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Deliverable C4.2: Report on the estimation of future climate change vulnerability on the agricultural sectors of Cyprus, Crete and Sicily (including relative database) Date: 30/09/2017
Report on Activity 4.2
'Use of crop models for assessing the vulnerability of agriculture to climate change'
(Final Version)
Lorenzo Brilli1*, Paolo Merante1, Luisa Leolini2, Camilla Dibari2, Marco Moriondo1
1 CNR-Ibimet, Florence, Via Caproni 8, 50145, Italy
2 University of Florence, P.le delle Cascine 18, 50144, Italy
* l.brilli@ ibimet.cnr.it, [email protected]
Executive summary This report focus on the climate change vulnerability assessment on the agricultural sectors of Cyprus, Crete and Sicily. Future impacts on crop systems were evaluated considering different general circulation models, climate scenarios, crop models and crop types…..to be finished
2 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2
Table of Contents
Executive summary .......................................................................................... 2
1. Introduction ............................................................................................ 4
2. Description of the simulation models ...................................................... 5
3. Calibration and validation ....................................................................... 8
3.1. CropSyst .......................................................................................... 8
3.2. OLIVEmodel.CNR ......................................................................... 12
3.3. GrapeModel ................................................................................... 15
4. Simulation scheme and Vulnerability Assessment ............................... 20
4.1. Crop Simulations ........................................................................... 20
4.2. Vulnerability Assessment .............................................................. 20
4.3. Crop response to future climate variability ..................................... 22
4.4. Crop response to sowing seasons, precocity levels and future climate scenarios ..................................................................................... 24
4.5. Overall Crop Vulnerability .............................................................. 41
5. Conclusions ......................................................................................... 42
Acknowledgements .................................................................................... 43
References ................................................................................................. 44
3 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2
1. Introduction The Action 4.2 (‘Report on the estimation of future climate change
vulnerability on the agricultural sectors of Cyprus, Crete and Sicily (including
relative database’) of the Adapt2Clima (A2C) project is triggered by the need to
understand the impacts of future climate on agricultural areas of three
European islands in the Mediterranean basin namely Crete (Greece), Sicily
(Italy) and Cyprus. The interaction of mid-latitude and tropical atmospheric
circulation processes makes this area of the world as one of the most sensitive
area to climate change (Giorgi, 2006). For instance in the last century the
Mediterranean basin experienced a generalized decrease of precipitation and
the related water availability (Sousa et al., 2011; NorranT and Douguédroit,
2006), a significant annual warming trend (+0.75°C) especially during winter
and summer (Vautard et al., 2007; EEA, 2012) and an increase in climate
extremes (i.e. day with minimum and maximum temperature, heavy
precipitation, etc.) (Kostopoulou and Jones, 2005; Zolina et al., 2008; Costa
and Soares, 2009; Kyselý, 2009; Durão et al., 2010; Rodda et al., 2010; Ulbrich
et al., 2013). These changes in climate conditions have negatively affected the
whole agricultural sector of the regions around and within the basin. A joint
combination between drought and heat stress can negatively affect the
physiological status of the plants (Barnabás et al., 2008; Alves and Setter,
2004), thus reducing crop growth and gross primary production of terrestrial
ecosystems (Ciais et al., 2005). A brief overview about the impacts of climate
extremes on European agriculture in 2003 and 2012, years with a climate
pattern comparable to that expected for the last decades of the 21st century, is
highlighted in Brilli et al. (2014), determining economic losses for the agriculture
and forestry sectors more than 13 billion euros (i.e. 2003) (Copa-Cogeca,
2003). In particular, for the regions around the Mediterranean basin, the main
losses were due to a strong decrease in quantity and quality of harvests, with
sensible damages for cereals (FAO, 2013; JRC/IES MARS Unit, 2012) and tree
crops. Despite future climate projections are affected by uncertainties, the
major part of the studies over the Mediterranean basin suggested a worsening
of climate pattern, which is expected to show a further increase of temperature
4 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2
and a decrease of total amount of precipitation associated with an increase in
intensity.
In this context, the Activity 4.2 ('Use of crop models for assessing the
vulnerability of agriculture to climate change') is focused on assessing the
impacts of future climate on agricultural sector of the study areas by means of
crop models. This activity is therefore essential in order to provide indications
to reduce crop vulnerability. The use of crop models, indeed, can enable
evaluating benefits provided by the adoption of adaptation strategies compared
to the current management. In the specific, alternative practices can be tested
through modelling so as to prevent production losses, which might be expected
under unchanged management. On these bases, the report firstly (Section 2)
describes the structure of the models used for simulating the main crops
cultivated in the three islands (i.e. wheat and barley amongst sowing crops,
olive tree and grapevine amongst perennial crops together with potatoes and
tomatoes amongst vegetables). Then, in the section 3, the most important
parameters used for crop calibration and statistics of calibration processes are
reported. In section 4, vulnerability of the crop systems has been assessed by
comparing phenology and yields using the same management under future and
current climate conditions. Also, some adaptation practices have been
simulated in order to assess those practices which may reduce crop
vulnerability under future climate. Finally, in section 5 discussions and brief
conclusions were reported.
2. Description of the simulation models
The simulation models used in this activity are the following:: i) CropSyst
(for wheat, barley, tomato and potato); ii) Olive model (for olive tree); iii)
UNIFI.GrapeML (for grapevine).
i) CropSyst (CS) model (Stöckle et al., 2003) is a “multi-year, multi-crop,
daily time step cropping systems simulation model”. CS has been developed in
order to encompass procedures and functions “to simulate productivity of crops
and crop rotations in response to weather, soil and management”. Based on
detailed information about climate, soils, crops and management, CS can
5 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2
assess both the climate impact on crop performances and the environmental
impacts determined by crop rotations and by different cropping (or farming)
system management (Dalla Marta et al., 2011; Jalota et al., 2011; Moriondo et
al., 2009). Furthermore, CropSyst simulation model may provide a wide array
of simulation’s outputs ranging from the soil water budget, soil plant budget and
soil erosion to specific crop-related outputs as phenology, canopy and root
growth, yield, and biomass production (Stöckle et al., 2003). CS simulates a
“land block fragment” which is an area with uniform soil, weather, crop rotation
and management (i.e. the case study’s sites). CS has been used for various
applications with different purposes as, for instance: a) to identify the efficient
best management practice (BMP) with regard to water and nitrogen use, the
CS model was used in a combined approach of field experimentation and
simulation (Jalota et al., 2011); b) to include environmental impacts in the
assessment of sustainability of farming system (Merante et al., 2014) in a
changing climate (Moriondo et al., 2009); c) to simulate crop production, water
requirement, and cultivation techniques to appraise the energy and water use
related to the cultivation of energy crops (Marta et al., 2011); d) to assess the
climate change impact on crop performances (Stöckle et al., 2009).
ii) The OLIVEmodel.CNR (Moriondo et al., under revision) simulates the
growth and development of olive agroecosystem at daily time step. Growth of
both olive tree and grass cover is also simulated by the model, considering the
competition for water between the two layers (Fig.1). A phenological sub model
reproduces changes in biomass allocation and the final yield, which is
calculated at the end of the growing season as a fraction of total olive tree
biomass accumulation (harvest index, HI). The key process of the model is the
simulation of daily potential biomass increase (g dry matter m-2) for both layers
as dependent on the relevant intercepted radiation (INT.RAD, %), daily
photosynthetic active radiation (RAD, MJ m-2), and Radiation Use efficiency
(RUE, g MJ-1). The fraction of transpirable soil water, (FTSW) is used as index
to rescale potential growth and leaf area of olive tree and grass cover to their
actual values.
6 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2
iii) UNIFI.GrapeML (Leolini et al., submitted) is a BioMA
(http://www.biomamodelling.org/) software model library jointly developed by
UNIFI and CREA-AA and used for simulating vine development and growth
under different pedo-climatic conditions. The model architecture is an
implementation of the original model of Bindi et al. (1997a, 1997b) and it is
based on simple or composite strategies (model units) that allow a fine
granularity and an easier implementation and maintenance of the code. This
aspect assumes a relevant importance when the collection of the main plant
processes must be implemented in alternative ways maintaining the original
modelling approach. The sharing of the new/alternative modelling approaches
is a prerogative of the BioMA software environment in which the model is
developed (Bregaglio and Donatelli, 2015; Cappelli et al., 2014; Donatelli et al.,
2014; Stella et al., 2014). UNIFI.GrapeML takes in account eight main plant
processes: i) Phenological development, which estimates the main phenology
stages over the grape growing season (i.e. Bud-break, Flowering, Veraison and
Maturity Day Of Year); ii) Leaf Area Growth, which reproduces the plant leaf
are growth and leaf area index (i.e. shoot leaf number, shoot leaf area, plant
leaf area and leaf area index); iii) Biomass accumulation and light interception
(i.e. daily photosynthesis); iv) Extreme event impacts of high/low maximum
temperature around flowering; v) Biomass partitioning between single plant
organs (i.e. fruit biomass, vegetative and total biomass, root deepening); vi)
Evapotranspiration process (i.e. soil evaporation and plant transpiration); vii)
Grape quality (i.e. sugar accumulation); viii) AgroManagement events. The
main model inputs are climate (Maximum and minimum air temperature,
rainfall, wind global solar Radiation), soil water content, management, and
specific phenological data (i.e. number of shoot per plant, bud-break, flowering,
veraison and maturity stages).
7 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2
3. Calibration and validation 3.1. CropSyst
CropSyst has been calibrated by adjusting the cultivar parameters of
different crops. The calibration process has invested barley, wheat, potato and
tomato. In particular, the calibration was performed for two barley cultivars (i.e.
Mattina and Aliseo) and seven wheat cultivars (i.e. Bronte, Ciccio, Claudio,
Duilio, Iride, Platani and Simeto). By contrast, no specific cultivars have been
used for potato and tomato.
The phenological and biomass data used to calibrate the modelled crops
were retrieved from several Long Term Experiment (LTE): i) Foggia (Apulia,
Italy) provided data for barley and tomato; ii) Caltagirone (Sicily, Italy) provided
data for several wheat cultivars; iii) potato was calibrated using data retrieved
from four different locations: Chinoli (Bolivia), Gisozi (Burundi), Jyndevad
(Denmark), Washington (USA). For tomato has been used the default
calibration.
Below are reported the calibrated parameters and the related statistics for
Barley, Wheat and Potatoes.
a) Barley:
BARLEY Variety Default ALISEO MATTINA Above ground biomass-transpiration 4.5 6 6 Light to above ground biomass conversion 3 4 5.5 Degree days Emergence (°days) 100 100 100 Peak LAI (°days) 600 500 500 Begin flowering (°days) 632 520 530 Begin grain filling (°days) 732 732 732 Physiological maturity (°days) 1100 1100 1075 Unstressed harvest index 0.48 0.5 0.5
Table 1 – Calibrated parameters for Barley
BARLEY Variety Parameter Obs Sim MBE nMBE RMSE nRMSE pearson R2 ALISEO Flowering 125 125 0.6 0.5 3.0 2.4 0.8 0.7
8 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2
Yield 5820 5300 -520.0 -8.9 1473.0 25.3 0.5 0.2
MATTINA Flowering 127 126 -0.5 -0.4 3.7 2.9 0.8 0.7 Yield 5750 5940 190.2 3.3 1172.9 20.4 0.6 0.4
Table 2 – Overview of statistics of the calibrated barley cultivars Aliseo and Mattina
The statistics regarding the phenology of Aliseo cv. showed contrasting
performances (i.e. flowering date R2: 0.7.; RMSE: 3.0;), whilst overall lower
performances resulted for yield (R2: 0.2; RMSE: 1473).
Fig.1 – Statistical correspondence of the simulated values with the observed values: Flowering date and yield of barley cv Aliseo.
9 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2
Similarly, Mattina cv. showed contrasting performances in phenology (i.e.
flowering date R2: 0.7; RMSE: 3.7), whilst yields gained good indicators (R2:
0.4; RMSE: 1172.9).
Fig.2 - Statistical correspondence of the simulated values with the observed values: Flowering date and yield of barley cv Mattina.
b) Wheat:
WHEAT
Cultivar Bronte Ciccio Claudio Duili
o Iride
Platani
Simeto
Abv biomass-transp. 4.5 4 5 3.5 3.5 3.5 4 5 Light to ABV biom.conv. 3 3 5 4.5 3.5 4
Initial green leaf area index 0.011 0.03
Peak LAI 600 400 420 420 400 400 400 410 10 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2
(°days) Begin flowering (°days) 632 430 450 450 410 440 430 440
Table 3 - Calibrated parameters for the seven wheat cultivars.
WHEAT
Variety Parameter Obs Sim MBE nMBE RMSE nRMSE pearson R2
BRONTE Flowering 114 114 0.0 0.0 5.6 4.9 0.9 0.8 Yield 3073 3060 -12.6 -0.4 913.9 29.7 0.5 0.2
CICCIO Flowering 115 117 2.3 2.0 5.3 4.6 0.9 0.7 Yield1 3053 3364 311.3 10.2 1149.9 37.7 0.1 0.0 Yield2 306 3039 -14.1 0.5 1017.8 33.3 0.1 0.0
CLAUDIO Flowering 121 117 -3.7 -3.1 6.9 5.7 0.9 0.8 Yield 3258 3281 22.4 0.7 994.6 30.5 0.5 0.2
DULIO Flowering 115 111 -4.5 -3.9 6.8 5.9 0.9 0.8 Yield 3224 3196 -28.1 -0.9 1551.8 48.1 -0.1 0.0
IRIDE Flowering 117 116 -1.0 -0.9 5.3 4.5 0.9 0.8 Yield 2921 3219 298.7 10.2 817.0 28.0 0.4 0.2
PLATANI Flowering 113 114 0.4 0.4 4.2 3.7 0.9 0.8 Yield 3113 3261 148.5 4.8 950.3 30.5 0.6 0.3
SIMETO Flowering 117 116 -1.4 -1.2 4.8 4.1 0.9 0.8 Yield 3316 2295 -1021.3 -30.8 1771.8 53.4 0.4 0.2
Table 4 - Overview of statistics of the calibrated seven wheat cultivars.
Also simulations of wheat showed contrasting results (Fig. 3). The
phenological pattern of all varieties has been well reproduced by the model.
This has been confirmed by statistics (Tab .4). The lowest performances were
observed using Ciccio cv (R=0.84, RMSE= 5.3), which in contrast, has been
well simulated by the model.
Conversely, the model was unable to well simulate the final yields. The
best performance has been observed for Platani cv (R=0.55; RMSE= 950), the
worst for Ciccio and Duilio varieties.
11 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2
Fig.3 - Statistical correspondence of the simulated values with the observed values: Flowering dates and yields of the seven wheat cultivars.
3.2. OLIVEmodel.CNR
The olive model has been calibrated and validated using experimental
data from two different sites located in Tuscany region (Italy): i) FTSW
measured in a 25 years old orchard located at Istituto Tecnico Agrario Statale
(ITAS) farm (Florence, 10.35 E 43.5 N); ii) Net Primary Production (NPP)
extrapolated from three years eddy covariance data (2010-2012) in a rainfed
olive orchard located in “S. Paolina” experimental farm of National Research
Council (Follonica. 42.55N, 10.45E).
Preliminary results showed the model reliability at reproducing both soil
water and growth dynamics. For the FTSW simulation over the soil profile 12 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2
indicated that the model correctly simulated the daily course of FTSW in the
layer explored by both grasses and olive tree rooting system (0-30 cm,
r2=0.91). In particular, the model simulated correctly both the drought stress
experienced by the orchard during summer 2016 and the soil water recharge
after early autumnal rainfalls (i.e. DOY 262).
Fig.4 - Daily course of observed and simulated FTSW in 2016 at 30 cm.
The model was additionally tested against three years of eddy covariance
data (2010-2012) showing high performances also to reproduce the daily NPP.
In 2010 (Fig. 5a) the model was able to detect and reproduce the two main
peaks of dry matter production of the orchards (i.e. early and late springtime)
whilst, on yearly basis, it only slightly underestimated the whole olive orchard
biomass (745 g DM m-2) compared to the observed (827 DM g m-2). For the
following years (2011 and 2012, Fig. 5b, c) the model confirmed its ability to
reproduce the daily NPP, also considering the effect of prolonged drought
periods on NPP in both years. Overall, the model was able to simulate the
yearly trend of three contrasting years, capturing changes of carbon driven by
climatic variables, resulting to be a reliable tool for olive orchards biomass
prediction.
13 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2
Fig.5 - Simulated and observed daily course of NPP for the three years of study.
14 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2
3.3. GrapeModel
UNIFI.GrapeML has been calibrated for two grape cultivars, namely
Chardonnay and Sangiovese. For Chardonnay cv. the model was calibrated
with observed data of phenology, soil water and grape yield retrieved in a
vineyard located in Spain (lat. 41.53 N, long. 1.7 E, 340 m. a.s.l.). Climate, soil
and management practices were monitored during the period 1998-2012. For
Sangiovese cv. phenology and grape quality data from Susegana (45°51"N,
12°15E, 83 m a.s.l, Treviso, Italy) and Montalcino (43°03'N; 11°29'E; 326 m
a.s.l., Siena, Italy) were used for model calibration. More specifically, the
phenological pattern was calibrated considering bud-break, flowering, veraison
and maturity over the period 1964-2005, whilst the grape quality was calibrated
using data collected only at Montalcino during the period 1998-2015.
For assessing the most sensitive model parameters a sensitivity analysis
(SA) was performed. The SA evidenced that parameters driving the leaf area
appearance and expansion showed the most relevant effect on final fruit
biomass production (Table 5).
15 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2
Parameter Description Default Calibration Units aParam Curve shape parameter 0.005 0.006 unitless cParam Optimal chilling temperature 2.8 1.79 °C ChillingReq Chilling requirement 78.692 59.28 CU db Slope of forcing unit eq.for bud-break -0.26 -0.23 unitless df Slope of forcing unit eq.for flowering -0.26 -0.13 unitless dv Slope of forcing unit eq. for veraison -0.26 -0.4 unitless dm Slope of forcing unit eq. for maturity -0.26 -0.4 unitless eb Optimal forcing temperature for bud-break 16.06 15.15 °C ef Optimal forcing temperature for flowering 16.06 9.43 °C ev Optimal forcing temperature for veraison 16.06 10.93 °C em Optimal forcing temperature for maturity 16.06 17.24 °C Col Curve shape parameter 176.26 108.36 unitless co2 Curve shape parameter -0.015 -0.014 unitless LimitForcingReq Last day of chilling effect on forcing requirement 234 200 day FloweringReq Forcing requirement for flowering 24.7 38.81 FU VeraisonReq Forcing requirement for veraison 51.14 57.31 FU MaturityReq Forcing requirement for maturity - 30.73 FU Leaf Area Growth ShootLeafAreaExp Curve shape parameter of Shoot Leaf Area equation 2.13 1.5 unitless ShootLeafNumberIntercept Curve shape parameter of Shoot Leaf Number equation -0.28 -0.22 n° of leaves d-1
LeafAppearanceRate1 Curve shape parameter of Shoot Leaf Number equation 0.04 0.06 n° of leaves d-1
°C-1 SLN1 Coefficient of water stress equation on leaf development 25.9 39.92 unitless SLN2 Coefficient of water stress equation on leaf development 17.3 39.16 unitless Light interception and biomass accumulation PHO1 Coefficient for water stress effect on photosynthesis 6.01 12.9 unitless PHO2 Coefficient for water stress effect on photosynthesis 8.59 14.1 unitless
Biomass partitioning HarvestIndex Slope of Fruit Biomass Index equation 0.0035 0.00443 d-1 Evapotranspiration WUE Water use efficiency 3.86 6.1 Pa Extreme events impact Tmax Maximum temperature for fruit-set at flowering 40 41 °C Tmin Minimum temperature for fruit-set at flowering 1 1 °C Topt Optimum temperature for fruit-set at flowering 25 25 °C q Curve shape parameter 1.9 1.9 unitless
Table 5 – Calibrated parameters of the UNIFI.GrapeML
17 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2
Calibration results showed satisfactory performances for both cultivars.
Statistics related to different phases are hereafter reported:
i) Phenology: for Chardonnay cv. using the chilling-forcing approach of
Caffarra and Eccel (2010) the higher performances were found for
flowering (R: 0.64; RMSE: 4.39) and maturity (R: 0.69; RMSE: 4.45),
whilst little lower performances were obtained for bud-break (R: 0.52;
RMSE: 5.05) and veraison (R: 0.44; RMSE: 4.88; Fig. 6). For
Sangiovese cv. satisfactory performances were observed for all
phases considered (i.e. flowering, RMSE: 4.54; R: 0.78; bud-break,
RMSE: 5.96; R: 0.63; veraison, RMSE: 6.85; R: 0.57; and maturity,
RMSE: 10.84; R: 0.39).
Fig.6 – Phenology calibration of the Chardonnay grape variety
ii) Soil Water Content: for Chardonnay cv. the calibration carried out at
four different soil depth (0.3, 0.5 ,0.7 and 1 m) showed good
performances (Layer 10-30: R: 0.62 RRMSE: 26.8; Layer 30-50: R:
0.66 RRMSE: 12.9; Layer 50-70: R: 0.81, RRMSE: 8.34; Layer 70-
100: R: 0.72, RRMSE: 10.17)
iii) Fruit biomass: the calibration of fruit biomass showed satisfactory
performance (R: 0.59; RRMSE: 23.00; EF: 0.10) over the period
1998-2012 (Fig.7).
Fig.7 -Fruit Biomass (g/m2 dry matter) calibration of the Chardonnay variety
19 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2
4. Simulation scheme and Vulnerability Assessment 4.1. Crop Simulations
After the calibration phase, the crop simulations of wheat, barley,
potato and tomato were performed with CropSyst (version 3.2), while
simulations of olive trees and grapevine were run with OliveModel.CNR
and UNIFI.GrapeML models, respectively. Simulations have been
performed under three different climate conditions: the Baseline-
Historical, which is based on a CO2 concentration of 360 ppm in the time
slice 1971-2000, the RCPs (Representative Concentration Pathways)
4.5 and 8.5 referring to a CO2 concentration of 485 and 540 ppm
respectively both, covering the temporal period 2031-2060. The
aforementioned scenarios were produced by NOA through a dynamic
downscaling of the climatic outcomes of two Global Climate Models, MPI
(Max Planck Institute for Meteorology model MPI-ESM-LR) and MOHC
(Met Office Hadley Centre) and applying a Regional Climate Model
SMHI (Swedish Meteorological and Hydrological Institute). Besides, the
Harmonized World Soil Database by FAO has been used to take into
consideration the main various soil textures and hydrologic properties
characterizing the soils of the three islands. In order to effectively
analyse the crop performances under different climatic conditions, six
performance indicators were selected: Flowering date and the Maturity
date regarding the crop phenology; the Actual Evapotranspiration (AE),
the Potential Evapotranspiration (PE), and the rate between the two
AE/PE, covering physiological aspects; the yield to analyse the biomass
changes. Furthermore, to appreciate the different sensitivity of the
selected crops in different climatic conditions, the simulations outcomes
were provided both in absolute and relative terms where the latter refers
to the difference between the performance value of the scenario RCP
4.5 or RCP 8.5 and the performance value of the Baseline (Historical).
This was done for each performance indicator.
4.2. Vulnerability Assessment
Assessing the degree to which a cropping system experience harm
because of specific hazards and or threats plays a crucial role in planning
effective response to changes of the surrounding environment. In order to
appreciate the variation of the crops’ vulnerability degree to climate change, the
crop vulnerability has been assessed by changes of four out of the six crop
performance indicators (i.e. flowering and maturity dates, yield, ETA/ETP ratio)
that were used in the simulation phase. Vulnerability was calculated by the
difference between the baseline (historical) and the two different future climate
scenarios (i.e. RCP4.5 and RCP8.5, time slice 2031-2060), further taking into
consideration different sowing seasons and precocity levels. Results were
following summarized for each island reporting the global average and the
related changes according to the climatic scenario and the sowing seasons (for
cereals and vegetables)/ precocity levels (for grapevine and olive tree) adopted
(Tab 6). It is underlined that, because of the considerable amount of simulations
and related outcomes, in this document the only simulations results under the
RCM-MOHC were discussed in terms of crop vulnerability.
21 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2
Crop type
Sowing season/Precocity
Sowing date
Budbreak (DOY)
Flowering (DOY)
Harvest (DOY)
Barley - Wheat
Early Autumn 10 November - - - - - - - - - Late Autumn 30 November - - - - - - - - - Winter 31 January - - - - - - - - - Spring 15 February - - - - - - - - -
Tomato
Early Winter 30 January - - - - - - - - - Late Winter 28 February - - - - - - - - - Early Spring 30 March - - - - - - - - - Late Spring 31 April - - - - - - - - -
Potato Late Autumn 20 October - - - - - - - - - Early winter 15 November - - - - - - - - - Late Winter 25 December - - - - - - - - -
Grapevine Early - - - 80-90 - - - 235-240 Medium - - - 100-110 - - - 260-270 Late - - - 110-130 - - - 265-275
Olive tree Early - - - - - - 145-155 240-270 Late - - - - - - 160-170 300-330
Table 6 – Description of sowing dates and phenological phases of the simulated crops.
4.3. Crop response to future climate variability
The crops response to both future climate scenarios has been firstly
evaluated by averaging the different sowing seasons and precocity levels
adopted (Table 7). This allowed to have a first indication about the crop
response to only future climate variability, regardless sowing time and precocity
levels.
Site Crop type Flowering (DOY) Maturity (DOY) Yield (Kg/ha) ETA/ETP (mm) - - - - - - Baseline RCP4.5 RCP8.5 Baseline RCP4.5 RCP8.5 Baseline RCP4.5 RCP8.5 Baseline RCP4.5 RCP8.5
Sicily
Wheat 114 -11 -13 185 -13 -15 5557.5 6% 9% 0.4 -1% 7% Barley 130 -14 -17 181 -15 -18 5675.4 7% 9% 0.4 1% 10% Tomato 153 -12 -15 226 -18 -20 8488.5 1% 3% 0.3 -7% -1% Potato 143 -21 -26 180 -21 -25 3410.2 15% 17% 0.3 3% 12% Grapevine 154 -6 -8 255 -10 -11 1444.3 -11% -3% 0.5 -11% -6% Olive tree 162 -15 -19 310 -41 -47 962.8 -1% 8% 0.6 -4% 0%
Cyprus
Wheat 99 -10 -12 169 -12 -13 5245.6 -2% 4% 0.4 0% -4% Barley 111 -13 -16 162 -14 -18 5277.2 -1% 5% 0.4 6% 4% Tomato 138 -12 -15 209 -16 -19 8718.4 -7% -6% 0.3 -8% -10% Potato 114 -22 -27 153 -21 -26 3440.8 17% 22% 0.4 9% 8% Grapevine 148 -4 -4 247 -6 -7 990.4 -7% -17% 0.3 -6% -16% Olive tree 144 -16 -20 271 -33 -40 926.3 0% 3% 0.5 4% 0%
Crete
Wheat 105 -9 -10 178 -12 -14 5053.7 -7% 8% 0.3 0% 3% Barley 120 -13 -16 172 -15 -18 5108.1 -6% 9% 0.4 4% 9% Tomato 149 -13 -16 222 -19 -22 11240.0 -2% -1% 0.3 -8% -1% Potato 129 -22 -26 168 -21 -26 3051.8 11% 22% 0.3 5% 10% Grapevine 152 -7 -8 254 -10 -12 1190.3 -9% 0% 0.4 -11% -7% Olive tree 156 -16 -20 299 -40 -48 976.7 2% 10% 0.5 2% 6%
Table 7 – Crops' response to the only future climate variability for each test site. Values were obtained by averaging results from the
different sowing seasons and precocity levels adopted.
Looking at Table 7, an advancement of the phenological pattern for all
crops, which is higher under RCP8.5 than RCP4.5, was observed in the all
three sites compared to the baseline.
In Sicily, cereals and vegetables showed a yield increase compared to the
baseline. The highest increase was found for potato (+17%), whilst the lowest
for tomato (+3%). Both wheat and barley showed a general increase of 9%
compared to the baseline. For the woody crops, olive tree showed low variation
under RCP4.5 and an increase of 8% under RCP8.5, compared to the baseline.
By contrast, grapevine showed a production decrease under both scenarios,
with the greatest losses under RCP4.5 (-11%).
In Cyprus, wheat and barley showed a slight yield decrease under RCP4.5
(-2% and -1%, respectively) and a more pronounced yield increase under
RCP8.5 (+4% and +5%, respectively) compared to the baseline. Vegetables
showed an opposite pattern: whilst tomato showed a similar yield decrease for
both RCPs (-6.5%, on average), potato showed a considerable yield increase
both under RCP4.5 (+17%) and RCP8.5 (+22%). For the woody crops, olive
tree showed no variation under RCP4.5 but a slight yield increase under
RCP8.5 (+3%) compared to the baseline. By contrast, grapevine showed the
greatest yield decrease among all crops, with production reduction of 7% and
17% under RCP4.5 and RCP8.5, respectively.
In Crete, the general yield pattern reflected that observed at Cyprus.
Wheat and barley showed a yield decrease under RCP4.5 (-7% and -6%,
respectively) and a considerable yield increase under RCP8.5 (+8% and +9%,
respectively) compared to the baseline. Concerning vegetables, tomato
showed a slight yield decrease for both RCP (-1.5%, on average), whilst potato
showed a considerable yield increase both under RCP4.5 (+11%) and RCP8.5
(+22%). For the woody crops, grapevine showed a strong yield decrease under
RCP4.5 (-9%), whilst no changes where found under RCP8.5, compared to the
baseline. The olive tree showed a slight yield increase under RCP4.5 (+2%)
and a stronger yield increase under RCP8.5 (+10%) compared to the baseline.
4.4. Crop response to sowing seasons, precocity levels and future climate
scenarios
The response of the crops to the interaction among sowing seasons or
crop precocity levels (henceforth identified as early, medium and late precocity)
and future climate scenarios has been reported for each site (Tables 8, 9, 10,
11,12). Results allowed to evaluate the best management practices and
genotype to adopt to limit the impacts due to the expected future climate
variability in each area analyzed.
4.4.1. Wheat
In Sicily, the phenological pattern indicated an earlier flowering and
maturity time under warmer scenarios compared to the baseline. In particular,
the maximum advance was found using the RCP8.5 in all possible scenarios
(management + crop type + site). This advance is found in a range of 1-3 weeks
for both flowering and maturity date by progressively anticipating the sowing
time (Table 8). Concerning crop production, under the baseline scenario the
early autumn provided the highest yield (6326.3 Kg/ha). Delaying the sowing
date production tended to decrease, reaching the minimum production using
the Spring sowing (-24.6%). Looking at future projections, the highest yield
increase was found under RCP8.5 using the Early Autumn sowing (+12.0%),
whilst the highest decrease was found under RCP4.5 using spring sown (-
25.3%) (Table 11). Under the baseline, the Early Autumn sowing provided the
highest ETA/ETP ratio (0.0.47 mm). Delaying the sowing date, ETA/ETP ratio
tended to decrease, reaching the minimum using spring sowing (-33.2%).
Looking at future projections, the highest ETA/ETP ratio increase was found
under RCP8.5 using the Early Autumn sowing (+9.9%), whilst the highest
ETA/ETP ratio decrease was found under RCP4.5 using spring sowing (-
36.7%) (Table 12).
In Cyprus, the phenological pattern indicated an earlier flowering and
maturity time under warmer scenarios compared to the baseline. In particular,
the maximum advance was found using the RCP8.5 in all possible scenarios
(management + crop type + site). This advance is found in a range of 1-3 weeks
for both flowering and maturity date by progressively anticipating the sowing
time (Table 9). Concerning crop production, under the baseline the early
25 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2
autumn sowing provided the highest yield (6017.1 Kg/ha). Delaying the sowing
date, production tended to decrease, reaching the minimum production using
spring sowing (-28.2%). Looking at future projections, the highest yield increase
was found using the early autumn sowing under RCP8.5 (+5.3%), whilst the
highest decrease was found under RCP4.5 using spring sowing (-31.1%)
(Table 11). Under the baseline, sowing at early autumn provided the highest
ETA/ETP ratio (0.49 mm). Delaying the sowing date, ETA/ETP ratio tended to
decrease, reaching the minimum using spring sowing (-45.3%). Looking at
future projections, the highest ETA/ETP ratio increase was found using the
early autumn sowing under RCP8.5 (+0.9%), whilst the highest ETA/ETP ratio
decrease was found under RCP8.5 using spring sowing (-50.4%) (Table 12).
In Crete, the phenological pattern indicated an earlier flowering and
maturity time under warmer scenarios compared to the baseline. In particular,
the maximum advance was found using the RCP8.5 in all possible scenarios
(management + crop type + site). This advance is found in a range of 1-3 weeks
for both flowering and maturity date by progressively anticipating the sowing
time (Table 10). Concerning crop production, under the baseline the early
autumn sowing provided the highest yield (5881.5 Kg/ha). Delaying the sowing
date, production tended to decrease, reaching the minimum production using
spring sowing (-25.8%). Looking at future projections, the highest yield increase
was found using the early autumn sowing under RCP8.5 (+7.8%), whilst the
highest decrease was found under RCP4.5 using spring sowing (-31.6%)
(Table 11). Under the baseline, sowing at early autumn showed the highest
ETA/ETP ratio (0.44 mm). Delaying the sowing date, ETA/ETP ratio tended to
decrease, reaching the minimum using spring sowing (-41.7%). Looking at
future projections, the highest ETA/ETP ratio increase was found using the
early autumn sowing under RCP8.5 (+5.95%), whilst the highest ETA/ETP ratio
decrease was found under RCP4.5 using spring sowing (-44.9%) (Table 12).
4.4.2. Barley
In Sicily the phenological pattern indicated an earlier flowering and
maturity time under warmer scenarios compared to the baseline. In particular,
26 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2
the highest advance was found using the RCP8.5 in all possible scenarios
(management + crop type + site). This advance is found in a range of 1-3 weeks
for both flowering and maturity date by progressively anticipating the sowing
time (Table 8). Concerning crop production, under the baseline the highest
yield were obtained using the early autumn sowing (6515.8 Kg/ha). Delaying
the sowing date the production tended to decrease, reaching the minimum
using spring sowing (-27%). Looking at future projections, the highest yield
increase was found using the early autumn sowing under RCP8.5 (+11.5%),
whilst the highest decrease was found using spring sowing under RCP4.5 (-
25.9%) (Table 11). Under the baseline, the early autumn sowing provided the
highest ETA/ETP ratio (0.50 mm). Delaying the sowing date, ETA/ETP ratio
tended to decrease, reaching the minimum using the spring sowing (-35.4%).
Looking at future projections, the highest ETA/ETP ratio increase was found
using the early autumn sowing under RCP8.5 (+13.1%), whilst the highest
ETA/ETP ratio decrease was found under RCP4.5 using the spring sowing (-
37.5%) (Table 12).
In Cyprus the phenological pattern indicated an earlier flowering and
maturity time under warmer scenarios compared to the baseline. In particular,
the maximum advance is found using the RCP8.5 in all possible scenarios
(management + crop type + site). This advance is found in a range of 1-3 weeks
for both flowering and maturity date by progressively anticipating the sowing
time (Table 9). Concerning crop production, under the baseline the early
autumn sowing provided the highest yield (6339 Kg/ha). Delaying the sowing
date, production tend to decrease, reaching the minimum production using the
spring sowing (-35.1%). Looking at future projections, the highest yield increase
was found using the early autumn sowing under RCP8.5 (+3.9%), whilst the
highest decrease was found under RCP4.5 using spring sowing (-34.8%)
(Table 11). Under baseline, the early autumn sowing provided the highest
ETA/ETP ratio (0.54 mm). Delaying the sowing date, ETA/ETP ratio tended to
decrease, reaching the minimum using the spring sowing (-45.6%). Looking at
future projections, the highest ETA/ETP ratio increase was found using the
27 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2
early autumn sowing under RCP8.5 (+6.7%), whilst the highest ETA/ETP ratio
decrease was found under RCP8.5 using spring sowing (-45.7%) (Table 12).
In Crete the phenological pattern indicated an earlier flowering and
maturity time under warmer scenarios compared to the baseline. In particular,
the maximum advance was found using the RCP8.5 in all possible scenarios
(management + crop type + site). This advance is found in a range of 1-3 weeks
for both flowering and maturity date by progressively anticipating the sowing
time (Table 10). Concerning crop production, under the baseline the early
autumn sowing provided the highest yield (6011.5 Kg/ha). Delaying the sowing
date, production tend to decrease, reaching the minimum production using the
spring sowing (-28.8%). Looking at future projections, the highest yield increase
was found using the early autumn sowing under RCP8.5 (+8%), whilst the
highest decrease was found under RCP4.5 using spring sowing (-32.7%)
(Table 11). Under the baseline, the early autumn sowing provided the highest
ETA/ETP ratio (0.47 mm). Delaying the sowing date, the ETA/ETP ratio tended
to decrease, reaching the minimum using spring sowing (-43.5%). Looking at
future projections, the highest ETA/ETP ratio increase was found using the
early autumn sowing under RCP8.5 (+10.0%), whilst the highest ETA/ETP ratio
decrease was found under RCP4.5 using the spring sowing (-44.0%) (Table
12).
4.4.3. Tomato
In Sicily the phenological pattern indicated an earlier flowering and
maturity time under warmer scenarios compared to the baseline. In particular,
the maximum advance was found using the RCP8.5 in all possible scenarios
(management + crop type + site). This advance is found in a range of 3-4 weeks
for both flowering and maturity date by progressively anticipating the sowing
time (Table 8). Concerning crop production, under the baseline the late winter
sowing provided the highest yield (9524 Kg/ha). Delaying the sowing date,
production tended to decrease, reaching the minimum production using the late
spring sowing (-36.1%). Looking at future projections, the highest yield increase
was found under RCP8.5 using the early winter sowing (+12.7%, on average),
28 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2
whilst the highest decrease was found using the late spring sowing under both
RCPs (-47.4%, on average) (Table 11). Under the baseline, the early winter
sowing provided the highest ETA/ETP ratio (0.34 mm). Delaying the sowing
date, ETA/ETP ratio tended to decrease, reaching the minimum using the late
spring sowing (-30.5%). Looking at future projections, the highest ETA/ETP
ratio increase was found using the Early Winter sowing under RCP8.5 (+7.2%),
whilst the highest ETA/ETP ratio decrease was found under RCP4.5 using late
spring sowing (-41.35%) (Table 12).
In Cyprus the phenological pattern indicated an earlier flowering and
maturity time under warmer scenarios compared to the baseline. In particular,
the maximum advance was found using the RCP8.5 in all possible scenarios
(management + crop type + site). This advance is found in a range of 3-4 weeks
for both flowering and maturity date by progressively anticipating the sowing
time (Table 9). Concerning crop production, under the baseline the early winter
sowing provided the highest yield (10781.2 Kg/ha). Delaying the sowing date,
production tended to decrease, reaching the minimum production using the late
spring sowing (-50%). Looking at future projections, the highest yield increase
was found under RCP8.5 using the early winter sowing (+9.2%), whilst the
highest decrease was found using the late spring sowing under both RCPs (-
63.4%, on average) (Table 11). Under the baseline, the early winter sowing
provided the highest ETA/ETP ratio (0.34 mm). Delaying the sowing date, the
ETA/ETP ratio tended to decrease, reaching the minimum using the late spring
sowing (-45.2%). Looking at future projections, the highest ETA/ETP ratio
increase was found using the early winter sowing under RCP8.5 (+4.5%),
whilst the highest ETA/ETP ratio decrease was found under RCP8.5 using late
spring sowing (-58.3%) (Table 12).
In Crete the phenological pattern indicated an earlier flowering and
maturity time under warmer scenarios compared to the baseline. In particular,
the maximum advance was found using the RCP8.5 in all possible scenarios
(management + crop type + site). This advance is found in a range of 3-4 weeks
for both flowering and maturity date by progressively anticipating the sowing
time (Table 10). Concerning crop production, under the baseline the late winter
29 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2
sowing provided the highest yield (12070.3 Kg/ha). Delaying the sowing date,
production tended to decrease, reaching the minimum production using the late
spring sowing (-21.9%). Looking at future projections, the highest yield increase
was found under RCP8.5 using the early winter sowing (+8.1%, on average),
whilst the highest decrease was found using the late spring sowing under both
RCPs (-39.3%, on average) (Table 11). Under the baseline, the early winter
sowing provided the highest ETA/ETP ratio (0.33 mm). Delaying the sowing
date, the ETA/ETP ratio tended to decrease, reaching the minimum using the
late spring sowing (-23.4%). Looking at future projections, the highest ETA/ETP
ratio increase was found using early winter sowing under the RCP8.5 (+8.4%),
whilst the highest ETA/ETP ratio decrease was found under RCP4.5 using late
spring sowing (-42.5%) (Table 12).
4.4.4. Potato
In Sicily the phenological pattern indicated an earlier flowering and
maturity time under warmer scenarios compared to the baseline. In particular,
the maximum advance was found under the RCP8.5 in all possible scenarios
(management + crop type + site). This advance is found in a range of 1-3 weeks
for flowering and in a range of 2-3 weeks for maturity date by progressively
anticipating the sowing time (Table 8). Concerning crop production, under the
baseline the late winter sowing provided the highest yield (3451.8 Kg/ha), which
tended to slight decrease by anticipating the sowing time until to reach the
minimum production using the late autumn sowing (-3.6%). Looking at future
projections, all scenarios (i.e. sowing dates + RCPs) showed a yield increase
compared to the baseline, varying in a range between 10.0% to 18.2%. This
maximum yield increase was found under RCP8.5 using the early winter sowing
(Table 11). Under the baseline, the late autumn sowing provided the highest
ETA/ETP ratio (0.40 mm). Delaying the sowing date, ETA/ETP ratio tended to
decrease, reaching the minimum using the late winter sowing (-27.6%). Looking
at future projections, the highest ETA/ETP ratio increase was found under
RCP8.5 using the late autumn sowing (+16.7%), whilst the highest ETA/ETP
30 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2
ratio decrease was found under RCP4.5 using the late winter sowing (-22.2%)
(Table 12).
At Cyprus the phenological pattern indicated an earlier flowering and
maturity time under warmer scenarios compared to the baseline. In particular,
the maximum advance was found under the RCP8.5 in all possible scenarios
(management + crop type + site). This advance is found in a range of 1-3 weeks
for flowering and in a range of 2-3 weeks for maturity date by progressively
anticipating the sowing time (Table 9). Concerning crop production, under the
baseline the late winter sowing provided the highest yield (3582.7 Kg/ha).
Yields tended to decrease by advancing the sowing time, reaching the minimum
production using the late autumn sowing (-11.5%). Looking at future
projections, all scenarios (i.e. sowing dates + RCPs) showed a yield increase
compared to the baseline, which varied in a range between 2.9% and 21.8%.
The highest yield increase was found under RCP8.5 using the early winter
sowing. Under the baseline, the late autumn sowing provided the highest
ETA/ETP ratio (0.44 mm) (Table 11). Delaying the sowing date, ETA/ETP ratio
tended to decrease, reaching the minimum using late winter sowing (-36.7%).
Looking at future projections, the highest ETA/ETP ratio increase was found
under both RCPs using the late autumn sowing (+7.2%, on average), whilst the
highest ETA/ETP ratio decrease was found under both RCPs using the late
winter sowing (-32.1%) (Table 12).
At Crete the phenological pattern indicated an earlier flowering and
maturity time under warmer scenarios compared to the baseline. In particular,
the maximum advance was found under the RCP8.5 in all possible scenarios
(management + crop type + site). This advance is found in a range of 1-3 weeks
for flowering and in a range of 2-3 weeks for maturity date by progressively
anticipating the sowing time (Table 10). Concerning crop production, under the
baseline the late winter sowing provided the highest yield (3159.1 Kg/ha), which
slightly decreased by anticipating the sowing time, with the minimum production
found using the late autumn sowing (-7.0 %). Looking at future projections, all
scenarios (i.e. sowing dates + RCPs) showed a yield increase compared to the
baseline which varied in a range between 4.4-23.1%. The highest yield increase
31 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2
was found under RCP8.5 using the late winter sowing (Table 11). Under the
baseline, the late autumn sowing provided the highest ETA/ETP ratio (0.38
mm). Delaying the sowing date, the ETA/ETP ratio tended to decrease,
reaching the minimum using the late winter sowing (-31.4%). Looking at future
projections, the highest ETA/ETP ratio increase was found under RCP8.5 using
the late autumn sowing (+10.4%), whilst the greatest decrease of ETA/ETP
ratio was found under RCP4.5 using the late winter sowing (-29.7%) (Table 12).
4.4.5. Grapevine
In Sicily, the phenological pattern indicated an earlier flowering and
maturity time under warmer scenarios compared to the baseline. In particular,
the maximum advance was found using the RCP8.5 in all possible scenarios
(management + crop type + site). This maximum advance was in a range of 1-
2 weeks for both flowering and maturity using the medium precocity (Table 8).
Concerning crop production, under the baseline the grapevine showed highest
yield using the late precocity (2094.9 Kg/ha), whilst strong yield decrease was
observed using both early (-51.1%) and medium (-42.1%) precocity levels.
Looking at future projections, all scenarios (i.e. sowing dates + RCPs) showed
a strong yield decrease compared to the baseline, with the highest losses (-
54.3%) using the early precocity under RCP4.5. The lower decrease was
observed under RCP8.5 (-8.3%) using the late precocity (Table 11). Under the
baseline, the early precocity provided the highest ETA/ETP ratio (0.49 mm),
whilst the minimum was found using the medium precocity (-15.0%). Looking
at future projections, all scenarios (i.e. sowing dates + RCPs) showed an
ETA/ETP ratio decrease compared to the baseline, with the highest decrease
(-26.0%) using the medium precocity under RCP4.5 and the lowest decrease
(-2.8%) using the early precocity under RCP8.5 (Table 12).
In Cyprus, the phenological pattern indicated an earlier flowering and
maturity time under warmer scenarios compared to the baseline. In particular,
the maximum advance was found using the RCP8.5 in all possible scenarios
(management + crop type + site). This maximum advance was in a range of 1-
2 weeks for both flowering and maturity using the medium precocity (Table 9).
32 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2
Concerning crop production, under the baseline the grapevine showed highest
yield using the late precocity (1416.8 Kg/ha), whilst strong yield decrease was
observed using both early (-45.2%) and medium (-45.1%) precocity. Looking at
future projections, all scenarios (i.e. sowing dates + RCPs) showed a strong
yield decrease compared to the baseline, with the highest losses (-55.5%) using
the medium precocity under RCP8.5. The lower decrease was observed under
RCP4.5 (-8.3%) using the late precocity (Table 11). Under the baseline, the
early precocity provided the highest ETA/ETP ratio (0.36 mm), whilst the
minimum was found using the medium precocity (-18.6%). Looking at future
projections, all scenarios (i.e. sowing dates + RCPs) showed an ETA/ETP ratio
decrease compared to the baseline, with the highest decrease (-33.0%) using
the medium precocity under RCP8.5 and the lowest decrease (-0.8%) using the
early precocity under RCP4.5 (Table 12).
In Crete, the phenological pattern indicated an earlier flowering and
maturity time under warmer scenarios compared to the baseline. In particular,
the maximum advance was found using the RCP8.5 in all possible scenarios
(management + crop type + site). This maximum advance was in a range of 1-
2 weeks for both flowering and maturity using the medium precocity (Table 10).
Concerning crop production, under the baseline the grapevine showed highest
yield using the late precocity (1755.7 Kg/ha), whilst strong yield decrease was
observed using both early (-55.2%) and medium (-41.4%) precocity. Looking at
future projections, all scenarios (i.e. sowing dates + RCPs) showed a strong
yield decrease compared to the baseline, with the highest losses (-56.8%) using
the early precocity under RCP4.5. The lower decrease was observed under
RCP8.5 (-6.2%) using the late precocity (Table 11). Under baseline, the late
precocity provided the highest ETA/ETP ratio (0.38 mm), whilst the minimum
was found using the medium precocity (-7.5%). Looking at future projections,
all scenarios (i.e. sowing dates + RCPs) showed an ETA/ETP ratio decrease
compared to the baseline, with the highest decrease (-18.5%) using the
medium precocity under RCP4.5 and the lowest decrease (-7.9%) using the
early precocity under RCP8.5 (Table 12).
33 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2
4.4.6. Olive tree
In Sicily, the phenological pattern indicated an earlier flowering and
maturity time under warmer scenarios compared to the baseline. In particular,
the maximum advance was found using the RCP8.5 in all possible scenarios
(management + crop type + site). The maximum advance was in a range of 2-
3 weeks for flowering and in a range of 5-8 weeks for maturity using the late
precocity (Table 8). Concerning crop production, under the baseline the olive
showed highest yield using the late precocity (1048.8 Kg/ha), whilst strong yield
decrease was observed using early precocity (-16.4%). Looking at future
projections, the highest yield decrease (-17.8%) was found using the early
precocity under RCP4.5, whilst the highest yield increase (+7.9%) was found
using the late precocity under RCP8.5 (Table 11). Under baseline, the late
precocity provided the highest ETA/ETP ratio (0.56 mm), whilst the early
precocity showed an ETA/ETP ratio reduction of 1.7%. Looking at future
projections, the highest ETA/ETP ratio decrease was found using the late
precocity under RCP4.5 (-7.14%) whilst the highest increase (+3.07) was
observed using the early precocity under RCP8.5 (Table 12).
In Cyprus, the phenological pattern indicated an earlier flowering and
maturity time under warmer scenarios compared to the baseline. In particular,
the maximum advance was found using the RCP8.5 in all possible scenarios
(management + crop type + site). The maximum advance was in a range of 2-
3 weeks for flowering and in a range of 5-8 weeks for maturity using the late
growing cycle (Table 9). Concerning crop production, under the baseline the
olive showed highest yield using the late precocity (999.2 Kg/ha), whilst strong
yield decrease was observed using early precocity (-14.6%). Looking at future
projections, the highest yield decrease (-13.4%) was found using the early
precocity under RCP4.5, whilst the highest yield increase (+1.8%) was found
using the late precocity under RCP8.5 (Table 11). Under baseline, the early
precocity provided the highest ETA/ETP ratio (0.54 mm), whilst the late
precocity showed an considerable ETA/ETP ratio reduction (-11.4%). Looking
at future projections, the highest ETA/ETP ratio decrease was found using the
34 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2
late precocity under RCP8.5 (-13.4%) whilst the highest increase (+6.4) was
observed using the early precocity under RCP4.5 (Table 12).
In Crete, the phenological pattern indicated an earlier flowering and
maturity time under warmer scenarios compared to the baseline. In particular,
the maximum advance was found using the RCP8.5 in all possible scenarios
(management + crop type + site). The maximum advance was in a range of 2-
3 weeks for flowering and in a range of 5-8 weeks for maturity using the late
precocity (Table 10). Concerning crop production, under the baseline the olive
showed highest yield using the late precocity (1055 Kg/ha), whilst strong yield
decrease was observed using early precocity (-14.9%). Looking at future
projections, the highest yield decrease (-12.7%) was found using the early
precocity under RCP4.5, whilst the highest yield increase (+9.6%) was found
using the late precocity under RCP8.5 (Table 11). Under baseline, late
precocity provided the highest ETA/ETP ratio (0.51 mm), whilst the early
precocity showed an ETA/ETP ratio reduction of 7.4%. Looking at future
projections, the highest ETA/ETP ratio decrease was found using the late
precocity under RCP4.5 (-10.2%) whilst the highest increase (+10.3) was
observed using the early precocity under RCP8 (Table 12).
35 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2
Site Crop type Sowing season- Precocity Flowering (DOY) Maturity (DOY) Yield (Kg/ha) ETA/ETP (mm)
- - - - - - - - - Baseline
RCP4.5
RCP8.5
Baseline
RCP4.5
RCP8.5
Baseline
RCP4.5
RCP8.5
Baseline
RCP4.5
RCP8.5
Sicily
Wheat
Early autumn 79 -16 -20 164 -16 -20 6326.3 10.0% 12.0% 0.47 3.5% 9.9% Late autumn 98 -14 -17 174 -15 -18 6093.0 11.3% 12.1% 0.44 1.8% 9.0% Winter 135 -8 -10 198 -11 -12 5039.7 3.2% 7.1% 0.34 -3.2% 6.1% Spring 143 -7 -6 203 -10 -10 4771.0 -0.9% 3.9% 0.32 -5.2% 2.3%
Barley
Early autumn 97 -18 -22 158 -18 -21 6515.8 10.5% 11.5% 0.50 5.5% 13.1% Late autumn 115 -16 -20 170 -16 -20 6233.2 11.9% 12.9% 0.46 3.3% 11.7% Winter 150 -11 -13 195 -13 -15 5195.5 4.9% 8.2% 0.35 -1.7% 9.6% Spring 156 -10 -12 200 -12 -14 4757.1 1.4% 4.5% 0.33 -3.2% 7.5%
Tomato
Early winter 148 -17 -21 222 -20 -24 9487.4 11.7% 13.6% 0.34 -2.1% 7.2% Late winter 150 -14 -17 223 -19 -22 9524.0 8.5% 12.3% 0.32 -4.7% 3.4% Early spring 153 -11 -13 226 -18 -20 8861.1 1.4% 3.6% 0.29 -3.5% 1.1% Late Spring 162 -7 -8 232 -15 -16 6081.6 -18.0% -17.1% 0.24 -15.6% -14.2%
Potato Late autumn 123 -27 -32 165 -25 -30 3327.3 14.8% 18.5% 0.40 8.2% 16.7% Early winter 144 -21 -25 180 -21 -25 3451.6 16.6% 18.2% 0.35 3.9% 13.2% Late winter 163 -17 -20 195 -18 -21 3451.8 12.2% 15.8% 0.29 -2.2% 7.5%
Grapevine Early 143 -5 -6 237 -9 -10 1024.3 -6.4% 2.8% 0.49 -9.2% -2.8% Medium 160 -8 -10 266 -11 -13 1213.7 -11.6% -4.6% 0.42 -12.9% -7.2% Late 158 -6 -8 263 -10 -11 2094.9 -14.2% -8.3% 0.47 -12.3% -6.8%
Olive tree Early 148 -15 -19 271 -34 -39 876.7 -1.6% 8.9% 0.55 -0.3% 4.8% Late 177 -15 -18 348 -47 -56 1048.8 -0.8% 7.9% 0.56 -7.1% -4.5%
Table 8 – Crops’ response to the interaction among sowing seasons, precocity levels and future climate scenarios for Sicily.
Site Crop type Sowing season- Precocity Flowering (DOY) Maturity (DOY) Yield (Kg/ha) ETA/ETP (mm)
- - - - - - - - - Baseline
RCP4.5
RCP8.5
Baseline
RCP4.5
RCP8.5
Baseline
RCP4.5
RCP8.5
Baseline
RCP4.5
RCP8.5
Cyprus
Wheat
Early autumn 56 -17 -20 142 -15 -20 6017.1 -1.2% 5.3% 0.49 0.8% 0.9% Late autumn 79 -13 -16 155 -11 -12 5997.7 -1.4% 4.3% 0.45 1.5% -1.8% Winter 125 -3 -4 186 -10 -12 4650.1 -2.3% 3.1% 0.30 -0.9% -7.6% Spring 137 -5 -6 194 -12 -9 4317.5 -4.0% 3.2% 0.27 -1.8% -9.2%
Barley
Early autumn 72 -18 -22 134 -18 -23 6339.0 -2.2% 3.9% 0.54 5.1% 6.7% Late autumn 94 -15 -19 148 -16 -20 6188.7 -1.4% 4.3% 0.48 7.7% 7.8% Winter 135 -10 -13 179 -12 -15 4468.3 0.6% 5.5% 0.32 7.0% 1.9% Spring 143 -9 -12 186 -11 -14 4112.8 0.5% 7.5% 0.29 5.2% -0.3%
Tomato
Early winter 127 -18 -23 202 -20 -24 10781.2 5.3% 9.2% 0.34 5.1% 4.5% Late winter 132 -13 -17 205 -18 -21 10228.9 -0.6% 3.3% 0.31 -1.0% -2.1% Early spring 140 -10 -12 210 -15 -18 8467.4 -6.2% -9.9% 0.27 -12.1% -17.4% Late Spring 155 -7 -8 220 -12 -13 5396.0 -26.3% -27.4% 0.19 -22.6% -23.9%
Potato Late autumn 86 -27 -33 131 -25 -31 3169.9 16.3% 23.7% 0.44 7.1% 7.2% Early winter 116 -21 -26 153 -21 -26 3569.7 18.5% 22.3% 0.37 10.0% 10.2% Late winter 142 -17 -21 174 -17 -22 3582.7 15.3% 20.1% 0.28 8.7% 5.9%
Grapevine Early 139 -5 -6 229 -7 -8 777.0 4.1% -1.9% 0.36 -0.8% -10.0% Medium 153 -6 -7 257 -8 -9 777.3 -7.9% -18.8% 0.30 -7.0% -17.6% Late 152 -2 -1 255 -4 -3 1416.8 -18.0% -29.6% 0.35 -10.1% -20.2%
Olive tree Early 129 -15 -19 239 -26 -32 853.4 1.4% 5.0% 0.54 6.4% 1.9% Late 159 -16 -20 303 -40 -49 999.2 -0.8% 1.8% 0.47 2.3% -2.2%
Table 9 – Crops’ response to the interaction among sowing seasons, precocity levels and future climate scenarios for Cyprus.
37 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2
Site Crop type Sowing season- Precocity Flowering (DOY) Maturity (DOY) Yield (Kg/ha) ETA/ETP (mm)
- - - - - - - - - Baseline
RCP4.5
RCP8.5
Baseline
RCP4.5
RCP8.5
Baseline
RCP4.5
RCP8.5
Baseline
RCP4.5
RCP8.5
Crete
Wheat
Early autumn 66 -16 -18 154 -16 -19 5881.5 -6.9% 7.8% 0.44 3.8% 6.0% Late autumn 87 -13 -15 166 -14 -17 5513.2 -6.7% 8.2% 0.40 4.6% 6.9% Winter 130 -6 -7 192 -9 -9 4454.9 -6.8% 9.3% 0.28 -1.4% 2.3% Spring 139 -3 1 199 -7 -9 4365.5 -7.8% 7.9% 0.25 -5.5% -2.9%
Barley
Early autumn 83 -18 -21 147 -18 -22 6011.5 -6.1% 8.0% 0.47 7.0% 10.0% Late autumn 103 -16 -19 160 -16 -20 5659.5 -6.6% 8.7% 0.42 7.1% 10.2% Winter 144 -10 -13 189 -12 -15 4483.2 -5.0% 9.7% 0.29 1.7% 8.6% Spring 151 -10 -12 195 -12 -14 4278.3 -5.4% 9.3% 0.26 -0.9% 6.4%
Tomato
Early winter 140 -18 -23 217 -22 -26 11932.4 7.6% 9.3% 0.33 1.5% 8.4% Late winter 144 -14 -18 219 -20 -23 12070.3 6.2% 8.1% 0.30 -2.4% 4.7% Early spring 149 -11 -14 222 -18 -21 11528.6 1.6% 0.7% 0.28 -6.8% 0.1% Late Spring 161 -8 -9 231 -16 -18 9428.6 -23.6% -21.1% 0.25 -25.0% -16.2%
Potato Late autumn 104 -27 -31 150 -25 -30 2936.6 12.3% 22.2% 0.38 6.7% 10.4% Early winter 130 -21 -26 168 -21 -25 3059.6 10.3% 21.8% 0.33 6.6% 11.1% Late winter 154 -17 -21 186 -18 -22 3159.1 11.5% 23.1% 0.26 3.2% 8.1%
Grapevine Early 141 -5 -6 234 -9 -11 786.6 -3.7% 6.2% 0.37 -7.2% -4.1% Medium 159 -9 -11 265 -12 -15 1028.6 -9.5% -0.5% 0.36 -11.9% -7.9% Late 157 -6 -8 262 -9 -11 1755.7 -14.5% -6.2% 0.38 -13.3% -9.8%
Olive tree Early 141 -15 -20 261 -32 -38 898.3 2.5% 11.3% 0.51 6.9% 10.3% Late 171 -16 -20 337 -48 -58 1055.0 1.2% 9.6% 0.47 -3.1% 0.7%
Table 10 – Crops’ response to the interaction among sowing seasons, precocity levels and future climate scenarios for Crete
38 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2
Yield Sowing season - Precocity Sicily Cyprus Crete (Kg/ha) - - - Baseline RCP4.5 RCP8.5 Baseline RCP4.5 RCP8.5 Baseline RCP4.5 RCP8.5
Wheat
Early autumn 0.0% 10.0% 12.0% 0.0% -1.2% 5.3% 0.0% -6.9% 7.8% Late autumn -3.7% 7.2% 7.9% -0.3% -1.7% 4.0% -6.3% -12.5% 1.4% Winter -20.3% -17.8% -14.7% -22.7% -24.5% -20.3% -24.3% -29.4% -17.2% Spring -24.6% -25.3% -21.6% -28.2% -31.1% -26.0% -25.8% -31.6% -19.9%
Barley
Early autumn 0.0% 10.5% 11.5% 0.0% -2.2% 3.9% 0.0% -6.1% 8.0% Late autumn -4.3% 7.0% 8.0% -2.4% -3.7% 1.9% -5.9% -12.0% 2.3% Winter -20.3% -16.3% -13.7% -29.5% -29.1% -25.6% -25.4% -29.2% -18.2% Spring -27.0% -25.9% -23.7% -35.1% -34.8% -30.3% -28.8% -32.7% -22.2%
Tomato
Early winter -0.4% 11.3% 13.2% 0.0% 5.3% 9.2% -1.1% 6.3% 8.1% Late winter 0.0% 8.5% 12.3% -5.1% -5.7% 3.3% 0.0% 6.2% 8.1% Early spring -7.0% -5.7% -3.6% -21.5% -26.3% -29.2% -4.5% -2.9% -3.8% Late Spring -36.1% -47.6% -47.1% -50.0% -63.1% -63.7% -21.9% -40.3% -38.4%
Potato Late autumn -3.6% 10.7% 14.2% -11.5% 2.9% 9.5% -7.0% 4.4% 13.6% Early winter -0.01% 16.6% 18.2% -0.4% 18.0% 21.8% -3.1% 6.8% 17.9% Late winter 0.00% 12.2% 15.8% 0.0% 15.3% 20.1% 0.0% 11.5% 23.1%
Grapevine Early -51.1% -54.3% -49.7% -45.2% -42.9% -46.2% -55.2% -56.8% -52.4% Medium -42.1% -48.8% -44.7% -45.1% -49.5% -55.5% -41.4% -47.0% -41.7% Late 0.0% -14.2% -8.3% 0.0% -18.0% -29.6% 0.0% -14.5% -6.2%
Olive tree Early -16.4% -17.8% -8.9% -14.6% -13.4% -10.3% -14.9% -12.7% -5.3% Late 0.0% -0.8% 7.9% 0.0% -0.8% 1.8% 0.0% 1.2% 9.6%
Table 11 – Yield variations (%) under current and future climate conditions using different sowing dates and varieties compared to the current maximum production.
ETA/ETP Sowing season - Precocity Sicily Cyprus Crete (mm) - - - Baseline RCP4.5 RCP8.5 Baseline RCP4.5 RCP8.5 Baseline RCP4.5 RCP8.5
Wheat
Early autumn 0.00% 3.47% 9.88% 0.00% 0.78% 0.92% 0.00% 3.81% 5.96% Late autumn -7.26% -5.55% 1.10% -9.15% -7.82% -10.83% -8.70% -4.46% -2.38% Winter -28.80% -31.05% -24.45% -38.01% -38.58% -42.71% -35.80% -36.69% -34.33% Spring -33.21% -36.67% -31.70% -45.33% -46.34% -50.38% -41.72% -44.91% -43.39%
Barley
Early autumn 0.00% 5.47% 13.06% 0.00% 5.09% 6.71% 0.00% 7.00% 10.01% Late autumn -8.73% -5.71% 1.90% -11.27% -4.39% -4.31% -10.20% -3.83% -1.02% Winter -31.17% -32.33% -24.54% -39.73% -35.50% -38.58% -37.93% -36.89% -32.61% Spring -35.38% -37.48% -30.56% -45.58% -42.77% -45.76% -43.52% -44.05% -39.89%
Tomato
Early winter 0.00% -2.14% 7.22% 0.00% 5.14% 4.54% 0.00% 1.51% 8.41% Late winter -5.16% -9.64% -1.91% -10.22% -11.15% -12.07% -8.87% -11.07% -4.60% Early spring -14.25% -17.28% -13.30% -22.36% -31.74% -35.88% -15.21% -20.97% -15.12% Late Spring -30.49% -41.35% -40.38% -45.17% -57.55% -58.26% -23.34% -42.52% -35.76%
Potato Late autumn 0.00% 8.21% 16.67% 0.00% 7.11% 7.20% 0.00% 6.67% 10.41% Early winter -12.67% -9.23% -1.18% -16.73% -8.43% -8.26% -13.53% -7.83% -3.91% Late winter -27.59% -29.17% -22.16% -36.68% -31.18% -32.97% -31.93% -29.74% -26.39%
Grapevine Early 0.00% -9.25% -2.84% 0.00% -0.83% -9.98% -4.04% -10.98% -7.94% Medium -14.99% -25.99% -21.07% -18.62% -24.36% -32.97% -7.50% -18.48% -14.84% Late -4.24% -16.03% -10.79% -5.05% -14.65% -24.21% 0.00% -13.27% -9.85%
Olive tree Early -1.69% -1.99% 3.07% 0.00% 6.38% 1.89% 0.00% 6.92% 10.28% Late 0.00% -7.14% -4.49% -11.42% -9.34% -13.36% -7.36% -10.20% -6.69%
Table 12 – ETA/ETP ratio variations (%) under current and future climate conditions using different sowing dates and precocity levels compared to the current maximum production.
4.5. Overall Crop Vulnerability
Crop vulnerability is measured by taking into consideration only the
yield fluctuations that will occur in the two scenarios RCP 4.5 and RCP 8.5
compared to the Baseline, as summarized in the following equations:
Performance Deficit = Performance baseline - Performance future
climate scenario (RCP 4.5 and RCP 8.5)
(1)
Vulnerability category (%) = Deficit/Performance baseline * 100 (2)
Examples:
1) Yield loss barley 4.5 = Yield baseline (sowing date 10 November) –
Yield at RCP 4.5 (sowing date 10 November);
Vulnerability category (%) = Yield loss 4.5 / Yield baseline (sowing
date 10 November) * 100
2) Yield loss tomato 8.5 = Yield baseline (sowing date 30 March) –
Yield at RCP 8.5 (sowing date 30 March)
Vulnerability category (%) = Yield loss 8.5 / Yield baseline (sowing
date 30 March) * 100
The categorization will refer to different level of performances (i.e. losses or
gains) and will include six categories: very high, high, medium, low and very
low. A first attempt of categorization is following proposed:
• Very high ≥ 80%
• 80 > High ≥ 60
• 60 > Medium ≥ 40
• 40 > Low ≥ 20
• 20 > Very low ≥ 0.
This scale will then be normalized to a numerical range i.e. 0 to 5, where 0
corresponds to no increase of vulnerability while 5 corresponds to the maximum
increase of vulnerability. The rationale is summarized in Table 8. This
procedure will be applied for each of the four sowing dates in the annual crops
and precocity levels for perennial crops.
Category Very high High Medium Low Very low Performance Deficit (X) X ≥ 80% 80 > X ≥ 60 60 > X ≥ 40 40> X ≥ 20 20 > X ≥ 0 Vulnerability level 5 4 3 2 1
Table 8. Vulnerability categories definition (Table 2).
5. Conclusions The Activity 4.2 ('Use of crop models for assessing the vulnerability of
agriculture to climate change'), focused on assessing the impacts of future
climate on agricultural sector over the three Mediterranean islands of Crete,
Cyprus and Sicily, has provided important indication about the main strategies
which could be used to cope with the forecasted impacts of climate on crop
productivity.
Our results showed as an increase in warm conditions can lead to
contrasting results depending on the location of the area (Crete, Cyprus and
Sicily), the sowing period (early or late autumn and winter), and the crop
analyzed. In general, the RCP4.5 scenario resulted with higher yields with
respect to RCP8.5. This response was consistent for all the crops expected for
grape which showed a generalized decrease of yield when temperature
increased.
These results, providing an indication of the level of vulnerability of the
studied crops, should be considered as a starting point to cope with issues on
climate change as well as the agronomic requests of farmers in the study areas.
42 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2
Acknowledgements
This report was produced under co-finance of the EC LIFE programme for the Environment and Climate Action (2014-2020), in the framework of Action E.3 “Informative material” of the project LIFE ADAPT2CLIMA (LIFE14 CCA/GR/000928) “Adaptation to Climate change Impacts on the Mediterranean islands' Agriculture”.
The project is being implemented by the following partners:
National Observatory of Athens - NOA
Agricultural Research Institute - ARI
Institute of Biometeorology - IBIMET
National Technical University of Athens - NTUA
Department of Agriculture, Rural Development and Mediterranean Fisheries, Region of Sicily - SICILY
Region of Crete - CRETE
43 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2
References
Alves A.C. and Setter T.L.: Response of cassava leaf area expansion to water deficit: cell proliferation, cell expansion and delayed development. Ann. Bot. 94, 605-613 (2004).
Barnabás B., Jäger K. and Fehér A.: The effect of drought and heat stress on reproductive processes in cereals. Plant Cell Environ. 31, 11-38 (2008).
Bindi, M., Miglietta, F., Gozzini, B., Orlandini, S., Seghi, L.: A simple model for simulation of growth and development in grapevine (Vitis vinifera L.). I. Model Description. Vitis 36, 67–71(1997a).
Bindi, M., Miglietta, F., Gozzini, B., Orlandini, S., Seghi, L.: A simple model for simulation of growth and development in grapevine (Vitis vinifera L.). II. Model validation. Vitis 36, 73–76 (1997b).
Bregaglio, S., Donatelli, M.: A set of software components for the simulation of plant airborne diseases. Environ. Model. Softw. 72, 426–444. (2015). doi:10.1016/j.envsoft.2015.05.011
Brilli, L., Moriondo, M., Ferrise, R., Dibari, C., Bindi, M., Climate change and Mediterranean crops: 2003 and 2012, two possible examples of the near future. Agrochimica, Vol. LVIII – Special Issue
Caffarra, A., Eccel, E.: Increasing the robustness of phenological models for Vitis vinifera cv. Chardonnay. Int. J. Biometeorol. 54, 255–267. (2010). doi:10.1007/s00484-009-0277-5
Cappelli, G., Bregaglio, S., Romani, M., Feccia, S., Confalonieri, R.: A software component implementing a library of models for the simulation of pre-harvest rice grain quality. Comput. Electron. Agric. 104, 18–24. (2014). doi:10.1016/j.compag.2014.03.002
Ciais P., Reichstein M., Viovy N., Granier A., Ogee J., Allard V. et al.: Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437, 529-523 (2005).
Copa-Cogeca: Assessment of the impact of the heat wave and drought of the summer 2003 on agriculture and forestry. Committee of Agricultural Organizations in the European Union and the General Committee for Agricultural Cooperation in the European Union, Brussels (2003).
44 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2
Costa A.C. and Soares A.: Trends in extreme precipitation indices derived from a daily rainfall database for the South of Portugal. Int. J. Climatol. 29, 1956-1975 (2009).
Dalla Marta, A., Natali, F., Mancini, M., Ferrise, R., Bindi, M., Orlandini, S.: Energy and water use related to the cultivation of energy crops: A case study in the Tuscany region. Ecology and Society, 16 (2):2. (2011).
Donatelli, M., Bregaglio, S., Confalonieri, R., De Mascellis, R., Acutis, M.: A generic framework for evaluating hybrid models by reuse and composition - A case study on soil temperature simulation. Environ. Model. Softw. 62, 478–486. (2014) doi:10.1016/j.envsoft.2014.04.011
Durao R.M., Pereira M.J., Costa A., Delgado J., Del Barrio G. and Soares A.: Spatial temporal dynamics of precipitation extremes in southern Portugal: a geostatistical assessment study. Int. J. Climatol. 30, 1526-1537 (2010).
EEA 2012: Climate change, impacts and vulnerability in Europe 2012. An indicator based report. Report No.12 (2012).
Giorgi F.: Climate change hot-spots. Geophys. Res. Lett. 33, L08707 (2006).
Jalota, S.K., Vashisht, B.B., Kaur, H., Arora, V.K., Vashist, K.K., Deol, K.S.: Water and nitrogen-balance and -use efficiency in a rice (Oryza sativa) wheat (Triticum aestivum) cropping system as influenced by management interventions: Field and simulation study. Experimental Agriculture, 47 (4) 609-628. (2011).
JRC/IES: Available at http://mars.jrc.ec.europa.eu/mars/Bulletins-Publications (2012).
Kyselý J.: Recent severe heat waves in central Europe: how to view them in a long term prospect?. Int. J. Climatol. 30, 89-109 (2010).
Kostopoulou E. and Jones P.D.: Assessment of climate extremes in the Eastern Mediterranean. Meteorol. Atmos. Phys. 89, 69-85 (2005).
Leolini, L., Bregaglio, S., Moriondo, M., Ramos, M.C., Bindi, M., Ginaldi, F. (submitted). A model library to simulate grapevine growth and development: software implementation, sensitivity analysis and field level application. Eur J Agron. (Submitted).
Merante, P., Van Passel, S., Pacini, C.: Using agro-environmental models to design a sustainable benchmark for the sustainable value method. Agric. Syst. 136, 1 – 13. (2015).
45 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2
Moriondo M., Leolini L., Brilli L., Dibari C., Tognetti R., Giovannelli A., Rapi B., Battista P., Caruso G., Gucci R., Argenti G., Raschi A., Centritto M., Bindi M. (under revision) A simple model simulating development and growth of an olive grove. Agricultural and Forest Meteorology (Under revision).
Moriondo, M. , Pacini, C., Trombi G., Vazzana, C., Bindi, M.: Sustainability of dairy farming system in Tuscany in a changing climate. European Journal of Agronomy 32, 80–90(2010)
Norrant C. and Douguédroit A.: Monthly and daily precipitation trends in the Mediterranean (1950-2000). Theor. Appl. Climatol. 83, 89-106 (2006).
Rodda J.C., Little M.A., Harvey J.E. and McSharry P.E.: A comparative study of the magnitude, frequency and distribution of intense rainfall in the United Kingdom. Int. J. Climatol. 30, 1776 1783 (2010).
Sousa P.M., Trigo R.M., Aizpurua P., Nieto R., Gimeno L. and Garcia-Herrera R.: Trends and extremes of drought indices throughout the 20th century in the Mediterranean. Nat. Hazard. Earth Sys. 11, 33-51 (2011).
Stella, T., Frasso, N., Negrini, G., Bregaglio, S., Cappelli, G., Acutis, M., Confalonieri, R.: Model simplification and development via reuse, sensitivity analysis and composition: A case study in crop modelling. Environ. Model. Softw. 59, 44–58. (2014). doi:10.1016/j.envsoft.2014.05.007
Stöckle, C.O., Donatelli, M., Nelson, R.: CropSyst, a cropping systems simulation model. European Journal of Agronomy 18, 289–307. (2003).
Stöckle, C.O., Nelson, R.L., Higgins, S., Brunner, J., Grove, G., Boydston, R., Whiting, M., Kruger, C.: Assessment of climate change impact on Eastern Washington agriculture. Climatic Change, 102 (1-2), 77-102. (2009).
Ulbrich U., Xoplaki E., Dobricic S., García-Herrera R., Lionello P., Adani M. et al.: Past and current climate changes in the Mediterranean region. In: Regional Assessment of Climate Change in the Mediterranean (Navarra A. and Tubiana L., eds.). Springer Science Business Media, Dordrecht (2013).
Vautard R., Yiou P., D’andrea F., De Noblet N., Viovy N., Cassou C. et al.: Summertime European heat and drought waves induced by wintertime Mediterranean rainfall deficit. Geophys. Res. Lett. 34, L07711 (2007).
46 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2
Zolina O., Simmer C., Gulev S.K. and Kollet S.: Changing structure of European precipitation: Longer wet periods leading to more abundant rainfalls. Geophys. Res. Lett. 37, L06704 (2010).
47 | ADAPT2CLIMA-D e l i v e r a b l e C 4 . 2