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Ciência Téc. Vitiv. 30(1) 29-42. 2015
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This is an Open Access article distributed under the terms of the Creative Commons Attribution License
(http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original
work is properly cited.
APPLICATION OF CROP MODELLING TO PORTUGUESE VITICULTURE:
IMPLEMENTATION AND ADDED-VALUES FOR STRATEGIC PLANNING
APLICAÇÃO DA MODELAÇÃO DE CULTURAS À VITICULTURA PORTUGUESA:
IMPLEMENTAÇÃO E VALOR ACRESCENTADOS PARA O PLANEAMENTO ESTRATÉGICO
Ricardo Costa1; Helder Fraga1*; Aureliano C. Malheiro1; João A. Santos1
1 Centre for the Research and Technology of Agro-Environmental and Biological Sciences, Universidade de Trás-os-Montes e Alto Douro, 5000-
801 Vila Real, Portugal
*corresponding author: Tel: +351259350389, fax: +351259350480, e-mail: hfraga@utad.pt
(Received 11.02.2015.Accepted 20.07.2015)
SUMMARY
Grapevine (Vitis vinifera L.) is a very important crop in Portugal, where the viticultural sector plays a central role in the national economy. The
present study provides a review of most relevant research on grapevine modelling, giving particular emphasis to its past and future applicability to
Portuguese conditions. A brief overview of the national sector, as well as of the prevailing physical and biological environments and viticultural
practices is provided. Further, the terroir concept is discussed in view of the main controlling factors of grapevine development. Several crop
models, either statistical or dynamic, that have reliably simulated grapevine/vineyard parameters, such as phenology, yield and quality, are
referred. Statistical models are based on statistically significant relationships between a number of predictors and a given grapevine parameter.
Dynamic crop models simulate plant growth and development and holistically integrate crop phenotype, soil profiles, weather and climate data
and management practices in their simulations. Dynamic crop models are then becoming important decision support systems in viticulture.
Additionally, they allow testing the effects of soils, management decisions and weather on crops. However, only a few dynamic models can
properly simulate grapevine performance. Several studies also apply crop models under future conditions to assess the detrimental climate change
impacts on grapevines. These crop models can be either applied to real-time monitoring and short-range predictions or to develop long-term
climate change projections for the Portuguese viticulture. These studies will represent important added-values for the competitiveness and future
sustainability of the winemaking sector in Portugal.
RESUMO
A videira (Vitis vinifera L.) é uma cultura de grande relevo em Portugal, onde o sector vitivinícola desempenha um papel central na economia
nacional. O presente estudo fornece uma revisão de pesquisas mais relevantes sobre a modelação da videira, dando especial ênfase à sua
aplicabilidade às condições específicas portuguesas. É dada uma visão sucinta do sector nacional, bem como das condições físicas, biológicas e
práticas vitivinícolas predominantes. O conceito de terroir é discutido tendo em conta os principais fatores condicionantes do desenvolvimento da
videira. São referidos vários modelos de culturas, estatísticos e dinâmicos, que têm simulado com sucesso as características da videira/vinha,
fenologia, produtividade e qualidade. Os modelos estatísticos são baseados em relações estatisticamente significativas entre um número de
preditores e uma determinada característica da videira. Os modelos dinâmicos simulam o crescimento e desenvolvimento da planta de forma
holística, integrando nas suas simulações o fenótipo da cultura, perfis de solo, dados meteorológicos e práticas culturais. Por este motivo, os
modelos dinâmicos estão a tornar-se importantes sistemas de apoio à decisão em viticultura. Além disso, permitem prever os efeitos do solo, das
práticas culturais e da meteorologia nas culturas. No entanto, apenas alguns modelos dinâmicos conseguem simular adequadamente o desempenho
da videira. Vários estudos também aplicam modelos de cultura para condições futuras para avaliar os impactos das mudanças climáticas sobre a
vinha. Estes modelos podem ser aplicados quer na monitorização e previsão de curta duração, quer no desenvolvimento de cenários de alterações
climáticas para a viticultura portuguesa. Estes estudos representarão um importante valor acrescentado para a competitividade e sustentabilidade
futura do setor vitivinícola em Portugal.
Key words: Crop models, Vitis vinifera, viticulture, climate change.
Palavras-chave: Modelos de culturas, Vitis vinifera, viticultura, alterações climáticas.
1. CHARACTERIZATION OF PORTUGUESE
VITICULTURE
The present study provides a succinct review of the
Portuguese viticulture and addresses the application
of crop models as key decision supporting systems
Article available at http://www.ctv-jve-journal.org or http://dx.doi.org/10.1051/ctv/20153001029
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towards a more competitive, efficient and sustainable
viticulture, under current and future climates.
1.1 The sector
All around the world, viticulture and winemaking are
very important activities with relevant impacts on
local and regional economies. For the specific case of
Portugal, which is the 11th wine producer and the
10th wine exporter in the world (OIV, 2013),
viticulture and the winemaking socioeconomic sector
play a key role in its economic growth. The vineyard
area in Portugal is over 229 kha, with an annual wine
production of about 6.7 Mhl (OIV, 2013). Portugal
exports almost half of its total national wine
production. Table wines represent 55% of the total
national exports, followed by DO wines with 36%,
which include fortified wines, such as Port wine as
well as Regional wines with 12%. These exports have
a large impact on the local economies, accounting for
nearly 2% of national export income (IVV, 2013).
Portugal is divided in 14 viticultural regions
(mainland Portugal, Azores and Madeira), with 25
Denominations of Origin – DO (Figure 1). The most
important winemaking regions are Douro/Porto,
Minho (“Vinho Verde”), Alentejo and Lisboa. In
terms of production, Douro/Porto is the main region,
with almost 1.5 Mhl in 2013, approximately
corresponding to 25% of all wine produced in
Portugal (IVV, 2013). This region has also the largest
vineyard area in the country (ca. 45 kha). Alentejo is,
however, the highest yielding region, with an average
production of 49 hl/ha, followed by Península-de-
Setúbal, with 47 hl/ha, and Lisboa, with 38 hl/ha
(2013 estimates).
1.2 The physical and biological environment
Portuguese winemaking regions commonly present
very specific environmental and climatic
characteristics. Portuguese climates show prevailing
Mediterranean-like characteristics, with warm dry
summers and wet autumn-winter periods. The
Portuguese warm-dry summers critically limit crop
growth, mainly due to summertime low water
availability (Fraga et al., 2014c). Furthermore,
extreme winter/spring weather events, such as hail
and frost, tend to occur in some northern regions and
to infringe important damages to this crop. In general,
Portugal presents higher temperatures in the south
(e.g. Alentejo) and lower temperatures in the north
(e.g. Minho and Douro/Porto) (Figure 2a). The lowest
temperatures are found in the mountainous areas of
the northern half of the country. With respect to
precipitation (Figure 2c), Portugal presents the
highest amounts in the northwest and the lowest
amounts in its southernmost part. Concerning
temperatures and precipitation during the growing
season (April–October; Figure 2b and d), similar
spatial patterns to the yearly average and cumulative
totals are found, but with much lower amounts in
precipitation, as it mostly falls in the winter half of
the year, a typical feature of the Mediterranean
climates.
Figure 1. Wine regions in mainland Portugal.
Regiões vitivinícolas em Portugal Continental
The predominant soils in Portugal (FAO, 2006) are
cambisols, mostly in the north, and lithosols/luvisols,
in the south (Figure 3a). In the northern half of the
country there is a large area of cambisols in Minho,
Beiras-Atlântico and Terras-da-Beira, while
Douro/Porto predominantly presents lithosols, which
has been greatly affected by human activity
(anthrosols). In the south, the region of Lisboa also
presents cambisols, while Peninsula-de-Setubal and
Tejo mostly show podzols. The most representative
soil types in Alentejo and Algarve are lithosols and
luvisols. With respect to topography (Figure 3b), the
most mountainous areas are located in inner-northern
Portugal, whereas flatlands and plateaus prevail in
coastal and southern areas.
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Figure 2. Climate-means for the 1950–2000 baseline of the: a) annual mean temperature; b) April–October mean temperature: c) annual
precipitation; d) April–October precipitation in Portugal, calculated using the WORLDCLIM dataset (www.worldclim.org).
Normais climatológicas para 1950–2000 da: a) temperatura média anual; b) temperatura média de Abril–Outubro: c) precipitação anual; d)
precipitação de Abril–Outubro em Portugal, utilizando a base de dados WORLDCLIM (www.worldclim.org).
Regarding intraspecific biodiversity, a large number
of native varieties can be found in Portugal,
according to their adaptation to the different soils,
climates and topographic conditions, with red
varieties typically predominant in the south and white
varieties in the northwest (Fraga et al., 2014b). Some
of the most well-known Portuguese winegrapes are:
Alvarinho, Castelão, Fernão-Pires, Touriga-Nacional
and Touriga-Franca. All these varieties present
unique agronomic and oenological characteristics that
ultimately result in distinctive wines. They give origin
to high quality wines, ranging from the iconic
fortified Port wine to the fresh and light “Vinho
Verde” (IVV, 2013), which are typically
characterized by blending varietals. Therefore, other
winemaking regions worldwide are already growing
some of these varieties (Anderson, 2014), namely
Touriga-Nacional.
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Figure 3. a) Portuguese soils according to the classification system from FAO (2006). b) Elevation in meters over mainland Portugal (source:
‘Atlas do Ambiente’; http://sniamb.apambiente.pt/).
a) Solos portugueses de acordo com a classificação FAO (2006). b) Elevação em metros sobre Portugal continental (Fonte: ‘Atlas do
Ambiente’; http://sniamb.apambiente.pt/).
Regarding the main phenological stages (Real et al.,
2014), though depending on the grapevine varieties
and environmental conditions in a given year,
budburst occurs annually from March to April (Figure
4). It is then followed by a phase of intensive growth
until flowering that generally occurs in May–June.
Immediately after flowering, the number of potential,
and viable fruits, is determined. This stage is followed
by the veraison that initiates the grapevine ripening,
usually in July–August. Full maturity is then reached,
typically in September to October (Magalhães, 2008).
At the end of the developing season, the grape
clusters are harvested and finally leaves begin to fall,
initiating the dormancy stage. Cultural practices have
also several particularities in each region. As an
example, in Douro/Porto, due to steep slopes, walled
terraces are common. Regarding the training systems,
the cordon (unilateral and bi-lateral) prevails, though
‘pergola’ (in Minho) and ‘gobelet’ (e.g. Alentejo) can
also be found. In this way, vine training determines
density, orientation and microclimate of the vines.
2. FACTORS OF INFLUENCE IN
VITICULTURE
A highly complex and interactive system, formed by
climate, soil and management practices, greatly
influences grapevine development (Magalhães, 2008).
This system evolved towards a concept of terroir
defined as “an area in which collective knowledge of
the interactions between the identifiable physical and
biological environment and applied vitivinicultural
practices develops, providing distinctive
characteristics for the products originating from this
area” (OIV, 2010). All these terroir elements strongly
influence growth and development of the different
varieties of Vitis vinifera L. (van Leeuwen et al.,
2004; van Vaudour et al., 2010; Yau et al., 2013;
Fraga et al., 2014b), as well as wine type, yield and
quality.
Figure 4. Typical grapevine annual growth cycle and phenological
stages in Portugal.
Ciclo de crescimento anual típico e estados fenológicos da videira
em Portugal
Climate is considered the main element of terroir
(Jones and Davis, 2000; Carbonneau, 2003; van
Leeuwen et al., 2004). One of the most important
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climatic thresholds is the classical 10 °C base
temperature for budburst (Winkler, 1974). In fact,
temperature conditions drive the timing and length of
all grapevine phenological stages (Kose, 2014), thus
also affecting the inter-annual variability in yield,
production and quality (de Orduna, 2010; Fraga et al.,
2014d). Berry colour, flavour and aroma, tannin and
sugar levels are also affected by temperature at
ripening (Jones and Davis, 2000; Malheiro et al.,
2010). Precipitation is another important element,
which widely controls soil water availability affecting
grapevine water use (Ferreira et al., 2012). Overall,
high-quality wines are associated to moderate water
stress during maturation (Koundouras et al., 1999;
van Leeuwen et al., 2004; Storchi et al., 2005). A
severe water stress during early stages may
considerably delay growth and grapevine
development (Hardie and Considine, 1976). On the
other hand, excessive vigour, diseases and other
problems negatively affecting wine quality may occur
when excessive soil moisture is verified over the
growing season (During, 1986; Magalhães, 2008).
Extreme weather events, such as late spring frosts,
hail and heat waves (above 35 ºC), may severely
damage grapevine leaves and berries (e.g. sunscald)
and are a significant risk to this crop (Trought et al.,
1999; Chuine et al., 2004; Molitor et al., 2014).
Soil is another important factor for viticulture and is
an important part of the terroir concept (Magalhães,
2008). Soils are composed by organic and inorganic
materials and a source of water and nutrients (e.g.
nitrogen) that are crucial for grapevine physiology,
growth and yield attributes (Winkler, 1974; Morlat
and Jacquet, 2003). In fact, grapevine composition
can be influenced by soil structure and chemistry and
can thereby affect wine quality (Mackenzie and
Christy, 2005). Vineyard installation is usually
preferred in deep soils with good drainage, either
natural or manmade. Root growth and development is
limited in compact and shallow soils that can obstruct
their access to oxygen, water and nutrients (Jackson
and Lombard, 1993). Soil water holding properties
are also essential, as they can have an effect on
grapevine performance (Field et al., 2009; Yau et al.,
2013).
Topographic elements are another important factor.
Elevation, slope and aspect are very relevant in
viticulture (Jones et al., 2004; Yau et al., 2013).
Elevation influences temperatures on vineyards
(through the vertical temperature gradient), exerting
this way a strong influence in site and varietal
selection (Magalhães, 2008). Slope degree affects
grapevines through solar exposure, thus having an
impact on canopy microclimate, soil erosion, water
drainage and viticultural management (Zsofi et al.,
2011).
The quality and growth of grapevines can also be
influenced by management practices, such as the
choice of rootstock and scion, training systems, crop
load, pruning type and cultural timings (Winkler,
1974). Knowledge of the varietal specificities allows
optimizing viticultural practices and is required for
production of high quality wines (Jones and Davis,
2000). Additionally, oenological practices also
greatly affect wine quality (Unwin, 1996).
3. CROP MODELS IN VITICULTURE
Many studies have been undertaken in order to
identify statistical relationships between grapevine
parameters and the abovementioned terroir factors.
These relationships are often rendered in crop models,
which have proven to be useful in predicting yields,
phenology, berry development and biomass. Crop
model simulations integrate current scientific
knowledge from many different areas, including crop
physiology, climatology/agrometeorology, plant
breeding, agronomy, soil physics/chemistry and
pathology. Given the growing awareness for the need
to implement these types of models, several
international projects have been created with this aim,
such as the MACSUR (Modelling European
Agriculture with Climate Change for Food Security)
or the AgMIP (Agricultural Model Intercomparison
and Improvement Project) project. Crop models can
be either statistical or dynamic in their formulation.
Statistical models are computationally cheaper than
dynamic models, but may present inconsistencies
among variables (Shin et al., 2009). These
weaknesses may happen mainly during non-linear
crop-environment interactions that cannot be properly
resolved by statistical approaches. As dynamic
methods holistically integrate the different
environmental interactions, they have the potential to
outperform statistical techniques. Nevertheless,
statistical models may still be a very useful tool when
neither dynamic models nor sufficient computing
resources are available (Shin et al., 2009). A more
detailed discussion of these models is presented in the
following sections.
3.1 Statistical crop models
A statistical model establishes relationships between
variables in the form of mathematical equations
(McCullagh, 2002). These models usually use
historical data of grapevine (or other crop) attributes
as dependent variables, while the terroir elements act
as independent variables (Lobell and Burke, 2010).
Those models allow simulating e.g. grapevine yield
34
(Williams et al., 1985; Nemani et al., 2001; Lobell et
al., 2006; Quiroga and Iglesias, 2009), wine quality
(Jones et al., 2005; Moriondo et al., 2011), grapevine
growth and development (Schultz, 1992; Bindi et al.,
1997; Jones and Davis, 2000; Lopes and Pinto, 2005),
water stress conditions (Pellegrino et al., 2006) and
risk of pests/diseases (Calonnec et al., 2008).
Regarding yield and quality, some studies have been
accomplished to identify statistical relationships
between grapevine yield and some environmental
variables. For the Portuguese Douro/Porto and Minho
regions, statistically significant relations between
grapevine yield and monthly mean temperatures or
precipitation totals were found (Santos et al., 2011;
Santos et al., 2013; Fraga et al., 2014d). These studies
demonstrated that anomalously high March rainfall
(during budburst), as well as anomalously high
temperatures and low precipitation in May and June
(flowering and berry development) are needed to
achieve moderate-to-high production. Cunha et al.
(2010) developed a forecast model for evaluating the
annual variability in regional wine yield based on
remote sensing indices in vineyards in Alentejo,
Minho and Douro. For the latter region, Gouveia et
al. (2011) developed a vintage model using climatic
and remote sensing data. Cunha et al. (2003) also
demonstrated that it is possible to obtain early season
estimates of wine production using grapevine
airborne pollen concentrations as a predictor.
Still focusing on yield or quality attributes, similar
results have also been found for other winemaking
regions worldwide. Rovira-Más and Sáiz-Rubio
(2013) applied crop metrics (e.g. vegetation amount,
berry size, grape yield, elevation, soil compaction and
pH) to predict grapevine yield. In another study,
Webb et al. (2008b) modelled the climatic sensitivity
of premium quality grapevines by developing
grapevine quality-temperature models for varieties
grown in Australia. Through a statistical approach it
was possible to identify key phenological periods
influencing phenolic concentration at maturity for
Pinot noir (Nicholas et al., 2011). The authors
demonstrated that warm temperatures from budburst
to flowering increase phenolic concentrations, which
are beneficial for wine quality.
Regarding phenology, several studies identified
statistically significant relationships with climate. The
standard growing degree-day (GDD) model measures
accumulated temperatures above 10ºC (Winkler,
1974) and is commonly used for evaluating grapevine
phenology (Winkler, 1974; Moncur et al., 1989;
Oliveira, 1998; Jones and Davis, 2000; Chuine et al.,
2003; van Leeuwen et al., 2008; Duchene et al.,
2010; Parker et al., 2011). In Portugal, Lopes et al.
(2008) estimated the usefulness of these thermal
models for several grapevine varieties in the Lisboa
wine region. For the Portuguese Douro/Porto region,
Real et al. (2014) also demonstrated their reliability
on monitoring phenology, despite the need for local
calibrations. For the Lisbon region, Malheiro et al.
(2013) and Fraga et al. (2014a) applied a linear
regression models to show that phenological timings
are deeply tied to air temperatures and remote sensing
indices. Many other studies assessing relationships
between air temperature, remote sensing indices and
grapevine phenology have been conducted worldwide
(Williams et al., 1985; Chuine et al., 2003; de
Cortazar-Atauri et al., 2009; Caffarra and Eccel,
2010; Bock et al., 2011; Cunha and Richter, 2012;
Fila et al., 2012; Parker et al., 2013; Rodrigues et al.,
2013).
Concerning vineyard water management, few studies
have been devoted to Portugal, while some advances
have been made in other winemaking regions in
Europe (Lebon et al., 2003; Berdeja et al., 2014;
Pellegrino et al., 2014; Roux et al., 2014; Schreiner
and Lee, 2014). Pellegrino et al. (2006) used a simple
soil water balance model, specifically parameterised
for grapevine, to characterize soil water deficits on 24
fields within 4 vineyards in Mediterranean southern
France. A similar approach was carried out by Gaudin
et al. (2014), using a water balance model to classify
water stress in French Mediterranean vineyards. In
preparation of a water stress alert system, Salinari et
al. (2014) developed a water stress model to early
detect the best management options as a function of
soil water content.
Statistical models have also been developed to
estimate the risk of pests and diseases, while none
was carried out in the Portuguese vineyards to our
knowledge. Caffarra et al. (2012) modelled the
impact of insect pests on the eastern Italian Alps.
Calonnec et al. (2008) used an epidemiological model
to simulate the dynamics of the powdery mildew
pathogen affecting the viticultural production
systems, while Hoppmann and Berkelmann-
Loehnertz (2000) used phenological models to
establish suitable plant protection regimes. However,
on the whole, the number of statistical models applied
to pests or diseases is relatively small compared to
other parameters.
3.2 Dynamic crop models
Dynamical crop models simulate/monitor plant
growth and development on a daily basis and at a
given location. These models integrate crop
phenotype, soil profiles, weather data and
management options in their simulations. Site-
specific parameters for climate, soil, plants and crop
35
management, among others, are defined as input in
model runs (Figure 5). Weather data, such as
maximum and minimum near-surface temperatures,
rainfall and incoming solar radiation are updated on a
daily basis, while other parameters are generally kept
invariant throughout the model run (e.g. soils
parameters). Dynamic crop models are becoming
important decision support systems for monitoring
crops and for assessing the impact of soils,
management decisions, weather and climate change
on crops (Paz et al., 2007; Semenov and Doblas-
Reyes, 2007; Challinor and Wheeler, 2008). In fact,
the simulation of crop parameters under different
conditions, scenarios and stresses are key outcomes
from crop models. Although many models have been
applied to evaluate crop development and growth,
only a few can properly simulate grapevine systems
(Valdes-Gomez et al., 2009).
Applied to viticulture, dynamic crop modelling can be
either focused on the simulation of a particular
process or on the entire plant growth (Moriondo et
al., 2015). As an example of the simulation of a
particular process, Lebon et al. (2003) used a
dynamic model to simulate the seasonal dynamics of
soil water balance on vineyards, demonstrating that
this process can be adequately replicated by the
model. In another study, Nendel and Kersebaum
(2004) skilfully simulate nitrogen dynamics in
vineyard soils using the NVINE model. Ben-Asher et
al. (2006) assessed the skill of the SWAP (soil, water,
atmosphere and plant) model to estimate the salinity
effects in grapevine production. Their results
demonstrate that the model can generate realistic
responses to salinity when water quality is the only
variable used. To help choose adequate training and
pruning strategies, Poni et al. (2006) used the
STELLA software to build a model to predicting the
daily carbon balance and dry matter accumulation on
grapevine vertical shoots. The Walis model was
evaluated by Celette et al. (2010) to simulate water
partitioning on a intercropped vineyard. Webb et al.
(2007) used the VineLOGIC model (Godwin et al.,
2002) to determine grapevine phenology. A broader
approach was developed by Cola et al. (2014), which
developed a new dynamic model MoDeM_IVM DSS
(Monitoring and Decision Making in Integrated
Vineyard Management Decision Supporting Systems)
for predicting grapevine seasonal dynamics, source-
sink balance and yield, showing high potential for its
inclusion in viticultural decision supporting systems
and technical assistance.
An example of a dynamic crop model that can be
used to simulate the whole plant growth process is the
STICS (Simulateur mulTIdisciplinaire pour les
Cultures Standard) (Brisson et al., 2003). STICS was
developed by the French National Institute for
Agronomic Research (INRA), comprising a large
multidisciplinary community of researchers, from
microclimate and soils to crop sciences (Brisson,
2004). This is a generic crop model that can be
applied to a wide variety of crops, such as wheat
(Brisson et al., 2002; Rodriguez et al., 2004), maize
(Bruckler et al., 2000; Brisson et al., 2002; Debaeke,
2004), sugarcane (Valade et al., 2014) and banana
(Brisson et al., 1998) and for many other purposes
such as irrigation strategies (Katerji et al., 2010),
carbon balances (de Noblet-Ducoudre et al., 2004),
soil drainage (Tournebize et al., 2004) and nitrate
contamination (Ledoux et al., 2007; Jego et al., 2008)
and climate change impact assessment (Courault and
Ruget, 2001; Juin et al., 2004; Gonzalez-Camacho et
al., 2008). de Cortazar-Atauri (2006) adapted this
model for grapevines assessing the necessary
parameterizations. STICS is one of the few freely
available and well-documented crop models
integrating grapevine, a short overview of its
applications to viticulture will be presented
henceforth.
STICS runs on a daily time-step and variables related
to climate, soil and crop system are used as input
(Brisson et al., 2003). It simulates the whole
processes of crop growth and development, also
including water and nitrogen balances (Figure 5). As
output, it provides variables related to yield and
development parameters. The model dynamics is
described as follows (de Cortazar-Atauri, 2006;
Brisson et al., 2008):
1 – Phenology is modelled by temperature
accumulation;
2 – Fruit growth is defined by the dynamics of dry
matter accumulation and water content;
3 – Dry matter content is computed using thermal
time and potential final dry weight;
4 – Dry matter partitioning is calculated through the
sink strength of several types of plant organs;
5 – The harvest date is determined according to berry
water content, which is greatly correlated with sugar
content;
6 – Soil water balance results from precipitation,
irrigation and soil evaporation, while crop
transpiration is estimated from the energy balance,
which is constrained by soil water availability,
drainage and run-off;
7 – Grape berry water content dynamics is modelled
by berry growth and plant water status;
36
8 – Plant water stress is calculated based on crop
demand and water supply, which influences leaf area
and consequently light interception and net
assimilation rate (Pellegrino et al., 2005), with
implications on grape sugar concentrations
(Pellegrino et al., 2006);
9 – Light intercepted by vines is evaluated using a
simple geometrical approach, by separating diffuse
and direct radiation components.
Figure 5. Representation of a dynamic crop model, main parameterization categories and output variables.
Representação do modelo cultural dinâmico, categorias principais de parametrização e variáveis de saída.
Focusing on grapevines, STICS has been used for
simulating grapevine yield and quality attributes
(Valdes-Gomez et al., 2009), soil water balance
(Valdes-Gomez et al., 2011), climate change impact
assessment (de Cortazar-Atauri, 2006), crop practices
(Brisson et al., 2011), soil management (Brisson et
al., 1998) and risks of pests/diseases (Leroy et al.,
2013).
Valdes-Gomez et al. (2009) assessed STICS skill in
replicating grapevine phenology, yield, soil water
content and biomass. Irrigated and non-irrigated
vineyards in Chile and France were selected for these
simulations. The model showed a high skill in
estimating grapevine phenology, with differences
between simulated and observed timings less than six
days. STICS also reliably simulated grapevine water
stress, biomass production and yield. In a subsequent
study, STICS allowed simulating water and nitrogen
balances (Valdes-Gomez et al., 2011) for the
“Cabernet Sauvignon” variety in Chile.
Celette et al. (2008) also assessed STICS ability to
represent vineyard water balance in two situations for
Chile and France, differing in annual rainfall and in
water management practices. Results revealed an
accurate estimation of grapevine phenology, as well
as of total dry matter production and yield. Soil water
content was also well estimated in both situations.
Nevertheless, the authors point out that water balance
modelling using STICS may not be appropriate in
situations with significant surface runoff. These
authors also highlighted some modelling
uncertainties, namely in the simulation of surface soil
layer humidity, which may have a significant effect
on nitrogen balance simulation.
37
de Cortazar-Atauri (2006) assessed the influences of
climate change on French grapevines by coupling
STICS with the ARPEGE climate model. This study
assesses water balance changes and impacts on
production and quality. Results allowed simulating
current and future water stress conditions, nitrogen
stress, biomass and yield, which may help planning
mitigation actions against climate change impacts. In
another study, potential climate change impacts on
production and phenology were also assessed for two
grapevine varieties in Sardinia (Italy) (Muresu, 2012),
by coupling climate models with STICS to generate
future scenarios. Model performance was also
assessed using historical phenology and yield data,
showing a high skill.
STICS was also used to assess the environmental
impacts of crop fertilization. Ruiz-Ramos et al.
(2011) used STICS coupled with a geographic
information system to estimate the amount of NO3-
leaching in La Rioja (Spain). Its performance was
examined by comparing simulations and
measurements of irrigated vineyards. Using STICS
simulations, the authors identified environmentally
safe agricultural practices for mitigating NO3-
pollution. With respect to pests and diseases, Leroy et
al. (2013) applied a bioeconomic model, coupled with
STICS, to test different fungicide treatment strategies
so as to diminish pesticide use in vineyards. In a
recent study, Coucheney et al. (2015) evaluated
STICS performance for several crops, and found poor
performances for N prediction on vine, that can be
due to the fact that the model is more focused on
carbon functioning making simpler assumptions on N
simulation.
In Portugal, some studies have been devoted to the
application of STICS model to grapevines. Under the
SIAMVITIS project (Climate change in Viticulture:
Scenarios, Impacts and Adaptation Measures) an
initial application of this model was achieved,
showing promising results (Coelho et al., 2013; Pinto,
2013). More recently, Fraga et al. (2015), calibrated
the STICS model for three of the most important
Portuguese varieties (Aragonez, Touriga-Franca and
Touriga-Nacional), obtaining a high model skill in the
simulation of yield, phenology and water status.
4. STRATEGIC PLANNING FOR PORTUGAL
4.1 Monitoring and short-to-medium range
prediction
Although grapevine production and phenology in
Portugal were already skilfully modelled by several
statistical approaches, the use of dynamic crop
models in Portuguese viticulture is still in very early
stages and future research on this topic is essential,
particularly taking into account the relevance of this
sector to the national economy, as previously
described. After these models are properly calibrated
and validated for the Portuguese varieties and
environmental conditions, they may become useful
decision support systems for stakeholders in the
national winemaking sector. These models enable the
adjustment of all cultural practices for each specific
location, such as vineyard intervention dates, plant
density, soil and water management, irrigation
efficiency and nutrient management. These models
may be used to predict annual yields and phenological
timings, allowing a more accurate preparation of
vineyard activities, such as spray scheduling and
harvest planning. Crop models also provide
preventive measures on soil conservation options in
order to improve tillage, mulching and application of
fertilizers. Due to the wide range of different terroirs
throughout Portugal, crop models may also allow
identifying the most suitable varieties for a given
region based not only on their thermal requirements,
but also on their tolerance to stress factors, such as
high temperatures, drought, pests and diseases. Given
the benefits of the application of these types of
models to grapevine monitoring and short-to-medium
range prediction they are expect to yield efficiency
gains at the vineyard level, resulting in higher profit
margins for growers.
4.2 Long-range prediction under climate change
Climate change projections are expected to have
significant impacts on viticulture, mostly owing to
changes in the temperature and precipitation patterns
(IPCC, 2013). The expected warming and drying
trends in southern Europe may bring some additional
challenges for grapevine production (Santos et al.,
2011). Temperature projections for the main
viticultural regions in Europe highlight increases in
the growing-season mean temperatures (Duchene and
Schneider, 2005; Jones et al., 2005; Neumann and
Matzarakis, 2011). This warming leads to longer
growing seasons but earlier phenological events
(Chuine et al., 2004; Dalla Marta et al., 2010; Bock et
al., 2011), which may have harmful impacts on wine
quality (Webb et al., 2008a). Some regions are
projected to become excessively dry to grapevine
production without adequate irrigation (Kenny and
Harrison, 1992; Koundouras et al., 1999; Malheiro et
al., 2010; Santos et al., 2013). All these factors
suggest a general lowering of the suitability of the
winemaking regions in southern Europe (Stock et al.,
2005; Malheiro et al., 2010; Santos et al., 2012; Fraga
et al., 2013). More specifically for Portugal,
projections reveal similar changes to other European
regions with Mediterranean-like climates (i.e.
38
warming and drying trends), which can lead to
changes in the suitability of the national viticultural
regions (Fraga et al., 2012). Climate change may
indeed lead to perceivable changes in traditional wine
styles. Furthermore, changes on the frequency and
strength of precipitation and temperature extremes are
also projected for Portugal (Costa et al., 2012;
Andrade et al., 2014).
Crop models are a key tool to determine the impact of
climate change on crop growth and to evaluate the
potential of new viticultural regions (Webb et al.,
2007; Bois et al., 2008; Kwon et al., 2008; Scaglione
et al., 2008; Caffarra and Eccel, 2010; Duchene et al.,
2010; Caffarra and Eccel, 2011) and to prevent, or
minimize, the impact of climate extremes (Molitor et
al., 2014). Crop models can also be used to assess
carbon sequestration and emissions of other
greenhouse gases, assisting the design of mitigation
measures in conformity with the 20-20-20
commitments of the EU Directive 2009/28/EC. Only
by using crop models is possible to fully integrate all
these aspects (Tomasi et al., 2011) and to develop
adequate mitigation and adaptation measures for the
viticultural sector under a changing climate.
Therefore, in forthcoming research, dynamic crop
models will be applied to the Portuguese viticultural
regions under climate change scenarios.
5. CONCLUSIONS
Given the above description, crop models, such as
STICS, can then be considered key decision making
tools for short- and long-term strategic planning in
viticulture. The possibility of obtaining early
predictions of production and development will
contribute to a more efficient winemaking process.
This knowledge is crucial for a timely planning of
field activities, such as determining harvest dates, and
for achieving premium quality vintages. Furthermore,
several studies use crop models to evaluate the impact
of climate change on grapevine. Owing to the above-
mentioned relevance of the viticultural sector to
Portugal, it is vital to improve its resilience and future
sustainability. The efficiency gains, of the integration
of these types of models in the industry, are expected
to increase the competitiveness and sustainability of
the wine sector in Portugal.
ACKNOWLEDGEMENTS
This study was supported by national funds by FCT -
Portuguese Foundation for Science and Technology,
under the project UID/AGR/04033/2013. This work
was also supported by the project “ModelVitiDouro”
- PA 53774”, funded by the Agricultural and Rural
Development Fund (EAFRD) and the Portuguese
Government by Measure 4.1 - Cooperation for
Innovation PRODER program - Rural Development
Programme.
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