Garuda - Garba Rujukan Digital

Moh Zulfajrin

Bachelor of Agriculture, Department of Soil Science, Faculty of Agriculture, IPB University, Bogor, 16680, West Java, Indonesia

Author-ID : 5555583

Agriculture, Biological Sciences & Forestry

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Evaluating the Effect of Fire on Cultivated Tropical Peat Properties: Lessons Learned from Observation in East Kutai Peatlands: The effect of fire on cultivated tropical peat properties Heru Bagus Pulunggono; Moh Zulfajrin; Nabila Hanifah; Lina Lathifah Nurazizah
CELEBES Agricultural Vol. 3 No. 1 (2022): CELEBES Agricultural
Publisher : Faculty of Agriculture, Tompotika Luwuk University

Fire and its associated impact highly affect peatland, particularly the peat properties, the plant cultivated above it, and its surrounding environment. Despite much research focused on fire monitoring or susceptibility assessments, peat consumption during fire occurrence, emissions from burned peat, and rehabilitation or restoration of burned peat, little attention is given to studying the changes of peat bio-physicochemical after burning. This small-scale study aims to examine the fire’ effect on the upper 30 cm of peat’s bio-physicochemical properties two months after being burned, using unburned peat as a reference. The result of this study indicated that fire-affected peat at all of our sampling depths. The impacted changes on peat chemical variables were varied. This study also found that sampling methods, fire magnitude and severity, peat physicochemical properties, laboratory determination, and statistical analyses were paramount in examining the fire effect on peat properties. This study also promotes the combination approach that represents both local and global phenomena to analyze and interpret the change of burned peat properties from its initial unburned state. More efforts are required to verify the initial results reported in this study and to gain in-depth information concerning the intricate relationships of organic materials, climate, hydrology, and vegetation across spatial and temporal scales in cultivated tropical peat as affected by fire events.

Identifying the Underlying Factors and Variables Governing Macronutrients in Cultivated Tropical Peatland Using Regression Tree Approach: Using Tree Regression Approach to Determine the Factors Influencing Total N, P, and K in Cultivated Tropical Peat Heru Bagus Pulunggono; Yusuf Azmi Madani; Moh Zulfajrin; Yusrizal
CELEBES Agricultural Vol. 3 No. 1 (2022): CELEBES Agricultural
Publisher : Faculty of Agriculture, Tompotika Luwuk University

The capability of machine learning/ML algorithms to analyze the effect of human and environmental factors and variables in controlling soil nutrients has been profoundly studied over the last decades. Unfortunately, ML utilization to estimate macronutrients and their governing factors in cultivated tropical peat soil are extremely scarce. In this study, we trained regression tree/RT, ML-based pedotransfer models to predict total N, P, and K in peat soils based on oil palm/OP and OP+bush datasets. Our results indicated that the dataset might contain outliers, non-linear relationships, and heteroscedasticity, allowing RT-based models to perform better compared to multiple linear regression/MLR models (as a benchmark) in estimating total N and P in both datasets, contrastingly, not in K. The difference of important variables in each RT-based model partially showed the vital role of land use in nutrient modeling in peat. The depth of sample collection, organic C, and ash content became the prominent factor and variables in regulating the entire predicted nutrients. Meanwhile, the distance from the oil palm tree and pH were the salient features of total P prediction models in OP and OP+bush sites, respectively. This study proposed employing ML-based pedotransfer models in analyzing and interpreting complex tropical peat data as an alternative to linear-based regression. Our initial study also shed more light on the development possibility of the pedotransfer models that agricultural practician, researchers, companies, and farmers can use to predict macronutrients, both in tabular and spatial terms, in cultivated tropical peatlands

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