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University of Twente (2013)

A spatial statistical study on upscaling in the SDI framework : the case of yield and poverty in Burkina Faso

Imran, Muhammad

Titre : A spatial statistical study on upscaling in the SDI framework : the case of yield and poverty in Burkina Faso

Auteur : Imran, Muhammad

Université de soutenance : University of Twente

Grade : Doctoral Thesis (2013)

Résumé partiel
Cropping conditions in West-Africa are highly spatially and temporally variable. Because of this, a variety of computational models have been developed based on understanding agricultural processes at different spatial scales. Farmers and extension workers need scientific tools that allow accessing, combining, and assessing data and models to obtain sustainable solutions at a farm location. Ideally such tools should be part of an agricultural spatial data infrastructure (SDI) so that wall to wall services are possible. In this work, we carried out four studies that support the creation of such an agricultural SDI in Burkina Faso. The first study proposes and deploys a flexible framework system for upscaling datasets and for linking such datasets with regional simulation models. The proposed framework is based on SDI technology. The service-oriented architecture of SDI allows datasets and models to be deployed as re-usable web services. The study investigates how to use an open and interoperable SDI environment to integrate data and models for deploying location-based wall to wall services. It also studies how this environment can allow models to be adapted for variables upscaled from ground-based surveys. It provides access to datasets and models as re- usable web services by means of standard wrapper implementations. The proposed framework is deployed for on-farm decision-making in Burkina Faso. To do so, the wrapper implementation deploys a farm simulation model following the “Model-as-a-Service” paradigm and the datasets as spatial data services. Orchestrating these services enables community participation by integrating the several farming resources. The study found that the model benefits from various spatial data services in state- of-the-art SDI-based implementations. It concluded that adaptation of the variables from the country’s agricultural surveys in the application of SDI services required the application of spatial statistical models and the use of remote sensing to upscale the survey data to the national scale. The second study uses data on biophysical, socioeconomic and human resources of terroirs in Burkina Faso to estimate crop yields and to upscale the yield estimates to the national scale. The study explores the application of remote sensing (RS) data to investigate yield spatial variability. A time series of SPOT-VEGETATION (NDVI) data 1 km 10- day composites for the period covering the crop growing season was used. Field observations for crop yields were obtained from groundsurveys published in the national statistical database and sub-Saharan auxiliary datasets, originally developed using RS, were obtained from online repositories. Geographically weighted regression was applied to interpolate crop data from the field scale towards the national scale. Estimates thus obtained were stored in the geodatabase. The spatial data services deployed on top of the geodatabase can adequately initialize a farm simulation model for a terroir location. Uncertainty due to limited data availability, likely prohibits the stability of statistical models to fully capture the high spatial variability of yields in a highly heterogeneous landscape. This required to model uncertainty associated with crop yield models at regional scales. The study concludes that statistical methods and RS technology can be used for upscaling crop yield estimates for the entire country. The third study quantifies the uncertainty in crop yield modeling at a national scale, using the crop yield observations obtained from coun- trywide georeferenced surveys and the spatial statistical upscaling. It presents a hybrid approach integrating ordinary kriging and geographic- ally weighted regression. This geographically weighted regression-kriging approach was applied to crop yields in Burkina Faso. The study shows that quantifying uncertainties in large-area crop models can help to improve the sources of uncertainty given by the sampling design and the model structure. Moreover, the uncertainty maps obtained in this way can increase the confidence of end-users by taking into account the accurately estimated prediction uncertainty of crop yields.

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Page publiée le 1er septembre 2014, mise à jour le 2 février 2018