In a theater near you P.C.S. Traor &

In a theater near you P.C.S. Traor &

In a theater near you P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 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: downscaling climate forecasts Focus: re-engineering cropping 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. 2. 3. 4. A decision-support matrix for producers to minimize climatic risk An evaluation of current forecasting skills for the region A digital land surface scheme of the region, including soils, topography and vegetation A method to downscale and apply climate forecasts to identify production options in sudanosahelian agriculture. Sahel = another buzzword promoted by climate science? P.C.S. Traor & al.

WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 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 Citron model called 2 chevaux. Actually he did not even remember whether it was a Citron 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 friends life. He was (maybe unconsciously) motivated by the prospect of a pay rise promised by his boss if he could secure a quick sale. P.C.S. Traor & al. WMO CLIMAG workshop, May 2005

ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 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 consumers 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 nations 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 P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 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 Citron model called 2 chevaux. Actually he did not even remember whether it was a Citron 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. 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 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 friends 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 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 farmers life. He was (maybe unconsciously) motivated by the prospect of a grant proposed by a donor if he could write an encouraging report. P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 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! 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 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 consumers confidence). 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 day the friend tried the car. The test occurred at a period when executives in the country were less concerned about the nations 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 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 P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 21st Century XXX Corporation Oops !! Croprotationpresents P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 in partnership with : with funding from : Seasonal forecasting and climate risk in the sudano-sahelian zone: progress towards new opportunities for improved sorghum varieties P.S. Traor, J.E. Bounguili, M. Kouressy, M. Vaksmann, J.W. Jones P.C.S. Traor & al.

WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 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 P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 The context P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 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) P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005

Climate: what is different about West Africa? Sahel: higher variations on decadal time steps (low frequency) High variability in both cases but does this mean relatively more risk for an annual crop / farmer in SEA? not necessarily because : Predictability is higher in SEA (both yearly and in the long term) SEA: higher variations on yearly time steps (high frequency) P.C.S. Traor & al. Risk = uncertainty x vulnerability (reproduced from IPCC, 2001)

WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 Climate: what is different about West Africa? Regional climate difficult to model Regional climate (+change) difficult to predict P.C.S. Traor & al.

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 WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 What would you do if you were an annual plant? Sotuba (1239N, 755W) Rainfall (mm) Decreasing daylengths

P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 Daylength (h) Favorable rainfed cropping conditions: May-November ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 What would you do if you were an annual plant? 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 P.C.S. Traor & al.

WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 What would you do if you were an annual plant? Grouped flowering towards end of rainy season Minimize grain mold, insect & bird damage (early maturing varieties) Avoid incomplete grain filling (late maturing varieties) Photoperiod sensitivity = adaptation trait West Africa : highest PP sensitivity levels worldwide Sudanian ag. systems = MONROE shock absorbers Global Environmental Change special issue 2001 x2 South Dr. Hoogenboom (2m) North x3 P.C.S. Traor & al. WMO CLIMAG workshop, May 2005

ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 The place of sorghum in West Africa Gadiaba variety Durra race Sahelian zone Ntenimissa variety Guinea x Caudatum hybrid Sudanian zone Major staple crop Mali: 30% of cereal production With millet, 4th cereal worldwide More nutritive than maize, but tannins Losing ground to maize P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 Millet and sorghum in a cotton-intensive year (2003)

Class P.C.S. Traor & al. Number of samples Bare Soil Cotton Grass + pasture + fallow Groundnut / legumes Maize Millet Rock Outcrops Sorghum Wetland + ponds Wild vegetation 10 154 32 32 51 104 2 51 15 21 total

472 WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 Resolution: the proof (panchromatic) P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 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 P.C.S. Traor & al.

WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 The problem P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 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) P.C.S. Traor & al. WMO CLIMAG workshop, May 2005

ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 Challenges for cropping systems modelers Uncertainties associated with: climate 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 P.C.S. Traor & al. cropping systems models plant WMO CLIMAG workshop, May 2005

soil ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 The problem with landracist climate models African regions with robust (green) and weak (orange) ENSO signals (after Nicholson, 1997). P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 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: Nio-3 index (5N-5S,150-90W). P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 The problem with landracist climate models

SST-Rainfall-Vegetation feedbacks affecting the monsoon rainfall over the NRB (after Zeng et al., 2003). P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 The problem with landracist crop models P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 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?) P.C.S. Traor & al. Diagnostic underestimate photoperiod (PP) sensitivity

+ do not parameterize PP sensitivity optimally = underestimate vegetative phase duration + do not partition biomass correctly = overestimates grain yield = underestimates vegetative biomass WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 Modeling: current approaches Phases of development P0 P1 Sowing End juvenile phase P2 P3 Panicle initiation P4

P5 Flag leaf Flowering Start grain filling P6 Maturity Harvest Emergence P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 Modeling: current approaches Phases of development Photoperiod induced phase (PIP) Juvenile phase Fixed duration No PI possible T control P0 Duration=f(P,T) Ends at PI

P control P1 Sowing Modeling approaches will differ depending on how they handle temperature photoperiod interactions during the PIP End juvenile phase P2 P3 Panicle initiation P4 P5 Flag leaf Flowering Start grain filling P6 Maturity Harvest

Emergence P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 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!) P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 Approach P.C.S. Traor & al.

WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 Approach Daily / decadal weather data [1950-2004] P Pt seasonal totals [1950-2004] reorganization in terciles Rn 2004 weather aggregation in time tercile probability extraction

IRI seasonal forecasts over specific locations [1997-2004] Tn, Tx Sotuba 2004 Expmt. (Kouressy, Vaksmann et al.) soils cultivars mngmt observations comparison Bipode water balance statistical analysis tercile limits determination yearly statistics: rains onset, end dates, LGP seasonal 30year normals 50-79, 55-84, 60-89, 65-94, 70-99, 75-04

1. analogue 1959 2. 3. normalization seasonal anomalies [1950-2004] dynamic process based model (DSSAT4) Pta 2004 regenerated weather sequences (100) Yield component predictions (2004, 1959), probabilities (using regenerated 2004 weather sequences) IRI FD seasonal forecast fields stochastic disaggregation FORECAST EVALUATION

P.C.S. Traor & al. YIELD PREDICTION WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 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. P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 Results P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 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 P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 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] P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 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? P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 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 P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005

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) P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 2003 IRI FD seasonal forecasts 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) P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005

2004 IRI FD seasonal forecasts Climatology 6 out of 9 Observed: above normal rainfall in July-August, abrupt end around mid-September P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 PP response options Response curves : thermal time to PI as a function of photoperiod 2500 Purpose: model the delay imposed by non-optimal P on plant development (how it slows down its speed or development rate) TTPI (Cd) f Pi P 1 P 2 R Pi P 2O PI will eventually occur

P2R P1 0 Photoperiod (h) Linear : rice (Vergara & Chang, 1985), other SD/LD crops (Major & Kiniry, 1991) sorghum (Ritchie & Alagarswamy, 1989) 2500 P P f Pi P1 sat base Pi Pbase TTPI (Cd) Hyperbolic (Franquin, 1976; Hadley, 1983; Hammer, 1989; Brisson, 2002) PI may not occur P1 Consequences for qualitative plants P.C.S. Traor & al.

P20 WMO CLIMAG workshop, May 2005 0 Psat Pbase Photoperiod (h) ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 DR calculation options Even more important is the procedure for calculating development rates (DR) DR = inverse of phase duration Case 1: cumulative photo-thermal ratios Case 2: threshold on thermal time requirements 1 DRj f Pj j dtti DRj

i 1 f Pi j dtt i i 1 Physiological interpretation Plant progresses every day towards flowering with a variable rate function of T and P P.C.S. Traor & al. Requires that daylength conditions be met for flowering to take place WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 Experimental design Typical Guinea cultivar CSM388, avg. cycle duration 130 days, P1=413C.days (Vaksmann & al., 1996) Calibration: 1996 planting date experiment

in Sotuba (1239N), June-August, PI dates observed by dissections every 5 days Genetic coefficients: screening ranges and increments Flag Leaf Sowing date = June 20 Validation: 1994 planting date experiment in Sotuba (1239N), Cinzana (1315N) and Koporo (1414N), February-September, FL expansion dates observed and translated into PI dates Flag Leaf Sowing date = July 20 P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 Results (PP) 1996 experimental observations used for calibration. All durations computed from emergence 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 P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 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 P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 Results (PP) Scatterplots of calculated emergence-flag leaf expansion durations (EFLcalc) against observations from the 1994 experiment (EFLobs) Cumulative Threshold 180 160 EFL calc 140 Linear

R2=0.89 R2=0.41 RMSE= 38 120 100 80 60 40 40 60 80 100 120 140 160 180 180 180

160 160 140 RMSE = 46 120 R =0.13 EFL calc Hyperbolic EFL calc EFL obs 2 100 RMSE = 8 120 R2=0.97

100 80 80 60 60 40 40 40 60 80 100 120 140 160 180 EFL obs

P.C.S. Traor & al. 140 WMO CLIMAG workshop, May 2005 40 60 80 100 120 140 160 180 EFL obs ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 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 180

Threshold hyperbolic 160 Cumulative hyperbolic 140 Threshold linear Cumulative linear EFL (d) 120 EFLobs Sotuba 94 100 80 60 40 20 M 3/31/A4/30/M 5/30/J 6/29/J 7/29/A8/28/S 9/27/O10/27N11/26 1/1/1J 1/31/F 3/1/1 996 1996 996 1996 1996 1996 1996 1996 1996 1996 /1996 /1996 Sowing dates P.C.S. Traor & al.

WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 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 plant growth and qualitative plant development Need to incorporate more knowledge of plant physiology & genetics into phenological crop models (shifts in hormone balances rather than florigen concept, ) P.C.S. Traor & al.

WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 New SG phenology in next DSSAT release Implementation in CERES-Sorghum is straightforward : replace 1 parameter, re-write 3 lines of code Source code: RATEIN = 1.0/102.0 IF (TWILEN .GT. P2O) THEN RATEIN = 1.0/(102.0+P2R*(TWILEN-P2O)) ENDIF SIND = SIND + RATEIN*PDTT Modifications: RATEIN = 1.0/P1 IF (TWILEN .GT. P2O) THEN RATEIN = (TWILEN-PBASE)/(P2O-PBASE) ENDIF SIND = RATEIN*SUMDTT P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 Simulated development stages rcolte

initiation paniculaire: 3 jours de diffrence pour ICSMH 13 " " W33 m aturit physiologique phase de rem plissage effectif des grains fin croissance feuilles dbut rem plissage grains initiation paniculaire fin croissance feuilles Grow th stage (W33-Jun22) Grow th stage (ICSMH-Jun22) Grow th stage (W33-Jul16) Grow th stage (ICSMH-Jul16) fin phase juvnile initiation paniculaire m ergence fin phase juvnile fin phase juvnile: 2 jours de diffrence pour ICSMH entre les semis du 22/6 et 16/7 2 " "

W33 " " Sem is 20 40 60 80 100 120 140 160 Jours aprs sem is P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 Simulated growth (leaf biomass)

3000 Masse foliaire (kg/ha) 2500 2000 1500 1000 500 0 20 40 60 80 100 120 140 160 Jours aprs sem is P.C.S. Traor & al.

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 WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 Simulated growth (stem biomass) 16000 Masse des tiges (kg/ha) 14000 12000 10000 8000 6000

4000 2000 0 20 40 Stem w t kg/ha (W33-Jun22) P.C.S. Traor & al. 60 80 100 120 140 160 Jours aprs sem Stem w is 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 WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 Publications Folliard A., P.C.S. Traor, M. Vaksmann, M. Kouressy, 2004. Modeling of sorghum response to photoperiod: a thresholdhyperbolic approach, Field Crops Research 89:1, 59-70 Traor, 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 2004 Traor, 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 (MSAS2004), Bamako, Mali, 2-5 Aug. 2004 Soumar, M., M. Vaksmann, P.C.S. Traor, M. Kouressy, 2004. Recent evolution of climate and consequences on adaptation for sorghum varieties in Mali (in French), MSAS2004 Traor, 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) Traor, 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 Research Traor, P.C.S., Kouressy, M., Vaksmann, M., Bostick, W.M., 2005. Modeling biomass partitioning in West African sorghum landraces, in preparation Bounguili, 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. Traor, 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 Dveloppement, IRD, Paris (in revision). Tabo, R., Bationo, A., Kandji, S., Traor, 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). P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 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 P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 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? P.C.S. Traor & al. WMO CLIMAG workshop, May 2005

ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 Trailer section P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005 Thank you to 1. ATI participants !! we need to think of 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) P.C.S. Traor & al. WMO CLIMAG workshop, May 2005 ICRISAT-IPR-IER-CIRAD-U. Florida, 2005

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