Investigating the relationships between wheat-specific rainfall characteristics, large-scale modes of climate variability and wheat yields in the Swartland region, South Africa
Includes bibliographical references. Wheat producers in the South Western Cape (SWC) of South Africa need to cope with biophysical and socio-economic systems exposing farmers to a multidimensional decision- making environment. The rain fed wheat production in the Swartland region is highly susceptib...
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| Other Authors: | , |
| Format: | Master Thesis |
| Language: | English |
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University of Cape Town
2014
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| Online Access: | http://hdl.handle.net/11427/13214 https://open.uct.ac.za/bitstream/11427/13214/1/thesis_sci_2014_kloppers_p.pdf |
| Summary: | Includes bibliographical references. Wheat producers in the South Western Cape (SWC) of South Africa need to cope with biophysical and socio-economic systems exposing farmers to a multidimensional decision- making environment. The rain fed wheat production in the Swartland region is highly susceptible to the interannual variability of winter rainfall. Producers, therefore, need relevant climatic information to identify ways to improve profitability and to make sound economic decisions. Seasonal forecasting has the potential to provide wheat producers with invaluable information regarding the climatic conditions. However, due to the complex nature of the atmospheric dynamics associated with winter rainfall in South Africa, seasonal forecasting models have been found to have very little skill in predicting the variability of winter rainfall. Such a shortfall has created a gap for which this study has attempted to bridge. This study aimed to investigate the relationship between wheat-specific rainfall characteristics, large-scale modes of climate variability and wheat yields in the Swartland region to assess whether these relationships could provide useful climatic information to the wheat farmers. Six wheat-specific rainfall characteristics (total rainfall number of wet days number of ‘good’ rainfall events; number of heavy rainfall events; percentage ‘good’ rainfall and the number of dry dekads ) on various time scales (winter; seasonal; monthly and dekadal) were correlated against wheat yield records over a 17 year period from 1994 to 2010. From this analysis, the distribution and timing of the rainfall throughout the wheat growing season (April to September) emerged as an important determinant of wheat yield. An accurate statistical wheat prediction model was created using farmer stipulated rainfall- wheat yield thresholds. Three teleconnections (El Niño-Southern Oscillation [ENSO], Antarctic Oscillation [AAO] and South Atlantic sea surface temperatures [SSTs]) represented by eight climate indices (Nino ... |
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