Sains Tanah: Journal of Soil Science and Agroclimatology (Jan 2022)

Estimates of methane and nitrous oxide emission from a rice field in Central Java, Indonesia, based on the DeNitrification DeComposition model

  • Umi Munawaroh,
  • Komariah Komariah,
  • Dwi Priyo Ariyanto,
  • Muhamad Khoiru Zaki,
  • Keigo Noda

DOI
https://doi.org/10.20961/stjssa.v19i1.56928
Journal volume & issue
Vol. 19, no. 1
pp. 1 – 11

Abstract

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Indonesia is the world’s third largest rice producer, with most rice being cultivated (estimated 3.1 million ha) in Central Java. However, one of the environmental challenges in producing rice is greenhouse gas (GHG) emissions from rice fields. Therefore, understanding the GHG emissions (methane and nitrous oxide) from the rice farming system is important for better management practices. The objective of this study is to estimate the GHG emissions supported by a satellite database, namely, the DeNitrification DeComposition (DNDC) model, at three regencies at Central Java, Indonesia, Cilacap, Karanganyar, and Pati, as well as the factors determining the emissions. The DNDC model was obtained from https://www.dndc.sr.unh.edu, which consists of three main submodels that worked together in simulating N2O and N2 emissions: (1) the soil-climate/thermal-hydraulic flux submodel, (2) the decomposition submodel, and (3) the denitrification submodel. The results showed that the N2O emissions from rice farming in Karanganyar, Cilacap, and Pati were 19.0, 18.8, and 12.8 kg N ha−1 yr−1, respectively, while they were 213.7, 270.6, and 360.6 kg C ha−1 yr−1 for CH4 emissions, respectively. Consecutive dry or high precipitation, which resulted in cumulative depleted or elevated soil moisture, respectively, along with warmer temperature likely promoted higher methane and nitrous oxide. Experimental fields for validating the model in accordance with various agricultural practices are suggested for further study. Overall, the DNDC model has successfully estimated the CH4 and N2O emissions in Central Java when incorporated with various secondary climatic and land management big data resources.

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