Using Biophysical Variables and Stand Density to Estimate Growth and Yield of <i>Pinus patula</i> in Antioquia, Colombia

Abstract

Timberland investment opportunities in Colombia are expected to increase as a result of the peace agreement recently signed between the Colombian government and the Revolutionary Armed Forces of Colombia. This new socio-political environment may facilitate the expansion of commercial forest plantations on a wider range of site conditions that are currently considered in existing biometric tools. Data from 1119 temporary plots of unthinned, unmanaged, and genetically unimproved Pinus patula plantations in the Antioquia region were combined with a large set of biophysical attributes to identify spatial variation in yield. A wide array of biophysical covariates was explored to characterize the most favorable environmental conditions for the species, and to identify potential explanatory variables to be included in forest yield models. The mathematical form of the model is the von Bertalanffy-Chapman-Richards type, with parameters: asymptote, intrinsic growth rate and allometric constant. The parameters were expressed as linear functions of soil pH, terrain slope, the mean annual temperature to mean annual precipitation ratio, and stand density. The statistical contribution of selected covariates was evaluated using the likelihood ratio test. The model was validated using an independent set of 133 observations. The spatial representation of the model depicts the timber production potential and allows for the identification of the most suitable geographical areas to establish Pinus patula plantations in Antioquia, Colombia. The estimated yield model provides a reliable baseline for timber production, and insight into timberland investments in Colombia.

Financial analysis of potential Pinus patula plantations in Antioquia, Colombia

“…Factors from 0.7 to 1.3 were applied to both costs and incomes, and the LEV and IRR variations, ceteris paribus, were assessed. The 95% prediction interval of the volume estimates is within +/-30% of the mean yield curve (Restrepo et al, 2019). Besides, although the volatility in the timber market in Colombia has not been assessed, other authors have found volatility of ~23% for more mature markets (Restrepo et al, 2020).…”

“…The parameter ϕ is estimated as a linear function of an intercept, and soil pH (a dummy variable that equals one if pH is in the range 5.1-6, and zero if pH is in the range 4.1-5), slope (S, degrees), and stand density (N, trees per hectare) (Equation 2). The parameter β was estimated as a linear function of an intercept, and the mean annual temperature (T e , °C) by the mean annual precipitation (P r , mm) ratio (T e P r ) (Brown and Lugo, 1982;Restrepo et al, 2019).…”

“…In Colombia, site selection for establishing forest plantations has been carried out mainly through descriptive biophysical analyses, including variables such as temperature, precipitation, and soil depth (UPRA, 2015). Financial analyses of forest plantations have also been developed using the Net Present Value (NPV), Land Expected Value (LEV) and Internal rate of Return (IRR) for plantation species in the Andean Region (Alnus jorullensis, Cordia alliodora, Pinus patula, Cupressus lusitanica, and Eucalyptus grandis) (Gutiérrez et al, 2006), and the Caribbean region (Tectona grandis) of Colombia (Restrepo and Orrego, 2015). Notwithstanding, efforts to identify potential areas for new forest plantations have not had a commercial focus that allows for the identification of areas with higher profitability (UPRA, 2018).…”

Influencia de los nutrientes del suelo sobre el crecimiento arbóreo en bosques del Pacífico colombiano

“…Los asteriscos indican la significancia de las correlaciones (* p<0.05; **p<0.01; *** p<0.001; ns p>0.05). Los árboles del Pacífico presentan una amplia variación en el CDA; se presentaron diferencias a nivel de localidades, categorías diamétricas y mecanismos nutricionales, lo cual es similar a lo reportado en estudios desarrollados en bosques tropicales que evidencian este tipo de variación (Turner, 2001;Toledo et al, 2011;Baribault et al, 2012), además de corroborar la influencia de factores como la calidad del sitio o condiciones ambientales, la edad del bosque y características intrinsecas como la genética, la densidad de individuos, los grupos taxonómico y ecológico, entre otros, que tienen incidencia aunque no se evaluaron (Russo et al, 2005;Restrepo et al, 2019).…”

“…En bosques tropicales, aunque la mayoría de los árboles crecen muy lentamente (promedio 1.0 mm.año -1 ) (Turner, 2001;Baribault et al, 2012), presentan un rango amplio de variación: entre 0.5 y 6.0 mm.año -1 en diámetro, con una tasa máxima cercana a 15 mm.año -1 (Turner, 2001). En tal sentido, se ha planteado que el crecimiento de los árboles es una función de cuatro factores: 1) la edad del bosque, 2) la calidad del sitio o las condiciones ambientales, 3) las características intrínsecas como la genética, y 4) la densidad de individuos y las prácticas de manejo del bosque, incluyendo la fertilización, el manejo de plagas y el control de competencia por vegetación (Restrepo et al, 2019). Esto explica por qué en bosques tropicales primarios y secundarios se presenta un amplio rango de variación en el crecimiento.…”