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Climate change
Knowledge Sharing Week
      May 2009
Contents
• Climate change in 3 short minutes
• The data we have in CIAT
• Analysing impacts in agriculture
  – Our commodi...
El clima esta
cambiando,
no me diga
que no
S o u r c e s o f A g r ic u lt u r a l G r e e n h o u s e
Ga s e s
excluding land use change Mt CO2-eq




Source: Cool ...
El pasado y el presente – El “hockey
                stick”
El arctico esta descongelando
Modelos GCM : “Global Climate
               Models”




• 21 “global climate models” (GCMs) basados en ciencias
  atmosfé...
Y que dicen los modelos?
What do the 21 models say?




   The world gets warmer
….and wetter, but not everywhere
The Data We have in CIAT
• First, data from Stanford (Lobell)
• Second, data downloaded from IPCC
• Now….strategic partner...
Climate change data
• Statistically downscaled from 18 GCM models
                       Originating Group(s)             ...
General climate change description

                                                                                   Ave...
Incertidumbre
                     Site-specific predicted values of each GCM model (IPCC, 2007) for principal bioclimatic...
Incertidumbre
   Site-specific monthly coefficient of variation using 14 GCM models (IPCC, 2007) for precipitation and
   ...
Colombia y el mundo en cambio
                                                       climático
                           ...
5.0




                                         4.0




                                         3.0
Temperature




    ...
Cambio en
                                                      Cambio en     Cambio en                   Incertidumbre
  ...
Estimating Likely Impacts in
            Agriculture
• Three modelling approaches:
  – Niche-based
  – Empirical
  – Mecha...
Ecocrop approach

                     1600

                     1400
                                                   ...
Pros and cons of the approach
       • Simple to use and apply
PROS




       • Available for “minor” crops which are
   ...
Current Cassava Suitability




Gmin: 150, Gmax: 365.
KTmp: 0, Tmin:15, TOPmn:24, TOPmx: 32, Tmax: 45.
Rmin: 300, ROPmn: 8...
Future Cassava Suitability (2020)




Gmin: 150, Gmax: 365.
KTmp: 0, Tmin:15, TOPmn:24, TOPmx: 32, Tmax: 45.
Rmin: 300, RO...
Change in Cassava Suitability




Gmin: 150, Gmax: 365.
KTmp: 0, Tmin:15, TOPmn:24, TOPmx: 32, Tmax: 45.
Rmin: 300, ROPmn:...
TECHNOLOGY OPTION:
    +2-3oC HEAT TOLERANCE




Only India benefits from heat tolerance. This is a
national strategy for ...
Changes in
 adaptability in
Green Mite 2020
Change in
adaptability of
Whitefly 2020
Arachis pintoi Krap.& Greg.




          Gmin: 180, Gmax: 300
KTmp: 0, Tmin: 12, TOPmn: 22, TOPmx: 28,
                Tm...
Clitoria ternatea L.




           Gmin: 50, Gmax: 365
KTmp: -2, Tmin: 15, TOPmn: 19, TOPmx: 28,
                 Tmax: 3...
Leucaena leucocephala (La.)




          Gmin: 180, Gmax: 365
KTmp: 0, Tmin: 10, TOPmn: 20, TOPmx: 32,
                Tm...
Brachearia x hybrid




          Gmin: 120, Gmax: 365
KTmp: 0, Tmin: 20, TOPmn: 24, TOPmx: 30,
                Tmax: 35
 ...
Cow peas             Vigna unguiculata unguic. L    10176
                                                             Gra...
Change in global suitability
Number of crops that lose out
Number of crops that gain
Cassava and maize in Africa and
    India – not all bad news
Crop adaptability anomaly




                 -80
                       -60
                             -40
           ...
COFFEE SUITABILITY


COFFEE: 14.3% of GDP in
Nicaragua, 2006.
COFFEE SUITABILITY
              0,6


              0,5


              0,4
Suitability




              0,3


         ...
COFFEE ACIDITY SUITABILITY
COFFEE ACIDITY SUITABILITY
              1,2


              1,0


              0,8
Suitability




              0,6


 ...
ADAPTATION STRATEGIES


 1. Areas not anymore suitable for coffee
                (alternatives)
     2. Areas only suitab...
IMPLICATIONS ON SUPPLY
              CHAIN
1. Shortage of commodity coffee and high value
                       coffee
  ...
Adaptation, adaptation, adaptation

• We see four types of agriculture within the
  context of climate change:
  –   Tradi...
Different strategies
• Traditional staple, short-cycle
   – Technology development, community-based adaptation
     strate...
Adaptive management across the supply
               chain
MARKET


                        Short-cycle high
             ...
Conclusions
• In multiple CIAT crops no panaceae in breeding
  strategies – we need to do more work on this
• Regional-lev...
Climate change
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Climate change

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Presentation by Andy Jarvis for the CIAT KSW 2009

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Climate change

  1. 1. Climate change Knowledge Sharing Week May 2009
  2. 2. Contents • Climate change in 3 short minutes • The data we have in CIAT • Analysing impacts in agriculture – Our commodities – Others • Adaptation, adaptation, adaptation but what does it really mean?
  3. 3. El clima esta cambiando, no me diga que no
  4. 4. S o u r c e s o f A g r ic u lt u r a l G r e e n h o u s e Ga s e s excluding land use change Mt CO2-eq Source: Cool farming: Climate impacts of agriculture and mitigation potential, Greenpeace, 07 January 2008
  5. 5. El pasado y el presente – El “hockey stick”
  6. 6. El arctico esta descongelando
  7. 7. Modelos GCM : “Global Climate Models” • 21 “global climate models” (GCMs) basados en ciencias atmosféricas, química, física, biología, y, dependiendo de las creencias, algo de astrología • Se corre desde el pasado hasta el futuro • Hay diferentes escenarios de emisiones de gases
  8. 8. Y que dicen los modelos?
  9. 9. What do the 21 models say? The world gets warmer
  10. 10. ….and wetter, but not everywhere
  11. 11. The Data We have in CIAT • First, data from Stanford (Lobell) • Second, data downloaded from IPCC • Now….strategic partnership with the Tyndell Centre in UK who will provide us with the latest projections (7 GCM models, 4 emissions scenarios) through their AVOID project
  12. 12. Climate change data • Statistically downscaled from 18 GCM models Originating Group(s) Country MODEL ID OUR ID GRID Year Bjerknes Centre for Climate Research Norway BCCR-BCM2.0 BCCR_BCM2 128x64 2050 Canadian Centre for Climate Modelling & Analysis Canada CGCM2.0 CCCMA_CGCM2 96x48 2020-2050 Canadian Centre for Climate Modelling & Analysis Canada CGCM3.1(T47) CCCMA_CGCM3_1 96x48 2050 Canadian Centre for Climate Modelling & Analysis Canada CGCM3.1(T63) CCCMA_CGCM3_1_T63 128x64 2050 Météo-France France CNRM-CM3 CNRM_CM3 128x64 Centre National de Recherches Météorologiques 2050 CSIRO Atmospheric Research Australia CSIRO-MK2.0 CSIRO_MK2 64x32 2020 CSIRO Atmospheric Research Australia CSIRO-Mk3.0 CSIRO_MK3 192x96 2050 Max Planck Institute for Meteorology Germany ECHAM5/MPI-OM MPI_ECHAM5 N/A 2050 Meteorological Institute of the University of Bonn Germany ECHO-G MIUB_ECHO_G 96x48 Meteorological Research Institute of KMA Korea 2050 LASG / Institute of Atmospheric Physics China FGOALS-g1.0 IAP_FGOALS_1_0_G 128x60 2050 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 USA GISS-AOM GISS_AOM 90x60 2050 Institut Pierre Simon Laplace France IPSL-CM4 IPSL_CM4 96x72 2050 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 Japan MRI-CGCM2.3.2 MRI_CGCM2_3_2a N/A 2050 National Center for Atmospheric Research USA PCM NCAR_PCM1 128x64 2050 Hadley Centre for Climate Prediction and Research UK UKMO-HadCM3 HCCPR_HADCM3 96x73 Met Office 2020-2050 Center for Climate System Research (CCSR) National Institute for Environmental Studies (NIES) Japan NIES-99 NIES-99 64x32 2020
  13. 13. General climate change description Average Climate Change Trends of Colombia The rainfall increases from 2672.92 millimeters to 2739.29 millimeters in 2050 passing through 2613.89 in 2020 General Temperatures increase and the average increase is 2.45 ºC passing through an increment of 0.94 ºC in 2020 climate The mean daily temperature range increases from 9.52 ºC to 9.69 ºC in 2050 characteristics The maximum number of cumulative dry months keeps constant in 2 months The maximum temperature of the year increases from 30.81 ºC to 33.97 ºC while the warmest quarter gets hotter by 2.58 ºC in 2050 Extreme The minimum temperature of the year increases from 19.08 ºC to 21.16 ºC while the coldest quarter gets hotter by 2.4 ºC in 2050 conditions The wettest month gets wetter with 358.48 millimeters instead of 353.03 millimeters, while the wettest quarter gets wetter by 5.17 mm in 2050 The driest month gets wetter with 96.32 millimeters instead of 84.75 millimeters while the driest quarter gets wetter by 45.47 mm in 2050 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 3.62% 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 350 40 Current precipitation Precipitation 2050 Precipitation 2020 35 Mean temperature 2020 300 Mean temperature 2050 Current mean temperature 30 250 Maximum temperature 2020 Maximum temperature 2050 Precipitation (mm) 25 Current maximum temperature Temperature (ºC) 200 Minimum temperature 2020 Minimum temperature 2050 20 Current minimum temperature 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 18 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
  14. 14. 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 1000 15 10 500 5 0 MIROC3 2 HIRES 0 MPI ECHAM 5 MIROC3 2 MIUB ECHO G CSIRO MK3 0 CCCMA CGCM3 CCCMA CGCM3 CNRM CM3 CCCMA CGCM2 BCCR BCM2 0 GFDL CM2 1 HCCPR HADCM3 GFDL CM2 NCAR PCM 1 MEDRES 1 T63 1 Total annual precipitation (bio 12) Annual mean temperature (bio 1) Annual maximum temperature (bio 5) Annual minimum temperature (bio 6)
  15. 15. 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
  16. 16. Colombia y el mundo en cambio climático 2950 27.5 Colombia 2900 27.0 +8.1% 26.5 +3.1ºC Precipitación total anual (mm) 2850 Temperatura media anual (ºC) 2800 26.0 2750 25.5 2700 25.0 2650 24.5 2600 24.0 Temperatura media anual (ºC) Precipitación total anual (mm) Tendencia temporal 2550 Tendencia temporal 23.5 Intervalo de confianza (95%) Intervalo de confianza (95%) 2500 23.0 1870 1890 1910 1930 1950 1970 1990 2010 2030 2050 2070 2090 1870 1890 1910 1930 1950 1970 1990 2010 2030 2050 2070 2090 Año Año 810 12.0 Mundo 790 +14% 11.0 +4.5ºC Precipitación total anual (mm) Temperatura media anual (ºC) 770 10.0 750 730 9.0 710 8.0 690 Temperatura media anual (ºC) Precipitación total anual (mm) 7.0 Tendencia temporal 670 Tendencia temporal Intervalo de confianza (95%) Intervalo de confianza (95%) 650 6.0 1870 1890 1910 1930 1950 1970 1990 2010 2030 2050 2070 2090 1870 1890 1910 1930 1950 1970 1990 2010 2030 2050 2070 2090 Año Año
  17. 17. 5.0 4.0 3.0 Temperature 2.0 1.0 1870 0.0 -200.0 -100.0 Baseline 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
  18. 18. Cambio en Cambio en Cambio en Incertidumbre Cambio en meses Region Departamento Temperatura estacionalidad de entre modelos Precipitacion consecutivos media precipitacion (StDev prec) secos Amazonas Amazonas 12 2.9 1.4 0 135 Amazonas Caqueta 138 2.7 -1.3 0 193 Amazonas Guania 55 2.9 -3.2 0 271 Amazonas Guaviare 72 2.8 -2.9 -1 209 Amazonas Putumayo 117 2.6 0.6 0 170 Andina Antioquia 18 2.1 1.3 0 129 Andina Boyaca 50 2.7 -3.9 -1 144 Andina Cundinamarca 152 2.6 -2.6 0 170 Andina Huila 51 2.4 1.0 0 144 Andina Norte de santander 73 2.8 -0.4 0 216 Andina Santander 51 2.7 -2.4 0 158 Andina Tolima 86 2.4 -3.1 0 148 Caribe Atlantico -74 2.2 -2.9 2 135 Caribe Bolivar 90 2.5 -1.8 0 242 Caribe Cesar -119 2.6 -1.3 0 160 Caribe Cordoba -11 2.3 -3.8 0 160 Caribe Guajira -69 2.2 -1.8 0 86 Caribe Magdalena -158 2.4 -1.8 0 153 Caribe Sucre 10 2.4 -4.1 -1 207 Eje Cafetero Caldas 252 2.4 -4.2 -1 174 Eje Cafetero Quindio 153 2.3 -4.1 -1 145 Eje Cafetero Risaralda 158 2.4 -3.5 -1 141 Llanos Arauca -13 2.9 -6.4 -1 188 Llanos Casanare 163 2.8 -5.7 -1 229 Llanos Meta 10 2.7 -5.4 -1 180 Llanos Vaupes 46 2.8 -1.4 0 192 Llanos Vichada 59 2.6 -2.6 0 152 Pacifico Choco -157 2.2 -1.2 0 148 Sur Occidente Cauca 172 2.3 -1.6 0 168 Sur Occidente Narino 155 2.2 -1.4 0 126 Sur Occidente Valle del Cauca 275 2.3 -5.1 -1 166
  19. 19. Estimating Likely Impacts in Agriculture • Three modelling approaches: – Niche-based – Empirical – Mechanistic model- based
  20. 20. Ecocrop approach 1600 1400 Marginal 1200 conditions Precipitation (mm) 1000 800 Death Optimum 600 Not conditions suitable 400 conditions 200 0 -5 0 5 10 15 20 25 30 35 40 Temperature (ºC)
  21. 21. Pros and cons of the approach • Simple to use and apply PROS • Available for “minor” crops which are important components of food and nutritional security • Captures the broad niche of the crop, including within crop genetic diversity CONS • Fails to capture complex physiological responses of within season climate • Only provides index of suitability – not productivity • Inferior model to those available for the “big” crops
  22. 22. Current Cassava Suitability Gmin: 150, Gmax: 365. KTmp: 0, Tmin:15, TOPmn:24, TOPmx: 32, Tmax: 45. Rmin: 300, ROPmn: 850, ROPmx: 1500, Rmax: 2000.
  23. 23. Future Cassava Suitability (2020) Gmin: 150, Gmax: 365. KTmp: 0, Tmin:15, TOPmn:24, TOPmx: 32, Tmax: 45. Rmin: 300, ROPmn: 850, ROPmx: 1500, Rmax: 2000.
  24. 24. Change in Cassava Suitability Gmin: 150, Gmax: 365. KTmp: 0, Tmin:15, TOPmn:24, TOPmx: 32, Tmax: 45. Rmin: 300, ROPmn: 850, ROPmx: 1500, Rmax: 2000.
  25. 25. TECHNOLOGY OPTION: +2-3oC HEAT TOLERANCE Only India benefits from heat tolerance. This is a national strategy for technology development. CIAT’s strategy probably better placed in pests/diseases.
  26. 26. Changes in adaptability in Green Mite 2020
  27. 27. Change in adaptability of Whitefly 2020
  28. 28. Arachis pintoi Krap.& Greg. Gmin: 180, Gmax: 300 KTmp: 0, Tmin: 12, TOPmn: 22, TOPmx: 28, Tmax: 30 Rmax: 1600, ROPmn: 1800, ROPmx:2000, Rmax: 3000
  29. 29. Clitoria ternatea L. Gmin: 50, Gmax: 365 KTmp: -2, Tmin: 15, TOPmn: 19, TOPmx: 28, Tmax: 32 Rmax: 400, ROPmn: 1200, ROPmx:1800, Rmax: 4300
  30. 30. Leucaena leucocephala (La.) Gmin: 180, Gmax: 365 KTmp: 0, Tmin: 10, TOPmn: 20, TOPmx: 32, Tmax: 42 Rmax: 250, ROPmn: 600, ROPmx: 3000, Rmax: 5000
  31. 31. Brachearia x hybrid Gmin: 120, Gmax: 365 KTmp: 0, Tmin: 20, TOPmn: 24, TOPmx: 30, Tmax: 35 Rmax: 800, ROPmn: 1200, ROPmx:1800, Rmax: 3000
  32. 32. Cow peas Vigna unguiculata unguic. L 10176 Grapes Vitis vinifera L. 7400 Groundnut Arachis hypogaea L. 22232 The geography of crop suitability Lentil Lens culinaris Medikus 3848 Linseed Linum usitatissimum L. 3017 Maize Zea mays L. s. mays 144376 Mango Mangifera indica L. 4155 Millet Panicum miliaceum L. 32846 Area Natural rubber Hevea brasiliensis (Willd.) 8259 Crop Species Harvested Oats Avena sativa L. 11284 (k Ha) Oil palm Elaeis guineensis Jacq. 13277 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
  33. 33. Change in global suitability
  34. 34. Number of crops that lose out
  35. 35. Number of crops that gain
  36. 36. Cassava and maize in Africa and India – not all bad news
  37. 37. Crop adaptability anomaly -80 -60 -40 -20 0 20 40 60 80 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
  38. 38. COFFEE SUITABILITY COFFEE: 14.3% of GDP in Nicaragua, 2006.
  39. 39. COFFEE SUITABILITY 0,6 0,5 0,4 Suitability 0,3 0,2 0,1 current 2050 0,0 0 500 1000 1500 2000 Altitude (m asl)
  40. 40. COFFEE ACIDITY SUITABILITY
  41. 41. COFFEE ACIDITY SUITABILITY 1,2 1,0 0,8 Suitability 0,6 0,4 0,2 current 2050 0,0 0 500 1000 1500 2000 2500 Altitude (m asl)
  42. 42. ADAPTATION STRATEGIES 1. Areas not anymore suitable for coffee (alternatives) 2. Areas only suitable with adapted management (varieties, irrigation, shade, etc) 3. New potential areas (expand where viable and possible)
  43. 43. IMPLICATIONS ON SUPPLY CHAIN 1. Shortage of commodity coffee and high value coffee 2. Possible increase in prices and income for supply chain actors 3. Change in sourcing areas and channels 4. Possible loss of product reputation (Denomination of Origin in Veracruz)
  44. 44. Adaptation, adaptation, adaptation • We see four types of agriculture within the context of climate change: – Traditional staple, short-cycle – Short-cycle low investment cash crop – Short-cycle high investment cash crop – Perennial long-term high investment system
  45. 45. Different strategies • Traditional staple, short-cycle – Technology development, community-based adaptation strategies (first practices) • Short-cycle low investment cash crop – Technology development, crop substitution • Short-cycle high investment cash crop – Careful planning, supply chain level adaptation strategies • Perennial long-term high investment system – Technology development, entire supply-chain level adaptation strategies (The Anchor)
  46. 46. Adaptive management across the supply chain MARKET Short-cycle high investment cash crops Perennial long-term high investment system FARM
  47. 47. Conclusions • In multiple CIAT crops no panaceae in breeding strategies – we need to do more work on this • Regional-level challenges at both all ends of spectrum (heat, drought, excess water) • We need to strengthen the analysis of economic and social implications of climate change • Technology development today for 2020 • Scientific gap in understanding of crop substitution (how common, implications, nutrition etc.)

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