Climate Change in the Senegal River Valley and implications for

rice cropping systems

BALDE A.B. (AfricaRice); MULLER B. (Cirad/AfricaRice);

VAN Oort P.(AfricaRice); NDIAYE O. (ANACIM); STUERZ S. (Hohenheim University); SOW A. (AfricaRice); DIACK B.S. (SAED);

DINGKUHN M. (Cirad/IRRI)

Study funded by ANR ESCAPE and CCAFS

Introduction (1)• Irrigated rice is

important crop in Sahel• More than 70 000 ha in

Senegal River valley• Rice double cropping

possible

• Cold (November-March) and hot (April-October) extreme temperatures are risks -> sterility

• Recommended sowing periods were elaborated in 90s (Dingkhun and al., 1995) based on 1950-80 climate data and RIDEV1 model• 15 Feb - 15 March for Dry Season• 01 July – 15 August for Wet Season

Sterile spikelets

Climate and sowings

BUT farmers say …« sowing date are more and more out of the recommended  sowing  windows,  and  that works  :  even  sowing  in  September  farmers get good yields! »

« It looks like the climate have changed »

Questions : Did farmer’s practices change (% of farmers)?

Why ? Due to climate ? others constraints ?

Did climate change ? How ?

What can we learn from those evolutions ?

Are recommended sowing periods still valid ?

How to protect farmers against climatic risks ?

Introduction (2)

Saint-Louis

Rosso

Matam

Tillaberi

Sterility(heat)

Sterility(cold)

Sowing date

% Sterility

1 year

RIDEV1 1950-80 sterility analysis(Dingkhun, 1995)

ObjectivesFinal objective is to define the most adapted sowing windows for recommended irrigated

rice varieties of Senegal River Valley (and Niger River Valley in Mali) in order to reduce climate risks and then maximize production, and to assess the “residual” risks related to those sowing windows ; - for “present” climate- also for “future” one using data coming from climatic scenario

Second main objective is to assess if cropping systems have changed in the last years and if it is the case, to understand why ?

Third main objective is to have a crop model (or several) able to correctly simulate developments and yields of recommended irrigated rice varieties in Senegal River Valley (and Niger River Valley in Mali)

Fourth: capacity building for production and management of climatic risks

Fifth : to elaborate insurance systems based on scientific results (risks analysis)

Material and MethodsFocus-groups and surveys: to understand possible changes in farmer’s practices and theirconstraints, and their eventual evolutions during the last yearsto understand farmers climate perception

Modeling analysis : RIDEV V2to assess through RIDEV2 crop model simulations the impacts of climatesince 30 years for different sowing dates-> Case of Sahel 108 cultivar ; Podor weather data (Tmin, Tmax, Mean Hum, 

and Rg) from 1950 to 2010Sowing every 15 days

Climate (temperature) analysis :Podor (16.35N/15.02W) temperature data from 1950 to 2011.

RIDEV2 : to simulate Phenology and Heat & cold sterility Calibration/ValidationSimulations

Results (1) : from focus and surveys Due to several constraints (delay for inputs, credit, machines) many farmers are sowing later than before, i.e. outside the recommended sowing windows : this is particularly the case for wet season cropping (July/August -> November) All interwied farmers mentioned difficulty and delay to get inputs, credits and machines as their main constraint Some farmers having sowed late (September) obtained good results Many farmers are now sowing regularly at the beginning of September and have good yields since few years. Exception 2011: “bad yields” (farmers words) for those late sowing

“Since 2000 yields are decreasing” ; except in the dry season of 2008 with 7t/ha (exceptional year) “Dry season yields are better than wet season ones” (for 90% of farmers)

Farmers notice climate “changes”: on average 94.5% and 72% of interviewed farmers say respectively that rainfall and temperature pattern have changed.

“the  cold  period  shifted  (delayed)  by  about  1  month” : October/November-February/March -> November-March/April “the cold comes latter”, “excepted in 2011 it came as before” “the February-April period is very hieratic, with small cold and hot periods”

“rainfall increased in the last 5-7 years, except in 2011”(Podor rainfall data shown an annual average of 175, 200 and 270 mm respectively between 1980-1989, 1990-1999 and 2000-2010)

Results (2) : from focus and surveys

Results (3) : temperatures analysis

30313233343536373839404142

1 2 3 4 5 6 7 8 9 10 11 12

1950-1980

1970-1990

1990-2000

2001-2011

14151617181920212223242526

1 2 3 4 5 6 7 8 9 10 11 12

1950-1980

1970-1990

1990-2000

2001-2011Month TMAX+1°C to +3°C / reference period 1950-1980but quite similar than 1990-2000

Month TMIN+1°C to +2°C / reference period 1950-1980but quite similar than 1990-2000

02468

101214161820222426

1 2 3 4 5 6 7 8 9 10 11 12

1950-1980

1970-1990

1990-2000

2001-2011

Days TMAX>40°C more than reference period 1950-1980but quite similar than 1990-2000

0123456789

101112

1 2 3 4 5 6 7 8 9 10 11 12

1950-1980

1970-1990

1990-2000

2001-2011

Days TMIN<14°C less than reference period 1950-1980but quite similar than 1990-2000

Results (4) : 2011 temp. analysis

2223242526272829303132333435

1 4 7 10 13 16 19 22 25 28 31 34

2011

2006-10

2000-10

1990-99

TMEAN

14151617181920212223242526

1 4 7 10 13 16 19 22 25 28 31 34

2011

2006-10

2000-10

1990-99

TMIN

0

1

2

3

4

5

6

7

8

9

10

11

1 4 7 10 13 16 19 22 25 28 31 34

2011

2006-10

2000-10

1990-99

0

1

2

3

4

5

6

0 3 6 9 12 15 18 21 24 27 30 33 36

2011

2006-10

2000-10

1990-99

TMIN<18°C

TMIN<14°C

Oct.-Nov. 2011 is rather hot ! Only end of December is colder than other years .. So ..???

60 70 80 90 100 110 120 130 140 150 16060

110

160

f(x) = 1.046959174151 x − 1.289307460638R² = 0.823689535059515

(b) Maturity

Observed

Sim

ulat

ed

60 70 80 90 100 110 120 130 1406080

100120140

f(x) = 1.0200468218 x + 1.0872144951R² = 0.845738285405555

(a) Flowering

Observed

Sim

ulat

ed

Model parameters (Ndiaye site, MD):Tbase=10.6°CTopt=31.7°CSumBVP=748SumRPR=400SumMATU=409CritsterCold1=10.6°CCritsterCold2=18.7°CCritsterHeat=33°C

Sterility is underestimated by the model (with present calibration; can be improved)

Results (5) : model validation (Ndiaye, Fanaye)

0 10 20 30 40 50 60 70 80 90 1000

102030405060708090

100

f(x) = 0.572904701794689 x − 1.21923102102214R² = 0.473557527030153

(c) Sterility

Observed

Sim

ulat

ed

Crop duration decreased (- 5-10 days)but variable through sowing dates(from 90 to 137 DAS) Compared to 1950-1980, cycles are shorter in recent years

Results (6) : simulated cycles and sterilities

For both Wet and Dry Season (WS, DS) sterility (out of recomandated calendar) globally decreased However differences are smallFor 15 September small sowing sterility difference existsIt is still very dangerous to sow after this date

The evolution of sterility (for 15 Sept sowing date) is going down through yearsSame with DAS to FloweringIn the past flowering period met low temperature that make high Stercold2

Results (7) : Focus on 15 Sept sowing date(Simulations from 1980 to 2010)

Present climate is different than during the reference period (1950-80) used for elaboration of sowing recommendations with RIDEV1 : temperatures clearly increased ; less cold days

But it seems that the climate (at least Temp) is already “different” since 20 years .. not only from 2000 as farmers say (“it recently changed”)And up to now we don’t understand the specificity of 2011 ?

We need more data to check climate evolution for all SRV (we are waiting for St Louis, Matam, Bakel data)

Sterility is underestimated by the model and quite low

Due to temperatures increase cycles are shorter (- 5-10 days) now(no “genetic change” in Sahel 108)

Conclusions (1)

Conclusions (2)Cold stress risks are lower, particularly for late 15 September sowing : it seems possible to sow up to this datebut not recommended to sow after this date

Risks due to high temperatures might be higher : must be analyzed with more accuracy

We need to check more the model functioning(we have several fields and experiments data)

We need to go back to farmers with simulations results in order to improve our understanding

If climate seems to be more favorable, why farmers say yields are lower ? …

Conclusions (3)

Still many work to do to clearly understand the evolution of things

Once we will be sure that our model functions well, we will see how to elaborate new sowing recommendations and how to develop temperatures based indicators for insurance

Thank you for your attention!

Alpha Bocar BALDEa.balde@cgiar.org