Potential impacts of climate change on corn, soybeans and barley ...

Potential impacts of climate change on corn, soybeans and barley yields in Atlantic Canada A. Bootsma1, S. Gameda1, and D. W. McKenney2 1Agriculture

and Agri-Food Canada, Eastern Cereal and Oilseed Research Centre, Ottawa, Ontario, Canada K1A 0C6; and 2Canadian Forest Service, Great Lakes Forestry Centre, Sault Ste. Marie, Ontario, Canada P6A 5M7. Received 2 April 2004, accepted 2 March 2005. Bootsma, A., Gameda, S. and McKenney, D. W. 2005. Potential impacts of climate change on corn, soybeans and barley yields in Atlantic Canada. Can. J. Plant Sci. 85: 345–357. In this paper, relationships between agroclimatic indices and average yields of grain corn (Zea mays L.), soybeans (Glycine max L. Merr.) and barley (Hordeum vulgare L.) in field trials conducted in eastern Canada are explored and then used to estimate potential impacts of climate change scenarios on anticipated average yields and total production of these commodities for the Atlantic region for the 2040 to 2069 period. Average yields of grain corn and soybeans were highly correlated (R2 = 0.86 and 0.74, respectively) with average available crop heat units (CHU), with yields increasing by about 0.006 t ha–1 CHU–1 for corn and 0.0013 t ha–1 CHU–1 for soybeans. The explained variance was not improved significantly when water deficit (DEFICIT) was included as an independent variable in regression. Correlations between average yields of barley and effective growing degree-days (EGDD) were low (R2 ≤ 0.26) and negative, i.e., there was a tendency for slightly lower yields at higher EGDD values. Including a second-order polynomial for DEFICIT in the regression increased the R2 to ≥ 0.58, indicating a tendency for lower barley yields in areas with high water deficits and with water surpluses. Based on a range of available heat units projected by multiple General Circulation Model (GCM) experiments, average yields achievable in field trials could increase by about 2.6 to 7.5 t ha–1 (40 to 115%) for corn, and by 0.6 to 1.5 t ha–1 (21 to 50%) for soybeans by 2040 to 2069, not including the direct effect of increased atmospheric CO2 concentrations, advances in plant breeding and crop production practices or changes in impacts of weeds, insects and diseases on yield. Anticipated reductions in barley yields are likely to be more than offset by the direct effect of increased CO2 concentrations. As a result of changes in potential yields, there will likely be significant shifts away from production of barley to high-energy and high-protein crops (corn and soybeans) that are better adapted to the warmer climate. However, barley and other small grain cereals will likely remain as important crops as they are very suited for rotation with potatoes. There is a need to evaluate the potential environmental impacts of these possible shifts in crop production, particularly with respect to soil erosion in the region. Key words: Crop heat units, growing degree-days, water deficits, crop yields, climate change, Atlantic region Bootsma, A., Gameda, S. et McKenney, D. W. 2005. Incidence éventuelle du changement climatique sur le rendement du maïs, du soja et de l’orge dans la région canadienne de l’Atlantique. Can. J. Soil Sci. 85: 345–357. Les auteurs examinent les liens entre les indices agroclimatiques et le rendement moyen du maïs (Zea mays L.), du soja (Glycine max L. Merr.) et de l’orge (Hordeum vulgare L.) lors d’essais sur le terrain effectués dans l’est du Canada et s’en servent pour évaluer les répercussions éventuelles d’un changement climatique sur le rendement prévu et la production totale de ces cultures dans la région de l’Atlantique, entre 2040 et 2069. Le rendement moyen du maïs grain et du soja est fortement corrélé (R2 = 0,86 et 0,74, respectivement) au nombre moyen de degrés-jours (DJ), le rendement augmentant d’environ 0,006 tonne par hectare et par DJ pour le maïs et de 0,0013 tonne par hectare et par DJ pour le soja. L’inclusion du déficit hydrique comme variable indépendante à l’analyse de régression n’améliore pas la variance expliquée de manière significative. Le rendement moyen de l’orge est peu corrélé (R2 _ 0,26) et de manière négative avec le nombre de degrés-jours de croissance (DJC), à savoir le rendement diminue légèrement quand le nombre de DJC augmente. L’inclusion d’un polynôme du deuxième degré pour le déficit hydrique à l’analyse de régression augmente la valeur R2 à ≥ 0,58, signe que le rendement de l’orge a tendance à être plus faible dans les régions qui connaissent de forts déficits hydriques ou des excédents d’eau. Selon la fourchette de degrés-jours que prévoient maintes expériences réalisées avec le modèle de circulation générale (MCG), le rendement moyen obtenu lors des essais sur le terrain pourrait augmenter de 2,6 à 7,5 tonnes par hectare (de 40 à 115 %) pour le maïs et de 0,6 à 1,5 tonne par hectare (de 21 à 50 %) pour le soja, d’ici 2040 à 2069, sans compter l’incidence directe d’une plus forte concentration de CO2 dans l’atmosphère, des progrès réalisés au niveau de l’hybridation et des pratiques agricoles ainsi que des conséquences variables des changements que subiront les mauvaises herbes, les insectes et les maladies sur le rendement. La baisse prévue du rendement d’orge sera sans doute plus que compensée par l’incidence directe de l’augmentation de la concentration de CO2. Avec la modification du rendement potentiel, il se pourrait qu’on assiste à l’abandon de l’orge pour des cultures plus protéiques (maïs et soja), mieux adaptées à des températures plus élevées. Toutefois, l’orge et les céréales secondaires resteront vraisemblablement des cultures importantes, car ils se prêtent bien à l’assolement avec la pomme de terre. On doit évaluer les répercussions éventuelles des changements environnementaux sur les modifications que pourraient subir les rotations, surtout en ce qui concerne l’érosion des sols dans la région. Mots clés: Degrés-jours, degrés-jours de croissance, déficit hydrique, rendement, changement climatique, région de l’Atlantique

Abbreviations: CHU, crop heat units; DEFICIT, water deficit; EGDD, effective growing degree-days; GCM, General Circulation Model; GHG, greenhouse gases; P, precipitationl; PE, potential evapotranspiration 345

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The gradual increase of greenhouse gases (GHG) in the earth’s atmosphere is expected to induce changes in climate that could significantly affect the suitability/capability of the Atlantic region of Canada to produce specific crops. The potential impacts of climate change on agroclimatic indices of significance to crop production in the region were examined by Bootsma et al. (2005) for the 2040 to 2069 time period, using the outputs from General Circulation Model experiments. Information is needed on the potential impacts that these changes in climate are likely to have on crop yields and production to make decisions on policies and management practices that will take advantage of the positive impacts or opportunities and minimize the negative effects (Smit and Skinner 2002). The indices evaluated were crop heat units (CHU), effective growing degree-days (EGDD) and water deficits (DEFICIT), defined as potential evapotranspiration minus precipitation. Average CHU available in the main agricultural areas of the region were expected to increase by 500 to 700 units, i.e., from 2300 to 2600 CHU for the baseline (1961 to 1990) period to 3000 to 3200 CHU by the year 2040 to 2069, based on the output from the Canadian GCM (Bootsma et al. 2005). Variability among GCMs indicated that increases in CHU could range from 450 to 1650 units, thus resulting in available CHU that range from 2650 to 4000 units. Average EGDD were expected to increase by about 400 units, i.e., from 1400 to 1600 units for the baseline period to 1800 to 2000 units by the year 2040 to 2069. However, on the basis of multiple GCM outputs, a range of values from 1700 to 2700 EGDD for the future period was considered realistic. Average water deficits for the baseline period were typically from –25 mm (a surplus) to over 100 mm in the main agricultural areas. These increased by 25 to 50 mm in central New Brunswick but decreased by up to 25 mm in some other agricultural areas for the future time period. Thus by the year 2040 to 2069, a significant area of the interior of New Brunswick had a deficit of over 150 mm, while in some other agricultural areas the surplus could exceed 50 mm. Consideration of multiple GCM outputs indicated that decreases in DEFICIT up to 100 mm (i.e., increased surpluses) were realistic, while the range for increases was not significantly changed. There has been little research done on the potential impacts of climate change on crop production in the Atlantic region of Canada. Many studies have simulated the potential effects of climate change on crop yields using crop growth models such as EPIC, SERES and CROPGRO (e.g., Easterling et al. 1993; Brklacich and Stewart 1995; Phillips et al. 1996; Dhakhwa et al. 1997; Singh et al. 1998; Alexandrov and Hoogenboom 2000; Laurila 2001; Southworth et al. 2002; Tubiello et al. 2002), but none of these dealt with the Atlantic region of Canada. De Jong and Li (2001) simulated the effects of climate change on barley yields in Canada using the EPIC model. They included one site from the Atlantic region (Charlottetown) where simulated yields of barley increased by approximately 10%. There is also extensive literature on the effects of environmental factors such as temperature, precipitation and water deficits on the yields of corn, soybeans and barley (Hunter

et al. 1977; Dwyer and Stewart 1987; Tollenaar and Bruulsema 1988; Ziska and Bunce 1995), although the focus of much of the work has been on phenological development. Much work has been done in controlled environments to investigate the response of specific hybrids/cultivars to environmental conditions, which does not consider the impacts that changes in hybrid/cultivars can have under a changed climatic regime. For example, shorter grain-filling period at higher temperatures have been associated with lower grain yields in corn (Hunter et al. 1977; Badu-Apraku et al. 1983; Muchow et al. 1990), suggesting that climate warming will result in lower corn yields. However, when corn hybrids are grown that are adapted to each heat unit zone in Ontario, higher yields are generally achieved in areas with longer and warmer growing seasons (Ontario Corn Committee 1998). The purpose of our study was to (i) examine relationships between average agroclimatic indices and average crop yields, based on yield data gathered from field variety/cultivar trials conducted in eastern Canada, and (ii) use these relationships to postulate possible scenarios of change in yield and production of these commodities for the Atlantic region by the year 2040 to 2069, based on the projected changes from the GCM experiments. We consider our method to be a type of spatial climate analogue approach, i.e., where crop yields in the Atlantic region under a warmer climate are anticipated to be similar to present-day yields achieved in warmer regions under comparable moisture regimes. Spatial analogues have been used in the past for gaining insight into adaptation to climate change, and are implicit in the Ricardian or hedonic approach used in assessing agricultural consequences of global climate change (Mendelsohn et al. 1996; Schneider 1997; IPCC 2001). However, spatial analogues cannot account for the direct effect of increased CO2 concentrations on yield, which may be substantial, depending on the type of crop (Warrick 1988), and thus they tend to underestimate the benefits or exaggerate the negative impacts of climate change. Spatial analogues also do not account for potential impacts of technology and genetic improvements on crop yields over time, which may also be substantial (Voldeng et al. 1997; Specht et al. 1999; Tollenaar and Wu 1999). METHODS General Procedures To assess the potential impacts of climate change on crop yields and production in the Atlantic Provinces, data on average yields were assembled from corn, soybean and barley trials conducted at various locations in Ontario, Quebec and the Atlantic region. Crop variety trials are managed using accepted agronomic practices with appropriate fertilizer and herbicide applications for each region (Ontario Corn Committee 1998; Atlantic Field Crops Committee 1999; Cober and Guillemette 2000). Therefore, nutrients and weeds are normally not limiting factors in yields obtained from these trials. However, trials are conducted under rainfed conditions and therefore yields may be affected by moisture stress in some years. Agroclimatic indices

BOOTSMA ET AL. — CLIMATE CHANGE AND CROP YIELDS IN ATLANTIC CANADA

were computed as described in Bootsma et al. (2005), using climate data interpolated to the approximate latitude and longitude of each field trial location. Regression analyses were used to quantify the relationship between average crop yields and the various agroclimatic indices. These algorithms were then used to approximate potential impacts of changes in the climate on crop yields and production in the Atlantic region. Yield data were included from both within the Atlantic Provinces and from areas outside of the region (i.e., Ontario and Quebec), to obtain a broader range of existing climate regimes that might be more typical under the change scenarios for the Atlantic region. However, since anticipated future climate conditions are outside of the range of conditions presently experienced in the Atlantic region, it is not possible to conduct a rigorous validation of the relationship using average yield data from field trials conducted in the region. Crop Yield Information from Field Trials Grain Corn Annual yields based on the average of all hybrids tested in Ontario Hybrid Corn Performance Trials were extracted for selected locations from annual reports (e.g., Ontario Corn Committee 1998) or obtained directly from trial co-operators for the 1990 to 2000 period and then averaged over all years for each location. Hybrids included in the trials varied depending on the CHU zone in which trials were conducted. Hybrids also changed over time, as new ones were introduced and older ones were dropped from the field trials. Average yields for locations that had years with missing data were adjusted using yield data from neighbouring sites where possible. For the Atlantic Provinces, average yields of “standard hybrids” in grain corn trials were obtained from Corn Hybrid Testing Reports published for the Atlantic region (e.g., Atlantic Field Crops Committee 1999). Standard hybrids are used to provide a common reference point over different trials and years to which hybrids being evaluated can be compared. Data were extracted for the years 1991 to 1997 and 1999. Data for 1998 were not included as relatively few locations had data available for that year. In most years, there were six standard hybrids grown at each location. Standard hybrids were the same at all trials in a given year, but could vary from year to year as new hybrids were introduced and older ones were dropped from the trials. Yields were adjusted for missing years as in the Ontario data. The following equation describes the procedure used for estimating yield data for missing years: Y1est = (Y1ave/Y2ave) × Y2obs where Y1est is the estimated yield in the missing year at location 1, Y1ave and Y2ave are average yields for locations 1 and 2 for years when yield data were available at both sites, and Y2obs is the observed yield at location 2 in the year with missing data at location 1. Soybeans Soybean yield data for selected locations were obtained for the years 1996 to 2000 from reports of variety trials con-

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ducted in Ontario and in the Maritime provinces (e.g., Ontario Oil and Protein Seed Crop Committee 1998; Agriculture and Agri-Food Canada 2000). Average yields for all varieties included in the trials were used. Varieties that were included in the trials could vary, depending on the CHU zone in which trials were conducted. Varieties also changed over time, as new varieties were introduced and older ones dropped from the field trials. Yield averages were adjusted for years with missing data using neighbouring trials as was done for corn. Barley Barley yields for both two-row and six-row varieties were obtained from field variety trials reports for selected locations in Ontario, Quebec and the Maritime Provinces. Results from registration trials, advanced trials, screening trials and early-maturing trials were included in these data. Averages were based on data from the years 1994 to 1999 for Ontario and Quebec (1994 to 1998 for six-row barley in Ontario), and for 1994 to 2000 for the Maritime Provinces. Data were not available for all years for some locations, and in these cases averages were adjusted based on comparisons with neighbouring trials where possible, as was done for corn. Annual yields were based on averages for two to three standard varieties in each trial. Standard varieties sometimes changed over time but were consistent for all trials conducted in a region. Yields for two-row and six-row barley varieties were determined separately, as significant differences in yield could be expected (Jui et al. 1997). Agroclimatic Indices at Field Trial Locations Monthly weather station data (derived from averaging daily maximum and minimum air temperatures and summing daily precipitation) were interpolated to the approximate location of each of the crop trials used in our study, using Digital Elevation Model (DEM) data and a thin plate smoothing spline fitting technique (Hutchinson 1995; McKenney et al. 2001; Bootsma et al. 2005). The monthly values were then averaged over the same period of years used in calculating average yields for each location. Average values of daily maximum (Tmax) and minimum (Tmin) air temperature for each of 365 d of the year were generated from monthly average values using the Brooks sine wave interpolation procedure (Brooks 1943). Average daily values of precipitation (P) were generated by dividing the monthly value by the number of days in the month. Average values of CHU and DEFICIT were then determined for locations with yield data for corn and soybeans, using the procedures described by Bootsma et al. (2005) and briefly summarized below. In the case of barley, the agroclimatic variables computed were EGDD, DEFICIT, and EGDD/day (REGDD) over the growing season length (GSL), where GSL is the number of days over which the EGDD were accumulated. Stepwise linear regression analyses were used to determine relationships between average yields of each crop and these agroclimatic variables. Average daily values of CHU were computed after Brown and Bootsma (1993) using the following formula:

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Ymax = 3.33 (Tmax – 10.0) – 0.084 (Tmax – 10.0)2 (if Tmax < 10.0, Ymax = 0.0) Ymin = 1.8 (Tmin – 4.44) (if Tmin < 4.44, Ymin = 0.0) where Ymax and Ymin are the contributions to CHU from average daily maximum (Tmax) and minimum (Tmin) air temperatures, respectively. Then, average daily CHU = (Ymax + Ymin) / 2.0 CHU were accumulated between average starting and stopping dates derived from appropriate criteria and then adjusted as described by Bootsma et al. (2005). Average daily values of Growing Degree-Days above 5°C (GDD) were calculated from mean daily air temperatures (Tmean) using the formula: Average daily GDD = Tmean – 5.0 (if Tmean is < 5.0, GDD = 0.0) EGDD were then computed by summing GDD between average starting and stopping dates in spring and autumn determined from appropriate criteria for spring-seeded small grain cereals (Bootsma et al. 2005). DEFICIT values were calculated by subtracting average daily precipitation (P) from potential evapotranspiration (PE) and accumulating values over the same period as EGDD. PE was estimated from Tmax, Tmin and solar radiation at the top of the atmosphere (Baier and Robertson 1965; Baier 1971). Future Climate Scenarios for Estimating Changes in Yield The relationships developed between observed crop yields and the agroclimatic indices were used to estimate impacts of potential climate change on average crop yields for the year 2040 to 2069. The climate change scenarios used in these analyses were determined by Bootsma et al. (2005). The scenarios were based mainly on the output from a Canadian General Circulation Model with an aerosol effect (CGCMI-A) experiment (Boer et al. 2000). However, the probable range of values for the agroclimatic indices resulting from variability introduced by multiple GCM experiments was also taken into consideration. RESULTS AND DISCUSSION Relationships between Crop Yields and Agroclimatic Indices Grain Corn Average yields of grain corn from field trial locations are shown in Table 1 in relation to agroclimatic indices. CHU at Harrington were reduced by 150 units as CHU are known to be less effective in maturing corn on Prince Edward Island than at other locations in the Atlantic region (Smith et al. 1982). Average yields tended to increase linearly with average CHU (Fig. 1). A linear regression line fitted to the data had an R2 of 0.86 and a slope of 0.00583 t ha–1 CHU–1, indicating that with each increase of 100 CHU, average yields of grain corn increased by about 0.6 t ha–1. The increase in corn yields with higher available CHU is largely due to

Table 1. Average yields of grain corn from hybrid trials conducted at specified locations eastern Canada, and average crop heat units (CHU) and water deficits (DEFICIT) from the nearest grid point, based on data from within the period 1990 to 2000 (ON = Ontario, NB = New Brunswick, NS = Nova Scotia, PEI = Prince Edward Island)

Location Ridgetown, ON Grande Pointe, ON Croton, ON Kerwood, ON Inwood, ON Ilderton, ON Woodstock, ON Winchester, ON Ailsa Craig, ON Pakenham, ON Elora, ON Cobden, ON Alma, ON Woodside, NS Centreville, NS Waterville, NB Stewiacke, NS Sussex, NB Nappan, NS Harrington, PEI

Average yieldz @15 to 15.5% moisture (t ha–1)

CHUy

DEFICIT (mm)

12.0 11.7 11.3 10.5 10.4 10.1 10.1 9.7 9.6 9.5 8.7 8.1 7.3 7.6 7.2 6.8 6.6 6.5 6.0 4.6

3406 3540 3420 3238 3237 3102 3014 2921 3144 2825 2683 2634 2665 2786 2782 2524 2595 2579 2626 2462

137.9 150.3 143.0 121.3 125.6 97.0 109.3 135.9 92.5 175.0 112.9 199.6 96.3 85.0 98.0 152.6 53.0 102.6 63.2 11.0

zOntario

yields were based on averages for all hybrids contained in trials; Maritime yields were based on averages for standard hybrids only. yFor Harrington, available CHU were reduced by 150 units as CHU are known to be less effective in maturing corn on PEI (Smith et al. 1982).

longer season hybrids that can take advantage of the longer and warmer growing season. Longer growing seasons allow more time for the corn plant to accumulate dry matter and thus achieve higher yields. Dry matter also accumulates at a faster rate in the higher CHU areas where temperatures are closer to the optimum for growth and development, which is near 30°C (Stewart et al. 1998; Yan and Hunt 1999). Adding DEFICIT as a second independent variable in the regression resulted in a higher R2 value of 0.93. However, the regression coefficient for DEFICIT was positive, indicating increased average deficits were associated with higher average yields. This result is likely due, in part, to a positive correlation between CHU and DEFICIT (r = 0.36). However, it could also imply that surplus moisture in areas with relatively low CHU may have a negative impact on yields. Research has shown that corn yields can be significantly reduced by excess moisture, particularly during the establishment and early vegetative stages (Kanwar et al. 1988; Lizaso and Ritchie 1997). In the context of this study, which is based on average yields, including the DEFICIT term in the regression is not meaningful since in most years yields are not under any significant water stress. Soybeans Average yields of soybeans from trials along with the relevant agroclimatic variables are shown in Table 2. A linear regression line fitted to the data had an R2 of 0.74 and a slope of 0.00133 t ha–1 CHU–1 (Fig. 2). These results suggest that for each increase of 100 CHU, soybean yields could potentially increase by 0.13 t ha–1. Although yields


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Table 2. Average yields of soybeans from variety trials conducted at specified locations in eastern Canada, and average crop heat units (CHU) and water deficits (DEFICIT) from the nearest grid point, based on data from the period 1996 to 2000) (ON = Ontario, NB = New Brunswick, NS = Nova Scotia, PEI = Prince Edward Island)

Location

Fig. 1. Relationship between present-day average yields of grain corn from hybrid trials and average crop heat units (CHU), based on data from within the period 1990 to 2000.

increased linearly with CHU, the yield response to CHU was lower than for corn. For example, when CHU are increased from 2600 to 3400 CHU, yields of grain corn increase by 67%, while soybean yields only increase 38%. Yield data from trials on clay soils in southern Ontario were not included in the analyses, since trials on these soils tended to yield lower than those on clay loam soils in the same area. We did not subtract 150 CHU from the Harrington data as was done in the case of the corn data, since there is no prior evidence that CHU are less effective in maturing soybeans on Prince Edward Island (Dr. J. MacLeod, personal communications). Although soybean yields on Prince Edward Island are about 0.5 t ha–1 lower than other locations with equivalent CHU values (Table 2, Fig. 2), this does not appear to be due to delayed maturity. Subtracting 150 CHU from Harrington data resulted in a R2 value of 0.80. Adding DEFICIT as an additional variable in the regression increased the explained variance (R2 increased from 0.74 to 0.82), but the regression coefficient was positive, indicating a tendency to higher yields in areas with greater deficits. The correlation coefficient (r) between CHU and DEFICIT was 0.74. The discussion in the previous paragraph on the relevance of the DEFICIT term in the corn results applies equally well to soybeans. Similarly to corn, there is evidence that excess moisture can have negative impact on soybean growth and yield (Scott et al. 1989; Sullivan et al. 2001; Boru et al. 2003). Barley Correlation between average yield and EGDD was relatively weak for both two- and six-row barley (Tables 3 and 4, Figs. 3 and 4) and negative, i.e., there was a tendency for lower yields at higher EGDD values. Correlation coefficients between yield and DEFICIT and between DEFICIT and EGDD were not statistically significant (Table 4). However, using a second-order polynomial for DEFICIT resulted in R2 values of 0.41 and 0.18 for two- and six-row barley, respectively, with yields peaking around the 90 mm DEFICIT value. When EGDD was added to this fit, R2 val-

Malden, ON Chatham, ON Ridgetown, ON Talbotville, ON Exeter, ON Woodstock, ON Winchester, ON St. Pauls, ON Ottawa, ON Brussels, ON Elora, ON Truro, NS Harrington, PEI Woodstock, NB

Average yield @ approx. 14% moisture (t ha–1)

CHU

DEFICIT (mm)

4.2 4.2 4.1 3.8 3.7 3.3 3.3 3.6 3.3 2.8 3.2 3.2 2.2 2.9

3808 3597 3487 3312 3192 3082 3019 3014 3027 2886 2798 2588 2711 2597

209.7 159.4 155.0 130.7 73.1 109.4 112.7 80.6 127.3 61.4 110.4 69.7 –21.6 116.2

ues increased to 0.59 and 0.58 for two- and six-row barley, respectively (Table 4). Adding REGDD as a second independent variable in addition to EGDD in linear regression did not improve the R2 significantly (data not shown). Overall, the average yields of six-row barley exceeded those of two-row barley by about 0.7 t ha–1. There was a significant tendency (P < 0.06) for yield differences between two- and six-row barley to be less in the warmer (higher EGDD) areas (Fig. 5), although the confidence is low due to scatter in the relationship. Linear correlation between yield differences and DEFICIT (data not shown) was not significant (r = 0.11). However, there was a slight tendency towards higher yield differences at the extreme high and low DEFICIT values, and a quadratic fit resulted in an R2 value of 0.10, with yield differences peaking at DEFICIT values around 80 to 90 mm. The relatively weak relationship between barley yields and EGDD is due, in part, to the fact that barley varieties do not utilize the longer growing season in higher heat unit areas, but instead are seeded earlier and reach maturity earlier than in lower heat unit regions. The net result is that change in the overall exposure to heat units is relatively small in barley in comparison to corn and soybeans. The tendency towards lower yields in areas with higher EGDD is likely due, in part, to the fact that warmer temperatures tend to hasten development, thus reducing the time available for assimilation of dry matter (Davidson and Campbell 1983; Laurila 2001). Other factors that may have reduced yields in warmer regions are increased diseases, reduced net carbon dioxide exchange (Ormrod et al. 1967) and greater water deficits at higher temperatures. Diseases such as powdery mildew, leaf rusts, barley yellow dwarf virus and fusarium head blight tend to be more common in the higher heat unit areas of southern Ontario than elsewhere in eastern Canada (T. M. Choo, personal communications). The lower yield differences between two- and six-row barley in warmer areas may be partly due to six-row barley being

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Fig. 2. Relationship between present-day average soybean yields from variety trials and average crop heat units (CHU), based on data from the period 1996 to 2000.

more susceptible to some of these diseases. The tendency towards lower yields at extreme high and low DEFICIT values suggest that both surplus and water shortages may have some negative impact on average barley yields in eastern Canada. Yield reductions at high deficits may be a result of water stress (Dwyer and Stewart 1987), while excess water could result in reduced yields due to oxygen deficiency, nitrogen leaching and/or increased disease pressure (Delin et al. 2005). Unexplained variances in the regression analyses were likely due to factors not evaluated in our study, such as differences in varieties, fertility, soil conditions and disease incidence at the various field trial locations. Furthermore, it is likely that not all climatic conditions that influence barley yield were fully incorporated in the indices used in our study. Crop Production Scenarios for the Atlantic Region Plausible scenarios of the potential impacts on crop production can be constructed, based on the expected changes in agroclimatic indices from Bootsma et al. (2005). These impacts are outlined for corn, soybeans and barley in the following sections. Grain Corn Production Our climate change scenario based on the Canadian GCM (CGCMI) experiment, indicated that an increase of about 600 CHU is possible for the main agricultural areas of the Atlantic Provinces by the year 2040 to 2069 (Bootsma et al. 2005). Grain corn yields in experimental field trials would potentially increase by 3.5 t ha–1 in response to this change. Assuming an average yield of 6.5 t ha–1 under the presentday climate (Table 1), this is a 54% increase in yield. When variability among multiple GCMs is considered, future increases in CHU ranged from 450 to 1650 units, which translate in yield increases ranging from 2.6 to 9.6 t ha–1. However, this makes the unlikely assumption that average corn yields will increase linearly with CHU beyond the maximum of 3400 CHU shown in Fig. 1. We compared

average CHU with corn yields in the USA corn belt and found that yields under irrigated conditions did not continue to increase as average CHU increased from 3400 to about 4500 units, but reached a plateau of about 14 t ha–1 (Bootsma 2002). Therefore, it seems unlikely that corn yields would increase by more than 7.5 t ha–1 in the Atlantic region under climate change, given present-day production practices and available hybrids. Thus, the potential yield of grain corn in field trials by the year 2040 to 2069 is projected to be approximately 9.1 to 14.0 t ha–1 (40 to 115% above present-day yields). Average DEFICIT values projected for the year 2040 to 2069 for the Atlantic region used in our study [derived by Bootsma et al. (2005)] were within the range of values used in our analyses of the impact of present-day climate on yield. Since higher DEFICIT values were not associated with lower average corn yields observed in field trials in our study, it seems unlikely that increases in water stress anticipated in some areas by our scenarios will influence average corn yields significantly, unless the corn is grown on shallow soil with low water-holding capacity. On the contrary, with the possibility of increased surpluses, by as much as 100 mm, projected by multiple GCMs, there is greater likelihood yields would be reduced by excess moisture. The potential increase in corn yield does not include the possible direct effects of elevated atmospheric CO2 concentrations, nor does it include increases that may be achieved through breeding or improved technology. The impacts of weeds, insects and diseases on yield under a changed climate are also not taken into consideration in our study. Since corn is a C4 plant, the direct effect of increased CO2 on photosynthesis is not expected to be large (Warrick 1988; Hocking and Meyer 1991). Tollenaar and Wu (1999) provide statistics from Ontario indicating that farm yields have increased approximately 80 kg ha–1 yr–1 over five decades beginning in the mid-1940s, as a result of genetic improvement, changes in cultural management and climate change. They suggest that potential for continued genetic yield improvement is substantial. Specht et al. (1999) indicate that irrigated corn yields in Nebraska, USA, have increased by 98 kg ha–1 yr–1, or about 2.8 times faster than irrigated soybean yields for the 25-yr period ending in 1997, although the relative rates of improvement were identical. They suggested an upper biological limit to corn yield of about 22.5 t ha–1. Statistics on field crop production in the Atlantic region indicate that average on-farm yields are somewhat lower than yields from regional corn hybrid trials. Statistics Canada reported that from 1995 to 1998, a yearly average of 2327 ha of grain corn were harvested in Nova Scotia, with an average yield of 5.6 t ha–1, or about 80% of yields achieved in field trials, for a total production of about 13 000 t yr–1 (Robinson 2003). If we assume a similar ratio between yields from field trials and on-farm statistics, our climate scenario indicates average on-farm yields could range from about 7.0 to 11.0 t ha–1 by the year 2040 to 2069. This range does not include possible technological and genetic improvements, nor factors such as weeds, pests or diseases that may decrease yields. If we assume that yield


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Table 3. Average yields of barley from variety trials conducted at specified locations in eastern Canada, and average climate data from the nearest grid point, based on data from within the 1994 to 2000 period. EGDD = effective growing degree-days above 5ºC, DEFICIT = water deficit, GSL = growing season length Yield (t ha–1) 2-row

6-row

EGDD

EGDD/GSL (EGDD/day)

DEFICIT (mm)

GSL (d)

3.1 3.6 3.9

3.5 4.7 4.5 4.3

2122 2040 1971 1921 1967 1967 1834 1802 1551 1419 1459 1326 1251 1283

11.79 11.79 11.26 12.01 12.22 12.22 11.25 10.92 11.24 10.67 10.81 9.97 10.43 10.43

152.3 172.6 127 137.8 154 154 167.9 133 156.7 154.1 150.5 177 166.2 168.3

180 173 175 160 161 161 163 165 138 133 135 133 120 123

6.2

2002 1923 1907 1737 1667 1450 1394 1384

12.51 12.17 12.22 11.74 11.26 10.36 10.64 10.56

142.3 102.8 111.3 55.4 –10.6 44.3 105.2 89.7

160 158 156 148 148 140 131 131

3.8 4.2 4.3

4.2 5.4 5.0

1698 1658 1559

10.42 10.36 11.13

21.3 63.3 149.7

163 160 140

4.1

4.9

1638

10.64

78.1

154

Location

Trialz

Ontario Nairn/Alsa Craig Woodstock Winthrop Ottawa Ottawa Ottawa Elora Harriston/Palmerston Emo New Liskeard New Liskeard Thunder Bay Kapuskasing Kapuskasing

A A A R R A A A E R R/E E R R/E

Quebec St-Anne-de-Bellevue Ste-Rosalie Ste-Simone Deschambault Pintendre LaPocatiere Normandin Normandin

R R R R R R R R

4.6 4.9 4.1 4.2 3.1 5.0 5.3

R R R

Maritime Provinces Charlottetown PEI Nappan NS Hartland NB Average for Maritime Provinces

3.6 3.9 4.4 4.2 4.4 3.8

4.3 4.7 4.4 5.7 5.8 6.0 5.7 4.5 4.6 4.5 5.8 5.0 5.3 3.4 6.2

Yields were adjusted for years with no data when possible, and may be averages from more than one trial at a location. zR = Registration trials, 1994 to 1999 averages, except 1994 to 1998 period for six-row barley in Ontario and 1994 to 2000 period for Maritimes; A = Advanced and screening trials, 1994 to 1999 averages; E = Early trials, six-row barley only, 1994 to 1999 averages.

increases over the past several decades documented by Tollenaar and Wu (1999) and by Specht et al. (1999) continue at a similar rate into the future, then these yields could be about 5 t ha–1 higher, or about 114 to 186% higher than present-day on-farm yields. In the 1996 census year, Statistics Canada (1999) reported over 100 000 ha of land seeded to small grain cereals (wheat, oats, barley, mixed grain, etc.) and silage corn in the Atlantic region. It is reasonable that at least 30% of this area would switch to grain corn as this crop becomes more economically advantageous under a warmer climate. Therefore, a projected range in production of 210 000 to 330 000 t of grain corn from over 30 000 ha by the year 2040 to 2069 seems plausible using present-day technology and available hybrids. These projections could be exceeded by an additional 150 000 t if technological and genetic improvements continue at rates similar to the present. Soybean Production Average yields of soybeans grown in field trials were shown to increase by 0.13 t ha–1 for each increase of 100 CHU.

Consequently, the addition of 600 CHU by the year 2040 to 2069 based on the Canadian GCM scenario (Bootsma et al. 2005) may be expected to increase soybean yields by about 0.8 t ha–1. This represents an increase of about 29%, based on present-day average yield from variety trials in the Atlantic region of about 2.8 t ha–1 (Table 2). Variability among multiple GCMs resulted in a range of increases in CHU from 450 to 1650 units, which translate into increases in soybean yields ranging from 0.6 to 2.1 t ha–1, or 21 to 75%. However, this assumes that average soybean yields will increase linearly above 4 t ha–1 as CHU exceed the maximum of 3800 CHU shown in Fig. 2. This seems unlikely, as maximum soybean yields from variety trials in the United States corn/soybean belt areas with CHU in excess of 3800 was typically in the range of 4.0 to 4.5 t ha–1 in the late 1990s (Day et al. 1999; Kansas State University 1999). It seems unlikely that increases in soybean yields would exceed 1.5 t ha–1, or 54%, at the high end of the GCM range in CHU. Thus, we estimate a range in average yields of soybeans in field trials of approximately 3.4 to 4.3 t ha–1 by the year 2040 to 2069. These estimates do not include the direct

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Table 4. Summary of results of regression analyses of average barley yields with climate variables, based on data from within the 1994 to 2000 period. EGDD = effective growing degree-days above 5ºC, DEFICIT = water deficit, REGDD = rate of accumulation of EGDD (EGDD/GSL) Regression constant

Regression coefficient(s)

Standard error(s) of coefficient

R2

SEE

N

P level

EGDD DEFICIT REGDD

5.84 4.16 5.89

–0.00098 –0.00029 –0.15586

0.0005 0.0027 0.1941

0.16 0.00 0.04

0.56 0.61 0.60

19 19 19

0.09 0.91 0.43

EGDD + DEFICIT + DEFICIT2

5.20

–0.00106 0.02735 –0.0001468

0.00042 0.00698 0.0000374

0.59

0.42

19

0.003

EGDD DEFICIT REGDD

7.57 4.88 9.19

–0.00155 0.00055 –0.37871

0.0006 0.0033 0.2230

0.26 0.00 0.12

0.71 0.83 0.77

23 23 23

0.01 0.87 0.10

EGDD + DEFICIT + DEFICIT2

7.25

–0.00196 0.03409 –0.0001799

0.00046 0.00919 0.0000478

0.58

0.56

23

0.0008

–0.00023

0.0433

0.00

54.12

23

0.996

Dependent variable

Independent variable(s)

2-row barley yield

6-row barley yield

DEFICIT

EGDD

121.56

effects of elevated CO2, which are likely to be greater than for corn, nor do they include potential improvements due to breeding or management practices. Factors such as weeds, pests or diseases that may decrease yields are also not accounted for. In our analyses, adding DEFICIT as a second independent variable in regression in addition to CHU did not improve the explained variance of average soybean yields significantly. Thus there was no evidence of a trend towards lower average yields at higher DEFICIT values within the range of existing climate in Eastern Canada. Increases in DEFICIT anticipated in some areas of the Atlantic region by our scenario are within the present-day range for Eastern Canada, and therefore are unlikely to impact average soybean yields significantly in the Atlantic region. Soybean yields will be reduced in years with high DEFICIT under both present-day and future climates. The influence that such years will have on average yields for the future for the Atlantic region is likely to be similar to the influence on present-day yields for eastern Canada. However, similarly to corn, the increased range in water surpluses anticipated by the full suite of GCM outputs could have a negative impact on average soybean yields that may be achievable in some areas of the Atlantic region under a changed climate. Soybeans are plants with the C3 photosynthetic pathway, and higher CO2 concentrations directly increase the rate at which carbon is fixed and reduce the rate of photorespiration. Doubling of atmospheric CO2 concentrations could increase yields of major C3 crops by 10 to 50% (Warrick 1988). Heagle et al. (1998) reported an increase in soybean yield of 16% for a doubling of atmospheric CO2 concentration at slightly elevated ozone levels. Allen et al. (1991) measured a 20% increase in soybean yields with doubled CO2 concentrations in outdoor, controlled environment chambers. Southworth et al. (2002), using the SOYGRO model, simulated a 30% increase in soybean yield in eastern

Illinios with a 54% increase in CO2 concentration under conditions of no moisture or nutrient stress. Phillips et al. (1996), using the EPIC model, simulated a 27% increase in soybean yields, or about 0.7 t ha–1, in the US corn belt, with a 79% increase in CO2 concentration. Overall, it seems likely that the direct effect of double CO2 concentrations on photosynthesis and respiration could increase soybean yields by an additional 20% or more. We estimated average soybean yields in field trials in the range of 4.1 to 5.2 t ha–1 by the year 2040 to 2069, using present-day management and available varieties, when the effect of CO2 fertilization is included. This represents increases of 46 to 86% over present-day yields. Some evidence of the potential yield increases in soybeans due to technology and plant breeding is provided by Specht et al. (1999). They indicate that soybean yields in the United States have increased by 31 kg ha–1 yr–1 for a 25-yr period ending in 1998. The yield trend for irrigated production in Nebraska was an increase of 35 kg ha–1 yr–1. Only a small portion of this increase is likely due to atmospheric CO2 concentrations, which have risen by only about 10% over that time period. A high proportion of the yield gain was attributed to genetic improvements. Voldeng et al. (1997) evaluated cultivars for public release in Ontario and estimated genetic improvement of 30 kg ha–1 yr–1 for the period from 1976 to 1992. Specht et al. (1999) proposed an upper biological limit for soybean yields of about 8 t ha–1, with average farm yields not to exceed 80% of the highest potential, i.e., 6.4 t ha–1. If we assume that the soybean yield trends due to technology and genetic improvement will continue at the present rate, and enhance the yields by 20% due to enrichment under double CO2 concentrations, then we may anticipate average soybean yields in field trials in the range of 6.3 to 7.4 t ha–1 by the year 2040 to 2069, given the range of GCM climate scenarios under consideration. Statistics on soybean production in the Atlantic region indicate that on-farm soy-


Fig. 3. Relationship between present-day average yields of tworow barley from variety trials and average effective growing degree-days above 5ºC (EGDD), based on data from within the period 1994 to 2000.

bean yields are lower than the yields based on regional variety trials. Statistics Canada reported that in the 1990’s, an average of about 3500 ha soybeans were harvested annually in Prince Edward Island, with an average yield around 2.3 t ha–1 (P.E.I. Department of Agriculture and Forestry 2000). Production was too low in the other Atlantic provinces to include in the statistics. Average on-farm soybean yields are typically about 80% of the average yield from field trials in the region, a ratio that has also been used in other studies on soybean production (Specht et al. 1999). Thus, a reasonable projection for average on-farm yields by the year 2040 to 2069 would be in the range of 5.0 to 5.9 t ha–1, or increases of 117 to 157% over present-day yields, when technology, genetic improvements and direct effects of CO2 are all considered. The projected absolute changes in on-farm yields for soybeans are about half of that for corn, although the relative changes are similar. With a warmer climate and double CO2 concentration, substantial increases in areas seeded to soybeans can be expected, as it will likely gain competitive advantage over small grain cereals. Soybeans would likely remain competitive with corn due to lower production costs, high level of protein content and considerably higher dollar value per tonne. It is reasonable to suggest that by the year 2040 to 2069 there could be at least 20 000 ha under soybean production in the Atlantic region. Under present-day technology and management, this would result in a total production ranging from about 66 000 to 83 000 t. These projections would be exceeded by an additional 35 000 t if yield increases due to technology and genetic improvements documented by Specht et al. (1999) and by Voldeng et al. (1997) continue at a similar rate into the future. Barley Production The weak relationship between barley yield and EGDD (Figs. 3 and 4, Table 4) suggests that climate warming is

353

Fig. 4. Relationships between present-day average yields of sixrow barley from variety trials and average effective growing degree-days above 5ºC (EGDD), based on data from within the period 1994 to 2000.

likely to have considerably less impact on average yields of barley than of corn and soybeans in the Atlantic region. The regression coefficients in Table 4 indicate that expected increases of about 400 EGDD by the year 2040 to 2069 based on the Canadian GCM (Bootsma et al. 2005) could decrease the yields of two- and six-row barley in field trials by about 0.35 and 0.60 t ha–1, or about 8 and 12% of present-day yields (Table 3) of around 4.1 and 4.9 t ha–1, respectively. Present-day differences in average yields of two-row barley compared with six-row barley of about 0.8 t ha–1 (Table 3) can be expected to decline to 0.5 t ha–1 by the year 2040 to 2069. Variability introduced by using multiple GCMs indicate EGDD could increase by 300 to 1100 units, giving a range in EGDD between 1700 to 2700, by the year 2040 to 2069. This suggests decreases in yield could range from 0.5 to 1.7 t ha–1 for six-row barley and 0.3 to 1.0 t ha–1 for two-row barley. These estimates assume that barley yields will continue to decline as EGDD increase beyond the upper limit of about 2000 units in Figs. 3 and 4. Data were not available to determine if this was a valid assumption, leaving considerable uncertainty in the potential impacts of relatively large temperature increases, indicated by some GCMs, on barley yields. Water deficits may have a stronger role than temperature in determining barley yields under climate change (Table 4). We estimated potential impacts of the combined effect of changes in temperature and water deficit regimes by using the regression coefficients (Table 4) for equations that included EGDD and second-order polynomial for DEFICIT. Predicted yields based on present-day climate (1961 to 1990) were compared with yields based on climate projected for the future (2040 to 2069) period. Based on the scenario constructed from the Canadian GCM, average yields for two-row barley in field trials are predicted to decrease by as much as 25% in areas with currently low deficit values

354


Fig. 5. Relationships between average effective growing degreedays above 5ºC (EGDD) and present-day average yield differences between two-row and six-row barley from variety trials, based on data from within the period 1994 to 2000.

(around 25 mm) that are expected to decrease or become small surpluses, and by 15% in areas where high deficits (>125 mm) are expected to increase (e.g., in central New Brunswick). Predicted yields in areas with moderate deficits (currently around 75 mm) decreased by only about 10%. Predicted yields of six-row barley decreased about 5% more than two-row barley. When scenarios based on multiple GCM experiments were considered in this analysis, the possible range of yield reductions extended to approximately double the values estimated using only the Canadian GCM scenario. Yield estimates for scenarios that included large surpluses of water (i.e., 50 to 100 mm or more) were not considered reliable, as extrapolation of the quadratic regression well beyond the observed data resulted in yields that were negative. It should be noted that the yield estimates that include consideration of projected changes in DEFICIT have a high level of uncertainty due to i) large scatter in the yield versus DEFICIT relationship, (ii) uncertainty in extrapolated estimates outside the range of observed data, and (iii) high level of uncertainty in the precipitation amounts projected by the GCM models. The above yield projections for barley do not include the direct effect that increased CO2 may have on barley yields, nor do they include the potential impacts of genetic improvements and technological advances in management. Yield decreases in barley due to increased temperature and changes in water deficits are likely to be counteracted by the direct effect of CO2 fertilization on yield. Barley is a C3 crop and therefore elevated CO2 concentrations are likely to promote higher yields by increasing photosynthesis and water use efficiency, and by suppressing photorespiration (Warrick 1988). However, simulated or measured effects of CO2 enrichment on spring cereals reported in the literature vary considerably, and thus make it difficult to estimate precisely the extent to which barley yields will be affected. De Jong and Li (2001) reported almost no change

in simulated barley yields at Charlottetown using the EPIC model with a doubling of atmospheric CO2 scenario when the direct effect of CO2 was not included, and an increase in yield of about 10% when the CO2 fertilization effect was included. They concluded from their simulations that the positive and negative effects of climate change on barley yields would largely offset each other in most agricultural regions of Canada. Sæbø and Mortensen (1996) reported a 20% increase in total biomass but no significant change in grain yield when barley was grown in open-top chambers in the field at CO2 concentrations elevated about 60 to 100% above ambient levels in Norway. Weigel et al. (1994) reported that grain yields of two barley cultivars increased by 52% and 89% when grown at CO2 concentrations 87% above ambient in open-top chambers in Germany. Laurila (2001) reported that doubling CO2 concentrations in opentop chambers in Finland resulted in 12% increase in yield of spring wheat under ambient temperatures and a 20% increase at temperatures 3ºC above ambient. Kleemola et al. (1994) reported a 76% increase in kernel yield of barley grown in a greenhouse under high nitrogen regime when CO2 concentration was increased by 75%. Overall, is seems reasonable to assume that the direct effect of double CO2 concentration alone could increase yields by 30 to 50%, and this would more than offset the yield reductions anticipated due to effects of rising temperature and changes in water deficit anticipated by the Canadian GCM scenario in the main agricultural areas of the Atlantic region. All things considered, we estimate that barley yields would increase by an average of about 15% under this scenario. Statistics on crop production indicate that on-farm barley yields are, on average, somewhat lower than average yields from variety trials. On-farm yields of barley from 1990 to 1999 averaged near 3 t ha–1 (Statistics Canada 2003), somewhat lower than the 4 to 5 t ha–1 that are typically observed in variety trials in the region (Table 3). The area seeded to barley production in New Brunswick, Nova Scotia and Prince Edward Island for the 1993 to 1997 period averaged about 55 000 ha yr–1. At an average yield of about 3 t ha–1, this resulted in total farm production of about 165 000 t. If we assume a 15% increase in barley yield by the year 2040 to 2069, then average yields would increase to 3.45 t ha–1. The area that is seeded to barley and other small grain cereals is likely to be significantly reduced as producers switch more to corn and soybeans, which will have a competitive advantage under a warmer climate. A 50% reduction in area seeded to barley and other spring cereals by the year 2040 to 2069 would seem plausible. Under this scenario, 25 000 ha seeded to barley, with an average yield of 3.45 t ha–1, would result in the production of about 86 000 t of barley in the region. This does not include increases in production that may accrue due to genetic advances or improvements in management, and therefore these estimates are likely on the low side. There is a lack of information on potential yield trends as a result of technology and breeding in the literature, and hence we avoid making projections that include these factors for barley. The climate change scenario from the Canadian GCM used in our study indicates that the main agricultural areas


of the Atlantic region will have 2800 to 3200 CHU available for crop production by the year 2040 to 2069 (Bootsma et al. 2005). Areas in southern Ontario that presently have these amounts of CHU (Bootsma and Brown 1995) have a high proportion of land area used for grain corn and soybean production (OMAFRA 2001). Since barley is well suited for rotation with potatoes, which are grown extensively in the region, the proportion of land area under barley production will likely remain higher in the Atlantic region than in southern Ontario. It is also likely that there will be considerable production of other cereals. Nevertheless, relatively large increases land areas used for corn and soybean production may be expected, and the potential detrimental effects that this may have as a result of increased soil erosion (van Vliet et al. 1976) need to be evaluated. CONCLUSIONS AND RECOMMENDATIONS The climatic changes expected to occur in the Atlantic region within the next 50 yr or so, based on GCM experiments and a “business as usual” emission scenario for GHG, are likely to have significant impacts on crop production in the region. Increased availability of heat units with only slight to moderate changes in water deficits may result in substantial increases in yields and production of corn and soybeans. Yields of barley are likely to change only slightly, but the competitive advantage in relation to corn and soybeans will be reduced. This will likely lead to significant reduction in land area seeded to barley and increases in corn and soybean acreage. Overall, the crop productivity will be increased by increased yields and the switch to high-energy and high-protein-content crops (corn and soybeans) that are better adapted to the warmer climate. However, there will likely still be a considerable area of land seeded to barley and other small grain cereals, as these are very desirable in rotation with potatoes. The potential impact of these possible shifts in crop production on soil erosion in the region needs to be evaluated. Land use changes may become a significant issue as forestry is currently the dominant land use in much of the region. Our study was primarily based on a single GCM scenario, with some consideration of a range of outputs from multiple GCM experiments. There is a need for more rigorous evaluation of the potential impacts of climate change using multiple GCMs and a wider range of GHG emission scenarios. Such an expanded analysis would provide a better indication of the variations and uncertainties that may be associated with climate change scenarios and their impacts in the Atlantic region. Our investigations have assumed that there will be little or no change in variability of climate in the future. Further analyses are needed to determine if changes in variability are anticipated by different GCMs, and how these would affect the conclusions of this study. There is also a need to evaluate the effects of other methods of downscaling the GCM data to spatial scales appropriate for agricultural impact studies. Bootsma et al. (2005) indicated that a statistical downscaling method may result in significantly different climate patterns, which could have an impact on the anticipated yields of corn and soybeans. However it is important to note that the use of more sophisticated down-

355

scaling approaches may actually be overwhelmed by the variation in GCMs and hence have limited benefit. The certainties in our proposed yield and production scenarios are limited since not all possible factors were included in our study. The estimates are based primarily on the effect of increasing temperatures on average yield as determined from field trials conducted under existing climates. There are numerous factors such as pests, diseases, direct effects of CO2, plant breeding, soil fertility, management practices and socio-economic factors, which could affect future crop production under a changed climate. We have included some of these factors in only a cursory manner, based on literature surveys. Additional research is needed to determine the potential effects of these factors in a more rigorous fashion. Effects of climate change on other crops such as potatoes, forages and winter wheat will also have impact on cropping decisions by producers. In the case of winter wheat, overwintering conditions are also extremely important, as increased potential for winter injury under climate change could limit production of winter wheat in the region (Bélanger et al. 2002). Adaptive response to climate change by producers would also affect future changes in crop production. For example, if producers delay selecting hybrids/varieties that are best adapted to changed climatic conditions, potential gains in crop production would be delayed. However, the potential for increased crop production would be realized earlier if confidence in long-term climate forecasts improves and producers adopt a pro-active mode in adapting to climate change. ACKNOWLEDGEMENTS The technical assistance of the following individuals with software development and data analyses is acknowledged with thanks: Dirk Anderson and Allan Jones, AAFC, Eastern Cereal and Oilseed Research Centre (ECORC), Ottawa, Ontario; Kathy Campbell, Y-Q. Yang, and Pia Papadopol, Canadian Forest Service, Sault Ste. Marie, Ontario; Jeremy Goodier and Randy Hutson, co-op students, Algonquin College of Applied Arts and Technology, Ottawa, Ontario. Thanks are due to the following individuals for providing data on crop yields from field trials: for corn yields, Byron Good, University of Guelph, Guelph, Ontario, Scott Jay, Ridgetown College, Ridgetown, Ontario, Jeff Horn, Huron Research Station, and Cheryl Wightman, Kemptville College, Kemptville, Ontario; for soybean yields, Ron Guillemette and Elroy Cober, ECORC, Ottawa, Dr. John MacLeod and Dave Grimmett, Charlottetown Research Centre, PE; for barley yields, Drs. T. M. Choo and K. M Ho, ECORC, Ottawa, Ontario, provided field trial reports. Thanks are also expressed to Drs. R. de Jong, T. M. Choo, H. N. Hayhoe and P. Schut, AAFC, Eastern Cereal and Oilseed Research Centre, Ottawa, Ontario, for their helpful review comments on this work. This research was supported in part by the Government of Canada’s Climate Change Action Fund. Agriculture and Agri-Food Canada. 2000. 2000 Canadian Soybean Coop Registration Trial Report. Report compiled by E.

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