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Fig. IV.31: Principal component analysis (PCA) of the results output by TNT2 and the soil phosphorus model. Projection of the variables on the F1-F2 (a) and F1-F3 PCA (b) planes. Projection of the individuals grouped by factor levels: climate levels on F1-F2 (c), cropping system levels on F1-F2 (d), soil pattern levels on F1-F3 (e). IV. Évaluation environnementale multi-critères et multi-ressources – p. 124
Exploratory analysis of the simulation results The first three axes of the PCA carried 70% of the variability of the simulation results (Fig. IV.31), 33.4% for axis F1, 24.1% for F2, and 12.4% for F3. The projection of the variables on the F1-F2 factorial plane shows that F1 is positively correlated with hydrological variables such as annual discharge (which is highly correlated with the annual rainfall illustrative variable), the mean annual area proportion of the catchment soil layer reached or saturated by groundwater. F2 is positively correlated with the annual soil P budget (and with illustrative N and P inputs), and negatively with P efficiency ratio. On the first diagonal of the F1-F2 plane, the N soil pressure and the N load at the outlet are negatively correlated with the N efficiency ratio and harvested amounts; on the second diagonal, strong correlations lied between groundwater and stream N concentrations, the total annual duration for which stream N concentration exceeded the regulation 11.3 mg N.l -1 threshold and the corresponding mean concentration value in excess situation. F3 is highly correlated with denitrified N amounts. The projection of data values on the factorial planes and their grouping according to soil, climate and cropping system levels show that (i) the centroids of climate groups are aligned with F1 (higher discharges with wetter climates), (ii) cropping system group centroids are aligned with F2 (higher N and P efficiencies with moderate cropping system), (iii) soil pattern group centroids are aligned with F3 (higher denitrified amounts with extended area of waterlogged soils, lower amounts with thin soils). Effects of the cropping system, climate and soil factor levels on model responses Normality tests failed for all the studied variables (except mineralized N and the leachable N soil budget) for most of the factor levels (results not shown). Homoscedasticity tests passed more successfully, failing generally for one factor out of three, but failures often occurred for the most explicative factor of the ANOVA model (Table IV.9). ANOVA is known to be more robust for departure from the normality assumption than from homoscedasticity, which modifies the power of the tests (probability to reject the false null hypothesis of ANOVA, that all groups have the same mean value). To normalize the value distribution and homogenize the variance, variable transformation was not considered since it hardly improves the situation for all groups at a time in a multi-factorial analysis. Alternate non-parametric tests equivalent to ANOVA, like Kruskall-Wallis and Friedman tests (Groggel and Skillings, 1986) or rank transformation tests (Conover and Iman, 1981) are respectively inappropriate or little sensitive to factor interaction (Helsel and Hirsch, 2002; Seaman et al., 1994). As a compromise between the ability to study factor interactions and a weakened statistical power, we choose the multi-factorial ANOVA approach and accepted that significant effects may have been rejected. IV. Évaluation environnementale multi-critères et multi-ressources – p. 125
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Thèse de Doctorat présentée deva
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Abstract and key words The environm
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Remerciements « [Il] émergeait de
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de connaître et de collaborer avec
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Table des matières Abstract and ke
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Annexes I : Application d’une mod
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Introduction générale Les paysage
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- dans un second temps : (i) constr
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Chapitre I Modélisation intégrée
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Fig. I.1: Trois regards disciplinai
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surface et le sous-sol est limitée
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Fig. I.2: (a) Variogramme expérime
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des interactions entre les exploita
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Fig. I.4: Schémas du cycle hydrolo
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Fig. I.5: Flux d’azote (a) et de
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influencent les phénomènes de tra
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Fig. I.6: Courbes d’absorption au
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Fig. I.7 : Diversité d’indicateu
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ecyclage et de transformation (min
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Modélisation et évaluation intég
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conceptuelles et techniques : - un
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La démarche adoptée pour cette é
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Chapitre II : La plateforme Qualsca
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L’occupation du sol conditionnant
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(a) (b) Fig. II.11 : Cartes des sol
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Formalisme du paysage « occupation
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Une représentation hiérarchique d
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surface=y), et la décision attendu
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Fig. II.16 : Les différentes class
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Représentation du milieu physique
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Sources de la plateforme Qualscape
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Chapitre III Stochastree, un modèl
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Les arbres de décision de Stochast
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Introduction Despite their role in
Page 74 and 75: Fig. III.19 : Agricultural land are
Page 76 and 77: The life-cycle analysis survey cond
Page 78 and 79: - farm-types have specific spatial
Page 80 and 81: eached. The graphical structure of
Page 82 and 83: Comparative analysis of the simulat
Page 84 and 85: GrMz: 1023m Wh: 858m dairy AWD: 636
Page 86 and 87: Fal 33% TempP 71% PermP 100% Forage
Page 88 and 89: simulation method Rotomatrix Stocha
Page 90 and 91: of significant differences was gene
Page 92 and 93: crop class simulation method perman
Page 94 and 95: Spatial distribution of crops over
Page 96 and 97: The decision trees used in this res
Page 98 and 99: Gabrielle B., B. Mary, R. Roche, P.
Page 100 and 101: In Cheverry C. (ed.). Agriculture i
Page 103 and 104: Chapitre IV : Une approche multi-re
Page 105 and 106: Multi-resource and multi-criteria l
Page 107 and 108: ecology (Ruiz et al., 2006) and for
Page 109 and 110: as cattle excreta (Bodet et al., 20
Page 111 and 112: Factorial simulation design A set o
Page 113 and 114: Cropping system factor levels Two l
Page 115 and 116: TNT2 simulations TNT2 calibration w
Page 117 and 118: Results Virtual landscape construct
Page 119 and 120: The NST transformations of measured
Page 121 and 122: Cropping system factor levels Fig.
Page 123: Scenario comparison and analysis St
Page 127 and 128: The variance of all simulation resu
Page 129 and 130: The effects of the factor levels va
Page 131 and 132: higher denitrification rates, thus
Page 133 and 134: Conclusion The study develops innov
Page 135 and 136: Gabrielle B., B. Mary, R. Roche, P.
Page 137 and 138: Chapitre V Conclusion générale
Page 139 and 140: Chapitre V : Conclusion générale
Page 141 and 142: par le système de culture ; − au
Page 143 and 144: La plateforme Qualscape Les modules
Page 145 and 146: Cependant, afin de maintenir un cer
Page 147 and 148: Perspectives - de l’évaluation e
Page 149 and 150: Bibliographie générale
Page 151 and 152: Bibliographie générale Aarts H.F.
Page 153 and 154: Deffontaines, J.P., C. Thenail, J.
Page 155 and 156: in considering landscape trajectori
Page 157 and 158: Ecosystems & Environment, 120 (2-4)
Page 159 and 160: on global change research. 124 p. U
Page 161: Annexes Annexes I : Article soumis
Page 165 and 166: Annexes I : Application d’une mod
Page 167 and 168: Application of virtual landscape mo
Page 169 and 170: Introduction The increasing develop
Page 171 and 172: Material and methods Construction o
Page 173 and 174: Fig. A.I.35: Construction of the in
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Virtual landscape evolution Three i
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Fertilization practices We consider
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PEi,y= PexportYcrop = PexportN(mean
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RMSE= 1 N Year 1 N Realization N
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The rules we followed to interpret
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The sill of the experimental variog
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Strategy evaluation Accuracy assess
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− samples stratified by landuse (
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est design worst design BIAS curren
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Papritz A. and R. Webster. 1995. Es
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Annexes II : Matrices de probabilit
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Arbres de décision utilisés par S
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Fig. A.II.43 : Ensemble des parcell
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Fig. A.II.45 : Ensemble des parcell
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Arbre de décision utilisé pour si
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1. Exemple de fichier texte produit
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Méthode principale de manipulation
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