Modeling Whole-Plant Carbon Stock in Olea europaea L. Plantations Using Logarithmic Nonlinear Seemingly Unrelated Regression

Abstract

Carbon stock (CS) is an important indicator of the structure and function of forest ecosystems, and plays an important role in mitigating climate change, maintaining ecological system balance, promoting carbon trading, and other socioeconomic and ecological values. Olea europaea L. is a species of high economic and ecological value, and its excellent nutritional composition, strong drought tolerance, sustainable production characteristics, and promotion of agrodiversity make it important in guaranteeing food security. Accurately estimating the CS of Olea europaea L. offers a reliable reference for its artificial breeding and yield prediction. Firstly, an independent estimation model of Olea europaea L. CS was constructed, while a compatibility model of Olea europaea L. unitary and binary CS was constructed using nonlinear metric error. Secondly, in the CS compatibility model system, the total CS model of Olea europaea L. was constructed by the Logarithmic Nonlinear Seemingly Unrelated Regression (LNSUR) method with D and D2H as independent variables. The results show: (1) The independent model of Aboveground CS (AGCS) was C = 0.0014D1.92876H0.67174 (R2 = 0.909), and the independent model of Belowground CS (BGCS) was C = 0.00723D1.23578H0.48553 (R2 = 0.686). The AGCS compatibility model effectively addresses the issue of component sums not equaling the total, while maintaining a low RMSE (1.918); (2) The LNSUR model improved the accuracy of the BGCS model more significantly (R2 = 0.787), and the estimated total CS also had a smaller RMSE (0.241~0.418); (3) Whole-plant CS of Olea europaea L. in 15 sample plots was estimated using the CS independent model and the LNSUR model with an R2 of 0.964. This study is the first attempt to construct a CS estimation model for Olea europaea L., which provides a scientific and technological basis for the monitoring of its economic and ecological value indicators, such as yield and carbon sink capacity.

Plant phenotyping relevance

オリーブ個体の全体炭素蓄積量という植物状態を推定する回帰モデルを構築・比較しており、炭素蓄積量の取得・推定手法が研究の中心である。

Code and data availability

The supplied blocks describe field-measured carbon stock data (120 modeling trees, 284 validation trees) and modeling in SPSS, Origin, PyCharm/Python, and Stata, but contain no data availability statement, no deposited phenotype dataset, no author code repository, and no public URL beyond the article DOI itself. Noqual

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