Declined peat heterotrophic respiration as consequences from zeolite amendment simulation: coupling descriptive and predictive modelling approaches

Authors

  • Heru Bagus Pulunggono Department of Soil Science and Land Resource, Faculty of Agriculture, IPB University http://orcid.org/0000-0003-3924-7839
  • Nabila Hanifah Graduate Program of Soil Science and Land Resources Department, Faculty of Agriculture, IPB University, 16680, West Java, Indonesia
  • Desi Nadalia Department of Soil Science and Land Resource, Faculty of Agriculture, IPB University http://orcid.org/0000-0001-5495-7340
  • Moh Zulfajrin Graduate Program of Soil Science and Land Resources Department, Faculty of Agriculture, IPB University, 16680, West Java, Indonesia http://orcid.org/0000-0001-8231-3622
  • Lina Lathifah Nurazizah Graduate Program of Agronomy and Horticulture Department, Faculty of Agriculture, IPB University, 16680, West Java, Indonesia http://orcid.org/0000-0002-4229-6896
  • Husni Mubarok Agronomy Research, Astra Agro Lestari Tbk, Jakarta
  • Nizam Tambusai Agronomy Research, Astra Agro Lestari Tbk, Jakarta, Indonesia
  • Syaiful Anwar Department of Soil Science and Land Resource, Faculty of Agriculture, IPB University http://orcid.org/0000-0002-9928-5821
  • Supiandi Sabiham Department of Soil Science and Land Resource, Faculty of Agriculture, IPB University http://orcid.org/0000-0002-1995-5670

DOI:

https://doi.org/10.15243/jdmlm.2022.101.3889

Keywords:

artificial intelligence, CO2 emission, machine learning, multivariate analysis, pedotransfer modelling

Abstract

Nowadays, halting greenhouse gasses (GHG) emission is the world's major concern to mitigate global climate change. In oil palm cultivated tropical peatland, GHG emission is primarily constituted of CO2 flux emitted from aerobic heterotrophic respiration (Rh), the natural degradation process of organic material in an oxidative environment. By coupling descriptive and predictive statistical approaches, this study attempt to gain an in-depth understanding of the effects of zeolite rates and incubation time on CO2 emission that came from aerobic Rh in peat, as well as their decomposition process. This study found that zeolite amelioration up to 30% of the peat at field capacity and starting from the first month of observation (month-1) significantly restricted peat Rh, denoted by a reduced amount of observed CO2 flux (0.021 and 0.019-0.012 mg m-2 sec-1, respectively). Both factors and several soil variables exhibited some non-linear relationships with Rh at different magnitudes and importance, showing the limitation of the traditional linear-based approach to interpreting their complex interrelationships, as well as predicting CO2 flux. This study highlights the vital role of a polynomial (GAM) and artificial intelligence (Cubist and GBM) -based pedotransfer models in improving our understanding regarding the dynamic of the peat decomposition process as affected by zeolite amendment.

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Submitted

14-06-2022

Accepted

02-08-2022

Published

01-10-2022

How to Cite

Pulunggono, H. B., Hanifah, N., Nadalia, D., Zulfajrin, M., Nurazizah, L. L., Mubarok, H., Tambusai, N., Anwar, S., & Sabiham, S. (2022). Declined peat heterotrophic respiration as consequences from zeolite amendment simulation: coupling descriptive and predictive modelling approaches. Journal of Degraded and Mining Lands Management, 10(1), 3889–3904. https://doi.org/10.15243/jdmlm.2022.101.3889

Issue

Vol. 10 No. 1 (2022)

Section

Research Article

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