Seasonal maize forecasting for South Africa and Zimbabwe derived from an agroclimatological model
Seasonal maize water-stress forecasts were derived for area averages of the primary maize-growing regions of South Africa and Zimbabwe. An agroclimatological model was used to create a historical record of maize water stress as a function of evapotranspiration for 1961-94. Water stress, the primary...
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Format: | Journal article |
Langue: | English |
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Americal Meteorological Society
2000
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author | Martin, R Washington, R Downing, T |
author_facet | Martin, R Washington, R Downing, T |
author_sort | Martin, R |
collection | OXFORD |
description | Seasonal maize water-stress forecasts were derived for area averages of the primary maize-growing regions of South Africa and Zimbabwe. An agroclimatological model was used to create a historical record of maize water stress as a function of evapotranspiration for 1961-94. Water stress, the primary determinant of yield in water-limited environments such as southern Africa, was correlated with two well-known indices of the El Niño - Southern Oscillation: the Southern Oscillation index (SOI) and the Niño-3 region of the equatorial Pacific. Forecasts for South Africa using only the SOI at a 4-month lead yielded a hindcast correlation of 0.67 over 17 seasons (1961-78) and a forecast correlation of 0.69 over 16 seasons (1978-94). Forecasts for Zimbabwe were less remarkable. |
first_indexed | 2024-03-06T19:09:25Z |
format | Journal article |
id | oxford-uuid:16431f86-2f45-4e67-9564-22ca96af32e7 |
institution | University of Oxford |
language | English |
last_indexed | 2024-03-06T19:09:25Z |
publishDate | 2000 |
publisher | Americal Meteorological Society |
record_format | dspace |
spelling | oxford-uuid:16431f86-2f45-4e67-9564-22ca96af32e72022-03-26T10:30:15ZSeasonal maize forecasting for South Africa and Zimbabwe derived from an agroclimatological modelJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:16431f86-2f45-4e67-9564-22ca96af32e7EnvironmentEnvironmental changeEnglishOxford University Research Archive - ValetAmerical Meteorological Society2000Martin, RWashington, RDowning, TSeasonal maize water-stress forecasts were derived for area averages of the primary maize-growing regions of South Africa and Zimbabwe. An agroclimatological model was used to create a historical record of maize water stress as a function of evapotranspiration for 1961-94. Water stress, the primary determinant of yield in water-limited environments such as southern Africa, was correlated with two well-known indices of the El Niño - Southern Oscillation: the Southern Oscillation index (SOI) and the Niño-3 region of the equatorial Pacific. Forecasts for South Africa using only the SOI at a 4-month lead yielded a hindcast correlation of 0.67 over 17 seasons (1961-78) and a forecast correlation of 0.69 over 16 seasons (1978-94). Forecasts for Zimbabwe were less remarkable. |
spellingShingle | Environment Environmental change Martin, R Washington, R Downing, T Seasonal maize forecasting for South Africa and Zimbabwe derived from an agroclimatological model |
title | Seasonal maize forecasting for South Africa and Zimbabwe derived from an agroclimatological model |
title_full | Seasonal maize forecasting for South Africa and Zimbabwe derived from an agroclimatological model |
title_fullStr | Seasonal maize forecasting for South Africa and Zimbabwe derived from an agroclimatological model |
title_full_unstemmed | Seasonal maize forecasting for South Africa and Zimbabwe derived from an agroclimatological model |
title_short | Seasonal maize forecasting for South Africa and Zimbabwe derived from an agroclimatological model |
title_sort | seasonal maize forecasting for south africa and zimbabwe derived from an agroclimatological model |
topic | Environment Environmental change |
work_keys_str_mv | AT martinr seasonalmaizeforecastingforsouthafricaandzimbabwederivedfromanagroclimatologicalmodel AT washingtonr seasonalmaizeforecastingforsouthafricaandzimbabwederivedfromanagroclimatologicalmodel AT downingt seasonalmaizeforecastingforsouthafricaandzimbabwederivedfromanagroclimatologicalmodel |