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Seasonal maize yield simulations for South Africa using a multi-model ensemble system
Dissertation (MSc)--University of Pretoria, 2009.
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University of Pretoria
2013
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| _version_ | 1867613647616868352 |
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| access_status_str | Open Access |
| author2 | Landman, Willem Adolf |
| author_browse | Landman, Willem Adolf |
| author_facet | Landman, Willem Adolf |
| collection | Thesis |
| dc_rights_str_mv | © 2009, University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MSc)--University of Pretoria, 2009. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/29970 |
| institution | University of Pretoria (South Africa) |
| last_indexed | 2026-06-10T12:39:28.478Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2013 |
| publishDateRange | 2013 |
| publishDateSort | 2013 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/29970 Seasonal maize yield simulations for South Africa using a multi-model ensemble system Landman, Willem Adolf somersn@arc.agric.za Engelbrecht, Francois Alwyn Le Roux, Noelien Observed weather data Seasonal maize yield simulations Rainfall UCTD Dissertation (MSc)--University of Pretoria, 2009. Agricultural production is highly sensitive to climate and weather perturbations. Maize is the main crop cultivated in South Africa and production is predominantly rain-fed. South Africa’s climate, especially rainfall, is extremely variable which influences the water available for agriculture and makes rain-fed cropping very risky. In the aim to reduce the uncertainty in the climate of the forthcoming season, this study investigates whether seasonal climate forecasts can be used to predict maize yields for South Africa with a usable level of skill. Maize yield, under rain-fed conditions, is simulated for each of the magisterial districts in the primary maize producing region of South Africa for the period from 1979 to 1999. The ability of the CERES-Maize model to simulate South African maize yields is established by forcing the CERES-Maize model with observed weather data. The simulated maize yields obtained by forcing the CERES-Maize model with observed weather data set the target skill level for the simulation systems that incorporate Global Circulation Models (GCMs). Two GCMs produced the simulated fields for this study, they are the Conformal Cubic Atmospheric Model (CCAM) and the ECHAM4.5 model. CCAM ran a 5 and ECHAM4.5 a 6- member ensemble of simulations on horizontal grids of 2.1° x 2.1° and 2.8° x 2.8° respectively. Both models were forced with observed sea-surface temperatures for the period 1979 to 2003. The CERES-Maize model is forced with each ensemble member of the CCAM-simulated fields and with each ensemble member of the ECHAM4.5-simulated fields. The CERES-CCAM simulated maize yields and CERES-ECHAM4.5 simulated maize yields are combined to form a Multi-Model maize yield ensemble system. The simulated yields are verified against actual maize yields. The CERES-Maize model shows significant skill in simulating South Africa maize yields. CERES-Maize model simulations using the CCAM-simulated fields produced skill levels comparable to the target skill, while the CERES-ECHAM4.5 simulation system illustrated poor skill. The Multi-Model system presented here could therefore not outscore the skill of the best single-model simulation system (CERES-CCAM). Notwithstanding, the CERES-Maize model has the potential to be used in an operational environment to predict South African maize yields, provided that the GCM forecast fields used to force the model are adequately skilful. Such a yield prediction system does not currently exist in South Africa. Geography, Geoinformatics and Meteorology Unrestricted 2013-09-07T17:25:12Z 2009-12-08 2013-09-07T17:25:12Z 2009-09-02 2009-12-08 2009-11-30 Dissertation Le Roux, N 2009, Seasonal maize yield simulations for South Africa using a multi-model ensemble system, MSc dissertation, University of Pretoria, Pretoria, viewed yymmdd < http://hdl.handle.net/2263/29970 > E1510/ag http://hdl.handle.net/2263/29970 http://upetd.up.ac.za/thesis/available/etd-11302009-211655/ © 2009, University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf application/pdf application/pdf application/pdf University of Pretoria |
| spellingShingle | Observed weather data Seasonal maize yield simulations Rainfall UCTD Seasonal maize yield simulations for South Africa using a multi-model ensemble system |
| title | Seasonal maize yield simulations for South Africa using a multi-model ensemble system |
| title_full | Seasonal maize yield simulations for South Africa using a multi-model ensemble system |
| title_fullStr | Seasonal maize yield simulations for South Africa using a multi-model ensemble system |
| title_full_unstemmed | Seasonal maize yield simulations for South Africa using a multi-model ensemble system |
| title_short | Seasonal maize yield simulations for South Africa using a multi-model ensemble system |
| title_sort | seasonal maize yield simulations for south africa using a multi model ensemble system |
| topic | Observed weather data Seasonal maize yield simulations Rainfall UCTD |
| url | http://hdl.handle.net/2263/29970 http://upetd.up.ac.za/thesis/available/etd-11302009-211655/ |