Growth Indicators of Main Species Predict Aboveground Biomass of Population and Community on a Typical Steppe.

Abstract

The objective was to explore a fast, accurate, non-destructive, and less disturbance method for predicting the aboveground biomass (AGB) of the typical steppe, by using plant height and canopy diameter of the dominant species, Stipa bungeana , Artemisia capillaris , and Lespedeza davurica , data were observed from 165 quadrats during the peak plant growing season, and the product of plant height (PH) and canopy diameter (PC) were calculated for each species. AGB of population were predicted for the same species and other species through using 2/3 of the measured data, and the optimal predictive equation was linear in terms of determination coefficient. The other 1/3 of the data, which was measured from no grazing paddocks or rotational grazing paddocks, was substituted into the predictive equations for validation. Results showed that PC of one dominant species could be used to predict aboveground biomass (AGB) of the same species or other species well. The predicted and measured values were significantly correlative, and most of the predictive accuracy was above 80%, and not affected by the managements of grassland, grazing or no grazing. A combination of 3 to 6 representative species was used to predict AGB of the community, and the predictive equations with PC of six species as an independent variable were the most optimal because explaining 83.5% variation of AGB. The predictive methods cost 1/15, 1/9, and 1/51 of time, labor, and capital as much as the destructive sample method (quadrat sampling method), respectively, and thus improved the efficiency of field study and protecting the fragile study areas, especially the long-term study sites in grassland.

Plant phenotyping relevance

植物高とキャノピー径から地上部バイオマスを非破壊推定する測定・予測法を開発し、独立データで検証しており、表現型取得法が中心である。

Code and data availability

The article reports field-measured plant height, canopy diameter, and aboveground biomass data (165 quadrats) and regression models, but contains no data availability statement, no public dataset deposit, no code or model repository, and no supplementary asset URL. The only URL present is the CC BY license link, which,

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