| Title | Optimization of sampling design for total organic carbon assessment using spatial simulated annealing: comparison of different variogram models performances. |
| Abstract | Assessment of soil organic carbon is of primary interest for evaluating soil quality and its variation as
effect of agronomic management. Appropriate sampling strategy and data analysis play a crucial role to
take into account variability that occurs at a scale smaller than the block size, assess spatial dependence
between observations and residuals, avoiding in this way erroneous conclusions about treatment
significance (Littell et al., 2006).
However, an over-sampling of the investigated field could be time-consuming, labor-intensive and
costly without a consequent significant knowledge gain. To avoid this pitfall, optimization of sampling
schemes allows reducing the number of sampling points with a negligible impact on the accuracy of the
estimate of the investigated attribute (Barca et al., 2015). In defining optimal sampling schemes,
important issues and decisions concern, among others, the choice of the optimization approach to be
used (model-based or design-based), the optimal variogram model when a model-based approach is
considered, the use of covariate information.
In this preliminary study, spatial simulated annealing was used as a method to optimize a TOC
sampling scheme. Two theoretical variogram models were used in order to reduce a previously defined
experimental design and to assess the impact of model selection on the optimal configuration. |
| Source | XLVIII Congress of the Italian Society of Agronomy (SIA),, perugia, 18-20/09/2019. |
| Keywords | Optimization of Sampling DesignTotal Organic Carbon AssessmentSpatial Simulated AnnealingVariogram Models |
| Year | 2019 |
| Type | Contributo in atti di convegno |
| Authors | Barca E., Stellacci A.M., De Benedetto D |
| Text | 424089 2019 Optimization of Sampling Design Total Organic Carbon Assessment Spatial Simulated Annealing Variogram Models Optimization of sampling design for total organic carbon assessment using spatial simulated annealing comparison of different variogram models performances. Barca E., Stellacci A.M., De Benedetto D Istituto di ricerca sulle acque IRSA , Consiglio Nazionale delle Ricerche CNR , Bari, IT Dip. di Scienze del Suolo, della Pianta e degli Alimenti, Univ. Bari, IT, annamaria.stellacci@uniba.it Consiglio per la ricerca in agricoltura e l analisi dell economia agraria CREA AA , sede di Bari, IT Assessment of soil organic carbon is of primary interest for evaluating soil quality and its variation as effect of agronomic management. Appropriate sampling strategy and data analysis play a crucial role to take into account variability that occurs at a scale smaller than the block size, assess spatial dependence between observations and residuals, avoiding in this way erroneous conclusions about treatment significance Littell et al., 2006 . However, an over sampling of the investigated field could be time consuming, labor intensive and costly without a consequent significant knowledge gain. To avoid this pitfall, optimization of sampling schemes allows reducing the number of sampling points with a negligible impact on the accuracy of the estimate of the investigated attribute Barca et al., 2015 . In defining optimal sampling schemes, important issues and decisions concern, among others, the choice of the optimization approach to be used model based or design based , the optimal variogram model when a model based approach is considered, the use of covariate information. In this preliminary study, spatial simulated annealing was used as a method to optimize a TOC sampling scheme. Two theoretical variogram models were used in order to reduce a previously defined experimental design and to assess the impact of model selection on the optimal configuration. 978 88 99407 01 8 Published version XLVIII Congress of the Italian Society of Agronomy SIA , perugia 18 20/09/2019. Nazionale Su invito Contributo in atti di convegno emanuele.barca BARCA EMANUELE |
