Open Journal of Applied Sciences, 2013, 3, 369-380 Published Online October 2013 (
Expert Systems Modeling for Assessing Climate Change
Impacts and Adaptation in Agricultural Systems at
Regional Level
Victor Sposito1, Robert Faggian2, Harmen Romeijn1, Mark Downey3
1Department of Infrastructure Engineering, Faculty of Engineering, University of Melbourne, Parkville, Australia
2Department of Agriculture and Food Systems, School of Land and Environment, University of Melbourne, Parkville, Australia
3Victorian Department of Environment and Primary Industries, Mildura, Australia
Received January 22, 2013; revised March 8, 2013; accepted March 20, 2013
Copyright © 2013 Victor Sposito et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Australian agriculture is very susceptible to the adverse impacts of climate change, with major shifts in temperature and
rainfall projected. In this context, this paper describes a research methodology for assessing potential climate change
impacts on, and formulating adaptation options for, agriculture at regional level. The methodology was developed and
applied in the analysis of climate change impacts on key horticultural commodities—pome fruits (apples and pears),
stone fruits (peaches and nectarines) and wine grapes—in the Goulburn Broken catchment management region, State of
Victoria, Australia. Core components of the methodology are mathematical models that enable to spatially represent the
degree of biophysical land suitability for the growth of agricultural commodities in the region of interest given current
and future climatic conditions. The methodology provides a sound analytic approach to 1) recognise regions under
threat of declines in agricultural production due to unfolding climatic changes; 2) identify alternative agricultural sys-
tems better adapted to likely future climatic conditions and 3) investigate incremental and transformational adaptation
actions to improve the problem situations that are being created by climate change.
Keywords: Climate Change; Expert Systems; Analytic Hierarchy Process; Land Suitability Analysis; Chilling;
Adaptation Actions
1. Introduction
The Earth’s climate has changed in the past, is changing
now, and will continue to change in the future. There is
nevertheless extensive scientific evidence that the obser-
ved rapid warming of the Earth since the Industrial Revo-
lution has been caused by human-induced climatologi-
cally phenomena interacting with natural climate pro-
cesses. The enhanced (anthropogenic) greenhouse effect
is a consequence of modifications to the Earth’s atmos-
phere from gases emitted by industrial, transportation
and agricultural activities—termed Greenhouse Gases
(GHG)—and variations in the land surface reflectivity
caused by deforestation, cropping and irrigation. The
impacts are likely to become more serious over coming
decades as global warming is projected to accelerate,
with an increasing risk of drastic changes to natural and
human systems [1-3].
Reducing the vulnerability of coupled socio-ecological
systems (including human settlements, biodiversity, land
and water resources, and economic activities) to the im-
pacts of climate change by means of adaptation is there-
fore critical. “[Planned] adaptation involves changes in
socio-ecological systems in response to actual and ex-
pected impacts of climate change in the context of inter-
acting non-climatic changes. Adaptation strategies and
actions can range from short-term coping to longer-term,
deeper transformations.” [4]
Australia is likely to be one of the most adversely af-
fected countries in the world with respect to potential
climate change impacts on economic activities, especial-
ly possible declines in agricultural production [5-7]. Pro-
duction must nevertheless increase in the future to feed
and clothe the Earth’s growing population—the world
population is expected to increase from approximately
6.9 billion people to over 9 billion by 2100 (based on
United Nations 2009 medium world population fore-
casts). However, without major adaptations to the un-
opyright © 2013 SciRes. OJAppS
folding climatic changes, agriculture in Australia will
struggle to even maintain current production levels [7,8].
In this context, this paper describes a research method-
ology developed for assessing potential climate change
impacts on, and formulating adaptation options for, agri-
culture at regional level in Australia. It is explained by its
application in the Goulburn Broken catchment manage-
ment region (hereafter “Goulburn Broken”) in the State
of Victoria, Australia; see Map 1.
The Study Area—Goulburn-Broken, Victoria
Goulburn Broken covers an area of approximately
24,800 square kilometres, or about 10.5% of the state’s
total area; the region is part of the largest river basin in
the country—the Murray Darling Basin (MDB). It is home
to over 190,000 people, with around 40% of the popula-
tion living in rural areas. Cities and rural towns in Goul-
burn Broken are shown in Map 1. The regional land-
scape comprises low lying floodplains along the Murray
River in the region’s north and mountainous areas in the
south, including Mount Buller with an elevation of 1806
metres above sea level. The main waterways are the
Map 1. Land use in Goulburn Broken, State of Victoria,
Australia, according to the ALUM (Australian Land Use
and Management) classification.
Goulburn and Broken Rivers, whilst Lake Eildon is a
major water storage. Groundwater is also an important
source of water for irrigation within the region.
Goulburn Broken is considered the food bowl of the
MDB with the main agricultural industries being horti-
culture, dairy, cropping, viticulture, forestry and grazing
for sheep (both for wool and meat) and cattle (meat,
dairy or both). The region also supports a large fruit and
vegetable food processing industry centred on its largest
city, Shepparton. The production of timber is also an
important regional employer, and tourism is becoming
increasingly significant to the region. Major land uses in
Goulburn Broken include dryland agriculture (approxi-
mately 51%), irrigated agriculture (about 9%), native
forests (27%), plantation forests (1%), urban uses (4%)
and water bodies (2%).
In 2006-2007, the average area dedicated to irrigated
horticulture in Goulburn Broken accounted for approxi-
mately 280 hectares. The main irrigated crops were pome
and stone fruits (including apples and pears) and wine
grapes. Irrigated horticulture farms in the region held an
estimated 285 megalitres of water entitlements, compri-
sed mainly of surface water rights. In 2006-2007, these
farms used an average total of nearly 200 megalitres of
irrigated water at an estimated rate of about 4 megalitres
per hectare [9].
2. Methodology and Research Objectives
The complete methodology1 developed is shown in Fig-
ure 1. The main research objectives (Phase 1 in the pro-
cess) were the following. 1) To systematically investigate
the climate change impacts on agriculture at regional
level (through a generic approach that can be replicated
elsewhere), 2) To understand the adaptation options avai-
lable to agriculturalists in the region of interest. The fo-
cus in this paper is on the first objective, with some dis-
cussion of the second objective (Section 8, below). Fur-
thermore, current research work in other regions of Vic-
toria is exploring which combination of adaptation mea-
sures might be more beneficial according to different cli-
mate change scenarios. A future paper will explain this
At the core of the methodology there are mathematical
models that enable to spatially represent (i.e. map) the
degree of biophysical land suitability for the growth of
agricultural commodities in the region of interest given
1As the word indicates, a methodology is a logos of methods; is a
structured set of principles which can be adapted for use in a way that
suits the specific nature of each situation in which it will be used [10].
As methodology is essentially theoretical, we can accept a plurality o
theories flowing into methodology, and hence a variety of methods and
models can be seen as legitimate. Our methodology uses a Multiple-
Criteria Evaluation method, climate change models, and Geographic
Information Systems as a tool (and platform) for generating the prod-
ucts of the methodology.
Copyright © 2013 SciRes. OJAppS
Figure 1. Methodology for assessing climate change impacts
and adaptation in agriculture at regional level.
current and projected future climatic conditions (Phase 3
in the process). Land suitability is defined as a measure
of how well the characteristics of a parcel of land match
the requirements of a particular type of land use [11,12].
Modifications in land suitability likely to be caused by
climate change can be assessed by comparing future
suitability maps with current suitability maps. The sec-
tions below describe key components of the methodol-
2.1. Climate Change Projections
In Phase 2, global climate change scenarios derived from
the Intergovernmental Panel on Climate Change (IPCC)
Special Report on Emissions Scenarios—SRES [13] were
scaled down to the regional level. Model-specific monthly
climate projection data were prepared by CSIRO Marine
and Atmospheric Research for this phase. This included
data of its latest global climate change model—termed
CSIRO Mark 3.5—downscaled to a spatial resolution of
only 5 square km grid (0.05˚) for nine key climatic vari-
ables: temperature (maximum, minimum and mean),
rainfall, solar radiation, potential evapotranspiration, re-
lative humidity (at 9 am and 3 pm), and wind speed
[14,15]. Although information for three “marker” SRES
scenarios (B1, A2 and A1FI) were generated for the
years 2030, 2050 and 2070, reference to the A1FI (inten-
sive fossil fuel) scenario and 2050 will mostly be made
in this paper.
Climate data for historical observations were provided
by the Department of Natural Resources and Mines
(Queensland, Australia), together with the Australian Bu-
reau of Meteorology (BoM) weather recordings, through
their SILO program. These data are produced as text files
which then can be presented in a map grid at the same
spatial resolution as the climate change projections.
2.2. Biophysical Land Suitability Analysis
Multi-Criteria Evaluation in a GIS Environment—In
order to define the biophysical suitability of an area for a
specific land use (or activity), several criteria need to be
considered simultaneously. For this purpose, the models
developed in our research integrated a Multi-Criteria
Evaluation (MCE) method with a Geographic Informa-
tion System (GIS). MCE has been developed to investi-
gate a number of alternatives (or choice possibilities) in
the light of multiple objectives (or criteria) and conflict-
ing preferences (or priorities) [16,17]. It is a very useful
method when a set of alternatives need to be evaluated
on the basis of conflicting and incommensurate criteria
MCE has been utilised around the world for land suit-
ability modeling where it is primarily concerned with
how to combine the information from several criteria to
form a single index of evaluation [19-24]. A “criterion”
is a basis for a decision that can be measured and evalu-
ated. “Factors” and “constraints” are the two types of
criteria and they can pertain either to attributes of an in-
dividual decision or to an entire decision set. A “factor”
is a criterion than enhances or detracts from the suitabi-
lity of a specific alternative for the use, or activity, under
consideration. It is generally measured on a continuous
scale. A “constraint” serves to limit the alternatives being
considered [25].
MCE is most commonly applied by one of two proce-
dures. The first, deployed in traditional land suitability
approaches, uses the Boolean aggregation method of
constructing suitability indexes based on a rigid binary
choice of acceptance or rejection. An example is the
Most Limiting Factor (MLF) method that is based on the
land capability classification of the US Department of
Agriculture [26,27].
The second procedure is known as Weighted Linear
Combination (WLC) which uses a soft or “fuzzy” con-
cept of suitability in standard criteria [16]. Instead of the
hard Boolean decision of assigning absolute suitability or
unsuitability to a location for a given criteria, it is scaled
to a particular common range where suitable and unsuit-
able areas are continuous measures. The WLC retains the
variability of continuous criteria and allows criterion to
trade-off with each other. A low suitability defined by
one criterion may be compensated by a high suitability
score in another criterion [25]. Trade-offs between crite-
ria depends on weights assigned to them, and a wide va-
riety of techniques exist for the development of weight-
ing [28]. As depicted in the decision strategy space in
Figure 2, the WLC model is a significant improvement
over the Boolean approach by avoiding its extreme risk
aversion (binary rejection) and extreme risk taking (bi-
nary acceptance) nature [25].
Analytic Hierarchy Process (AHP) and Experts’ Judg-
Copyright © 2013 SciRes. OJAppS
Figure 2. Decision strategy space. Source: [23, p. 17].
ment—In our research, the MCE was implemented using
an Expert Systems Modeling—the Analytic Hierarchy
Process (AHP) developed by Saaty [29,30]. AHP pro-
vides a framework that incorporates experts’ participa-
tion in the decision-making process. Compared to empi-
rical models based purely on the correlation amongst
data, this Expert Systems Modeling incorporates the
knowledge of experts who can address significant issues
and have an understanding of the system of concern that
may be relevant to the required decision-making. The
method is also better suited when the availability of good
data is limited.
As explained by Saaty [30], “basically the AHP is a
method of breaking down a complex unstructured situa-
tion into its component parts; arranging these parts, or
variables, into a hierarchical order [or decision tree]; as-
signing numerical values to subjective judgments on the
relative importance of each variable; and synthesizing the
judgments to determine which variables have the highest
priority and should be acted upon to influence the out-
come of the situation. AHP incorporates both the qualita-
tive and the quantitative aspects of human thought: the
qualitative to define the problem and its hierarchy and
the quantitative to express judgments and preferences
concisely. The process itself is designed to integrate
these dual properties.” When AHP is integrated with GIS,
it can deal with criteria that are interdependent, both
from the effect on land and in the interaction between
spatial units [31].
It is required when using a WLC in MCE that the
weights sum to one. In the AHP, the weights are calcu-
lated by taking the principal eigenvector of a square re-
ciprocal matrix of pair-wise comparisons between the
criteria. All possible pairs of criteria are compared by
experts on a 9-point continuous scale for their relative
importance to determining the suitability of the stated
objectives. The ratings are then introduced in a matrix to
calculate the weights. The accuracy of the weight results
at each level of the AHP hierarchy depends on the con-
sistency of judgments in the pair-wise comparisons. A
consistency ratio (CR) measures the logical inconsis-
tency of judgments and enables the identification of pos-
sible errors in the matrix. If CR 0.10 the pair-wise
comparison matrix must be reformulated [25,29,30].
3. Example of a Biophysical Land Suitability
Model (LSA)
The methodology was developed and applied in the ana-
lysis of the potential climate change impacts on key hor-
ticultural commodities—pome fruits (apples and pears),
stone fruits (peaches and nectarines) and wine grapes—in
Goulburn Broken [30]. However, for illustration purpo-
ses, we explain only the LSA model for apples (Royal
Gala variety—the most common in Australia).
This model was developed in a workshop-type situa-
tion held during approximately 6 hours with participation
of a large group of experts including: land use and envi-
ronmental planners, natural resource analysts/managers,
climate and soil scientists, hydrologists, geographers, and
agriculturalists involved in pome fruits cultivation. Rele-
vant material was distributed prior to the workshop com-
prising information on the methodology, particularly on
the AHP, climate, soil, topography, and scientific cri-
teria for the growth of the crop being examined. At the
commencement of the workshop, the agenda for the day
was discussed, and explanations on the methodology and
available data (including gaps and accuracy) were pro-
vided. A workshop Facilitator assisted in the develop-
ment of the following steps in model formulation 1) con-
struction of the AHP hierarchy, 2) making pair-wise
comparisons and 3) calculating the priorities. To under-
take these steps on-the-fly, we used a computer program
developed by us. It is interesting to note that, in general,
there was agreement in relation to weights and ratings,
and, if there was disagreement, the experts with the
greatest knowledge on the factor in question ultimately
prevailed. Much discussion occurred in relation to the
weightings at the top of the AHP hierarchy, especially
between soils and climate; (as expected) the soil scien-
tists argued that soil was the most important factor influ-
encing pome fruits growth. A counter-argument was
however put that climate, on account of its significant
projected changes should be given a greater weight; and
this position was finally accepted.
The apple LSA model was developed setting a goal to
produce a yield within a range (as agreed at the work-
shop) of 30 to 60 tonnes per hectare per year (t/ha/y).
These are desirable values on the assumption that the
agricultural land is utilised to its maximum potential
through Best Management Practices (BMP). Current
yields, as reported by the Australian Bureau of Statistics
(ABS), show that the average production of all current
orchards in Goulburn Broken is at the lower range of the
stated yields at around 33 t/ha/y. And Figure 3 shows the
overall structure of the model and the landscape branch’s
hierarchy. Because of its significance for this paper, of
the other two branches of the LSA model—soil and cli-
Copyright © 2013 SciRes. OJAppS
Figure 3. Apple land suitability model—overall structure
and landscape hierarc hy.
mate—Figure 4 depicts only the latter.
The AHP hierarchy shows that Climate is the most in-
fluential factor with an assigned weight of 0.65, followed
by Soil (0.30) and Landscape (0.05). These main branch-
es are further divided down the model; each with their
own weighting factors. For example, if one progresses
down the Climate branch, shown in Figure 4, the tem-
perature is the most influential factor with a weight of
0.45. Down this division, temperature at flowering is the
most important with a weight of 0.70. Overall, branches
with a higher weighting will have a large influence on
the model output, whereas branches with smaller weight-
ings will have a lesser impact. The GIS model-based on
the AHP tree was developed using ArcGIS (ArcGIS 9.3,
2008) and PyScripter for Python 2.5 (PyScripter,
2008). The model was then populated with necessary
data. For comparative purposes, climate data were gene-
rated taking the average historical values of the key cli-
matic variables for a 10-year period from 1996 to 2005;
i.e. five years either side of 2000. To simplify the expla-
nation, we will refer to 2000 as the “baseline” year.
Through the use of a decade baseline, any (relatively in-
frequent) climate events, such as extreme weather, are
smoothed out and an even spread of climate data are ge-
Calculation of Chill Units—an interesting factor in-
cluded in the Climate branch of the model is “chill units”
(CU). Deciduous fruit plants, including pome, stone and
grape trees, develop their vegetative and fruiting buds in
the summer. As winter approaches, the already formed
buds go dormant in response to both shorter day lengths
and cooler temperatures. This dormancy, or sleeping
stage, protects buds from the effects of cold weather. The
buds remain dormant until they have accumulated suffi-
cient chilling, or vernalisation, to break the dormancy
[32]. As long as there has been enough accumulated
chilling, the flower and leaf buds would develop nor-
mally. One chill unit is accumulated per hour when tem-
peratures are between defined limits. Maximum chilling
tends to occur between 6˚C - 8˚C, with very little chilling
accumulated below 2˚C or above 12˚C - 14˚C [33].
A modified version of the widely used Utah Vernalisa-
tion Model [34] was employed in our research. In the
Modified Utah Model, proposed by Linvill [35], CU are
accumulated in the following steps:
CU = 0 when (1)
CU=sinwhen021 C
Tt Tt
CU = 1 when (3)
21 CTt
where: CU is chill units and T(t) is temperature at time
t” hours.2
When this model is plotted over a range of tempera-
tures from 5˚C to 25˚C, a smooth curve is formed where
chilling becomes positive after 0˚C, peaks at 1 CU at 7˚C,
reaches negative chilling after 14˚C and strikes 1 CU at
21˚C (Figure 5).
For Goulburn Broken, chilling accumulation was cal-
culated for each individual grid point in the 5 square
kilometre grid, where climate information was available
for the present and the future. A GIS-based version (us-
ing ArcGIS and Python 2.5) of this model was used to
convert daily mean temperatures into CU from the start
of autumn to the end of spring (i.e. from March to No-
vember). The calculated CU for each day were then con-
secutively added together (e.g. day 1 + day 2, day 1 +
day 2 + day 3, and so on) to form an accumulated chill-
ing curve for the entire study region over that nine month
period. This procedure was repeated for the three future
years (2030, 2050, 2070) using the IPCC emission sce-
nario and mean temperatures. See Figure 5.
In this figure, chilling starts on March 1st (Day 0), de-
clines during early autumn due to relative warm condi-
tions, increases once the cooler weather begins during
late autumn and winter, and declines again in late spring
as the temperature increases. Stone, pome and grape trees
enter bud dormancy when they discern the cooler wea-
2For the calculation of CU in this model, hourly temperatures are re-
quired. Also, the observed temperature used for this step should be
determined based on time of day and length of day. For example,
minimum temperatures should be used during night time and maxi-
mum temperatures during daylight hours. For our research, only daily
mean temperatures were available; therefore, to estimate hourly mean
temperatures the value was multiplied by twenty four.
Copyright © 2013 SciRes. OJAppS
ther, or when positive chilling begins. [Some other
species respond to day/night length]. The annual chill
accumulation is hence defined as the largest section of
the curve with a positive gradient, i.e., the difference be-
tween the maximum value and the minimum which pre-
cedes it [33].
For Goulburn Broken, in the baseline, the positive
chill begins at the end of March and continues to the end
of October and the start of November. The accumulated
chilling value is 2723 CU where the minimum is 179.7
CU on day 30 (relative to March 1st) and the maximum
is 2543.8 CU on day 250; this reflects a chilling period of
about 220 days.
When the baseline curve is compared with the future
years of 2030, 2050 and 2070, a noticeable decrease in
accumulated chilling over the nine month period occurs.
For example, 2050 has an accumulated chilling of 1690.6
CU (maximum of 1193.1 CU, minimum of 497.1 CU).
Accompanying this decrease, accumulated chilling shows
a delay in the start of the period when positive accu-
mulation occurs. Thus, 2050 has a starting point for po-
sitive chilling on approximately day 60 (relative to
March 1st) compared with day 30 for the base years.
There is also a shortening of the chilling period.
This pattern of delayed start to the beginning of the
positive chilling period and its shortening resulting in
lower accumulated chilling becomes more pronounced in
each following future year. For example, by 2050 the
maximum accumulated chilling occurs at about day 210
giving a chilling period of roughly 150 days. By 2070,
the maximum chilling occurs at around day 213 with a
total chilling period of about 122 days.
4. Biophysical LSA–Model Robustness and
The execution of the LSA model produces a composite
map that ranks areas in terms of suitability for the growth
of apples; it has an index range of 0 to 1, where 0 means
a site which is deemed to have no potential for growing
apples and 1 represents a site deemed ideal for growing
apples (i.e., 100% suitability). The production of a
particular area can be estimated by multiplying a value in
the yield range defined at the top of the AHP hierarchy
by the suitability index. For example, in an area with a
suitability of 0.8 (or 80%) we could expect, other things
being equal, a yield of (0.8 × 30 t/ha/y = 24 t/ha/y).
Model Robustness—the resultant suitability map was
sent for validation purposes to the same group of experts
that participated in the development of the LSA model. If
inconsistencies were perceived by any of the experts,
weights and ratings in the model were adjusted and a
new suitability map was generated. This process was
repeated until every participating expert was satisfied
with the output map. The model was also examined in
relation to its robustness through two (desk-top) methods:
climate sensitivity analysis and Pearson’s product-mo-
ment correlation coefficient.
Analysis of the sensitivity of the LSA model to cli-
matic variables, as per the former method, is important
because if the model does not respond as expected, its
predictive capacity is low and it is not fit-for-purpose.
Due to the strong influence that climate has in the AHP
hierarchy, there should be a response to seasonal vari-
ability in the suitability ratings produced by the model.
Sensitivity analysis hence focused on examining the im-
pacts of maximum and minimum temperature and total
annual rainfall, as these variables have a heavy weighting
in the LSA model. LSA outputs were developed for sev-
eral seasons and the influence of each season on the land
suitability was then assessed. It was seen that shifts in the
climate patterns caused concomitant modifications in the
suitability of the land resource for grape production. The
results thus showed that the biophysical land suitability
model for apples behaved in a responsive fashion and
was sensitive to the climatic changes.
The Pearson’s product-moment correlation coefficient
is a measure of the correlation, or linear dependence,
between two variables. It gauges the strength of the rela-
tionship between two variables and gives a value in the
range of 1 to 1. A strong negative or positive correlation
would typically have a value near to the upper (+1) or
lower maximums (1) of the range, whereas no correla-
tion would have a value close to, or at, 0. For validation
purposes, the two variables analysed were 1) change in
land suitability due to climate, landscape and soils and 2)
change in grape production in terms of yield. The values
of the LSA model were compared with actual production
figures provided by the ABS. The application of this
method showed that there was a strong positive relation-
ship between land suitability and actual apple production
in a given year: as land suitability in a particular area
increased or decreased, apple production was seen to
increase or decrease in response. This result confirmed
that LSA model outputs correlated well with actual pro-
duction figures.
It should be noted that existing land uses are the con-
sequence of many factors, including past and current
market conditions and tradition, whilst the LSA maps
primary reflect biophysical conditions.
Results—The desk-top validation confirmed the LSA
model performed as expected. Land suitability was then
ascertained using widely diverging climate change sce-
narios to cover a broad range of plausible futures. The set
of resultant maps illustrate where and how the suitability
of the land resource is likely to alter if climatic changes
unfold as projected by the IPCC SRES scenarios consi-
dered. The metrics of the model are assumed to remain
constant. As previously mentioned, only the results from
the IPCC SRES A1FI scenario are discussed in this paper.
Copyright © 2013 SciRes. OJAppS
Copyright © 2013 SciRes. OJAppS
Figure 4. Apple Land Suitability Model—climate hierarchy.
Accumulated Chilling from M arch 1st to November 30th
2543. 770191
-179.65397 6
1911. 265987
1193.1429 26
371.98986 1
0306091121 152183 213244 274
Day number f r om M arch 1st
Accumul at ed Chil ling ( CU)
Figure 5. Accumulated chilling from March 1st to November 30th. CU refers to chill units; Base refers to the years 1996 to
Maps 2-4 show, respectively, the results of LSA for the
baseline (average climatic conditions in the period 1996-
2005; noted as year 2000), year 2050 and the change in
land suitability between these two years. Red, orange,
yellow and green colours in Maps 2 and 3 and associated
legends represent, respectively, very low (0% to 10%),
low (20% to 40%), moderate (50% to 70%) and high
(80% to 100% suitability) ranges. A large area in the
south (shown in grey colour in the maps) and portions of
the northwest and east of the region are restricted to pro-
tect native vegetation and other natural landscapes.
Map 2 shows that most areas in Goulburn Broken are
currently high in suitability to produce 30 - 60 t/ha/y of
apples. Only a small scattering of areas show a moderate
suitability; in particular, areas between Numurkah and
Shepparton and north of Kyabram display this ranking.
Map 3 for 2050 depicts a large change in the land suit-
ability for apples from the high to the moderate catego-
ries. The northern regional areas (north of the town of
Euroa) have shifted from a high land suitability category
to moderate land suitability ranges. In the southern re-
gional areas, land suitability has changed from the high
suitability categories of 90% and 100% to the high suit-
ability category of 80%. These changes are largely a
consequence of the projected increases in temperature
and concomitant decreases in rainfall across the region.
In the comparison between the baseline and 2050
(Map 4), a decrease indicates that there has been a 10%
change from a higher suitability class to a lower suitabi-
lity class (e.g. from 90% to 80%); similarly an increase
indicates that there has been a 10% change from a lower
class to a higher class (e.g. from 80% to 90%). A high
increase denotes that there has been a 20% change from a
low suitability class to a higher suitability class and a
high decrease indicates that there has been a 20% reduc-
tion from a higher suitability class. No change means that
there has been no variation between the baseline and the
year of the comparison. Therefore, from the baseline to
the year 2050 changes are quite pronounced with over
half of the region displaying a decrease in land suitability.
This is primarily seen in the areas south of the town of
If a new variety of apples (or any commodity) is better
adapted to the likely future climatic conditions, a new
LSA model can be developed and parameterised with
new weights (based on the new data) to investigate
whether an anticipated improvement in performance will
actually occur.
5. Discussion
The integration of MCE methods, particularly the AHP,
with GIS is fairly well established in theory and practice
in the analysis of land uses [18-24]. The linking of the
AHP with climate change projections to determine pos-
sible changes in agricultural land suitability as a conse-
quence of potential climate change, we believe, a novelty
of the methodology outlined in this paper. It proved
Copyright © 2013 SciRes. OJAppS
Map 2. Apple (Royal Gala) land suitability for Goulburn
Broken Region—2000 (Baseline).
to be particular useful in assessing likely biophysical
impacts in the future according to a multiplicity of sce-
narios (i.e. possibilities). The methodology was applied
to examining the climate change impacts on the produc-
tion of several horticultural commodities in Goulburn
Broken. The following general comments hence refer to
the results of the application of the methodology to all
the commodities analysed in that region—pome fruits
(apples and pears), stone fruits (peaches and nectarines)
and wine grapes [36].
The assessment indicated that these horticultural com-
modities will be impacted in various ways by projected
climatic changes.
Biophysical land suitability will likely change for all
of the modeled commodities; in particular, there will be
reductions and shifts in the areas most suitable for grow-
ing these crops. Increased temperatures will bring for-
ward the timing of crop developmental stages with likely
effects on flowering, pollination and harvest dates. This
will be especially important in wine grapes cultivation,
where a warmer climate could accelerate the progression
of phenological stages of the vine—budburst, flowering
and veraison so that ripening occurs earlier in the season
[37]. Modifications in both the total amount of rainfall
Map 3. Apple (Royal Gala) land suitability for Goulburn
Broken Region—2050 (A1FI Scenario).
and periods in which rain falls, combined with changes
in evapotranspiration, are likely to reduce water runoff to
storages and affect soil moisture. The possible increase in
water demand combined with reduced water availability
indicates that the efficient use of water resources will be
a major consideration for horticultural industries in the
Australia’s agricultural industries have always expe-
rienced highly variable climatic conditions. Nevertheless,
projected climate change will pose new and major chal-
lenges. For moderate levels of climate change (e.g. below
1.5˚C), incremental adaptations or adjustments to exist-
ing agricultural systems could lessen impacts and enable
producers to take advantage of opportunities. Importantly,
there are many adaptation actions of this type; they in-
1) Crop management: reduced sunburn, improved co-
lour and elimination of bird damage can be achieved
through hail netting, which was found to be particularly
suited in high-yielding, intensive apple orchards systems
[38] and in table grape production [39]. Strategies to
protect grapevines during heatwaves (i.e. a prolonged
period of excessive warm conditions) include a) maxi-
mise transpirational cooling with, for example, the use of
Copyright © 2013 SciRes. OJAppS
Map 4. Apple (Royal Gala) land suitability change Goul-
burn Broken Region—2000 (Baseline) to 2050 (A1FI Sce-
sprinklers, and b) minimise incoming radiation intensity
(or bunch exposure) by natural shading provided by the
canopy or through new orientation (applicable to new
vineyards) [40].
2) Varietal selectionintroduction of breeding varie-
ties of the horticultural crops that may be more suited to
the new climatic conditions will assist in reducing some
of the impacts mentioned above [6,41].
3) Water management: improve water application effi-
ciency and minimise water loss by adopting the most
efficient water saving techniques will assist growers to
cope with the limiting supply of water. For example, a
study by O’Connell and others [42] in Goulburn Broken
on the responses of fruit trees to reduced irrigation in
micro-irrigated, high-density apple and peach orchards
demonstrated that better matching of water application to
the evaporative surface of the orchard canopies (i.e. ef-
fective canopy cover—ECC) can substantially reduce ir-
rigation water use in that region without compromising
yield and fruit quality.
4) Weed, pest and disease management: several eco-
logical approaches now exist for this purpose; they are
based on integrated practices that affect pest population
shifts and management rather than the complete “con-
trol” of a particular organism [43].
There are however limits to the effectiveness of the
types of incremental adjustments that have proven useful
in maintaining or increasing agricultural production in
the highly variable Australian climatic conditions [6].
Climate change will probably expose existing agricul-
tural systems to conditions which are likely to be outside
the coping ranges of incrementally adaptive management
practices and technologies. As the comprehensive defini-
tion of adaptation [4] highlights, adaptation also encom-
passes far-reaching change: “longer-term, deeper trans-
formations”. Therefore, a range of non-incremental, or
transformational, adaptations must also be considered.
Transformational adaptations of agriculture were recently
recognised as a priority by the Australian Primary Indus-
tries National Adaptation Research Plan [http//
au/about-piarnn/national-adaptation-research-plan; accessed
on 2 March 2012] and is an emerging field of research
[44,45]. Transformational adaptations could involve chan-
ges from existing activities to production systems or pro-
ducts that have been previously rare or unknown in the
region of interest (i.e. changes in purpose), or transfer-
ring a production system and/or agriculturalists to ano-
ther region that has (or will become) more suitable (i.e.
changes in location). The latter could be however costly
for established agricultural systems such as pome and
stone fruit orchards and grapevines yards.
Agricultural research organised along traditional dis-
ciplines seems increasingly inadequate to address the
growing complexity, uncertainty and risk brought about
by climate, and other powerful drivers of, change. Such
research needs to be holistic and trans-disciplinary in de-
veloping systems of adaptive governance and be under-
taken through inclusive and participatory processes
6. Concluding Remarks
The Earth’s climate is changing and further changes
seem unavoidable in the foreseeable future, even if ap-
propriate actions are taken to reduce GHG emissions. For
agricultural industries to continue to thrive in a complex,
uncertain and risky future, we need to anticipate the
likely changes and develop and implement adaptation
strategies now. The methodology described in this paper
provides a sound analytic approach to 1) recognise re-
gions under threat of declines in agricultural production
due to unfolding climatic changes, 2) identify alternative
agricultural systems better adapted to likely future cli-
matic conditions and 3) investigate incremental and
transformational adaptation actions to improve the prob-
lematic situations that are being created by climate chan-
Copyright © 2013 SciRes. OJAppS
7. Acknowledgements
The authors would like to thank: the Department of En-
vironment and Primary Industries for providing funding
for this research; Kevin Hennessy, Penny Whetton and
John Clarke, CSIRO Marine and Atmospheric Research,
for providing climate change information and advice,
particularly a version of CSIRO OzClim tailored to the
delivery of high resolution data for Victoria; DEPI staff
including David Rees, Karl Sommer, Ian Goodwin, An-
gie Grills, Cathy Mansfield, Sue Richards and Henry
Schneider, who greatly contributed to the initial deve-
lopment of the LSA models for horticultural production.
Two anonymous reviewers are also thanked for their
thoughtful suggestions to improve the submitted manu-
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