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Modelling maize (Zea Mays L.) phenology seasonal forecast data

Muswera, Tanyaradzwa Joy Nyarai LU (2016) In Lund University GEM thesis series NGEM01 20161
Dept of Physical Geography and Ecosystem Science
Abstract
Agriculture is an essential economic activity which sustains the livelihood of millions of people around the world. Maize is one of the most grown, consumed and traded cereals in the world mostly because of its adaptability to varied environmental conditions. Maize farming depends on climatic factors like temperature, rainfall and radiation to thrive but this also means that it is very susceptible to variabilities in climatic conditions. Farmers every season are vulnerable to the risk of losing their crops and in turn losing their income. In order to reduce the impact of climate variability on crop production, there is need to make use of available climate forecast information to anticipate, plan for and cope with the related seasonal... (More)
Agriculture is an essential economic activity which sustains the livelihood of millions of people around the world. Maize is one of the most grown, consumed and traded cereals in the world mostly because of its adaptability to varied environmental conditions. Maize farming depends on climatic factors like temperature, rainfall and radiation to thrive but this also means that it is very susceptible to variabilities in climatic conditions. Farmers every season are vulnerable to the risk of losing their crops and in turn losing their income. In order to reduce the impact of climate variability on crop production, there is need to make use of available climate forecast information to anticipate, plan for and cope with the related seasonal climate risks. In this study the potential use of ensemble seasonal climate forecasts from the new The European Centre for Medium-Range Weather Forecasts (ECMWF) System 4 coupled ocean-atmosphere general circulation model is evaluated for predicting maize (Zea mays L.) phenology, particularly the date of silking and the date of maturity in Zimbabwe, Spain and Sweden. Linear-scaling approach was used as a bias correction method to improve the prediction skill of the ensemble forecasts, whilst a temperature driven growing degree days (GDD) model was developed to simulate the development of early and late maize varieties. Verification of the model results was done using Brier skill scores. Results indicate very low skill scores by the model, showing that contrary to the initial study hypothesis, the ECMWF System 4 ensemble data cannot successfully be used to determine the day of silking and day of maturity for both the early and late varieties of maize. Interpretation of results attained in this study have to take into account a number of limitations, which can also be subjects of further research, such as observed and ensemble forecast data uncertainties as well use of more comprehensive bias correction methods like quantile mapping. (Less)
Popular Abstract
Agriculture is an essential economic activity which sustains the livelihood of millions of people around the world. Maize is one of the most grown, consumed and traded cereals in the world mostly because of its adaptability to varied environmental conditions. Maize farming depends on climatic factors like temperature, rainfall and radiation to thrive but this also means that it is very susceptible to changes in climatic conditions. Farmers every season are vulnerable to the risk of losing their crops and in turn losing their income.

In order to reduce the impact of climate variability on crop production, there is need to make use of available climate forecast information to anticipate, plan for and cope with the related seasonal... (More)
Agriculture is an essential economic activity which sustains the livelihood of millions of people around the world. Maize is one of the most grown, consumed and traded cereals in the world mostly because of its adaptability to varied environmental conditions. Maize farming depends on climatic factors like temperature, rainfall and radiation to thrive but this also means that it is very susceptible to changes in climatic conditions. Farmers every season are vulnerable to the risk of losing their crops and in turn losing their income.

In order to reduce the impact of climate variability on crop production, there is need to make use of available climate forecast information to anticipate, plan for and cope with the related seasonal climate risks. In this study the potential use of seasonal climate forecasts from the new The European Centre for Medium-Range Weather Forecasts (ECMWF) System 4 climate forecast model is evaluated for predicting maize (Zea mays L.) phenology, particularly the date of silking and the date of maturity in Zimbabwe, Spain and Sweden.

Linear-scaling approach was used as a bias correction method to improve the prediction skill of the forecast data, whilst a temperature driven growing degree days (GDD) model was developed to simulate the development of early and late maize varieties. Verification of the model results was done using Brier skill scores.

Results indicate very low skill scores by the model, showing that contrary to the initial study hypothesis, the ECMWF System 4 forecast data cannot successfully be used to determine the day of silking and day of maturity for both the early and late varieties of maize. Interpretation of results attained in this study have to take into account a number of limitations, which can also be subjects of further research, such as observed and ensemble forecast data uncertainties as well use of more comprehensive bias correction methods like quantile mapping. (Less)
Please use this url to cite or link to this publication:
author
Muswera, Tanyaradzwa Joy Nyarai LU
supervisor
organization
course
NGEM01 20161
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Growing Degree Days (GDD), ECMWF System 4, Physical Geography and Ecosystem analysis, agriculture, maize phenology, GEM
publication/series
Lund University GEM thesis series
report number
16
funder
Erasmus Mundus Programme
language
English
id
8887289
date added to LUP
2016-08-22 12:47:02
date last changed
2016-08-22 12:47:02
@misc{8887289,
  abstract     = {Agriculture is an essential economic activity which sustains the livelihood of millions of people around the world. Maize is one of the most grown, consumed and traded cereals in the world mostly because of its adaptability to varied environmental conditions. Maize farming depends on climatic factors like temperature, rainfall and radiation to thrive but this also means that it is very susceptible to variabilities in climatic conditions. Farmers every season are vulnerable to the risk of losing their crops and in turn losing their income. In order to reduce the impact of climate variability on crop production, there is need to make use of available climate forecast information to anticipate, plan for and cope with the related seasonal climate risks. In this study the potential use of ensemble seasonal climate forecasts from the new The European Centre for Medium-Range Weather Forecasts (ECMWF) System 4 coupled ocean-atmosphere general circulation model is evaluated for predicting maize (Zea mays L.) phenology, particularly the date of silking and the date of maturity in Zimbabwe, Spain and Sweden. Linear-scaling approach was used as a bias correction method to improve the prediction skill of the ensemble forecasts, whilst a temperature driven growing degree days (GDD) model was developed to simulate the development of early and late maize varieties. Verification of the model results was done using Brier skill scores. Results indicate very low skill scores by the model, showing that contrary to the initial study hypothesis, the ECMWF System 4 ensemble data cannot successfully be used to determine the day of silking and day of maturity for both the early and late varieties of maize. Interpretation of results attained in this study have to take into account a number of limitations, which can also be subjects of further research, such as observed and ensemble forecast data uncertainties as well use of more comprehensive bias correction methods like quantile mapping.},
  author       = {Muswera, Tanyaradzwa Joy Nyarai},
  keyword      = {Growing Degree Days (GDD),ECMWF System 4,Physical Geography and Ecosystem analysis,agriculture,maize phenology,GEM},
  language     = {eng},
  note         = {Student Paper},
  series       = {Lund University GEM thesis series},
  title        = {Modelling maize (Zea Mays L.) phenology seasonal forecast data},
  year         = {2016},
}