P.C.S. Traoré & al.© ICRISAT-IPR-IER-CIRAD-U. Florida, 2005WMO CLIMAG workshop, May 2005 In a theater near you…

P.C.S. Traoré & al. © ICRISAT-IPR-IER-CIRAD-U. Florida, 2005WMO CLIMAG workshop, May 2005

In a theater near you…

Objectives: Analyze crop responses to climate variability in the sudano-sahelian zone. Develop a method to translate seasonal climate forecasts into agricultural production strategies that further minimize risk for rural communities

Focus: downscalingclimate forecasts

Focus: re-engineeringcropping systems models


In retrospect… oh, donors!

Problem statement : “Climate variability is an urgent problem in the Sahel” – not quite in fact !! “There are tools to develop crop yield forecasts” – not yet in fact !! “But these tools have limitations” – oh yes, quite a few !! “The scope of this project” – [quote – review panel] […] Likely too ambitious and would

take 3 years but encouraged to go ahead and start. […] [unquote]

Goal = enhance food security in rural communities of the West African semi-arid tropics.

Expected outputs:

1. A decision-support matrix for producers to minimize climatic risk2. An evaluation of current forecasting skills for the region3. A digital land surface scheme of the region, including soils, topography and vegetation4. A method to downscale and apply climate forecasts to identify production options in sudano-

sahelian agriculture.

Sahel = another buzzword promoted by climate science?


The 2cv and the Ferrari (part 1)

Once upon a time… a long time ago…… a car dealer went to visit his old school pal in a popular neighborhood. That pal owned an old Citroën model called ‘2 chevaux’. Actually he did not even remember whether it was a Citroën or a Peugeot. He had inherited the vehicle from his father, who had inherited it from his grandfather. The car was not looking very attractive – many bumps and scars and anything but aerodynamic. It was also desperately slow – but he just valued it, he had been through so many tough roads with it. It was lightweight, and could handle sand and gravel.

The car dealer was determined to help his friend experience more comfort, more speed, more exhilaration, even more security. Actually, he was committed to changing his friend’s life. He was (maybe unconsciously) motivated by the prospect of a pay rise promised by his boss if he could secure a quick sale.


The 2cv and the Ferrari (part 2)

So said the car dealer: “Look at this Ferrari Testarossa… there has not been any car like this one for years: it can reach 200mph within seconds, yet it is non-polluting. It can make you the most admired man in town!”

The neighbor was visibly impressed. So he asked his friend – “can I have a free ride?” “Sure”, replied the car dealer (he knew that in a competitive economy there was no such thing as consumer’s confidence).

The same day the friend tried the car. The test occurred at a period when executives in the country were less concerned about the nation’s communication infrastructure, its economy and more about their own political future. Potholes proved the car was too low, spare parts were too scarce, and the Ferrari Testarossa eventually ran out of gas…


The 2cv and the Ferrari revisited (part 1)

Once upon a time… a long time ago…… a car dealer went to visit his old school pal in a popular neighborhood. That pal owned an old Citroën model called ‘2 chevaux’. Actually he did not even remember whether it was a Citroën or a Peugeot. He had inherited the vehicle from his father, who had inherited it from his grandfather. The car was not looking very attractive – many bumps and scars and anything but aerodynamic. It was also desperately slow – but he just valued it, he had been through so many tough roads with it. It was lightweight, and could handle sand and gravel.

The car dealer was determined to help his friend experience more comfort, more speed, more exhilaration, even more security. Actually, he was committed to changing his friend’s life. He was (maybe unconsciously) motivated by the prospect of a pay rise promised by his boss if he could secure a quick sale.

Once upon a time… not so long ago…… an ag. scientist went to visit a farmer in a remote village. That farmer relied on an old variety called ‘Sanko’. Actually he did not even remember whether it was a sorghum or a millet. He had inherited the seed from his father, who had inherited it from his grandfather. The plant was not looking very attractive – many leaves and stems and anything but aerodynamic. It was also desperately ??? – but he just valued it, he had been through so many hard times with it. It was sturdy, and could handle crusting and drought.

The scientist was determined to help the farmer experience more nutrients, more yield, more satiety, all in all more food security. Actually, he was committed to changing the farmer’s life. He was (maybe unconsciously) motivated by the prospect of a grant proposed by a donor if he could write an encouraging report.


The 2cv and the Ferrari revisited (part 2)

So said the car dealer: “Look at this Ferrari Testarossa… there has not been any car like this one for years: it can reach 200mph within seconds, yet it is non-polluting. It can make you the most admired man in town!”

The school pal was visibly impressed. So he asked his friend – “can I have a free ride?” “Sure”, replied the car dealer (he knew that in a competitive economy there was no such thing as consumer’s confidence).

The same day the friend tried the car. The test occurred at a period when executives in the country were less concerned about the nation’s communication infrastructure, its economy and more about their own political future. The Ferrari Testarossa soon ran out of (expensive) gas. By then potholes had proved the car was too low, spare parts were too scarce, and rust took good care of the remaining

So said the scientist: “Look at this JKS8273… there has not been any sorghum like this one for years: it can produce 5t/ha of grain within days, yet it is a dwarf. It can make you the most admired farmer in the village!”

The farmer was visibly impressed. So he asked his friend – “can you give me a few seeds?” “Sure”, replied the scientist (he knew that in a donor-driven world there was no such thing as participatory testing).

The same season the farmer sowed the seeds. The trial occurred at a time when climate modelers had forgotten about demand-driven research, agricultural applications and were heavily involved in data crunching. JKS8273 soon suffered from water shortage. Later birds proved the plant was too early, as alternate feed was too scarce, and grain mold took good care of remaining panicles…


Croprotation21st Century XXX Oops !!Corporation presents…


Seasonal forecasting and climate risk in the sudano-sahelian zone: progress towards new opportunities for improved sorghum varietiesP.S. Traoré, J.E. Bounguili, M. Kouressy, M. Vaksmann, J.W. Jones

in partnership with : with funding from :


Outline

The context A unique blend of competing variability modes… … resulting in high, distinctive seasonal climatic uncertainty So: what would you do if you were an annual plant? PP-traits: a sine qua non for farm resiliency Population growth, intensification, climate forecasts: what next?

The problem “landracist” climate models (when continentality is underrepresented) “landracist” crop models (when landraces are underrepresented) higher forecast skill lower risk more climate-sensitive, higher yielding varieties

Methods Climate: assess forecast skill ( capacity to reduce climate risk), and then? Crops: revise development, growth in models

Results: case studies Vegetative Phase Duration Biomass Production

Discussion: advances, challenges and the way forward


The context


Climate: what is different about West Africa?

There are no such things as climate ‘normals’ in sudano-sahelian West Africa “What is ‘normal’ to the Sahel is not some […] rainfall total […] but variability of the rainfall

supply in space and from year-to-year and from decade-to-decade” (Hulme, 2001)



High variability in both cases but…

(reproduced from IPCC, 2001)

Sahel: higher variations on decadal time

steps (low frequency)

SEA: higher variations on yearly time steps (high frequency)

does this mean relatively more risk for an annual crop /

farmer in SEA?

not necessarilybecause :

Predictability is higher in SEA (both yearly and in the long term)

Risk = uncertainty x vulnerability


Regional climate among the most variable in the world (also most pronounced decadal change: -0.3% rainfall over 20th century)

Largest tropical land mass with 6,000km east-west extent high sensitivity to small surface boundary forcings (yearly changes in land cover)

Regional climate modeling more complex – reliance on SST predictors not sufficient, + weak ENSO signal

Ability of GCMs to simulate observed interannual Sahelian rainfall generally rather poor

Projections call for African climate warming, esp. in semi-arid margins, but future changes in rainfall less well defined – in the Sahel : inconsistent projections, no or little change

Forecasting skill consistently lower over the Sahel than for other regions of the globe, especially at inter-annual time scales important to agriculture (HF)

Total rainfall amounts have decreased, but no significant change in LGP Under SRES scenarii, precipitation may decrease during the growing season

and may increase at other times of the year Date of rains onset and distribution much more critical to farmers than total

amount, but rarely in the set of predictands

Regional climate difficult to model

Regional climate (+change) difficult to predict



What would you do if you were an annual plant?

Favorable rainfed cropping conditions: May-November

Decreasing daylengths

Dayle

ngth

(h)

Rain

fall (

mm

)

Sotuba (12°39’N, 7°55’W)


Limiting factor: high rainfall variability Spatially along a N-S transect Temporally: inter-annual Function of rains onset date

Need to fit crop cycle to probable duration of rains

Flexibility required from varieties to handle climatic uncertainty

Photoperiod sensitivity in crops = strategy to avoid climatic risk



Grouped flowering towards end of rainy season Minimize grain mold, insect & bird damage (early

maturing varieties) Avoid incomplete grain filling (late maturing

varieties)

Dr. H

ooge

nboo

m (2

m)

x 2

x 3

North South


Photoperiod sensitivity = adaptation traitWest Africa : highest PP sensitivity levels worldwideSudanian ag. systems = MONROE shock absorbers

Global Environmental Change special issue 2001


The place of sorghum in West Africa

Gadiaba varietyDurra raceSahelian zone

N’tenimissa varietyGuinea x Caudatum hybridSudanian zone

Major staple crop Mali: 30% of

cereal production With millet, 4th

cereal worldwide More nutritive

than maize, but tannins

Losing ground to maize


Millet and sorghum in a cotton-intensive year (2003)

Class Number of samples

Bare Soil 10Cotton 154Grass + pasture + fallow 32Groundnut / legumes 32Maize 51Millet 104Rock Outcrops 2Sorghum 51Wetland + ponds 15Wild vegetation 21

total 472


Resolution: the proof (panchromatic)


Ridge tillage detection…

ridges (‘ados’)

87% of proposed ridge tillage fields confirmed by survey

7% of total actual ridge tillage fields missed

Real potential for simple operational detection method based on edge detection filters


The problem


Flashback on the car thing…

Rephrased question: how do you bring a specialist in risk avoidance (also fatalist at times) to consider investing in risk management?

better have very good arguments!! Like…

Reliable supply systems (for spare parts and the like) = seed systems, fertilizer / market accessibility…

Good paved road network infrastructure (reducing uncertainties linked to potholes (= typhoons), unexpected Desert Storms / gas shortages (= forecasting skill)

Affordable insurance policies (to supplement prayers after accidents)


climate

soilplant

Challenges for cropping systems modelers

Uncertainties associated with:

croppingsystemsmodels

Spatio-temporal scale mismatches and resulting low

prediction skill of rainfall onset, distribution and amount

Incomplete understanding of gene-environment interaction and resulting inaccurate local crop development and growth

High level of measurement error relative to C accretion

rates, and need to extrapolate to meet tradable quantities


African regions with robust (green) and weak (orange) ENSO signals (after Nicholson, 1997).

The problem with “landracist” climate models


Correlation of July-August-September (JAS) rainfall with Atlantic SSTs and ENSO - after O. Baddour, cited in CLIVAR, 1999 – Note: Niño-3 index (5°N-5°S,150°-90°W).



SST-Rainfall-Vegetation feedbacks affecting the monsoon rainfall over the NRB (after Zeng et al., 2003).



The problem with “landracist” crop models


Diagnosticunderestimate photoperiod (PP) sensitivity

+ do not parameterize PP sensitivity optimally= underestimate vegetative phase duration

+ do not partition biomass correctly= overestimates grain yield

= underestimates vegetative biomass

The problem with “landracist” crop models

Crop models and landrace cereals : improvements are needed

Cause(range of genetic coefficients – P2R)(choice of response curve, coefficients, DR calculation approach)(begin. stem growth, others?)


Modeling: current approaches

Phases of development

P1 P2 P3 P4 P5 P6

Emergence

Flag leafPanicleinitiation

End juvenilephase

Flowering Maturity Harvest

P0

Start grain fillingSowing


Modeling: current approaches

Phases of development

P1 P2 P3 P4 P5 P6

Emergence

Flag leafPanicleinitiation

End juvenilephase

Flowering Maturity Harvest

P0

Start grain fillingSowing

Juvenile phase

Fixed durationNo PI possible

T control

Photoperiod induced phase (PIP)

Duration=f(P,T)Ends at PIP control

Modeling approaches will differ depending on how they handle temperature – photoperiodinteractions during the PIP


Recap… in a nutshell

Assumption 1 farmers lack critical information about upcoming climate and their current coping strategies

would gain from incorporating modern science climate forecasts to adapt to possible increases in climate risk hmmmmm (yes and no!)

Assumption 2 there is a capacity to generate seasonal forecasts of local climate that meet farmers interest in

additional information hmmmmm (I still have doubts!)

Assumption 3 selected process-based models can simulate conditions actually encountered by farmers, and

they can be driven by downscaled climate forecasts hmmmmm (not always!)


Approach


Approach

FORECAST EVALUATION YIELD PREDICTION

IRI FD seasonal forecast fields

tercile probabilityextraction

aggregation in time

Ptseasonal

totals[1950-2004]

IRI seasonal forecasts over specific locations

[1997-2004]statistical analysis

normalization

seasonal anomalies

[1950-2004]

Pta

tercile limits determinationreorganization

in terciles

Daily / decadal weather data [1950-2004]

P Tn, Tx Rn

Sotuba 2004 Expmt.(Kouressy, Vaksmann et al.)

2004 weather

soils cultivars mngmt observations

“Bipode”water balance

yearly statistics: rains onset, end dates, LGP

analogue1959

2004 regenerated weather sequences (100)

stochastic disaggregation

1.

2.

3.dynamic process

based model (DSSAT4)

Yie

ld c

ompo

nent

pre

dict

ions

(20

04, 1

959)

,pr

obab

ilitie

s (u

sing

reg

ener

ated

200

4 w

eath

er

sequ

ence

s)

comparison

seasonal 30-year normals 50-79, 55-84, 60-89, 65-94, 70-99, 75-04


Hansen&al, 2004

“This convention [expressing operational seasonal climate forecasts as climatic anomalies or tercile probability shifts averaged in space… and time] maximizes prediction skill by reducing the ‘noise’ associated with weather variability in time and space that can mask predictable seasonal climatic variations.”


Results


Yearly rainfall variability (Sotuba)

Observed reduction in rainfall of ~300mm (~25%), LGP by about 12 days (~10%) over 50 yrs Similar data available for 89 rainfall stations (1950-2004), + satellite


Energy & Water Balance Products

Radiation (W.m-2, x 0.5) Rainfall (mm)

Meteosat-derived observations, August 2002, Decad 2. Other variables in the database include surface temperatures (at noon and midnight), top boundary layer temperature, air temperature at 2 meters, number of cloud free days, potential and actual evapotranspiration. Decadal data available

for [1993-2002]


1998-2004 IRI FD seasonal forecasts (AMJ)

1-mth lead time Above normal predictions 5 years out of 7: tendency to overestimate rainfall outside the

core of the rainy season? Which reference period for IRI normals?


1998-2004 IRI FD seasonal forecasts (JAS)

1-mth lead time Apparent better performance at predicting rainfall totals for core of rainy season Very humid/dry 1999-2000 sequence well predicted, but not 2001


2002 IRI FD seasonal forecasts

Observations: 2002 rainfall = 873mm, normal (1975-2004) = 876mm Seems to have some skill at predicting observed above average rainfall outside of core

of rainy season (obs: 84mm, normal: 68mm) and relative dryness during core (obs: 562mm, normal: 658mm)



Relative stability of 3-mth forecasts (33-33-33 thrice, 40-35-25 thrice in a row) seems to match the very homogeneous temporal distribution observed (best year in more than 20 years)

Observed annual rainfall = 912mmm (normal: 873mm)



Climatology 6 out of 9 Observed: above normal rainfall in July-August, abrupt end around mid-September


PP response options

Response curves : thermal time to PI as a function of photoperiod

Purpose: model the delay imposed by non-optimal P on plant development (how it slows down its speed or development rate)

Linear : rice (Vergara & Chang, 1985), other SD/LD crops (Major & Kiniry, 1991) sorghum (Ritchie & Alagarswamy, 1989)

Hyperbolic (Franquin, 1976; Hadley, 1983; Hammer, 1989; Brisson, 2002)

P200

2500

Photoperiod (h)

TT

PI (°

Cd)

P2R

P1

0

2500

Photoperiod (h)

TT

PI (°

Cd)

Psat

P1

Pbase Consequences for ‘qualitative’ plants

OPPRPPPf ii 22 1

basei

basesati PP

PPPPf 1

PI will eventually occur

PI may not occur


DR calculation options

Even more important is the procedure for calculating development rates (DR) DR = inverse of phase duration

j

i i

ij Pf

dttDR

1

j

ii

jj dtt

PfDR

1

1

Case 1: cumulative photo-thermal ratios

Case 2: threshold on thermal time requirements

Physiological interpretation

Plant progresses every day towards flowering with a variable rate function of T and P

Requires that daylength conditions be met for flowering to take place


Experimental design

Typical Guinea cultivar CSM388, avg. cycle duration 130 days, P1=413°C.days (Vaksmann & al., 1996)

Calibration: 1996 planting date experiment in Sotuba (12°39’N), June-August, PI dates observed by dissections every 5 days

Genetic coefficients: screening ranges and increments

Validation: 1994 planting date experiment in Sotuba (12°39’N), Cinzana (13°15’N) and Koporo (14°14’N), February-September, FL expansion dates observed and translated into PI dates

Flag Leaf – Sowing date = June 20

Flag Leaf – Sowing date = July 20


Sowing date

Photoperiod at PI (h)

TTPI, thermal time to PI

(°C.d)

EPI, days to PI (d)

EFL, days to Flag Leaf

(d)

TLN, total leaf

number

10 Jun. 96 13.366 1063 54 87 32

25 Jun. 96 13.313 851 44 76 30

10 Jul. 96 13.187 756 40 68 26

25 Jul. 96 13.104 603 32 56 18

10 Aug. 96 13.033 413 22 47 16

Results (PP)

1996 experimental observations used for calibration.All durations computed from emergence


Results (PP)

Model calibration. Best estimate of genetic coefficients for the 4 model types

Model type Coefficients RMSE

P2O (h) P2R (°C.d.h-1)

Cumulative-linear case 13.05 1160 2.7

Threshold-linear case 13 1660 1.2

Psat (h) Pbase (h)

Cumulative-hyperbolic case 13.05 13.9 2.0

Threshold-hyperbolic case 12.85 13.7 1.7


Results (PP)

Cumulative Threshold

EFL obs

40 60 80 100 120 140 160 180

EF

L c

alc

40

60

80

100

120

140

160

180

RMSE= 38

EFL obs

40 60 80 100 120 140 160 180

EF

L c

alc

40

60

80

100

120

140

160

180

RMSE = 46

EFL obs

40 60 80 100 120 140 160 180

EF

L c

alc

40

60

80

100

120

140

160

180

RMSE = 8

Linear

Hyperbolic

R2=0.41 R2=0.89

R2=0.13 R2=0.97

Scatterplots of calculated emergence-flag leaf expansion durations (EFLcalc) against observations from the 1994 experiment (EFLobs)


Results (PP)

Predictions of EFL as a function of planting dates for the 4 approaches, as compared to 6 observations (EFLobs) from the 1994 experiment in Sotuba, Mali

20

40

60

80

100

120

140

160

180

1/1/1996

1/31/1996

3/1/1996

3/31/1996

4/30/1996

5/30/1996

6/29/1996

7/29/1996

8/28/1996

9/27/1996

10/27/1996

11/26/1996

Sowing dates

EFL

(d)

Threshold hyperbolic

Cumulative hyperbolic

Threshold linear

Cumulative linear

EFLobs Sotuba 94

J F M A M J J A S O N


Growth = quantitative, development = qualitative

Growing Degree Days appropriate to describe quantitative processes such as plant growth, but…

Photo-thermal time concept appears inappropriate for simulation of plant progress towards flowering (= plant development)

“Short Day” plants… rather “decreasing day” Threshold-hyperbolic approach may be more consistent with crop physiology as it

associates:

cumulative (temperature) processes

and … that better reflect trigger (photoperiod)

events

quantitative plantgrowthand

qualitative plant development

Need to incorporate more knowledge of plant physiology & genetics into phenological crop models (shifts in hormone balances rather than ‘florigen’ concept, …)


Source code:

RATEIN = 1.0/102.0

IF (TWILEN .GT. P2O) THEN RATEIN = 1.0/(102.0+P2R*(TWILEN-P2O))ENDIFSIND = SIND + RATEIN*PDTT

New SG phenology in next DSSAT release

Implementation in CERES-Sorghum is straightforward : replace 1 parameter,re-write 3 lines of code

Modifications:

RATEIN = 1.0/P1

IF (TWILEN .GT. P2O) THEN RATEIN = (TWILEN-PBASE)/(P2O-PBASE)ENDIFSIND = RATEIN*SUMDTT


Simulated development stages

20 40 60 80 100 120 140 160

Jours après semis

Grow th stage (W33-Jun22)Grow th stage (ICSMH-Jun22)Grow th stage (W33-Jul16)Grow th stage (ICSMH-Jul16)

Semis

émergence à finphase juvénile

fin phase juvénile àinitiation paniculaire

initiation paniculaire à fincroissance feuilles

fin croissance feuilles à débutremplissage grains

phase de remplissageeffectif des grains

maturité physiologique

récolte

f in phase juvénile:2 jours de différence pour ICSMH entre les semis du 22/6 et 16/72 " " W33 " "

initiation paniculaire:3 jours de différence pour ICSMH13 " " W33


Simulated growth (leaf biomass)

0

500

1000

1500

2000

2500

3000

20 40 60 80 100 120 140 160

Jours après semis

Mas

se f

olia

ire

(kg

/ha)

Leaf w t kg/ha (W33-Jun22) Leaf w t kg/ha (ICSMH-Jun22)

Leaf w t kg/ha (W33-Jul16) Leaf w t kg/ha (ICSMH-Jul16)

Leaf w t kg/ha (IMSO0401 SGT) TRT 1 Leaf w t kg/ha (IMSO0401 SGT) TRT 2

Leaf w t kg/ha (IMSO0401 SGT) TRT 3 Leaf w t kg/ha (IMSO0401 SGT) TRT 4


Simulated growth (stem biomass)

0

2000

4000

6000

8000

10000

12000

14000

16000

20 40 60 80 100 120 140 160

Jours après semis

Mas

se d

es t

iges

(kg

/ha)

Stem w t kg/ha (W33-Jun22) Stem w t kg/ha (ICSMH-Jun22)

Stem w t kg/ha (W33-Jul16) Stem w t kg/ha (ICSMH-Jul16)

Stem w t kg/ha (IMSO0401 SGT) TRT 1 Stem w t kg/ha (IMSO0401 SGT) TRT 2

Stem w t kg/ha (IMSO0401 SGT) TRT 3 Stem w t kg/ha (IMSO0401 SGT) TRT 4


Publications

Folliard A., P.C.S. Traoré, M. Vaksmann, M. Kouressy, 2004. Modeling of sorghum response to photoperiod: a threshold-hyperbolic approach, Field Crops Research 89:1, 59-70Traoré, P.C.S., A. Folliard, M. Vaksmann, C. Porter, M. Kouressy, J.W. Jones, 2004. Enhanced photoperiod response modeling for improved biomass simulation in a Sudanian carbon accounting framework, NASA Scientific Workshop on Land Management and Carbon Sequestration in West Africa (SW-LMCS), Bamako, Mali, 26-28 February 2004Traoré, P.C.S., N. Sakana, M.D. Doumbia, R.S. Yost, 2004. Accuracy assessment of ASTER digital elevation models for topography extraction at field and watershed levels, Mali Symposium on Applied Science (MSAS’2004), Bamako, Mali, 2-5 Aug. 2004Soumaré, M., M. Vaksmann, P.C.S. Traoré, M. Kouressy, 2004. Recent evolution of climate and consequences on adaptation for sorghum varieties in Mali (in French), MSAS’2004Traoré, P.C.S., 2005. The legacy of climate variability management in sudano-sahelian cropping systems: what prospects for the future? 6th Open Meeting of the Human Dimensions of Global Environmental Change Research Community, U. Bonn, Oct. 9-13, 2005 (also accepted for publication in Dovie, D.B.K., Chipanshi, A.C., Eds., Reframing sustainability issues in response to global governance and environmental change in Africa)Traoré, P.C.S., Vaksmann, M., Kouressy, M., Porter, C.H., 2005. Modeling of sorghum and millet development: simple phenotyping for photoperiod sensitivity assessment, to be submitted to Field Crops ResearchTraoré, P.C.S., Kouressy, M., Vaksmann, M., Bostick, W.M., 2005. Modeling biomass partitioning in West African sorghum landraces, in preparationBounguili, J.-E., 2004. Seasonal climate forecasting and agricultural risk in sudanian regions: what opportunities for improved sorghum varieties? The case of Sotuba, Mali. Ing. Degree dissertation (in French), Institut Polytechnique Rural de Katibougou, Univ. Bamako.Traoré, P.C.S., 2004. Current knowledge and explanatory models of climatic trends in the Niger River Basin. Contribution to Chapter 3 of the Expert Panel on the Future of the Niger River Basin, Institut de Recherche pour le Développement, IRD, Paris (in revision).Tabo, R., Bationo, A., Kandji, S., Traoré, P.C.S., 2005, Effects of global change on food systems in Africa, Chap. 7 in L. Otter et al. (Eds), Global Change and Africa (in preparation).


Where are we now?

Advances: mostly on the crop modeling side Adapted “landrace-friendly” models for phenology (SG, ML) – in theory, more

consistent with short-day plant physiology and have more universal applicability Increasing # of parameterized landraces (as of today: 13) through simple

phenotyping method for PP sensitivity – in practice, change of 1 genetic coefficient requires re-computation of crop genetic sets in DSSAT-Century

impact on simulation of VPD using a modified PP response most important for crop cycles of 120+ days (ie, applicable to sudanian and northern guinean AEZ)

Ongoing work on biomass partitioning will further improve the simulation of yield components in landraces (e.g. stem growth before flowering)

Better prepared for future climate modeling breakthroughs? (AMMA,…) Strong interest from cotton parastatal CMDT (crop yield forecasting) – probably a

better entry point into smallholder livelihoods than staple cereals


Where are we now? (continued)

Challenges: mostly on the climate modeling side From a risk-adverse farmer standpoint, skill remains modest at best (performance

of total rainfall prediction, relevance of predictands) Needed: a task force on rains onset prediction! Upfront model improvement – how can we help? (dynamic boundary conditions –

land surface, dynamical downscaling) – comparative advantages? Climate Prediction Tool?

Lack of communication between climate modelers, agricultural scientists, physiologists – e.g. CLIMAG-WA, AMMA…

Needed: improved access to RCM outputs – little usefulness of satellite-derived agro-meteorological surfaces for forecast validation from very coarse grids

Problem with .LAN formats in IRI online data library?


Trailer section…


Thank you to…

1. ATI participants !! – we need to thinkof ways to “institutionalize” this group !!

2. ATI sponsors and organizers – for their patience in bearing with a “ghost” trainee – I will improve I promise !

3. Hawa and Seyni – for the same reasons (they now qualify to start working for… START)

Documents

P.C.S. Traoré & al.© ICRISAT-IPR-IER-CIRAD-U. Florida, 2005WMO CLIMAG workshop, May 2005 In a theater near you…