In a theater near you…

1. In a theater near you…

2. 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

3. 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: • 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 sudano-sahelian agriculture. Sahel = another buzzword promoted by climate science?

4. 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.

5. 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…

6. 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.

7. 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…

8. 21st Century XXX Corporation presents… Oops !! Croprotation

9. 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. Traoré, J.E. Bounguili, M. Kouressy, M. Vaksmann, J.W. Jones

10. 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

11. The context

12. 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)

13. 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) Risk = uncertainty x vulnerability (reproduced from IPCC, 2001)

14. Climate: what is different about West Africa? • 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

15. What would you do if you were an annual plant? Sotuba (12°39’N, 7°55’W) Favorable rainfed cropping conditions: May-November Decreasing daylengths Daylength (h) Rainfall (mm)

16. 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

17. 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) x 3 Photoperiod sensitivity = adaptation trait West Africa : highest PP sensitivity levels worldwide Sudanian ag. systems = MONROE shock absorbers  Global Environmental Change special issue 2001 x 2 North South Dr. Hoogenboom (2m)

18. The place of sorghum in West Africa N’tenimissa variety Guinea x Caudatum hybrid Sudanian zone Gadiaba variety Durra race Sahelian zone • Major staple crop • Mali: 30% of cereal production • With millet, 4th cereal worldwide • More nutritive than maize, but tannins • Losing ground to maize

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

20. Resolution: the proof (panchromatic)

21. 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

22. The problem

23. 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)

24. Challenges for cropping systems modelers Uncertainties associated with: climate Spatio-temporal scale mismatches and resulting low prediction skill of rainfall onset, distribution and amount cropping systems models Incomplete understanding of gene-environment interaction and resulting inaccurate local crop development and growth plant soil High level of measurement error relative to C accretion rates, and need to extrapolate to meet tradable quantities

25. The problem with “landracist” climate models African regions with robust (green) and weak (orange) ENSO signals (after Nicholson, 1997).

26. 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).

27. The problem with “landracist” climate models SST-Rainfall-Vegetation feedbacks affecting the monsoon rainfall over the NRB (after Zeng et al., 2003).

28. The problem with “landracist” crop models

29. The problem with “landracist” crop models • Crop models and landrace cereals : improvements are needed 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 Cause (range of genetic coefficients – P2R) (choice of response curve, coefficients, DR calculation approach) (begin. stem growth, others?)

30. Modeling: current approaches • Phases of development P0 P1 P2 P3 P4 P5 P6 Panicle initiation Sowing End juvenile phase Flag leaf Flowering Start grain filling Harvest Maturity Emergence

31. Modeling: current approaches • Phases of development Modeling approaches will differ depending on how they handle temperature – photoperiod interactions during the PIP Juvenile phase Fixed duration No PI possible T control Photoperiod induced phase (PIP) Duration=f(P,T) Ends at PI P control P0 P1 P2 P3 P4 P5 P6 Panicle initiation Sowing End juvenile phase Flag leaf Flowering Start grain filling Harvest Maturity Emergence

32. 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!)

33. Approach

34. P Tn, Tx Rn IRI seasonal forecasts over specific locations [1997-2004] Pt seasonal totals [1950-2004] Approach Daily / decadal weather data [1950-2004] Sotuba 2004 Expmt. (Kouressy, Vaksmann et al.) IRI FD seasonal forecast fields observations mngmt soils cultivars 2004 weather S aggregation in time tercile probability extraction comparison yearly statistics: rains onset, end dates, LGP Yield component predictions (2004, 1959), probabilities (using regenerated 2004 weather sequences) “Bipode” water balance 1. analogue 1959 statistical analysis seasonal 30-year normals 50-79, 55-84, 60-89, 65-94, 70-99, 75-04 2. tercile limits determination 3. reorganization in terciles dynamic process based model (DSSAT4) normalization Pta seasonal anomalies [1950-2004] 2004 regenerated weather sequences (100) stochastic disaggregation FORECAST EVALUATION YIELD PREDICTION

35. 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.”

36. Results

37. 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

38. 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] Energy & Water Balance Products

39. 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?

40. 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

41. 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)

42. 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)

43. 2004 IRI FD seasonal forecasts • Climatology 6 out of 9 • Observed: above normal rainfall in July-August, abrupt end around mid-September

44. 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) PI will eventually occur PI may not occur • Consequences for ‘qualitative’ plants

45. 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 • 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

46. 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

47. 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

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

49. Results (PP) Scatterplots of calculated emergence-flag leaf expansion durations (EFLcalc) against observations from the 1994 experiment (EFLobs) Cumulative Threshold R2=0.89 R2=0.41 Linear Hyperbolic R2=0.13 R2=0.97

50. 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