LIFE AgriAdapt Vulnerability assessment in Southern European pilot - PowerPoint PPT Presentation

LIFE AgriAdapt Vulnerability assessment in Southern European pilot farms V. Sánchez, N. Metayer , J. Domingo, L. García, S, Doublet, C. Wackerhagen 2nd International Conference ADAPTtoCLIMATE 24-25 June 2019 Heraklion, Crete island, Greece With the support of:

AgriAdapt partnership With the support of:

Life AgriAdapt IN PRACTICE 4 BASELINE REPORTS WITH AGRO CLIMATE 4 BASELINE REPORTS WITH AGRO CLIMATE GRIDS PER CLIMATE ZONE GRIDS PER CLIMATE ZONE COMPILATION OF SUSTAINABLE COMPILATION OF SUSTAINABLE ADAPTATION MEASURES ADAPTATION MEASURES ONE DECISION SUPPORTING TOOL FOR THE ONE DECISION SUPPORTING TOOL FOR THE FARM VULNERABILITY ASSESSMENT FARM VULNERABILITY ASSESSMENT 5 STEERING COMMITTEE BOARDS: FARMER UNIONS, 5 STEERING COMMITTEE BOARDS: FARMER UNIONS, COOPERATIVES, EXPERTS, RESEARCHERS, COOPERATIVES, EXPERTS, RESEARCHERS, AGRONOMIC SCHOOLS, DECISION MAKERS, ETC. AGRONOMIC SCHOOLS, DECISION MAKERS, ETC. 120 PILOT FARMS WITH DOMINANT AND 120 PILOT FARMS WITH DOMINANT AND MINOR FARMING PRACTICES. MINOR FARMING PRACTICES. With the support of:

From vulnerability to adaptation… A learning process for farmers … AND CLIMATE TRAJECTORIES CLIMATIC HAZARDS... Erosion Drought Hail Intense Heat frost wave Flooding AGRIADAPT ROADMAP FOR ADAPTATION Awareness of Awareness of Vulnerability, 2019 Vulnerability, solutions identifjed Awareness of solutions identifjed and their effjciency Vulnerability Vulnerability, but no idea of their (advantages and 2017 unknown but no solutions effjciency disadvantages) is identifjed quantifjed With the support of:

AGRIADAPT VULNERABILITY ASSESSMENT The vulnerability level (or risk level) combine the probability of occurrence of climate stress (exposure) and the extent of the consequences (crop impact). EXPOSURE IMPACT OR Frequency of SENSITIVITY climate stress (i.e., key climatic % of crop yield reduction parameters) Vulnerability experienced Impact Exposure Impact Exposure VULNERABILITY = EXPOSURE X IMPACT With the support of: 5

AGRIADAPT VULNERABILITY ASSESSMENT The assessment help to prioritize the level of vulnerability. No scientifjc unit to measure a risk. To assess the levels of Exposure and Sensitivity, qualitative evaluation trough rating scale is then required. AGRIADAPT VULNERABILITY MATRIX V e ryfre q u e n t(>5 0 %) 6 6 12 18 24 30 36 41-50% 5 5 10 15 20 25 30 E R U 31- 40% 4 4 8 12 16 20 24 S O 21-30% 3 3 6 9 12 15 18 P X 11-20% 2 2 4 6 8 10 12 E R a re<1 0 % 1 1 2 3 4 5 6 1 2 3 4 5 6 In s ig n ific a n t Ma jor <5 % 6- 10% 11-15% 16-25% 26-30% >3 0 % S E V E R IT YO FC O N S E Q U E N C E S(Y ie ldim p a c t% ) With the support of: 6

COMMON DECISION TOOL: A MUL TISTEP APPROACH FROM THE AGRO CLIMATE ZONE TO FARM SCALE 1. Agro Climate Zone 1. Agro Climate Zone The analysis provide a framework for analysis The analysis provide a framework for analysis at the farm level: identifjed in a recent past at the farm level: identifjed in a recent past period the strongly impacted years, main period the strongly impacted years, main climate events,… climate events,… 2. Farm Scale 2. Farm Scale Once the farm is characterized, assessment of Once the farm is characterized, assessment of vulnerability of the farm’s crops and reduction of vulnerability of the farm’s crops and reduction of Near Future Farm vulnerability Near Future Farm vulnerability With the support of: 7

COMMON DECISION TOOL: RELEVANT POINTS Climatic data : Climatic data : Crop yields : Crop yields : Climate daily observations Climate daily observations Regional scale (statistics): Regional scale (statistics): (30 last years) for the (30 last years) for the annual yield of the last 15 annual yield of the last 15 Recent Past (RP) Recent Past (RP) years years Climate daily projection Climate daily projection Farm scale (average, Farm scale (average, (30 years) for the Near (30 years) for the Near minimum & maximum) minimum & maximum) Future (NF) Future (NF) COMMON COMMON DECISION DECISION TOOL TOOL Vulnerability scoring : Vulnerability scoring : Farm interview : Farm interview : Qualitative (agronomic Qualitative (agronomic expertise & bibliography) expertise & bibliography) Agronomic, livestock, Agronomic, livestock, and quantitative and quantitative economic, climatic data economic, climatic data information information With the support of: 8

CLIMATE DATA ACZ TOOL Agri4Cast Resources Portal Covering all the EU Member states and free access Climate observations available from 1975 to the last calendar year completed (25x25 km grid) Future daily weather data for Europe (25x25 km grid) for time horizon 2030, (SRES Scenario A1B, 3 GCM RCMs available). For pilot farms assessment, only one climate model (ETHZ-CLM-HadCM3Q0 model) was used in order to show the pilot farmers the impacts of climate change in a simplifjed way. With the support of: 9

AgriAdapt pilot farms Southern Region. Spain Arable Tomat Vineyard Fruits Dair Beef Sheep o y Pilot Farms 6 6 7 1 6 5 1 Minimum size (ha 11 15 4 87 232 UAA) Average size (ha 146 138 24 156 780 980 UAA) Maximum size (ha 400 230 130 230 1715 UAA) With the support of: A g r i A d a p t - E C C A 2 0 1 7

SOFT WHEAT Yield variability Yields 1990 - 1990 2015 2068,00 1991 1819,00 1992 175,00 Barley – Valladolid (Spain) Rainfall & hot days - Observed 1993 4011,00 1994 2993,25 1995 1428,00 180 1996 3044,67 Rainfall 01/05 & 30/06 160 1997 1808,87 1998 2750,00 140 1999 2778,00 120 2000 4183,36 2001 1716,46 100 2002 1611,27 2003 2766,08 80 2004 2913,75 60 2005 1258,50 2006 2119,25 40 2007 3609,92 2008 4139,00 20 2009 1618,07 0 2010 2870,00 0 5 10 15 20 25 30 35 2011 2954,00 2012 Nb of days >30°C. 01/05 & 30/06 2023,00 2013 3848,00 2014 2240,00 Solagro from Agri4Cast 2015 2470,00 With the support of:

Yields Yield variability 1990 - 2015 1990 2068,00 1991 1819,00 1992 175,00 Barley – Valladolid (Spain) Rainfall & hot days - Observed 1993 4011,00 1994 2993,25 1995 1428,00 180 1996 3044,67 Rainfall 01/05 & 30/06 160 1997 1808,87 1998 2750,00 140 1999 2778,00 120 2000 4183,36 2001 1716,46 100 2002 1611,27 2003 2766,08 80 2004 2913,75 60 2005 1258,50 2006 2119,25 40 2007 3609,92 2008 4139,00 20 2009 1618,07 0 2010 2870,00 0 5 10 15 20 25 30 35 2011 2954,00 2012 Nb of days >30°C. 01/05 & 30/06 2023,00 2013 3848,00 2014 2240,00 Solagro from Agri4Cast 2015 2470,00 With the support of:

Agro Climate Indicators (ACIs) A C I -C 3 -H y d ric d e fic it (Ma y to Ju n e ) A C I -C 3 -H y d ric d e fic it (Ma y to Ju n e ) 0 0 Automatic calculation of 70 different ACIs -5 0 -5 0 - General (x13): rainfall, temperatures, etc. -1 0 0 -1 0 0 ) m ) m (m (m P P - T Fodder (x11): date for grass regrowth, date for 1 st ll -E T -1 5 0 ll -E -1 5 0 fa fa in in grazing , etc. a R a R -2 0 0 -2 0 0 - Cereal crops (x12): end of cycle thermal and hydric -2 5 0 -2 5 0 stress, etc. -3 0 0 -3 0 0 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 8 9 9 9 9 9 0 0 0 0 0 1 1 1 1 2 2 2 2 2 3 3 3 3 3 4 4 4 4 - 9 8 9 8 9 9 9 9 9 9 9 9 9 9 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 0 2 0 2 0 2 0 2 0 2 0 3 0 3 0 3 0 3 0 3 0 4 0 4 0 4 0 4 Summer crops (x9) : temperatures > 32°C, summer 1 9 1 9 1 9 1 9 1 9 1 9 1 9 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 A C I-M1-H e a ts tre s sC orn A C I-M1-H e a ts tre s sC orn hydric deficit, etc. (T x>3 2 ° C0 1 -0 6to3 0 -0 9 ) (T x>3 2 °C0 1 -0 6to3 0 -0 9 ) 3 5 3 5 - Vineyards (x13): date of late frost, Huglin index, etc 3 0 3 0 - Rapeseed (x4), Field tomatoes (x5), Field peas (x1) r 2 5 a r 2 5 a e ry e ry e e sp sp 2 0 - 2 0 Irrigation (x2): winter reload, etc. y y a rofd a rofd 1 5 1 5 e - e b Livestock (x3): Temperature-Humidity Index, etc. m b m u u 1 0 N 1 0 N 5 5 0 0 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 7 9 1 3 5 7 9 1 3 5 7 9 1 3 5 8 0 2 4 6 8 0 2 4 6 8 0 2 4 6 8 8 9 9 9 9 9 0 0 0 0 0 1 1 1 1 2 2 2 2 2 3 3 3 3 3 4 4 4 4 8 8 9 9 9 9 9 0 0 0 0 0 1 1 1 1 2 2 2 2 2 3 3 3 3 3 4 4 4 4 9 9 9 9 9 9 9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 9 1 9 1 9 1 9 1 9 1 9 1 9 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 2 0 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 With the support of: