Species in the genus Populus (poplars) have shown the potential to be utilized as short rotation woody crops for bioenergy production in the Southeast. A lack of knowledge on which poplar taxa perform best on marginal sites throughout the Southeast exists. Through measurement of relationships between growth metrics, water usage and physiological parameters of 2400 poplar trees, I was able to assess: 1) early rotation suitability of numerous poplar varietals to be grown as bioenergy feedstocks in northeast Mississippi, and 2) the effectiveness of early rotation physiological parameters in predicting future productivity and water usage. Overall findings from this study suggest that trees with D x M parentage may be best fit for large-scale plantation growth in the Southeast. They demonstrated low mortality and collectively grew the tallest of all taxa. Early-rotation physiology demonstrated mixed results in predicting future growth and water usage. Findings from this study will be used in future bioenergy feedstock selection.

This study aimed at developing allometric equations for the estimation of aboveground biomass of black willow and eastern cottonwood and determine biomass production by these species under several planting spacing and harvest frequency combinations. Logarithmic model with dbh and tree height was the best fitting model for individual tree aboveground biomass estimation of both species. At area level, logarithmic models with stand age, dominant height, and planting density produced the best results. Mixed-effects modeling showed statistically significant effects of harvest frequency for eastern cottonwood but not for black willow. Overall, we conclude that, biomass production of black willow and eastern cottonwood would play a critical role in the fulfillment of the wood energy demands and biomass yields can be enhanced by considering management factors during plantation. These findings will be useful to forest owners in Lower Mississippi Alluvial Valley for estimating biomass without destructive sampling and have optimal biomass production.