Factors affecting forestland owners' allocation of non-forested land to pine plantation for bioenergy in Virginia. (February 2016)

• Record Type: Journal Article
• Authors: Wolde, Bernabas; Lal, Pankaj; Alavalapati, Janaki; Burli, Pralhad; Munsell, John
• Is Part Of: Biomass and bioenergy. Volume 85(2016:Feb.)
• Page Start: 69
• Page End: 75
• Publication Date: 2016-02
• Subjects: Woody bioenergy -- Land use change -- Forestland owners -- Recursive partitioning -- Tobit model -- Virginia; Biomass energy -- Periodicals; Biomass -- Periodicals; Energy-Generating Resources -- Periodicals; Bioénergie -- Périodiques; 333.9539
• DOI: 10.1016/j.biombioe.2015.12.007
• Languages: English
• ISSNs: 0961-9534

Abstract

Abstract: Studies have shown that woody bioenergy can have potential economic, social, and environmental benefits. One of the ways to meet the growing biomass demand for woody bioenergy is by allocating currently non-forested land for growing feedstocks such as pine. Towards this end, we conducted a survey on 900 randomly selected private forestland owners in Virginia and asked what proportion of their non-forested land they would allocate for loblolly pine at given bid prices. We then used recursive partitioning based Tobit regression to analyze data. Our results suggest that the experience of having supplied wood for chip-n-saw mills in the past five years, large land holding, prior experience with state/federal financial/technical support programs, among other factors, lead to smaller proportion of non-forested land being allocated for pine. However, a higher price offer, stronger preference for producing non-timber forest products such as evergreen boughs and grapevine, and lesser dependence on working the land for annual income lead to larger proportion of non-forested land being allocate for pine. Our results could assist policy makers in developing and improving land use and energy policies, certification programs, and extension and outreach services. Our contribution also includes the use of threshold analyses to delineate tipping points in variables associated with different response rates and showing the different effect of variables in terms of how they affect the supply of biomass and the supply of land for bioenergy purposes. Highlights: Respondents will change land use pattern to benefit from growing biomass demand. Higher price increases proportion of non-forested land allocated for pine. Dependence on the land for income reduces non-forested land allocated for pine. Role of some factors is opposite to that in supplying biomass from existing stand.