Modelling maize (Zea Mays L.) phenology seasonal forecast data
(2016) In Lund University GEM thesis series NGEM01 20161Dept 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:
http://lup.lub.lu.se/student-papers/record/8887289
- author
- Muswera, Tanyaradzwa Joy Nyarai LU
- supervisor
- organization
- course
- NGEM01 20161
- year
- 2016
- 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}},
language = {{eng}},
note = {{Student Paper}},
series = {{Lund University GEM thesis series}},
title = {{Modelling maize (Zea Mays L.) phenology seasonal forecast data}},
year = {{2016}},
}