Random Forests as a Tool for Analyzing Partial Drought Stress Based on CO₂ Concentrations in the Rootzone of Longan Trees

• FUKUDA Shinji

  Institute of Agriculture, Tokyo University of Agriculture and Technology

• SPREER Wolfram

  Institute of Agricultural Engineering, University of Hohenheim Faculty of Agriculture, Chiang Mai University

• WIRIYA-ALONGKORN Winai

  Department of Soil Resources and Environment, Mae Jo University

• SPOHRER Klaus

  Institute of Agricultural Engineering, University of Hohenheim

• YASUNAGA Eriko

  Institute for Sustainable Agro-ecosystem Services, Graduate School of Agricultural and Life Sciences, The University of Tokyo

• TIYAYON Chantalak

  Faculty of Agriculture, Chiang Mai University

Bibliographic Information

Other Title

• Random Forests as a Tool for Analyzing Partial Drought Stress Based on CO₂ Concentrations in the Rootzone of Longan Trees

Abstract

This study aims at establishing a relationship between water supply and CO₂ concentration in the rootzone, and to identify disturbing factors using data-driven modelling. In our previous study, 10 longan trees were planted in split-root technique and kept under controlled conditions. During six months, 5 trees were partially irrigated on one side of the root system, while the other side was kept non-irrigated. The sides were switched in a two-week interval. Five control trees received full irrigation on both sides. Monitoring results on CO₂ concentration in the rootzone, soil moisture and stomatal conductance indicated a weak correlation between the CO₂ concentration in the rootzone and the soil moisture, but without a statistically significant correlation, partially because air temperature was a main disturbing factor. In this study, Random Forests was applied to establish a CO₂-water stress relationship based on air temperature, relative humidity, vapour pressure deficit and soil moisture. It was shown that the most important factor on CO₂ concentration in the rootzone was soil moisture, followed by air temperature. Together with the information retrieved, the results suggest a potential of CO₂ monitoring in the rootzone for assessing plant water status continuously and with a minimum level of invasion.

Journal

• Environment Control in Biology

  Environment Control in Biology 56 (2), 25-31, 2018

  Japanese Society of Agricultural, Biological and Environmental Engineers and Scientists

Keywords

• data-driven model
• deficit irrigation
• <i>Dimocarpus longan</i>
• machine learning

Details

• CRID: 1390282680237102592
• NII Article ID: 130006712463
• NII Book ID: AA12029220
• DOI: 10.2525/ecb.56.25
• ISSN: 18830986
• ISSN: 1880554X
• NDL BIB ID: 028942106
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I028942106
• Web Site: https://www.jstage.jst.go.jp/article/ecb/56/2/56_25/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • NDL
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed