Developing Additive Systems of Biomass Equations for <i>Robinia pseudoacacia</i> L. in the Region of Loess Plateau of Western Shanxi Province, China
The accurate estimation of forest biomass is important to evaluate the structure and function of forest ecosystems, estimate carbon sinks in forests, and study matter cycle, energy flow, and the effects of climate change on forest ecosystems. Biomass additivity is a desirable characteristic to predi...
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MDPI AG
2020-12-01
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| Online Access: | https://www.mdpi.com/1999-4907/11/12/1332 |
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| author | Yanhong Cui Huaxing Bi Shuqin Liu Guirong Hou Ning Wang Xiaozhi Ma Danyang Zhao Shanshan Wang Huiya Yun |
| author_facet | Yanhong Cui Huaxing Bi Shuqin Liu Guirong Hou Ning Wang Xiaozhi Ma Danyang Zhao Shanshan Wang Huiya Yun |
| author_sort | Yanhong Cui |
| collection | DOAJ |
| description | The accurate estimation of forest biomass is important to evaluate the structure and function of forest ecosystems, estimate carbon sinks in forests, and study matter cycle, energy flow, and the effects of climate change on forest ecosystems. Biomass additivity is a desirable characteristic to predict each component and the total biomass since it ensures consistency between the sum of the predicted values of components such as roots, stems, leaves, pods, and branches and the prediction for the total tree. In this study, 45 <i>Robinia pseudoacacia</i> L. trees were harvested to determine each component and the total biomass in the Loess Plateau of western Shanxi Province, China. Three additive systems of biomass equations of <i>R. pseudoacacia</i> L., based on the diameter at breast height (<i>D</i>) only and on the combination of <i>D</i> and tree height (<i>H</i>) with <i>D<i><sup>2</sup></i>H</i> and <i>D<sup>b</sup>H<sup>c</sup></i>, were established. To ensure biomass model additivity, the additive system of biomass equations considers the correlation among different components using simultaneous equations and establishes constraints on the parameters of the equation. Seemingly uncorrelated regression (SUR) was used to estimate the parameters of the additive system of biomass equations, and the jackknifing technique was used to verify the accuracy of prediction of the additive system of biomass equations. The results showed that (1) the stem biomass contributed the most to the total biomass, comprising 51.82% of the total biomass, followed by the root biomass (24.63%) and by the pod and leaf biomass, which accounted for the smallest share, comprising 1.82% and 2.22%, respectively; (2) the three additive systems of biomass equations of <i>R. pseudoacacia</i> L. fit well with the models and were effective at making predictions, particularly for the root, stem, above-ground, and total biomass (<i>R<i><sup>2</sup></i><sub>adj</sub></i> > 0.812; root mean square error (<i>RMSE</i>) < 0.151). The mean absolute error (MAE) was less than 0.124, and the mean prediction error (MPE) was less than 0.037. (3) When the biomass model added the tree height predictor, the goodness of fit <i>R</i><sup>2</sup><i><sub>adj</sub></i> increased, RMSE decreased, and the accuracy of prediction was much improved. In particular, the additive system, which was developed based on <i>D<sup>b</sup>H<sup>c</sup></i> combination prediction factors, was the most accurate. The additive system of biomass equations established in this study can provide a reliable and accurate estimation of the individual biomass of <i>R. pseudoacacia</i> L. in the Loess region of western Shanxi Province, China. |
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| spelling | doaj.art-323de6369e7143cbb59394dab28803d22023-11-21T00:48:15ZengMDPI AGForests1999-49072020-12-011112133210.3390/f11121332Developing Additive Systems of Biomass Equations for <i>Robinia pseudoacacia</i> L. in the Region of Loess Plateau of Western Shanxi Province, ChinaYanhong Cui0Huaxing Bi1Shuqin Liu2Guirong Hou3Ning Wang4Xiaozhi Ma5Danyang Zhao6Shanshan Wang7Huiya Yun8College of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, ChinaCollege of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, ChinaCollege of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, ChinaCollege of Forestry, Sichuan Agricultual University, Sichuan 611130, ChinaCollege of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, ChinaCollege of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, ChinaCollege of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, ChinaCollege of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, ChinaCollege of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, ChinaThe accurate estimation of forest biomass is important to evaluate the structure and function of forest ecosystems, estimate carbon sinks in forests, and study matter cycle, energy flow, and the effects of climate change on forest ecosystems. Biomass additivity is a desirable characteristic to predict each component and the total biomass since it ensures consistency between the sum of the predicted values of components such as roots, stems, leaves, pods, and branches and the prediction for the total tree. In this study, 45 <i>Robinia pseudoacacia</i> L. trees were harvested to determine each component and the total biomass in the Loess Plateau of western Shanxi Province, China. Three additive systems of biomass equations of <i>R. pseudoacacia</i> L., based on the diameter at breast height (<i>D</i>) only and on the combination of <i>D</i> and tree height (<i>H</i>) with <i>D<i><sup>2</sup></i>H</i> and <i>D<sup>b</sup>H<sup>c</sup></i>, were established. To ensure biomass model additivity, the additive system of biomass equations considers the correlation among different components using simultaneous equations and establishes constraints on the parameters of the equation. Seemingly uncorrelated regression (SUR) was used to estimate the parameters of the additive system of biomass equations, and the jackknifing technique was used to verify the accuracy of prediction of the additive system of biomass equations. The results showed that (1) the stem biomass contributed the most to the total biomass, comprising 51.82% of the total biomass, followed by the root biomass (24.63%) and by the pod and leaf biomass, which accounted for the smallest share, comprising 1.82% and 2.22%, respectively; (2) the three additive systems of biomass equations of <i>R. pseudoacacia</i> L. fit well with the models and were effective at making predictions, particularly for the root, stem, above-ground, and total biomass (<i>R<i><sup>2</sup></i><sub>adj</sub></i> > 0.812; root mean square error (<i>RMSE</i>) < 0.151). The mean absolute error (MAE) was less than 0.124, and the mean prediction error (MPE) was less than 0.037. (3) When the biomass model added the tree height predictor, the goodness of fit <i>R</i><sup>2</sup><i><sub>adj</sub></i> increased, RMSE decreased, and the accuracy of prediction was much improved. In particular, the additive system, which was developed based on <i>D<sup>b</sup>H<sup>c</sup></i> combination prediction factors, was the most accurate. The additive system of biomass equations established in this study can provide a reliable and accurate estimation of the individual biomass of <i>R. pseudoacacia</i> L. in the Loess region of western Shanxi Province, China.https://www.mdpi.com/1999-4907/11/12/1332<i>Robinia pseudoacacia</i> L.Loess Plateauadditive biomass modelserror structurelikelihood analysisseemingly unrelated regression |
| spellingShingle | Yanhong Cui Huaxing Bi Shuqin Liu Guirong Hou Ning Wang Xiaozhi Ma Danyang Zhao Shanshan Wang Huiya Yun Developing Additive Systems of Biomass Equations for <i>Robinia pseudoacacia</i> L. in the Region of Loess Plateau of Western Shanxi Province, China Forests <i>Robinia pseudoacacia</i> L. Loess Plateau additive biomass models error structure likelihood analysis seemingly unrelated regression |
| title | Developing Additive Systems of Biomass Equations for <i>Robinia pseudoacacia</i> L. in the Region of Loess Plateau of Western Shanxi Province, China |
| title_full | Developing Additive Systems of Biomass Equations for <i>Robinia pseudoacacia</i> L. in the Region of Loess Plateau of Western Shanxi Province, China |
| title_fullStr | Developing Additive Systems of Biomass Equations for <i>Robinia pseudoacacia</i> L. in the Region of Loess Plateau of Western Shanxi Province, China |
| title_full_unstemmed | Developing Additive Systems of Biomass Equations for <i>Robinia pseudoacacia</i> L. in the Region of Loess Plateau of Western Shanxi Province, China |
| title_short | Developing Additive Systems of Biomass Equations for <i>Robinia pseudoacacia</i> L. in the Region of Loess Plateau of Western Shanxi Province, China |
| title_sort | developing additive systems of biomass equations for i robinia pseudoacacia i l in the region of loess plateau of western shanxi province china |
| topic | <i>Robinia pseudoacacia</i> L. Loess Plateau additive biomass models error structure likelihood analysis seemingly unrelated regression |
| url | https://www.mdpi.com/1999-4907/11/12/1332 |
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