Climate change impacts and adaptation options for the Greek agriculture in 2021 2050 A monetary assessment Accepted Manuscript Climate change impacts and adaptation options for the Greek agriculture i[.]
Accepted Manuscript Climate change impacts and adaptation options for the Greek agriculture in 2021-2050: A monetary assessment E Georgopoulou, S Mirasgedis, Y Sarafidis, M Vitaliotou, D.P Lalas, I Theloudis, K.-D Giannoulaki, D Dimopoulos, V Zavras PII: DOI: Reference: S2212-0963(16)30046-8 http://dx.doi.org/10.1016/j.crm.2017.02.002 CRM 104 To appear in: Climate Risk Management Received Date: Revised Date: Accepted Date: 12 October 2016 14 February 2017 23 February 2017 Please cite this article as: E Georgopoulou, S Mirasgedis, Y Sarafidis, M Vitaliotou, D.P Lalas, I Theloudis, K.D Giannoulaki, D Dimopoulos, V Zavras, Climate change impacts and adaptation options for the Greek agriculture in 2021-2050: A monetary assessment, Climate Risk Management (2017), doi: http://dx.doi.org/10.1016/j.crm 2017.02.002 This is a PDF file of an unedited manuscript that has been accepted for publication As a service to our customers we are providing this early version of the manuscript The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain CLIMATE CHANGE IMPACTS AND ADAPTATION OPTIONS FOR THE GREEK AGRICULTURE IN 2021-2050: A MONETARY ASSESSMENT E Georgopouloua,*, S Mirasgedisa, Y Sarafidisa, M Vitaliotoub, D P Lalasb I Theloudisc, K.-D Giannoulakic, D Dimopoulosc, V Zavrasc a b c National Observatory of Athens/IERSD, I Metaxa & Vas Pavlou, GR-15236 Palea Penteli, Greece FACE3TS S.A., Agiou Isidorou str., GR-11471 Athens, Greece Piraeus Bank S.A., Environment Unit, Amerikis str., GR-10564 Athens, Greece * Corresponding author Tel.: +30 210 8109215, elenag@noa.gr E-mail addresses: elenag@noa.gr, seba@noa.gr, sara@noa.gr, mabita@otenet.gr, lalas@facets.gr, TheloudisI@piraeusbank.gr, GiannoulakiK@piraeusbank.gr, DimopoulosD@piraeusbank.gr, v.zavras@piraeusbank.gr Abstract: The paper presents a quantitative assessment of mid-term (2021-2050) climate change impacts on and potential adaptation options for selected crops in Greece that are of importance in terms of their share in national agricultural production and gross value added Central points in the assessment are the monetary evaluation of impacts and the cost-benefit analysis of adaptation options To address local variability in current and future climate conditions, analysis is spatially disaggregated into geographical regions using as an input downscaled results from climatic models For some crops (cereals, vegetables, pulses, grapevines), changes in future agricultural yields are assessed by means of agronomic simulation models, while for the rest crops changes are assessed through regression models The expected effects on crop yields of a number of potential adaptation options are also investigated through the same models, and the costs and benefits of these options are also quantitatively assessed The findings indicate that climate change may create winners and losers depending on their agricultural activity and location, while adaptation can mitigate adverse effects of climate change under cost-effective terms Keywords: climate change; agriculture; impacts; adaptation; economic assessment 1 Introduction Agriculture is one of the major economic sectors where climate change can have large impacts, affecting crop growth and consequently productivity As agricultural activities ensure food supply and represent an important source of income for local economies, especially in southern Europe, the investigation of these impacts is particularly important as it can provide the necessary scientific input for proper planning of adaptation strategies Especially in the Mediterranean region where Greece is located (as well as in the rest southern Europe), the recent findings of the Intergovernmental Panel of Climate Change (IPCC) reveal that the duration and intensity of droughts -as projected by regional and global climate modelswill increase and will be accompanied by significant reductions in summer soil moisture (Kovats et al., 2014) These, together with temperature increase, entail dangers for crop cultivations Agriculture in Greece is important both at national and regional level (as is also the case in other southern European countries like Italy, Spain and Portugal) In 2015, agriculture generated 4% of the Greek gross value added, while its share in some regions is even higher (of the order of 7-10%) In the case of Greece, agriculture is viewed, together with tourism, is viewed as a sector whose development will contribute substantially in providing the development that the local economy needs to mitigate the financial difficulties that have plagued it in the last years To date very few studies have attempted to provide quantitative estimates of the climate change impacts on crop cultivations in Greece together with the expected economic effects of adaptation Giannakopoulos et al (2011) estimated the change of regional climate indices with relevance to agriculture (but not the change of crop yields) In another study (Bank of Greece, 2011), which is so far the only available study of climate change impacts on crops at national level, semi- quantitative (i.e order of magnitude) estimates of future crop yield change are provided These estimates were obtained through the use of crop models for only crops, namely wheat, maize and cotton while for the rest of the crops examined (olive trees, grapevines and vegetables) the estimations were based on findings in international literature published during 1994-2010 and concerning regions other than those of Greece The present study represents a significant addition to the existing knowledge on the impacts of climate change on crops in Greece as it examines a larger range of crops, it provides quantitative estimations of climate change impacts on crop yield change and agricultural income per region and crop Furthermore these estimations are based on models 'tailored' to crop cultivations at different agricultural locations in Greece, capturing in this way the regional/ local dimension of climate change impacts As indications of the accelerating rate of climate change multiply, including the record breaking mean global temperatures of the last years, and the recent international understandings for mitigation, the builtin increase will continue and its impacts will be felt at least in the years till 2050 It behooves policy makers to start considering adaptation measures as soon as possible To address this urgent need, in this paper the effects of potential adaptation options on crop yields are also examined, to assess their economic attractiveness and quantify their expected direct economic effects on agricultural income As the crops examined and the conditions expected in Greece are similar to those of neighboring countries such as Italy, Spain, southern France and Cyprus but also some regions of the Balkan peninsular and Turkey, these findings could provide useful insights on applying similar adaptation measures in these regions The findings may also be of value to enterprises involved in the agricultural sector in both production and in providing focused and innovative financing and insurance In this respect, financial institutions with a considerable fraction of their total exposure being in the agricultural sector, need to ascertain better the corresponding climate change risk To estimate the climate risk of financial institutions, Georgopoulou et al (2014) developed a methodology applicable to many activity sectors The methodology was applied, as a case study, to one of the systemic Greek banks (Piraeus Bank) and the amount at risk was found to be not negligible In view of this finding and considering the fact that the agricultural sector contributes considerably to Greek GDP, the need for adaptation becomes clear, and the choice and effectiveness of measures to be adopted becomes of interest including their cost benefit analysis and fuller coverage of crop varieties and cultivars The approach for achieving these targets comprises at first the assessment of climate change impacts on important crops under no adaptation by applying an analytical methodology which directly relates climatic parameters with crop yields, and allows the quantitative estimation of impacts in physical terms (% change of crop yield per unit area cultivated) and then in monetary values (change of agricultural income) Next, a number of adaptation options per crop and region are assessed, both in terms of their expected impact on crop yields as well as (to the extent possible) on their private costs and benefits A review of climate change impacts on crops in Europe This section gives an overview of climate change impacts on crops under no adaptation, paying more attention to southern Europe and the period up to 2050 which is the focus of this study 2.1 Cereals Earlier studies projected a reduction of crop yield for maize in almost all cases, and a small overall increase albeit with considerable regional variation for wheat (e.g Brandao and Pinto, 2002; Trnka et al., 2004; Ministry of the Environment and University of Castilla de la Mancha, 2005; Alexandrov and Eitzinger, 2005; Wiggering et al., 2008) Recent studies confirm the negative effect of climate change on maize (particularly in southern Europe), while for wheat the previously positive impacts are now reconsidered (e.g Asseng et al., 2013; Thaler et al., 2012; Kersebaum and Nendel, 2014; Vanuytrecht et al., 2015; Graß et al., 2015; Valverde et al., 2015) According to a global assessment study (Balkovič et al., 2014) utilizing Representative Concentration Pathways (RCPs) scenarios, wheat yields in 2041-2060 will decrease up to 40% from 2000 in eastern Europe, and change by -8% up to +15% in southern Europe, by -12% up to +5% in northern Europe and by -10% up to +2% in western Europe (depending on the RCP) Supit et al (2012) carried out crop simulations covering 35 European countries for SRES scenarios (A2 and B1) Wheat yields in 2030 will increase from 1990-2008 in most countries (Greece: 21-22%, rest southern Europe: 7-13%, other: 0-44%); this trend continues up to 2050 For maize, yields in the Balkans and south-eastern Europe by 2030 decrease or remain stable (Greece: -4%, other: -2% up to -7%), while by 2050 they will decrease further in southern Europe (Greece: -16%, rest southern countries: -10-16%) Donatelli et al (2012) examined wheat production in the-27 and for the period up to 2030 under the A1B scenario Under the ‘cold’ version of A1B (ECHAM5 data), wheat yields decrease by 5-30% in almost all parts of Spain, Portugal, and Italy, increase by 5-30% in a large part of Greece and Balkans (as well as in almost all Western Europe), and decrease elsewhere However, under the ‘hot’ version (HadCM3 data), wheat yields increase by 5-30% in almost all southern Europe and decrease or remain unchanged in the rest continent The differences in southern Europe are due to the very different rainfall patterns projected by ECHAM5 and HadCM3 For the same emissions scenario (A1B), Tatsumi et al (2011) predicted an increase of wheat yield in 2090-99 compared to 1990-99 in southern and western Europe (+11% and +8% respectively), and a decrease in eastern Europe, northern Europe and Russia 2.2 Vegetables, pulses and legumes Regarding potato, Supit et al (2012) found that in 2030 and under the A2 and B1 scenarios, yields remain stable or increase in almost all countries compared to 1990-2008 levels (Greece: +6-8%, rest southern Europe: +6-17%, other: -4-+17%) In 2050 and under A2, yields remain stable in most countries or decrease slightly in some (compared to 2030) apart from northern Europe where they increase further Under B1 y,ields slightly increase in the Atlantic coast of western and northern Europe, Italy and Portugal, and remain unchanged or decrease elsewhere (Greece: +6-8%, rest southern Europe: +6-15%, other: -19-+18%) Vanuytrecht et al (2015) found for Belgium an increase by +16-26% in 2031-2050 under the A1B compared to 1981–2010, while an increase by 3-16% was also found for the UK in 2050s depending on water and fertilization rates (Daccache et al., 2011) On tomato, a recent study covering the Mediterranean region (Saadi et al., 2015) concluded that yields will not change by 2050 under the A1B scenario as tomato is mostly an irrigated crop; however, under mild or severe water stress, relative yield losses by 10-60% were estimated for most of the region For southern Italy, in particular, Ventrella et al (2012b) found that tomato yield will decrease by 10% during 2030-2059 As for other outdoor vegetables and grain legumes, published research for Europe is limited In southern Portugal, under the A2, A1B and B1 emissions scenarios, the yield of grain legumes was found to decrease by 0.6-1.8% in 2011-2040 and by 1.2-3.8% in 2041-2070 compared to 1961-1990 (Valverde et al., 2015) 2.3 Olive trees and grapevines The link between olive yield, rainfall and CO2 concentration was explored in Viola et al (2014) for Italy; they concluded that (a) under the present CO2 concentration but a lower rainfall the olive yield will decrease, (b) under a stable rainfall but higher CO2 concentration the yield will increase, (c) under the combined effect of an increased CO2 concentration and a reduced rainfall the increase of yield would be much lower than in (b), i.e of the order of 14% Another study on south-eastern Italy concluded that under the A1B scenario the yield of olive trees by 2050 will be by 8-19% lower than the historic (19512000) one (Lionello et al., 2014) In southern Portugal, the yield of rain fed olives under the A2, A1B and B1 scenarios was found to decrease by 4-7.4% in 2011-2040 and by 8-15% in 2041-2070 compared to 1961-1990 (Valverde et al., 2015) Similarly, in Andalucía, Spain, by 2030-2050 a 15-30% rainfall reduction in the fall (combined with a 7%-9% annual reduction) will cause a decrease of yields by 7% and 3.5% by 2030-50 for rain-fed and irrigated olive trees respectively (Ronchail et al., 2014) Regarding grapevines, modified climatic conditions are expected to have an impact on yields, as well as on the wine quality by changing the ratio between sugar and acids (Bock et al., 2011; Santos et al., 2011; Duchêne et al., 2010) As for yields, these were found to decrease by 1.5-2% in 2011-2040 and by 35.4% in 2041-2070 in southern Portugal compared to 1961-1990 under the A2, A1B and B1 emissions scenarios (Valverde et al., 2015) On the contrary, in northern Portugal (Douro Valley), an increase in wine production by about 10% by the end of the 21st century was estimated under the A1B scenario (Santos et al., 2013) For the Apulia region in southern Italy, a decrease of must and wine production by 20-26% in 2021-2050 compared to 1961-1990 was estimated (Lionello et al., 2014), while for the Tuscany region an average decrease of yield by 12% at 0-200m elevations and by 27% at 400-600m elevations by 2100 compared to 1975-2005 was predicted under the A2 and B2 scenarios (Moriondo et al., 2011) One should keep in mind though that grapevine cultivation may start in new areas not cultivated at present because of a thermal deficit 3.1 Materials and methods Regional disaggregation and future climate As climate change impacts on crops may differ significantly between geographical regions, a suitable spatial scale should be chosen for impact assessment In this work, the present division of Greece into administrative regions (Figure 1), with some aggregations performed (i.e ‘Kentriki and Ditiki Makedonia’, ‘Peloponissos and Ditiki Ellada’) was considered suitable as they are broadly representative of the climatic classification and at the same time correspond to the disaggregation of the national statistics (Figure is to be inserted here) The assessment of impacts per crop was performed in regions where the share of regional crop production to the relevant national total exceeds 10% If by this rule the cumulative share to national total was lower than 85%, then more regions were added to the set until the desired percentage was reached In total, 77 cases were modelled (Table 1) (Table is to be inserted here) Regarding future climate, this study focuses on short to midterm time horizon, i.e up to 2050 The simulation of the historic (1961-1990) and future (2021-2050) climate in Greece is based on the results of the regional climate model RACMO2 (developed by the Netherlands Meteorological Service) for the SRES A1B global emissions scenario (Nakicenovic et al 2000) In each region, 1-2 representative (in terms of historical climatic conditions) locations were selected, and for each of them the outputs of the regional climate model were utilized to provide daily values for maximum, mean and minimum temperature, precipitation, relative humidity, wind, and sunshine duration for each year of the climatic periods examined 3.2 Crop modelling for impact assessment For the assessment of climate change impacts on crops, agronomic simulation models and regression models were utilized Agronomic models simulate in detail all phases of crop growth and are thus more reliable and allow for a quantitative examination of potential adaptation measures The models were adjusted to the regions examined and thus were 'tailored' to the spatial scale selected For crops where agronomic models are not available, regression models have been developed for each region and crop linking climatic parameters and crop yields based on regional historical data For adaptation, the same models were applied with input data modified accordingly to introduce the operational changes brought in by each adaptation measure In regression models where this was not possible, the examination was limited to the effects from an increase of irrigation 3.2.1 Agronomic simulation models For the assessment of climate change impacts on cereals, vegetables, legumes, pulses, sunflower, rice and cotton, the Decision Support System for Agrotechnology Transfer (DSSAT) was utilized DSSAT (Hoogenboom et al., 2015; Jones et al., 2003) has been in use for more than 20 years by researchers, policy makers and others in several countries worldwide It includes detailed crop simulation models which cover a large part of main crops cultivated in Greece, and which simulate crop growth, development and yield as a function of the soil-plant-atmosphere dynamics DSSAT also comprises a very rich database of soil types and agronomic experiments for each crop, and allows the introduction of desired crop management schemes (sowing date, irrigation, fertilization, etc.) Thus, this tool allows for a comprehensive simulation and assessment of the impact of climate variability and climate change on crop growth and yield, and for the assessment of potential adaptation options For grapevines, the VineLOGIC Virtual Vineyard simulation tool developed by the Cooperative Research Centre for Viticulture (CRCV) in Australia was utilized VineLOGIC (Godwin et al., 2002) is a simulation model of grapevine growth and development incorporating a model of the soil water balance and soil salt balance It uses daily meteorological data as inputs and includes simulation drivers such as soil type, row spacing, pruned bud number, variety, and irrigation Thus, VineLOGIC allows for a detailed simulation of the effects on vine growth and yield from water deficits and waterlogging (associated with reduced/ extreme rainfall under future climate) These characteristics make VINELOGIC a particularly useful tool for climate change impact assessments in vineyards and for evaluating appropriate adaptation strategies Management and cultivation-related input data to the DSSAT include information on planting date, soil characteristics, planting density, row spacing, planting depth, crop variety, irrigation and fertilizer practices, environmental modifications (e.g CO2 atmospheric concentration), organic residue application, chemical application, and harvest management Weather-related input data include latitude of the weather stations to be used in the simulations, daily values of incoming solar radiation, maximum and minimum air temperature, and precipitation Regional models were ‘tailored’ to the reality of each geographical region in terms of soil types, management practices, and local climate Regarding soils, three basic categories were considered (loam, clay loam, sandy loam), with different sub-types per region Cultivars were derived from the relevant DSSAT database, with an effort to select those closer to the ones used in Greece Management practices were compiled based on information collected by consultation with agronomists and field visits The ambient CO2 concentration was kept stable at present levels (390 ppm) as its change up to 2030 (the middle of the 2021-2050 period) is not large; in this, the results obtained could be considered somehow 'conservative' as they omit the potential benefits of the CO2 fertilization effect in some cases As for VineLOGIC, in its Vines 950-VINES model for the simulation of the growing season of grapevine the atmospheric CO2 concentration has a pre-set value of 350 ppm (which cannot be modified by the end-user) As local grapevine varieties are not included in the Vinelogic database, some highvalue added foreign varieties cultivated in Greece were examined instead (Chardonnay, Cabernet, and Shiraz) Historical data on the main climatic parameters (maximum, minimum and average temperature, rainfall, sunshine) from representative meteorological stations in each region were used as input data to the models The total number of ‘tailored' cases (i.e combinations of soil types, cultivars/varieties, and regions) simulated by agronomic models amounted to 2,042 These were examined under both the historic and the future climate As for the models' calibration, to date in Greece a database with agronomic experiments and other relevant information does not exist Thus, to minimize, to the extent possible, the deviation between the models' simulated yields and the historic ones per crop and region, the following two-step 'screening process' was developed: (1) For each combination of soil type - cultivar per crop and region, the deviation between the simulated crop yield under the historic climate (1961-1990) and the real annual yield (from data published by the National Statistical Service) during 2000-2006 was calculated (2) For each region and crop, only the combinations with a deviation of 10% of less for at least one year in 2000-2006 were retained for impact assessment, unless all figures were above this limit (and thus all combinations had to be retained) This approach provided for a rough 'calibration' of agronomic models utilized Table shows the median deviations of the final combinations retained (Table is to be inserted here) As seen, the models’ performance can be considered as satisfactory in most of cases, with resulting median deviations being less than ±15% Only six cases have a deviation higher than 30%; however, since it is the difference between the simulated present and future crop yield (and not the absolute figures) that matters for the assessment, this large deviation was not regarded as critical 3.2.2 Regression models In case of crops for which agronomic simulation models are not available (i.e olive trees, tobacco, orange trees, peach trees, cucumber), annual yields were simulated by linear regression models connecting the crop yield (expressed in tons per ha) with statistically important climatic parameters Μodels were developed on the basis of statistical data on climatic parameters, cultivated areas and production per crop for the time period 1980-2006 Data on climatic parameters for this period derived from the official annual statistical yearbooks of Greece (ESYE 1980-2006(a)), and data on cultivated areas and production per crop derived from the official annual agricultural statistics of Greece (ESYE 1980-2006(b)) As climatic data are available on a monthly basis, climatic parameters in the models are also expressed on the same time basis The models are presented in Table (Table is to be inserted here) The R2 and significance parameters (sF) of the models (also shown in Table 3) are a measure of their deviation from real figures on crop yields As seen, in almost all cases the R2-value is equal to or greater than 0.6 while the F-values are small 3.3 Economic evaluation of adaptation measures The assessment of costs and benefits from the introduction of adaptation measures was done vise-a-vis the 'no adaptation' case where these measures are not implemented Therefore, only the additional cost and benefits from the 'no adaptation' case were considered The economic evaluation of each adaptation measure was performed separately for each region and crop This was done only where the potential measure was found to reduce yield losses compared to the 'no adaptation' case The elements included in the evaluation comprised the following: Cost: purchase and installation of equipment, consulting services for the proper implementation of the measure, irrigation water supply, fertilizers' supply, etc Benefits: Decrease of yield losses / increase of yield gains as a result of the measure, conservation of water for irrigation, etc The economic evaluation was performed on a unit cultivated area, i.e one Ha Since the lifetime of each adaptation measure is different, in order to be able to compare the cost and benefits of different measures, capital (investment) costs had to be annualized This was done by applying the equation: T ACi , j ,k = ICi , j ,k ⋅ r × (1 + r ) (1 + r )T (1) −1 where i: adaptation measure, j: crop, k: geographical region ACi,j,k: annualized capital cost of measure i for crop j in region k (€/year) ICi,j,k: capital cost of measure i for crop j in region k (€) r: discount rate (%) T: lifetime of measure (years) The annual operational and maintenance costs of adaptation measures include the use of any additional irrigation water, the application of additional quantities of chemical N-fertilizers, and rest costs (namely the cost of farmers’ consulting from specialized agronomists on how to properly apply the adaptation measures in field to reduce the adverse effects of climate change) The equivalent annual cost EACi,j,k were: EAC i , j , k = AC i , j , k + OMC i , j , k = AC i , j , k + CW i , j , k + CFi , j , k + restOM i , j , k (2) where OMCi,j,k: annual operational and maintenance cost of measure i for crop j in region k (€/year) CWi,j,k: annual cost of additional irrigation water as a result of measure i for crop j in region k (€/year) CFi,j,k: annual cost of additional N-fertilizers as a result of measure i for crop j in region k (€/year) restOMi,j,k: annual additional rest O&M cost as a result of measure i for crop j in region k (€/year) The annual benefits Bi,j,k from the implementation of each adaptation measure i for crop j in region k are given by: B i , j , k = BPi , j , k + BW i , j , k + BFi , j , k = = (YC i , j , k − YC NoA , j , k ) ⋅ P j + (W NoA , j , k − W i , j , k ) ⋅ PW k + ( F NoA , j , k − Fi , j , k ) ⋅ PFN (3) where YCi,j,k: yield of crop j in region k when measure i is implemented (kg/ha) YCNoA,j,k: yield of crop j in region k under no adaptation (kg/ha) Pj: producer price of crop j (€/kg) Wi,j,k: annual consumption of irrigation water for crop j in region k when measure i is implemented (m3/ha) WNoA,j,k: annual consumption of irrigation water for crop j in region k under no adaptation (m3/ha) PWk: price of irrigation water in region k (€/m3) Fi,j,k: annual consumption of N-fertilizers for crop j in region k when measure i is implemented (kg N/ha) FNoA,j,k: annual consumption of N-fertilizers for crop j in region k under no adaptation (kg N/ha) PFN: price of N-fertilizer (€/ kg N) On the basis of the parameters and methods explained above, the Cost-Benefit Ratio (i.e the ratio between the equivalent annual cost EACi,j,k and the annual benefits Bi,j,k) was calculated A value of CBR less than indicates that measure i is economically attractive for farmers, whereas the opposite (CBR > 1) shows that benefits of the measure are lower than its cost CBR allows comparing adaptation measures which are very different in terms of their lifetime and the magnitude of their costs and benefits 4.1 Results Estimated impacts on crop yields and agricultural income under no adaptation By applying the models of section 3.3, the percentage estimated change of crop yields between the future (2021-2050) and the historic (1961-1990) climate under no adaptation was calculated (Table 4) The regional figure for each crop simulated by agronomic models corresponds to the median of yield changes estimated for the different 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IMPACTS AND ADAPTATION OPTIONS FOR THE GREEK AGRICULTURE IN 2021- 2050: A MONETARY ASSESSMENT E Georgopouloua,*, S Mirasgedisa, Y Sarafidisa, M Vitaliotoub, D P Lalasb I Theloudisc,... potential adaptation options are also investigated through the same models, and the costs and benefits of these options are also quantitatively assessed The findings indicate that climate change may