L'actualité du développement durable avec Médiaterre, le système d'information mondial francophone pour le développement durable concourt à la mise en oeuvre du développement durable dans l'espace francophone par la diffusion et l'échange d'informations, et l'aide à la constitution de réseaux de coopération.
The morphological responses of seedlings of eight African provenances of Vitellaria paradoxa (Shea tree or Karité) to imposed draught stress were compared under nursery experimental conditions. The potted seedlings were subjected to three different watering regimes (87 days after sowing): no water stress (100% of the field capacity, C), moderate water stress (75% of C) and severe water stress (50% of C).
Publication date: 1 October 2017
Source:Science of The Total Environment, Volume 595
Author(s): Elisa Terzaghi, Melissa Morselli, Matteo Semplice, Bruno Enrico Leone Cerabolini, Kevin C. Jones, Michele Freppaz, Antonio Di Guardo
Current modelling approaches often ignore the dynamics of organic chemicals uptake/release in forest compartments under changing environmental conditions and may fail in accurately predict exposure to chemicals for humans and ecosystems. In order to investigate the influence of such dynamics on predicted concentrations in forest compartments, as well as, on air-leaf-litter fluxes, the SoilPlusVeg model was developed including a forest compartment (root, stem, leaves) in an existing air-litter-soil model. The accuracy of the model was tested simulating leaf concentrations in broadleaf woods located in Northern Italy and resulted in satisfying model performance. Illustrative simulations highlighted the “dual behaviour” of both leaf and litter compartments. Leaves appeared to behave as “filters” of air contaminants but also as “dispensers”, being deposition flux exceeded by volatilization flux in some periods of the day. Similarly, litter seemed to behave as a dynamic compartment which could accumulate and then release contaminants recharging air and vegetation. In just 85days, litter could lose due to volatilization, diffusion to depth and infiltration processes, from 6% to 90% of chemical amount accumulated over 1year of exposure, depending on compound physical and chemical properties. SoilPlusVeg thus revealed to be a powerful tool to understand and estimate chemical fate and recycling in forested systems.
Publication date: September 2017
Source:Geoderma Regional, Volume 10
Author(s): Andrew Sila, Ganesh Pokhariyal, Keith Shepherd
In this study, the utility of regression-kriging was investigated in building prediction models for soil properties using mid-infrared (7498 to 600cm−1) spectral data for soil samples collected from Nyando, Nzoia and Yala catchment areas in Kenya, sampled at 0–20cm and 20–50cm depths. Using a systematic technique, 158 samples were selected for analysis of a number of soil properties of interest using wet chemistry methods. We randomly divided the dataset into two groups: 118 samples in the calibration and 40 samples in the holdout validation set. The calibration set was first used to develop partial least squares regression (PLS) models for all the soil properties. Residuals from these models were used to generate semivariograms, which revealed a strong spatial dependence as determined by the ratio of nugget to sill for nitrogen, 9%; Al, 12%; and B, 36%, but with weak spatial dependence for exchangeable Ca (ExCa), 100%; and carbon, 76%. The fitted theoretical semivariograms were used to fit regression-kriging models. Lastly, both the PLS and regression-kriging models were assessed with the validation set and their prediction performance evaluated by R2 and root mean square error (RMSE). The results showed that regression kriging method gave lower RMSE values for all the evaluated soil properties except for ExCa, B and exchangeable acidity, with the best predictions, compared with the PLS model, obtained for ExMg (R2, 0.93 vs 0.88; RMSE, 6.1 vs 8.4cmolc kg−1) and total nitrogen (R2 =0.92 vs R2 =0.74; RMSE, 0.11%, RMSE=0.2%). In this study, regression-kriging, which takes into account spatial variation normally ignored by other methods, improved use of infrared spectroscopy for predicting soil properties.