Density Survey of Sago Palms in Sago-identified Areas in Mindanao, Southern Philippines

Sago palm, locally known as lumbia, is one among the many sources of flour. In the Philippines, especially in Mindanao however, it is an untapped resource. Thus, the density of natural sago stands in several areas of Mindanao was determined in this study. Sample locations were identified in several areas of Mindanao and were surveyed using the transect method to determine the population density of sago palms in various growth stages. The number of sago palms in rosette, bole formation, inflorescence, and fruiting stages were determined and categorized according to their age by counting the number of leaf scars and fronds in stand. Transect survey was conducted according to dry, wet, and submerged soil conditions. Further stratification was also done to delineate sago density in areas where local community used sago shingles for roofing and household business. Results showed that sago palms are very dense in Agusan del Sur and Agusan del Norte, and approximately 8,800 trees are in the stage of inflorescence which can be harvested within 1 to 2 years; a total of 209,000 stand in bole formation stage which can be harvested after 4 to 5 years; and about 6.5 million rosette in varying ages. These results indicate that the sago palm can be a potential economic enterprise for farmers within Mindanao

Assessment of Potential Sago Starch Content in Sago Palm Forests in Mindanao, Southern Philippines

This paper presents the results of the assessment of the potential yield of sago palms in Mindanao, Southern Philippines. Previous studies have characterized the growth parameters of sago palms as influenced by their environmental growth conditions and anthropogenic activities of the local residents in the area, thereby affecting the potential yield of starch. In this study, a total of 60 sago palms were harvested and cut into logs and brought to central facility where each log was manually debarked, stripped, solar dried, milled, and sieved. Samples were obtained from identified 3 environmental growth environments (dry, wet, and submerged soil conditions) and from undisturbed and disturbed areas where the local community utilized the leaves for roofing. The results showed that on average, starch yield was maximum in palms grown in wet environment and without anthropogenic disturbance. Furthermore, the results were consistent with previous works that starch is at the maximum at inflorescent stage (247.5 kg/palm), and in bole formation stage (127.1 kg/palm), and lowest at fruiting stage (63.1 kg/palm). With these results and utilizing the sago density data from a previous study, we conclude that 2,000 tons of starch can be harvested within 1 to 2 years and about 20,000 tons in another 4 to 5 years. Further study is recommended to include cost of production in the sustainability assessment

Starch Yield Based on Physical Dimensions and Age of Sago Palm: A Mathematical Model

This study employed firefly algorithm (FA) to generate a mathematical model of sago palm’s potential starch yield based on the physical dimensions, namely, diameter breast height (DBH), palm height, and age. Three environmental conditions (i.e., dry, wet, and submerged) were taken into consideration in the modelling process using the general linear and nonlinear models. Moreover, the resulting models were assessed using sum of squared residuals (SSR) as FA’s fitness function and mean absolute percentage error (MAPE) for the models’ accuracy. Results show that general linear models are the best fit models for the sago palms growing in the three different environmental conditions with respect to the considered parameters. These models were used to quantitatively describe the underlying relationships between the starch yield with respect to the physical dimensions and age in order to determine the maximum potential starch yield of sago palm for the different environmental conditions. The models estimate that the maximum potential starch yield for dry, wet, and submerged environmental conditions are as follows: 0.75 m, 0.35 m, and 0.75 m for DBH, respectively; 10.5 m for palm height for all three; and 11.5 years, 15.5 years, and 15.5 years for age, respectively. These results will be able to aid farmers and potential investors in maximizing their sago starch produce. This will also help them as a guide for identifying harvestable sago palms which can be incorporated in their harvest plan

Sago Palm Flour Weight in Different Environmental Conditions: A Mathematical Model

For the past decades, the demand for starch from the sago palm (Metroxylon sagu Rottb.) from the starch industry is increasing because of the palm’s high starch yield and low cost of production. This study presented a model which illustrates the relationship of sago palm flour weight with respect to its age depending on sago palm environmental condition (i.e., dry, wet, or submerged). Five different growth models were considered in this study, namely, the quadratic, cubic, quartic, power, and logarithmic models, which were ran using a metaheuristic approach, specifically genetic algorithm (GA), in order to estimate the weights associated with the independent variable age and to generate an estimate for the dependent variable flour weight. GA performance was measured using sum of squared residuals (SSR) as the fitness function while the accuracy of the models were measured using the mean absolute percentage error (MAPE). The results show that the best fit model models for dry, wet, and submerged environmental conditions are cubic, cubic, and quartic models, respectively. The best fit models generated SSR values closer to the tolerance value of 0.000001 and have MAPE values of 2.820, 1.366, and 4.316, respectively, which indicate high accuracy. These models will help aide potential investors or land owners to identify the maximum potential starch yield of sago palm in areas where data with respect to growth stages are only available

A Forecast for Cocoa Bean Farm Gate Prices in Davao Region, Southern Philippines, Using Generalized Autoregressive Conditional Heteroscedasticity

The Davao Region is one of the top cocoa producers in Southern Philippines. The region aims to invest in cocoa production by enticing cocoa bean farmers to increase their production of the said commodity. This is possible if their cocoa beans produced have a good farm-gate price. Thus, this study was done to forecast the cocoa bean farm-gate prices in the Davao Region. The data for monthly cocoa bean farm-gate prices in Davao Region for the period of January 1990 to December 2015 was used as an input to the generalized autoregressive conditional heteroscedasticity (GARCH) to come up with a time series model. Mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and Theil’s inequality coefficient (U-statistics) were used as the forecasting accuracy criteria in identifying the best fit model. The results of the study revealed that the time series data was influenced by a positive linear trend factor and also indicates that no seasonal factor exists. Moreover, the best model is GARCH (1, 2). Furthermore, a ten-year forecast was done for fiscal years 2016–2025. By discovering the price movement of the cocoa beans in the next ten years, farmers should maximize their production and sell their produce when the prices are estimated to increase. In contrast, when the prices are estimated to decline, farmers should use storing techniques and employ timed planting decisions in order to lessen the reduction of their profits