Paysages virtuels et analyse de scénarios pour évaluer les impacts ...

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output kg N/input kgN kg N/ha 80% 78% 76% 74% 72% 70% 68% 66% 100 95 90 85 80 75 70 Nefficiency NS crop syst. climate soils crop syst. climate soils 45% int mod dry obs wet wtrlog obs thin int mod dry obs wet wtrlog obs thin NsoilBudget NS NS crop syst. climate soils int mod dry obs wet wtrlog obs thin Fig. IV.32: Effects of cropping system, climate and soil pattern levels on selected simulation results computed with an analysis of variance (ANOVA). For a given variable, the horizontal line shows the intercept of the ANOVA model, which is its global mean value. Estimates of the soil-climate interaction levels are shown for the simulated stream nitrogen concentration. Factor levels are: int=intensive, mod=moderate, obs=observed, and wtrlog=extended waterlogged soil. Non-significant effects are signaled with the tag 'NS'. output kg P/input kgP mg N/l 80% 75% 70% 65% 60% 55% 50% 22 20 18 16 14 12 10 Pefficiency [N]stream crop syst. climate soils int mod dry obs wet wtrlog obs thin -1.5 wtrlog obs thin Climate dry observed wet IV. Évaluation environnementale multi-critères et multi-ressources – p. 128 mg N/l 1.5 1.0 0.5 0 -0.5 -1.0 NS Soil-climate interactions NS Soils
The effects of the factor levels varied among the studied variables (Fig. IV.32). Nitrogen and P efficiency ratios were influenced similarly by the factor levels, although P efficiency was mainly sensitive to the cropping system. The efficiency ratios both improved with the moderate cropping system; they decreased with the thin and extended waterlogged levels; and also decreased with wetter climates. The effect of the cropping system and climate factor levels on the N soil budget were opposite than for the N efficiency ratio, but similar for soil pattern levels. The P soil budget presented responses similar to the N soil budget that were not represented on Fig. IV.32. The P soil budget varied mainly with the cropping systems and equaled 17 and 5 kg P.cultivated ha -1 for the intensive and moderate cropping system respectively. Finally, stream N concentration decreased with a moderate cropping system, wetter climates, and thicker or extended waterlogged soils. However, with the observed climate and soil levels, neither the intensive nor the moderate cropping systems allowed the mean stream N concentration to be below the 11.3 mg N.l -1 regulation threshold. The magnitude of the significant soil-climate interactions was around 1 mg N.l -1 , which makes them as important as the magnitude of the cropping system level effects. For the observed climate level (the intermediate climate level), switching from the extended waterlogged soil level to the thin level increased the stream N concentration, which is the opposite behavior than for the dry and wet climate levels. None of the soil-climate interactions involving the observed soil pattern were significant. Discussion The use of virtual landscapes allowed the design of a full factorial experiment to compare the effects and the interactions between agricultural, climate, and soil factors on a wide range of multi-resource indicators. Exploratory and statistical analyses permitted to identify the overall controlling factors and to quantify their relative and absolute influences. Construction of the virtual landscapes The creation of the virtual landscapes relied on the combination of initial states (initial landcover, soil thickness and waterlogging classes) with evolution processes (summer crop transition, crop growth and field management), all derived from survey data. While the reference landscape used for calibration was constructed with observed structures, the alternate landscapes were artificial combinations of soil and cropping system components submitted to different climate series. These combinations are obviously in contradiction with the commonly admitted concept in geomorphology that soils are at the interface of the atmosphere, the lithosphere and hydrosphere and serve as a support for human activities, thus making soil morphology a very complex result of interrelated and dynamic processes. However, this approach served our analytical purposes which assumed no hierarchy in the importance of the factor effects. Incorporating correlations between the landscape components would have possibly biased the statistical analyses or complicated the interpretation. Moreover, in order to ensure the most reliable response of the diffuse nutrient transfer model for each virtual landscape, (i) the model was calibrated on a reference scenario which was composed of the observed IV. Évaluation environnementale multi-critères et multi-ressources – p. 129
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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
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Fig. III.19 : Agricultural land are
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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 and 124: Scenario comparison and analysis St
Page 125 and 126: Exploratory analysis of the simulat
Page 127: The variance of all simulation resu
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
Page 175 and 176: Virtual landscape evolution Three i
Page 177 and 178: 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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