet been empirically validated. Nevertheless qualitative verification (Teng, 1981) of the model by a major buyer
Table 1 Economic inputs required by the model. All values can be changed by the user to predict outcomes under specific economic, labour or premium circumstances Description
Units
International cocoa price (US$ per tonne) Proportion of international cocoa price paid to co-operative Harvest labour (man days per hectare) Phytosanitary stripping (man days per hectare) Labour (US$ per day)
$750.00 2 0.50 1.00 $3.00
A.W. Leach et al. / Crop Protection 21 (2002) 317–326 Table 2 Example parameter and variable listing for the model Variable/parameter description
Example value
Trees per hectare Choose years No of pods per kg of cocoa (year 1) No of pods per kg of cocoa in (year 2) Harvest interval (weeks) Harvest efficiency (%) Weeks from infection when Moniliophthora infection is visible Weeks after infection to sporulating Age at which infected pods are discernible from cherelle wilt (weeks after flowering) Efficiency of pod stripping (%) Daily losses of ripe pods to rodents (%) Minimum age for harvesting (weeks after flowering) Maximum age for harvesting (weeks after flowering)
800 1994–1995* 25 25 4* 95* 7
85* 3* 30 38
Gompertz sporulating function Lower asymptote (a) Upper asymptote (c) Point of max growth (m) Growth rate (b)
0 1 6000 0.0002
Infectivity curve Intercept Slope Asymptote
0.2 0.2 1
8 12*
All values listed may be varied to match local circumstances or to explore management outcomes or disease dynamics etc. Those values marked with * will be most changed regularly to explore different management activities or conditions.
and exporter of Costa Rican cocoa and from two CATIE scientists indicate that the model provides a correct framework for analysis and intuitively comprehensible output with sensible assumptions. Each part of the model is on a separate worksheet within the file. The main page for strategy testing is the first worksheet, the ‘‘User Interface’’ (Fig. 5). From here the tree density, fruiting pattern, pod counts and different management and economic criteria can be added. These parameter values are passed directly into the model. The outputs can also be viewed from this page either from tables of economic outputs or graphics of cocoa weights, pods harvested or the percentage of infected pods in the field (Fig. 5). Spreadsheets are very useful tools for modelling because they are very user-friendly and transparent in their working (as opposed to compiled programs). With sympathetic design they can be split into separate organisational units (worksheets) to help the user understand how the various sub-models interact. Cells can be coloured to indicate processes such as pod ageing in the pod sub-model and also to indicate cells to be manipulated to test management ideas. Not only scientists but also managers and trainers in growers’ co-operatives quickly learned to use it and recognised its
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usefulness as a decision-making tool. The model is ! de Pequenos currently being evaluated by the Asociacion * Productores de Talamanca (APPTA) and the Organic Commodity Project. 3.1. Harvesting frequency The model indicates that, in the absence of infected pod stripping, harvesting more frequently than current practice (4–5 weeks) does not offer any relative economic benefits. More regular harvesting does marginally reduce the number of infected pods but the cost involved in more frequent harvests outweighs any benefits (at the current labour and cocoa prices). Harvesting alone is not enough to outweigh the benefits accrued from reduced rodent losses because, without conducting stripping, the net return is not increased because of the paucity of non-infected ripe pods available. 3.2. Pod stripping By contrast, pod stripping, when it is ‘‘switched on’’, does provide a considerable benefit and allows the more frequent harvesting/stripping option to reap considerable benefits (Table 3). The benefits of increased frequency of phytosanitary measures are even more accentuated in the presence of high rodent losses (Table 3). Moniliasis has a latent period of approximately 7 weeks (Evans, 1981). After that period symptoms become recognisable initially only to the trained eye but within a week they are very clear and are soon accompanied by prolific sporulation. The model enables the user to change the age at which Moniliophthorainfected pods can be recognised and be discerned from cherelle wilt and it becomes possible to evaluate the economic impact of an extension programme aimed at the earlier identification of infected pods. In the hypothetical scenario described below it is envisaged that a training programme could get farmers to separate cherelle wilts from infected pods 5 weeks earlier than they are currently able. A policy maker may want to know what effect this may have on the income of farmers trained in this method. The model suggests that earlier recognition (infected pods identified at 10 weeks old rather than 15 weeks after flowering) provides an increase in net return of about 50% over two years when harvesting is performed at 4 week intervals (US$ 931 vs. US$ 613 per hectare over two years). However, the relative benefits were greatly reduced as harvesting, and thus pod stripping, frequency increased to weekly intervals (2% improvement on net return, US$ 1326 vs. US$ 1299). This provides an important insight into the economics of decision-making and provides an extension strategist more options for potential programmes. For example, there may be less resistance
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Fig. 5. Screen-capture image of the User Interface page of the model. In the model itself some cells are coloured blue to denote where the user may make inputs and outputs are denoted by yellow background cells. All of the lightly shaded cells in columns B and D are input cells (blue in colour) and the shaded cells in column G are output cells (yellow).
A.W. Leach et al. / Crop Protection 21 (2002) 317–326 Table 3 Table of model outputs of net returns under various management options and rodent loss burdens Stripping turned on?
Harvest and/or stripping interval (weeks)
Rodent losses?
Net return (2 years) US$/ha
F F F F * * * *
4 1 4 1 4 1 4 1
F F * * F F * *
9 101 37 149 752 1393 314 1117
A * denotes where stripping and/or rat losses were included in the simulation. All harvests were conducted at 95% efficiency. Where stripping is included these are conducted at 85% efficiency. Where rodent losses were specified in the simulation they occurred at 3% loss of ripe pods per day.
from farmers to improving their identification of the disease (at current harvest/stripping intervals) than in encouraging farmers to strip pods more frequently. The model’s most important recommendation is to conduct phytosanitary stripping which, even if conducted at 4-week intervals (at the same time as harvesting), will reap a many-fold increase in net return compared with harvesting alone. If the frequency of harvesting/stripping can then be increased, then benefits increase further still (85% increase in net return when going from 4 to 1 week harvest/stripping intervals). When losses of 3% of ripe pods per day to rodents are included in the model, this difference is even more marked (256% increase in net return between harvest/ stripping at 1-week intervals compared with 4-week intervals). This is because losses to rats will be compounded day by day and week by week so the shorter time between harvests will greatly decrease rodent losses and increase net returns. This model has the potential to evaluate and explore any management permutations so that extension agents and buyers, in conjunction with farmers, can test strategies where the returns on employment could be investigated and evaluated prior to implementation. Apart from providing a skeleton for directing fieldwork the model also provides a tool for regions where the disease is not yet present (such as Bahia, Brazil). Assuming that the entry of the disease is inevitable then the model, when adapted for the region, could provide a tool for extensionists, unfamiliar with the management of M. roreri, to develop some ‘‘field’’ experience and to understand the issues involved in the economic control of the disease. It would also allow for the strategic assessment of potential losses and assist in contingency planning.
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4. Summary A model has been developed that simulates much of the crop phenology of cocoa, the pod age-structure and the disease dynamics within the management and economic context of Costa Rican cocoa. The model demonstrates the value of frequent pod stripping (when harvests are conducted) in terms of the reduction of losses and the associated economic net benefits. The most important output of the model is the recommendation to conduct phytosanitary stripping and harvesting as often as possible since this will reduce losses to M. roreri as well as reduce the compounding losses to rodents that accumulate day by day as ripe clean pods remain on the tree. The model presents great potential value as a training and extension strategy evaluation tool. A number of different management actions can be simulated in the model, which can be tested singly or as integrated strategies. The model allows the user to test permutations of those strategies and gain insight into the dynamics of the disease and its monetary considerations such as cocoa prices, premiums, labour requirements and costs. The model is still in development and the two main assumptions about the disease behaviour require fielddata derived functions to enhance the model’s realism. The functions are currently based on available literature and discussions with research staff in Costa Rica and the United Kingdom. The model could be adapted to the conditions and crop phenology of other countries with the M. roreri problem such as Ecuador, Peru and Colombia. Regions currently unaffected (but likely to succumb to the disease) may find the model of some value. In the latter case the model would be useful for preparing buyers and extension bodies for the likely effects of the disease, in advance, so that appropriate extension strategies and training are developed. The format of the model is such that it could be adapted to other diseases such as Phytophthora in West Africa and Asia or witches’ broom in South America. The user interface renders the model very user-friendly. Acceptance at the major Costa Rican growers’ association is good.
Acknowledgements The authors would like to thank the Biscuit, Cake, Chocolate and Confectionery Alliance and the US Department of Agriculture for co-funding this research. Many thanks to Harry Evans, Serge Savary and Laetitia Willocquet for their perceptions of the disease functions and to Robert Mack, Mario Pierda, Eduardo Somarriba and Philipe Vaast for useful
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discussion. Adrian Leach would also like to thank Ulrike Krauss and all the CATIE staff, for their kind hospitality when he visited CATIE, Turrialba in November 2000.
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