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

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variable unit description Model inputs (illustrative in PCA) rainfall mm cumulated rainfall NfertiMin kg N.cultivated ha -1 amount of N spread as synthesis fertilizer per cultivated hectare NfertiOrg kg N.cultivated ha -1 amount of spread N manure and excreta inputs to grazed grassland per cultivated hectare NOrg/Tot % total organic N (NfertiOrg) fraction of total N inputs (NfertiMin plus NfertiOrg) Pinputs kg P.cultivated ha -1 amount of P inputs (spread mineral and organic fertilizers plus excreta inputs to grazed grassland) per cultivated hectare Agricultural indicators Soil indicators Nharvest kg N.cultivated ha -1 amount of N harvested with crops Ngraze kg N.cultivated ha -1 amount of N grazed by cattle Nmow kg N.cultivated ha -1 amount of N mowed from grasslands Nefficiency % agricultural efficiency ratio of N inputs (grazed, harvested, mowed) over inputs(fertilization, excreta inputs to grazed grassland) Poutputs kg P.cultivated ha -1 total amounts of P outputs (harvested, grazed, mowed) Pefficiency % agricultural efficiency ratio of P inputs over outputs Nmine kg N.ha -1 amount of mineralized N Ndenit kg N.ha -1 amount of denitrified N Nawc kg N.ha -1 mean N amount dissolved in the available water content NsoilBudget kg N.cultivated ha -1 leachable N pressure: N budget between ammoniacal inputs (fertilization and grazing restitution, soil N mineralization) and N inputs (harvests, mows, grazing) PsoilBudget kg P.cultivated ha -1 soil surface budget between P inputs and outputs Hydrological indicators discharge mm.day -1 catchment specific discharge (water volume divided by the catchment area) saturatedArea % penetratedArea % [N]groundwater mg N.l -1 mean groundwater N concentration mean fraction of the catchment area for which the groundwater reached the soil surface (soil saturation) mean fraction of the catchment area for which the groundwater table reached the soil layer (partial soil saturation) [N]stream mg N.l -1 mean stream N concentration at the outlet [N]streamExcessDuration day total duration during which the daily stream N concentration was above the 11.3 mgN.l -1 EU regulation threshold [N]streamExcess mg N.l -1 mean stream N concentration in situation of excess Nstream kg N load of N discharged at the outlet Nstream/fertiTot % N transmission ratio between the N load over the amount of N spread as synthesis and organic fertilizers Table IV.6: Description of active and illustrative variables used in the statistical analyses. IV. Évaluation environnementale multi-critères et multi-ressources – p. 114
TNT2 simulations TNT2 calibration with a reference scenario The so-called “reference virtual landscape”, which combines the “intensive” cropping system, “observed” soils, and “observed” climate levels, was used to calibrate the TNT2 model over the 1994-2005 decade for which we had measured data. The calibration was performed by a trial and error method considering both graphical and mathematical criteria. Nitrogen fate processes of TNT2 rely on water fluxes (infiltration and exfiltration, vertical leaching, surface and sub-surface horizontal fluxes), hydrological parameters related to soil lateral transmissivity and vertical conductivity were adjusted to allow a good reproduction of the stream specific daily discharge, which was assessed by the Nash-Sutcliffe coefficient (Nash and Sutcliffe, 1970) defined by equation 2: E=1− n ∑ O − P i i i=1 2 n ∑ i=1 O i − o 2 where Oi and Pi correspond respectively to the observations and the predictions and µo is the average of the observations. Then, nitrogen transformation parameters related to mineralization rate were adjusted to reproduce both fluctuations of measured N concentration and the global mean value. However, the N stream concentration sampling was sparse on occasions and not regular in time. Therefore instead of computing biased Nash- Sutcliffe coefficients, a graphical interpretation was preferred. Initialization of all the virtual landscapes Eighteen different virtual landscapes were created by combining each level of the different soil, climate and cropping system factors. Each one served as an input dataset for TNT2 and the soil P model with a common initial state, which was created by a six-year run of TNT2 with the reference scenario to produce distributed maps of soil nutrient contents, and groundwater and soil available water content (AWC) reservoirs and N concentration. Statistical analysis Since all landscapes were similarly initialized but then submitted to different climate series and cropping systems, the period of time necessary for the systems to stabilize was ignored when performing the following analyses, which do not expect any temporal trend in the data. Simulation outputs were aggregated annually by either summing fluxes or averaging concentrations and amounts over the whole catchment (Table IV.6) in order to remove seasonal variability. Assuming that landscapes reached stationary state, annual values were considered as independent repetitions and their variance thus reflects interannual variability. Statistical analyses were conducted using the R software (R Development Core Team, 2007). IV. Évaluation environnementale multi-critères et multi-ressources – p. 115 (2)
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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
Page 63 and 64: Représentation du milieu physique
Page 65 and 66: Sources de la plateforme Qualscape
Page 67: Chapitre III Stochastree, un modèl
Page 70 and 71: Les arbres de décision de Stochast
Page 72 and 73: 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: Cropping system factor levels Two l
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 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
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Annexes I : Application d’une mod
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Application of virtual landscape mo
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Introduction The increasing develop
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Material and methods Construction o
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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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