Soil organic carbon dynamics under perennial energy crops

The European renewable energy directive 2009/28/EC (E.C. 2009) provides a legislative framework for reducing GHG emissions by 20%, while achieving a 20% share of energy from renewable sources by 2020. Perennial energy crops could significantly contribute to limit GHG emissions through replacing equivalent fossil fuels and by sequestering a considerable amount of carbon into the soil through the large amounts of belowground biomass produced. The objective of this study is to evaluate the effects of land use change that perennial energy crops have on croplands (switchgrass) and marginal grasslands (miscanthus). For that purpose above and belowground biomass, SOC variation and Net Ecosystem Exchange were evaluated after five years of growth. At aboveground level both crops produced high biomass under cropland conditions as well as under marginal soils. At belowground level they also produced large amounts of biomass, but no significant influences on SOC in the upper layer (0-30 cm) were found. This is probably because of the "priming effect" that caused fast carbon substitution. In switchgrass only it was found a significant SOC increase in deeper layers (30-60 cm), while in the whole soil profile (0-60 cm) SOC increased from 42 to 51 ha-1. However, the short experimental periods (for both switchgrass and miscanthus), in which land use change was evaluated, do not permit to determine the real capacity of perennial energy crops to accumulate SOC. In conclusion the large amounts of belowground biomass enhanced the SOC dynamic through the priming effect resulting in increased SOC in cropland but not in marginal grassland.

Modeling the spatial dynamics of economic models

The advances that have been characterizing spatial econometrics in recent years are mostly theoretical and have not found an extensive empirical application yet. In this work we aim at supplying a review of the main tools of spatial econometrics and to show an empirical application for one of the most recently introduced estimators. Despite the numerous alternatives that the econometric theory provides for the treatment of spatial (and spatiotemporal) data, empirical analyses are still limited by the lack of availability of the correspondent routines in statistical and econometric software. Spatiotemporal modeling represents one of the most recent developments in spatial econometric theory and the finite sample properties of the estimators that have been proposed are currently being tested in the literature. We provide a comparison between some estimators (a quasi-maximum likelihood, QML, estimator and some GMM-type estimators) for a fixed effects dynamic panel data model under certain conditions, by means of a Monte Carlo simulation analysis. We focus on different settings, which are characterized either by fully stable or quasi-unit root series. We also investigate the extent of the bias that is caused by a non-spatial estimation of a model when the data are characterized by different degrees of spatial dependence. Finally, we provide an empirical application of a QML estimator for a time-space dynamic model which includes a temporal, a spatial and a spatiotemporal lag of the dependent variable. This is done by choosing a relevant and prolific field of analysis, in which spatial econometrics has only found limited space so far, in order to explore the value-added of considering the spatial dimension of the data. In particular, we study the determinants of cropland value in Midwestern U.S.A. in the years 1971-2009, by taking the present value model (PVM) as the theoretical framework of analysis.