Ecological Modelling 329 (2016) 86–99
Contents lists available at ScienceDirect
Ecological Modelling journal homepage: www.elsevier.com/locate/ecolmodel
Development and evaluation of new modelling solutions to simulate hazelnut (Corylus avellana L.) growth and development Simone Bregaglio a,∗ , Francesca Orlando a , Emanuela Forni b , Tommaso De Gregorio c , Simone Falzoi d , Chiara Boni c , Michele Pisetta e , Roberto Confalonieri a a
Università degli Studi di Milano, DiSAA, Cassandra Lab, via Celoria 2, 20133 Milan, Italy 3a Srl, via Le Chiuse 68, 10144, Torino, Italy c Ferrero S.p.A., Piazzale P. Ferrero 1, 12051 Alba (CN), Italy d Piedmont Region, Phytosanitary Sector, Via Livorno 60, 10144 Torino, Italy e Ferrero Trading Lux, Findel Center, 2632, Luxembourg b
a r t i c l e
i n f o
Article history: Received 23 July 2015 Received in revised form 1 March 2016 Accepted 2 March 2016 Keywords: Orchard management Soil water uptake Radiation use efficiency Model composition Tree modelling
a b s t r a c t The worldwide increase in the hazelnut demand and cultivated area are explained by the rising importance of this species for the food industry. However, no simulation models are available to support the analysis of the impact of environmental conditions and management practices on hazelnut production. This paper presents two modelling solutions to simulate hazelnut tree growth and development under potential and water-limiting conditions, partly based on generic approaches used in tree modelling and on new modules specifically developed to address hazelnut tree peculiarities. The two solutions differed in the simulation of the photosynthetic rate, in one case reproduced using a Farquhar-type model (gross photosynthesis), whereas a radiation use efficiency approach (net photosynthesis) was used in the other. The coherence of simulation outputs at leaf, organ and plant level and the models responsiveness to weather conditions was verified and discussed with literature data, and the performances of the modelling solutions were evaluated using experimental data collected between 2002 and 2013 growing seasons in Piedmont region (northern Italy). Results highlighted the reliability of both the solutions in reproducing phenological development (mean relative root mean squared error, RRMSE = 8.61%), as well as the time trend of specific leaf area (RRMSE = 26.32%) and leaf area index (RRMSE = 18.46%). Also, the simulation of the temporal dynamics of soil water content and temperature along the soil profile led to outputs very close to observations (RRMSE = 14.02% and 10.32%, respectively). The solution based on gross photosynthesis resulted slightly more accurate in reproducing the year-to-year fluctuation in yields (RRMSE = 25.03%) compared to the one based on net photosynthesis (RRMSE = 30.40%). These results proved the suitability of these modelling solutions to be used as simulation engines within a variety of applications, ranging from decision support systems for the management of the orchard to complex yield forecasting systems. © 2016 Elsevier B.V. All rights reserved.
1. Introduction The raising demand for hazelnut fruits is leading to a marked expansion of the global production, that reached 1,500,000 Mt in 2012, corresponding to a 53% increase with respect to 2000 (FAOSTAT, 2014). Nowadays hazelnut production contributes for the 35% to the global market of hard-shelled fruits and are mainly used in the confectionary and chocolate industries (Kilic and Alkan, 2006). Turkey and Italy lead the ranking of producing countries, accounting more than 80% of the total volume (Fideghelli and De Salvador, 2009). Among the peculiarities of hazelnut tree (Corylus ∗ Corresponding author. Tel.: +39 02 50316578; fax: +39 02 50316575. E-mail address:
[email protected] (S. Bregaglio). http://dx.doi.org/10.1016/j.ecolmodel.2016.03.006 0304-3800/© 2016 Elsevier B.V. All rights reserved.
avellana L.) cultivation, a key issue is the large variability of average yields obtained in different production districts (Pisetta, 2011), ranging between 0.9 t ha−1 in Turkey to more than 4 t ha−1 in Armenia (FAOSTAT, 2014). The variability in the performance of hazelnut cropping systems is mainly due to climatic conditions and to the degree of technical advancement in orchard management, rather than being related to differences in genotypes (Erdogan and Mehlenbacher, 2000). Indeed, the number of cultivated varieties is less than 20 (Mehlenbacher, 2008). The extension of hazelnut growing areas from the traditional hilly cultivation to lowlands (Yavuz et al., 2005) and the impact of climate change on the year-to-year yield variability (Ustao˘glu, 2012) are increasingly leading scientists to deepen their understanding of the influence of agro-environmental conditions on key
S. Bregaglio et al. / Ecological Modelling 329 (2016) 86–99
plant processes. Available studies indicate that hazelnut fruits have a strong sink activity during ripening, which in turn induces a steep decline in soil water status (Marsal et al., 1997); consequently, even a slight water stress during the sensitive period can markedly reduce the final yield (Mingeau et al., 1994). Furthermore, the plant is very susceptible during the same period to high temperatures and vapour pressure deficit (Girona et al., 1994; Hogg et al., 2000). For this reason, irrigation systems targeting water stress prevention and allowing reserve accumulation for the following growing season (Tous et al., 1994) are increasingly adopted in Nebraska (Awada and Josiah, 2007), Spain (Gispert et al., 2005) and Italy (Bignami et al., 2011). For herbaceous species, simulation models are used since decades to increase the efficiency of production systems, e.g., by maximizing yields while reducing costs and negative externalities (e.g., Giménez et al., 2013), to support agricultural policies (e.g., Cappelli et al., 2015), to develop crop yield forecasting systems (Bregaglio et al., 2015) or to evaluate the impact of climate change and identify adaptation plans (Rosenzweig et al., 2014). To a lesser extent, they are also used to support breeding programs through in silico analysis of genotype × environment × management interaction (Confalonieri et al., 2013; Paleari et al., 2015). Conversely, the higher complexity of production systems based on tree species has delayed the development of supporting tools based on models, despite the availability of fruit trees simulators, partly sharing concepts and modelling approaches (Le Roux et al., 2001), e.g., for apple tree (Lakso and Johnson, 1990), peach tree (Grossman and De Jong, 1994), grapevine (Cola et al., 2013) and kiwifruit (Buwalda, 1991). This paper presents two modelling solutions for hazelnut tree growth and development, which were based on approaches adopted in other tree models as well as on specific algorithms developed to address peculiar features of hazelnut tree, such as the independent development of male and female reproductive organs and their relation to the allocation of photosynthates to the nuts. The two solutions differed in the approach used for photosynthesis: one was based on a Farquhar-type model (gross photosynthesis), the other on the concept of radiation use efficiency (net photosynthesis). For both the solutions, algorithms used for reproducing processes involved with main biophysical processes were described, and an in-depth evaluation using observed data was presented. 2. Materials and methods 2.1. Model description The following sections present the main equations implemented in the two modelling solutions, i.e., RUE-MS (based on radiation use efficiency) and GROSS-MS (based on gross photosynthesis). The two solutions shared the approaches for the phenological development (Section 2.1.1), the increase in tree size (Section 2.1.2), the leaf area expansion (Section 2.1.3), the partitioning of assimilates (Section 2.1.6), and the soil water dynamics and root water uptake (Section 2.1.7). The solutions differed in the approaches used to simulate light interception (Section 2.1.4) and photosynthesis (Section 2.1.5). The modelling solutions were implemented in a software component extending the first prototype developed within the project System for Environmental and Agricultural Modelling; Linking European Science and Society (SEAMLESS, the sixth European Framework Programme) for vineyard simulation (http://www.apesimulator.org/help/models/vineyards/). The flow diagram of the main processes implemented in the two modelling solutions is presented in Fig. 1. The program flow starts on a fixed day of year (November 1st in this study), when the dimension and the organ carbon content of hazelnut tree are initialized. The chilling and forcing thermal time accumulation are then computed,
87
and when the day of bud break is reached, the simulation of green leaf area index starts (see Section 2.1.1). The approach used for light interception is the first difference between the two modelling solutions, with RUE-MS computing a daily interception and GROSS-MS splitting daily global solar radiation into hourly direct and diffuse components (see Section 2.1.4). The simulation of root water uptake is then shared by the two modelling solutions and it is driven by soil temperature and rainfall (see Section 2.1.7). The resulting actual transpiration is then used to derive a water stress index, as the ratio to potential transpiration. The simulation of photosynthesis is performed via a daily radiation use efficiency approach (RUE-MS) or with a stomatal conductance model coupled to a photosynthesis model (GROSS-MS), thus requiring the subtraction of the assimilates lost via maintenance and growth respiration (see Section 2.1.5). The daily rate of photosynthates is then partitioned to the different tree organs according to rules driven by development stage code (see Section 2.1.6). The last step in the flow allows to update the value of green leaf area index, basing on the daily specific leaf area and on the rate of biomass partitioned to the leaves. The values of the model parameters are reported in Appendix A. 2.1.1. Phenological development Phenological development is reproduced by considering the effects (i) of chilling temperatures during dormancy in triggering the emission of female flowers, and (ii) of hourly air temperature in driving the transition to vegetative and reproductive phases. The simulation of phenological development starts at D0 (November 1st in this study) with the accumulation of chilling degree hours (Ch , h, Eq. (1)).
Ch =
1 if 0 ≤ T ≤ 7 0
(1)
elsewhere
where T (◦ C) is hourly air temperature. The threshold of chilling hours to trigger the emission of female flowers (Cfemale , hour) was ˇ set to 530 according to Crepinˇ sek et al. (2012). The accumulation of thermal time starts when Cfemale is reached and it is computed using the temperature response function (f(T), unitless) reported in Eq. (2) (Yan and Hunt, 1999):
f (T ) =
Tmax − T Tmax − Topt
T − Tmin Topt − Tmin
(Topt −Tmin )/(Tmax −Topt )
(2)
where Tmax , Tmin and Topt (◦ C) are the maximum, minimum and optimum temperature for phenological development. The hourly rate of growing degree hours for the vegetative and reproductive phases (GDDh , ◦ C h−1 ) is then derived according to Eq. (3):
⎧ ⎨ f (T ) · (Topt − Tmin ) if T ≤ T ≤ Tmax min 24 GDDh = ⎩ 0
(3)
elsewhere
The vegetative phases corresponding to leaf coloring and fall are simulated using the approach proposed by Delpierre et al. (2009), based on a photoperiod threshold to trigger senescence, and the accumulation of degree days as modulated by day length (Eq. (4)):
GDDsen =
⎧ Day b ⎪ l ⎪ (Tthreshold − Tavg )a · if Dayl < Pthreshold ⎪ ⎨ P threshold
⎪ ⎪ ⎪ ⎩
0
and Tavg < Tthreshold elsewhere (4)
where GDDsen (◦ C day) is the daily senescence rate, Tthreshold (◦ C) is the temperature threshold to trigger leaf senescence, Tavg (◦ C) is the mean daily air temperature, Dayl (hour) is the day length, Pthreshold (hour) is the photoperiod threshold to trigger leaf senescence; a and
88
S. Bregaglio et al. / Ecological Modelling 329 (2016) 86–99
start day=0
model initialization
1st November
dimensions
dimension parameters organs C content
C pools
Legend: Process shared between modelling solutions Process adopted by RUE-MS Process adopted by GROSS-MS
day = day+1 DVSrep>=14
phenological development
Yes
end
chilling parameters
chilling
air temperature
DVSrep 7
No
daily PAR intercepted
day length
Yes
reproductive parameters
forcing
vegetative parameters
DVSveg 4
light interception extinction coefficient
hourly PAR intercepted
global solar radiation
soil temperature
water stress impact
root water uptake
root depth
water stress
albedo
rainfall ET0
photosynthesis and respiration net assimilation RUE
temperature parameters Vcmax respiration parameters
stomatal conductance gross assimilation maintenance respiration
VPD
growth respiration
growth efficiency
growth of nuts, branches and leaves
CO2 temperature
partitioning update dimensions update LAI
leaf area index evolution
Fig. 1. Flow diagram of the two modelling solutions (i.e., RUE-MS and GROSS-MS) developed and tested in this study. The modelling solutions share most part of the approaches and differ in the models used to simulate light interception and photosynthesis. RUE-MS on the left of the diagram (black boxes), based on daily time step and radiation use efficiency. GROSS-MS on the right (grey boxes), based on hourly time step and on a Farquhar-type model for gross photosynthesis.
b (unitless) are empirical parameters allowing to account for any absent/proportional/more than proportional effect of temperature and photoperiod, and were set to 2 and 0, respectively. The model reproduces the development stages defined by the phenological scale of the Italian Phenological Gardens network (IPG; Malossini, 1993), and it provides the corresponding BBCH codes (Lancashire et al., 1991) when available, according to the conversion proposed by Puppi and Zanotti (2011). Two independent continuous development stage codes (DVS, reflecting the IPG codes in Table 1) are calculated for vegetative and reproductive phases by (i) normalizing the GDDs cumulated since the beginning of the current phase by those needed to complete the next phase and (ii) by adding the value obtained to the integer values indicated in Table 1 (V1–V14 and R7–R14).
2.1.2. Increase in tree size The simulation of tree size increase requires crown volume (Crownvol , m3 ) to be initialized (Eq. (5)): Crownvol,ini = (2 · Radiusinter,ini ) · (2 · Radiusintra,ini ) ·(Heightini − Crownbase )
(5)
where Radiusinter,ini (m) is the inter-row crown radius, Radiusintra,ini (m) is the intra-row crown radius, Heightini is the height of the tree, and Crownbase (m) is the height of crown base. The initial carbon stored in the branches (BranchC,ini , kg C) is then computed according to Eq. (6): BranchC,ini = Crownvol,ini · Branchratio · WoodC · Wooddensity
(6)
S. Bregaglio et al. / Ecological Modelling 329 (2016) 86–99 Table 1 Codes and description of the vegetative and reproductive phenological phases simulated by the hazelnut modelling solutions, according to the Italian Phenological Garden (IPG) scale. The corresponding Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie (BBCH) code is reported when available. IPG code
BBCH code
Description
11
V5 V6
12–18 19
V7 V8 V9 V10 V11 V12 V13 V14
91 92 94 93 95 93 95 97
Bud dormancy End of bud swelling Bud breaking: swollen and opening buds with folded leaves Open buds and first young leaves with unfolded blade Young leaves unfolded, not yet full size Young leaves unfolded together with leaves fully expanded Leaves fully developed Beginning of leaf discoloring Leaves mostly discolored Beginning of leaf dryness Leaves mostly dried up Beginning of leaf fall Leaves mostly fallen End of leaf fall, plants dormant
Reproductive phases 71 R7 72–77 R8 R9 79–87 89 R10 R11 – – R12 R13 R14
Beginning of female flowering Full female flowering End of female flowering Beginning of ovary growing Beginning of fruit ripening Fruits visible but mostly unripe Fruits maximum ripening Beginning of fruits fall and seed dispersal
where Branchratio (m3 m−3 ) is the branch to crown volume ratio, WoodC (kg C kg−1 ) is the wood C content, and Wooddensity (kg m−3 ) is the wood volumetric mass density. The daily increase in tree size is estimated basing on the fraction of assimilates partitioned to the branches (Branchgrowth,d , kg C d−1 ). The updated volume of branches (Branchvol,d , m3 ) at day d is computed according to Eq. (7) Branchvol,d =
BranchC,ini +
d=DOYV 14 d=DOYV 4
Branchgrowth,d
WoodC · Wooddensity
(7)
where DOYV4 is the day of the year in which bud burst occurs (V4 stage), and DOYV14 is the day of year in which the leaf fall phase is completed and the plant is dormant (V14 stage). The crown volume at day d (Crownvol,d , m3 ) is then estimated (Eq. (8)): Crownvol,d =
Branchvol,d Branchratio
(8)
The values of Radiusinter,d and Radiusintra,d are daily updated by the model. 2.1.3. Leaf area dynamics Leaf area index (LAId , m2 m−2 ) is computed each day since bud burst according to Eq. (9): LAId =
Leavesarea,d 4 · Radiusinter,d · Radiusintra,d
(9)
where Leavesarea,d (m2 ) is the leaf area of the plant, in turn obtained by multiplying the fraction of assimilates partitioned to leaves (Leavesgrowth,d , m2 d−1 ) by the specific leaf area (SLAd , m2 kg−1 ) after senescent tissues are removed (Eq. (10)): Leavesarea,rate =
where LeavesC (kg C) is the leaf carbon content, derived from Crownvol,d , leaf area density (m2 m−3 ), leaf mass to area (kg m−2 ) and relative leaf carbon content (kg C kg−1 ). SLAd is modulated by the impact of water stress (WS, unitless) according to Eq. (11). SLAd = (SLAmax − SLAmin ) · WS SLAsens + SLAmin
Vegetative phases 00 V1 03 V2 7–10 V3 V4
89
Leavesgrowth,d · SLAd WoodC
Leavessen,d − · Leavesarea,d−1 LeavesC (10)
(m2
kg−1 )
(m2
(11)
kg−1 )
where SLAmax and SLAmin are the maximum and minimum SLA, which were set according to experimental data (see Section 2.2 Experimental data) and SLAsens (unitless) is an empirical parameter which was calibrated to express the sensitivity of SLA to water stress. 2.1.4. Light interception The interception of solar radiation is simulated according to Pronk et al. (2003). This model considers that the alternate presence of paths and rows tends to reduce the canopy interception because of the more pronounced shading effect of the tree leaves than in an homogeneous canopy, and of the amount of radiation reaching the soil surface. The RUE-MS estimates the amount of intercepted radiation adopting a daily time step. Conversely, the GROSS-MS decomposes the hourly solar radiation into diffuse and direct components (Spitters et al., 1986), and it discriminates between the radiation intercepted by shaded and sunlit leaves, according to the crop growth model WOrld FOod STudies (WOFOST, van Keulen and Wolf, 1986) and by Teh (2006). 2.1.5. Photosynthesis and growth The RUE-MS estimates daily net photosynthesis using the concept of radiation use efficiency (RUE, Wilson, 1967). The RUE approach proved to be suitable to simulate tree growth within forest stands (Bartelink et al., 1997) and it is widely adopted in orchard simulations (i.e., Villalobos et al., 2006). The daily increase in carbon accumulation (Cnet , kg m−2 ) is modulated by mean air temperature and water stress (Eq. (12)). Cnet = PARint · RUEmax · f (T ) · WS
(12)
where PARint is the intercepted photosynthetically active radiation (MJ m−2 d−1 ), RUEmax is the maximum radiation use efficiency (g C MJ−1 m−2 day−1 ) and WS is the water stress (unitless), computed as the ratio of actual to potential evapotranspiration. The GROSS-MS simulates hourly gross photosynthesis of individual leaves using the analytical solution of the Farquhar equation (Farquhar et al., 1980) proposed by Chen et al. (1999). Details on the model used to simulate stomatal conductance and gross photosynthetic rate are provided in Appendices B and C, respectively. Carbohydrates for maintenance and growth respirations are then subtracted to the gross carbon assimilation (de Vries et al., 1989). Maintenance respiration is simulated for each organ with a hourly time step, considering the impact of air temperature in modulating the turnover rates of the plant tissues with a Q10 coefficient. Daily growth respiration is considered by reducing the gross C assimilation by a coefficient representing the ratio of structural dry matter to the total amount of assimilates needed for the new growth. Both the solutions divide the C stored into the plant in structural (SC) and non-structural (NSC) pools, the latter considered to be homogeneously distributed in stem, branches and coarse roots tissues. The C flow from NSC pool occurs in the case of a relevant imbalance in tree structure (Imb, unitless, Eq. (13)) during the period from V4 (i.e., bud burst) to V8 (i.e., beginning of leaf discoloring) phenological phases. Imb = 1 −
Leavescarbon Leavesdensity · Leavescarbon · Leavesmass · Crownvol
(13)
where Leavesdensity (m2 m−3 ) is the leaf area density and Leavesmass (kg m−2 ) is the leaf mass to area. The denominator in Eq. (14)
90
S. Bregaglio et al. / Ecological Modelling 329 (2016) 86–99
expresses the estimated carbon content in leaves. In case the tree structure is imbalanced (i.e., Imb > 0), the C accumulation (Cgrowth , kg C d−1 ) occurs through the remobilization of NSC. When remobilization of NSC is not allowed, NSC pool acts as a sink. This sink is assumed to be proportional to the total woody biomass and has priority over growth of woody tissues. 2.1.6. Partitioning of assimilates The amount of assimilates partitioned to leaves (Leavesp , unitless) is derived according to Eq. (14): it is maximum (RipL0 , unitless) from stage V4 to stage V7, and it decreases until V9 stage.
Leavesp =
⎧ Rip L0 ⎪ ⎪ ⎨ ⎪ ⎪ ⎩
RipL0 ·
−
DVSveg 2.824
0
if 4 ≤ DVSveg ≤ 7
2 + 1.125 · DVSveg · RipL0
if 7 < DVSveg ≤ 9 elsewhere
(14) Partitioning to fruit (Fruitp , unitless) is estimated using the parabolic function reported in Eq. (15), which is maximum (Fruitmax , unitless) between stages R11 (beginning of fruit ripening) and R13 (maximum fruit ripening), and decreases until R14 stage (beginning of fruit fall and seed dispersal).
Fruitp =
2 Fruitmax · (−0.004 · DVSrep + 0.038 · DVSrep + 0.966) if 13 ≤ DVSrep ≤ 14
0
elsewhere
(15) Partitioning to roots (Rootsp ) is assumed to be 0.1 until R14 stage, whereas partitioning to branches is derived as 1 – (Leavesp + Fruitp + Rootsp ). 2.1.7. Soil water dynamics and root water uptake Water redistribution along the soil profile is simulated using a tipping bucket approach (Ritchie, 1998), assuming water moving downward through the soil profile (bottom depth 1 m). Root water uptake is simulated with the approach proposed by Williams (1995) and implemented in the EPIC model, with root depth set at 0.8 m according to Costa Leme and Assunc¸ão (2005). According to Donatelli et al. (2014), surface soil temperature was estimated using the model developed by Parton (1984), whereas the approach implemented in the SWAT model was used for heat propagation along the soil profile (SWAT, Neitsch et al., 2011). The impact of soil temperature on root water uptake was simulated according to Lv et al. (2013), who demonstrated an exponential decrease of the water uptake rate as a function of decreasing root temperature.
LAI-2000 Plant Canopy Analyzer (LI-COR Inc., Lincoln, NE), whereas SLA was determined on 10 g of leaves from each tree, after scanning the leaves and processing images using the WinSEEDLE Pro 5.1a software (Regent Instruments, Inc., Canada). Soil water content (SWC, m3 m−3 ) and temperature (◦ C) were continuously monitored at 0.15, 0.3, 0.4, 0.55, and 0.7 m depth using dielectric probes (Sentek Technologies, Kent Town, Australia). The second datasets – used for evaluating model performance in reproducing the year-to-year variability in hazelnut yields (t ha−1 of dried nuts) – was collected in a hazelnut orchard during the 2002–2011 growing seasons in Cravanzana (44◦ 34 N, 8◦ 07 E, 523 m a.s.l.), where the same cultivar was grown and the same orchard management practices were applied. Details on this dataset are provided by Pisetta (2011). For both the datasets, weather data were collected using stations placed close to the orchards. Daily measured variables were maximum and minimum air temperature (◦ C), precipitation (mm), maximum and minimum air relative humidity (%) and global solar radiation (MJ m−2 d−1 ). Table 2 shows information on key weather variables measured in the growing seasons (from April to October) 2011–2013 in Baldissero d’Alba, and in the seasons 2002–2011 in Cravanzana. Ambient air CO2 was set to 390 ppm. Model performances in reproducing observed data were quantified using mean absolute error (MAE; Schaeffer, 1980; from 0 to +∞, optimum 0, Eq. (16)), relative root mean square error (RRMSE; Jørgensen et al., 1986; from 0 to +∞, optimum 0, Eq. (17)), coefficient of residual mass (CRM; Loague and Green, 1991; from −∞ to +∞, optimum 0, Eq. (18)) and modelling efficiency (EF, Nash and Sutcliffe, 1970; from −∞ to 1, optimum 1, Eq. (19)).
n i=1
MAE =
|Si − Mi |
n (S i=1 i
RRMSE =
Mi −
n
i=1
CRM =
− Mi )2
n
n
i=1
n EF =
(16)
n
i=1
·
100 M
(17)
n
S i=1 i
(18)
Mi 2
¯ − (Mi − M)
n
i=1
n
(S i=1 i 2
− Mi )2
¯ (Mi − M)
(19)
where Si is the ith simulated variable, n is the number of measured and simulated couples of values, Mi is the ith measured variable ¯ is the average value of measured variables. and M
2.2. Experimental data 3. Results The two modelling solutions were evaluated using two datasets. The first (2011–2013 growing seasons) was collected in a four years old hazelnut orchard of two hectares in Baldissero d’Alba (44◦ 45 N, 7◦ 54 E, 361 m a.s.l.). The variety grown was the Italian “Tonda Gentile delle Langhe”, which is highly appreciated by the confectionary industry because of the nut size and flavor intensity (Valentini et al., 2001). Plants were grown as bushes (height = 2 m, crown base height = 0.4 m), in a rainfed system with a planting distance of 5 m × 5 m (400 plants ha−1 ). The management of the orchard followed a standard protocol in the area: pruning and suckering were performed annually, and the application of fertilizers was 20 kg N ha−1 , 10 kg P2 O5 ha−1 , 14 kg K2 O ha−1 . The soil was silt loam (sand = 38.8%, clay = 8.8%, silt = 52.3%), alkaline (pH 8.4), with low organic matter content; cation exchange capacity was 8 meq 100 g−1 . Data on phenological stages, LAI and SLA were collected on ten plants (Awada and Josiah, 2007). LAI was measured with the
3.1. Comparison of simulation outputs with literature data The coherence of the modelling solutions in reproducing hazelnut growth and development was verified by comparing simulation outputs at leaf, organ and plant level with literature data, prior to apply the modelling solution on the experimental datasets. The modelling solutions were run in two contrasting growing seasons, from bud burst to leaf fall, in Cravanzana site in 2002 – with low temperatures and high precipitation (Table 2) – and in 2003, which is considered an extreme arid year in Italy (Diodato and Bellocchi, 2008). Simulated outputs referred to the increase in the weight of plant organs and plant dimension, and to the patterns of assimilation and respiration (Section 3.1.1); Section 3.1.2 presents the dynamics of stomatal conductance and photosynthetic rate in sunlit and shaded leaves during the growing season.
S. Bregaglio et al. / Ecological Modelling 329 (2016) 86–99
91
Table 2 Average maximum (Tmax ) and minimum (Tmin ) air temperature, global solar radiation and cumulated rainfall in the experimental sites of Baldissero d’Alba and Cravanzana in the period from April to October. Site
Cropping season
Tmax (◦ C)
Tmin (◦ C)
Rain (mm)
Radiation (MJ m−2 d−1 )
Baldissero d’Alba
2011 2012 2013
26.54 25.10 24.25
9.81 10.23 10.42
422.8 456.1 490.6
24.63 23.04 18.63
Cravanzana
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
20.21 24.02 21.92 21.75 23.10 22.52 21.83 23.03 21.50 23.45
11.88 10.91 10.27 10.72 10.92 10.44 10.61 11.55 10.41 10.81
719.2 201 418.4 362.8 480.2 402.6 358.4 501 541.8 255.6
19.07 20.05 17.97 17.1 18.84 18.20 16.96 17.59 17.06 18.93
3.1.1. Hazelnut growth dynamics during the growing season Fig. 2a presents the cumulated daily growth rate of the different plant organs (i.e., fine roots, stem, branches, leaves and fruits) expressed in kg dry matter plant−1 from bud burst to leaf fall in two contrasting growing seasons. Rates of increase of the weight of leaves and branches were low after bud burst (V4), and then presented a steep increment until full leaves expansion (V7), when maintenance respiration started to be prominent, according to
Bradshaw (2005), who analyzed hazelnut growth dynamics in an Australian orchard; fruit ripening (R11) began in the second half of May and ended in the middle of August (R13), according to Pisetta (2011). The percentage of fruit biomass on the total annual increase of plant biomass was in the range 40–45%, which were in line with the value of 46% measured by Bignami et al. (2005) on an Italian hazelnut cultivar similar to Tonda Gentile delle Langhe. The percentages of leaves biomass were in the range 8–18%, and simulated
Fig. 2. Simulated dynamics of hazelnut plant organs weight (a), gross and net photosynthesis, and growth and maintenance respiration (b) and soil water content, measured precipitation and vapor pressure deficit (c) in two growing seasons in Cravanzana (2002–2003) characterized by contrasting weather conditions.
92
S. Bregaglio et al. / Ecological Modelling 329 (2016) 86–99
weather conditions diffuse PAR MJ m-2 h-1 1.4 1.2 a) 1 0.8 0.6 0.4 0.2 0 0 4 8
leaf area index
direct PAR
air temperature
shaded
°C 35 30 25 20 15 10 5 0 12 16 20
0
4
8
12 16 20 hour
b)
3 2 1 0 0
4
8
shaded
sunlit
c)
0.15 0.1 0.05 0 0
4
8
12 16 20
4
8
12 16 20 hour
µmol m-2 s-1 14 12 d) 10 8 6 4 2 0 0 4 8
shaded
sunlit
e)
0.15 0.1 0.05 0 1
3
5
7
9
11 13 15 17 19 21 23
0
4
8
shaded
12 16 20
hour
µmol m-2 s-1 14 12 f) 10 8 6 4 2 0 1 3 5
0
shaded
7
9
12 16 20 hour
sunlit
4
8
photosynthetic rate
stomatal conductance µmol m-2 s-1 0.2
12 16 20
photosynthetic rate
stomatal conductance µmol m-2 s-1 0.2
sunlit
m2 m-2 4
12 16 20 hour
sunlit
11 13 15 17 19 21 23 hour
Fig. 3. Comparison of simulation outputs related to stomatal conductance (c) and photosynthetic rate (d) of shaded and sunlit leaves in two days in Cravanzana characterized by low temperature and high direct/diffuse PAR ratio (a, May 26, 2002, left side) and vice versa (a, July 28, 2003, right side). Daily dynamics of sunlit and shaded leaves are presented (b). Average hourly values of stomatal conductance (e) and net photosynthetic rate (d) in 2002 and 2003 are presented, with error bards corresponding to ±one standard deviation.
branches biomass accounted for 17–19% of total plant biomass, according to literature data reported by Bignami et al. (2005) and Cannell (1985), who showed a similar partitioning pattern in apple, peach, citrus and coffee plants. Fig. 2b presents the seasonal dynamics of gross and net photosynthesis and growth and maintenance respiration expressed in g C d−1 . During 2002 growing season, gross assimilation rates were higher than in 2003, with maximum values of 15.53 g C d−1 at V7 phenological stage. Growth respiration reached a maximum of 5.90 g C d−1 whereas the dynamic of maintenance respiration presented a delay and accounted for an increasing portion during the growing season according to organs weight increase and air temperature, with a shifted peak just before leaf fall (7.05 g C d−1 ). In both years, the simulated maintenance component exceeded the growth respiration only when fruits were fully developed, leading to null or negative net photosynthetic rates at the end of the growing season. The average percentage of total gross assimilation used for net photosynthesis (25.4–27.7%) and maintenance respiration (34.3–36.5%) were in the range simulated by Grossman and De Jong (1994) for peach, as well as the maximum rate of simulated daily carbon assimilation. These results, as well as the sharp declines of gross and net photosynthesis in the drought year, were comparable with findings by Campioli et al. (2008), who analysed the dynamic of carbon assimilation and growth/maintenance respiration of Fagus sylvatica. Fig. 2c shows that the reduction in net photosynthetic rates occurred in days with high vapor pressure deficit and low soil water content, according to Mingeau et al. (1994) and Girona et al. (1994), who reported the high sensitivity of hazelnut plants to unfavourable weather conditions. This reflected in the marked reduction of the rates of increase in above and below ground biomass in 2003 growing season. Fruit
biomass ranged between 4.0 kg plant−1 in 2003 and 6.7 kg plant−1 in 2002, according to Santos et al. (1998), who measured the hazelnut yield fluctuations in a ten year period, reporting a strong negative correlation between high temperatures and low precipitation and yield per plant, with comparable values. The same authors showed a strong dependence of the annual shoot length increase on weather conditions, with average values of 25–50 cm and measured ranges between 16 and 56 cm (Santos and Silva, 2001). This was in agreement with the simulated increment of crown dimension, which was 37.78 cm in 2002 and 18.91 cm in 2003. Measured crown volumes by Santos et al. (1994) ranged between 18 and 44 m3 in adult hazelnuts orchards, with an annual increment in the range 5.35–10.32 m3 year−1 in different cultivars. This agreed with simulations, showing a simulated crown volumes of 37.77 m3 plant−1 in 2002 and 32.67 m3 plant−1 in 2003, with a starting crown volume of 27.21 m3 plant−1 . 3.1.2. Differentiating assimilation rates and stomatal conductance Fig. 3 presents an insight of the simulated dynamics of shaded and sunlit LAI, stomatal conductance and assimilation. Fig. 3a presents the direct and diffuse photosynthetically active radiation (PAR) and hourly air temperature in two days – May 26, 2002 and July 28, 2003 – the latter presenting a lower ratio between direct and diffuse PAR and higher temperatures. Fig. 3b presents the hourly dynamic of shaded and sunlit leaf area index (LAI, m2 m−2 ), with sunlit LAI starting to increase at sunrise, reaching the maximum value at midday and decreasing until sunset (Xin et al., 2015). Fig. 3c presents the simulated hourly stomatal conductance (mol m−2 s−1 ) in the two days of interest, as separated for shaded and sunlit LAI. Simulated values were in the range reported by
S. Bregaglio et al. / Ecological Modelling 329 (2016) 86–99
93
Table 3 Performances of the hazelnut model in reproducing the soil dynamics and plant processes. GROSS-MS and RUE-MS are the modelling solutions based on gross and net photosynthesis, respectively. RRMSE is relative root mean square error, EF is modelling efficiency, MAE is mean absolute error, CRM is coefficient of residual mass. Process Phenology (day of the year)
Data 17 21
Modelling solution Phase Reproductive Vegetative
RRMSE
EF
MAE
CRM
10.84 6.38
0.98 0.99
7.20 6.67
0.01 0.00
GROSS-MS RUE-MS GROSS-MS RUE-MS GROSS-MS RUE-MS GROSS-MS net GROSS-MS RUE-MS
13.92 12.55 13.15 10.21 17.71 12.85 18.01 17.55 14.24 10.06
0.51 0.60 0.87 0.92 0.89 0.94 0.90 0.90 0.37 0.69
0.03 0.02 0.02 0.02 0.03 0.02 0.03 0.03 0.03 0.02
−0.06 −0.01 0.03 0.01 0.02 −0.02 0.10 0.10 0.08 −0.05
GROSS-MS RUE-MS GROSS-MS net GROSS-MS RUE-MS GROSS-MS RUE-MS GROSS-MS RUE-MS
11.95 13.30 8.45 8.48 5.87 5.89 9.46 9.54 14.95 15.33
0.71 0.64 0.92 0.92 0.96 0.96 0.82 0.81 0.92 0.91
1.83 2.05 1.18 1.18 0.82 0.83 1.46 1.47 1.63 1.63
−0.06 −0.06 −0.06 −0.06 −0.02 −0.02 −0.07 −0.07 −0.12 −0.12
Depth
Soil water content (m3 m−3 )
543
0.15 m
494
0.3 m
430
0.4 m
426
0.55 m
543
0.70 m Depth
Soil temperature (◦ C)
543
0.15 m
524
0.3 m
524
0.4 m
543
0.55 m
450
0.70 m
Specific leaf area (m−2 kg−1 )
12
GROSS-MS RUE-MS
23.88 28.77
−0.74 −1.53
2.15 3.39
−0.08 −0.39
Leaf area index (m2 m−2 )
21
GROSS-MS RUE-MS
14.66 22.27
0.51 −0.14
0.57 0.80
0.04 −0.06
Hazelnut dry weight (t ha−1 )
10
GROSS-MS RUE-MS
25.03 30.40
0.52 0.29
0.53 0.58
−0.08 0.03
Bradshaw (2005), who measured a maximum stomatal conductance of 0.15 mol m−2 s−1 for sunlit and 0.075 mol m−2 s−1 for shaded leaves: an almost optimal value was simulated for sunlit LAI during the milder day (May 26, 2002), with stomatal conductance of shaded LAI being limited by diffuse radiation flux. Stomatal conductance in the hottest day showed a marked reduction due to high temperature and low soil water content (data not shown) in the mid hours of the day, with similar values for sunlit and shaded LAI. These findings were in agreement with Marsal et al. (1997), who proved a strong dependence of stomatal conductance on soil water availability. Fig. 3d reports the CO2 assimilation patterns in the two days, with May 26, 2002 presenting a higher photosynthetic rate for shaded leaves than July 28, 2003, as the result of the higher direct/diffuse radiation ratio and stomatal conductance. On the other hand, the assimilation rate of sunlit LAI was comparable in the two days, due to higher direct PAR on July 28, 2003, when a decrease was simulated in the period 12:00 a.m.–4:00 p.m. due to low values of stomatal conductance. Fig. 2d shows the hourly average values of stomatal conductance of shaded and sunlit LAI, simulated during the 2002–2003 growing seasons in Cravanzana, with error bars corresponding to ±one standard deviation. Simulated values for shaded LAI (maximum 0.11 mol m−2 s−1 , minimum 0.01 mol m−2 s−1 ) and sunlit LAI (maximum 0.14 mol m−2 s−1 , minimum 0.07 mol m−2 s−1 ) were in the range measured by Bradshaw (2005), who reported similar daily dynamics. Maximum average assimilation rates were 10.4 mol m−2 s−1 for sunlit LAI and 5.64 mol m−2 s−1 for shaded LAI: the simulated hourly pattern was in line with findings by Marsal et al. (1997) and Hampson et al. (1996), who measured 12 mol m−2 s−1 as the maximum net photosynthetic rate for hazelnut sunlit leaves and 6.12 mol m−2 s−1 for shaded leaves, with an exponential increase as a function of intercepted PAR.
3.2. Performances of the modelling solutions in reproducing experimental data 3.2.1. Phenological development The approach used to simulate the phenological development was common to the two modelling solutions and showed good performances in the simulation of the occurrence of the main growth stages during the seasons 2011–2013 in Baldissero d’Alba (Table 3). MAE was around 7 days for both the vegetative and reproductive phases, which was in line with the values reported in literature for other tree species (e.g., Olsson et al., 2013). RRMSE between observed and simulated dates of onset of the main stages was 6.24% and 10.84% for the vegetative and reproductive phase, respectively. The values of modelling efficiency were very close to the optimum (i.e., 0.98 and 0.99 for the reproductive and vegetative development) and no systematic bias was observed, with CRM always close to 0. Fig. 4a shows the simulated and observed timing of occurrence of the main stages (IPG scale) during the three growing seasons. The bud break onset (V4) was simulated from March 10th (2012) to March 25th (2013), the period of maximum leaf area expansion (V7) in mid-May, and the late vegetative phases (leaves drying and fall, V10–V14) during the first half of October. The model simulated fruit ripening from the beginning of May (R11) to mid August (R14). 3.2.2. Leaf area index dynamics Both the modelling solutions allowed achieving a high correlation between measured and simulated LAI (Table 3). EF values revealed a higher accuracy of the GROSS-MS solution (EF = 0.51 versus EF = −0.14). The poor EF value for RUE-MS was partly due to a tendency to overestimate the observations (CRM = −0.06). RRMSE values (14.66% for GROSS-MS and 22.27% for RUE-MS) were in agreement with what reported for other species (Duan et al., 2014).
94
S. Bregaglio et al. / Ecological Modelling 329 (2016) 86–99
Fig. 4. Comparison between the simulated and observed data concerning: phenological development (a), leaf area index (b), specific leaf area (c) and the average water content in the rooted soil profile (0–0.7 m, d). The red dots are the values measured in the hazelnut orchard of Baldissero d’Alba in the growing seasons 2011–2013 (error bars represent 1 standard deviation).
S. Bregaglio et al. / Ecological Modelling 329 (2016) 86–99
95
Fig. 5. Measured (red dots) versus simulated soil water content in the growing seasons 2011–2013 in Baldissero d’Alba at 0.15, 0.3, 0.4, 0.55 and 0.7 m depth. The outputs of the modelling solutions are indicated as black continuous lines (modelling solution based on gross photosynthesis) and dotted lines (modelling solution based on net photosynthesis).
96
S. Bregaglio et al. / Ecological Modelling 329 (2016) 86–99
Fig. 6. Measured (red dots) versus simulated soil temperature in the growing seasons 2011–2013 in Baldissero d’Alba at 0.15, 0.3, 0.4, 0.55 and 0.7 m depth. The outputs of the modelling solutions are indicated as black continuous lines (modelling solution based on gross photosynthesis) and dotted lines (modelling solution based on net photosynthesis).
S. Bregaglio et al. / Ecological Modelling 329 (2016) 86–99
Fig. 4b highlights the tendency of both modelling solutions to simulate higher LAI values in 2011 with respect to 2012, in agreement with field data.
97
Measured measured yield
Fruits weight (t ha -1) 4
GROSS modelling gross photosynthesis solution
NET modelling net photosynthesis solution
3.5
3.2.3. Specific leaf area GROSS-MS simulated SLA dynamics with more accuracy (RRMSE = 23.88%; MAE = 2.15 m2 kg−1 ) with respect to RUE-MS (RRMSE = 28.77%; MAE = 3.39 m2 kg−1 ). The slightly negative values of EF (Table 3) were partially explained by an overestimation of measured data in 2013. Fig. 4c shows observed and simulated SLA values during the three cropping seasons whereas, in Fig. 4d, the corresponding SWC data were reported. The highest SLA values were measured at the beginning of the season (average 18.76 m2 kg−1 ). Then, a marked decrease was observed, with relevant fluctuations during the summer months, according to the water availability in the soil profile. The lowest SLA was measured on July 27th and September 25th in 2012 (10.97 and 10.70 m2 kg−1 ), and on August 14th in 2013 (12.69 m2 kg−1 ). No SLA measurements were available for 2011, although simulated values were higher because of a lower impact of water stress. 3.2.4. Soil water content and soil temperature Both the modelling solutions succeeded in properly reproducing SWC values at different depths (Table 3), with mean RRMSE in the soil profile ranging from 12.67% (RUE-MS) to 15.40% (GROSSMS). These values were consistent with those obtained by other authors in similar works (e.g., Markevitz et al., 2010). The EF values were always positive (average EF = 0.88 and 0.93 for GROSSMS and RUE-MS, respectively), and they did not present relevant differences across the different soil depths. Fig. 4d shows the comparison between measured and simulated SWC averaged in the 0.15–0.70 m depth, according to available measurements. The highest SWC values were measured (and simulated) in 2011, and led to higher SLA and LAI values simulated by both the solutions. Seasons 2012 and 2013 were characterized by a sharp decrease in SWC values since June, i.e., at the beginning of the fruit ripening phase. Both solutions tended to anticipate the period of water stress and to overestimate the root water uptake in this period. Conversely, they showed good performances in reproducing the dynamics of SWC after rainfall events. The simulation of SWC in the period from July to September confirmed the capability of the solutions to correctly reproduce measured data. Fig. 5 presents the comparison between measured and simulated SWC values at different soil depths. The two solutions showed an opposite behavior, with GROSS-MS overestimating water uptake in the deepest soil layers and vice versa. Fig. 6 shows the comparison between measured and simulated soil temperature at different depths. The outputs of the two modelling solutions are overlapped at each soil layer, and they present good matching with reference data (Table 3). Indeed, despite results showed a certain tendency to slightly overestimate measured data, RRMSE values were satisfactory and in agreement with those shown by other authors (e.g., Archontoulis et al., 2014). The differences between measured and simulated soil temperature data increased with soil depth. 3.2.5. Yield simulation The historical series of hazelnut yields recorded at the Cravanzana site was characterized by marked year-to-year fluctuations (Fig. 7). Both the modelling solutions obtained satisfactory results, although GROSS-MS was more accurate (RRMSE = 25.03%; EF = 0.52). Indeed, RUE-MS presented marked overestimations in 2003 and 2009 and underestimation in 2010, achieving less satisfying values for all the agreement metrics (RRMSE = 30.40%; EF = 0.29). In general, both the solutions correctly simulated the impact of water stress on yields in seasons characterized by low rainfall volumes, such as 2005 (368 mm), 2008 (358.4 mm) and
3 2.5 2 1.5 1 0.5 0 2002
2003
2004
2005
2006
2007
2008
2009
2010
2011 year
Fig. 7. Measured (histogram) versus simulated hazelnut yields in the growing seasons 2002–2011 in Cravanzana. The outputs of the two modelling solutions are indicated as black dots and continuous lines (modelling solution based on gross photosynthesis) and white dots and dotted lines (modelling solution based on net photosynthesis).
2011 (255.6 mm). Conversely, the highest yields were usually estimated for years where favorable rainfall patterns were recorded, such as 2002 (719 mm), 2006 (480 mm) and 2010 (541.8 mm), in agreement with field observations 4. Discussion Parameter values with related sources of information are reported in Appendix A. The values of the parameters involved in initial tree size refer to the orchard in Baldissero d’Alba, whereas maximum and minimum SLA values are derived from the measured data. Minimum and maximum temperature (Tmin and Tmax , ◦ C) for hazelnut development are set according to Hunter and Lechowicz (1992) and Gerosa et al. (2012), whereas the optimum temperature (Topt , ◦ C, Eq. (2)) is calibrated and set to 18 ◦ C. Parameters related to late vegetative phases are derived by Delpierre et al. (2009), who calibrated them for Fagus sylvatica L. in France. Wood density (Wooddensity , kg m−3 ) is set according to Korkut et al. (2008), who measured it on Corylus colurna L. in Turkey, and Pisetta (2011), who derived it on C. avellana L. in northern Italy. Leaf mass to area (kg m−2 ) is derived from hazelnut leaves with high nitrogen content by Kull and Niinemets (1993). Cardinal temperatures for photosynthesis and stomatal conductance are coherent with those used by von Stamm (1994). The maximum carboxylation rate is derived by Manter and Kerrigan (2004), who estimated specific parameters involved with leaf photosynthesis and gas exchange measurements on Corylus cornuta L. No specific values for hazelnut maximum radiation use efficiency are available in literature, therefore this parameter is set according to Pothier and Margolis (1991), who measured it on white birch (Betula verrucosa), which belongs to the same family. The thermal sums needed to reach vegetative and reproductive stages are calibrated by minimizing RRMSE between measured and simulated values. The same procedure is followed to derive the initial assimilate partitioning to leaves and fruits, by using LAI as the reference state variable. Simulation models of deciduous tree species are available mainly for apple (Hester and Cacho, 2003), and peach (Grossman and De Jong, 1994) orchards. The approaches used to simulate main plant processes, e.g., photosynthesis, are often derived by growth models for herbaceous crops (e.g., SUCROS, van Keulen et al., 1982) and adapted to the species of interest via dedicated algorithms, e.g., to take into account the discontinuity of the canopy. Other authors developed simulation models targeting the support to orchard management (Cahn et al., 1997; Lescourret et al., 1999), adopting empirical approaches to simulate main plant processes. In the case of hazelnut, the sensitiveness to water stress and high temperatures especially during the period of fruit ripening is widely investigated and documented, therefore there is the need to develop a
98
S. Bregaglio et al. / Ecological Modelling 329 (2016) 86–99
process-based model to dynamically reproduce the impacts of variable agro-environmental conditions on hazelnut growth. Despite the differences in the modelling approaches used to simulate light interception and photosynthetic rate, the two modelling solutions proposed here prove to be adequate in reproducing the time trend of leaf area expansion, as well as the impact of soil water availability on the dynamics of specific leaf area. The performances of the two solutions in reproducing the detailed dataset from Baldissero d’Alba are very similar. However, the RUE-MS leads to slightly better results in the simulation of soil water dynamics, whereas the GROSS-MS shows a higher accuracy in the reproduction of plant state variables. The decreasing accuracy in the simulation of soil water content for increasing soil depth highlights the need for improving the formalization of processes involved in root water uptake in the bottom layers. This could be achieved by substituting the simplified tipping bucket approach adopted in this study with a more process-based approach, e.g., based on the Richards equation (Richards, 1931). The application of the two solutions to the independent dataset collected in the Cravanzana site leads to higher differences in their performance. The GROSS-MS is capable to reproduce with more accuracy the high year-to-year variability in hazelnut yields. This suggests that the additional layers of complexity implemented in this solution (hourly time step, decomposition of light into diffuse and direct components, different interception from sunlit and shaded leaves, explicit simulation of stomatal conductance and maintenance and growth respiration) allow a more reliable representation of the underlying system. 5. Conclusions The two modelling solutions presented here target the simulation of the interactions between hazelnut trees and environmental conditions, through a detailed characterization of phenological development and biomass growth as affected by meteorological variables and water availability in soil. The good agreement between the year-to-year fluctuations of measured and simulated yields confirmed the suitability of the two solutions in estimating plant responses as affected by environmental conditions. These results encourage the application of the modelling solutions developed here to simulate hazelnut growth and yield in research and operational contexts, both for management support and for studies performed in climate change scenarios. However, the selection of the most suitable modelling solution to perform operational yield forecasting studies is still an open issue, and it requires a further extended model evaluation including different sites and years to enlarge the variability of the explored conditions. Further developments will envisage the implementation of modules for the impacts of agro-environmental conditions on the qualitative aspects of hazelnut production (i.e., oil content, fatty acids, proteins). Acknowledgments The research leading to these results has received funding from the Piedmont Region, GECORSIST project “Development of a decision support system for hazelnut” and from the European Community’s Seven Framework Programme-FP7 (KBBE.2013.1.409) under Grant Agreement No. 613817. 2013–2016. MODelling vegetation response to EXTREMe Events (MODEXTREME, modextreme.org) Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.ecolmodel.2016. 03.006.
References Archontoulis, S.V., Miguez, F.E., Moore, K.J., 2014. Evaluating APSIM maize, soil water, soil nitrogen, manure, and soil temperature modules in the Midwestern United States. Agron. J. 106, 1025–1040. Awada, T., Josiah, S., 2007. Physiological responses of four hazelnut hybrids to water availability in Nebraska. Great Plains Res. 17, 193–202. Bartelink, H.H., Kramer, K., Mohren, G.M.J., 1997. Applicability of the radiation-use efficiency concept for simulating growth of forest stands. Agric. For. Meteorol. 88, 169–179. Bignami, C., Cammilli, C., Moretti, G., Sallusti, L., 2005. Growth analysis and nitrogen dynamics in hazelnut ‘Tonda Gentile Romana’. Acta Hortic. 686, 193–200. Bignami, C., Cristofori, V., Bertazza, G., 2011. Effects of water availability on hazelnut yield and seed composition during fruit growth. Acta Hortic. 922, 333–340. Bradshaw, B., Ph.D. Thesis 2005. Physiological aspects of Corylus avellana associated with the French black truffle fungus Tuber melanosporum and the consequence for commercial production of black truffles in Western Australia. School of Biological Sciences and Biotechnology, Murdoch University Perth, Western Australia. Bregaglio, S., Frasso, N., Pagani, V., Stella, T., Francone, C., Cappelli, G., Acutis, M., Balaghi, R., Ouabbou, H., Paleari, S., Confalonieri, R., 2015. New multi-model approach gives good estimations of wheat yield under semi-arid climate in Morocco. Agron. Sustain. Dev. 35, 157–167. Buwalda, J.G., 1991. A mathematical model of carbon acquisition and utilization by kiwifruit vines. Ecol. Model. 57, 43–64. Cahn, M.B., Stevens, R.B., van de Klundert, A.C.A., 1997. Economic outcomes of early cropping and tree decisions in Braeburn apples on Malling 9 rootstock. Acta Hortic. 451, 551–558. Campioli, M., Verbeek, H., Lemeur, R., Samson, R., 2008. C allocation among fine roots, above-, and belowground wood in a deciduous forest and its implication to ecosystem C cycling: a modelling analysis. Biogeosci. Discuss. 5, 3781–3823. Cannell, M.G.R., 1985. Dry matter partitioning in tree crops. In: Cannell, M.G.R., Jackson, J.E. (Eds.), Attributes of Trees as Crop Plants. Institute of Terrestrial Ecology, Abbotts Ripton, pp. 160–193. Cappelli, G., Yamac¸, S.S., Stella, T., Francone, C., Paleari, L., Negri, M., Confalonieri, R., 2015. Are advantages from the partial replacement of corn with secondgeneration energy crops undermined by climate change? A case study for giant reed in northern Italy. Biomass Bioenergy 80, 85–93. Chen, J.M., Liu, J., Cihlar, J., Goulden, M.L., 1999. Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications. Ecol. Model. 124, 99–119. Cola, G., Mariani, L., Salinari, F., Civardi, S., Bernizzoni, M., Gatti, M., Poni, S., 2013. Description and testing of a weather-based model for predicting phenology, canopy development and source–sink balance in Vitis vinifera L. cv. Barbera. Agric. For. Meteorol. 184, 117–136. Confalonieri, R., Bregaglio, S., Cappelli, G., Francone, C., Carpani, M., Acutis, M., El Aydam, M., Niemeyer, S., Balaghi, R., Dong, Q., 2013. Wheat modelling in Morocco unexpectedly reveals predominance of photosynthesis versus leaf area expansion plant traits. Agron. Sustain. Dev. 33, 393–403. Costa Leme, P., Assunc¸ão, A., 2005. Relationship between the above and underground parts of hazelnut variety ‘Tonda di Giffoni’. Acta Hortic. 686, 173–178. ˇ Crepinˇ sek, Z., Stampar, F., Kajfez-Botagaj, L., Solar, A., 2012. The response of Corylus avellana L. phenology to rising temperature in north-eastern Slovenia. Int. J. Biometeorol. 56, 681–694. de Vries, P.F.W.T., Jansen, D.M., ten Berge, H.F.M., Bakema, A., 1989. Simulation of ecophysiological processes of growth in several annual crops. Simul. Monogr. 29, 271, Wageningen, The Netherlands. Delpierre, N., Dufrêne, E., Soudani, K., Ulrich, E., Cecchini, S., Boé, J., Franc¸ois, C., 2009. Modelling interannual and spatial variability of leaf senescence for three deciduous tree species in France. Agric. For. Meteorol. 149, 938–948. Diodato, N., Bellocchi, G., 2008. Drought stress patterns in Italy using agro-climatic indicators. Clim. Res. 36, 53–63. Donatelli, M., Bregaglio, S., Confalonieri, R., De Mascellis, R., Acutis, M., 2014. A generic framework for evaluating hybrid models by reuse and composition – a case study on soil temperature simulation. Environ. Model. Softw. 62, 478–486. Duan, S.B., Li, Z.L., Wu, H., Tang, B.H., Ma, L., Zhao, E., Li, C., 2014. Inversion of the PROSAIL model to estimate leaf area index of maize, potato, and sunflower fields from unmanned aerial vehicle hyperspectral data. Int. J. Appl. Earth Obs. Geoinf. 26, 12–20. Erdogan, V., Mehlenbacher, S.A., 2000. Phylogenetic relationships of Corylus Species (Betulaceae) based on nuclear ribosomal DNA ITS region and chloroplast matK gene sequences. Syst. Bot. 25, 727–737. FAOSTAT, 2014. Food and Agriculture Organization of the United Nations Cropping Database. http://faostat3.fao.org/home/index.html (accessed on 01.03.16). Farquhar, G.D., von Caemmerer, S., Berry, J.A., 1980. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90. Fideghelli, C., De Salvador, F.R., 2009. World hazelnut situation and perspectives. Acta Hortic. 845, 39–52. Gerosa, G., Mereu, S., Finco, A., Marzuoli, R., 2012. Stomatal conductance modelling to estimate the evapotranspiration of natural and agricultural ecosystems. In: Irmak, A. (Ed.), Evapotranspiration – Remote Sensing and Modelling. , ISBN: 978953-307-808-3. Giménez, C., Gallardo, M., Martínez-Gaitán, C., Stöckle, C.O., Thompson, R.B., Granados, M.R., 2013. VegSys, a simulation model of daily crop growth, nitrogen uptake and evapotranspiration for pepper crops for use in an on-farm decision support system. Irrig. Sci. 31, 465–477.
S. Bregaglio et al. / Ecological Modelling 329 (2016) 86–99 Girona, J., Cohen, M., Mata, M., Marsal, J., Miravete, C., 1994. Physiological, growth and yield responses of hazelnut (Corylus avellana L.) to different irrigation regimes. Acta Hortic. 351, 463–472. Gispert, J.R., Tous, J., Romero, A., Plana, J., Gil, J., Company, J., 2005. The influence of different irrigation strategies and the percentage of wet soil volume on the productive and vegetative behaviour of the hazelnut tree (Corylus avellana L.). Acta Hortic. 686, 333–342. Grossman, Y.L., De Jong, T.M., 1994. PEACH: a simulation model of reproductive and vegetative growth in peach trees. Tree Physiol. 14, 329–345. Hampson, C., Azarenko, A.N., Potter, J.R., 1996. Photosynthetic rate, flowering, and yield component alteration in hazelnut in response to different light environments. J. Am. Soc. Hortic. Sci. 121, 1103–1111. Hester, S.M., Cacho, O., 2003. Modelling apple orchard systems. Agric. Syst. 77, 137–154. Hogg, E.H., Saugier, B., Pontailler, J.Y., Black, T.A., Chen, W., Hurdle, P.A., Wu, A., 2000. Responses of trembling aspen and hazelnut to vapor pressure deficit in a boreal deciduous forest. Tree Physiol. 20, 725–734. Hunter, A.F., Lechowicz, M.J., 1992. Predicting the timing of budburst in temperate trees. J. Appl. Ecol. 29, 597–604. Jørgensen, S.E., Kamp-Nielsen, L., Christensen, T., Windolf-Nielsen, J., Westergaard, B., 1986. Validation of a prognosis based upon a eutrophication model. Ecol. Model. 32, 165–182. Kilic, O., Alkan, I., 2006. The developments in the world hazelnut production and export, the role of Turkey. J. Appl. Sci. 6, 1612–1616. Korkut, D.S., Korkut, S., Bekar, I., Budakc¸, M., Dilik, T., C¸akıcıer, N., 2008. The effects of heat treatment on the physical properties and surface roughness of Turkish hazel (Corylus colurna L.) wood. Int. J. Mol. Sci. 9, 1772–1783. Kull, O., Niinemets, U., 1993. Variations in leaf morphometry and nitrogen concentration in Betula pendula Roth., Corylus avellana L. and Lonicera xylosteum L. Tree Physiol. 12, 311–318. Lakso, A.N., Johnson, R.S., 1990. A simplified dry matter production model for apple using automatic programming simulation software. Acta Hortic. 276, 141–148. Lancashire, P.D., Bleiholder, H., Langelüddecke, P., Stauss, R., Van den Boom, T., Weber, E., Witzenberger, A., 1991. An uniform decimal code for growth stages of crops and weeds. Ann. Appl. Biol. 119, 561–601. Le Roux, X., Lacointe, A., Escobar-Gutierrez, A., Le Dizès, S., 2001. Carbon-based models of individual tree growth: a critical appraisal. Ann. For. Sci. 58, 469–506. Lescourret, F., Blecher, N., Habib, R., Chadoeuf, J., Agostini, D., Pailly, O., Vaissiere, B., Poggi, I., 1999. Development of a simulation model for studying kiwi fruit orchard management. Agric. Syst. 59, 215–239. Loague, K., Green, R.E., 1991. Statistical and graphical methods for evaluating solute transport models: overview and application. J. Contam. Hydrol. 7, 51–73. Lv, G., Hu, W., Kang, Y., Liu, B., Li, L., Song, J., 2013. Root water uptake model considering soil temperature. J. Hydrol. Eng. 18, 394–400. Malossini, A., 1993. Procedure per il rilevamento fenologico. Gruppo di Lavoro Nazionale per i Giardini Fenologici. Assessorato Agricoltura Regione Emilia Romagna, Bologna, Italy, http://cma.entecra.it/reteGFI/documenti/procedure per il rilevamento fenologico 1993.pdf (accessed on 01.03.16). Manter, D.K., Kerrigan, J., 2004. A/Ci curve analysis across a range of woody plant species: influence of regression analysis parameters and mesophyll conductance. J. Exp. Bot. 55, 2581–2588. Markevitz, D., Devine, S., Davidson, E.A., Brando, P., Nepstad, D.C., 2010. Soil moisture depletion under simulated drought in the Amazon: impacts on deep root uptake. New Phytol. 187, 592–607. Marsal, J., Girona, J., Mata, M., 1997. Leaf water relation parameters in Almond compared to hazelnut trees during a deficit irrigation period. J. Am. Soc. Hortic. Sci. 122, 582–587. Mehlenbacher, S., 2008. Genetic resources for hazelnut: state of the art and future perspectives. Acta Hortic. 845, 33–38. Mingeau, M., Ameglio, T., Pons, B., Rousseau, P., 1994. Effects of water stress on development, growth and yield of hazelnut trees. Acta Hortic. 351, 305–314. Nash, J.E., Sutcliffe, J.V., 1970. River flow forecasting through conceptual models part I. A discussion of principles. J. Hydrol. 10, 282–290. Neitsch, S.L., Arnold, J.G., Kiniry, J.R., Williams, J.R., 2011. Soil and Water Assessment Tool. Theoretical Documentation. Version 2009. Texas Water Resources Institute Technical Report No. 406. Texas A&M University System, College Station, Texas http://twri.tamu.edu/reports/2011/tr406.pdf (accessed on 01.03.16). Olsson, C., Bolmgren, K., Lindström, J., Jönsson, A.M., 2013. Performance of tree phenology models along a bioclimatic gradient in Sweden. Ecol. Model. 266, 103–117. Paleari, L., Cappelli, G., Bregaglio, S., Acutis, M., Donatelli, M., Sacchi, G.A., Lupotto, E., Boschetti, M., Manfron, G., Confalonieri, R., 2015. District specific, in silico
99
evaluation of rice ideotypes improved for resistance/tolerance traits to biotic and abiotic stressors under climate change scenarios. Clim. Change, http://dx. doi.org/10.1007/s10584-015-1457-4. Parton, W.J., 1984. Predicting soil temperatures in a shortgrass steppe. Soil Sci. 138, 93–101. Pisetta, M., Ph.D. Thesis 2011. Relazioni idriche in nocciolo (Corylus avellana L.). University of Padua http://paduaresearch.cab.unipd.it/4688/1/TesiPisetta.pdf (accessed on 01.03.16). Pothier, D., Margolis, A., 1991. Analysis of growth and light interception of balsam fir and white birch saplings following precommercial thinning. Ann. For. Sci. 48, 123–132. Pronk, A., Goudriaan, J., Stilma, E., Challa, H., 2003. A simple method to estimate light interception by nursery stock conifers: a case study of eastern white cedar. NJAS – Wageningen J. Life Sci. 51, 279–295. Puppi, G., Zanotti, A.L., 2011. Comparison of phytophenological data: a proposal for converting between GFI and BBCH scales. Italian J. Agrometeorol. 3, 29–37. Richards, L.A., 1931. Capillary conduction of liquids through porous mediums. Physics 1, 318–333. Ritchie, J.T., 1998. Soil water balance and plant water stress. In: Tsuji, G.Y., Hoogenboom, G., Thornton, P.K. (Eds.), Understanding Options of Agricultural Production. Kluwer Academic Publishers, International Consortium for Agricultural Systems Applications, Dordrecht, The Netherlands, pp. 41–53. Rosenzweig, C., Elliot, J., Deryng, D., Ruane, A.C., M"uller, C., Arneth, A., Boote, K.J., Folberth, C., Glotter, M., Khabarov, N., Neumann, K., Piontek, F., Pugh, T.A.M., Schmid, E., Stehfest, E., Yang, H., Jones, J.W., 2014. Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc. Natl. Acad. Sci. U. S. A. 111, 3268–3273. Santos, A., Silva, A.P., 2001. Hazelnut productivity in northern Portugal. Acta Hortic. 556, 97–102. Santos, A., Silva, A.P., Colac¸o, J., 1994. Annual growth dynamics of eleven hazelnut varieties in northern Portugal. Acta Hortic. 351, 93–98. Santos, A., Silva, A.P., Rosa, E., 1998. Shoot growth and yield of hazelnut (Corylus avellana L.) and the influence of climate. Ten years of observations. J. Hortic. Sci. Biotechnol. 73, 145–150. Schaeffer, D.L., 1980. A model evaluation methodology applicable to environmental assessment models. Ecol. Model. 8, 275–295. Spitters, C., Toussanint, H., Goudriaan, J., 1986. Separating the diffuse and direct component of global radiation and its implications for modelling canopy photosynthesis Part I. Components of incoming radiation. Agric. For. Meteorol. 38, 217–229. Teh, C., 2006. Introduction to Mathematical Modelling of Crop Growth: How the Equations are Derived and Assembled into a Computer Model. Brown Walker Press, Florida, US. Tous, J., Girona, J., Tasias, J., 1994. Cultural practices and cost of hazelnut production. Acta Hortic. 351, 395–418. Ustao˘glu, B., 2012. The effect of climatic conditions on hazelnut (Corylus avellana) yield in giresun (Turkey). Marmara Geogr. Rev. 26, 302–323. Valentini, N., Me, G., Vallania, R., 2001. New hazelnut selections for direct consumption. Acta Hortic. 556, 103–108. van Keulen, H., Wolf, J., 1986. Modelling of agricultural production: weather, soils and crops. Simul. Monogr., Pudoc, Wageningen, The Netherlands. van Keulen, H., Penning de Vries, F.W.T., Drees, E.M., 1982. A summary model for crop growth. In: Penning de Vries, F.W.T., van Laar, H.H. (Eds.), Simulation of Plant Growth and Crop Production. Centre for Agricultural Publishing and Documentation (Pudoc), Wageningen, The Netherlands, pp. 87–97. Villalobos, F.J., Testi, L., Hidalgo, J., Pastor, M., Orgaz, F., 2006. Modelling potential growth and yield of olive (Olea europaea L.) canopies. Eur. J. Agron. 24, 296–303. von Stamm, S., 1994. Linked stomata and photosynthesis model for Corylus avellana (hazel). Ecol. Model. 75/76, 345–357. Williams, J.R., 1995. The EPIC model. In: Singh, V.P. (Ed.), Computer Models of Watershed Hydrology. Water Resources Publications, Littleton, CO. Wilson, W.J., 1967. Ecological data on dry-matter production by plants and plant communities. In: Bradley, E.F., Denmead, O.T. (Eds.), The Collection, Processing of Field Data. Interscience, New York, US, pp. 77–123. Xin, Q., Gong, P., Li, W., 2015. Modelling photosynthesis of discontinuous plant canopies by linking the Geometric Optical Radiative Transfer model with biochemical processes. Biogeosciences 12, 3447–3467. Yan, W., Hunt, L.A., 1999. An equation for modelling the temperature response of plants using only the cardinal temperatures. Ann. Bot. (Lond.) 84, 607–614. Yavuz, F., Birinci, A., Peker, K., Atsan, T., 2005. Econometric modelling of turkey’s hazelnut sector: implications on recent policies. Turk. J. Agric. For. 29, 1–7.
