Ph.D OPEN DEFENCE ON FACTORING CLIMATE VARIABILITY AND CHANGE INTO CROP MODELS FOR ENHANCING SORGHUM PERFORMANCE IN THE WEST AFRICAN SEMI-ARID TROPICS

1. FACTORING CLIMATE VARIABILITY AND CHANGE
INTO CROP MODELS FOR ENHANCING SORGHUM
PERFORMANCE IN THE WEST AFRICAN
SEMI-ARID TROPICS
AKINSEYE, Folorunso Mathew
Major Supervisor: Prof. S. O Agele (FUTA-Nigeria)
Co-Supervisor: Dr. P. C. S. Traore (ICRISAT- Mali)
German Adviser: Prof. Dr. A. M Whitbread(UG& ICRISAT-India)
Department of Meteorology and Climate Science
Federal University of Technology, Akure, Ondo State.
PhD Final Presentation

2. Why is climate variability so important
to agriculture ?
Agriculture is the largest employer of labour, a guarantee for food
security in the world and is probably the most weather-dependent of all
human activities.
Climate variability has been, and continues to be, the principal source of
fluctuations in global food production, particularly in the semi- arid
tropics.
Throughout history, climatic extremes has wreaked havoc on
agriculture, water resources etc.
In addition, with other physical, social, political and economic factors,
climate variability contribute to vulnerability of economic losses,
hunger, famine and dislocation

3. Introduction
West African semi-arid is home to some 300 million people with at least 70%
engaged in agricultural activity (FAO,2007), it accounts for 35% of the GDP,
(World bank, 2000) and ~ 90% of cropland managed under rainfed conditions
(FAOSTAT,2005).
 Rainfall is one of the most important natural resources and rainfall variability
manifests intra-annual, inter-annual and decadal scales.
Crucial problem for rainfed agriculture: Decision about the optimal planting date
for current season
- Planting as early as possible to avoid wastage of valuable growth time
- Planting too early /late may lead to crop failures and high economic losses
Low crop yield (productivity) of major cereal crops attributed to constraining
environmental conditions ,depleted soil fertility (Nitrogen and phosphorus),
diseases ( e.g. Midges),high costs of fertilizers (Winterbottom et al., 2013)

4. Introduction Cont’d
• In the semi-arid tropics, sorghum and millet contribute to more
than 80% of the food needs and has mean yield of 800kg/ha
(Maredia et al., 1998, 2000)
• In 2008, sorghum was cultivated in Mali on an area of 990 995
ha with a production of 1, 027,202 tons and yield average is
1036kg/ha(http://faostat.fao.org/site)
• Crop growth models are used around the world as a research
tool for yield forecast because models
– provide dynamical estimates of climate driven potential yield,
and yield components as well as water balance
– useful for assessing the agricultural risks of climate change in
the 21st Century

5. Introduction Cont’d
 Decision Support for Agro-technology Transfer (DSSAT) (Jones et al., 2003).
 Agricultural Productions Systems sIMulator (APSIM) (McCown et al., 1996;
Keating et al., 2003).
 Samara Version 2 implemented on the Ecotrop platform of the Centre
International de Recherche Agronomique pour le De´veloppement (CIRAD)
Dingkhun et al., (2003)
• DSSAT model was previously used in simulation studies by Adiku et al., 2007;
MacCarthy et al., (2013) over Ghana) and Traore et al., (2007) in the Sahel zone
• APSIM model was also used in previous studies in West Africa by MacCarthy
et al., 2009 and Apkonikpe et al., (2010).
• comparative evaluation of these models has not been undertaken for
sorghum growth and development in West Africa
 Crop simulation models integrate the interaction of genotypic traits,
environmental factors (e.g. soils, weather) and management (G x E x M)

6. Literature Review
• Lobell et al., (2011), the potential yield loss due to the climate change impact is
about 5% for each degree Celsius of global warming.
• IPCC (2014) predicts an approximate 50% decrease in yields from rain-fed
agriculture by 2020 in some countries.
Reference
Climate
model Crop model Scenario Area Horizon Crop Baseline
Adejuwon (2006) HadCM2 EPIC 1%/year in CO2 Nigeria
2035/2055/
2085
Cassava, maize,
millet, rice, sorghum 1960/1990
Jones and
Thornton (2003) HadCM2
CERES maize
(DSSAT) Not found WA (details) 2055 Maize
1990
climate
normals
Liu et al., (2008) HadCM3 GEPIC A1FI, B1, A2, B2
SSA, WA
(details) 2030
Global, cassava,
maize, millet, rice,
sorghum, wheat 1990/1999
Lobell et al.,
(2008) 20 GCMs Empirical A1B, A2, B1 WA 2030
Cassava, groundnut,
maize, millet,
rice, sorghum, wheat,
yams 1998/2002
Parry et al.,
(2004) HadCM3
Empirical +
BLS
A1FI, A2A, A2B,
A2C,
B1A, B2A, B2B WA
2020/2050/
2080 Global 1990
Salack (2006) Scenario DSSAT 4
(+1 8C, +1.5 8C,
+3 8C)/ (+5%,
+10%, +20%)
Niger/
Burkina
2020/2050/
2080
Millet( mtdo/ zatib
genotypes), sorghum 1961/1990
Table 1:

7. Future projections suggest a drier
western Sahel (e.g., Senegal, part of Mali)
A wetter eastern Sahel (e.g., Mali, Niger)
No change or slight increases in annual
rainfall towards more southern locations
(e.g., Ghana, Nigeria) (Hulme et al.,
2001,Adiku et al.,2014).
Literature Review Cont’d
Fig.1b: Median Temperature change (%) for Mid-
century RCP8.5 over West Africa
Fig.1a: Median Precipitation Change (%) for Mid-
century RCP8.5 over West Africa

8. RESEARCH QUESTIONS
 How do process-based crop models perform on diverse photoperiod
sensitive sorghum varieties under current climate system and near
future climate change scenarios in the terms of yield potentials across
semi-arid region?
 Which definition of onset of rain is most appropriate to define the start
of growing season (OGS) and fitted into farmer’s planting time, for
major cereal crops(maize, millet and sorghum) across agroecological
zones of Mali?

9. AIMS AND OBJECTIVES
Aims:
“To address the need for substantial improvement in the characterization of food
security risks and enhance the development of adaptation measures for Sub-
Sahara Africa (SSA) in the circumstances of the changing growing environmental
(biophysical) conditions”.
Specific objectives are to;
 evaluate the onset and length of growing season in order to establish the
most suitable dates for planting major cereal crops in the agro-ecological zones
of Mali;
 determine the effect of sowing date on photoperiod sensitive sorghum
genotypes and yield potentials under non-limiting water and nutrient supply;
 assess the process-based crop growth models (DSSAT, APSIM and Samara)
improvements through model calibration and validation for phenology and
yield prediction in sorghum;
 provide comparison of the sensitivity of the current system to climate change,
and then recommend the most suitable adaptation strategies.

10. Fig.3: Map of the Mali showing the selected rainfall station and
ecological zones in accordance with the annual mean rainfall
OGS was evaluated
from four (4)
definitions of onset
of rain by;
Def_1 -Sivakumar,
(1988)
Def_2 -Kasei and
Afuakwa, (1991).
Def_3 - Omotosho et
al., (2000)
Def_4 –FAO, (1978)
CGS - Cessation of
growing season
defined after Traore
et al., (2000)
 LGS = CGS - OGS
Research Methodology – PART 1

11. Hypothesis
• OGS - onset dates was validated with farmers sowing window
for maize, millet and sorghum
– Accept null: if the mean onset date provided at least 7days
to farmers planting date
• LGS was evaluated with duration to maturity of some major
crops varieties (FAO, 2008)
Crop type Local name Selected name Breeder Variety
maturity
Duration from
planting to
Maturity(days)
Maize Zangueréni Zangueréni IER Early 80 - 90
Dembagnuman Obatanpa CIMMYT/CRI Medium 105-110
Sotubaka Suwan 1-SR CIMMYT/IITA Late 110–120
Millet Sossat Sossat c-88 ICRISAT/IER Early 90
Toroniou Toroniou IER Medium 100 -110
M9D3 M9D3 IER Late 125 -130
Sorghum Jakumbe CSM63E IER Early 100
Jigui Seme CSM388 IER Medium 125
Soumalemba IS15-401 CIRAD/ICRISAT Late 145
Table 2: Characteristics of the most cultivated crop varieties within the West Africa semiarid tropics.

12. Research Methodology –PART 2
Fig. 4: Study Area
The field experiment was
conducted under non-limiting
water and nutrients supply

13. CLIMATIC CONDITION AT FIELD SITE
Fig.4c: Climatic pattern of the experimental site
Fig.4b: An Automatic weather station less than 500m away from sorghum field trial

14. Code Genotypes
Name
Race/type Geographical
Origin
Target use quality of
Stover
grain quality Plant
type
G1 CSM63E Guinea Mali Biomass Poor Good int
G2 621 B Caudatum Senegal Dual purpose High Good short
G3 Soumba Caudatum Senegal Dual purpose High; stay
green
intermediate
/mold
int
G4 Nieleni Hybrid Senegal Dual purpose High good int
G5 Fadda Guinea
(Hybrid)
Burkina Faso Dual purpose High guinea
grain(good)
int
G6 Pablo Guinea Senegal Biomass Poor good tall
G7 Grinkan Caudatum Mali Dual purpose High int/mold short
G8 CSM335 Guinea Mali Biomass Poor poor tall
G9 IS15401 Guinea Cameroon Biomass High good tall
G10 SK5912 Caudatum Nigeria Dual purpose High int/mold int
MATERIALS AND METHODS
Table3b: Characterization of the Genetics materials

15. Experimental Design: Randomized complete block design,
2 factors, 4 replications, Plot size: 8x4.8m(Fig.5a)
Sowing: Jun 14, Jul 09 and Aug 05. Spacing: 75 x 20cm (Fig.
5b)
EXPERIMENTAL DESIGN FIELD LAY-OUT
Fig.5a
Fig.5b
L1 L2 L3 L4 L5 L6 L7

16. Table 5: Comparison of modeling approaches applied regarding the major
processes that determine crop growth and development
DSSAT APSIM SAMARA
Leaf area
development
Simple function estimation of
rate of leaf appearance, PHINT
(in degrees day/leaf)
Phyllochron (leaf apperance
rate) specific leaf area
(respectively leaf size)
Phyllochron ,detailed Light
extiction and coversion based on
some morphological detail of the
canopy(Dingkuhn et al., 2008)
Light
utilization/
DM estimates
RUE based on Beer-Lambert’s
law that estimates light
interception
RUE based on Beer-Lambert’s
law
Beer-Lambert’s law on the basis of
leaf blade aggregate LAI
Crop
Phenology
Estimation of thermal time (T)
through developmental phases,
Photoperiod (day length),
water/nutrient effects
simulated through a number of
development phases, using a
thermal time approach
(Muchow and Carberry, 1990;
Hammer and Muchow, 1994),
with the temperature response
characterized , Photoperiod (day
length) and water
Estimation of thermal time (T), air
temperatures at 2m, Photoperiod
(day length), water without nutrient
effects (Dingkuhn et al.,
2003,2008) .
Yield
formation
Yield is a function of harvest
index(HI) based on number of
grain and biomass production
yield formation depends on
grain number and grain size
yield formation depends on
(Coefficient of Panicle Sink
Population* Panicle Structured
Mass Maximum / Grain weight).
Stress involved Water and Nitrogen stress
shorten the growth stages
Water and Nitrogen stress
shorten the growth stages
Water stress shorten the growth
stages
Evapo-
transpiration
Priestley- Taylor /Ritchie
approach
Priestley- Taylor approach FAO-method based on Penman-
Monteith

17. OBJECTIVE 1 - RESULTS
zone Hypothesis Def_1 Def_2 Def_3 Def_4 Maize Sorghum Millet
Sahelian
Mean Onset 193 191 193 173 194 (Jul 13 ) 188 (Jul 07) 188 (Jul 07)
St.dev 15 14 14 15 6 6 6
Time_lag -5 -3 -5 15
Mean LGS 70 72 70 90
Sudano-
sahelian
Mean Onset 179 177 178 158 174(Jun 23) 178(Jun 27) 178(Jun 27
St.dev 15 16 17 16 6 6 6
Time_lag -5 -3 -4 19
Mean LGS 101 103 102 123
Sudanian
Mean Onset 160 159 162 144 172 (Jun 21) 177(Jun 26 ) 177(Jun 26)
St.dev 14 13 14 13 8 8 8
Time_lag 12 13 10 28
Mean LGS 132 133 131 149
Guinea
savanna
Mean Onset 147 146 145 133 156(Jun 05) 156(Jun 05) 156(Jun 05)
St.dev 15 15 13 9 12 12 12
Time_lag 9 10 11 23
Mean LGS 151 152 154 165
Table 6: Comparison of mean onset dates according to each method estimates with the farmers planting time for
maize, sorghum and millet. The bold part indicates the most suitable method found closed to the hypothesis set.

18. Fig. 6: (a) –Sahelian; (b) –Sudano-sahelian; zone: Probability distribution Length of growing season (in
days) based on the most appropriate OGS
OBJECTIVE 1 – RESULTS Cont’d

19. Fig. 6: (c) Sudanian; (d)- Guinea savannah zone: Probability distribution Length of growing season (in
days) based on the most appropriate OGS
OBJECTIVE 1 – RESULTS Cont’d

20. Fig.7a: Effect of sowing date on flowering time for 10 sorghum genotypes
0
500
1000
1500
2000
2500
G1 G2 G3 G4 G5 G6 G7 G8 G9 G10
Thermaltime(0Cdays)
Genotypes
I II III
Low PPsen Moderate PPsen
High PPsen
 G1 – G4 represent early maturity genotypes(85-110days), observed the lowest cumulative thermal time
to flowering and less sensitive to variation of sowing date
 G5-G8 represent medium maturity genotypes(110-135days), observed medium cumulative thermal time
to flowering and moderate sensitive to variation of sowing
 G9-G10 represent medium maturity genotypes(115-155days), flowering time remains more or less
constant independent of sowing dates observed highest cumulative thermal time to flowering and
highly decreased to variation of sowing
OBJECTIVE 2 – EXPERIMENTATION-RESULTS

21. Fig.7b: Effect of sowing date on Total leaf Number(TLN) per plant for 10 sorghum genotypes
• As observed, TLN reduced up to 7 leaves for cultivars that
are very sensitive to day length because of the shortened
vegetative phase.
0
5
10
15
20
25
30
35
G1 G2 G3 G4 G5 G6 G7 G8 G9 G10
TotalLeavesNumber(TLN)
Genotypes
I
II
III
OBJECTIVE 2 – EXPERIMENTATION-RESULTS

22. Total biomass produced varied among the cultivars especially for the medium
and high photoperiod sensitive genotypes
And also observed significant decreased with late planting date, this is due
shortened of the growth phases
All the genotypes were efficient for biomass production with the highest value
in early planting dates (I &II)
As observed, the estimated RUE among the genotypes showed a significant
reduction up to 1/3 value of the early planting date
0
5000
10000
15000
20000
25000
30000
G1 G2 G3 G4 G5 G6 G7 G8 G9 G10
Totalbiomass(kg/ha)
Genotypes
I II III
Fig. 7c: Effect of sowing date on total biomass and Grain yield
OBJECTIVE 2 – EXPERIMENTATION-RESULTS Cont’d

23. 0
500
1000
1500
2000
2500
3000
3500
4000
4500
G1 G2 G3 G4 G5 G6 G7 G8 G9 G10
Grainyield(kg/ha)
Genotypes
I II III
Fig. 7d: Effect of sowing date on Grain yield
Highest grain yield values was obtained in early planting dates (I & II) except for
G1 , G8 and G10 respectively.
OBJECTIVE 2 – EXPERIMENTATION-RESULTS Cont’d

24. OBJECTIVE 3: CROP MODELING - RESULTS
Fig: 9 a: Model-fitted cultivars responses to day length between emergence to
Flag leaf initiation over the three sowing date as observed from the field
The duration to flag leaf initiation is driven by thermal time and
genotypic response to photoperiod changes - that varied from
low to highly sensitivity.
0
100
200
300
400
500
600
12 13 14
ThermaltimetoFlagleafinitiation
(0Cdays)
Photoperiod length (h)
CSM63E
CSM335
Fadda
IS15401

25. 0
500
1000
1500
2000
2500
3000
CSM63E CSM335 Fadda IS15401
GDD(°Cdays)
SAMARA
APSIM
DSSAT
Observed
Fig. 9b: Comparison of model-estimated growing degree days (GDD) with
the field-observed estimated between emergence and maturity (exclusive
of PSP) across cultivars
OBJECTIVE 3: CROP MODELING - RESULTS
APSIM and DSSAT estimates were close to observed compared
to Samara, the difference is due to model parameterization

26. Flowering DAP Maturity DAP
Cultivar Observed APSIM DSSAT SAMARA Observed APSIM DSSAT SAMARA
CSM63E 63 64 (1) 65 (3) 62 (1) 92 95 (4)) 94 (3) 94 (3)
CSM335 89 92 (5) 94 (7) 86 (3) 116 124 (8) 127 (12) 121 (6)
Fadda 83 87 (5) 86 (3) 82 (3) 113 119 (7) 119 (7) 116 (4)
IS15401 107 107 (7) 113 (14) 97 (12) 130 139 (14) 139 (11) 136 (14)
Models calibration results average over three sowing dates
Table 8a: Phenology
Yield (kg/ha) Total biomass(kg/ha)
Cultivar Observed APSIM DSSAT SAMARA Observed APSIM DSSAT SAMARA
CSM63E 1105 2145 2177 1446 9142 8448 8849 9917
CSM335 2007 2438 2579 2263 14907 14461 16916 15061
Fadda 2971 3505 3674 3584 15116 16798 13735 13338
IS15401 2022 2551 2185 2280 10341 13437 10361 10051
Table8b: Grain yield and Total biomass
Brackets(): RMSE

27. 0
10
20
30
40
0 10 20 30 40
SimulatedTLN
Observed TLN
APSIM
DSSAT
SAMARA
Fig. 10a: Model-simulated total leaf numbers (TLN) against the observed TLN
values for all cultivars used over the three sowing dates (Jun14, July 09, Aug.05).
0
2
4
6
8
10
0 2 4 6 8 10
SimulatedMaxLAI(m2/m2)
Observed Max LAI(m2/m2)
APSIM
DSSAT
SAMARA
Fig. 10b: Model-simulated maximum leaf area Index (MaxLAI) against the
observed MaxLAI values for all cultivars used over the three sowing dates (Jun14,
July 09, Aug.05).
 APSIM: RMSE =2.2, NRMSE =
10.6 %, R2= 0.88;
 DSSAT: RMSE =2.0, NRMSE =
9.6%, R2= 0.86;
 Samara: RMSE =1.3,NRMSE =
6.4 %, R2= 0.96
APSIM:
RMSE=2.4,NRMSE = 85
%, R2= 0.1;
DSSAT:
RMSE=2.6,NRMSE = 92
%, R2= 0.5;
Samara: RMSE=
0.9,NRMSE = 33 %, R2=
0.4
Model-calibrated and observed for TLN and Max LAI

28. Fig.11: Comparison of model-validation for duration to flowering and maturity with field
observed
Model performance against independent trials for phenology under different growing
season, locations and planting densities

29. 0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
CSM63E CSM335 Fadda IS15401
Grainyield(kg/ha)
Cultivars
Observed
DSSAT
APSIM
SAMARA
Fig12a
0
5000
10000
15000
20000
25000
CSM63E CSM335 Fadda IS15401
Totalbiomass(kg/ha)
Cultivars
Observed
DSSAT
APSIM
SAMARA
Fig12b
Table 9: Grain yield(kg/ha)
APSIM DSSAT SAMARA
RMSE(kg/ha) 833 753.0 810.0
NRMSE(%) 40.0 36 38
R2 0.6 0.6 0.4
Total biomass(kg/ha)
APSIM DSSAT SAMARA
RMSE(kg/ha) 3798 3144 3653
NRMSE(%) 40 33 39
R2 0.8 0.8 0.5
Models performance against an independent dataset for grain yield and total biomass
under different growing season, locations and planting densities

30. OBJECTIVE 4- CLIMATE CHANGE SCENARIOS AND IMPACTS ON
SORGHUM PRODUCTION
• Climate scenarios from CMIP5 GCMs using a 30-year baseline daily
weather of MODERN-ERA RETROSPECTIVE ANALYSIS FOR RESEARCH AND
APPLICATIONS (MERRA) dataset(1980-2009)
• For future projections (2040-2069), five GCMs namely CCSM4, GFDL-
ESM2M, Had GEM2-ES, MIROC5, and MPI-ESM-MR (Rosenzweig et al.
2013) were used for the RCP 8.5 scenario that assumes an elevated
CO2 concentration of 571 ppm compared with the current 390 ppm.

31. Projected decline change towards
western Sahel significant increase
change towards eastern and southern
Sahel
All the GCMs seasonal rainfall
projected changes differs across the
station, CCSM4 and MIROC5 projected
above baseline except Nioro du Rip
Fig. 13: Projected change (%) in the growing season (May to October) rainfall between Baseline (1980-2009)
and GCM’s future projection (2040- 2069) .
RCP8.5 analyses – Climate change impact on moisture regime between Baseline and
GCM’s future projectionSeasonal Rainfall
-40
-30
-20
-10
0
10
20
30
40
Changeinseasonalrainfall(%)
Nioro du Rip, Senegal Current average = 720 mm(a)
-40
-30
-20
-10
0
10
20
30
40
Changeinseasonalrainfall(%)
Sadore, Niger Current Average = 517 mm(b)
-20.0
-15.0
-10.0
-5.0
0.0
5.0
10.0
15.0
20.0
Changeinseasonalrainfall(%)
Navrongo, Ghana, Current Average = 903mm(c)

32. Onset of growing season (OGS)
Fig. 14: Comparison between Baseline(1980-2009) and GCMs mean
projection (2040-2069) for estimated Onset of growing season
 Low significant change ( -5 to +7days) -
uncertainty would lies in the distribution of
rainfall during the growing period
 Sadore and Navrongo projected early OGS
– corroborate the projection of more wetter
future climate.
08-Jun
13-Jun
18-Jun
23-Jun
28-Jun
03-Jul
08-Jul
Onsetofgrowingseason
Nioro du Rip, Senegal
Baseline (1980-2009)
Median_RCP 8.5 (2040-2069)
(a)
02-Jun
04-Jun
06-Jun
08-Jun
Onsetofgrowingseason
Sadore, Niger
Baseline (1980-2009)
Median_RCP 8.5 (2040-2069)
(b)
04-Jun
05-Jun
06-Jun
07-Jun
08-Jun
Onsetofgrowingseason
Navrongo, Ghana
Baseline (1980-2009)
Median_RCP 8.5 (2040-2069)
(f)

33. Length of growing season (LGS)
Fig. 15: Comparison between Baseline(1980-2009) and GCMs mean projection
(2040-2069) for estimated length of growing season (LGS)
 Sadore: LGS shows significant increase in
(4) and decrease in (1); inter-annual
variability is high
 Nioro: LGS decrease (3), no change (2),
variability remains high
 Navrongo: No change variability remains
moderate
 CCSM4 projected increase across the
stations except Nioro du Rip
80
100
120
140
160
180
Lengthofgrowingseason(days)
Nioro du Rip
Baseline (1980-2009)
Median_RCP 8.5 (2040-2069)
(a)
80
90
100
110
120
130
140
Lengthofgrowingseason(days)
Sadore, Niger
Baseline (1980-2009)
Median_RCP 8.5 (2040-2069)
(b)
80
100
120
140
160
180
Lengthofgrowingseason(days)
Navrongo, Ghana
Baseline (1980-2009)
Median_RCP 8.5 (2040-2069)
(c)

34. Fig. 16: Comparison of average monthly variability of minimum temperature between the
Baseline (1980-2009) and GCMs Scenario (2040-2069) for the selected stations
 Both Tmax and Tmin uniformly increase
throughout growing season between
baseline and the GCMs projection
Tmin projected faster in magnitude than
Tmax
Suggests increase in GDD for the
crops,
Exacerbated moisture stress in rainfed
agriculture leads to grain weight loss
Climate change impact on temperatures regime between Baseline and
GCM’s future projection
10
15
20
25
30
35
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
AverageTmin(0C)
Nioro du Rip, Senegal
BASELINE
CCSM4
GFDL-ESM2M
HadGEM2-ES
MIROC5
MPI-ESM-MR
(a)
Growing season
10
15
20
25
30
35
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
AverageTmin(0C)
Sadore,Niger
BASELINE
CCSM4
GFDL-ESM2M
HadGEM2-ES
MIROC5
MPI-ESM-MR
(b)
Growing season
20
25
30
35
40
45
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
AverageTmax(0C)
Navrongo, Ghana
BASELINE
CCSM4
GFDL-ESM2M
HadGEM2-ES
MIROC5
MPI-ESM-MR
Growing season
(c)

35. Fig. 17: Projected change for minimum temperature between baseline (1980-2009
and GCMs scenario (2040-2069)
 All GCMs project increased
temperature at varying
magnitudes across six stations
Highest value was projected
by HadGEM2-ES followed by
MPI-ESM-MR while the least
warming is projected by CCSM4
except at Nioro du Rip
Minimum temperatures projection change
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
ChangeinAverageTmin(0C)
Nioro du Rip, Senegal, Current average = 23.7 0C, ∆=0.12 0C(a)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
ChangeinAverageTmin(0C)
Sadore, Niger Current average = 25.6 0C ∆= 0.14 0C(b)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
ChangeinaverageTmin(0C)
Navrongo, Ghana, Current average = 22.9 0C ∆=0.11 0C(c)

36. Fig. 18: Projected change for maximum temperature between baseline
(1980-2009 and GCMs scenario (2040-2069)
Maximum temperatures projection change
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
ChangeinAverageTmax(0C)
Nioro du Rip, Senegal , Current Average = 34.40C , ∆=0.140C(a)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
ChangeinAverageTmax(0C)
Sadore, Niger Current average = 36.9 0C ∆= 0.19 0C(b)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
ChangeinaverageTmax(0C)
Navrongo,Ghana, Current average = 33 0C ∆ =0.13 0C
(c)

37. 0
1000
2000
3000
4000
5000
6000
Baselineyield(kg/ha)
CSM63E
APSIM
DSSAT
Samara
(a)
0
1000
2000
3000
4000
5000
6000
Baselineyield(kg/ha)
CSM335
APSIM
DSSAT
Samara
0
1000
2000
3000
4000
5000
6000
Baselineyield(kg/ha)
Fadda
APSIM
DSSAT
Samara
(c)
0
1000
2000
3000
4000
5000
6000
Baselineyield(kg/ha)
IS15401
APSIM
DSSAT
Samara
(d)
Models sensitivity under baseline climate
Fig. 19: Simulated yield of CSM63E, CSM335, Fadda and IS15401 under the baseline climate (1980–
2009). Error bars indicates inter-annual variability

38. Results
• CSM63E- DSSAT simulated lower grain yield compared to
APSIM and Samara, low inter-annual variability except at Mopti
and Kano by DSSAT
• CSM335 -DSSAT and Samara shows higher inter-year
variability across the sites compared to APSIM model, highest
grain yield simulated at Koutiala and lowest grain yield at
Sadore.
• Fadda –exhibited high grain yield potential, inter-annual
variability remains high across the models and sites
• IS15401 – model simulated low grain yield across the sites

39. -30
-20
-10
0
10
20
30
RelativeChangeingrainyield(%)
CSM63E - without Adaptation
Mopti
Sadore
Nioro du Rip
Kano
Koutiala
Navrongo
(a)
Impact of projected GCMs scenario on sorghum cultivars without adaptation
-30
-20
-10
0
10
20
30
RelativeChangeingrainyield(%)
CSM335 - without Adaptation
Mopti
Sadore
Nioro du Rip
Kano
Koutiala
Navrongo
(b)
-30
-20
-10
0
10
20
30
RelativeChangeingrainyield(%)
Fadda - without Adaptation
Mopti
Sadore
Nioro du Rip
Kano
Koutiala
Navrongo
(c)
-30
-20
-10
0
10
20
30
RelativeChangeingrainyield(%)
IS15401 - without Adaptation
Mopti
Sadore
Nioro du Rip
Kano
Koutiala
Navrongo
(d)
Fig. 20: Comparison of the relative change (%) in yield projection for the cultivars between the baseline and
future projected climate scenario (2040-2069) without Adaptation across selected sites

40. -30
-20
-10
0
10
20
30
Relativechangeingrainyield(%)
CSM63E With Adaptation
Mopti
Sadore
Nioro du Rip
Kano
Koutiala
Navrongo
(a)
-30
-20
-10
0
10
20
30
Relativechangeingrainyield(%)
FADDA – With Adaptation
Mopti
Sadore
Nioro du Rip
Kano
Koutiala
Navrongo
(c)
-30
-20
-10
0
10
20
30
RelativeChangeingrainyield(%)
IS15401 – With Adaptation
Mopti
Sadore
Nioro du Rip
Kano
Koutiala
Navrongo
(d)
-40
-30
-20
-10
0
10
20
30
40
RelativeChangeingrainyield(%)
CSM335- With Adaptation
Mopti
Sadore
Nioro du Rip
Kano
Koutiala
Navrongo
(b)
Impacts of adaptation measure on genotypic difference under climate change
Fig. 21 : Comparison of the relative change (%) in yield projection for the cultivars between the baseline
and future projected climate scenario (2040-2069) with Adaptation across selected sites

41. Discussions
• Medium and late maturity cultivars found to be photoperiodically sensitive
and strong response to variation in sowing dates
• Calibration shows the models capability to predict crop duration for the
agronomically relevant range of sowing dates.
– A near perfect fit was observed for the phenological growth stages
between the crop model-simulated and field-observed values
– the uncertainty lied in the prediction of total grain yield and biomass
• Total biomass and grain yield varied strongly among the models, the
variation from models output could be linked to model internal mechanism
or quality of the field data.
• On the sensitivity of current systems to climate change:
– Decline changes in yield output between baseline and 5GCMs for all the
models across sites
– Models showed effect of the latitude and photoperiod on the cultivars
(e.g. Fadda)
– High demand for water (CSM335 and IS15401) which resulted in low yield
– the increase in rainfall amounts projected by some GCMs (e.g. CCSM4)
does not match with the projected increase in mean simulated grain
yields
– Tmin projected faster than Tmax that suggests increase in GDD

42. Conclusions
 The determination of onset date of growing season from single
method across AEZ of Mali may lead to false onset or too late date
estimation.
 Based on the estimated LGS across AEZ and evaluation with
duration to maturity of major crops varieties, the results suggest
 early-maturing varieties for Sahelian zone,
 early and medium maturing varieties for Sudano-sahelian zone,
 All level of maturity for Sudanian and Guinean zones provided the flowering
time would occur 15-20days prior to CGS (e.g. sorghum and millet) or varieties
that can withstand the terminal drought(CGS) during grain filling
 The novel and apparent merit of this study is that
 Crop modelling is found as a valuable tool to understand
genotype × environment × management (G × E × M) interactions
on crop growth and yield potential
 Nearly all the widely used crop models tested showed their
capability in assessing climate impacts/risk for range of
photoperiod sensitive sorghum cultivars

43. Conclusions cont’d
 The study confirmed warming across the dryland West
Africa (high confidence) – seemingly faster in cooler areas
(e.g. Nioro du Rip, Senegal).
 Rainfall may likely increase eastwards, decrease westwards
and slight increase/no change southward: this suggests
climate adaptation will be local
 Impacts of projected changes by GCM’s vary significantly
across different study sites compared and cultivars.
 Projected yields changes from three crop models at different
contrasted sites, it suggests an insight on the need for climate-
smart varieties as long-time plan adaptation strategy to
ensure increase productivity under warming projected climate.

44. CONTRIBUTION OF THE RESEARCH TO
KNOWLEDGE
 Strengthened the prediction skill to define the onset of growing
season, as well as the length of growing season in semi-arid region in
order to minimize climatic risk especially for staple crops(maize, millet
and sorghum)
 Crop models improvement through calibration of photoperiod sensitive
sorghum for the growth parameters and yield development was
established
 Application of multi-model climate change scenarios projection (GCMs)
into dynamic crop models for enhancing sorghum productivity in West
Africa semi-arid tropics and the development of the adaptation
strategies.

45. Recommendations
 Further evaluations of onset date via participatory approach
with farmers, agrometeorologists and agriculture extension
officers, for ‘on-line’ dissemination to farmers;
 As modelling can help reduce number of field experiments and
can save resources, it is therefore recommended that a reliable
yield projection should be cultivar specific through model
calibration and validation with data sets from carefully-
conducted experiments;
 Crop breeders should work closely with both climate and crop
modellers in the region to improve on climate-smart traits in
sorghum varieties that would be more resilient to elevated
mean temperature during the growing period;
 Many, many more models exist and much, much more
uncertainty subsists. Regional capacity to operate models
and interpret projections is lacking and must be aggressively
developed – e.g. through science-policy platforms

46. ACKNOWLEDGEMENT
• This research study was funded by Federal Ministry
of Education and Research (BMBF) through the
West African Service Centre on Climate Change and
Adapted Land Use (WASCAL), Graduate Research
Program (GRP). Financial support is gratefully
acknowledged.
• Grateful to the University management and
Department for my study leave.

47. THANKS FOR YOUR
ATTENTION
MERCI POUR VOTRE ATTENTION
Danke für Ihre Aufmerksamkeit