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Practical yield loss models for infestation of cocoa with cocoa pod borer moth, Conopomorpha cramerella (Snellen)
Crop Protection 66 (2014) 19e28
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Practical yield loss models for infestation of cocoa with cocoa pod borer moth, Conopomorpha cramerella (Snellen) Isabel Valenzuela a, *, Hussin Bin Purung b, Richard T. Roush a, Andrew J. Hamilton a, c a
Melbourne School of Land and Environment, The University of Melbourne, Parkville, Victoria 3010, Australia Mars Indonesia, Makassar, South Sulawesi, Indonesia c Department of Agriculture and Food Systems, The University of Melbourne, Dookie Campus, Victoria 3647, Australia b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 29 April 2014 Received in revised form 26 August 2014 Accepted 27 August 2014 Available online
The cocoa pod borer, Conopomorpha cramerella (Snellen) (Gracillariidae: Gracillariinae), is an important pest of cocoa in Southeast Asia and Oceania, with devastating effects on yields. Using data on cocoa pod borer (CPB) infestation and cocoa yield from mixed-variety plantations in South Sulawesi, Indonesia, we developed models for estimating yield and yield loss under CPB attack. For six yield variables, two types of models were constructed: non-linear regressions based upon the presence or absence of infestation of pods (PI model), and multiple linear regressions for a four-point graded system of infestation severity (IS model). The IS models performed markedly better than PI models, in terms of percentage of variance explained, for all variables, also supported by Corrected Akaike Information Criterion values. But the explanatory power of the best-fit models was still poor for some variables. The fits were strongest for arguably the two most important variables in the industry, dry weight/pod and pod value (the number of pods required to achieve 1 kg of dry cocoa), with 62% and 69% of the variance accounted for, respectively. Validation of the dry weight/pod and pod value models against an independent dataset from South Sulawesi indicated that the models slightly under-estimated both yield indicators that increase concomitantly with the degree of yield loss. We propose the IS models, particularly that for pod value, as useful tools for industry, and argue that they will have broad utility given that they are based on mixedcultivar plantations. Not only are these the first CPB yield-loss models to be based on commercial mixed plantings, they also represent the first attempt to employ a gradation of infestation severity based on simple visual assessment, which proved to be an important advance. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Cacao Pest damage Yield evaluation Non-linear regression Multiple linear regression Lepidoptera
1. Introduction Cocoa is one of the most important cash crops in the tropics. Originally from South America, cocoa has seen an expansion into West Africa, which accounts for 70% of the world's production, and more recently into Southeast Asia (FAOSTAT; Neilson, 2007). The majority of the world's cocoa (~70%) is grown by smallholders with low input/output economics, typically organized into family units for whom cocoa is their main or sole source of income (Gotsch, 1997; WCF). In Southeast Asia production of cocoa is also carried out largely by smallholders (Neilson, 2007), with Indonesia as the main contributor in the share of the total production; Southeast Asia exported 526,000 tonnes in 2010e2011 of which 440,000 tonnes were produced in Indonesia alone (ICCO). However, the
* Corresponding author. Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, 30 Flemington Road, Parkville, VIC 3010, Australia. E-mail address: [email protected] (I. Valenzuela). http://dx.doi.org/10.1016/j.cropro.2014.08.018 0261-2194/© 2014 Elsevier Ltd. All rights reserved.
prospect of a profitable farming model has been jeopardized by a number of pests and diseases that have been affecting yield and quality (Entwistle, 1972; Keane, 1992; Mumford, 2005; Ploetz, 2007). Conopomorpha cramerella (Snellen), the cocoa pod borer (CPB) moth, is the most injurious invertebrate pest of cocoa in Southeast Asia (Wardojo, 1980). The larvae bore inside the pod and feed on the placenta and pulp that supply nutrients to the beans. The infestation interrupts the normal development of the beans and also causes them to clump together, making the harvest difficult because the beans are not easily removed and cannot be separated for fermenting and drying. This pest is now well-known in Southeast Asia and is spreading across the Pacific region, with a recent incursion being reported in East New Britain Province of Papua New Guinea in 2006 and in Bougainville Island (Autonomous Region of Bougainville) in 2008 (CCI; Jonah, 2009), and the most recent being from Northern Queensland, Australia where eradication programs are being carried out to stop this pest from becoming established in the area (Royer pers. comm.).
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Qualitative and quantitative assessments of yield are common measures in agricultural production, and understanding how yield is affected by pests and diseases is paramount for the development of efficient crop management strategies (Wiese, 1982). Growers need to know in quantitative terms the effects particular pests and diseases have on their crop yields, so the appropriate measures can be applied when necessary to avoid the predicted losses (Chiarappa, 1981; Walker, 1983; Zadoks, 1985). A widespread method for making associations between pest densities (or through their injuries when pest density cannot be measured) and yield is the use of regression methods (Teng, 1981). These were used in Southeast Asia when CPB appeared in Sabah, Borneo Malaysia in the 1980s. Surveys and studies were set that examined the effects of CPB on yield soon after the pest was first detected in the region, which developed models based on the number of pods infested with CPB (Lim and Phua, 1986; Day, 1989). Lim and Phua (1986) related pod infestation to wet weight of beans and derived yield loss (measured in an unspecified way) from 59 cocoa varieties representing the 10 most important groups in terms of production. This was a crucial exercise that characterized yield responses to CPB infestation by testing commercially important clones used for breeding. It revealed significant differences in yield outputs between varieties that had similar degrees of pod infestation and thus allowed the detection of CPB-resistant varieties (relative to the more susceptible ones). The application of this model is problematic if yield variables other than wet bean weight are being assessed, which is the current situation in Southeast Asia where assessments of yield are given as dry weight. Day (1989), using plantation data, related pod infestation to wet and dry bean weights for Amelonado and mixed Upper Amazon Hybrid clones, and derived non-linear models for yield and, consequently, yield loss. These non-linear models can be used to estimate yield and yield loss but, their usefulness is questionable if estimates are needed from smallholders, who, in contrast to large plantation operations, cultivate mixed cocoa varieties which typically display large differential production capacities. Thus, these models, which are based on data from plantations, are possibly unreliable for situations outside the range of conditions from which they were developed. Only the models from Day (1989) have been validated; Lim and Phua's (1986) model has not been validated, and in effect simply represents a fitting exercise to a specific data-set. Despite the pest's prevalence and significance in Southeast Asia, no formal assessments have since been made in other regions, with the exception of Sulawesi, Indonesia where an informal modeldhenceforth referred to as the Sulawesi Standard: SSdis being used in mixed cultivar plantations. Briefly, the SS model is based on a four-point scale of severity that classifies pods into groups that range from no damage to total damage, based on a visual assessment of the pods after they have been opened and inspected. The model estimates yield loss in terms of dry weight and it is based on the number of pods classified into particular categories of damage. How the model was derived has not been specified in the broader literature and remains unpublished, but the formula is given in a subsequent section of this manuscript and is used in a comparative exercise with our model of yield loss for dry weight of beans/pod. Here, we use yield information from a trial conducted in mixed variety plantations in Sulawesi to (1) construct models that describe yield as a function of pod damage and pest infestation due to CPB infestation, (2) validate their performance on an independent dataset and, (3) derive yield loss from the models of yield. First, in line with previous work (Lim and Phua, 1986; Day, 1989), we develop models based upon the overall infestation status (total number of pods infested), and this is followed by models based upon a four-point scale of severity (number of pods in 4 categories of damage). Second, we ask whether infestation severity is more
appropriate in relating CPB damage to yield than overall number of pods infested and we extend yield variables to include pod valuedthe number of pods required to achieve 1 kg of dried cocoa (Adomako, 2007)dwhich is the preferred measure of yield in current cocoa markets. We validate model performances through application to a dataset from an independent trial and derive yield loss from the models of yield for variables dry weight per pod (DW/ pod) and pod value. 2. Materials and methods 2.1. Trial conditions and CPB damage levels In 2003e2004, yield surveys were undertaken from cocoa farms in South Sulawesi while routine commercial harvest was taking place in the region, i.e., growers agreed to have a fraction of their harvest assessed for CPB infestation and yield. It is important to note that we are making opportunistic use of this large dataset and that it was not collected with the express purpose of informing the development of these yield loss models; rather it was part of a survey quantifying the severity of yield loss in Sulawesi. Overall there were 19 sites that were part of the survey which had 100 mature trees at each site planted with a mixture of unknown varieties and clones, with a planting density of 900 trees/ha approximately. All ripe pods only were harvested from marked 100 trees at each of 19 sites, every two weeks from June 2003 to December 2004 (38 sampling events in total). The number of pods harvested per site and sampling event was not constant due to the natural variation of pod production, while labor constraints determined a maximum number of 100 (sometimes 110) pods/site to be processed for yield and CPB infestation data collation. In other words, when more than 100 ripe pods were harvested a random sample of 100 pods was selected whereas when less than 100 ripe pods were harvested; all available pods were used for assessments of yield. The mean number of pods sampled per site and sampling event was 72.4 (SD ¼ 29.9). Logistic mishaps occurred that affected the smooth running of the survey, which was initially designed to occur every two weeks. Problems like heavy rains, vehicle breakdown and similar issues altered the total number of observations from the expected 722 to 682 as some sites were not visited every two weeks as was planned. More information about localities, precipitation and crop conditions related to the 2003e04 survey can be found in Appendix A, Table 1. In every sampling event, pods used for data collation were counted and after opening, separated into four categories according to their level of damage, i.e., after opening the pods and observing the amount of dark coloring and clumped beans, the level of damage was quantified using an AeD scale of severity: A ¼ none, B ¼ light damage, C ¼ moderate damage, and D ¼ heavy damage (Fig. 1). Many criteria were used to separate damaged pods, including: darkening and clumping of the beans, sluggish texture of the dark areas, visual larval trail imprints, and harvest by hand or spoon, i.e., beans harvested by hand were in category B (no clumping of the beans is observed in this category as all beans can be removed by hand from the pod), whereas beans that would have to be harvested with a spoon were placed in category C (some clumping is observed in this category and a spoon is needed to remove the beans from the pod) and when beans could not be extracted these made up the category D. Pods in category D were discarded, as no beans could be extracted from these pods, but their number was nevertheless recorded. Workers in Sulawesi have been conducting this type of pod classification for many years, for the reason that it gives growers a quick estimate of CPB infestation levels in the field and are skilled in this method, which is known as the A, B, C, D method of cocoa pod classification and was developed
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Fig. 1. Example of visual criteria used to classify pods into four categories A to D; no damage (pods A), light damage with up to 10% darkening of beans (pods B), moderate damage with 10%e50% darkening (pods C) and heavily infested with more than 50% darkening (pods D).
by Mars Inc. field research team. Number of cocoa pods (per category of damage) and month are shown in Fig. 6 and in Appendix A, Table 3. Subsequently, the number of pods, the number of beans, and the fresh and dry weights of beans were evaluated for each category. The number of pods in each damage category was recorded but yield variables were measured in categories A, B and C only, as category D pod's yield was nil (beans could not be extracted from this category). Yield variables were: dry and wet weight of beans per pod (WW/pod, DW/pod); the number of extractable beans per pod (No. beans/pod); the average dry weight of one bean (DW 1 bean); the pod value (the number of pods required to achieve 1 kg of dry cocoa) and bean value (the number of beans required to achieve 100 g of dry cocoa). Analyses of variance (ANOVA) between the entire above mentioned yield variables and factors: (1) month and (2) site were performed (per category of damage) with relevant post-hoc analyses shown in Appendix A, Tables 5 and 6 respectively. An additional dataset used for validation of the models was available from a field trial conducted in 2007e2008 in Noling, South Sulawesi (unpublished). This trial was carried out in 8 sites; each with 125 trees from a mixture of cocoa varieties with a planting density of approximately 900 trees/ha. Commercial practices determined the harvest of ripe pods to occur every two weeks from March 2007 to April 2008 inclusive (26 sampling events in total) and assessments for CPB and yield followed the abovementioned methodologies where up to 200 pods were used for data collation instead (mean number of pods ¼ 139.3, SD ¼ 58.1). Pods were also classified following categories of pod damage A through to D, while yield variables evaluated were also the same. The total number of observations was 208. More information about localities, precipitation and crop conditions from the 2007-08 survey used for the validation can be found in Appendix A, Table 2. 2.2. Yield models Each sampling event generated four distinct groups of pods: one category of healthy pods XA (%) and three categories of damaged pods namely XB, XC and XD (%). From these we calculated, for each sampling event, the overall infestation namely X ¼ XB þ XC þ XD. There were 682 observations for each category of damage XA through to XD. Therefore, all calculations are based on 682 data points (for individual categories of damage and therefore for the overall infestation calculated here).
First, we examined the relationships between each of the aforementioned yield variables and the overall infestation, X. Three non-linear models (all exponential in form) were investigated for each variable using Genstat V15 (see Appendix A, Table 4 for model descriptions, fit and selection analyses), but given none performed consistently better than the other, in terms of variance explained and Corrected Akaike Information Criterion, we chose the simple exponential model which is also the exact same model as used by Day (1989) for modeling CPB damage. The simple exponential (asymptotic) regression was applied to all variables:
YPI ¼ a þ bX c ;
(1)
where YPI is the general case for yield estimated from a nonlinear model based on “pod infestation” (PI). The asymptote a is the yield in the absence of CPB, b and c are shape parameters, and X is the overall infestation (i.e., irrespective of its degree), calculated for each sampling event (682 data points in total). Parameters were estimated using an Ordinary least-squares method. Second, we examined the relationship between yield and infestation severity through the following multiple linear regression model:
YIS ¼ a þ ðb1 XA Þ þ ðb2 XB Þ þ ðb3 XC Þ þ ðb4 XD Þ;
(2)
where YIS is the general case for yield estimated from a multiple linear model based on “infestation severity” (IS). XAeXD are the percentage of pods in categories A through to D, a is the intercept and b1eb4 are coefficients determined by multiple linear regression, calculated for each sampling event (682 for each category of damage). See Appendix A, Table 4 for a description of the model, and parameters. All regression analyses were conducted using Genstat V15 (Lawes Agricultural Trust, Institute of Arable Crops Research e Rothamsted, UK). Model selection was made using the Corrected Akaike's Information Criterion (AICc), where a lower value indicates a higher quality model (Sugiura, 1978; Hurvich and Tsai, 1989). 2.3. Yield loss models For all variables the multiple linear regression model, YIS, performed better than the non-linear model, YPI, in describing the relationship between damage and yield, in terms of variance explained also supported by the Corrected Akaike's Information
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Criterion (Appendix A, Table 4), and therefore was used to calculate yield loss. Yield loss (YL) for each yield variable was calculated as:
3. Results 3.1. Yield model statistics and validation
YL ¼ ðm YÞ 100=m;
(3)
where YL denotes the general case for yield loss, and m and Y are yield in the absence and presence of pest, respectively (Walker, 1983). Calculations of m and Y are based on the parameters from the YIS model. Yield in absence of pest (m) is calculated as:
m ¼ a þ ðb1 XA Þ;
(4)
where XA is 100, due to the absence of damaged pods, and Y, which is the yield in presence of pest, is given as:
Y ¼ YIS ;
(5)
where YIS is yield in presence of pest as determined by multiple linear regression shown in Eq. (2). We also investigated the performance of the Sulawesi Standard (SS) model used to estimate loss of dry weight of beans (percentage):
YLSS ¼ ðððXA 1Þ þ ðXB 1:025Þ þ ðXC 1:5Þ þ ðXD 2Þ=XÞ*100Þ 100; (6) where XA…D are the number of pods in categories A through to D and X is the total number of pods.
For all variables, the pod infestation model (YPI) predicted relatively constant yield at low to medium levels of infestation followed by a rapid decline in yield at higher levels (Fig. 2). These exponential models were very weak, explaining only 23.7%e49.3% of the variance, with DW 1 bean being the most poorly predicted variable, explaining only 23.7% of the variance, and No. beans/pod the best, explaining 49.3% of the variance, followed by pod value and DW/pod which accounted for 48.4% and 47.3% variance respectively (Fig. 2). Corrected Akaike's Information Criterion (AICc) was performed for all models obtaining values that ranged from 1.081 to 6.099 (Fig. 2). On the other hand, the yield models based on infestation severity (YIS) had greater explanatory power than the pod infestation models (Fig. 3). The coefficient of determination ranged from 32% to 69%. For all variables category D was excluded from the final model due to significant co-linearity (P < 0.05) (Zar, 1999). As with pod infestation model, the variable pod value was the best estimated yield variable with 69.2% of the variation explained, followed by No. beans/pod (66.5%) and DW/pod (62.0%) and WW/pod (55.4%), yet bean value and DW 1 bean were the poorest predicted variables with 32.9% and 31.7% of the variation explained (Fig. 3). Here, AICc values ranged from 1.157 and 6.014 (Fig. 3). AICc values from both PI and IS models were compared for each yield variable as an objective way of determining which model was most accurate (statistically sound) as the use of r2 is not an
Fig. 2. Non-linear relationships between yield variables and the percentage of pods infested with CPB. The exponential model is YPI ¼ a þ bX C , where YPI is the general case and X is the percentage of pods infested, regardless of the severity of infestation.
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Fig. 3. Relationship between yield variables as predicted on multiple linear regression (MLR) yield models based on infestation percentage and actual yield for each variable. MLR equation is YIS ¼ a þ ðb1 XA Þ þ ðb2 XB Þ þ ðb3 XC Þ where XAeXC are the percentage of infested pods in categories A through C, respectively (n ¼ 682). The dashed diagonal line denotes unity (i.e., perfect agreement).
appropriate parameter for this purpose. For all variables, the AICc values from the IS models were lower than the AICc values from the PI models, indicating that the PI models were better in terms of predicting power (Appendix A, Table 4). Using an additional dataset, we validated the broader application of both the PI and IS models on DW/pod and pod value (Fig. 4). We selected these variables since they are the preferred units of measure used by cocoa stake holders. Results showed that the IS models were better predictors of yield than the PI models (Fig. 4). Results also showed that the IS models (predicted data) when plotted against the observed data accounted for 50% and 63% of the observed variation on DW/pod and pod value respectively (Fig. 4). The model for DW/pod and pod value underestimated most predicted values for each respective model (Fig. 4). For instance, for DW/pod, most predicted vs observed values fell above the line of unity with the corresponding regression line showing the progressive deviation that underestimates at higher values. Out of the 208 observations, 58 values overestimated by 0.2%e68.2%, while 150 observations underestimated by 0.4%e62.5% (data not shown). Similarly, the model for pod value also underestimated (for the most part) shown by the position of the values which are below the line of unity and the progressive deviation that occurs at higher pod values. Again, out of the 208 observations, 33 were overestimates of 0.1%e33.4% while the remainder 175 observations underestimated in the number of pods by 0.5%e161.2% (data not shown).
3.2. Yield loss validation The IS and SS models produced similar % loss predictions for DW/pod (Fig. 5). The SS model underestimated yield loss slightly, with the magnitude of the underestimation increasing concomitantly with increase in yield loss. 3.3. Pod production and yield The number of pods used in the survey varied from 8 to 110 per site and sampling event (which was the maximum number of pods thought to be practical in terms of labor inputs). In total, 49,343, were used and these were harvested from 19 sites every 2 weeks from June 2003 to December 2004, with the average number of pods being 72 (SD ¼ 30) (data not shown). Generally speaking, the number of pods available for harvest varies according to the seasons since there are distinctive low and high production periods throughout the year. Studies have shown that pest fluctuations are in cycle with the number of ripe pods (Day, 1986). Inevitably this will have an effect on the number of healthy and overall infested pods and consequently on the number of infested pods if these are separated into categories of damage. For instance, we observed significant increases/decreases in the number of pods in categories A, B and C from one month to another month (Fig. 6), and particularly a decrease of pods from category A during the months of
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Fig. 4. Relation between observed and predicted yield for DW/pod and pod value, from trials conducted in Noling, South Sulawesi in 2007e2008, and used here as an additional dataset for the model validation (n ¼ 208). The IS models (infestation severity) are shown in figures A and C while the PI models (pod infestation) are shown in figures B and C. The fitted ordinary least squares linear regression model is shown as a solid line, and the dashed diagonal line denotes unity.
SeptembereNovember when the production is lower and there are fewer pods overall. Yet, observed changes in pod numbers (per category of infestation) did not affect yields associated to the corresponding categories, i.e., weights and bean numbers associated to each category of infestation were relatively stable throughout the duration of the study (Fig. 7). Monthly yield data were analyzed and results indicated that, for most variables and months, yields in categories A were not different from yields in category B while yields in category C were significantly lighter from A and B when analyses of variance (ANOVA) were performed by month (Appendix A, Table 5). Number of pods and yields associated were also examined by site. This was to identify additional causes of yield variability. Similar to the study by month, yields from category A were similar to yields from category B, which tended to be significantly higher than yields from category C (Appendix A, Table 6). There were however six sites out of 19 namely CTR-01 to CTR-05, and CTR-RR
Fig. 5. MLR relation between yield loss (DW/pod) predicted by the infestation severity (IS) and Sulawesi Standard (SS) models when applied to data from South Sulawesi 2003e2004 (n ¼ 682). For clarity of presentation, the regression line is not shown, but it clearly lies on the line of points. The dashed diagonal line denotes unity (i.e., perfect agreement).
that recorded lower category-A associated yields than category-B yields (although not significantly different by Tukey B test) with the exception of CTR-04 site (Appendix A, Table 6). 4. Discussion Comparative analyses of regression models based on the overall infestation and individual categories of infestation showed significant differences in predictive power. For instance, a non-linear exponential model based solely on the percentage of pod infestation, regardless of infestation severity, was able to account for only 35.1% of the variation found in WW/pod, but a multiple linear
Fig. 6. Cocoa pods used for data collation per category of infestation A through to D (monthly averages harvested from June 2003 to December 2004 in percentages). (Supplementary data in Appendix A, Table 3; Mean, SD., and no. of observations). Pods A d, pods B …, pods C , pods D d .
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Fig. 7. Yields associated to categories of pod damage A, B and C (monthly averages from June 2003 to December 2004). (Supplementary data in Appendix A, Table 5; Mean, SD., and no. of observations). Pods A d, pods B …, pods C .
regression model based on four categories of infestation severity, accounted for 55.4% of the variation, and this pattern was replicated for all variables. Thus, the use of a four-point severity system was more precise for predicting yield under the effects of CPB than the coarse pod infestation percentage approach. This was also supported by the Corrected Akaike Information Criterion (AICc) values which were always lower for the IS models by comparison to the PI models, for all variables. In general, our models explained less of the observed variance than those of previous studies. For instance, Day (1989) used a nonlinear regression model relating WW/pod and DW/pod to CPB attack, which accounted for 74% and 83% respectively of the observed variation, from a mixture of Amelonado and Upper Amazon Hybrid varieties. However, this was not unexpected, despite the much larger sample size in our study. Our investigation included a larger and more diverse collection of varieties, from clones and hybrids, and thus observed larger spread of response yield variables which resulted in a poorer fit (Fig. 2). This can also be viewed in terms of the two fundamental forms of uncertainty: aleatory (natural variability) and epistemic (incomplete knowledge) (Benke et al., 2007). By definition, aleatory uncertainty, clonal variation in this case, is irreducible. Given that in many Southeast Asian countries the norm is to have various plant varieties, we believe our model, despite lower fitting capacity than previous studies, better depicts those situations where multiple varieties are present and is thus more robust. Even then, our pod value model based on infestation severity fitted
reasonably well (r2 ¼ 0.69, and pod value is currently the preferred unit of measure of yield used by cocoa stakeholders (Lambert pers. comm.). Another important variable, DW/pod, was also predicted reasonably well (r2 ¼ 0.62), giving cocoa stakeholders an additional variable from which to estimate yield and derive yield loss with a sound level of confidence. Accordingly, we validated the models' function on an additional dataset from a subsequent trial in the same region for pod value and DW/pod. Overall, we found reasonable fitting power for both variables on the validation exercise. Interestingly, our IS model produced fairly similar predictions to the SS model in terms of yield loss of dry weight. Mathematically, the two models take the same general form, with each be a linear scaling of the other, and this is why virtually all of the variance is explained by the regression in Fig. 5. The underestimation of the SS could be considered negligible at lower yield losses but is likely to be of consequence at the higher losses. The coefficients in the SS model, which uses 4 categories achieves similar results to our proposed IS model that uses 3. Clearly, the infestation categories are related; for example, by considering categories A, B and C, one is implicitly including D because it is the complement of the sum of the other three. It is reasonable, therefore, to expect a certain degree of correlation between the categories, and indeed our model was simplified to three variables because of the high co-linearity of category D variable with another/others. More pragmatically, the SS model applies only to dry weight, whereas here we presented a model for estimating losses in an important indicator used in
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industry, pod value. This appears to be the first published model for estimating pod value losses in cocoa. To our knowledge, this is also the first time multiple linear regression has been used to predict cocoa yield (and yield loss drawn from it) under the effects of CPB infestation. The direct application of this method in the field is the distribution of pods into four categories, according to their differential bean harvest, and based on a preliminary visual assessment (Fig. 1). This method is easy to conduct in the field as pods that need to be opened in order to extract the beans can be simultaneously assessed for level of damage and recorded in spreadsheets or placed on the ground at the same time as they are assigned to a particular damage category. Once the pods have been counted, their number (in percentages) is applied to the multiple linear regression equation to obtain estimates of yield and/or yield loss in situ. Estimating yield loss due to CPB using this method can be used in any context of cocoa production i.e., from the small-scale plot to the large size plantation. In either context, there are no restrictions in terms of the frequency of sampling events or the number of plots to sample. These will be determined by the resources available to the landowners. There are however, some considerations regarding the number of pods to sample, with our recommendation being a minimum of 40 pods/ sampling event for plots containing 100 trees (as determined by the average number of pods used in the development of the models i.e., average of 72 ± 30 pods/sampling event for plots containing 100 trees). The models are easily applied to small plots of 10 10 m which normally contain 90e100 trees (at a standard density of 1000 trees/ha approximately) and can be scaled up for larger plantations by simply adding 10 10 m plots or 100-tree plots. With this in mind, cocoa stakeholders can conduct surveys of yield loss for various reasons. For instance, surveys could take place between plots to determine estimates of yield loss associated with different CPB management strategies and identify which method is more effective. Surveys could also take place between plots that have differential crop and climate conditions to estimate yield loss at these particular sites. Although the application of these models is broad in theory, their use will depend on specific needs of landholders and industry, almost certainly related to IPM programs which are being developed and tested at the present time. As shown in McMahon et al. (2009), CPB-susceptible cocoa clones had more heavily infested pods than the less susceptible ones, and similarly areas that contained black ants had significantly fewer heavily infested pods (See and Khoo, 1996). Based on these reports, heavily infested pods (category D in our study) are paramount for identifying CPB-resistant cocoa varieties and for monitoring the effects of pest control methods. It is important to note that while our MLR models do not require category D as an input, the category is still implicitly accounted for because the percentages for categories A, B and C are percentages of all pods. Cocoa yield variation depends on many factors: biotic and abiotic. Some abiotic factors listed here are rainfall and the intensity of the shading while biotic factors identified range from fruit morphology to photosynthesis and respiration (Alvim and Alvim, 1978; Zuidema et al., 2005; Schwendenmann et al., 2010). The effects of climate had an effect on the number of pods available for harvest with the uninfested pod category being the most affected (Fig. 6). Corresponding yields however were relatively stable throughout the seasons (Fig. 7) with a few exceptions such as the changes seen in October 2003 (noticeable increase although not significantly different from pods B associated yields, Appendix A, Table 5). The observed variation in October 2003-associated yields may be due to changes in the type of pod, i.e., larger pods that contained fewer bigger beans for categories A, B and C. Thus changes in yield are possible but are not directly associated with
changes in the number of pods but to other factors instead like type of pods being produced which are different with different yield characteristics e.g., bigger pods with fewer bigger beans, perhaps linked to climatic events. Other changes could come from the workers that separate pods into different categories, where pods A could have mistakenly been placed in pods B group and vice versa. This however is unlikely to happen in a significant way. Sulawesi workers have proven their skill in separating pods according to their level of damage, particularly for that of category C which is clearly different from A and B when yields were assessed (Fig. 7). There were however, six sites out of 19 with unusual yield patterns, where yields from category A were on average lower than yields from category B and even category C at two sites (Appendix A, Table 6). This is puzzling as by definition pods in category A have no CPB damage and therefore should have the highest yields. It is possible that in some instances mistakes were made at the time of recording the number of pods into spreadsheets or more likely that category A pods were smaller than all other pods and contained fewer and lighter beans. This phenomenon has been recorded in other studies where the mirid bug Helopeltis sulawesi Stonedahl, coexists with CPB (Wielgoss et al., 2012). In this study the authors showed that when mirid bugs are present on the pods, the cocoa pod borer moth does not oviposit on them. This behavior translates into an undamaged category (from a CPB management perspective), with an associated reduction in yield due to the injury effects caused by the mirids. Therefore, CPB undamaged pods can have lower yields than CPB damaged pods. Our survey identified six sites out of 19 which had distinctive lower yields from the undamaged category and were managed differently from the other 13 sites in that insecticide sprays were nonexistent. This could have allowed the buildup of H. sulawesi in an area where this secondary pest is common and widespread (S. lambert pers. comm.). The IS models were validated using data from a survey conducted in 2007e08 with results showing that models for DW/pod and pod value mostly underestimated predicted yield variables but with some overestimation also occurring. A possible explanation for the observed deviation may be that yield data from the 2007e08 survey were higher than yield data derived from 2003 to 04 survey used to develop the IS models (presumably due to higher rainfalls which averaged 4148 mm in a region where normal precipitation ranges from 1500 to 2000 mm/year) and these yields were inconsistent across damage categories. Observed yields from 2007 to 08 increased significantly for all categories of damage but particularly for that of pods A (data not shown). For instance, DW/ pod from pods A increased from 31.2 g to 38.5 g while pod value decreased from 29 to 27 pods between the two surveys, explaining some of the variation seen in the models' predictive ability in Fig. 4. Nevertheless the models are valuable tools as some degree of difference is always expected in agricultural systems. Changes in the number of pods per category of damage did not directly correspond to changes in yield and did not therefore affect the predictive power of the MLR yield models and yield loss models. Growers and researchers can use them to predict yield and yield loss knowing that pod numbers vary from month to month depending on various factors. The MLR-based models are suitable to use as long as (1) the visual criteria used for separating cocoa pods into four categories of damage remains the same and, (2) they are applied to a mixture of cocoa varieties, hybrids and clones with similar yield outputs as the ones found in this study, which represent the mixture of trees that determined the yields associated to each category of infestation. Changes in pod numbers however have a clear association with yield loss given that yield losses are higher when pod numbers in category A decrease during the months of SeptembereNovember (data not shown).
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These models are in effect management tools from which to estimate other important variables like economic impacts. For instance, using our model we estimated yield losses of dry weight due to CPB and found that yield reductions ranged from 10 to 30% during June 03eDecember 04 (data not shown). Similarly we applied our model to estimate yield loss of dry weight to the validation dataset and found that yield reductions ranged from 18 to 42% during March 2007eApril 2008. Cocoa is an important cash crop in Southeast Asia, with most of the production dominated by small landholders for whom cocoa-related earnings are critically important (Neilson, 2007; Perdew and Shively, 2009). In Sulawesi there are an estimated 400,000 small landholders that have varying small-size plots that yield an average 832 kg/ha (Perdew and Shively, 2009). Given that 69% of these households' incomes depend on the sale of cocoa beans, predictive tools for assessing yield loss such as the ones presented here are paramount for assisting growers with their decision-making processes in terms of choosing suitable control methods. Growers can change methods if estimates of yield losses increase for instance. Naturally, to make meaningful estimations of economic costs due to CPB, such models should include more data such as fertilizer, herbicide and pesticiderelated expenses, labor inputs and, include when possible the effects of spraying on beneficial insects such as black ants and pollinators and effects of secondary pests. These coupled with information on increase in profits should provide the adequate understanding of the true costs that CPB has on the economics of cocoa. A model that predicts profitability taking into account some of these factors has been produced for Sulawesi (Perdew and Shively, 2009). Furthermore, there are price reductions that apply at farm-gate to pods and beans of inferior quality. The industry standard in Sulawesi for bean and pod values is 26 pods and 110 beans (Mumford, 2005). These quality thresholds were exceeded for pods and beans from categories B and C in our dataset, creating additional economic losses. Quality downgrading of cocoa at the farmgate is well documented by industry which as a consequence has made huge efforts to change quality outputs of cocoa and its production through various programs such as SUCCESS Alliance project, PRIMA project, and some extension work, mainly to find out the effective methods that would control this pest (Mumford, 2005; Neilson, 2007; Lambert pers. comm.). In South Sulawesi, the current preferred method for controlling CPB is spraying synthetic pyrethroids, although other methods are being tested as part of IPM strategies that include the use of kairomones, using pheromone traps to reduce CPB numbers, sleeving the pods with biodegradable bags, planting resistant cultivars which have a hard pod husk and keeping high phytosanitary standards such as regular pod harvesting and pruning of the trees. These methods are being implemented in Sulawesi to see yields and quality increase in the near future with rational minimal and efficient use of permitted insecticides (Lambert pers. comm.). The array of methods that are being tested and others are testimony of how serious this pest is for the region and how critical are these efforts in controlling it, in order to increase yield, quality and overall economic returns. Yield loss models such as those presented here should prove efficient tools for assessing and monitoring the performance of such measures. We collated information on yield contributions from each pod category, and used this information to estimate yield and yield loss but, the question about the relationship between damage and pest density remains unanswered. However, this information could form part of an integrated system for setting economic thresholds upon which application of control measures are based. The current system of opening pods is effective in estimating yield loss but may not be feasible to conduct in some circumstances due to its
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laborious methods. Thus, in order to assist growers with making informed decisions it is possible to establish parallel monitoring studies, and develop systems that integrate information from yield and pest density as shown in previous studies with other lepidopterous pests that used pheromones as a predictor of their density (Sanders, 1988; Evenden et al., 1995). In the case of cocoa, an effective CPB monitoring system is possible given that sex pheromones that attract males have been developed and tested effectively in Southeast Asia, Papua New Guinea and in Northern Queensland, Australia for controlling CPB and for monitoring purposes (Zhang et al., 2008; Royer pers. comm.). Regrettably, pest density using pheromones was not surveyed in this study. The separation of pods into four categories of damage has been practised in Sulawesi for many years for determining levels of CPB infestation as part of programs designed to identify the effects of chemical and pheromone control methods (Lambert pers. comm.). Furthermore, the separation of pods into four categories of infestation is also used to estimate yield loss of dry weight of beans through the SS model (Hussin Purung pers. obs.). Thus, given that the methodology of separating pods into four categories of damage is well established in Sulawesi, there is no need to train or instruct new techniques in regards to the methods involved and, the adoption of our MLR-based model by cocoa stakeholders should be a straightforward process. In any case, the general trend supports a model based on a system with categories of infestation rather than pod infestation across conditions where multiple cocoa varieties are present. The models developed here particularly that for pod value, should have utility in mixed-cultivar plantations, and are stepping stones towards efficient management of CPB in Southeast Asia. Acknowledgments This work was funded by Mars Inc., Ballarat, Australia. The authors are grateful to Dr Smilja Lambert for providing important network support and information on the survey that took place during 2003e2004 and 2007e2008 in South Sulawesi. All datasets used in this study are unpublished and were produced as part of a collaborative effort to study the effects of chemical and biological management strategies against CPB in South Sulawesi. We are thankful to Jane Royer, Senior entomologist from the Queensland Government Department of Agriculture, Fisheries and Forestry for providing information on the program carried out in northern Queensland to eradicate CPB. Appendix A. Supplementary data Supplementary data associated with this article can be found in the online version, at http://dx.doi.org/10.1016/j.cropro.2014.08.018. References Adomako, B., 2007. Causes and extent of yield losses in cocoa progenies. Trop. Sci. 47, 22e25. Alvim, P.T., Alvim, R., 1978. Relation of climate to growth periodicity in tropical trees. In: Tomlinson, P.B., Zimmermann, M.H. (Eds.), Tropical Trees as Living Systems. Cambridge University Press, Cambridge, UK, pp. 445e464. Benke, K.K., Hamilton, A.J., Lowell, K.E., 2007. Uncertainty analysis and risk assessment in the management of environmental resources. Aust. J. Environ. Manag. 14, 243e249. Chiarappa, L., 1981. Crop loss Assessment Methods. Supplement 3. Commonwealth Agricultural Bureaux, Famham Royal, UK. Cocoa Coconut Institute (CCI) of Papua New Guinea. The Asian Cocoa pod borer (CPB) (Conopomorpha Cramerella snellen): a report on its containment within and eradication from east New Britain province, Papua New Guinea (March 2006eJanuary 2007). Rabaul, East New Britain, Papua New Guinea. Day, R.K., 1986. Population dynamics of cocoa pod borer Acrocercops cramerella: importance of host plant cropping cycle. In: Pushparajah, E., Chew, P.S. (Eds.),
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Cocoa and Coconuts: Progress and Outlook. Incorporated Society of Planters. Malaysia, Kuala Lumpur, pp. 255e263. Day, R.K., 1989. Effect of cocoa pod borer, Conopomorpha cramerella, on cocoa yield and quality in Sabah, Malaysia. Crop Prot. 8, 332e339. Entwistle, P.F., 1972. Pests of Cocoa. Tropical Science Series. Longman Group Limited, London, pp. 370e375. Evenden, M.L., Borden, J.H., van Sickle, G.A., 1995. Predictive capabilities of a pheromone-based monitoring system for western hemlock looper (Lepidoptera: Geometridae). Environ. Entomol. 24, 933e943. FAOSTAT (FAO Statistics Division), Food and Agriculture Organization of the United Nations, Rome. http://faostat.fao.org (accessed 24.11.10.). Gotsch, N., 1997. Cocoa crop protection: an expert forecast on future progress, research priorities and policy with help of the Delphi survey. Crop Prot. 16, 227e233. Hurvich, C.M., Tsai, C.L., 1989. Regression and time series model selection in small samples. Biometrika 76, 297e307. ICCO (International Cocoa Organization), London. http://www.icco.org (accessed 19.11.13.). Jonah, P., 2009. Bougainville hit by cocoa borer pest. ABC international, Radio Australia [cited 2010]. Available on URL: http://www.radioaustralia.net.au/ pacbeat/stories/200909/s2682634.htm. Keane, P., 1992. Diseases and pests of cocoa: an overview. In: Keane, P.J., Putter, C.A. (Eds.), Cocoa Pest and Disease Management in Southeast Asia and Australasia. FAO Plant Production and Protection Paper No. 112. FAO, Rome, Italy, pp. 1e11. Lim, G.T., Phua, P.K., 1986. Effect of the cocoa pod borer Acrocercops cramerella Snellen on yield and bean size. In: Pushparajah, E., Chew, P.S. (Eds.), Cocoa and Coconuts: Progress and Outlook. Incorporated Society of Planters, Kuala Lumpur, Malaysia, pp. 325e336. McMahon, P., Iswanto, A., Susilo, A.W., Sulistyowati, E., Wahab, A., Imron, M., Purwantara, A., Mufrihati, E., Dewi, V.S., Lambert, S., Guest, D., Keane, P., 2009. On-farm selection for quality and resistance to pest/diseases of cocoa in Sulawesi: (i) performance of selections against cocoa pod borer, Conopomorpha cramerella. Int. J. Pest. Manag. 55, 325e337. Mumford, J.D., 2005. Community actions to improve productivity, quality and markets in fruit and cocoa pest management in Asia. Asp. Appl. Biol. 75, 47e52. Neilson, J., 2007. Global markets, farmers and the state: sustaining profits in the Indonesian cocoa sector. Bull. Indones. Econ. Stud. 43, 227e250. Perdew, J.G., Shively, G.E., 2009. The economics of pest and production management in small-holder cocoa: lessons from Sulawesi. Bull. Indones. Econ. Stud. 45, 373e389.
Ploetz, R.C., 2007. Cacao diseases: important threats to chocolate production worldwide. Phytopathology 97, 1634e1639. Sanders, C.J., 1988. Monitoring spruce budworm population density with sex pheromone traps. Can. Entomol. 120, 175e183. € lscher, D., Ko €hler, M., Clough, Y., Schwendenmann, L., Veldkamp, E., Moser, G., Ho Anas, I., Djajakirana, G., Erasmi, S., Hertel, D., Leitner, D., Leuschner, C., Michalzik, B., Propastin, P., Tjoa, A., Tscharntke, T., van Straaten, O., 2010. Effects of an experimental drought on the functioning of a cacao agroforestry system, Sulawesi, Indonesia. Glob. Change Biol. 16, 1515e1530. See, Y.A., Khoo, K.C., 1996. Influence of Dolichoderus thoracicus (Hymenoptera: Formicidae) on cocoa pod damage by Conopomorpha cramerella (Lepidoptera: Gracillariidae) in Malaysia. Bull. Entomol. Res. 86, 467e474. Sugiura, N., 1978. Further analysis of the data by Akaike's information criterion and the finite corrections. Commun. Stat. A Theory 7, 13e26. Teng, P.S., 1981. Use of regression analysis for developing crop loss models. In: Chiarappa, L. (Ed.), Crop Loss Assessment Methods. Supplement 3. Commonwealth Agricultural Bureaux, Famham Royal, UK, pp. 51e55. Walker, P.T., 1983. Crop losses: the need to quantify the effects of pests, diseases and weeds on agricultural production. Agric. Ecosyst. Environ. 9, 119e158. Wardojo, S., 1980. The cocoa pod borer: a major hindrance to cocoa development. Indones. Agric. Res. Dev. J. 2, 1e9. WCF (World Cocoa Foundation), Washington. http://worldcocoafoundation.org (accessed 20.06.10.). Wielgoss, A., Clough, Y., Fiala, B., Rumede, A., Tscharntke, T., 2012. A minor pest reduces yield losses by a major pest: plant-mediated herbivore interactions in Indonesian cacao. J. Appl. Ecol. 49, 465e473. Wiese, M.V., 1982. Crop management by comprehensive appraisal of yield determining variables. Annu. Rev. Phytopathol. 20, 419e432. Zadoks, J.C., 1985. On the conceptual basis of crop loss assessment: the threshold theory. Annu. Rev. Phytopathol. 23, 455e473. Zar, J.H., 1999. Biostatistical Analysis, fourth ed. Prentice Hall, Upper Saddle River, NJ. Zhang, A., Kuang, L.F., Maisin, N., Karumuru, B., Hall, D.R., Virdiana, I., Lambert, S., Purung, H.B., Wang, S., Hebbar, P., 2008. Activity evaluation of cocoa pod borer sex pheromone in cacao fields. Environ. Entomol. 37, 719e724. Zuidema, P.A., Leffelaar, P.A., Gerritsma, W., Mommer, L., Anten, N.P.R., 2005. A physiological production model for cocoa (Theobroma cacao): model presentation, validation and application. Agric. Syst. 84, 195e225.
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Crop Protection journal homepage: www.elsevier.com/locate/cropro
Practical yield loss models for infestation of cocoa with cocoa pod borer moth, Conopomorpha cramerella (Snellen) Isabel Valenzuela a, *, Hussin Bin Purung b, Richard T. Roush a, Andrew J. Hamilton a, c a
Melbourne School of Land and Environment, The University of Melbourne, Parkville, Victoria 3010, Australia Mars Indonesia, Makassar, South Sulawesi, Indonesia c Department of Agriculture and Food Systems, The University of Melbourne, Dookie Campus, Victoria 3647, Australia b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 29 April 2014 Received in revised form 26 August 2014 Accepted 27 August 2014 Available online
The cocoa pod borer, Conopomorpha cramerella (Snellen) (Gracillariidae: Gracillariinae), is an important pest of cocoa in Southeast Asia and Oceania, with devastating effects on yields. Using data on cocoa pod borer (CPB) infestation and cocoa yield from mixed-variety plantations in South Sulawesi, Indonesia, we developed models for estimating yield and yield loss under CPB attack. For six yield variables, two types of models were constructed: non-linear regressions based upon the presence or absence of infestation of pods (PI model), and multiple linear regressions for a four-point graded system of infestation severity (IS model). The IS models performed markedly better than PI models, in terms of percentage of variance explained, for all variables, also supported by Corrected Akaike Information Criterion values. But the explanatory power of the best-fit models was still poor for some variables. The fits were strongest for arguably the two most important variables in the industry, dry weight/pod and pod value (the number of pods required to achieve 1 kg of dry cocoa), with 62% and 69% of the variance accounted for, respectively. Validation of the dry weight/pod and pod value models against an independent dataset from South Sulawesi indicated that the models slightly under-estimated both yield indicators that increase concomitantly with the degree of yield loss. We propose the IS models, particularly that for pod value, as useful tools for industry, and argue that they will have broad utility given that they are based on mixedcultivar plantations. Not only are these the first CPB yield-loss models to be based on commercial mixed plantings, they also represent the first attempt to employ a gradation of infestation severity based on simple visual assessment, which proved to be an important advance. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Cacao Pest damage Yield evaluation Non-linear regression Multiple linear regression Lepidoptera
1. Introduction Cocoa is one of the most important cash crops in the tropics. Originally from South America, cocoa has seen an expansion into West Africa, which accounts for 70% of the world's production, and more recently into Southeast Asia (FAOSTAT; Neilson, 2007). The majority of the world's cocoa (~70%) is grown by smallholders with low input/output economics, typically organized into family units for whom cocoa is their main or sole source of income (Gotsch, 1997; WCF). In Southeast Asia production of cocoa is also carried out largely by smallholders (Neilson, 2007), with Indonesia as the main contributor in the share of the total production; Southeast Asia exported 526,000 tonnes in 2010e2011 of which 440,000 tonnes were produced in Indonesia alone (ICCO). However, the
* Corresponding author. Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, 30 Flemington Road, Parkville, VIC 3010, Australia. E-mail address: [email protected] (I. Valenzuela). http://dx.doi.org/10.1016/j.cropro.2014.08.018 0261-2194/© 2014 Elsevier Ltd. All rights reserved.
prospect of a profitable farming model has been jeopardized by a number of pests and diseases that have been affecting yield and quality (Entwistle, 1972; Keane, 1992; Mumford, 2005; Ploetz, 2007). Conopomorpha cramerella (Snellen), the cocoa pod borer (CPB) moth, is the most injurious invertebrate pest of cocoa in Southeast Asia (Wardojo, 1980). The larvae bore inside the pod and feed on the placenta and pulp that supply nutrients to the beans. The infestation interrupts the normal development of the beans and also causes them to clump together, making the harvest difficult because the beans are not easily removed and cannot be separated for fermenting and drying. This pest is now well-known in Southeast Asia and is spreading across the Pacific region, with a recent incursion being reported in East New Britain Province of Papua New Guinea in 2006 and in Bougainville Island (Autonomous Region of Bougainville) in 2008 (CCI; Jonah, 2009), and the most recent being from Northern Queensland, Australia where eradication programs are being carried out to stop this pest from becoming established in the area (Royer pers. comm.).
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Qualitative and quantitative assessments of yield are common measures in agricultural production, and understanding how yield is affected by pests and diseases is paramount for the development of efficient crop management strategies (Wiese, 1982). Growers need to know in quantitative terms the effects particular pests and diseases have on their crop yields, so the appropriate measures can be applied when necessary to avoid the predicted losses (Chiarappa, 1981; Walker, 1983; Zadoks, 1985). A widespread method for making associations between pest densities (or through their injuries when pest density cannot be measured) and yield is the use of regression methods (Teng, 1981). These were used in Southeast Asia when CPB appeared in Sabah, Borneo Malaysia in the 1980s. Surveys and studies were set that examined the effects of CPB on yield soon after the pest was first detected in the region, which developed models based on the number of pods infested with CPB (Lim and Phua, 1986; Day, 1989). Lim and Phua (1986) related pod infestation to wet weight of beans and derived yield loss (measured in an unspecified way) from 59 cocoa varieties representing the 10 most important groups in terms of production. This was a crucial exercise that characterized yield responses to CPB infestation by testing commercially important clones used for breeding. It revealed significant differences in yield outputs between varieties that had similar degrees of pod infestation and thus allowed the detection of CPB-resistant varieties (relative to the more susceptible ones). The application of this model is problematic if yield variables other than wet bean weight are being assessed, which is the current situation in Southeast Asia where assessments of yield are given as dry weight. Day (1989), using plantation data, related pod infestation to wet and dry bean weights for Amelonado and mixed Upper Amazon Hybrid clones, and derived non-linear models for yield and, consequently, yield loss. These non-linear models can be used to estimate yield and yield loss but, their usefulness is questionable if estimates are needed from smallholders, who, in contrast to large plantation operations, cultivate mixed cocoa varieties which typically display large differential production capacities. Thus, these models, which are based on data from plantations, are possibly unreliable for situations outside the range of conditions from which they were developed. Only the models from Day (1989) have been validated; Lim and Phua's (1986) model has not been validated, and in effect simply represents a fitting exercise to a specific data-set. Despite the pest's prevalence and significance in Southeast Asia, no formal assessments have since been made in other regions, with the exception of Sulawesi, Indonesia where an informal modeldhenceforth referred to as the Sulawesi Standard: SSdis being used in mixed cultivar plantations. Briefly, the SS model is based on a four-point scale of severity that classifies pods into groups that range from no damage to total damage, based on a visual assessment of the pods after they have been opened and inspected. The model estimates yield loss in terms of dry weight and it is based on the number of pods classified into particular categories of damage. How the model was derived has not been specified in the broader literature and remains unpublished, but the formula is given in a subsequent section of this manuscript and is used in a comparative exercise with our model of yield loss for dry weight of beans/pod. Here, we use yield information from a trial conducted in mixed variety plantations in Sulawesi to (1) construct models that describe yield as a function of pod damage and pest infestation due to CPB infestation, (2) validate their performance on an independent dataset and, (3) derive yield loss from the models of yield. First, in line with previous work (Lim and Phua, 1986; Day, 1989), we develop models based upon the overall infestation status (total number of pods infested), and this is followed by models based upon a four-point scale of severity (number of pods in 4 categories of damage). Second, we ask whether infestation severity is more
appropriate in relating CPB damage to yield than overall number of pods infested and we extend yield variables to include pod valuedthe number of pods required to achieve 1 kg of dried cocoa (Adomako, 2007)dwhich is the preferred measure of yield in current cocoa markets. We validate model performances through application to a dataset from an independent trial and derive yield loss from the models of yield for variables dry weight per pod (DW/ pod) and pod value. 2. Materials and methods 2.1. Trial conditions and CPB damage levels In 2003e2004, yield surveys were undertaken from cocoa farms in South Sulawesi while routine commercial harvest was taking place in the region, i.e., growers agreed to have a fraction of their harvest assessed for CPB infestation and yield. It is important to note that we are making opportunistic use of this large dataset and that it was not collected with the express purpose of informing the development of these yield loss models; rather it was part of a survey quantifying the severity of yield loss in Sulawesi. Overall there were 19 sites that were part of the survey which had 100 mature trees at each site planted with a mixture of unknown varieties and clones, with a planting density of 900 trees/ha approximately. All ripe pods only were harvested from marked 100 trees at each of 19 sites, every two weeks from June 2003 to December 2004 (38 sampling events in total). The number of pods harvested per site and sampling event was not constant due to the natural variation of pod production, while labor constraints determined a maximum number of 100 (sometimes 110) pods/site to be processed for yield and CPB infestation data collation. In other words, when more than 100 ripe pods were harvested a random sample of 100 pods was selected whereas when less than 100 ripe pods were harvested; all available pods were used for assessments of yield. The mean number of pods sampled per site and sampling event was 72.4 (SD ¼ 29.9). Logistic mishaps occurred that affected the smooth running of the survey, which was initially designed to occur every two weeks. Problems like heavy rains, vehicle breakdown and similar issues altered the total number of observations from the expected 722 to 682 as some sites were not visited every two weeks as was planned. More information about localities, precipitation and crop conditions related to the 2003e04 survey can be found in Appendix A, Table 1. In every sampling event, pods used for data collation were counted and after opening, separated into four categories according to their level of damage, i.e., after opening the pods and observing the amount of dark coloring and clumped beans, the level of damage was quantified using an AeD scale of severity: A ¼ none, B ¼ light damage, C ¼ moderate damage, and D ¼ heavy damage (Fig. 1). Many criteria were used to separate damaged pods, including: darkening and clumping of the beans, sluggish texture of the dark areas, visual larval trail imprints, and harvest by hand or spoon, i.e., beans harvested by hand were in category B (no clumping of the beans is observed in this category as all beans can be removed by hand from the pod), whereas beans that would have to be harvested with a spoon were placed in category C (some clumping is observed in this category and a spoon is needed to remove the beans from the pod) and when beans could not be extracted these made up the category D. Pods in category D were discarded, as no beans could be extracted from these pods, but their number was nevertheless recorded. Workers in Sulawesi have been conducting this type of pod classification for many years, for the reason that it gives growers a quick estimate of CPB infestation levels in the field and are skilled in this method, which is known as the A, B, C, D method of cocoa pod classification and was developed
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Fig. 1. Example of visual criteria used to classify pods into four categories A to D; no damage (pods A), light damage with up to 10% darkening of beans (pods B), moderate damage with 10%e50% darkening (pods C) and heavily infested with more than 50% darkening (pods D).
by Mars Inc. field research team. Number of cocoa pods (per category of damage) and month are shown in Fig. 6 and in Appendix A, Table 3. Subsequently, the number of pods, the number of beans, and the fresh and dry weights of beans were evaluated for each category. The number of pods in each damage category was recorded but yield variables were measured in categories A, B and C only, as category D pod's yield was nil (beans could not be extracted from this category). Yield variables were: dry and wet weight of beans per pod (WW/pod, DW/pod); the number of extractable beans per pod (No. beans/pod); the average dry weight of one bean (DW 1 bean); the pod value (the number of pods required to achieve 1 kg of dry cocoa) and bean value (the number of beans required to achieve 100 g of dry cocoa). Analyses of variance (ANOVA) between the entire above mentioned yield variables and factors: (1) month and (2) site were performed (per category of damage) with relevant post-hoc analyses shown in Appendix A, Tables 5 and 6 respectively. An additional dataset used for validation of the models was available from a field trial conducted in 2007e2008 in Noling, South Sulawesi (unpublished). This trial was carried out in 8 sites; each with 125 trees from a mixture of cocoa varieties with a planting density of approximately 900 trees/ha. Commercial practices determined the harvest of ripe pods to occur every two weeks from March 2007 to April 2008 inclusive (26 sampling events in total) and assessments for CPB and yield followed the abovementioned methodologies where up to 200 pods were used for data collation instead (mean number of pods ¼ 139.3, SD ¼ 58.1). Pods were also classified following categories of pod damage A through to D, while yield variables evaluated were also the same. The total number of observations was 208. More information about localities, precipitation and crop conditions from the 2007-08 survey used for the validation can be found in Appendix A, Table 2. 2.2. Yield models Each sampling event generated four distinct groups of pods: one category of healthy pods XA (%) and three categories of damaged pods namely XB, XC and XD (%). From these we calculated, for each sampling event, the overall infestation namely X ¼ XB þ XC þ XD. There were 682 observations for each category of damage XA through to XD. Therefore, all calculations are based on 682 data points (for individual categories of damage and therefore for the overall infestation calculated here).
First, we examined the relationships between each of the aforementioned yield variables and the overall infestation, X. Three non-linear models (all exponential in form) were investigated for each variable using Genstat V15 (see Appendix A, Table 4 for model descriptions, fit and selection analyses), but given none performed consistently better than the other, in terms of variance explained and Corrected Akaike Information Criterion, we chose the simple exponential model which is also the exact same model as used by Day (1989) for modeling CPB damage. The simple exponential (asymptotic) regression was applied to all variables:
YPI ¼ a þ bX c ;
(1)
where YPI is the general case for yield estimated from a nonlinear model based on “pod infestation” (PI). The asymptote a is the yield in the absence of CPB, b and c are shape parameters, and X is the overall infestation (i.e., irrespective of its degree), calculated for each sampling event (682 data points in total). Parameters were estimated using an Ordinary least-squares method. Second, we examined the relationship between yield and infestation severity through the following multiple linear regression model:
YIS ¼ a þ ðb1 XA Þ þ ðb2 XB Þ þ ðb3 XC Þ þ ðb4 XD Þ;
(2)
where YIS is the general case for yield estimated from a multiple linear model based on “infestation severity” (IS). XAeXD are the percentage of pods in categories A through to D, a is the intercept and b1eb4 are coefficients determined by multiple linear regression, calculated for each sampling event (682 for each category of damage). See Appendix A, Table 4 for a description of the model, and parameters. All regression analyses were conducted using Genstat V15 (Lawes Agricultural Trust, Institute of Arable Crops Research e Rothamsted, UK). Model selection was made using the Corrected Akaike's Information Criterion (AICc), where a lower value indicates a higher quality model (Sugiura, 1978; Hurvich and Tsai, 1989). 2.3. Yield loss models For all variables the multiple linear regression model, YIS, performed better than the non-linear model, YPI, in describing the relationship between damage and yield, in terms of variance explained also supported by the Corrected Akaike's Information
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Criterion (Appendix A, Table 4), and therefore was used to calculate yield loss. Yield loss (YL) for each yield variable was calculated as:
3. Results 3.1. Yield model statistics and validation
YL ¼ ðm YÞ 100=m;
(3)
where YL denotes the general case for yield loss, and m and Y are yield in the absence and presence of pest, respectively (Walker, 1983). Calculations of m and Y are based on the parameters from the YIS model. Yield in absence of pest (m) is calculated as:
m ¼ a þ ðb1 XA Þ;
(4)
where XA is 100, due to the absence of damaged pods, and Y, which is the yield in presence of pest, is given as:
Y ¼ YIS ;
(5)
where YIS is yield in presence of pest as determined by multiple linear regression shown in Eq. (2). We also investigated the performance of the Sulawesi Standard (SS) model used to estimate loss of dry weight of beans (percentage):
YLSS ¼ ðððXA 1Þ þ ðXB 1:025Þ þ ðXC 1:5Þ þ ðXD 2Þ=XÞ*100Þ 100; (6) where XA…D are the number of pods in categories A through to D and X is the total number of pods.
For all variables, the pod infestation model (YPI) predicted relatively constant yield at low to medium levels of infestation followed by a rapid decline in yield at higher levels (Fig. 2). These exponential models were very weak, explaining only 23.7%e49.3% of the variance, with DW 1 bean being the most poorly predicted variable, explaining only 23.7% of the variance, and No. beans/pod the best, explaining 49.3% of the variance, followed by pod value and DW/pod which accounted for 48.4% and 47.3% variance respectively (Fig. 2). Corrected Akaike's Information Criterion (AICc) was performed for all models obtaining values that ranged from 1.081 to 6.099 (Fig. 2). On the other hand, the yield models based on infestation severity (YIS) had greater explanatory power than the pod infestation models (Fig. 3). The coefficient of determination ranged from 32% to 69%. For all variables category D was excluded from the final model due to significant co-linearity (P < 0.05) (Zar, 1999). As with pod infestation model, the variable pod value was the best estimated yield variable with 69.2% of the variation explained, followed by No. beans/pod (66.5%) and DW/pod (62.0%) and WW/pod (55.4%), yet bean value and DW 1 bean were the poorest predicted variables with 32.9% and 31.7% of the variation explained (Fig. 3). Here, AICc values ranged from 1.157 and 6.014 (Fig. 3). AICc values from both PI and IS models were compared for each yield variable as an objective way of determining which model was most accurate (statistically sound) as the use of r2 is not an
Fig. 2. Non-linear relationships between yield variables and the percentage of pods infested with CPB. The exponential model is YPI ¼ a þ bX C , where YPI is the general case and X is the percentage of pods infested, regardless of the severity of infestation.
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Fig. 3. Relationship between yield variables as predicted on multiple linear regression (MLR) yield models based on infestation percentage and actual yield for each variable. MLR equation is YIS ¼ a þ ðb1 XA Þ þ ðb2 XB Þ þ ðb3 XC Þ where XAeXC are the percentage of infested pods in categories A through C, respectively (n ¼ 682). The dashed diagonal line denotes unity (i.e., perfect agreement).
appropriate parameter for this purpose. For all variables, the AICc values from the IS models were lower than the AICc values from the PI models, indicating that the PI models were better in terms of predicting power (Appendix A, Table 4). Using an additional dataset, we validated the broader application of both the PI and IS models on DW/pod and pod value (Fig. 4). We selected these variables since they are the preferred units of measure used by cocoa stake holders. Results showed that the IS models were better predictors of yield than the PI models (Fig. 4). Results also showed that the IS models (predicted data) when plotted against the observed data accounted for 50% and 63% of the observed variation on DW/pod and pod value respectively (Fig. 4). The model for DW/pod and pod value underestimated most predicted values for each respective model (Fig. 4). For instance, for DW/pod, most predicted vs observed values fell above the line of unity with the corresponding regression line showing the progressive deviation that underestimates at higher values. Out of the 208 observations, 58 values overestimated by 0.2%e68.2%, while 150 observations underestimated by 0.4%e62.5% (data not shown). Similarly, the model for pod value also underestimated (for the most part) shown by the position of the values which are below the line of unity and the progressive deviation that occurs at higher pod values. Again, out of the 208 observations, 33 were overestimates of 0.1%e33.4% while the remainder 175 observations underestimated in the number of pods by 0.5%e161.2% (data not shown).
3.2. Yield loss validation The IS and SS models produced similar % loss predictions for DW/pod (Fig. 5). The SS model underestimated yield loss slightly, with the magnitude of the underestimation increasing concomitantly with increase in yield loss. 3.3. Pod production and yield The number of pods used in the survey varied from 8 to 110 per site and sampling event (which was the maximum number of pods thought to be practical in terms of labor inputs). In total, 49,343, were used and these were harvested from 19 sites every 2 weeks from June 2003 to December 2004, with the average number of pods being 72 (SD ¼ 30) (data not shown). Generally speaking, the number of pods available for harvest varies according to the seasons since there are distinctive low and high production periods throughout the year. Studies have shown that pest fluctuations are in cycle with the number of ripe pods (Day, 1986). Inevitably this will have an effect on the number of healthy and overall infested pods and consequently on the number of infested pods if these are separated into categories of damage. For instance, we observed significant increases/decreases in the number of pods in categories A, B and C from one month to another month (Fig. 6), and particularly a decrease of pods from category A during the months of
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Fig. 4. Relation between observed and predicted yield for DW/pod and pod value, from trials conducted in Noling, South Sulawesi in 2007e2008, and used here as an additional dataset for the model validation (n ¼ 208). The IS models (infestation severity) are shown in figures A and C while the PI models (pod infestation) are shown in figures B and C. The fitted ordinary least squares linear regression model is shown as a solid line, and the dashed diagonal line denotes unity.
SeptembereNovember when the production is lower and there are fewer pods overall. Yet, observed changes in pod numbers (per category of infestation) did not affect yields associated to the corresponding categories, i.e., weights and bean numbers associated to each category of infestation were relatively stable throughout the duration of the study (Fig. 7). Monthly yield data were analyzed and results indicated that, for most variables and months, yields in categories A were not different from yields in category B while yields in category C were significantly lighter from A and B when analyses of variance (ANOVA) were performed by month (Appendix A, Table 5). Number of pods and yields associated were also examined by site. This was to identify additional causes of yield variability. Similar to the study by month, yields from category A were similar to yields from category B, which tended to be significantly higher than yields from category C (Appendix A, Table 6). There were however six sites out of 19 namely CTR-01 to CTR-05, and CTR-RR
Fig. 5. MLR relation between yield loss (DW/pod) predicted by the infestation severity (IS) and Sulawesi Standard (SS) models when applied to data from South Sulawesi 2003e2004 (n ¼ 682). For clarity of presentation, the regression line is not shown, but it clearly lies on the line of points. The dashed diagonal line denotes unity (i.e., perfect agreement).
that recorded lower category-A associated yields than category-B yields (although not significantly different by Tukey B test) with the exception of CTR-04 site (Appendix A, Table 6). 4. Discussion Comparative analyses of regression models based on the overall infestation and individual categories of infestation showed significant differences in predictive power. For instance, a non-linear exponential model based solely on the percentage of pod infestation, regardless of infestation severity, was able to account for only 35.1% of the variation found in WW/pod, but a multiple linear
Fig. 6. Cocoa pods used for data collation per category of infestation A through to D (monthly averages harvested from June 2003 to December 2004 in percentages). (Supplementary data in Appendix A, Table 3; Mean, SD., and no. of observations). Pods A d, pods B …, pods C , pods D d .
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Fig. 7. Yields associated to categories of pod damage A, B and C (monthly averages from June 2003 to December 2004). (Supplementary data in Appendix A, Table 5; Mean, SD., and no. of observations). Pods A d, pods B …, pods C .
regression model based on four categories of infestation severity, accounted for 55.4% of the variation, and this pattern was replicated for all variables. Thus, the use of a four-point severity system was more precise for predicting yield under the effects of CPB than the coarse pod infestation percentage approach. This was also supported by the Corrected Akaike Information Criterion (AICc) values which were always lower for the IS models by comparison to the PI models, for all variables. In general, our models explained less of the observed variance than those of previous studies. For instance, Day (1989) used a nonlinear regression model relating WW/pod and DW/pod to CPB attack, which accounted for 74% and 83% respectively of the observed variation, from a mixture of Amelonado and Upper Amazon Hybrid varieties. However, this was not unexpected, despite the much larger sample size in our study. Our investigation included a larger and more diverse collection of varieties, from clones and hybrids, and thus observed larger spread of response yield variables which resulted in a poorer fit (Fig. 2). This can also be viewed in terms of the two fundamental forms of uncertainty: aleatory (natural variability) and epistemic (incomplete knowledge) (Benke et al., 2007). By definition, aleatory uncertainty, clonal variation in this case, is irreducible. Given that in many Southeast Asian countries the norm is to have various plant varieties, we believe our model, despite lower fitting capacity than previous studies, better depicts those situations where multiple varieties are present and is thus more robust. Even then, our pod value model based on infestation severity fitted
reasonably well (r2 ¼ 0.69, and pod value is currently the preferred unit of measure of yield used by cocoa stakeholders (Lambert pers. comm.). Another important variable, DW/pod, was also predicted reasonably well (r2 ¼ 0.62), giving cocoa stakeholders an additional variable from which to estimate yield and derive yield loss with a sound level of confidence. Accordingly, we validated the models' function on an additional dataset from a subsequent trial in the same region for pod value and DW/pod. Overall, we found reasonable fitting power for both variables on the validation exercise. Interestingly, our IS model produced fairly similar predictions to the SS model in terms of yield loss of dry weight. Mathematically, the two models take the same general form, with each be a linear scaling of the other, and this is why virtually all of the variance is explained by the regression in Fig. 5. The underestimation of the SS could be considered negligible at lower yield losses but is likely to be of consequence at the higher losses. The coefficients in the SS model, which uses 4 categories achieves similar results to our proposed IS model that uses 3. Clearly, the infestation categories are related; for example, by considering categories A, B and C, one is implicitly including D because it is the complement of the sum of the other three. It is reasonable, therefore, to expect a certain degree of correlation between the categories, and indeed our model was simplified to three variables because of the high co-linearity of category D variable with another/others. More pragmatically, the SS model applies only to dry weight, whereas here we presented a model for estimating losses in an important indicator used in
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industry, pod value. This appears to be the first published model for estimating pod value losses in cocoa. To our knowledge, this is also the first time multiple linear regression has been used to predict cocoa yield (and yield loss drawn from it) under the effects of CPB infestation. The direct application of this method in the field is the distribution of pods into four categories, according to their differential bean harvest, and based on a preliminary visual assessment (Fig. 1). This method is easy to conduct in the field as pods that need to be opened in order to extract the beans can be simultaneously assessed for level of damage and recorded in spreadsheets or placed on the ground at the same time as they are assigned to a particular damage category. Once the pods have been counted, their number (in percentages) is applied to the multiple linear regression equation to obtain estimates of yield and/or yield loss in situ. Estimating yield loss due to CPB using this method can be used in any context of cocoa production i.e., from the small-scale plot to the large size plantation. In either context, there are no restrictions in terms of the frequency of sampling events or the number of plots to sample. These will be determined by the resources available to the landowners. There are however, some considerations regarding the number of pods to sample, with our recommendation being a minimum of 40 pods/ sampling event for plots containing 100 trees (as determined by the average number of pods used in the development of the models i.e., average of 72 ± 30 pods/sampling event for plots containing 100 trees). The models are easily applied to small plots of 10 10 m which normally contain 90e100 trees (at a standard density of 1000 trees/ha approximately) and can be scaled up for larger plantations by simply adding 10 10 m plots or 100-tree plots. With this in mind, cocoa stakeholders can conduct surveys of yield loss for various reasons. For instance, surveys could take place between plots to determine estimates of yield loss associated with different CPB management strategies and identify which method is more effective. Surveys could also take place between plots that have differential crop and climate conditions to estimate yield loss at these particular sites. Although the application of these models is broad in theory, their use will depend on specific needs of landholders and industry, almost certainly related to IPM programs which are being developed and tested at the present time. As shown in McMahon et al. (2009), CPB-susceptible cocoa clones had more heavily infested pods than the less susceptible ones, and similarly areas that contained black ants had significantly fewer heavily infested pods (See and Khoo, 1996). Based on these reports, heavily infested pods (category D in our study) are paramount for identifying CPB-resistant cocoa varieties and for monitoring the effects of pest control methods. It is important to note that while our MLR models do not require category D as an input, the category is still implicitly accounted for because the percentages for categories A, B and C are percentages of all pods. Cocoa yield variation depends on many factors: biotic and abiotic. Some abiotic factors listed here are rainfall and the intensity of the shading while biotic factors identified range from fruit morphology to photosynthesis and respiration (Alvim and Alvim, 1978; Zuidema et al., 2005; Schwendenmann et al., 2010). The effects of climate had an effect on the number of pods available for harvest with the uninfested pod category being the most affected (Fig. 6). Corresponding yields however were relatively stable throughout the seasons (Fig. 7) with a few exceptions such as the changes seen in October 2003 (noticeable increase although not significantly different from pods B associated yields, Appendix A, Table 5). The observed variation in October 2003-associated yields may be due to changes in the type of pod, i.e., larger pods that contained fewer bigger beans for categories A, B and C. Thus changes in yield are possible but are not directly associated with
changes in the number of pods but to other factors instead like type of pods being produced which are different with different yield characteristics e.g., bigger pods with fewer bigger beans, perhaps linked to climatic events. Other changes could come from the workers that separate pods into different categories, where pods A could have mistakenly been placed in pods B group and vice versa. This however is unlikely to happen in a significant way. Sulawesi workers have proven their skill in separating pods according to their level of damage, particularly for that of category C which is clearly different from A and B when yields were assessed (Fig. 7). There were however, six sites out of 19 with unusual yield patterns, where yields from category A were on average lower than yields from category B and even category C at two sites (Appendix A, Table 6). This is puzzling as by definition pods in category A have no CPB damage and therefore should have the highest yields. It is possible that in some instances mistakes were made at the time of recording the number of pods into spreadsheets or more likely that category A pods were smaller than all other pods and contained fewer and lighter beans. This phenomenon has been recorded in other studies where the mirid bug Helopeltis sulawesi Stonedahl, coexists with CPB (Wielgoss et al., 2012). In this study the authors showed that when mirid bugs are present on the pods, the cocoa pod borer moth does not oviposit on them. This behavior translates into an undamaged category (from a CPB management perspective), with an associated reduction in yield due to the injury effects caused by the mirids. Therefore, CPB undamaged pods can have lower yields than CPB damaged pods. Our survey identified six sites out of 19 which had distinctive lower yields from the undamaged category and were managed differently from the other 13 sites in that insecticide sprays were nonexistent. This could have allowed the buildup of H. sulawesi in an area where this secondary pest is common and widespread (S. lambert pers. comm.). The IS models were validated using data from a survey conducted in 2007e08 with results showing that models for DW/pod and pod value mostly underestimated predicted yield variables but with some overestimation also occurring. A possible explanation for the observed deviation may be that yield data from the 2007e08 survey were higher than yield data derived from 2003 to 04 survey used to develop the IS models (presumably due to higher rainfalls which averaged 4148 mm in a region where normal precipitation ranges from 1500 to 2000 mm/year) and these yields were inconsistent across damage categories. Observed yields from 2007 to 08 increased significantly for all categories of damage but particularly for that of pods A (data not shown). For instance, DW/ pod from pods A increased from 31.2 g to 38.5 g while pod value decreased from 29 to 27 pods between the two surveys, explaining some of the variation seen in the models' predictive ability in Fig. 4. Nevertheless the models are valuable tools as some degree of difference is always expected in agricultural systems. Changes in the number of pods per category of damage did not directly correspond to changes in yield and did not therefore affect the predictive power of the MLR yield models and yield loss models. Growers and researchers can use them to predict yield and yield loss knowing that pod numbers vary from month to month depending on various factors. The MLR-based models are suitable to use as long as (1) the visual criteria used for separating cocoa pods into four categories of damage remains the same and, (2) they are applied to a mixture of cocoa varieties, hybrids and clones with similar yield outputs as the ones found in this study, which represent the mixture of trees that determined the yields associated to each category of infestation. Changes in pod numbers however have a clear association with yield loss given that yield losses are higher when pod numbers in category A decrease during the months of SeptembereNovember (data not shown).
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These models are in effect management tools from which to estimate other important variables like economic impacts. For instance, using our model we estimated yield losses of dry weight due to CPB and found that yield reductions ranged from 10 to 30% during June 03eDecember 04 (data not shown). Similarly we applied our model to estimate yield loss of dry weight to the validation dataset and found that yield reductions ranged from 18 to 42% during March 2007eApril 2008. Cocoa is an important cash crop in Southeast Asia, with most of the production dominated by small landholders for whom cocoa-related earnings are critically important (Neilson, 2007; Perdew and Shively, 2009). In Sulawesi there are an estimated 400,000 small landholders that have varying small-size plots that yield an average 832 kg/ha (Perdew and Shively, 2009). Given that 69% of these households' incomes depend on the sale of cocoa beans, predictive tools for assessing yield loss such as the ones presented here are paramount for assisting growers with their decision-making processes in terms of choosing suitable control methods. Growers can change methods if estimates of yield losses increase for instance. Naturally, to make meaningful estimations of economic costs due to CPB, such models should include more data such as fertilizer, herbicide and pesticiderelated expenses, labor inputs and, include when possible the effects of spraying on beneficial insects such as black ants and pollinators and effects of secondary pests. These coupled with information on increase in profits should provide the adequate understanding of the true costs that CPB has on the economics of cocoa. A model that predicts profitability taking into account some of these factors has been produced for Sulawesi (Perdew and Shively, 2009). Furthermore, there are price reductions that apply at farm-gate to pods and beans of inferior quality. The industry standard in Sulawesi for bean and pod values is 26 pods and 110 beans (Mumford, 2005). These quality thresholds were exceeded for pods and beans from categories B and C in our dataset, creating additional economic losses. Quality downgrading of cocoa at the farmgate is well documented by industry which as a consequence has made huge efforts to change quality outputs of cocoa and its production through various programs such as SUCCESS Alliance project, PRIMA project, and some extension work, mainly to find out the effective methods that would control this pest (Mumford, 2005; Neilson, 2007; Lambert pers. comm.). In South Sulawesi, the current preferred method for controlling CPB is spraying synthetic pyrethroids, although other methods are being tested as part of IPM strategies that include the use of kairomones, using pheromone traps to reduce CPB numbers, sleeving the pods with biodegradable bags, planting resistant cultivars which have a hard pod husk and keeping high phytosanitary standards such as regular pod harvesting and pruning of the trees. These methods are being implemented in Sulawesi to see yields and quality increase in the near future with rational minimal and efficient use of permitted insecticides (Lambert pers. comm.). The array of methods that are being tested and others are testimony of how serious this pest is for the region and how critical are these efforts in controlling it, in order to increase yield, quality and overall economic returns. Yield loss models such as those presented here should prove efficient tools for assessing and monitoring the performance of such measures. We collated information on yield contributions from each pod category, and used this information to estimate yield and yield loss but, the question about the relationship between damage and pest density remains unanswered. However, this information could form part of an integrated system for setting economic thresholds upon which application of control measures are based. The current system of opening pods is effective in estimating yield loss but may not be feasible to conduct in some circumstances due to its
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laborious methods. Thus, in order to assist growers with making informed decisions it is possible to establish parallel monitoring studies, and develop systems that integrate information from yield and pest density as shown in previous studies with other lepidopterous pests that used pheromones as a predictor of their density (Sanders, 1988; Evenden et al., 1995). In the case of cocoa, an effective CPB monitoring system is possible given that sex pheromones that attract males have been developed and tested effectively in Southeast Asia, Papua New Guinea and in Northern Queensland, Australia for controlling CPB and for monitoring purposes (Zhang et al., 2008; Royer pers. comm.). Regrettably, pest density using pheromones was not surveyed in this study. The separation of pods into four categories of damage has been practised in Sulawesi for many years for determining levels of CPB infestation as part of programs designed to identify the effects of chemical and pheromone control methods (Lambert pers. comm.). Furthermore, the separation of pods into four categories of infestation is also used to estimate yield loss of dry weight of beans through the SS model (Hussin Purung pers. obs.). Thus, given that the methodology of separating pods into four categories of damage is well established in Sulawesi, there is no need to train or instruct new techniques in regards to the methods involved and, the adoption of our MLR-based model by cocoa stakeholders should be a straightforward process. In any case, the general trend supports a model based on a system with categories of infestation rather than pod infestation across conditions where multiple cocoa varieties are present. The models developed here particularly that for pod value, should have utility in mixed-cultivar plantations, and are stepping stones towards efficient management of CPB in Southeast Asia. Acknowledgments This work was funded by Mars Inc., Ballarat, Australia. The authors are grateful to Dr Smilja Lambert for providing important network support and information on the survey that took place during 2003e2004 and 2007e2008 in South Sulawesi. All datasets used in this study are unpublished and were produced as part of a collaborative effort to study the effects of chemical and biological management strategies against CPB in South Sulawesi. We are thankful to Jane Royer, Senior entomologist from the Queensland Government Department of Agriculture, Fisheries and Forestry for providing information on the program carried out in northern Queensland to eradicate CPB. Appendix A. Supplementary data Supplementary data associated with this article can be found in the online version, at http://dx.doi.org/10.1016/j.cropro.2014.08.018. References Adomako, B., 2007. Causes and extent of yield losses in cocoa progenies. Trop. Sci. 47, 22e25. Alvim, P.T., Alvim, R., 1978. Relation of climate to growth periodicity in tropical trees. In: Tomlinson, P.B., Zimmermann, M.H. (Eds.), Tropical Trees as Living Systems. Cambridge University Press, Cambridge, UK, pp. 445e464. Benke, K.K., Hamilton, A.J., Lowell, K.E., 2007. Uncertainty analysis and risk assessment in the management of environmental resources. Aust. J. Environ. Manag. 14, 243e249. Chiarappa, L., 1981. Crop loss Assessment Methods. Supplement 3. Commonwealth Agricultural Bureaux, Famham Royal, UK. Cocoa Coconut Institute (CCI) of Papua New Guinea. The Asian Cocoa pod borer (CPB) (Conopomorpha Cramerella snellen): a report on its containment within and eradication from east New Britain province, Papua New Guinea (March 2006eJanuary 2007). Rabaul, East New Britain, Papua New Guinea. Day, R.K., 1986. Population dynamics of cocoa pod borer Acrocercops cramerella: importance of host plant cropping cycle. In: Pushparajah, E., Chew, P.S. (Eds.),
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