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Pierre C. Sibiry Traoré & al.© ICRISAT-IER-U. Florida-CIRAD, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Enhanced photoperiod response modeling for.

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Presentation on theme: "Pierre C. Sibiry Traoré & al.© ICRISAT-IER-U. Florida-CIRAD, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Enhanced photoperiod response modeling for."— Presentation transcript:

1 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-U. Florida-CIRAD, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Enhanced photoperiod response modeling for improved biomass simulation in a Sudanian carbon accounting framework P.S. Traoré, A. Folliard, M. Vaksmann, C. Porter, M. Kouressy, J.W. Jones in partnership with :with funding from :

2 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-U. Florida-CIRAD, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Overview  The context  C sequestration is only one (still small) part of the livelihood portfolio  Climate risk will continue to reign in the poor West African SAT  Local cereals, at the crossroads of tactical and strategic opportunities  a working base for a win-win solution?  The problem  Crop simulation models and local cereals : enhancements needed  Why improve predictive models in a data assimilation framework anyway?  Methods  Development (PP response), then growth (partitioning)  DSSAT-Century  Results (preliminary)  Vegetative Phase Duration  Biomass Production  Next steps…

3 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-U. Florida-CIRAD, 2004Regional Carbon Workshop – Bamako, Feb. 2004 The context

4 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-U. Florida-CIRAD, 2004Regional Carbon Workshop – Bamako, Feb. 2004 The place of sorghum in West Africa Gadiaba variety Durra race Sahelian zone N’tenimissa variety Guinea x Caudatum hybrid Sudanian zone  Major staple crop  Mali: 30% of cereal production  With millet, 4 th cereal worldwide  More nutritive than maize, but tannins  Losing ground to maize

5 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-U. Florida-CIRAD, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Millet and sorghum in a cotton-intensive year (2003)

6 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-U. Florida-CIRAD, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Climate: but what is so different about West Africa? 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 necessarily because : Predictability is higher in SEA (both yearly and in the long term)

7 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-U. Florida-CIRAD, 2004Regional Carbon Workshop – Bamako, Feb. 2004  Regional climate among the most variable in the world (also most pronounced decadal change: -0.3% rainfall over 20 th 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 Climate: but what is so different about West Africa?

8 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-U. Florida-CIRAD, 2004Regional Carbon Workshop – Bamako, Feb. 2004 What would you do if you were an annual plant? Favorable rainfed cropping conditions: May-November Decreasing daylengths Daylength (h) Rainfall (mm) Sotuba (12°39’N, 7°55’W)

9 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-U. Florida-CIRAD, 2004Regional Carbon Workshop – Bamako, Feb. 2004  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 manage climatic risk What would you do if you were an annual plant?

10 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-U. Florida-CIRAD, 2004Regional Carbon Workshop – Bamako, Feb. 2004  Grouped flowering towards end of rainy season  Minimize grain mold, insect & bird damage (early maturing varieties)  Avoid incomplete grain filling (late maturing varieties) Dr. Hoogenboom (2m) x 2 x 3 NorthSouth What would you do if you were an annual plant? Photoperiod sensitivity = adaptation trait West Africa : highest PP sensitivity levels worldwide Bonus for C sequestration : large biomass production

11 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-U. Florida-CIRAD, 2004Regional Carbon Workshop – Bamako, Feb. 2004 The problem

12 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-U. Florida-CIRAD, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Diagnostic underestimate photoperiod (PP) sensitivity + do not parameterize PP sensitivity optimally = underestimate vegetative phase duration + do not partition biomass optimally = underestimate vegetative biomass production The problem  Crop models and local cereals : improvements are needed Cause (range of genetic coefficients – P2R) (choice of response curve, coefficients, DR calculation approach) to be determined  Why improve predictive models in a data assimilation framework anyway?  Uncertainty reduction is critical for EnKF performance and can be approached from either or both sides (measurements, predictions)  It may be easier to reduce uncertainty in point peak biomass estimates from models (as compared to remote sensing)

13 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-U. Florida-CIRAD, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Data assimilation framework for C accounting D D ATA Measurement Soil Sampling Biomass Soil C Weather Management Soil Properties Parameters Biomass Measured Soil C Measured Soil C Simulated Optimized Soil-C Estimation Optimized Biomass Estimation Optimized Parameter Estimation M M ODEL D D ATA A A SSIMILATION ENSEMBLE KALMAN FILTER Biomass Simulated DSSAT -CENTURY Crop/Soil C Model 1. reduce uncertainties in ‘measurements’ (adaptation = calibration, modification) 2. reduce uncertainties in predictions (adaptation = calibration, modification)

14 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-U. Florida-CIRAD, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Modeling: current approaches  Phases of development P1P2P3P4P5P6 Emergence Flag leaf Panicle initiation End juvenile phase Flowering Maturity Harvest P0 Start grain filling Sowing

15 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-U. Florida-CIRAD, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Modeling: current approaches  Phases of development P1P2P3P4P5P6 Emergence Flag leaf Panicle initiation End juvenile phase Flowering Maturity Harvest P0 Start grain filling Sowing Juvenile phase Fixed duration No PI possible T control Photoperiod induced phase (PIP) Duration=f(P,T) Ends at PI P control Modeling approaches will differ depending on how they handle temperature – photoperiod interactions during the PIP

16 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-U. Florida-CIRAD, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Methods

17 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-U. Florida-CIRAD, 2004Regional Carbon Workshop – Bamako, Feb. 2004 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)  Consequences for ‘qualitative’ plants PI will eventually occur PI may not occur

18 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-U. Florida-CIRAD, 2004Regional Carbon Workshop – Bamako, Feb. 2004 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

19 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-U. Florida-CIRAD, 2004Regional Carbon Workshop – Bamako, Feb. 2004 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

20 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-U. Florida-CIRAD, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Results

21 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-U. Florida-CIRAD, 2004Regional Carbon Workshop – Bamako, Feb. 2004 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 8732 25 Jun. 9613.313851447630 10 Jul. 9613.187756406826 25 Jul. 9613.104603325618 10 Aug. 96 13.033 413224716 Results (PP) 1996 experimental observations used for calibration. All durations computed from emergence

22 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-U. Florida-CIRAD, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Results (PP) Model calibration. Best estimate of genetic coefficients for the 4 model types Model typeCoefficientsRMSE P2O (h)P2R (°C.d.h -1 ) Cumulative-linear case13.0511602.7 Threshold-linear case1316601.2 Psat (h)Pbase (h) Cumulative-hyperbolic case13.0513.92.0 Threshold-hyperbolic case12.8513.71.7

23 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-U. Florida-CIRAD, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Results (PP) CumulativeThreshold Linear Hyperbolic R 2 =0.41 R 2 =0.89 R 2 =0.13 R 2 =0.97 Scatterplots of calculated emergence-flag leaf expansion durations (EFL calc ) against observations from the 1994 experiment (EFL obs )

24 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-U. Florida-CIRAD, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Results (PP)  Predictions of EFL as a function of planting dates for the 4 approaches, as compared to 6 observations (EFL obs ) from the 1994 experiment in Sotuba, Mali

25 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-U. Florida-CIRAD, 2004Regional Carbon Workshop – Bamako, Feb. 2004 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, …)

26 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-U. Florida-CIRAD, 2004Regional Carbon Workshop – Bamako, Feb. 2004 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 Prospects  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) ENDIF SIND = RATEIN*SUMDTT

27 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-U. Florida-CIRAD, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Impact on biomass estimates  Sotuba 1996 planting date experiment – Sorghum growth analysis

28 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-U. Florida-CIRAD, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Next steps…

29 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-U. Florida-CIRAD, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Next steps  DSSAT-Century : development OK, now look at growth  biomass partitioning in sorghum and millet (thesis M. Kouressy)  DSSAT-Century : compute new set of genetic coefficients for database cultivars  Using DSSAT-Century GIS interface, spatially simulate biomass production for the Oumarbougou study area (2004?)

30 Pierre C. Sibiry Traoré & al.© ICRISAT-IER-U. Florida-CIRAD, 2004Regional Carbon Workshop – Bamako, Feb. 2004 Conclusions  Improved DSSAT-Century PP response code has been included in a beta version and simulates plant development correctly  The modified function is more consistent with short-day plant physiology and has more universal applicability in theory – in practice, change of 1 genetic coefficient requires re-computation of crop genetic sets in DSSAT-Century  The impact on simulation of VPD using a modified PP response algorithm is negligible whenever the crop cycle is below 120 days (ie under most current normal planting conditions) – no impact on biomass expected under these conditions  Starting work on biomass partitioning will probably have a more significant impact on enhancing biomass estimation capability under normal planting conditions


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