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Root Processes (root + process)
Selected AbstractsReducing size distortions of parametric stationarity testsJOURNAL OF TIME SERIES ANALYSIS, Issue 4 2003MARKKU LANNE The use of asymptotic critical values in stationarity tests against the alternative of a unit root process is known to lead to over-rejections in finite samples when the considered process is stationary but highly persistent. We claim that, in recent parametric tests, this is caused by estimation errors which result when the autoregressive parameters used to describe the short-run dynamics of the process are replaced by estimators. We suggest a modification that corrects for these errors. Simulation results show that the modified test works reasonably well when the persistence is moderate and there is no time trend in the model but it is less effective when the model contains a time trend. An empirical illustration with inflation rate data is provided. [source] Fine root dynamics in a loblolly pine forest are influenced by free-air-CO2 -enrichment: a six-year-minirhizotron studyGLOBAL CHANGE BIOLOGY, Issue 3 2008SETH G. PRITCHARD Abstract Efforts to characterize carbon (C) cycling among atmosphere, forest canopy, and soil C pools are hindered by poorly quantified fine root dynamics. We characterized the influence of free-air-CO2 -enrichment (ambient +200 ppm) on fine roots for a period of 6 years (Autumn 1998 through Autumn 2004) in an 18-year-old loblolly pine (Pinus taeda) plantation near Durham, NC, USA using minirhizotrons. Root production and mortality were synchronous processes that peaked most years during spring and early summer. Seasonality of fine root production and mortality was not influenced by atmospheric CO2 availability. Averaged over all 6 years of the study, CO2 enrichment increased average fine root standing crop (+23%), annual root length production (+25%), and annual root length mortality (+36%). Larger increase in mortality compared with production with CO2 enrichment is explained by shorter average fine root lifespans in elevated plots (500 days) compared with controls (574 days). The effects of CO2 -enrichment on fine root proliferation tended to shift from shallow (0,15 cm) to deeper soil depths (15,30) with increasing duration of the study. Diameters of fine roots were initially increased by CO2 -enrichment but this effect diminished over time. Averaged over 6 years, annual fine root NPP was estimated to be 163 g dw m,2 yr,1 in CO2 -enriched plots and 130 g dw m,2 yr,1 in control plots (P= 0.13) corresponding to an average annual additional input of fine root biomass to soil of 33 g m,2 yr,1 in CO2 -enriched plots. A lack of consistent CO2× year effects suggest that the positive effects of CO2 enrichment on fine root growth persisted 6 years following minirhizotron tube installation (8 years following initiation of the CO2 fumigation). Although CO2 -enrichment contributed to extra flow of C into soil in this experiment, the magnitude of the effect was small suggesting only modest potential for fine root processes to directly contribute to soil C storage in south-eastern pine forests. [source] The evolution of, and revolution in, land surface schemes designed for climate modelsINTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 5 2003A. J. Pitman Abstract The land surface is a key component of climate models. It controls the partitioning of available energy at the surface between sensible and latent heat, and it controls the partitioning of available water between evaporation and runoff. The land surface is also the location of the terrestrial carbon sink. Evidence is increasing that the influence of the land surface is significant on climate and that changes in the land surface can influence regional- to global-scale climate on time scales from days to millennia. Further, there is now a suggestion that the terrestrial carbon sink may decrease as global temperatures increase as a consequence of rising CO2 levels. This paper provides the theoretical background that explains why the land surface should play a central role in climate. It also provides evidence, sourced from climate model experiments, that the land surface is of central importance. This paper then reviews the development of land surface models designed for climate models from the early, very simple models through to recent efforts, which include a coupling of biophysical processes to represent carbon exchange. It is pointed out that significant problems remain to be addressed, including the difficulties in parameterizing hydrological processes, root processes, sub-grid-scale heterogeneity and biogeochemical cycles. It is argued that continued development of land surface models requires more multidisciplinary efforts by scientists with a wide range of skills. However, it is also argued that the framework is now in place within the international community to build and maintain the latest generation of land surface models. Further, there should be considerable optimism that consolidating the recent rapid advances in land surface modelling will enhance our capability to simulate the impacts of land-cover change and the impacts of increasing CO2 on the global and regional environment. Copyright © 2003 Royal Meteorological Society [source] Linkages between plant functional composition, fine root processes and potential soil N mineralization ratesJOURNAL OF ECOLOGY, Issue 1 2009Dario A. Fornara Summary 1Plant functional composition may indirectly affect fine root processes both qualitatively (e.g. by influencing root chemistry) and quantitatively (e.g. by influencing root biomass and thus soil carbon (C) inputs and the soil environment). Despite the potential implications for ecosystem nitrogen (N) cycling, few studies have addressed the linkages between plant functional composition, root decay, root detritus N dynamics and soil N mineralization rates. 2Here, using data from a large grassland biodiversity experiment, we first show that plant functional composition affected fine root mass loss, root detritus N dynamics and net soil N mineralization rates through its effects on root chemistry rather than on the environment of decomposition. In particular, the presence of legumes and non-leguminous forbs contributed to greater fine root decomposition which in turn enhanced root N release and net soil N mineralization rates compared with C3 and C4 grasses. 3Second, we show that all fine roots released N immediately during decomposition and showed very little N immobilization regardless of plant composition. As a consequence, there was no evidence of increased root or soil N immobilization rates with increased below-ground plant biomass (i.e. increased soil C inputs) even though root biomass negatively affected root decay. 4Our results suggest that fine roots represent an active soil N pool that may sustain plant uptake while other soil N forms are being immobilized in microbial biomass and/or sequestered into soil organic matter. However, fine roots may also represent a source of recalcitrant plant detritus that is returned to the soil (i.e. fine roots of C4 and C3 grasses) and that can contribute to an increase in the soil organic matter pool. 5Synthesis. An important implication of our study is that the simultaneous presence of different plant functional groups (in plant mixtures) with opposite effects on root mass loss, root N release and soil N mineralization rates may be crucial for sustaining multiple ecosystem services such as productivity and soil C and N sequestration in many N-limited grassland systems. [source] Can forecasting performance be improved by considering the steady state?JOURNAL OF FORECASTING, Issue 1 2008An application to Swedish inflation, interest rate Abstract This paper investigates whether the forecasting performance of Bayesian autoregressive and vector autoregressive models can be improved by incorporating prior beliefs on the steady state of the time series in the system. Traditional methodology is compared to the new framework,in which a mean-adjusted form of the models is employed,by estimating the models on Swedish inflation and interest rate data from 1980 to 2004. Results show that the out-of-sample forecasting ability of the models is practically unchanged for inflation but significantly improved for the interest rate when informative prior distributions on the steady state are provided. The findings in this paper imply that this new methodology could be useful since it allows us to sharpen our forecasts in the presence of potential pitfalls such as near unit root processes and structural breaks, in particular when relying on small samples.,,Copyright © 2008 John Wiley & Sons, Ltd. [source] The representation of root processes in models addressing the responses of vegetation to global changeNEW PHYTOLOGIST, Issue 1 2000F. I. WOODWARD The representation of root activity in models is here confined to considerations of applications assessing the impacts of changes in climate or atmospheric [CO2]. Approaches to modelling roots can be classified into four major types: models in which roots are not considered, models in which there is an interplay between only selected above-ground and below-ground processes, models in which growth allocation to all parts of the plants depends on the availability and matching of the capture of external resources, and models with explicit treatments of root growth, architecture and resource capture. All models seem effective in describing the major root activities of water and nutrient uptake, because these processes are highly correlated, particularly at large scales and with slow or equilibrium dynamics. Allocation models can be effective in providing a deeper, perhaps contrary, understanding of the dynamic underpinning to observations made only above ground. The complex and explicit treatment of roots can be achieved only in small-scale highly studied systems because of the requirements for many initialized variables to run the models. [source] Panel vector autoregression under cross-sectional dependenceTHE ECONOMETRICS JOURNAL, Issue 2 2008Xiao Huang Summary, This paper studies estimation in panel vector autoregression (VAR) under cross-sectional dependence. The time series are allowed to be an unknown mixture of stationary and unit root processes with possible cointegrating relations. The cross-sectional dependence is modeled with a factor structure. We extend the factor analysis in Bai and Ng (2002, Econometrica 70, 91,221) to vector processes. The fully modified (FM) estimator in Phillips (1995) is used for estimation in panel VAR and we also propose a factor augmented FM estimator. Our simulation results show this factor augmented FM estimator performs well when sample size is large. [source] An Aggregation Theorem for the Valuation of Equity Under Linear Information DynamicsJOURNAL OF BUSINESS FINANCE & ACCOUNTING, Issue 3-4 2003David Ashton We state an Aggregation Theorem which shows that the recursion value of equity is functionally proportional to its adaptation value. Since the recursion value of equity is equal to its book value plus the expected present value of its abnormal earnings, it follows that the adaptation value of equity can normally be determined by a process of simple quadrature. We demonstrate the application of the Aggregation Theorem using two stochastic processes. The first uses the linear information dynamics of the Ohlson (1995) model. The second uses linear information dynamics based on the Cox, Ingersoll and Ross (1985),square root' process. Both these processes lead to closed form expressions for the adaptation and overall market value of equity. There are, however, many other processes which are compatible with the Aggregation Theorem. These all show that the market value of equity will be a highly convex function of its recursion value. The empirical evidence we report for UK companies largely supports the convexity hypothesis. [source] | |||||||||||||