Call Prefix : PR-T

Call Suffix : 2025

Call Cutter : D - Stat 5

Location : SEARCA Library

Format : Printed

Publisher : Institut Pertanian Bogor

Publication Date : 2025

Publication Place : Indonesia

Summary : Advancements in developing prediction models based on the characteristics of data are critical as they influence the accuracy of predictions across various fields. These advancements include models designed for data constrained by specific maximum and minimum values, such as the Four Parameter Beta distribution, which is recognized for its flexibility in accommodating diverse shapes, skewness, and heavy tails. Next, prediction models should also consider the variability of data that is commonly found in conditions where data is effected by geographical differences, temporal fluctuations, environmental variability, and socio-economic disparities. Furthermore, the development of prediction models should also account for the complexity of relationships that may occur when applying different datasets. Particularly in agriculture, developing prediction models can benefit from the integration of survey data and satellite data. Understanding and modelling these complex relationships is key to improving prediction accuracy. Therefore, we have developed three studies that discuss the methods and models suitable for predicting response data characterized by a Four Parameter Beta distribution, high variability, and inherit complex relationships within datasets. This study specifically highlights the significance of these advancements in predicting paddy productivity, with a particular focus on their application to the development of Area Yield Index (AYI) crop insurance for paddy. Accurate predictions of paddy productivity are essential for determining insurance premiums and assessing risks. The first study in Chapter 3 develops the Four Parameter Beta GLMM model by implementing a transformation process. The transformation maps the actual response variable y that has an interval (a,b) to Y* with the interval (0,1). This process enables us to model and predict data by applying beta GLM or GLMM models. We use a GLMM model when the response variable is measured groups/areas (i= 1, 2, .... ,q) and j = 1,2, ... ni or apply GLM model if the data structure is more straightforward. Results show that the GLMM model is better than the GLM approach, indicating that random effects and fixed effects are needed for predicting paddy productivity. Even though, the results of the Four Parameter Beta GLMM model is promising, the transformation process can cause bias in parameter estimates and complications in the interpretation of coefficient values. In the next study presented in Chapter 4, we have further developed Zhou and Huang's (2022) which means Four Parameter Beta regression model by introducing a random effect within the model. This model was developed based on a Bayesian approach through a Stan package in R software. Simulation studies showed that the parameter estimates of the model are considered relatively unbiased, except for precision parameter. Furthermore, empirical study shows that the proposed Four Parameter Beta GLMM predictions are more accurate than Zhou and Huang's benchmark model. For the third study in Chapter 5, we can further improve the prediction accuracy by addressing more complex data. As an example, when integrating farmer survey and satellite data in our model, both linear and non-linear relationships emerge. Thus, the four-parameter beta GLMM has been further developed into a Generalized Mixed Effect Tree (GMET) and a Generalized Mixed Effect Random Forest (GMERF). Empirical case study wise, these models proved to be more suitable for predicting paddy productivity compared to the Four Parameter Beta GLMM when using satellite data and farmer surveys. At the end of Chapter 5, we have also evaluated the developed model's prediction accuracy and selected the best model. By calibrating the best model to empirical data, extensive Bootstrap studies were performed to estimate the pure premium and VaR of AYI. It was shown that designing AYI at district level is more appropriate when productivity among areas vary. Consideration must also be given in defining the benchmark productivity when there is proof that the distribution of paddy productivity follows a Four Parameter Beta distribution. The use of satellite data in the model has proven a beneficiary as it provides valuable, large-scale, and temporally consistent information. It is also more efficient compared to conducting massive field surveys. However, satellite data still may need to be combined with survey data to capture localized, context-specific factors that satellites alone might not fully address. Typically, AYI premiums are calculated using average historical productivity data, which lacks flexibility and does not account for dynamic factors like climate or pest outbreaks. In contrast, predictive models such as the Four Parameter Beta GLMM, GMET, and GMERF offer a more refined, data-driven approach to estimating paddy productivity, improving adaptability, accuracy, and responsiveness to agricultural changes. Hence, enhancing risk assessment and leading to more effective insurance products. Consequently, farmers are ensured fair and adequate compensation in cases of crop failure, while insurers maintain financial stability.