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[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
[Day 2] Center Presentation: Bioversity and CIAT
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[Day 2] Center Presentation: Bioversity and CIAT

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Presented by Andy Jarvis (Bioversity), Andy Farrow (CIAT), and Glenn Hyman (CIAT) at the …

Presented by Andy Jarvis (Bioversity), Andy Farrow (CIAT), and Glenn Hyman (CIAT) at the
CGIAR-CSI Annual Meeting 2009: Mapping Our Future. March 31 - April 4, 2009, ILRI Campus, Nairobi, Kenya

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  • 1. Gap analysis of genetic resources in the CG and beyond Bioversity International and CIAT
  • 2. A renewed effort in identifying gaps • CG collections suffer from both over- and under- collecting • Duplications increase costs • But also signficiant gaps in the collections • GPG2 and GCDT project: – Crop wild relatives – Major cultivated crops
  • 3. Gap analysis of what? • What is a gap? Fundamental question goes at the root objective of a genebank! • “95% of all the alleles at a random locus occurring in the target population with a frequency greater than 0.05” (Marshall and Brown, 1975) • Trait-focused vs. Neutral diversity focus • Also function of use…breeders… • ….and time • The Jarvis-take on things: Today trait- focused, 2020 neutral diversity focused, 2050 Arabidopsis. Chao genebank!
  • 4. The Gap Analysis Protocol • What and where: taxonomic and geographic priorities • Gaps are GREaT: – Taxonomic underrepresentation in collections – Geographic holes in collections (geography is a decent indicator for all things, biotic, abiotic, quality traits) – Environmental underrepresentation in collections – Rare environmental conditions at the edges of collections (especially relevant for breeding for abiotic stress) • Final result: map and table of geographic and taxonomic priorities for completing the collection
  • 5. Worse than pulling teeth Crop Genus # species G H Total Avg. Records/species Barley Hordeum 27 1419 10965 12384 459 Bean Phaseolus 72 2435 2952 5387 75 Chickpea Cicer 23 314 19 333 14 Cowpea Vigna 64 2509 6306 8815 138 Faba bean Vicia 9 511 949 1460 162 Finger millet Eleusine 7 3 68 71 10 Maize Zea 4 228 143 371 93 Pearl millet Pennisetum 54 963 3409 4372 81 Pigeon pea Cajanus 26 197 601 798 31 Sorghum Sorghum 31 320 4138 4458 144 Wheat Aegilops 23 4016 2231 6247 272 Wheat Triticum 3 1374 1 1375 458
  • 6. Herbarium versus Germplasm • Herbarium samples essentially a reference set of data for comparison • Also used by collectors to re-locate populations to complete germplasm collections • Our point of entry for the development of the methodology, thanks to success in Vigna (Maxted et al. 2008)
  • 7. Geographic Gaps GENEBANK HERBARIUM
  • 8. Geographic Gaps SITES WITH NO SITES WITH GERMPLASM DEFICIENT GERMPLASM
  • 9. 1000 900 P. vulgaris 800 Number of samples 700 (germplasm) 600 500 400 300 P. acutifolius P. coccineus 200 100 P. lunatus 0 P. filiformis 0 200 400 600 800 1000 Num ber of sam ples TAXONOMIC GAPS (all collections) Extent to which each species has been adequately sampled • Compared with herbarium •Related to its distributional range
  • 10. ENVIRONMENTAL GAPS Sites with environmental conditions not yet captured in the germplasm collections •19 bioclimatic indices reduced to principal components •Holes or under-representation in PC classes mapped out 1000 Herbarium distribution 900 Germplasm distribution 800 700 Frequency 600 500 400 300 200 100 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 PC1 classes
  • 11. At species level, identification of species which are poorly represented across the environmental gradient For herbarium rich groups For herbarium poor groups 700 600 Real distribution Theoretical distribution 500 Frequency 400 300 200 100 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 PC1 classes
  • 12. Species Sampling (%) Coverage (%) Distribution (%) Outlier (%) Rarity Score albiviolaceus 0.0 N/A N/A N/A N/A 0.00 amabilis 0.0 N/A N/A N/A N/A 0.00 chacoensis 0.0 N/A N/A N/A N/A 0.00 Synthesis diversifolius 0.0 N/A N/A N/A N/A 0.00 elongatus 0.0 N/A N/A N/A N/A 0.00 fraternus 0.0 N/A N/A N/A N/A 0.00 laxiflorus 0.0 N/A N/A N/A N/A 0.00 micranthus 10.0 N/A N/A N/A N/A 0.00 mollis 0.0 N/A N/A N/A N/A 0.00 nitensis 0.0 N/A N/A N/A N/A 0.00 opacus 0.0 N/A N/A N/A N/A 0.00 pachycarpus 0.0 N/A N/A N/A N/A 0.00 Simple scoring system: texensis 10.0 N/A N/A N/A N/A 0.00 trifidus 0.0 N/A N/A N/A N/A 0.00 xolocotzii 0.0 N/A N/A N/A N/A 0.00 If species not in genebank, highest anisophyllus 0.0 N/A N/A N/A N/A 0.00 oaxacanus 0.0 N/A N/A N/A N/A 0.00 pauper 0.0 N/A N/A N/A N/A 0.00 priority plagiocylix 0.0 N/A N/A N/A N/A 0.00 rosei 0.0 N/A N/A N/A N/A 0.00 sonorensis 0.0 N/A N/A N/A N/A 0.00 If underrepresented in genebank, falciformis 0.0 N/A N/A N/A N/A 0.00 marechalii 6.7 N/A N/A N/A N/A 0.00 rotundatus 6.7 N/A N/A N/A N/A 0.00 with gaps, medium priority salicifolius 3.3 N/A N/A N/A N/A 0.00 altimontanus 7.5 N/A N/A N/A N/A 0.00 esquincensis 0.0 N/A N/A N/A N/A 0.00 If well represented with few gaps, novoleonensis 5.0 N/A N/A N/A N/A 0.00 tenellus 0.0 N/A N/A N/A N/A 0.00 albiflorus 10.0 N/A N/A N/A N/A 0.00 low priority macrolepis 8.0 N/A N/A N/A N/A 0.00 reticulatus 2.0 N/A N/A N/A N/A 0.00 jaliscanus 1.7 N/A N/A N/A N/A 0.00 macvaughii 3.3 N/A N/A N/A N/A 0.00 magnilobatus 3.3 N/A N/A N/A N/A 0.00 venosus 0.0 N/A N/A N/A N/A 0.00 carteri 7.1 N/A N/A N/A N/A 0.00 formosus 0.0 N/A N/A N/A N/A 0.00 polymorphus 2.9 N/A N/A N/A N/A 0.00 parvifolius 4.5 2.2 5.0 N/A 10.0 4.50 esperanzae 8.8 N/A N/A N/A N/A 0.00 filiformis 1.6 5.6 6.7 0.0 9.9 4.66 perplexus 1.3 N/A N/A N/A N/A 0.00 maculatus 2.2 4.4 8.0 1.0 9.1 4.89 talamancensis 1.1 10.0 4.0 2.0 7.1 4.97 polystachios 0.1 0.1 0.0 0.0 8.3 0.45 leptostachyus 2.9 6.5 6.7 0.0 9.9 5.32 amblyosepalus 0.0 0.0 0.0 N/A 10.0 1.00 glabellus 5.3 6.0 4.0 N/A 10.0 5.60 pachyrrhizoides 8.8 6.5 2.9 0.0 6.7 5.77 nelsonii 0.0 0.0 0.0 N/A 10.0 1.00 costaricensis 2.3 10.0 6.0 1.1 8.0 5.96 coccineus 4.8 8.1 5.7 0.0 9.7 6.06 pluriflorus 1.4 1.3 2.5 N/A 10.0 2.56 oligospermus 3.4 10.0 5.0 10.0 9.5 6.51 pedicellatus 0.9 2.7 3.3 0.0 9.5 2.56 hintonii 7.7 4.3 7.5 N/A 10.0 6.86 microcarpus 5.9 8.6 6.7 0.0 9.8 6.86 angustissimus 0.5 1.2 6.7 0.6 6.1 2.83 acutifolius 6.4 8.3 8.0 0.0 9.9 7.30 augusti 7.4 10.0 4.3 10.0 9.5 7.49 grayanus 5.2 2.0 4.0 0.0 7.5 3.72 neglectus 5.3 10.0 6.7 N/A 10.0 7.60 parvulus 1.3 5.0 5.0 0.0 8.6 3.82 vulgaris 8.7 9.9 5.4 3.8 7.4 7.76 dumosus 5.3 10.0 8.6 6.0 8.8 7.89 tuerckheimii 1.5 10.0 0.0 0.0 8.2 3.86 xanthotrichus 8.4 10.0 5.7 10.0 9.0 8.19 pauciflorus 0.2 4.0 6.7 N/A 10.0 4.27 chiapasanus 9.0 9.5 7.5 N/A 10.0 8.80 zimapanensis 8.8 10.0 10.0 N/A 10.0 9.63 lunatus 3.9 3.3 5.6 3.9 8.9 4.47
  • 13. Priority setting for a species • Predicted distribution • Limiting it to its true range • Eliminating already sampled sites • High probability of finding
  • 14. Worse than pulling teeth Crop Genus # species G H Total Avg. Records/species Barley Hordeum 27 1419 10965 12384 459 Bean Phaseolus 72 2435 2952 5387 75 Chickpea Cicer 23 314 19 333 14 Cowpea Vigna 64 2509 6306 8815 138 Faba bean Vicia 9 511 949 1460 162 Finger millet Eleusine 7 3 68 71 10 Maize Zea 4 228 143 371 93 Pearl millet Pennisetum 54 963 3409 4372 81 Pigeon pea Cajanus 26 197 601 798 31 Sorghum Sorghum 31 320 4138 4458 144 Wheat Aegilops 23 4016 2231 6247 272 Wheat Triticum 3 1374 1 1375 458
  • 15. Zea
  • 16. Vigna
  • 17. Vicia
  • 18. Aegilops and Triticum
  • 19. Sorghum
  • 20. Pennisetum
  • 21. Hordeum
  • 22. Cicer
  • 23. Cajanus
  • 24. Phaseolus
  • 25. Current geographic distribution Predicted future distribution of of diversity for the 343 crop diversity based on 18 GCM wild relative species studied models under the A2a scenario Total number of herbarium specimens and germplasm accessions available for each major crop wild relative genepool through the GBIF portal Crop Genus # species G H Total Avg. Records/species Barley Hordeum 27 1419 10965 12384 459 Bean Phaseolus 72 2435 2952 5387 75 Chickpea Cicer 23 314 19 333 14 Cowpea Vigna 64 2509 6306 8815 138 Faba bean Vicia 9 511 949 1460 162 Finger millet Eleusine 7 3 68 71 10 Maize Zea 4 228 143 371 93 Pearl millet Pennisetum 54 963 3409 4372 81 Pigeon pea Cajanus 26 197 601 798 31 Sorghum Sorghum 31 320 4138 4458 144 Predicted change in Wheat Aegilops 23 4016 2231 6247 272 Wheat Triticum 3 1374 1 1375 458 species richness to 2050.
  • 26. Decadal climate change 2000 – 2100, one GCM Trajectories of wild populations to “follow” their climate Two parameters: Max. migration rate Plasticity
  • 27. Wild relatives TOP 30 FAO PRODUCED CROPS WITH: • Phaseolus • Rice • Vigna • Cotton • Zea • Soy • • Sugar cane Vicia • Rapeseed • Sorghum • Cassava • Cajanus • Oil Palm • Cicer • Potato • Hordeum • Coconut • • Coffee Pennisetum • Sweet Potato • Triticum/Aegilops • Groundnut • Eleusine • Sunflower • Lentil
  • 28. Climate change data and analyses CIAT
  • 29. Climate change data • Statistically downscaled from 18 GCM models Originating Group(s) GRID Year Country MODEL ID OUR ID Bjerknes Centre for Climate Research 2050 Norway BCCR-BCM2.0 BCCR_BCM2 128x64 Canadian Centre for Climate Modelling & Analysis 2020-2050 Canada CGCM2.0 CCCMA_CGCM2 96x48 Canadian Centre for Climate Modelling & Analysis 2050 Canada CGCM3.1(T47) CCCMA_CGCM3_1 96x48 Canadian Centre for Climate Modelling & Analysis 2050 Canada CGCM3.1(T63) CCCMA_CGCM3_1_T63 128x64 Météo-France France CNRM-CM3 CNRM_CM3 128x64 Centre National de Recherches Météorologiques 2050 CSIRO Atmospheric Research 2020 Australia CSIRO-MK2.0 CSIRO_MK2 64x32 CSIRO Atmospheric Research 2050 Australia CSIRO-Mk3.0 CSIRO_MK3 192x96 Max Planck Institute for Meteorology 2050 Germany ECHAM5/MPI-OM MPI_ECHAM5 N/A Meteorological Institute of the University of Bonn Germany ECHO-G MIUB_ECHO_G 96x48 Meteorological Research Institute of KMA 2050 Korea LASG / Institute of Atmospheric Physics 2050 China FGOALS-g1.0 IAP_FGOALS_1_0_G 128x60 US Dept. of Commerce NOAA USA GFDL-CM2.0 GFDL_CM2_0 144x90 Geophysical Fluid Dynamics Laboratory 2050 US Dept. of Commerce NOAA USA GFDL-CM2.0 GFDL_CM2_1 144x90 Geophysical Fluid Dynamics Laboratory 2050 NASA / Goddard Institute for Space Studies 2050 USA GISS-AOM GISS_AOM 90x60 Institut Pierre Simon Laplace 2050 France IPSL-CM4 IPSL_CM4 96x72 Center for Climate System Research National Institute for Environmental Studies Japan MIROC3.2(hires) MIROC3_2_HIRES 320x160 Frontier Research Center for Global Change (JAMSTEC) 2050 Center for Climate System Research National Institute for Environmental Studies Japan MIROC3.2(medres) MIROC3_2_MEDRES 128x64 Frontier Research Center for Global Change (JAMSTEC) 2050 Meteorological Research Institute 2050 Japan MRI-CGCM2.3.2 MRI_CGCM2_3_2a N/A National Center for Atmospheric Research 2050 USA PCM NCAR_PCM1 128x64 Hadley Centre for Climate Prediction and Research UK UKMO-HadCM3 HCCPR_HADCM3 96x73 Met Office 2020-2050 Center for Climate System Research (CCSR) Japan NIES-99 NIES-99 64x32 National Institute for Environmental Studies (NIES) 2020
  • 30. Climate General climate change description characteristic Average Climate Change Trends of Colombia The rainfall increases from 2645.89 millimeters to 2702.41 millimeters General Temperatures increase and the average increase is 2.66 ºC climate The mean daily temperature range increases from 9.57 ºC to 9.85 ºC characteristics The maximum number of cumulative dry months keeps constant in 2 months The maximum temperature of the year increases from 30.84 ºC to 34.36 ºC while the warmest quarter gets hotter by 2.81 ºC Extreme The minimum temperature of the year increases from 19.05 ºC to 21.23 ºC while the coldest quarter gets hotter by 2.6 ºC conditions The wettest month gets wetter with 354.88 millimeters instead of 350.35 millimeters, while the wettest quarter gets wetter by 3.55 mm The driest month gets wetter with 94.2 millimeters instead of 83.6 millimeters while the driest quarter gets wetter by 40.25 mm Climate Overall this climate becomes more seasonal in terms of variability through the year in temperature and less seasonal in precipitation Seasonality The coefficient of variation of temperature predictions between models is 3.7% Variability Temperature predictions were uniform between models and thus no outliers were detected between The coefficient of variation of precipitation predictions between models is 5.72% models Precipitation predictions were uniform between models and thus no outliers were detected Current precipitation 350 40 Future precipitation Future mean temperature Current mean temperature 35 300 Future maximum temperature Current maximum temperature Future minimum temperature 30 Current minimum temperature 250 Precipitation (mm) Temperature (ºC) 25 200 20 150 15 100 10 50 5 0 0 1 2 3 4 5 6 7 8 9 10 11 12 Month These results are based on the 2050 climate compared with the 1960-2000 climate. Future climate data is derived from 14 GCM models from the 3th (2001) and the 4th (2007) IPCC assessment, run under the A2a scenario (business as usual). Further information please check the website http://www.ipcc-data.org
  • 31. Incertidumbre Site-specific predicted values of each GCM model (IPCC, 2007) for principal bioclimatic variables 3500 50 45 3000 40 2500 35 Precipitation (mm) Temperature (ºC) 30 2000 25 1500 20 15 1000 10 500 5 0 0 MPI ECHAM 5 MIROC3 2 HIRES MIUB ECHO G CNRM CM3 MIROC3 2 CCCMA CGCM3 CCCMA CGCM3 CSIRO MK3 0 CCCMA CGCM2 GFDL CM2 BCCR BCM2 0 GFDL CM2 1 NCAR PCM 1 HCCPR HADCM3 MEDRES 1 T63 1 Total annual precipitation (bio 12) Annual mean temperature (bio 1) Annual maximum temperature (bio 5) Annual minimum temperature (bio 6)
  • 32. Incertidumbre Site-specific monthly coefficient of variation using 14 GCM models (IPCC, 2007) for precipitation and temperature 16 35 14 30 Precipitation coefficient of variation (%) Temperature coefficient of variation (%) 12 25 10 20 8 15 6 10 4 5 2 0 0 1 2 3 4 5 6 7 8 9 10 11 12 Month Precipitation Mean temperature Maximum temperature Minimum temperature
  • 33. Yearly data too… 5.0 4.0 3.0 Temperature 2.0 1.0 1870 0.0 Baseline -200.0 -100.0 0.0 100.0 200.0 300.0 400.0 500.0 600.0 700.0 -1.0 Precipitation India Myanmar Burma Mexico Dominican Republic Rwanda Brazil Uganda Korea Guatemala United States Colombia
  • 34. Ecocrop approach 1600 1400 Marginal 1200 Precipitation (mm) conditions 1000 800 Death Optimum Not 600 conditions suitable conditions 400 200 0 -5 0 5 10 15 20 25 30 35 40 Temperature (ºC)
  • 35. Pros and cons of the approach • Simple to use and apply • Available for “minor” crops which are PROS important components of food and nutritional security • Captures the broad niche of the crop, including within crop genetic diversity • Fails to capture complex physiological CONS responses of within season climate • Only provides index of suitability – not productivity • Inferior model to those available for the “big” crops
  • 36. Cow peas Vigna unguiculata unguic. L 10176 Grapes Vitis vinifera L. 7400 Groundnut Arachis hypogaea L. 22232 Lentil Lens culinaris Medikus 3848 Linseed Linum usitatissimum L. 3017 The geography of crop suitability Maize Zea mays L. s. mays 144376 Mango Mangifera indica L. 4155 Millet Panicum miliaceum L. 32846 Natural rubber Hevea brasiliensis (Willd.) 8259 Area Oats Avena sativa L. 11284 Crop Species Harvested Oil palm Elaeis guineensis Jacq. 13277 (k Ha) Olive Olea europaea L. 8894 Alfalfa Medicago sativa L. 15214 Onion Allium cepa L. v cepa 3341 Apple Malus sylvestris Mill. 4786 Oranges Citrus sinensis (L.) Osbeck 3618 Banana Musa acuminata Colla 4180 Pea Pisum sativum L. 6730 Barley Hordeum vulgare L. 55517 Pigeon pea Cajanus cajan (L.) Mill ssp 4683 Common Bean Phaseolus vulgaris L. 26540 Plantain bananas Musa balbisiana Colla 5439 Common buckwheat Fagopyrum esculentum Moench 2743 Potato Solanum tuberosum L. 18830 Cabbage Brassica oleracea L.v capi. 3138 Rapeseed Brassica napus L. 27796 Cashew nuts Anacardium occidentale L. 3387 Rice Oryza sativa L. s. japonica 154324 Cassava Manihot esculenta Crantz. 18608 Rye Secale cereale L. 5994 Chick pea Cicer arietinum L. 10672 Perennial reygrass Lolium perenne L. 5516 Clover Trifolium repens L. 2629 Sesame seed Sesamum indicum L. 7539 Cocoa bean Theobroma cacao L. 7567 Sorghum Sorghum bicolor (L.) Moench 41500 Coconut Cocos nucifera L. 10616 Perennial soybean Glycine wightii Arn. 92989 Coffee Coffea arabica L. 10203 Sugar beet Beta vulgaris L. v vulgaris 5447 Cotton Gossypium hirsutum L. 34733 Sugarcane Saccharum robustum Brandes 20399 Cow peas Vigna unguiculata unguic. L 10176 Sunflower Helianthus annuus L v macro 23700 Grapes Vitis vinifera L. 7400 Sweet potato Ipomoea batatas (L.) Lam. 8996 Groundnut Arachis hypogaea L. 22232 Tea Camellia sinensis (L) O.K. 2717 Lentil Lens culinaris Medikus 3848 Tobacco Nicotiana tabacum L. 3897 Linseed Linum usitatissimum L. 3017 Tomato Lycopersicon esculentum M. 4597 Maize Zea mays L. s. mays 144376 Watermelon Citrullus lanatus (T) Mansf 3785 Mango Mangifera indica L. 4155 Wheat Triticum aestivum L. 216100 Millet Panicum miliaceum L. 32846 Yams Dioscorea rotundata Poir. 4591 Natural rubber Hevea brasiliensis (Willd.) 8259 Oats Avena sativa L. 11284 Oil palm Elaeis guineensis Jacq. 13277 Olive Olea europaea L. 8894 Onion Allium cepa L. v cepa 3341 Oranges Citrus sinensis (L.) Osbeck 3618
  • 37. Current suitability for agriculture
  • 38. Future suitability for agriculture 18 GCM models, A2a scenario
  • 39. Change in global suitability
  • 40. Number of crops that lose out
  • 41. Number of crops that gain
  • 42. Current suitability for common bean Gmin: 60, Gmax: 100 Ttmp:0, Tmin: 7, TOPmn: 16, TOPmx: 27, Tmax: 32 Rmin: 220, ROPmn: 350, ROPmx: 900, Rmax: 1500
  • 43. Future suitability for common bean Gmin: 60, Gmax: 100 Ttmp:0, Tmin: 7, TOPmn: 16, TOPmx: 27, Tmax: 32 Rmin: 220, ROPmn: 350, ROPmx: 900, Rmax: 1500
  • 44. Cassava and maize in Africa and India – not all bad news
  • 45. Crop adaptability anomaly -80 -60 -40 -20 20 40 60 80 0 Angola cass Angola maiz Congo cass Congo maiz Ghana cass Ghana maiz India cass India maiz Malawi cass Malawi maiz Mozambique cass Mozambique maiz Tanzania cass Tanzania maiz Nigeria cass Nigeria maiz Uganda cass Uganda maiz Differential response in maize
  • 46. Change in bean suitability
  • 47. Technological options • Impact of a 100mm more drought resistant bean in Africa • Change in the change with Ropt less 100mm • Green areas show regions that will benefit from such a technology
  • 48. Context What is drought? Where is drought? Who is affected? Will the weather cause the crop to fail or significantly reduce yields? – What variety? – When planted? – What kind of soil and terrain? – What management?
  • 49. Context What is drought? Where is drought? Who is affected? Types of drought I. Terminal drought II. Intermittent drought III. Predictable drought IV. Semi-arid Amede et al, 2004
  • 50. Context What is drought? Where is drought? Who is affected? Expert knowledge
  • 51. Context What is drought? Where is drought? Who is affected? Cons and Pros • Expert knowledge Only as good as the experts x Difficult to extrapolate x Some areas not considered x Consistency x Transparency x – Potentially quick – Useful for defining indicators – Validation of results
  • 52. Context What is drought? Where is drought? Who is affected? Homologue environments
  • 53. Context What is drought? Where is drought? Who is affected? Water Balance models • Failed Seasons – WATBAL model to determine length of season – MarkSim to simulate rainfall and temperature – Viable growing seasons >= 50 growing days (defined as Ea/Et > 0.5) with no more than 20 days in this period with stress (where Ea/Et < 0.5) (Thornton et al, 2006)
  • 54. Seasonal Drought Index Sorghum in Sub Saharan Africa
  • 55. Seasonal Drought Index Period 1, 0 to 20 days after sowing. 0 - 10 % 10 - 20 % 20 - 30 % Sorghum 30 - 40 % 40 - 50 % Proportion of 50 - 60 % days in sorghum 60 - 70 % regions with 70 - 80 % Ea/Et < 0.35 80 - 90 % 90 - 100 %
  • 56. Seasonal Drought Index Period 2, 20 to 40 days after sowing. 0 - 10 % 10 - 20 % 20 - 30 % Sorghum 30 - 40 % 40 - 50 % Proportion of 50 - 60 % days in sorghum 60 - 70 % regions with 70 - 80 % Ea/Et < 0.35 80 - 90 % 90 - 100 %
  • 57. Seasonal Drought Index Period 3, 40 to 60 days after sowing. 0 - 10 % 10 - 20 % 20 - 30 % Sorghum 30 - 40 % 40 - 50 % Proportion of 50 - 60 % days in sorghum 60 - 70 % regions with 70 - 80 % Ea/Et < 0.35 80 - 90 % 90 - 100 %
  • 58. Seasonal Drought Index Period 4, 60 to 80 days after sowing. 0 - 10 % 10 - 20 % 20 - 30 % Sorghum 30 - 40 % 40 - 50 % Proportion of 50 - 60 % days in sorghum 60 - 70 % regions with 70 - 80 % Ea/Et < 0.35 80 - 90 % 90 - 100 %
  • 59. Seasonal Drought Index Period 5, 80 to 100 days after sowing. 0 - 10 % 10 - 20 % 20 - 30 % Sorghum 30 - 40 % 40 - 50 % Proportion of 50 - 60 % days in sorghum 60 - 70 % regions with 70 - 80 % Ea/Et < 0.35 80 - 90 % 90 - 100 %
  • 60. Seasonal Drought Index Period 6, 100 to 120 days after sowing. 0 - 10 % 10 - 20 % 20 - 30 % Sorghum 30 - 40 % 40 - 50 % Proportion of 50 - 60 % days in sorghum 60 - 70 % regions with 70 - 80 % Ea/Et < 0.35 80 - 90 % 90 - 100 %
  • 61. Seasonal Drought Index Period 7, 120 to 140 days after sowing. 0 - 10 % 10 - 20 % 20 - 30 % Sorghum 30 - 40 % 40 - 50 % Proportion of 50 - 60 % days in sorghum 60 - 70 % regions with 70 - 80 % Ea/Et < 0.35 80 - 90 % 90 - 100 %
  • 62. Context What is drought? Where is drought? Who is affected? Drought for common beans • Stress due to water deficiency • Susceptible period – during flowering, pod set and early grain-fill – ranges from 30-60 DAP to 45-75 DAP in the case of later maturing varieties – varies according to elevation
  • 63. Pest and Disease Mapping • Over to Glenn…
  • 64. Step 1: collect literature and databases • CIAT Green mite database • Scientific literature on cassava mealybug and CIAT database • System-wide IPM data on whitefly and cassava mosaic disease • Scientific literature on cassava brown streak disease
  • 65. Step 2: geo-reference and characterize
  • 66. Step 3: Run models CLIMEX - CBSD Open Modeler
  • 67. Step 4: validation
  • 68. Potential for whitefly Potential for CBSD

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