Abstract
Original language  English 

Qualification  Doctor of Philosophy 
Awarding Institution  
Supervisors/Advisors 

Award date  19 Nov 1993 
Place of Publication  S.l. 
Publisher  
Print ISBNs  9789054851837 
Publication status  Published  1993 
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Keywords
 soil analysis
 sampling
 models
 research
 geostatistics
Cite this
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Incorporating models of spatial variation in sampling strategies for soil. / Brus, D.J.
S.l. : Brus, 1993. 211 p.Research output: Thesis › external PhD, WU
TY  THES
T1  Incorporating models of spatial variation in sampling strategies for soil
AU  Brus, D.J.
N1  WU thesis 1700 Proefschrift Wageningen
PY  1993
Y1  1993
N2  The efficiency of soil sampling strategies can be increased by incorporating a spatial variation model. The model can be used in the random selection of sample points i.e. in the sampling design, or in spatial estimation (prediction). In the first approach inference is based on a sampling design, in the second on a probabilistic model. The advantages and disadvantages of these two approaches, referred to as the designbased and modelbased approach, are dealt with from a theoretical and a practical point of view. Estimation by random sampling stratified by soil map unit, and kriging are taken as examples of the two approaches in several case studies.The commonly accepted belief in geostatistical literature that the designbased approach is not valid in areas with autocorrelation is incorrect. Furthermore, the claimed optimality of the modelbased approach is questionable. The two approaches use different criteria for assesment of the quality of estimates, consequently optimum estimation has a different meaning in each approach.In a regional survey with small observation density (1 observation per 25 ha), estimates of values at points were generally not significantly improved by soil map stratification (α=0.10), neither by estimation with variograms as in kriging. Stratified random sample estimates of values at points were as accurate as those provided by kriging.In the modelbased approach the quality of the estimates depends on the quality of the model. To avoid this, a new approach for spatial estimation is proposed, the modelassisted approach, making use of nonergodic variograms. This approach incorporates the sampling error of the nonergodic variogram in the kriging error, making the estimation variance estimates always valid. A set of new methods is presented for unbiased and robust estimation of the nonergodic variogram and its sampling error.Many factors determine the efficiency of an approach that incorporates spatial variation models, making the decision process rather complicated. A simple decisiontree is presented with seven questions related to the aim of the survey (local or global estimation, criteria for assessment of the quality of the estimates), the constraints (available budget and sampling costs) and prior information (soil map).
AB  The efficiency of soil sampling strategies can be increased by incorporating a spatial variation model. The model can be used in the random selection of sample points i.e. in the sampling design, or in spatial estimation (prediction). In the first approach inference is based on a sampling design, in the second on a probabilistic model. The advantages and disadvantages of these two approaches, referred to as the designbased and modelbased approach, are dealt with from a theoretical and a practical point of view. Estimation by random sampling stratified by soil map unit, and kriging are taken as examples of the two approaches in several case studies.The commonly accepted belief in geostatistical literature that the designbased approach is not valid in areas with autocorrelation is incorrect. Furthermore, the claimed optimality of the modelbased approach is questionable. The two approaches use different criteria for assesment of the quality of estimates, consequently optimum estimation has a different meaning in each approach.In a regional survey with small observation density (1 observation per 25 ha), estimates of values at points were generally not significantly improved by soil map stratification (α=0.10), neither by estimation with variograms as in kriging. Stratified random sample estimates of values at points were as accurate as those provided by kriging.In the modelbased approach the quality of the estimates depends on the quality of the model. To avoid this, a new approach for spatial estimation is proposed, the modelassisted approach, making use of nonergodic variograms. This approach incorporates the sampling error of the nonergodic variogram in the kriging error, making the estimation variance estimates always valid. A set of new methods is presented for unbiased and robust estimation of the nonergodic variogram and its sampling error.Many factors determine the efficiency of an approach that incorporates spatial variation models, making the decision process rather complicated. A simple decisiontree is presented with seven questions related to the aim of the survey (local or global estimation, criteria for assessment of the quality of the estimates), the constraints (available budget and sampling costs) and prior information (soil map).
KW  grondanalyse
KW  bemonsteren
KW  modellen
KW  onderzoek
KW  geostatistiek
KW  soil analysis
KW  sampling
KW  models
KW  research
KW  geostatistics
M3  external PhD, WU
SN  9789054851837
PB  Brus
CY  S.l.
ER 