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

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GrMz: 1023m Wh: 858m dairy AWD: 636m PermP: 624m TempP: 442m Veg: 429m dairy farmstead 500 m 1000 m Wh: 1439m other AWD: 1286m TempP: 921m PermP: 261m other farmstead 500 m 1000 m 1500 m Veg: 2153m dairy-pig AWD: 1340m TempP: 1191m PermP: 781m dairy-pig farmstead Veg: 988m pig AWD: 798m PermP: 486m pig farmstead Fig. III.21: Spatial distribution of the crops observed in Naizin (1993-2002) around the four farm-types (as if viewed from the sky): dairy, dairy-pig, pig, and other. The circles show the mean area-weighted distance (AWD) expressed in meters. Only the crops for which the AWD were significantly different than the global AWD of the farm-type are shown. Crop classes are: Fal=fallow, TempP=temporary pasture, PermP=permanent pasture, SRG=seed ray-grass, Wh=wheat, GrMz=grain maize, SlMz=silage maize, Pot=potato, Rape=rape, Veg=vegetable. III. Stochastree, un modèle de successions de cultures basé sur des arbres de décision stochastique – p. 84 500 m 1000 m 1500 m 2000 m 500 m 1000 m
Results Driving factors of crop transition in Naizin Results of the Wilcoxon tests performed on observed data of Naizin exhibited crop production objectives specific to each farm-type. The dairy farm-type was characterized by significantly larger proportions for fodder crops (silage maize, permanent and temporary pastures) and smaller proportions for wheat, grain maize and vegetables. On the contrary, pig farm-type was characterized by smaller proportions for fodder crops; and larger proportions for wheat, grain maize, and potato. The dairy-pig farm-type exhibited intermediate proportions between those of the dairy and the pig farm-types. The AWD spatial indicator revealed two levels of spatial structures in the crop pattern of Naizin: - most farm-type crop AWDs were significantly different than the crop AWDs computed over the whole catchment (except marginal crops like fallow, rapeseed, and potato), thus showing a farm- type effect on crop spatial structures; - the comparison of specific crop AWD (i.e. calculated for a given farm-type) to the global farm-type AWD showed that some crops were significantly close or far away from the farmstead (Fig. III.21). Dairy farm-type exhibited the most spatially-structured crop pattern, and the crops with significant spatial structures were permanent and temporary pastures (closer to farmstead), wheat, grain maize and vegetables (farther to farmstead). Concerning soil waterlogging, fallows and permanent pastures proved to be preferentially allocated on waterlogged soils (respectively 51% and 40% of each crop area), whereas all the other crops were preferentially allocated on well-drained soils. Structure of the crop transition models Rotomatrix models We tested the statistical independence of the generic and the four specific transition matrices constructed from the learning dataset (1993-1998). The dimension of these five matrices (m) was 10 since 10 crop classes were represented on the catchment. The values of α for the generic and farm-type specific ranged between 546 and 4767, largely exceeding the critical value of 103 (χ²p=0.05, df=9x9). Therefore, the annual crop transitions are not random and can be modeled with at least a first order Markov chain. Generic and specific transition matrices were all ergodic (the first eigenvalue equaled 1, others were strictly lower than 1) and the temporal stationarity of the Markovian processes can thus be assumed, and visually controlled (Fig. III.20) although we did not test it. Following the methodology of Coppedge et al. (2007), we used χ² tests to control that matrices were significantly different among farm-types. III. Stochastree, un modèle de successions de cultures basé sur des arbres de décision stochastique – p. 85
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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
Page 34 and 35: influencent les phénomènes de tra
Page 36 and 37: Fig. I.6: Courbes d’absorption au
Page 38 and 39: Fig. I.7 : Diversité d’indicateu
Page 40 and 41: ecyclage et de transformation (min
Page 42 and 43: Modélisation et évaluation intég
Page 44 and 45: conceptuelles et techniques : - un
Page 46 and 47: La démarche adoptée pour cette é
Page 49 and 50: Chapitre II : La plateforme Qualsca
Page 51 and 52: L’occupation du sol conditionnant
Page 53 and 54: (a) (b) Fig. II.11 : Cartes des sol
Page 55 and 56: Formalisme du paysage « occupation
Page 57 and 58: Une représentation hiérarchique d
Page 59 and 60: surface=y), et la décision attendu
Page 61 and 62: 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 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 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
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Gabrielle B., B. Mary, R. Roche, P.
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Chapitre V Conclusion générale
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Chapitre V : Conclusion générale
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par le système de culture ; − au
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La plateforme Qualscape Les modules
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Cependant, afin de maintenir un cer
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Perspectives - de l’évaluation e
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Bibliographie générale
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Bibliographie générale Aarts H.F.
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Deffontaines, J.P., C. Thenail, J.
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in considering landscape trajectori
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Ecosystems & Environment, 120 (2-4)
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on global change research. 124 p. U
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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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