Potential predictability of crop impacting climate variables for East Africa and application to sorghum in the Mt Kenya area

Defended on the 22nd November 2013

Funding: ANR PICREVAT (prévisibilité de l’information climatique pour reduction de vulnérabilité de l’agriculture tropicale)

Supervisor: Pierre Camberlin

Started in 2010

Abstract

In Southern countries with rural low income populations, the vulnerability of rainfed agriculture to rainfall variability requires effective solutions to mitigate the effects of climatic hazards on crops. Predicting the characteristics of rainy seasons some time before they start should help the establishment of agricultural adaptation strategies to rainfall hazards. This is the objective of the present study, focused on East Africa (Kenya and northern Tanzania), and divided in three parts:

– Define and document intra-seasonal descriptors (ISD) that will be considered in the predictability study. A new methodological approach has been developed in order to define the onset date (ORS) and the cessation date (CRS) of the rainy seasons at the regional level. Based on a multivariate analysis, it eliminates the subjective choice of rainfall thresholds imposed by the definitions commonly used in agroclimatology. An analysis of spatial coherence at interannual time-scale shows that for the two rainy seasons (“long rains” and “short rains”), the seasonal amount and the number of rainy days have a high spatial coherence, while it is medium for the onset and cessation dates and low for the average daily rainfall intensity.

– Analyze the predictability of the ISD at both regional and local scales based on numerical simulations from the global climate model ECHAM 4.5. Daily precipitation simulated by the model, even after bias correction, do not correctly capture the IDS interannual variability. A specification of the ORS and CRS variability using statistical models applied to observed climate indices, suggests quite a low predictability of the descriptors at the local (regional) scale, regardless of the season. The development of statistical-dynamical models from wind fields simulated by ECHAM 4.5, in experiments forced by either observed or predicted sea temperatures, also shows quite poor skills locally and regionally.

– Explore how the space-time variability of climatic and environmental factors modulate the variations of sorghum yields. Crop yields are simulated by the agronomic model SARRA-H using observed climate data (1973-2001) at three stations located at different elevations along the eastern slopes of Mt Kenya. The seasonal rainfall accumulation and the duration of the season account for a large part of the yields variability. Other rainfall variables also play a significant role, among which the number of rainy days, the average daily intensity and some ISD related to the temporal organization of rainfall within the season. The influence of other meteorological variables is only found during the long rains, in the form of a negative correlation between yields and both maximum temperature and global radiation. Sowing dates seem to play a role in modulating yields for high and medium altitude stations, but with notable differences between the two rainy seasons.

Keywords

East Africa, intra-seasonal rainfall descriptors, potential predictability, ECHAM 4.5, onset / cessation of the rainy season, crop yields, sorghum, SARRA -H

Jury

Gérard Beltrando, université Paris 7 – rapporteur
Benjamin Sultan, IRD – rapporteur
Christian Baron, CIRAD – examinateur
Alain Durand, université de Rouen – examinateur
Vincent Moron, université Aix-Marseille – examinateur
Nathalie Philippon, Biogéosciences – examinateur

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Potential predictability of crop impacting climate variables for East Africa and application to sorghum in the Mt Kenya area

Defended on the 22nd November 2013

Funding: ANR PICREVAT (prévisibilité de l’information climatique pour reduction de vulnérabilité de l’agriculture tropicale)

Supervisor: Pierre Camberlin

Started in 2010

 

Abstract

In Southern countries with rural low income populations, the vulnerability of rainfed agriculture to rainfall variability requires effective solutions to mitigate the effects of climatic hazards on crops. Predicting the characteristics of rainy seasons some time before they start should help the establishment of agricultural adaptation strategies to rainfall hazards. This is the objective of the present study, focused on East Africa (Kenya and northern Tanzania), and divided in three parts:

- Define and document intra-seasonal descriptors (ISD) that will be considered in the predictability study. A new methodological approach has been developed in order to define the onset date (ORS) and the cessation date (CRS) of the rainy seasons at the regional level. Based on a multivariate analysis, it eliminates the subjective choice of rainfall thresholds imposed by the definitions commonly used in agroclimatology. An analysis of spatial coherence at interannual time-scale shows that for the two rainy seasons ("long rains" and "short rains"), the seasonal amount and the number of rainy days have a high spatial coherence, while it is medium for the onset and cessation dates and low for the average daily rainfall intensity.

- Analyze the predictability of the ISD at both regional and local scales based on numerical simulations from the global climate model ECHAM 4.5. Daily precipitation simulated by the model, even after bias correction, do not correctly capture the IDS interannual variability. A specification of the ORS and CRS variability using statistical models applied to observed climate indices, suggests quite a low predictability of the descriptors at the local (regional) scale, regardless of the season. The development of statistical-dynamical models from wind fields simulated by ECHAM 4.5, in experiments forced by either observed or predicted sea temperatures, also shows quite poor skills locally and regionally.

- Explore how the space-time variability of climatic and environmental factors modulate the variations of sorghum yields. Crop yields are simulated by the agronomic model SARRA-H using observed climate data (1973-2001) at three stations located at different elevations along the eastern slopes of Mt Kenya. The seasonal rainfall accumulation and the duration of the season account for a large part of the yields variability. Other rainfall variables also play a significant role, among which the number of rainy days, the average daily intensity and some ISD related to the temporal organization of rainfall within the season. The influence of other meteorological variables is only found during the long rains, in the form of a negative correlation between yields and both maximum temperature and global radiation. Sowing dates seem to play a role in modulating yields for high and medium altitude stations, but with notable differences between the two rainy seasons.

Keywords

East Africa, intra-seasonal rainfall descriptors, potential predictability, ECHAM 4.5, onset / cessation of the rainy season, crop yields, sorghum, SARRA -H

 

Jury

Gérard Beltrando, université Paris 7 – rapporteur
Benjamin Sultan, IRD – rapporteur
Christian Baron, CIRAD – examinateur
Alain Durand, université de Rouen – examinateur
Vincent Moron, université Aix-Marseille – examinateur
Nathalie Philippon, Biogéosciences – examinateur