Mapping soil water holding capacity over large areas to predict potential production of forest stands

Abstract

Ingrid Seynave. Mapping soil water holding capacity over large areas to predict the potential production of forest stands. Geoderma, Elsevier, 2011, 160, pp.355-366. 10 AbstractEcological studies need environmental descriptors to establish the response of species or communities to ecological conditions. Soil water resource is an important factor but is poorly used by plant ecologists because of the lack of accessible data. We explore whether a large number of plots with basic soil information collected within the framework of forest inventories allows the soil water holding capacity (SWHC) to be mapped with enough accuracy to predict tree species growth over large areas. We first compared the performance of available pedotransfer functions (PTFs) and showed significant differences in the prediction quality of SWHC between the PTFs selected.We also showed that the most efficient class PTFs and continuous PTFs compared had similar performance, but there was a significant reduction in efficiency when they were applied to soils different from those used to calibrate them. With a root mean squared error (RMSE) of 0.046 cm 3 cm -3 (n = 227 horizons), we selected the Al Majou class PTFs to predict the SWHC in the soil horizons described in every plot, thus allowing 84% of SWHC variance to be explained in soils free of stone (n = 63 plots). Then, we estimated the soil water holding capacity by integrating the stone content collected at the soil pit scale (SWHC') and both the stone content at the soil pit scale and rock outcrop at the plot scale (SWHC") for the 100.307 forest plots recorded in France within the framework of forest inventories. The SWHC" values were interpolated by kriging to produce a map with 1 km² cell size, with a wider resolution leading to a decrease in map accuracy. The SWHC" given by the map ranged from 0 to 148 mm for a soil down to 1 m depth. The RMSE between map values and plot estimates was 33.9 mm, the best predictions being recorded for soils developed on marl, clay, and hollow silicate rocks, and in flat areas. Finally, the ability of SWHC' and SWHC" to predict 3 height growth for Fagus sylvatica, Picea abies and Quercus petraea is discussed. We show a much better predictive ability for SWHC" compared to SWHC'. The values of SWHC" extracted from the map were significantly related to tree height growth. They explained 10.7% of the height growth index variance for Fagus sylvatica (n = 866), 14.1% for Quercus petraea (n = 877) and 10.3% for Picea abies (n = 2067). The proportions of variance accounted by SWHC" were close to those recorded with SWHC" values estimated from the plots (11.5, 11.7, and 18.6% for Fagus sylvatica, Quercus petraea and Picea abies, respectively). We conclude that SWHC" can be mapped using basic soil parameters collected from plots, the predictive ability of the map and of data derived from the plot being close. Thus, the map could be used just as well for small areas as for large areas, directly or indirectly through water balance indices, to predict forest growth an...

Pedotransfer functions for water retention in different soil classes from the center-southern Rio Grande do Sul State

“…Out of the 26 analyzed conditions (2 potentials x 7 soil classes-depth from 0 to 20 cm + 2 potentials x 6 soil classes-depth from 40 to 70 cm), specific PTFs were shown less efficient in only four conditions, but with results very close to general PTFs, indicating that the stratifying of the data by soil classes presented better result i n most of th e cases, beca use th e more homogeneous the soils that compose the database for the generation of the PTFs, the better the data estimative, as also observed by Piedallu et al (2011);Poggio et al (2010) and Santra and Das (2008). …”

“…Examples of PTFs can be seen in: Balland et al (2008) who proposed equations to estimate the water retention and the hydraulic conductivity from the particle size distribution and organic matter of the soil for a wide variety of soils in Canada; Khodaverdiloo et al (2011) who generated PTFs to estimate the water retention in the soil at various potentials for soils derived from limestone in Iran; and Tomasella et al (2000) who used various soil survey data in Brazil to generate PTFs to predict the water retention parameters of the van Genuchten equation and to estimate the water retention; among other works as in Oliveira et al (2002), and Piedallu et al (2011). Michelon et al (2010) generated PTFs for the RS State, however they utilized data from undisturbed samples.…”

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