Abstract
Background and Objective: SRI paddy field occasionally experiences aerobic soil conditions that emit complicated greenhouse gases due to biophysical processes. Understanding these emission patterns, as well as the amount, is important to determine the proper mitigation action to take. This study was carried out to produce a simple method to estimate CH4 and N2O using easily measure environmental parameters that might be used to simulate the mitigation of non-CO2 emissions. Materials and Methods: Two Artificial Neural Network (ANN) models were developed to estimate methane (CH4) and nitrous oxide (N2O) fluxes based on three selected variables. Based on the models, patterns of CH4 and N2O emissions in SRI paddy fields were presented in the form of a triangle graph that can be used to estimate emissions from the easily measured soil pH, soil moisture and air temperature. A sensitivity test (Spearman’s correlation test) was used to statistically analyze data. Results: ANN models were developed to estimate emission fluxes based on three selected variables of soil pH, soil moisture and air temperature that could produce R2 0.96 and 0.82 for CH4 and N2O, respectively. CH4 emission in the SRI paddy field increased with air temperature but decreased when soil moisture decreased, while N2O emissions were mostly stable at all times regardless of changes in soil moisture and air temperature. In general, a higher soil pH produced higher CH4 and N2O emissions. The triangle graph shows that in SRI paddy field with soil pH, soil moisture and air temperature range 4.30-5.30, 0.354-0.524 m3 m-3, 29.0-32.5EC, respectively; it could be a sink or emission of methane until more than 45 mg m-2 day-1 and be able to emit nitrous oxide to more than 6 µg m-2 day-1. Conclusion: SRI paddy field can be emission source of CH4 and N2O. The graphs can be used to identify mitigation action that can be implemented to lower emissions by showing the set-point value of soil moisture. For example, in an air temperature of 29.4oC and soil pH condition of 4.8, to minimize the emission of CH4 and N2O, the soil moisture should be less than 0.418 m3 m-3.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 206-214 |
| Number of pages | 9 |
| Journal | Journal of Environmental Science and Technology |
| Volume | 10 |
| Issue number | 4 |
| DOIs | |
| State | Published - 2017 |
Bibliographical note
Funding Information:This work was supported by grant numbers 081/SP2H/PL/Dit.Litabmas/VI/2014, 157/SP2H/ PL/Dit. Litabmas/II/2015 and 180/SP2H/PL/Dit.Litabmas/II/2016 under PMDSU research project “Automation of Irrigation and Drainage to Improve the Productivity of Land and Water and Reduce Greenhouse Gas Emissions Factor” from the Ministry of Research, Technology and Higher Education, Indonesia. Paperwork was supported by the same ministry, especially by Directorate General of Resources for Science, Technology and Higher Education through PKPI scholarship. We greatly appreciate the warm reception from Department of Bioproducts and Biosystems Engineering, University of Minnesota and Department of Global Agricultural Sciences, The University of Tokyo during paperwork activity. I would additionally like to thank Prof. K. Noborio (Meiji University) for his scientific advice and discussion about greenhouse gases emission from agricultural soil.
Publisher Copyright:
© 2017 Nur Aini Iswati Hasanah et al.
Keywords
- Artificial neural network
- Emission estimation graphs
- Mitigation
- Soil moisture
- System of rice intensification
