Comparison of carbon content between plantation and natural regeneration seedlings in Durango, Mexico.

Forest plantations and natural forests perform a relevant role in capturing CO2 and reducing greenhouse gas concentrations. The objective of this study was to compare the diameter increment, biomass and carbon accumulation in a plantation of Pinus durangensis and a naturally regenerated stand. The data were collected from 32 circular plots of 100 m2 (16 plots in the planted site and 16 in naturally regenerated area). At each plot, the diameter at the base (cm) and height (m) of all seedlings were measured using a Vernier and tape measure, and a seedling was destructively sampled collecting one cross-section at the base of the stump. The annual ring-width increment of each sampled seedling was recorded to obtain its diameter at the base and estimate annual aboveground biomass and carbon accumulation through allometric equations. The response variables were evaluated using mixed-effects ANOVA models. Results indicated that there were significant differences (P ≤ 0.05) on annual tree-ring width growth, biomass and carbon accumulation. The plantation seedlings showed significantly higher growth rates, biomass and carbon accumulation at most evaluated years. After 7 years of growth the lines of current annual increment (CAI) and mean annual increment (MAI) in basal diameter for both the plantation and the natural regeneration have not yet intersected. Both forest plantations and naturally regenerated stands of the studied tree species may be suitable alternatives to promote CO2 capture and increase timber production.

Dummy regression to predict dry fiber in Agave lechuguilla Torr. in two large-scale bioclimatic regions in Mexico.

Agave lechuguilla Torr., of the family Agavaceae, is distributed from southwestern United States to southern Mexico and is one of the most representative species of arid and semiarid regions. Its fiber is extracted for multiple purposes. The objective of this study was to generate a robust model to predict dry fiber yield (Dfw) rapidly, simply, and inexpensively. We used a power model in its linear form and bioclimatic areas as dummy variables. Training, generation (80%) and validation (20%) of the model was performed using machine learning with the package 'caret' of R. Using canonical correlation analysis (CCA), we evaluated the relationship of Dwf to bioclimatic variables. The principal components analysis (PCA) generated two bioclimatic zones, each with different A. lechuguilla productivities. We evaluated 499 individuals in four states of Mexico. The crown diameter (Cd) of this species adequately predicts its fiber dry weight (R2 = 0.6327; p < 0.05). The intercept (ß0), slope [lnCd (ß1)], zone [(ß2)] and interaction [lnCd:Zona (ß3)] of the dummy model was statistically significant (p < 0.05), giving origin to an equation for each bioclimatic zone. The CCA indicates a positive correlation between minimum temperature of the coldest month (Bio 6) and Dwf (r = 0.84 and p < 0.05). In conclusion, because of the decrease in Bio 6 of more than 0.5°C by 2050, the species could be vulnerable to climate change, and A. lechuguilla fiber production could be affected gradually in the coming years.