Additivity on nonlinear stem taper functions: A case for Corsican pine in northern Spain

Producción CientíficaA system of additive equations was developed to predict whole-tree volume and the different components of Corsican pine. In this work, the nonlinear seemingly unrelated regression (NSUR) approach, which guarantees additivity in nonlinear equations, was evaluated. The effect of bark thickness on the accuracy of the results for all tree components was also assessed. Data for 351 trees, ranging in age from 10 to 72 years, were collected from 65 public and private sites. The volume estimates show average biases that range in absolute values from 2.19 to 31.02 dm3 for whole-tree, from 1.41 to 27.31 dm3 for wood, and from 1.05 to 16.52 dm3 for bark volume components. Errors in volume predictions were relatively small, representing less than 3% of the average observed wood volume and less than 6% of the average observed bark volume. This research showed that satisfactory predictions can be obtained from forcing additivity using NSUR approach with a minimal number of easily measurable tree variables, such as dbh and total height. FOR. SCI. 59(4):464–471.Ministerio de Ciencia, Innovación y Universidades (project AGL2011-29701-C02-01/02

Modelo de masa incorporada en pinares mediterráneos: Un caso de estudio en rodales de pino silvestre (Pinus sylvestris L.) y pino negral (Pinus pinister Ait.) en España

Producción CientíficaNational Forest Inventories (NFI) are a basic tool for forestry planning at the National level. A new two-step system for predicting ingrowth compatible with NFI data is presented in order to improve long-term estimation of stand condition. In growth and yield models, an ingrowth submodel is a key feature for long-term estimation. An accurate projection of ingrowth is needed to avoid model projection bias and inaccuracy. A two-step approach was used, which consisted of (I) estimating the probability of ingrowth occurrence on a sample plot and (II) quantifying the ingrowth in terms of basal area. Logistic regression was used for step I, while linear regression was used for step II. A good performance of the joint ingrowth model for Scots pine (Pinus sylvestris L.) and Mediterranean Maritime pine (Pinus pinaster Ait. Ssp mesogeensis) stands was observed. Logistic model include quadratic mean diameter as independent variables for both species while basal area is only included for Mediterranean Maritime. Quadratic mean diameter is the only independent variable in linear model for both species. The presented two-step modeling methodology for ingrowth is applicable to data from National Forest Inventories with concentric plots.Los Inventarios Forestales Nacionales (IFN) son un instrumento básico para la planificación forestal a nivel nacional. Con el objeto de predecir la masa incorporada, se presenta un nuevo sistema bietápico compatible con los datos del IFN para de esta forma mejorar las estimaciones a largo plazo. En los modelos de crecimiento y producción, un modelo de masa incorporada es clave para la proyección a largo plazo dado que se precisa una adecuada estimación de la masa incorporada para evitar sesgos e imprecisiones. Se utilizó un método bietápico basado en (I) estimar la probabilidad de presencia de masa incorporada en la parcela y (II) cuantificar la masa incorporada en área basimétrica. Para el paso I se utilizó la regresión logística mientras que para el paso II se utilizó regresión lineal. Se observó un buen comportamiento del modelo conjunto tanto para pino silvestre (Pinus sylvestris L.) como para pino negral (Pinus pinaster Ait. Ssp mesogeensis). El modelo logístico incluye el diámetro medio cuadrático como variable independiente para ambas especies mientras que el área basimétrica solo es significativa en el caso del pino negral. En el modelo lineal, el diámetro medio cuadrático es significativo para las dos especies. El método bietápico presentado para estimar la masa incorporada es aplicable a los datos de Inventarios Forestales Nacionales basados en parcelas concéntricas