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A Regional Analysis of Climate Change Impacts on Canadian Agriculture


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Climate change is expected to alter production opportunities facing agricultural producers. Global studies of climate change impacts on agriculture suggest positive benefits for Canada. Results from Canadian studies tend to be more pessimistic; however, most of these studies are regionally specific and focus on the impacts on specific crops, particularly grains and oilseeds. This paper examines the impact of climate change on Canadian agricultural land values. Changes in land values are used to impute expected changes to agricultural GDP. We find that all provinces benefit from climate change and that previous estimates may be overly pessimistic.
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A Regional Analysis of Climate Change Impacts on Canadian Agriculture 163
A Regional Analysis of Climate
Change Impacts on Canadian
Alberta Research Council
Edmonton, Alberta
Department of Rural Economy
University of Alberta
Edmonton, Alberta
Il faut s’attendre à ce qu’un changement des conditions climatiques modifie les possibilités de production
qui se présentent aux agriculteurs. Des études globales visant à déterminer les conséquences du changement
climatique sur l’agriculture semblent indiquer un effet bénéfique pour le Canada. Les résultats d’études
menées au Canada ont tendance à être plus pessimistes; cependant, la plupart de ces études concernent des
régions spécifiques et se concentrent sur des récoltes spécifiques, en particulier les céréales et les oléagineux.
Notre article examine les conséquences du changement climatique sur la valeur des terres agricoles
canadiennes. Les changements dans la valeur des terres sont utilisés pour évaluer les changements prévus
du PIB agricole. Nous en concluons que toutes les provinces bénéficient du changement climatique et que
les évaluations précédentes sont probablement trop pessimistes.
Climate change is expected to alter production opportunities facing agricultural producers. Global studies
of climate change impacts on agriculture suggest positive benefits for Canada. Results from Canadian studies
tend to be more pessimistic; however, most of these studies are regionally specific and focus on the impacts
on specific crops, particularly grains and oilseeds. This paper examines the impact of climate change on
Canadian agricultural land values. Changes in land values are used to impute expected changes to agricultural
GDP. We find that all provinces benefit from climate change and that previous estimates may be overly pessimistic.
limate change is expected to affect the ability
of land resources to support different types of
vegetation and alter production possibilities for ag-
ricultural producers. Climate change impacts on
global agricultural production are expected to be
negligible overall but positive for North America,
particularly for Canada, a country constrained by
short growing seasons and unfavourable moisture
regimes (e.g., Lewandrowski and Schimmelpfennig
1999; IPCC 2001). Studies that examine Canadian
agriculture specifically show more mixed results.
For example, estimated impacts of climate change
164 Marian Weber and Grant Hauer
on agricultural gross domestic product (GDP) in the
Prairies range from –7 to +7 percent (Brklacich et
al. 1999). However, it is difficult to draw general-
ized conclusions from these studies since they focus
geographically on the Prairies and Ontario, and on
the production responses of specific crops such as
grains and oilseeds.
Historically the agricultural sector has been very
responsive to changes in prices and climatic condi-
tions suggesting that adaptation will play a large role
in reducing the impact or increasing the benefit from
changing climate conditions (Easterling 1996). Pro-
duction response models, like those used in most
Canadian analyses, tend to overestimate the costs
of climate change because the range of adaptive
strategies considered is usually limited to changes
in management variables for particular crops. Pro-
ducers can adapt by changing seeding dates,
fertilization and irrigation applications, and switch-
ing cultivars (e.g., Brklacich and Stewart 1995;
Brklacich et al. 1997). However, producers can also
adapt by switching between agricultural land uses
such as from one crop to another, or from crop to
livestock operations. Production studies that allow
for land-use switching may still overestimate costs
of climate change since adjustments are limited to
a subset of potential land uses (see Brklacich and
Smit 1992; Mooney and Arthur 1990; Singh 1988;
Smit 1987).
The production approach is only valid for regions
having similar physical characteristics to those as-
sumed in the underlying yield models and is not
useful for large-scale interregional comparisons. An
alternative approach to modeling climate change
impacts in agriculture is to use regression analysis
to estimate the relationship between climate and the
value of agricultural production across Canada and
make inferences about the impact of climate change
on agricultural GDP. This is the basis of the
Ricardian approach outlined by Mendelsohn,
Nordhaus and Shaw (1994). The Ricardian approach
assumes that the benefits of location-specific at-
tributes such as climate and soil type which
influence land productivity are capitalized in agri-
cultural land values. It is assumed that producers at
every location behave optimally so that spatial vari-
ation in land values can be attributed to the effects
of variation in climate, soils, and other exogenous
factors on agricultural land productivity. By assum-
ing that farmers can replicate practices in regions
that currently have analogous climate and soil char-
acteristics, land-value estimates can be used to
project future agricultural productivity changes that
account for the full range of current adaptive re-
sponses (e.g., Mendelsohn, Nordhaus and Shaw
1994, 1996, 1999; Segerson and Dixon 1999).
Using the Ricardian approach, Reinsborough
(2003) predicts negligible benefits from climate
change for Canadian agriculture. However,
Reinsborough’s model is aggregated at a very coarse
spatial resolution, masking the responsiveness of
agricultural productivity to changing climate con-
ditions. Furthermore, it is expected that the
distribution of costs and benefits across regions will
be uneven. In this paper we extend Reinsborough’s
analysis and use the Ricardian approach to provide
a more detailed assessment of the impacts of cli-
mate change on Canadian agriculture at both a
national and regional scale. Estimates of agricultural
land values are obtained by overlaying soils and
agricultural census data with climate data projected
on to a 10x10 km grid for Canada. Future climate
scenarios are taken from simulated output generated
by the Canadian Climate Change Modeling and
Analysis Group’s CGCMII, and downscaled to fit
the 10x10 km resolution (Price et al. 2001). The
detailed spatial resolution allows us to focus on vari-
ations in productivity due to local climate and soil
conditions. Impacts are then aggregated at the pro-
vincial level in order to assess regional effects. We
find Canadian agriculture benefits significantly from
climate change, with land values increasing on av-
erage by 65 percent over the next 50 years. This is
equivalent to a $5.24 billion or 16 percent increase
in annual agricultural GDP relative to its current $32
billion value. While all regions benefit from climate
change, the relative gain is greatest for the Prairies.
A Regional Analysis of Climate Change Impacts on Canadian Agriculture 165
British Columbia and the Maritimes benefit the least,
with the coastal regions of these provinces experi-
encing negative impacts. Overall, the ranking of
agricultural land values does not change, indicating
that, in spite of positive climate change impacts, the
Prairies will continue to have the lowest productiv-
ity of land in agriculture.
The results of our model suggest that previous
studies have been overly pessimistic in estimating
the costs of climate change to Canadian agriculture.
However, while we think that the Ricardian approach
is a useful way to proceed, the results should be in-
terpreted as an upper bound on potential benefits
rather than estimates of what will actually occur.
First, the approach requires the regression variables to
be representative of actual factors influencing farm
level decisions, and relies on statistical analysis to iso-
late confounding effects such as interactions between
temperature and precipitation (Lewandrowski and
Schimmelpfennig 1999). In addition, the cross-
sectional approach is limited to adaptations based
on observations for current climate conditions and
may not accurately predict the effect of changes
outside the range of current conditions or changes
in other factors such as CO
concentration levels.
This is a potentially important caveat for Canadian
analysis since the variation in climate across agri-
culturally productive regions is not as large as in
other countries such as the United States where this
methodology has been previously applied. Finally,
adaptation is costly and may be prohibited by a
number of unmodeled factors such as soil chemis-
try, water constraints, the provincial policy regime,
and other location and historic factors that limit ac-
cess to markets (e.g., Adams et al. 1999; Chiotti
et al. 1997).
In the following section we discuss the effect of
the current Canadian climate on regional agricul-
tural production. In the third section we review
previous results illustrating the potential impact of
climate change on Canadian agriculture. Agricul-
tural land rents are estimated in section four and
used in the fifth section to project future climate
change impacts on Canadian agriculture over the
next 50 years. In spite of the limitations of our
model, the overwhelmingly positive results illustrate
the potential direction of change in agricultural pro-
ductivity due to global warming.
Agriculture in Canada is constrained by climate and
soils. Only 10 percent of Canada’s land is economi-
cally viable for agricultural production, which takes
place along a narrow east-west band across the
southern latitudes of the country (Environment
Canada 1976). The most important climate variables
affecting agricultural productivity are temperature,
soil moisture, length of growing season, and sever-
ity of winter conditions (Bootsma 1999). For
example, temperature affects the number of heat
units and growing degree-days available for long
season crops such as corn, sunflowers and potatoes.
Variations in autumn temperatures result in differ-
ences in maturation dates, frost risk, and optimum
seeding dates for over-wintering crops such as win-
ter wheat, clover, and alfalfa. Extreme cold and low
snow cover also affect survival of these crops. Hor-
ticultural crops and tree fruits require mild winter
temperatures and long warm growing seasons. Only
a small area of the country is currently suitable for
growing tree fruits for which moderate winter tem-
perature is the critical growing factor. Finally, the
timing of precipitation is a key factor in productiv-
ity. Excessive spring moisture delays planting and
increases the risk of disease while excessive fall
moisture delays harvest, reducing the quality of
crops and increasing frost risk. Precipitation varia-
tion also affects farm management decisions such
as machinery size, labour requirements, and the
number of days suitable for tillage. In the remain-
der of this section we draw from several agroclimatic
studies that interpret agricultural potential in rela-
tion to macro-climatic variations in order to provide
a regional context for current variations in agricul-
tural productivity (see Chapman and Brown 1966;
Beattie, Bond and Manning 1981; Bootsma 1999;
166 Marian Weber and Grant Hauer
McCrae and Smith 2000). The purpose is to give
the reader a context for climate-induced changes in
agricultural productivity rather than to fully repre-
sent regional agricultural potential.
Canada’s climate is moderated by the Pacific and
Atlantic oceans, as well as the Great Lakes. These
water bodies generate temperate influences in an
otherwise harsh continental climate characterized by
extreme temperatures and low precipitation. On the
West Coast, mountains trap warm moist Pacific air
in winter and slow the movement of high-pressure
systems in summer (Chapman and Brown 1966). The
climate is characterized by low diurnal variation in
summer temperature and infrequent occurrence of
frost in winter. This creates a long temperate grow-
ing season attractive for fruits, vegetables, and
horticultural crops that would otherwise be vulner-
able to frost risk or winter kill. The most productive
agricultural soils are found in the Fraser Valley and
southeastern Vancouver Island where horticulture
and mixed farming predominate. The interior cli-
mate has greater temperature extremes in terms of
both diurnal and seasonal variation. The warmer,
drier montane cordillera is dominated by horticul-
ture and livestock production while the Peace River
region of the Mackenzie Basin resembles the north-
ern area of the Prairies and supports oilseed and
grain production.
The absence of topographic features on the Prai-
ries allows cold air masses to move unimpeded south
from the arctic and warm air masses to move north
from the US. Short, hot summers and very cold win-
ters characterize the Prairie climate. Annual
precipitation is very low, particularly during fall and
winter months. The soils are fine textured with high
fertility and good moisture-holding capacity, mak-
ing crops amenable to irrigation, particularly in the
south, which suffers from regular drought. Produc-
tivity is limited in the north by the boreal shield and
short growing seasons. Plants grow more rapidly in
the north due to longer days so that moisture rather
than degree-days tends to be the limiting factor de-
termining productivity. In fact, high temperatures
may have a detrimental impact on Prairie produc-
tivity since they exacerbate moisture problems and
hasten maturity. The major commodities are grains
and oilseeds although there has been a recent ex-
pansion in red meat, particularly in areas with low
precipitation. Winter kill of forage crops, fruit trees,
and horticultural plants is a constraint on crop choice
resulting in low agricultural diversity.
Southern Ontario and the St. Lawrence valley lie
on a major storm track resulting in high precipita-
tion distributed uniformly throughout the year
(Chapman and Brown 1966). The temperate influ-
ence of the Great Lakes reduces the frequency of
late spring and early fall frosts giving the region a
comparative advantage in the production of fruit and
vegetable crops. As a result, Ontario’s agricultural
sector is the most diversified in the country. Most
of Ontario and Quebec’s agriculture occurs in the
mixed-wood plains ecozone which contains Cana-
da’s most valuable agricultural land. Agriculture
extends into the southern boreal shield and Atlantic
maritime ecozones, however, colder temperatures
and less productive soils restrict agricultural activ-
ity to livestock and forage. The Atlantic provinces
experience more storms than any other region in the
country. The winters are cold, springs late, and sum-
mers cool and cloudy. Moisture is excessive in
spring and fall leading to short growing seasons. In
addition, the soils tend to be acidic with poor tex-
ture. Agriculture in the Atlantic region is mixed with
a concentration on livestock operations. Potato, ce-
real, and hay are the dominant crops.
Table 1 provides a provincial breakdown of agri-
cultural production for the year 1997 and illustrates
the effect of climate on regional agricultural pro-
ductivity and land prices. For the purpose of this
analysis agricultural productivity is measured as the
value of production per hectare. Productivity can
vary either as a result of changes in soil and climate
factors that cause yield variations for outputs, or the
intensity of land use. Intensive land uses are char-
acterized by high value-added per unit of land and
are often accompanied by substantial capital
A Regional Analysis of Climate Change Impacts on Canadian Agriculture 167
Regional Agricultural Productivity
Region Commodity % of Regional Cash % of Canadian % of Canadian
Group Agricultural GDP Receipts Agricultural Land Agricultural GDP
(%) ($M) (%) (%)
British Columbia All 100.0 $1,700 3.5 7
Grains and oilseeds 1.5
Fruits and vegetables 18.5
Dairy 19.5
Poultry and eggs 19.0
Red meats 16.5
Other 25.0
Prairies All 100.0 $14,500 81.0 46
Grains and oilseeds 52.0
Fruits and vegetables 0.5
Dairy 4.0
Poultry and eggs 2.5
Red meats 33.5
Other 7.5
Ontario All 100.0 $6,600 8.3 25
Grains and oilseeds 19.0
Fruits and vegetables 10.0
Dairy 18.5
Poultry and eggs 12.0
Red meats 23.0
Other 17.5
Quebec All 100.0 $4,500 5.1 17
Grains and oilseeds 8.5
Fruits and vegetables 7.0
Dairy 30.5
Poultry and eggs 12.5
Red meats 31.0
Other 10.5
Atlantic All 100.0 $999 1.6 4
Grains and oilseeds 1.0
Fruits and vegetables 8.0
Dairy 22.0
Poultry and eggs 17.5
Red meats 18.5
Other 33.0
Source: McCrae and Smith (2000).
168 Marian Weber and Grant Hauer
investments to increase productivity per unit. Pro-
ducers choose the intensity of their agricultural
operation based on the underlying productivity char-
acteristics of soils and climate, as well as the price
of land, availability of other inputs, and access to
markets. Crops such as grains and forages that re-
quire large tracts of land tend to be grown on the
Prairies where land is relatively cheap. Table 1
shows that the Prairies dominate agricultural pro-
duction both in terms of the percentage of land in
agriculture and the share of Canadian agricultural
GDP. The Atlantic provinces have the least amount
of land in agriculture although on a per hectare ba-
sis, Atlantic farms generate more receipts than farms
in the Prairies. Ontario and Quebec generate the
highest GDP relative to the area in agriculture. High
agricultural land values in Ontario and Quebec are
associated with high-value crops, while high land
values in the Maritimes reflect the dominance of
intensive land uses. Productivity per hectare is low-
est in the Prairies where land is cheap and farms are
large-scale operations efficient for grain and oilseed
Based on climate change scenarios from several glo-
bal climate models (GCMs), the impact of climate
change on the mean temperature over Canada’s land
mass for the next 20 years is predicted to range from
1.39 to 2.68 degrees celsius relative to 1961–90
normals (Canadian Institute for Climate Studies
2001). Predicted increases in mean precipitation
range from 2.61 to 7.67 percent for the same pe-
riod. By 2080, predicted increases in mean
temperature and precipitation will range from 3.64
to 7.47 degrees celsius and 9.13 to 17.83 percent
These increases are not distributed
uniformly throughout the country. Temperature in-
creases are more extreme in the northern latitudes
due to the melting of the polar ice cap, which al-
lows heat from the ocean to escape into the
atmosphere. Most of the increase in precipitation
occurs over the ocean and coastal areas. However,
areas of significant decrease are expected in the mid-
continent. Decreased precipitation coupled with
increased temperatures is expected to exacerbate
moisture problems in regions already facing soil-
moisture constraints.
Estimates of climate change impacts on Canadian
agriculture vary regionally according to soil char-
acteristics and the extent to which the climate is a
current constraint on agricultural production.
ductions in frost risk and crop maturation times
benefit the north more than the south where shorter
maturation times may actually reduce crop yields
(Brklacich et al. 1999; Singh et al. 1998). In the
clay belts of northern Ontario and Quebec, climate
constraints on grain production and corn are relaxed
(Brklacich et al. 1999). Grain yield declines in re-
gions currently producing grain will be offset by
yield increases for higher value crops such as corn
and soybeans. Increased risk due to increased fre-
quency of drought may offset benefits from climate
change (Singh and Stewart 1991; Smit and Brklacich
1992; Smit 1987; Smit et al. 1989). An increase in
warming is also expected to benefit Atlantic Canada,
particularly through expansion of corn, soybeans,
tree fruits, and other specialty crops (Bootsma
1999). Again major changes in precipitation may
offset some of these gains.
It is generally believed that climate change will
have a positive impact on wheat yields in the Prai-
ries (Brklacich and Stewart 1995; Brklacich et al.
1999). Warmer frost-free seasons accelerate the de-
velopment of grain crops and reduce time between
seeding and harvest. Increased yields are also ex-
pected due to the fertilization effects of elevated
levels of CO
. However, increases in crop moisture
stress and accelerated crop maturation rates may
offset these effects in some regions, particularly in
the western Prairies. In the eastern Prairies elevated
levels and increases in precipitation are ex-
pected to increase cereal yields overall (Brklacich
A Regional Analysis of Climate Change Impacts on Canadian Agriculture 169
et al. 1999). Mooney and Arthur (1990) find that
where yields for wheat, barley, and canola decrease,
the substitution of higher value crops such as corn,
sunflowers, and soybeans offsets these reductions.
In addition, traditional crops are expected to migrate
into presently marginal areas. In the southern Prai-
ries (particularly Alberta and Saskatchewan), soil
moisture deficits are expected to limit opportuni-
ties for crop substitution and hasten the decline in
already marginal areas such as Palliser’s triangle
(Delcourt and Van Kooten 1995; Arthur and Van
Kooten 1992). Year-to-year variation in temperature
and precipitation is expected to increase, along with
the probability of extreme events leading to an in-
crease in agricultural risk. Studies of observed farm
level responses to climate variation find evidence
that farmers respond strategically to climate vari-
ability, particularly to dryness (e.g., Brklacich et al.
1997; Smit, McNabb and Smithers 1996). Pulse
crops such as chickpeas and lentils have been shown
to adapt well to dry conditions and are useful for
diversification and risk spreading (Bauder 1998).
While there have been numerous studies of the ef-
fect of climate change on crop yields, few studies
have estimated the economic impact of these
changes in productivity. Most economic studies fo-
cus on the Prairies and Ontario and on the impacts
of dry years, and thus do not provide a basis for
understanding overall impacts (Brklacich et al.
1999). For the Prairies, estimates of the effects of
climate change on agricultural GDP range from –7
to +7 percent, and Ontario exhibits similar trends
(ibid.). The results highlight the need for a compre-
hensive interregional model. In this section we
regress climate and soil characteristics on agricul-
tural land values in order to assess the impact of
climate on agricultural productivity. Land values are
used as an economic indicator for interregional
Data on agricultural land values are based on re-
ported market values of land and farm buildings
obtained from the 1996 Census of Agriculture by
agricultural census subdivision (Statistics Canada
1996a). These values are in 1995 Canadian dollars
and are assumed to reflect the discounted present
value of returns to agriculture.
Demographic in-
formation related to non-agricultural influences on
land values such as average housing values and
population density were obtained from the 1996
Census of Canada by census subdivision (Statistics
Canada 1996b). Soils data were obtained from the
Canadian National Soil Database by soil landscape
polygon (Centre for Land and Biological Resources
Research 1996). Baseline climate data were made
available by Natural Resources Canada and based
on monthly climate normals for temperature and
precipitation from 1961 to 1990 provided by weather
station by Environment Canada. Weather station data
were interpolated to generate historical normals for
mean monthly temperature and precipitation gridded
at 10x10 km
resolution (Price et al. 2000; Price et
al. 2001).
Census and soils data were intersected
with the climate data in a GIS database to define a
new gridded dataset at 20x20 km resolution.
Land values are spatially clustered due to
geoclimatic factors as well as influences such as
proximity to large populations and transportation
networks. Since both population and access to mar-
kets are concentrated in the south there is also a
positive southward trend in land values. We removed
several observations for which agricultural land val-
ues were deemed to be outliers based on the
studentized residual test (Belsey, Kuh and Welsch
1980). These observations were concentrated around
the BC Lower Mainland, Vancouver Island, and Lake
Ontario. Reported market values for land in these
locations are likely elevated by factors unrelated to
agricultural productivity such as proximity to
densely populated metropolitan centres and restric-
tions on transferring land from agricultural to
non-agricultural uses. The final dataset consists of
3,665 observations. The average value of agricultural
170 Marian Weber and Grant Hauer
land in the sample is $2285/ha. Minimum and maxi-
mum values are respectively $273/ha (Newfoundland),
and $33,273/ha (British Columbia).
Climate variables were derived for the midpoint
months of the four seasons (DJF, MAM, JJA, and
SON). January and July climate variables reflect the
effects of annual climate extremes, while April and
October variables reflect the effects of length of
growing season and seasonal variations in moisture.
All climate variables are expressed as deviations
from the mean. Climate variables are expected to
have non-linear impacts on agricultural productiv-
ity due to the existence of optimal heat and moisture
levels for producing crops (Grigg 1995). This im-
plies that the extra benefit of temperature and
precipitation will be lower for observations where
temperature and precipitation are already near opti-
mum levels, and may be negative if temperature and
precipitation exceed the optimum. Non-linearities
are captured with dummy variables (e.g., January
Median) for observations where the observed cli-
mate value is above the median value for the sample.
For example, the negative coefficient on the July
median precipitation variable indicates that the ben-
efit of an increase in July precipitation is lower for
observations where July precipitation is already
above the median.
The combined effect of low precipitation and in-
creased temperature on land values is expected to
be negative due to increased soil moisture stress.
This effect is captured through “temperature interac-
tion” dummy variables (e.g., January Temperature
Interaction) that reflect the effect of increased tem-
perature in areas of very low precipitation. The dummy
variables are constructed for observations where an-
nual precipitation is below the 25
percentile of annual
precipitation observed in the sample. These observa-
tions are concentrated in the southern Prairies.
In order to determine the impact of climate on
agricultural land values we regressed variables rep-
resenting climate, soil, and other related socio-
economic characteristics on the reported market
value of agricultural land. A full description of the
variables included as regressors is provided in the
Appendix. Each observation is weighted by the per-
centage of land in agricultural production. Areas
with a large percentage of land in agriculture are
assumed to provide a better reading on agricultural
practices. We also expect variances in average land
values to be negatively correlated with the percent-
age of land in agriculture. The weighting procedure
gives greater weight to observations on the Prairies
where the percentage of land in agriculture is high-
est. The coefficients and associated t-values for each
regressor are reported in columns 2 and 3 of Table
2. The regression has an R
of 0.75, indicating that
the model explains a large percentage of the varia-
tion in the data. Table 3 shows both actual and
predicted land values. The model significantly
under-predicts land values in Ontario and New-
foundland and over-predicts in Nova Scotia and
Prince Edward Island.
The relationship between climate coefficients and
agricultural land values can be seen by examining
the impact of a one-degree increase in January tem-
perature. According to Table 2, a one-degree
increase in January temperature decreases agricul-
tural land values by $44.20/ha. For observations
where January temperature is above the median, land
values increase by an additional $385.05/ha for a
total impact of $340.85/ha. Note that the net effect
of an increase in January temperature is negative
for observations below the median and positive for
those above, perhaps reflecting a threshold winter
temperature for higher value horticultural and over-
wintering crops. If the temperature increase occurs
in an area where annual precipitation is below the
percentile of observed annual precipitation, land
values decrease by an additional $86.96/ha. In a
similar fashion one can determine the impacts of a
one-degree increase in temperature and a one-
millimeter increase in precipitation for the
remaining climate variables.
Direct interpretation of the regression coefficients
in relation to specific impacts on yields and cropping
A Regional Analysis of Climate Change Impacts on Canadian Agriculture 171
Agricultural Land Value Model
Variable Coefficient t-value
Constant –33.72 (–0.10)
January Rain 2.82 (–0.73)
January Rain Median 13.82 **(4.17)
April Rain 24.60 **(5.72)
April Rain Median 24.06 **(6.10)
July Rain 41.99 **(11.43)
July Rain Median –32.39 **(–6.08)
October Rain –1.76 (–0.41)
October Rain Median –4.36 (–1.21)
January Temperature –44.20 **(–1.85)
January Temperature Median 385.05 **(13.27)
April Temperature 217.69 **(3.33)
April Temperature Median 611.90 **(7.84)
July Temperature 101.01 (–1.59)
July Temperature Median 48.97 (–0.99)
October Temperature –35.39 (–0.43)
October Temperature Median –322.96 **(–4.08)
January Temperature Interaction –86.96 **(–3.17)
April Temperature Interaction –375.89 **(–5.00)
July Temperature Interaction –174.38 **(–2.31)
October Temperature Interaction 488.95 **(4.48)
Housing Value 0.0045 **(9.80)
Rooting Depth 176.89 **(4.28)
Water Capacity 63.19 **(3.91)
British Columbia 1653.4 **(6.68)
Alberta 223.48 **(1.74)
Saskatchewan 61.81 (–0.53)
Ontario –204.45 (–0.98)
Quebec –124.28 (–0.59)
New Brunswick –1766.3 **(–5.91)
Nova Scotia –3626.8 **(–10.74)
Prince Edward Island 663.86 (–1.48)
Newfoundland –2046.8 **(–3.59)
Brunisol –328.36 **(–3.06)
Gleysol 195.66 **(1.76)
Luvisol 147.71 **(2.11)
Podzol –366.17 **(–2.73)
Regosol 21.63 (–0.25)
Solonetzic –18.30 (–0.23)
Adjusted R
= .75 N = 3665
Notes: **Indicates 95 percent level of significance.
Farm Values are in 1995 Canadian dollars.
172 Marian Weber and Grant Hauer
decisions is not possible within the Ricardian frame-
work. However, given the discussion in the second
section, we can speculate to some extent about what
might be driving the results, particularly with re-
spect to the direction of the signs on the coefficients.
The effect of precipitation is positive in all seasons
except the fall. This is not surprising given the im-
portance of soil moisture constraints on the Prairies.
The negative coefficient on October rainfall may
reflect the decrease in crop quality and increase in
frost risk associated with wet weather during har-
vest season. The positive effect of April temperature
may be related to an increase in the length of the
growing season, creating opportunities for switch-
ing to longer season crops that have higher value
per hectare. Similarly, the positive coefficient on
July temperature may indicate that switching to
higher value corn and soybean crops requiring
higher heat units offsets decreases in the value of
grain crops due to quicker maturation. As expected,
the coefficients on the interaction terms are nega-
tive for January, April, and July, reflecting the
combined negative effects of high temperature and
low precipitation on soil moisture. Interestingly, the
interaction effect is positive for October, perhaps
reflecting the association of low precipitation and
warm fall temperatures with reduced frost risk.
Several non-climate variables are included as
explanatory variables in the regression analysis.
Average dwelling value is a proxy for demographic
and urbanization trends that affect regional land
values. Not surprisingly, the results show a positive
correlation between dwelling values and land val-
ues. Provincial dummy variables are included to
reflect the effects of the provincial agricultural
policy regime on land values relative to Manitoba.
Provincial variables are positive for British Colum-
bia and Alberta, and negative everywhere else.
Dummy variables are also included for soil type as
defined by soil order in the Canadian System of Soil
Classification (Soil Classification Working Group
1998). Chernozems are fertile agricultural soils
found throughout the Prairies. The effects of the soil
Climate Change Impacts on Agricultural Productivity by Province 1995–2001
Change in Land Values per Hectare
Number of observations 869 822 755 318 359 244 114 111 7 46 3,665
Actual value 4,096 1,328 810 944 4,405 2,266 2,072 2,070 4,099 3,618 2,285
Predicted value 4,256 1,203 820 857 3,995 2,383 1,869 2,677 4,389 2,811 2,259
Change in value 1995–2051 1,147 1,676 1,553 1,424 2,216 1,462 1,224 776 798 568 1,485
Projected value 2051 5,403 2,879 2,373 2,281 6,211 3,845 3,093 3,453 5,187 3,379 3,744
Change in Annual Farmland Returns
(Percentage of 1995 Agricultural GDP)
Annualized Change* 7% 23% 38% 17% 5% 4% 6% 5% neg. 1% 16%
Note: *Annualized returns based on a 4.7 percent discount rate.
A Regional Analysis of Climate Change Impacts on Canadian Agriculture 173
variables are measured relative to chernozems. Soils
that commonly occur under forests such as brunisols,
and podzols have negative effects on land values
because they are poor quality for agriculture and can
constrain growth and farm management choices in
areas where they are dominant. Luvisols and
gleysols are often associated with chernozems, and
gray-brown luvisols are abundant in the St. Law-
rence lowlands. These soils have positive
coefficients. Although regosols are new undeveloped
soils not suitable for agricultural crops, they have a
positive coefficient reflecting their proximity to
fresh water networks in the southern Prairies. Fi-
nally solonetzic soils, which are associated with
saline parent materials, have a negative effect on
land values.
We use the results from the regression analysis to
project the effects of global warming on agricultural
land values over the period 2021–51. The climate-
change scenario is based on the CGCMII model
developed by the Canadian Centre for Climate
Modeling and Analysis. The climate scenario rep-
resents output for a single model run based on one
projection of GHG and aerosol emissions from
1950–2070. Output from the simulation is down-
scaled to 10x10 km resolution in order to provide a
spatially detailed projection of change in Canada’s
climate suitable for regional impact analysis (Price
et al. 2001). The 2021–51 projection used for this
analysis reflects a moving average calculated from
30 simulated years that are directly comparable to
1961–90 climate normals. This projection shows the
greatest increases in temperature occurring in the
northern and central regions of the country. Winter
minima increase the most, particularly in the south-
ern Prairies where average changes exceed six
degrees during the period. Increases in spring, sum-
mer, and fall temperatures are smaller. Precipitation
increases on average by 5 percent from 2000–70.
However, there are significant regional decreases,
particularly in the southern Prairies and BC (Price
et al. 2001). Overall, the implications for the inter-
action terms used in our study are that the number
of observations for which annual total precipitation
falls below the 25
percentile of 1961–90 normals
decreases by 16 percent. It should be noted that pre-
cipitation is notoriously variable both spatially and
temporally. Therefore, regional results from indi-
vidual GCM simulations can also be expected to
vary significantly.
Table 3 shows the predicted impact of climate
change on agricultural land values by province for
the 30-year average period 2021–51. Climate change
has a positive impact on agriculture in all provinces.
Geographic variation within and between provinces
is illustrated in Figure 1. Predicted climate driven
increases in land values average $1,485/ha for the
country. Relatively, increases are greatest in the Prai-
ries with average gains of $1,551/ha (an increase of
approximately 200 percent compared to 1995). On
the other hand, gains in the Maritimes are small,
perhaps because of limitations in soils and exces-
sive precipitation. In percentage terms the benefits
of climate change appear substantial for the Prai-
ries. However, it should be noted that percentage
changes are measured relative to initial values, and
under the current climate returns to agricultural land
are lowest in the Prairies.
Increases in Prairie land values are driven by sig-
nificant increases in January and April temperatures,
causing an increase in the length of the growing sea-
son and the production of more valuable crops. Dry
conditions temper but do not eliminate predicted
increases in land values in the southern Prairies. The
Peace River region, which was marginal to begin
with, experiences only moderate increases in tem-
perature and precipitation and here land values are
expected to decline, perhaps because winter tem-
peratures do not reach a minimum threshold for
switching to higher value crops. The impacts of cli-
mate change in the Maritimes are negligible and land
values in coastal regions decline, possibly as a result
174 Marian Weber and Grant Hauer
of excessive precipitation and only moderate tem-
perature increases. Land values also decline along
the West Coast. In spite of these trends, climate
change does not alter the relative distribution of land
value as the substantial gains predicted for the Prai-
ries are not large enough to overcome the current
advantages of other regions.
Changes in land value reflect the discounted
present value of changes in future returns to agri-
cultural land and can be used to infer changes in
annualized returns to agriculture relative to 1995
agricultural GDP. Annualized changes are reported
in Table 3 for Canada as well as for each province.
Annualized returns are calculated using a 4.7 per-
cent discount rate. This rate is based on the rate of
profit for farms defined as net income for farms di-
vided by the total value of farms and farmland (see
Mendelsohn, Nordhaus and Shaw 1994). Provincial
changes in annualized returns are based on 1995
provincial agricultural receipts. Farms with nega-
tive returns are assumed to leave the agricultural
sector and are assigned an implicit annual return rate
of zero. The Canadian increase in agricultural GDP
is 16 percent; however, there is a great deal of re-
gional variation in this figure. Newfoundland and
Change in Agricultural Land Values 1995–2051
Change in $/ha
< 0
0 – $1,485
$1,485 – $2,970
> $2,970
A Regional Analysis of Climate Change Impacts on Canadian Agriculture 175
Prince Edward Island experience negligible gains.
Gains for British Columbia, Ontario, Quebec, New
Brunswick, and Nova Scotia are in the range of 4 to
7 percent. On the other hand, gains in the Prairies
are significant — ranging from 17 percent for Mani-
toba, to a high of 38 percent for Saskatchewan.
These increases exceed the 7 percent upper bound
for increases in agricultural GDP reported in
Brklacich et al. (1997). However, the models sur-
veyed in that report do not consider the full range
of adaptive responses available to producers and
therefore underestimate the potential benefits of cli-
mate change. The results are also significantly
higher than those reported by Reinsborough (2003)
who, using the same methodology, finds increases
in agricultural GDP of only $0.9 to $1.5 million rela-
tive to $32 billion. A key reason for the difference
in results between the two models is the scale of
spatial resolution. Reinsborough’s data are aggre-
gated at very low spatial resolution with only 267
data points representing all of Canada. This has a
tendency to smooth the variation in the data and
dampen the effects of climate change. On the other
hand, with 3,665 observations we are able to cap-
ture a greater response in land values to climate and
other exogenous factors. Reinsborough also includes
latitude and solar radiation as regressors and these
variables are highly correlated with temperature in
Canada. As a result, more variance is attributed to
climate in our model, which may partially explain
the higher predicted impacts from climate change.
The results of our analysis may seem overly op-
timistic, given that high temperatures and drought
conditions over the past few years have created enor-
mous hardship for agricultural producers in some
parts of the Prairies. Conventional wisdom asserts
that greenhouse-gas-driven increases in soil mois-
ture deficits will lead to a decline in the agricultural
sector in the southern Prairies while our model pre-
dicts substantial increases in land values for this
area. Thus, some discussion of our results in this
context is warranted. First, it is necessary to em-
phasize that our climate projections are derived from
downscaled results from a single GCM run for a sin-
gle emissions scenario. Given the spatial and
temporal variability of precipitation it is more im-
portant to focus on the overall trend in impacts
suggested by the model than on particular values.
Second, the interaction terms are rough proxies for
potential moisture deficits in the current climate and
are not derived from an underlying water balance
model. Therefore they probably do not capture the
full costs associated with increased temperatures and
decreased precipitation. It does not necessarily fol-
low, however, that future increases in evapo-
transpiration in the Prairies will lead to a decline in
agricultural land values. This is because producers
currently adapt to dry climate through irrigation.
The fundamental assumption which needs to be
addressed is not the degree to which increased
evapotranspiration will lead to a decrease in soil
moisture. Rather it is the extent to which irrigation
will be feasible in regions that experience increases
in soil moisture deficits. This will depend on the
ability to augment supplies, particularly in the south-
ern Prairies where water resources are currently near
or fully allocated. Current water shortages should
not be used as the basis for predicting future water
constraints. Given that most water used for irriga-
tion in Canada is not priced, there is no incentive in
the current system to undertake conservation meas-
ures that might increase the availability of water in
the future. Finally, while the impact of climate
change on the hydrological cycle and potential sup-
ply is debatable, it should be acknowledged that
reductions in available water are possible and fu-
ture water supplies may be inadequate to sustain the
irrigation requirements assumed in this model (see
Lewis 1989).
The Ricardian approach represents an upper
bound on the potential benefits of climate change.
Difficulties with the approach can arise when pro-
jecting for climate impacts outside the natural range
of variability in the data. In particular, the model
176 Marian Weber and Grant Hauer
may underestimate damages associated with a wors-
ening of already poor climate conditions and
overestimate the benefits of temperature increases
in already warm areas. Another problem with the
approach is the implicit assumption that marginal
impacts from climate can be examined separately
and that a continuous gradient of adaptation over
the range of climate variables is feasible. It is likely
that the linkages between the various components
that contribute to agricultural productivity are more
important than individual factors — that is, the in-
fluence of the sum is greater than the individual
parts. For example, Adams et al. (1999) find that
while crops migrate easily within the same
geoclimatic zone, soil barriers prevent migration of
most crops and agriculture technology across large
regions. This would cause the Ricardian approach
to overstate the range of adaptations available to
farmers at different locations. It should be noted that
as returns to agriculture increase there will be pres-
sure to expand the margin of cultivation further north
leading to a climate-driven increase in agricultural
GDP. Darwin et al. (1995) find net increases of
cropland in Canada ranging from 54.5 to 115.4 per-
cent. In addition, Canada’s global position in the
trade of wheat and grain corn is expected to improve
as production prospects in the rest of the world de-
cline (Smit 1989). These additional benefits are also
not captured in the Ricardian framework.
Previous research on the impact of climate change
on Canadian agriculture has suggested that while
climate warming will relax constraints on the grow-
ing season, the benefits of warmer temperatures may
be offset in many regions by increases in
evapotranspiration, soil moisture deficits, and rapid
maturation of grain crops. Previous research has
focused on particular regions, and differing meth-
odologies have made interregional comparisons
difficult. In addition, by focusing on yield impacts
for particular crops, the full range of potential
adaptations has not been considered, overestimat-
ing the costs associated with climate change. We ad-
dress this gap in the literature by developing a
cross-sectional econometric model of agricultural
land values for Canada. Optimal adjustments to cli-
mate variation are assumed to be capitalized in
agricultural land values. Estimates of the impact of
climate on agricultural land values are used to
project changes in productivity due to climate
change. Our results suggest that previous studies
have been overly pessimistic in estimating the costs
of climate change. We find that while all regions
benefit from climate change, the relative gain is
greatest for the Prairies and lowest for coastal re-
gions. In absolute terms, Ontario experiences the
largest gain. The regional ranking of agricultural
land values does not change.
In spite of the limitations of the Ricardian ap-
proach we believe that the results illustrate the
potential direction of change in agricultural land
values. Future research that utilizes the approach
should aim at extending the method to identify
physical constraints to adaptation, such as climate
threshold effects, soil profiles, and soil moisture
deficits that are not fully captured in the model pre-
sented here. Finally, if any of these gains are to be
realized, governments will have to dismantle poli-
cies that may inhibit the adjustment process. Such
policies include provincial crop insurance programs
that cover only select crops based on current crop-
ping patterns, or other agricultural support policies
that target particular activities.
The authors would like to thank Bill Sherman for his in-
valuable assistance, as well as David Price, Mustapha
El-Maayer, Marty Siltanen and Rick Pelletier for com-
ments and data support. We would also like to thank the
referees for their detailed comments and suggestions. This
research was supported in part by the Government of
Canada’s Climate Change Action Fund.
A positive vegetative growth response from CO
tilization is well established. However, the magnitude of
A Regional Analysis of Climate Change Impacts on Canadian Agriculture 177
the response depends on the type of plant and other lim-
iting conditions in the soil (Allen, Baker and Boote 1996).
A growing degree-day is an index used to calculate
length of time it will take a crop to mature and the frost
risk. Growing degree-days reflect the fact that there are
minimum temperatures below which crops do not grow,
and that growth rates are increasing in temperature.
Predicted temperature increases are higher than the
C uniform warming scenarios assumed in Mendelsohn,
Nordhaus and Shaw (1994) and Reinsborough (2003).
However, the 2.5
C value reflects the mean increase in
global surface temperature rather than the increase in tem-
perature expected over North America. Recent global
model simulations suggest that land areas will warm more
rapidly than the global average, particularly at high lati-
tudes in the cold season. Warming in the northern regions
of North America is expected to exceed global mean
warming by more than 40 percent (IPCC 2001).
A summary of research results for Canadian agricul-
tural was compiled by Brklacich et al. (1999) for the
Canada Country Study.
While reported land values may be distorted, there is
no reason to believe that they are systematically biased.
Therefore these values are taken to be a fair proxy of ac-
tual market values of agricultural land. The regression
model is directly comparable to Mendelsohn, Nordhaus
and Shaw (1994), and Reinsborough (2003) who also use
reported market values of farmland including buildings
as the dependent variable.
Climate data were obtained from Meteorological
Service of Canada climate stations, and neighbouring sta-
tions in US states bordering on Canada. The data were
statistically interpolated as functions of latitude, longi-
tude, and station elevation. The resulting spline functions
were used to make maps for each monthly mean variable
in the dataset (Price et al. 2001).
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180 Marian Weber and Grant Hauer
Land Value: Reported market value of land and buildings ($CDN/ha) in 1996 Census of Agriculture.
January Rain: Mean January precipitation (mm) based on 1961–90 climate normals.
January Rain Median: Dummy variable = January precipitation (mm) if above median of mean January
precipitation, or else = 0.
January Temperature: Mean average temperature for January (degrees celsius) based on 1961–90 climate
January Temperature Median: Dummy variable = January temperature (degrees celsius) if above median
of mean January temperature, or else = 0.
January Temperature Interaction: Dummy Variable = January temperature if mean annual precipitation
is below the 25
percentile based on 1961–90 climate normals.
Housing Value: Average dwelling value reported by 1996 census subdivision.
Rooting Depth: Categorical variable reflecting unrestricted rooting depth class (cm) of the dominant soil
landscape from the Soil Landscapes of Canada Version 2.2 (SLCV2.2) database.
Water Capacity: Categorical variable reflecting available water capacity defined as the portion of water in
a soil that can be readily absorbed by plant roots in the upper 120 cm. of the dominant soil landscape
from SLCV2.2.
BC, AB, SK, ON, QUE, NB, NS, PEI, NFD: Dummy variables for all provinces.
Brunisol, Gleysol, Luvisol, Podzol, Regosol, Solonetzic: Dummy variables based on the soil name associ-
ated with the dominant soil landscape from SLCV2.2.
... Indeed, he concludes that the expected effect of climate changes on farmland prices are "unremarkable." Another Canadian study conducted by Weber and Hauer (2003) argues ". . . that previous studies have been overly pessimistic in estimating the costs of climate change. ...
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... This work tries to remedy the absence of a model, which links the following three factors: climate, agricultural production, and economic growth. Indeed, it was found that all studies in this area use partial models to assess the effect of the climate shock either on agriculture, or on economic growth (Molua, 2006;Deressa, 2007;Ginnakopoulous et al., 2005;Kumar & Parikh, 2001;Weber & Hauer, 2003). ...
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This study aims to analyze the direct and indirect impact of future climate changes on agricultural production and macroeconomic aggregates. A dynamic general equilibrium model of the Tunisian economy has been developed, which takes into account the effects of future climate shocks from 2020 to 2050 to assess the impact of future climate change on agricultural production and macroeconomic aggregates. The model is used to simulate various scenarios. The results of the climate shock simulations clearly show that long-term citrus fruits production is showing remarkable declines in the most citrus-producing governorates following a significant drop in water level in dams and level of groundwater table. In turn, cereals are the plants most affected by the long-term reduction in rainfall. As for the olive production, it would show a decline reaching –1.263% between 2020 and 2024 in the level of its production following reduction in rainfall. From a macro-economic point of view, climate change will result in the short- and long-term in a deterioration of certain quantities, notably household consumption, entrepreneurial investment, and the unemployment rate, which decreases by –0.139% between 2031 and 2040. These results underline the need for a long-term agricultural policy to reduce or limit the economic and social consequences of climate change and support economic development.
... 31-32. 8 See, for example, Mendelsohn et al. (1994); Sohngen et al. (1998); Mendelsohn and Dinar (1999); Weber and Hauer (2003)]. 9 See the striking differences in the conclusions of the studies by Tol (2009), p. 36 andHeal (2017), p. ...
... Within Canada, which is one of the world's most important food producing nations, however, relatively few empirical studies exist that explicitly draw together both sociotechnical and environmental data. A few study attempts to do this by exploring corn and wheat production, but these are predominantly grown in southern Ontario and not in the prairies [6,39]. However, among the field crops grown within the Canadian Prairie region, canola (Brassica napus, also known as oilseed rape) is the most valuable, contributing approximately 26.7 billion Canadian Dollars to the Canadian economy each year [28], Statistics Canada 2016. ...
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In most climate change research, agricultural yields are explained as a function of climatic and biophysical factors such as soil, rainfall and temperature. However, the increased use of integrated sensors, digital technologies and robotics within the agricultural sector has dramatically altered the way in which we produce food. Considering both the agriculture industry’s continuing widespread technological innovation and a rapidly changing biophysical environment, there is a need to explore how sociotechnical and climatic variables interact to determine yield. In this paper, we present a regression model derived from Agriculture and Agri-Food Canada (AAFC) yield data, Environment and Climate Change Canada (ECCC) climate and land capability data, and Statistics Canada Census of Agriculture databases that include sociotechnical variables such as farms that use GIS and GPS had access to high-speed internet alongside more traditional biophysical factors to predict canola (rape seed) yields in the southern prairies of Canada. We demonstrated that about 38% of canola yield variability could be explained by temperature and rainfall during the growing season (defined as 3 months of June, July and August) and access to high-speed internet, application of chemical fertilizer, fungicides and average age of farm operators. While of a preliminary nature, our results demonstrate that a better understanding of how climatic and sociotechnical factors interact is necessary to anticipate how climate change may affect the crop yield.
... Because of the rise in temperature at the harvesting stage cause the positive impact on production; furthermore these result also in line with the results of Dasgupta et al. (2013) when they estimated the effect of climatic change on food grain productivity in Indian states. Square form result of the result showed significant relationship at 1 percent level of significance with value -0.00017 these results were like the conclusive results of Weber and Hauer (2003) and Mendelsohn and Reinsborough (2007) both calculated that with the rise in temperature and precipitation Canadian farmland value also raised. Precipitation has a very important variable in the wheat production. ...
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HIGHLIGHTS  The impact of temperature and precipitation are both positive and negative on crop productivity.  High temperature at initial stage of wheat crop in December negatively affect the crop productivity, while high temperature has positive impact at later stages of wheat crop.  Gradually high precipitation on sowing stage positively affect seed germination.  Precipitation at vegetative growth stage positively affect wheat growth.  Increase in Precipitation at harvesting level increases the harvesting losses. ABSTRACT The current research study was conducted to estimate the impact of climate change on wheat production by using panel data from 1998-2014. For this purpose four districts were selected from southern Punjab, Pakistan. Panel model of fixed effect (FE) was estimated at region level for wheat productivity utilizing climatic and non-climatic variables based on season. The conclusion of the study showed that non-climatic, i.e. inputs, number of tractors, area under wheat, number of tube wells and fertilizer consumption in each district have significant impact on the wheat production. The fixed effect model results revealed that the increase in temperature has significance impact on the month of the November and January, while it showed negative impact in the month of April. The results also showed a non-linear relationship of precipitation for the months of April and November.
... Climate model projections show warming temperatures increased precipitation (i.e., in the amount and timing, as well as the form), but also increased evapotranspiration, which will result in decreased summer soil moisture [4,11,12]. Also, an increase in the number of frost-free days (FFD) and growing degree days (GDD) could improve Prairie wheat crop production as well as reduce the number of days between seeding and harvest seasons [13]. Rising temperature could also, however, be favorable for pests, disease, and weeds [14]. ...
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We established the statistical relationships between seasonal weather variables and average annual wheat yield (Hard Red Spring and Durum wheat: Triticum spp.) for the period of 1979–2016 for 296 rural municipalities (RMs) throughout six soil zones comprising the arable agricultural zone of Saskatchewan, Canada. Controlling climate variables were identified through Pearson’s product moment correlation analysis and used in stepwise regression to predict wheat yields in each RM. This analysis provided predictive regression equations and summary statistics at a fine spatial resolution, explaining up to 75% of the annual variance of wheat yield, in order to re-evaluate the climate factor rating in the arable land productivity model for the Saskatchewan Assessment and Management Agency (SAMA). Historical climate data (1885–2016) and Regional Climate Model (RCM) projections for the growing season (May–August) were also examined to put current climatic trends into longer-term perspective, as well as develop a better understanding of possible future climatic impacts on wheat yield in Saskatchewan. Historical trends demonstrate a decrease in maximum temperature and an increase in minimum temperature and precipitation throughout all soil zones. RCM projections also show a potential increase in temperatures and total precipitation by 5 °C and 10%, respectively. We recommended against a modification of the climate factor rating at this time because (1) any increase in wheat yield could not be attributed directly to the weather variables with the strongest trends, and (2) climate and wheat yield are changing more or less consistently across the zone of arable land, and one soil zone is not becoming more productive than another.
... The correlation coefficient ( (Brklacich ans. Smit, 1992, Arthur and Van Kooten, 1992, Weber and Hauer, 2003. ...
The knowledge of the expected production, allows managers of agro-industrial plantations to better organize their technical and financial management. The estimation methods must be easy to apply, while having sufficient precision. This study was initiated to contribute to the development of a method for estimating oil palm production through the use of rainfall data. Experiments were conducted on experimental stations Robert-Michaux of CNRA at Dabou, of PALMCI at Ehania and of PALMAFRIQUE at Anguédedou, located in the southeast of Côte d'Ivoire. The proposed methodology is based on the time between initiation and maturation of the palm regimes. This evolution of the inflorescence is influenced by the climate, through the effects of rainfall. The hydric deficit provides information on the tonnage that will be harvested in the next three years. The results obtained showed that the water deficit was higher in Dabou and Anguédédou than in Ehania. The yields over the four years studied were 16.07 tons/ha/year at Ehania, 12.77 tons/year at Anguédédou and 10.37 tons/ha/year at Dabou. The variations of hydric deficit and that of the production carried out previously make it possible to estimate the production in three years. The climate-based model shows satisfactory results, with error accuracies between 0 and 10% and determination coefficients (R 2) of more than 0.97. They demonstrate the economic and technical interest of such a method in the case of these production localities having information on the climatic conditions of oil palm cultivation.
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The Peace River region is one of the northern agricultural frontiers in Canada, undergoing farmland expansion as well as intensification with input-intensive industrial agriculture. The cropping systems evolved with the rotations between annual grain and perennial forage crops as a prudent adaptation to fragile, crust-forming, runoff prone, poorly-developed, platy-structured acidic Luvisolic soils. In the recent years, there is a decline in acreage of perennial forage seed crops leading to simplified low-diversity cropping systems with heavy reliance on external inputs. The production systems have been prone to rapid evolution of herbicide resistant weeds, and outbreaks of crop diseases and insect pests in the face of global warming. A number of studies conducted in the Peace River region and other parts of North America have shown multiple benefits of integrating perennial forage crops in the cropping systems. By virtue of high root: shoot ratio and perennial growth, forage seed crops can provide multiple ecological services in the fragile Luvisolic soil through increased soil organic matter, carbon sequestration, soil biological diversity, soil structural improvement, nutrient mobilization, crop protection and environmental health, and thereby creating conducive effects to resilient performance of the cropping systems. This review discusses merits of crop rotations in general and those of perennial forage seed crops in particular in the face of changing climate, with special reference to studies conducted in the Prairies and Peace region of western Canada. Research opportunities are highlighted to elucidate multi-dimensional ecosystems services from diversified cropping sequences integrating perennial forage seed crops.
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This paper presents estimates of the effects of changing climate on crop yields for grain corn and soybeans in Ontario, Canada, for 1959–2013. We were able to use a database that is more comprehensive with respect to explanatory variables than some previous efforts had available. Our model includes climate variables, prices, land quality, groundwater level, CO2 concentration, and a time trend. Our results indicate that trends in temperature and precipitation during our study period have not yet resulted in appreciable threats to crop yields in the region.
Agricultural land use in Canada varies greatly regionally in its intensity, vitality and economic prospects. Reviews experience along the agricultural margins. A comprehensive review of the literature is undertaken. Addresses the problem of definition of the margins, of marginal lands and of the 'marginal' condition. The physical, economic and social factors which create retreating margins and advancing frontiers are reviewed, as are the conditions of the frontier and margins and their socio-economic consequences. The role of government programs as a further factor in the advance and retreat is discussed. An annotated bibliography of Canadian sources is provided. -Authors
Canada has been one of the top three wheat (Triticum aestivum L.) exporting nations during the last decade and therefore changes in Canada's capacity to produce wheat could have profound effects on the Canadian economy and the international supply of wheat. Previous studies have indicated that wheat production systems in the Canadian prairies would be sensitive to a range of scenarios for global climate change. This study builds upon this foundation by evaluation the combined impacts of climate change, elevated CO2 levels, and selected adaptive strategies on wheat yields in the Canadian prairies. The CERES-wheat model was used to estimate yields and climate change scenarios were derived from the Goddard Institute for Space Studies (GISS), Geophysical Fluid Dynamics Laboratory (GFDL), and United Kingdom Meteorological Office (UKMO) global climate models (GCMs) 2 × CO2 experiments. Adaptive strategies considered in this study included earlier seeding dates for spring-seeded wheat, conversion to winter wheat and irrigation. The main conclusions were: (i) yield responses were sensitive to climatic change scenario and location, (ii) the climatic change scenarios tended to reduce and in some cases offset the beneficial effects stemming from elevated CO2 levels, and (iii) the effectiveness of response strategies varied according to the location, climatic change scenario, and selected response strategy. Please view the pdf by using the Full Text (PDF) link under 'View' to the left. Copyright © 1995. . Copyright © 1995 by the American Society of Agronomy, Inc., 5585 Guilford Rd., Madison, WI 53711 USA
Understanding the impacts of climate change on economic behaviour is an important aspect of deciding when to take policy actions to prevent or mitigate its consequences. This book applies advanced new economics methodologies to assess impacts on potentially vulnerable aspects of the US economy: agriculture, timber, coastal resources, energy expenditure, fishing, outdoor recreation. It is intended to provide improved understanding of key issues raised in the recent Intergovernmental Panel on Climate Change (IPCC) reports. It concludes that some climate change may produce economic gains in the agriculture and forestry sectors, whereas energy, coastal structures, and water sectors may be harmed. The book will serve as an important reference for the scientific, economic, and policy community, and will also be of interest to natural resource/environmental economists as an example of economic valuation techniques. The volume will clearly be of main importance to researchers and policymakers in the US, but will also be influential as a model for assessment of impacts on economies worldwide.