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Fig. A.I.41: Evolution of the detection rate as a function of sampling density for the different sampling designs and sampling time-intervals. Trend detection for a 2 years (a) and 8 years (c) sampling time-interval; accurate trend detection for a 2 years (b) and 8 years (d) sampling interval. Annexes I : Virtual landscapes to assess monitoring strategies – p. 188
− samples stratified by landuse (IL and CL) exhibited less bias than RG and Tr. IL was the only design to be negatively biased (although not significantly, considering the CI). During the landscape evolution process, the size of the permanent pastures stratum decreased from 28% to 7% of the total area. Therefore, the weights initially associated with the sampling sites located in permanent pastures slightly over-represented the low soil P content values carried by permanent pastures as the landscape evolved (see the cultural crop components in Table A.I.10). Finally, samples based on IL and CL designs were the most accurate in terms of bias. RG, IL, and CL designs were significantly better than Tr in terms of RMSE. Tr was the worst strategy for all sample sizes, in terms of DE, bias, and RMSE. Trend detectability The trend ratio (TR) was developed to assess trend detectability of sampling strategy. The approaches used here were based on a success rate. Each pair of sample median values separated by a time interval within a realization was characterised by a TR value. The success rate is the ratio of satisfying TR values to the total number of pairs, which depended on the considered time interval. Fig. A.I.41 shows the effect of sampling density and frequency on the trend detectability of the sampling strategies. A higher sampling density increased the success rates of all the strategies for both 2- and 8-year time intervals, and particularly for sampling densities comprised between 0.1 and 1.1 site/km 2 where success rates gained from 5 to 40%. The success rate improvements were non-linear with sampling densities and tended asymptotically toward 100%. Considering only trend detectability (TR>0), stationary designs had high success rates (greater than 85%), either with a 2-year or a 8-year interval. Despite its good accuracy, the trend detectability of the CL design was clearly disadvantaged by the relocation of the sampling sites: at a 2-year interval, the success rate was still below 75% with a sampling density of 4.6 site/km 2 . However, success rates of CL could be significantly improved by increasing the time interval between samplings to 8 years because the soil P content landscape was in a context of long term and uniform evolution (20 years of intensive fertilization and then 30 years of balanced fertilization): the success rate rose from 60% to nearly 80% for a sampling density of 0.6 site/km 2 . The framing of the trend ratio (0.5
Page 1:
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
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- farm-types have specific spatial
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eached. The graphical structure of
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Comparative analysis of the simulat
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GrMz: 1023m Wh: 858m dairy AWD: 636
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Fal 33% TempP 71% PermP 100% Forage
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simulation method Rotomatrix Stocha
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of significant differences was gene
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crop class simulation method perman
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Spatial distribution of crops over
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The decision trees used in this res
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Gabrielle B., B. Mary, R. Roche, P.
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In Cheverry C. (ed.). Agriculture i
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Chapitre IV : Une approche multi-re
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Multi-resource and multi-criteria l
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ecology (Ruiz et al., 2006) and for
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as cattle excreta (Bodet et al., 20
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Factorial simulation design A set o
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Cropping system factor levels Two l
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TNT2 simulations TNT2 calibration w
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Results Virtual landscape construct
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The NST transformations of measured
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Cropping system factor levels Fig.
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Scenario comparison and analysis St
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Exploratory analysis of the simulat
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The variance of all simulation resu
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The effects of the factor levels va
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higher denitrification rates, thus
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Conclusion The study develops innov
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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
Page 179 and 180: PEi,y= PexportYcrop = PexportN(mean
Page 181 and 182: RMSE= 1 N Year 1 N Realization N
Page 183 and 184: The rules we followed to interpret
Page 185 and 186: The sill of the experimental variog
Page 187: Strategy evaluation Accuracy assess
Page 191 and 192: est design worst design BIAS curren
Page 193: Papritz A. and R. Webster. 1995. Es
Page 197 and 198: Annexes II : Matrices de probabilit
Page 199 and 200: Arbres de décision utilisés par S
Page 201 and 202: Fig. A.II.43 : Ensemble des parcell
Page 203 and 204: Fig. A.II.45 : Ensemble des parcell
Page 205: Arbre de décision utilisé pour si
Page 208 and 209: 1. Exemple de fichier texte produit
Page 210 and 211: Méthode principale de manipulation
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