FACTORING CLIMATE VARIABILITY AND CHANGE INTO CROP MODELS FOR ENHANCING SORGHUM

PERFORMANCE IN THE WEST AFRICAN SEMI-ARID TROPICS

AKINSEYE, Folorunso MathewMajor 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 ScienceFederal University of Technology, Akure, Ondo State.

PhD Final Presentation

Why is climate variability so importantto 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

IntroductionWest 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)

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

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 CentreInternational 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)

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:

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

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?

AIMS AND OBJECTIVESAims:“To address the need for substantial improvement in the characterization of foodsecurity 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.

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

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.

Research Methodology –PART 2

Fig. 4: Study Area

The field experiment was

conducted under non-limiting

water and nutrients supply

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

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 METHODSTable3b: Characterization of the Genetics materials

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.5aFig.5b

L1 L2 L3 L4 L5 L6 L7

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

OBJECTIVE 1 - RESULTSzone 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.

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

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

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

Th

erm

al

tim

e(0

Cd

ays)

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

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

To

tal L

eaves N

um

ber

(TL

N)

Genotypes

I

II

III

OBJECTIVE 2 – EXPERIMENTATION-RESULTS

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

To

tal b

iom

as

s (

kg

/ha

)

Genotypes

I II III

Fig. 7c: Effect of sowing date on total biomass and Grain yield

OBJECTIVE 2 – EXPERIMENTATION-RESULTS Cont’d

0

500

1000

1500

2000

2500

3000

3500

4000

4500

G1 G2 G3 G4 G5 G6 G7 G8 G9 G10

Gra

in y

ield

(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

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

Th

erm

al ti

me

to

Fla

g le

af

init

iati

on

(0

Cd

ays

)

Photoperiod length (h)

CSM63E

CSM335

Fadda

IS15401

0

500

1000

1500

2000

2500

3000

CSM63E CSM335 Fadda IS15401

GD

D (

°C d

ays

)

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 - RESULTSAPSIM and DSSAT estimates were close to observed compared

to Samara, the difference is due to model parameterization

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

0

10

20

30

40

0 10 20 30 40

Sim

ula

ted

TL

N

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

Sim

ula

ted

M

ax L

AI(

m2/m

2)

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

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

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

CSM63E CSM335 Fadda IS15401

Gra

in y

ield

(k

g/h

a)

Cultivars

Observed

DSSAT

APSIM

SAMARA

Fig12a

0

5000

10000

15000

20000

25000

CSM63E CSM335 Fadda IS15401

To

tal b

iom

ass(k

g/h

a)

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

OBJECTIVE 4- CLIMATE CHANGE SCENARIOS AND IMPACTS ON SORGHUM PRODUCTION

• Climate scenarios from CMIP5 GCMs using a 30-year baseline dailyweather of MODERN-ERA RETROSPECTIVE ANALYSIS FOR RESEARCH ANDAPPLICATIONS (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.

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

Ch

an

ge

in

sea

so

na

l ra

infa

ll(%

)

Nioro du Rip, Senegal Current average = 720 mm(a)

-40

-30

-20

-10

0

10

20

30

40

Ch

an

ge

in

se

as

on

al ra

infa

ll(%

) 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

Ch

an

ge

in

sea

so

na

l ra

infa

ll(%

)

Navrongo, Ghana, Current Average = 903mm(c)

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

On

se

t o

f g

row

ing

sea

so

n

Nioro du Rip, Senegal

Baseline (1980-2009)

Median_RCP 8.5 (2040-2069)

(a)

02-Jun

04-Jun

06-Jun

08-Jun

On

se

t o

f g

row

ing

sea

so

n

Sadore, Niger

Baseline (1980-2009)

Median_RCP 8.5 (2040-2069)

(b)

04-Jun

05-Jun

06-Jun

07-Jun

08-Jun

On

se

t o

f g

row

ing

sea

so

n

Navrongo, Ghana

Baseline (1980-2009)Median_RCP 8.5 (2040-2069)

(f)

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

Len

gth

of

gro

win

g s

ea

so

n(d

ays

)

Nioro du Rip

Baseline (1980-2009)

Median_RCP 8.5 (2040-2069)

(a)

80

90

100

110

120

130

140

Len

gth

of

gro

win

g s

ea

so

n(d

ays

)

Sadore, Niger

Baseline (1980-2009)

Median_RCP 8.5 (2040-2069)

(b)

80

100

120

140

160

180

Len

gth

of

gro

win

g s

easo

n(d

ays)

Navrongo, Ghana

Baseline (1980-2009)

Median_RCP 8.5 (2040-2069)

(c)

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

Ave

rag

e T

min

(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

Ave

rag

e T

min

(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

Ave

rag

e T

ma

x (

0C

)

Navrongo, Ghana

BASELINE

CCSM4

GFDL-ESM2M

HadGEM2-ES

MIROC5

MPI-ESM-MR

Growing season

(c)

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

Ch

an

ge

in

Ave

rag

e T

min

(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

Ch

an

ge

in

Ave

rag

e T

min

(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

Ch

an

ge i

n a

vera

ge T

min

(0C

)

Navrongo, Ghana, Current average = 22.9 0C ∆=0.11 0C(c)

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

Ch

an

ge

in

Ave

rag

e T

ma

x(0

C)

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

Ch

an

ge

in

Ave

rag

e T

ma

x(0

C)

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

Ch

an

ge

in

ave

rag

e T

ma

x(0

C)

Navrongo,Ghana, Current average = 33 0C ∆ =0.13 0C (c)

0

1000

2000

3000

4000

5000

6000

Ba

se

lin

e y

ield

(k

g/h

a)

CSM63E

APSIM

DSSAT

Samara

(a)

0

1000

2000

3000

4000

5000

6000

Ba

se

lin

e y

ield

(k

g/h

a)

CSM335

APSIM

DSSAT

Samara

0

1000

2000

3000

4000

5000

6000

Ba

se

lin

e y

ield

(k

g/h

a)

Fadda

APSIM

DSSAT

Samara

(c)

0

1000

2000

3000

4000

5000

6000

Ba

se

lin

e y

ield

(k

g/h

a)

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

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

-30

-20

-10

0

10

20

30

Rela

tiv

e C

han

ge i

n

gra

in y

ield

(%

)

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

Rela

tiv

e

Ch

an

ge i

n g

rain

yie

ld (

%)

CSM335 - without Adaptation

Mopti

Sadore

Nioro du Rip

Kano

Koutiala

Navrongo

(b)

-30

-20

-10

0

10

20

30

Rela

tiv

e C

han

ge i

n g

rain

yie

ld (

%)

Fadda - without Adaptation

Mopti

Sadore

Nioro du Rip

Kano

Koutiala

Navrongo

(c)

-30

-20

-10

0

10

20

30

Rela

tiv

e C

han

ge i

n g

rain

yie

ld (

%)

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

-30

-20

-10

0

10

20

30

Rela

tiv

e c

han

ge i

n g

rain

yie

ld(%

)

CSM63E With Adaptation

Mopti

Sadore

Nioro du Rip

Kano

Koutiala

Navrongo

(a)

-30

-20

-10

0

10

20

30

Rela

tiv

e c

han

ge i

n g

rain

yie

ld (

%)

FADDA – With Adaptation

Mopti

Sadore

Nioro du Rip

Kano

Koutiala

Navrongo

(c)

-30

-20

-10

0

10

20

30

Rela

tive

Cha

ng

e in

g

rain

yie

ld(%

)

IS15401 – With Adaptation

Mopti

Sadore

Nioro du Rip

Kano

Koutiala

Navrongo

(d)

-40

-30

-20

-10

0

10

20

30

40

Rela

tiv

e C

han

ge i

n g

rain

yie

ld (

%)

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

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 theagronomically relevant range of sowing dates.

– A near perfect fit was observed for the phenological growth stagesbetween 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, thevariation from models output could be linked to model internal mechanismor 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 themodels 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 grainyields

– Tmin projected faster than Tmax that suggests increase in GDD

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

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.

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.

Recommendations Further evaluations of onset date via participatory approach

with farmers, agrometeorologists and agriculture extensionofficers, for ‘on-line’ dissemination to farmers;

As modelling can help reduce number of field experiments andcan save resources, it is therefore recommended that a reliableyield projection should be cultivar specific through modelcalibration and validation with data sets from carefully-conducted experiments;

Crop breeders should work closely with both climate and cropmodellers in the region to improve on climate-smart traits insorghum varieties that would be more resilient to elevatedmean temperature during the growing period;

Many, many more models exist and much, much moreuncertainty subsists. Regional capacity to operate modelsand interpret projections is lacking and must be aggressivelydeveloped – e.g. through science-policy platforms

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.

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