Intra- and inter-year variability of agricultural carbon footprints – A case study on field-grown tomatoes

The performance of agricultural systems and their environmental impacts can vary considerably within a single crop supply chain, due to differences in farming practices, soil properties, and yearly climatic conditions. In this paper, we characterised the variability of carbon footprints of open-field tomato production by analysing a comprehensive farm dataset gathered over 4 years. We also assessed the importance of the different drivers of variability as compared to model uncertainties. The primary data used in this study were collected from 189 farms from the Extremadura region in Spain and Portugal over a period of four years, from 2012 to 2015. We modelled the carbon footprint of these farms using the Cool Farm Tool model developed by Hillier et al. (2011) and conducted statistical analysis on the results to understand the relative importance of inter-year and intra-year variability. We performed sensitivity analysis to understand how sensitive the results were to variability in the farmers' input parameters and to the uncertainty in model parameters. This was done by varying all factors one-at-a-time, and then by running a Monte Carlo simulation where all uncertainties were propagated simultaneously. Results clearly show significant inter-year and intra-year variability in carbon footprints of tomato production within the study region. We observed approximately 20% variation for each annual carbon footprint (intra-year variability), resulting in an overall 28% coefficient of variation in the aggregated footprint across the different years. The carbon footprint of the whole tomato supply, calculated with the 4-year dataset, showed a weighted geometric mean of 51 kg CO2-eq/t and a weighted GSD of 1.32, meaning a 95% confidence interval of 29–89 kg CO2-eq/t. Results also show that small farms were characterised by a larger variability than larger ones. This highlights the need to weight farm results by production volumes if the objective is to obtain a carbon footprint for the total production in a given region. The carbon footprint was found to be mainly sensitive to variability in farm practices, notably extent of pump irrigation and choice and amount of fertiliser used, with model uncertainties influencing the results to a relatively smaller extent. Further work is needed to extend these findings to other crops, regions and impact categories

Analyse protéomique des glioblastones: Quel est le meilleur contrôle?

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Developing and testing a global-scale regression model to quantify mean annual streamflow

Quantifying mean annual flow of rivers (MAF) at ungauged sites is essential for assessments of global water supply, ecosystem integrity and water footprints. MAF can be quantified with spatially explicit process-based models, which might be overly time-consuming and data-intensive for this purpose, or with empirical regression models that predict MAF based on climate and catchment characteristics. Yet, regression models have mostly been developed at a regional scale and the extent to which they can be extrapolated to other regions is not known. In this study, we developed a global-scale regression model for MAF based on a dataset unprecedented in size, using observations of discharge and catchment characteristics from 1885 catchments worldwide, measuring between 2 and 10(6) km(2). In addition, we compared the performance of the regression model with the predictive ability of the spatially explicit global hydrological model PCR-GLOBWB by comparing results from both models to independent measurements. We obtained a regression model explaining 89% of the variance in MAF based on catchment area and catchment averaged mean annual precipitation and air temperature, slope and elevation. The regression model performed better than PCR-GLOBWB for the prediction of MAF, as root-mean-square error (RMSE) values were lower (0.29-0.38 compared to 0.49-0.57) and the modified index of agreement (d) was higher (0.80-0.83 compared to 0.72-0.75). Our regression model can be applied globally to estimate MAF at any point of the river network, thus providing a feasible alternative to spatially explicit process based global hydrological models. (C) 2016 Elsevier B.V. All rights reserved.FWN – Publicaties zonder aanstelling Universiteit Leide

A planetary boundary-based method for freshwater use in life cycle assessment: Development and application to a tomato production case study

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Caractérisation d'une population cellulaire stromale associée au glioblastome (GASC) en vue d'un ciblage thèrapeutique

National audienc

Caractérisation d'une population cellulaire stromale associée au glioblastome (GASC) en vue d'un ciblage thèrapeutique

National audienc

Les "GASC" (GBM-associated stromal cells): mise en évidence et rôle dans la croissance des cellules tumorales

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