Developing the plant disease model to manage the cocoa black pod disease

• Main Author: Ling, Albert Sheng Chang
• Format: Thesis
• Language: English; English
• Published: 2019
• Subjects: SB183-317 Field crops Including cereals, forage crops, grasses, legumes, root crops, sugar plants, textile plants, alkaloidal plants, medicinal plants
• Online Access: https://eprints.ums.edu.my/id/eprint/40521/1/24%20PAGES.pdf; https://eprints.ums.edu.my/id/eprint/40521/2/FULLTEXT.pdf; https://eprints.ums.edu.my/id/eprint/40521/
• Institution: Universiti Malaysia Sabah

Cocoa smallholder in Malaysia facing a problem of low productivity for the past 10 years and one of the reasons was due to the cocoa black pod disease. The resistant breeding was the best long term solution to the cocoa black pod disease problem that need a reliable screening technique to predict the disease severity progress and identify the resistance level of genotypes. As for the short term solutions, it is important to know which combination of control measures is effective in controlling the cocoa black pod disease incidence. Fungicide application was the most preferred control measure by the cocoa smallholders and can be effectively applied by forecasting when the cocoa disease incidence was likely to be at economic threat. Currently, there are no existing statistical model that can be used for predicting disease severity and estimating the area under disease progress curve to rank the genotype’s resistant level in cocoa and also statistical model that can forecast the disease incidence and used in decision making for fungicide application. This study has four objectives to be achieved. The first objective is to develop the nonlinear statistical model of the cocoa black pod severity progress using Monomolecular, Exponential, Logistic and Gompertz models and followed by estimating the Area Under Disease Progress Curve (AUDPC) on four cocoa genotypes of different resistant categories (i.e. KKM 4 (susceptible), KKM 5 (moderately susceptible), BR 25 (moderately resistant) and QH1003 (resistant) at two pod development stages (i.e. young pod and mature pod). The measurements for disease severity was made daily for a duration of six days after inoculated with Phytophthora palmivora. The results of study has identified Gompertz model as the best fitted nonlinear model with the smallest values of the Akaike Information Criterion test and the Bayesian Information Criterion test. The second objective is to determine resistance of cocoa genotypes against the cocoa black pod disease by using AUDPC value. The results of study showed the estimated AUDPC value proved that the new protocol of screening the mature genotype’s resistant level to the cocoa black pod disease severity gave 100% accuracy with similar results to the field assessment compared to standard assessment that gave 50% accuracy. The third objective is to develop a statistical model to forecast the cocoa black pod incidence by comparing ARIMA approach and ARIMAX approach. The results of the study proved ARIMAX models, known as combination linear regression and the ARIMA process performance better than ARIMA models based on the mean squared error, root mean squared error and mean absolute error. The fourth objective is to estimate losses from the cocoa black pod incidence by using the best fitted model developed in this study. The results showed integrated treatment of pruning, fungicide application and phytosanitary gave the lowest forecasted economic losses, followed by the integrated treatment of pruning and phytosanitary and then integrated treatment of fungicide application and phytosanitary. In conclusion, the Gompertz model built on disease severity in this study can potentially assist breeders to determine the genotypes’ resistant level to the cocoa black pod disease while the ARIMAX model built on disease incidence can guide the cocoa farmers to decide when to apply fungicide based on the expected losses and the cost of applying fungicide.