Agricultural productivity and water supply variability in Spain: a model to manage hydrological risks

Transcription

1 International Drought Symposium Integrating Science and Policy March 24th March 26th, 2010, California USA Agricultural productivity and water supply variability in Spain: a model to manage hydrological risks Marina Gil (CEIGRAM, UPM) Alberto Garrido (CEIGRAM, Technical University of Madrid, UPM) Almudena Gómez Ramos (U Valladolid) (Work funded by the Spanish Ministry of Environment and Rural and Marine Affairs)

2 Droughts have impacts on farmers Distribution of types of indemnified hazards (in % number). Source: Agroseguro (2006) Hail Fire Frost Drought Strong wind Excessive rain Flood Lack of curdled (fruits) Excessive (persisent) rain Other Number of indemnified hazards 82, ,006 89,037

3 Drought worries farmers Type of production Risk perception rankings among farmers in Spain (1-5 scale: 1=not important; 5=very important). Hail Price volati Frost Drought controllable Other risks not Ddieases Strong winds Torrental rain Flloods Excessive rain Fruit crops Vineyard Cereals Vegetables Citrus Fire Source: ENESA (2007); IKEFEL (2006)

4 Insurance has become a common practice Source: ENESA (2009)

5 Goals Model farm sector (only) economics based on water used for irrigation: Micro-level (Production value of irrigated land) Develop risk management monitor ex ante economic drought effects Carry-out preliminay analysis for designing insurance policy for irrigators

6 Study areas 13 Irrigated areas Sevilla, Córdoba & Jaén (Guadalquivir), Lleida & Huesca (Ebro), Murcia (Segura), Albacete, Castellón & Valencia (Júcar), Badajoz & Ciudad Real (Guadiana), León & Valladolid (Duero).

7 Method 1: attribution models Variables of interest: MICRO Irrig Prod Value= surface * yield * prices ( ) Easy to evaluate, but not necessarily what farmers focus on (profit)

8 Data Irrigation districts(14 provinces) Time data: (14 years) Provincial Agric statistics (94 crops: yield, area and prices)

9 Results: Micro-economic variable Irrigation production value IPV = a + bt + c WA + d Ip + u it i i t i it i it it IPV: Irrigation producion value ( ) T: Trend (Year) WA : Water Availability (3 alternative evaluations) Ip: Price index

10 Results: Micro-economic variables (Irrig Areas) Adjusted R2 (three models) it = ai + bitt + ciwait + diipit uit it = it + it 1 IPV + Crop demand Pennman-Monteih 2 (Déficit de agua) 1 u ε Extended demand Irrig District Ad R 2 N WA (Sign) Ad R 2 N WA (Sign) Ad R 2 N WA (Sign) Genil-Cabra ** 0, ,82 10 Vinalopó 0,50 6 0, ,36 10 RRTT Júcar 0,81 6 0, ,70 10 RRTT Valencia 0,25 6 0, ,67 10 Plana Castellón 0,35 6 0, ,79 10 M. Oriental 0,69 6 0, ,76 10 Campo Cartagena 0,49 6 0, ,56 10 R. de Lorca 0,72 6 0, ,26 10 Canal de Cinca 3 0,64 10 * 0,52 10 * ZR del Segre 0,84 6 0,86 10 * 0,38 10 M. Occidental 0,75 6 0, ,83 10 Eresma 0,24 6 0, ,45 10 Canal de Aves 0,88 6 0,77 10 * 0,83 10 ** ρε

11 it Results: Micro-economic variables Adjusted R2 (three models) = ai + bt i t uit IPV it = ai + bt i t + ciwait + uit it i i t i it i it it IPV + IPV = a + bt + c WA + d Ip + u COEFICIENT COEFFICIENTS COEFFICIENTS province Ad-R 2 year Ad-R 2 year WA Ad-R 2 year WA Ip Albacete (**) (**) (**) - - Badajoz (**) (**) (*) Castellón (*) Ciudad Real (**) (**) (**) - - Córdoba (**) (**) + (**) (**) + (**) + Huesca (*) (**) + (*) (**) + (**) - (*) Jaén (**) (**) (**) + + León (*) (*) + Lleida (*) (*) Murcia (**) (**) (**) + + (**) Sevilla (**) (**) + (*) (**) + (**) + (*) Valencia (*) (*) Valladolid (**) (**) (**) - + Zaragoza (**) (**) (**) + -

12 Results 2 Simulation of supply risks obtain ex ante economic drought effects: Micro-economic varible Production value in irrigated land (, probability terms)

13 Results: Economic Risk models Based on the fitted models for Irrig Produc Value (IPV) I PV ˆ ˆ u h h i, 1+ t = aˆ i + bt i t+ 1 + cˆ ibi,1+ t + diipt + i Pdfs of systems R h i, t+ 1 for storage B h h i,1+ t = Ri,1 + t Di, t+ 1 S i Pdfs for demand D i, t+ 1 crops water h B v ex ante water balance, i,1+ t calculated h months before the end of the season

14 Results: Economic Risk models Based on the fitted models for Irrig Produc Value (IPV) I PV ˆ ˆ u h h i, 1+ t = aˆ i + bt i t+ 1 + cˆ ibi,1+ t + diipt + i Pdfs of systems R h i, t+ 1 for storage B h h i,1+ t = Ri,1 + t Di, t+ 1 S i Pdfs for demand D i, t+ 1 crops water Environmental restriction h B v ex ante water balance, i,1+ t calculated h months before the end of the season

15 Results: Supply variability Risk models Pdfs of monthly R h, + 1 i t for two important reservoirs Increases of storage till March Increases of storage till May 1 Genil-Cabra 1 ZR. Canal del Cinca Oct-March Nov-March Dec-March Jan-March Freb-March Oct-May Nov-May Dec-May Dataset #4 Feb-May March-May Increasesof storage in million cubic meters (capacity 981 mcm) Increasesof storage in billion cubic meters (capacity 835 mcm)

16 Irrig Seas Results: Supply variability Risk models Probability of h h B R D S 0 Genil-Cabra (Guadalquivir, Andalusia) i, 1+ t = i,1+ t i, t+ 1 i < RR TT Valencia (Jucar, Valencia) Oct Nov Dec Jan Feb Mar Apr Oct Nov Dec Jan Feb Mar Apr Plana de Castellón (Júcar, Valencia) Canal del Cinca (Ebro, Aragón

17 Results: Economic Risk models Pdfs of h I PV i,1+ t for Genil-Cabra (Córdoba, Andalusia) Drought-evolving year (2007) Wet year (2003) prob prob

18 Results: Economic Risk models Pdfs of h I PV i,1+ t for Plana de Castellón Valencia Wet year (2007) Very 2et year (2003)

19 Results: Economic Risk models h Pdfs of I PV i for Plana de Castellón Valencia,1+ t Monthly follow-up ( ) Plana de Castellón (2001 Octobre -- July 2007) 5% - 95% +/- 1 Std. Dev. Mean / Octubre 2001 / Diciembre 2001 / Febrero 2001 / Abril 2001 / Junio 2001 / Agosto 2002 / Octubre 2002 / Diciembre 2002 / Febrero 2002 / Abril 2002 / Junio 2002 / Agosto 2003 / Octubre 2003 / Diciembre 2003 / Febrero 2003 / Abril 2003 / Junio 2003 / Agosto 2004 / Octubre 2004 / Diciembre 2004 / Febrero 2004 / Abril 2004 / Junio 2004 / Agosto 2005 / Octubre 2005 / Diciembre 2005 / Febrero 2005 / Abril 2005 / Junio 2005 / Agosto 2006 / Octubre 2006 / Diciembre 2006 / Febrero 2006 / Abril 2006 / Junio 2006 / Agosto 2007 / Octubre 2007 / Diciembre 2007 / Febrero 2007 / Abril 2007 / Junio 1000

20 Conclusions Economic impacts of drought can be measured at micro- and macro-levels (including employment or Value Added) Economic impacts of drought must be carefully analysed, using robust attribution models: Isolating the effects of water supply variability from other non water-related effects (trends & farm prices)

21 Conclusions If impact attribution models can be fitted and supply systems can be characterised in stochastic terms, then: Economic risks of drought can be evaluted Real-time ex ante evaluations inform preparatory and anticipatory drought strategies. Insurance policies can be designed and offered commercially to water users (under some specific conditions)

22 Thank you