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

机译:龙眼树根系中基于CO 2 浓度的随机森林作为分析局部干旱胁迫的工具

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This study aims at establishing a relationship between water supply and CO_(2) 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 CO_(2) concentration in the rootzone, soil moisture and stomatal conductance indicated a weak correlation between the CO_(2) 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 CO_(2)-water stress relationship based on air temperature, relative humidity, vapour pressure deficit and soil moisture. It was shown that the most important factor on CO_(2) concentration in the rootzone was soil moisture, followed by air temperature. Together with the information retrieved, the results suggest a potential of CO_(2) monitoring in the rootzone for assessing plant water status continuously and with a minimum level of invasion.
机译:这项研究旨在建立根部区域的供水与CO_(2)浓度之间的关系,并使用数据驱动的模型来识别干扰因素。在我们以前的研究中,采用分根技术种植了10棵龙眼树,并保持在受控条件下。在六个月的时间里,根系的一侧部分灌溉了5棵树,而另一侧则保持不灌溉。双方每两周更换一次。五棵对照树的两侧都进行了充分灌溉。对根区CO_(2)浓度,土壤水分和气孔导度的监测结果表明,根区CO_(2)浓度与土壤水分之间的相关性较弱,但无统计学意义的相关性,部分原因是气温是主要因素干扰因素。在这项研究中,随机森林被用于建立基于空气温度,相对湿度,蒸气压赤字和土壤湿度的CO_(2)-水分胁迫关系。结果表明,影响根区CO_(2)浓度的最重要因素是土壤湿度,其次是气温。连同检索到的信息一起,结果表明在根区中监测CO_(2)的潜力可用于连续评估植物水分状况并以最小的入侵水平进行评估。

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