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Benjamin P. Louis

Benjamin P. Louis

PhD in Agronomy and Biology, Statistician

About

17
Publications
2,662
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201
Citations
Introduction
I am currently a freelance statistician. I work with different kind of institutions and companies. Before that, I obtained a Ph.D in agronomy and biology on questions about integrating soil microbial diversity in soil carbon dynamic models.

Publications

Publications (17)
Article
Soil pH is one of the most common and important measurements used to assess soil quality and manage soil fertility. Soil acidification is a slow process that can have large consequences. Therefore, it is important to detect soil changes early. Using a French soil test database, we show that the soil pH increased in 36% of arable soils monitored fro...
Article
Full-text available
High-resolution mapping of soil phosphorus (P) concentration is necessary to identify critical source areas reliably where a large risk of transport coincides with a large potential source of P in agricultural landscapes. However, dense soil P data are not usually available to produce such maps and to obtain them is expensive. In this study, we mod...
Article
Full-text available
Mathematical models do not explicitly represent the influence of soil microbial diversity on soil organic carbon (SOC) dynamics despite recent evidence of relationships between them. The objective of the present study was to statistically investigate relationships between bacterial and fungal diversity indexes (richness, evenness, Shannon index, in...
Data
Information about soil sample locations. AMT, Average Monthly Temperature; AMP, Average Monthly Precipitation; AMETP, Average Monthly Evapotranspiration. a Coordinates follow the Lambert-93 projection. (XLSX)
Data
Minimal dataset of response variables and covariates used in the study. AMT, Average Monthly Temperature; AMP, Average Monthly Precipitation; AMETP, Average Monthly Evapotranspiration; SOC, Soil Organic Carbon; C/N, soil Carbon/Nitrogen ratio; H', Shannon index; J', Evenness; 1/D, inverse Simpson index; Rs, Respiration rate during control (Rs,contr...
Data
Correlation matrix between soil properties. Upper part of the matrix: Pearson correlation coefficients. Size of figures is proportional to the absolute value of the coefficient. Lower part of the matrix: scatter plots between soil properties. Solid red lines represent smoothed estimates of the relationship between soil properties. (TIFF)
Data
Comparison of quality between microbial diversity index-based models and phyla-based models. nVar, Total number of selected covariates; df, degrees of freedom; BIC, Bayesian Information Criterion; nDiv, Number of selected microbial diversity covariates (indexes or phyla abundance); %Div, Total percentage of variance explained by all selected microb...
Article
Full-text available
Industrial agriculture is yearly responsible for the loss of 55–100 Pg of historical soil carbon and 9.9 Tg of reactive nitrogen worldwide. Therefore, management practices should be adapted to preserve ecological processes and reduce inputs and environmental impacts. In particular, the management of soil organic matter (SOM) is a key factor influen...
Chapter
Full-text available
The design of a Soil Monitoring Network (SMN) poses numerous scientific challenges, especially for the assessment of national or continental areas. The task is particularly challenging because soil carbon content and stocks are driven by controlling factors of disparate origins and scales. Various approaches to the establishment of SMNs are reviewe...
Book
In France, soil test results from samples of cultivated topsoil requested by farmers have been collected to constitute the French Soil Testing database. Enriching soil maps with such data can be regarded as an important source of information to build GlobalSoilMap products when dense soil profile information does not exist. We inferred the Soil Org...
Article
Full-text available
The French Soil-testing database The French Soil-testing database (FSTD) gathers since twenty years the results of the soil-tests realized by commercial soil-testing laboratories approved by the Ministry of Agriculture. The objectives of this paper are three-fold : to present (i) the global gathering procedure, (ii) the inherent methodological deve...
Book
The design of a Soil Monitoring Network (SMN) poses numerous scientific challenges, especially for the assessment of national or continental areas. The task is particularly challenging because soil carbon content and stocks are driven by controlling factors of disparate origins and scales. Various approaches to the establishment of SMNs are reviewe...
Article
Full-text available
This report evidences factors controlling soil organic carbon at the national scale by modelling data of 2,158 soil samples from France. The global soil carbon amount, of about 1,500 Gt C, is approximately twice the amount of atmosphere C. Therefore, soil has major impact on atmospheric CO2, and, in turn, climate change. Soil organic carbon further...

Questions

Question (1)
Question
I am working on identify which explanatory variables could be interesting to add in a mechanistic model on soil carbon dynamic.
I am able to calibrate a simple model on experimental data from several sites. This model is like an average model without explanatory variables and so don't simulate the variability existing between the different sites. I have some informations about the different sites (soil properties) which could improve the predictive quality of my model.
I can estimate the MSEP of the "average" model and I'd like to estimate the population part (lambda) of the MSEP decomposition according to Bunke and Droge (1984) or Wallach and Goffinet (1987). This part represent the minimum MSEP we can get with the explanatory variables present in the model. The bigger this part is (relatively to the MSEP) the most we have to add explanatory variables to improve the predictive quality of the model. This term depends on how much the predicted variable (y) varies for fixed values of the explanatory variables (X) in the model : lambda=E[var(y|X)].
I found that when the explanatory variables are categorial, we can estimate lambda by the mean square error of the residuals of a linear model between y and X which seems logical for me. I first thought that we can do it the same way with continuous explanatory variables but I doubt now because of the linear hypothesis which can be a contribution of the squared biais part of the MSEP decomposition (Delta).
Have you any suggestions of how I estimate the lambda part of the MSEP decomposition?
Thanks for the help!
Benjamin

Network

Cited By
    • Ministère de l'agriculture, de l'alimentation, de la pêche, de la ruralité et de l' aménagement du territoire
    • French National Centre for Scientific Research / Sorbonne Université
    • Institute of Ecology and Environmental Sciences IEES-Paris
    • INRA - AgroParisTech
    • Agence de l'Environnement et de la Maîtrise de l'Energie