Assessing the effects of climate change on crop production and GHG emissions in Canada

Agriculture, Ecosystems and Environment 179 (2013) 139–150

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Assessing the effects of climate change on crop production and GHG emissions in Canada W.N. Smith a,∗ , B.B. Grant a , R.L. Desjardins a , R. Kroebel b , C. Li c , B. Qian a , D.E. Worth a , B.G. McConkey d , C.F. Drury e a

Eastern Cereal and Oilseed Research Centre, Agriculture and Agri-Food Canada, 960 Carling Avenue, Ottawa, ON K1A 0C6, Canada Lethbridge Research Centre, Agriculture and Agri-Food Canada, 5403 1st Avenue South, Lethbridge, AB T1 J 4B1, Canada c Institute for the Study of Earth, Oceans, and Space, Complex Systems Research Center, University of New Hampshire, Durham, NH 03824, USA d Semiarid Prairie Agricultural Research Centre, Agriculture and Agri-Food Canada, 1 Airport Road, P.O. Box 1030, Swift Current, SK S9H 3X2, Canada e Greenhouse & Processing Crops Research Centre, Agriculture & Agri-Food Canada, Harrow, ON N0R 1G0, Canada b

a r t i c l e

i n f o

Article history: Received 8 June 2013 Received in revised form 30 July 2013 Accepted 2 August 2013 Available online 13 September 2013 Keywords: Climate change DNDC Crop yield Nitrous oxide emissions Soil carbon

a b s t r a c t Regions in northern latitudes are likely to be strongly affected by climate change with shifts in weather that may be conducive to increased agricultural productivity. In this study the DNDC model was used to assess the effect of climate change on crop production and GHG emissions at long-term experimental sites in Canada. Crop production in the model was parameterized using measured data, and then simulations were performed using historical weather (1961–1990) and future IPCC SRES climate scenarios (2040–2069). The DNDC model predicted that for western Canada under the SRES scenarios and no change in cultivar, yields of spring wheat would increase by 37% and winter wheat by 70%. Corn responded favorably to an increase in heat units at the eastern site with a 60% increase in yields. At all locations, yields were projected to increase further when new cultivars with higher GDD requirements were assumed. These increases were notable considering that the estimated soil water deficit indices indicated that there could be less water available for crop growth in the future. However, when accounting for increased water use efficiency under elevated CO2 , DNDC predicted less crop water stress. Nitrous oxide emissions per ton of wheat were projected to increase across most of western Canada by about 60% on average for the A1b and A2 SRES scenarios and by about 30% for the B1 scenario. Nitrous oxide emissions per unit area were predicted to increase under corn production at the eastern location but to remain stable per ton of grain. Model results indicated that climate change in Canada will favor increased crop production but this may be accompanied by an increase in net GHG emissions for small grain production. Crown Copyright © 2013 Published by Elsevier B.V. All rights reserved.

1. Introduction On a global scale there are many indications that climate change is occurring (Tebaldi et al., 2006; Oreskes, 2004) and that this change will continue and could result in major shifts in biomes by the turn of the century (Bergengren et al., 2011). Staudinger et al. (2012) hypothesized that biomes can be tied to climate thresholds whereby a small change can result in a shift. Effects of climate change on regional ecology are predicted to be major in North America’s Great Plains and Great Lakes areas (Bergengren et al., 2011), areas which account for a large proportion of North American agricultural production. Changes in climate will affect biodiversity, soil fauna and microbial activity and could greatly influence the type and productivity of cropping systems in these regions. Increased temperature and water stress, and more extreme

∗ Corresponding author. Tel.: +1 613 759 1334; fax: +1 613 759 1432. E-mail address: [email protected] (W.N. Smith).

weather events over the next 50 years could decrease crop productivity in many regions of the world; however, in cooler regions such as in Canada the effect of climate change on production could be beneficial (Qian et al., 2013, 2012, 2011, 2010b). In a review focussing on the implications of climate change for crop yields, Parry (2007) concluded that crop yields have the potential to increase at mid and high-mid latitudes but may generally decrease in the tropics and subtropics. There is currently a general trend of increased crop production in Canada. For example, yields of grain corn in eastern Canada and spring wheat in western Canada have increased by 64% and 57%, respectively, since 1980 (Statistics Canada, 2010). The yield increase can be attributed to improved cultivars, improved management and changes in climate. Based on favorable temperature-based climate indices, it is expected that yields will continue to increase, however, there may be negative consequences that will impact agricultural production through the introduction of new diseases and pests and more frequent extreme climate events (Rosenzweig et al., 2001).

0167-8809/$ – see front matter. Crown Copyright © 2013 Published by Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.agee.2013.08.015

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Process-based models have been developed which examine the effects of climate, soil properties and agricultural management on crop productivity and several of these have been employed to estimate the effect that climate change may have on biomass production and sustainability (Pan et al., 2013; Abdalla et al., 2011; Wang et al., 2011; Smith et al., 2009a,b; Li, 2007; Berntsen et al., 2006; Olesen, 2005). The CLIMCROP model was used to evaluate the influence of climate change on productivity in Danish Agricultural systems (Olesen, 2005), and it predicted that the yields of winter wheat and spring barley would increase by 13% and 17% under the HCGG scenario by 2050 and potato and grass production would increase by 9% and 12%. A study by Smith et al. (2009a), using the Century model, indicated that even when crop yields increased in eastern Canada, soil carbon was often lost due to greater rates of decomposition. Pan et al. (2013) used the DAYCENT model to evaluate the vulnerability of agricultural soils to SOC loss under three climate scenarios (SRES-A1b, SRES-A2 and SRES-B1) for two GCM’s (Canadian Centre for Climate Modeling and Analysis Model [CaGCM] and Météo-France Centre National Recherches Météorologiques [FrGCM]) on a corn–soybean rotation in the Midwestern United States. Results suggested that under all climate scenarios SOC would decline slightly by the mid-21st century even though NPP would increase by 22% and 14% under both the CaGCM and FrGCM scenarios respectively. The increase in NPP was offset by an increase in soil respiration, which led to a slight loss in SOC. A study by Smith et al. (2009b) where the DNDC model was used to estimate the effect of climate change on net GHG emissions at 10 locations around the world also found that SOC was generally lost under climate change and there were greater rates of loss in cooler climatic zones. In this study the DNDC model did not include the effect of CO2 fertilization on increased water and N use efficiency nor the effect of temperature stress during anthesis, factors which could strongly influence crop production and C inputs. Wang et al. (2011) using the DSSAT model, predicted that wheat yields in southern Saskatchewan in the 2040–2069 time period would increase by 41–74% relative to the 1961–1990 historical period. The effect of heat shock during anthesis was also not included in this model; thus yield increases may have been overestimated. Although several modeling studies have examined the effect of climate change on specific components of agricultural cropping systems, there has been little effort to estimate the effect from an ecosystem perspective including both crop growth and environmental sustainability. Also, it is important that models be updated using the latest experimental data and that they respond well to water, nutrient and temperature stresses. It is our hypothesis that trends in increased temperature-based indices in Canada when coupled with increases in water and nitrogen use efficiency of crops under higher CO2 fertilization could result in a general positive feedback on crop production with the opportunity to shift to cultivars or crop species that favor more temperate climates The objectives of this study are to: (i) update the DNDC model with new information describing the effect of climate on crop growth, (ii) estimate temperature and water based climate indices as an indicator of changes in climate that may affect crop growth (iii) use the improved DNDC model to assess the effect of climate change on crop production, soil carbon change and N2 O emissions at several locations in Canada, and (iv) identify cropping systems that perform well under projected climate conditions.

2. Methodology 2.1. DNDC model The DNDC model (DNDC (i.e., DeNitrification–DeComposition) by Li (2000) is a well-known mathematical model used to simulate

carbon and nitrogen biochemistry for agricultural systems over a wide range of agricultural management, soil and climatic conditions. It is able to estimate crop growth, soil temperature and moisture regimes, C&N dynamics, and trace gas emissions, reporting at a daily time scale. DNDC has been parameterized and tested for its ability to predict soil temperature, soil water content, soil N content, and N2 O emissions at experimental sites in eastern and western Canada (Smith et al., 2002, 2008). It has been used to estimate the inter-annual variations in N2 O emissions at a national level (Smith et al., 2004) and effect of residue removal on soil organic matter (Smith et al., 2012). A tool, based on this model, was developed for predicting effects of agricultural management on GHG emissions (Smith et al., 2010). The model was also used to predict the effect that climate may have on GHG emissions at various locations around the world (Smith et al., 2009b). More recently, an empirical crop growth model was developed to improve estimates of crop biomass in Canada (Kröbel et al., 2011). 2.1.1. Improvement of the DNDC model to simulate effects of climate Initial investigations using the DNDC model to simulate crop productivity in our current study indicated that the temperature stress applied to crop growth in non-optimal temperature ranges was smaller than in published literature (i.e., Weikai and Hunt, 1999), particularly for temperatures significantly above the optimum. As a result we incorporated a formulation whereby estimated crop production is based on cardinal temperatures (Weikai and Hunt, 1999): r Rmax



=

Tmax − T Tmax − Topt



T

Topt /Tmax −Topt

Topt

where r is the daily rate of growth (or development) at any temperature (T), Rmax is the maximum rate of growth or development at the optimum temperature Topt , and Tmax is the maximum temperature that growth can occur. A number of studies have indicated that high maximum temperature during anthesis could result in lower kernel number and lower harvest index (McCaig, 1997; Ferris et al., 1998). The effect of grain exposure to high temperature during anthesis was incorporated into DNDC when temperature was greater than 22 ◦ C for winter wheat, 28 ◦ C for spring wheat and 38 ◦ C for corn. Timing of anthesis was assumed to occur between 0.5 and 0.6 of the modelled Plant Growth Index (PGI; the fraction of achieved crop growth as a function of GDD) which is just before grain filling. Thus, depending on climate, the timing that anthesis occurs is automatically adjusted by the model. Adapting results by Ferris et al. (1998), winter wheat and spring wheat harvest indices were reduced by 1.43% for every degree above 22 ◦ C and 28 ◦ C, respectively. For corn an approximation was taken from Carberry et al. (1989), who parameterized the CERES model for temperature effects on maize yield during anthesis. Yield was reduced when air temperature was above 38 ◦ C as follows; 1-(AirTemp–38.0) × 0.019. Further, an effect of cold winter temperatures on winter wheat survival was added to the model, whereby the crop potential biomass was reduced by 10% per day when crown temperatures dropped below −24 ◦ C (Fowler, 1992). In the calculation of evapotranspiration, albedo during the cropping season was adjusted to an average of 0.195, based on MODIS data (Davidson and Wang, 2005) for crops grown in Canada. The data indicated that albedo was relatively constant over the growing season and was generally higher than the previous DNDC estimate. The Penman–Monteith routine in DNDC was ported to Microsoft Excel® for testing and it was found that PET (potential evapotranspiration) estimates improved in comparison to Canadian ecodistrict averages (Agriculture and Agri-food Canada, 2008)

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when observed wind speed, and relative humidity were used instead of allowing the model to calculate these drivers based on air temperature. To improve PET estimates, when actual climate inputs were not available to drive the Penman–Monteith equations, we utilized average monthly wind speed and humidity from a database with ecodistrict climate normals (Environment Canada, 1994) and read them into the model directly. Additional modifications were made to the DNDC model to incorporate new research regarding the effects of CO2 fertilization on crop growth (Long et al., 2006; Leakey et al., 2009; Morison, 1993; Kimball and Idso, 1983). The changes were based on results from FACE experiments which indicate that the response of crop production to increases in CO2 concentration are substantially lower than those reported in laboratory studies (Long et al., 2006; Leakey et al., 2009). Water use efficiency in plants is also reported to be considerably lower in FACE experiments compared to previously reported values from laboratory experiments that had suggested a 1:1 relationship with CO2 increases (Morison, 1993; Kimball and Idso, 1983). For this study the modifications incorporated in the DNDC model include (i) the lowering of the effect of CO2 fertilization on assimilation in C3 crops to better correspond to results from FACE experiments (Long et al., 2006) and other exposure systems (Ziska and Bunce, 2007) excluding glasshouse methodology, (ii) the removal of the effect of CO2 fertilization on assimilation in C4 crops since it was found to be negligible in most open air studies (Leakey et al., 2006), (iii) the average effects of improved water use efficiency in response to elevated CO2 concentrations by lowering plant water requirements in DNDC under higher CO2 concentrations for both C3 and C4 crops (Barton et al., 2011; Leakey et al., 2009; Van de Geijn and Goudriaan, 1996), and (iv) the effect of elevated CO2 concentration on nitrogen use efficiency by adjusting the C:N ratio of crop fractions in response to elevated CO2 . To be consistent with the other included FACE observations, results from the Leakey et al. (2009) meta-analysis were employed whereby the maximum theoretical reduction in leaf N due to Rubisco acclimation under elevated CO2 was found to be smaller than anticipated from past experiments. Since this topic is still under study, further modifications in the response of leaf N content to CO2 fertilization might be required in the future. Increased assimilation in C3 crops can result in greater growth than the maximum potential biomass production for each crop type that is input into DNDC, thus the maximum potential growth was allowed to increase in the model as atmospheric CO2 concentration increased. 2.2. Experimental study locations This study was carried out using soils, climate and typical management inputs from well-established long-term experimental studies in Canada. The four selected long-term sites were: Swift Current SK (Campbell and Zentner, 1993; Grant et al., 2013), Lethbridge AB (Janzen et al., 1998; Grant et al., 2013), Melfort SK (Zentner et al., 1990; Moulin et al., 1997; Liebig et al., 2006) and Harrow ON (Drury and Tan, 1995; Drury et al., 2004). The soil at the Swift Current site is a Swinton Loam situated on a Canadian Orthic Brown Chernozem (FAO:Kastonozem–aridic) with an average annual precipitation of 334 mm. The Lethbridge site has a loam–clay loam textured soil on a Canadian Orthic Dark Brown Chernozem (FAO: Kastonzem–Haplic) with an average annual precipitation of 382 mm. The Melfort site is situated on a silty clay loam texture in a Canadian Orthic, thick Black Chernozem (FAO: Chernozem) with an annual precipitation of 404 mm. Lastly, the Harrow research site is on an Orthic Humic Gleysol (FAO: Mollic, Umbric, Calcic Gleysol) with annual precipitation of 968 mm. A wide range of cropping systems is investigated at each of these sites. These four sites characterize much of the diversity of semi-arid agriculture in

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western Canada along with a sample of the more humid eastern region. A detailed description of agricultural management is provided in the aforementioned sample of research papers associated with each site as well as in Grant et al. (2013) where DNDC and DAYCENT were used to simulate historical changes in crop yields and SOC at the Swift Current and Lethbridge locations. 2.3. Climate scenarios and weather generator The stochastic weather generator developed at Agriculture and Agri-Food Canada (AAFC-WG) uses historical climate data to derive climate parameters based on changes in the variance and means of climate variables (Hayhoe, 2000). Qian et al. (2008) found that the weather generator had a good capacity for estimating daily precipitation extremes, however, there were some difficulties in reproducing extreme values of daily maximum and minimum temperatures. In later work Qian et al. (2010a) successfully parameterized and tested the weather generator against historical climate data. AAFC-WG was then used to derive daily climate data for the 1961–1990 historical period and future 2040–2069 time periods using results from the Canadian Coupled Global Climate Model (CGCM3: Kim et al., 2003). Weather data, generated for 120 years in both the 1961–1990 and 2040–2069 time periods were used in this study. This dataset provides a large number of scenario years for simulating the expected range in crop production and C and N dynamics that can occur due to inter-annual variations in climate. A comparison of temperature and precipitation normals for each climate scenario is presented in Table 1. Atmospheric CO2 concentration was set to 331 ppm for the historical 1961–1990 time period and 547, 551, 496 ppm, respectively for the A1b, A2, and B1 future scenarios in 2040–2069. 2.4. Estimating climate indices Using methods employed by Qian et al. (2010b) and Bootsma (1994), agroclimatic indices were estimated for cool and warm season crops for historical climate and the three climate scenarios. Indices were calculated for each year then averaged for the 100 years of generated weather. These indices were estimated (i) to provide a “back of the envelope” estimate of possible changes in climate drivers between historical and projected scenarios that may affect crop growth, (ii) to compare climate indices and their likely influence on crop growth to projections made using the DNDC model and (iii) to allow for modification of DNDC inputs to adjust planting and harvest dates as effected by climate. The following indices were estimated: growing season start (GSS), growing season end (GSE), growing season length (GSL), last spring killing frost (SF), first fall killing frost (FF), average number of cool spell days (CD) and heat wave days (HD) during the growing season, growing degree days (GDD) for cool season crops, crop heat units (CHU) for warm season crops (e.g., corn), and precipitation deficit (SWD). 2.5. Modeling crop production and C and N dynamics under climate scenarios The DNDC model was employed using site specific agricultural activity data to simulate differences in yield and GHG emissions between historical climate and climate change scenarios. One hundred and twenty years of generated climate data was simulated from a historical period (1961–1990) and future period (2040–2069) for three climate scenarios (A1b, A2 and B1). The first 20 years was used solely for model stabilization and only the last 100 years is included in the results. Historical simulations were carried out using crops that were planted in the yield trials of each site, measured soil characteristics included SOC, bulk

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Table 1 Historical and future projections of SRES A1b, A2 and B1 IPCC climate scenarios using CGCM3 model for average annual temperature and average annual precipitation at four locations in Canada. Average annual temperature (◦ C)

Swift Current Lethbridge Melfort Harrow

Annual precipitation (mm)

Historical

SRES-A1b

SRES-A2

SRES-B1

Historical

SRES-A1b

SRES-A2

SRES-B1

(1961–1990) 3.7 5.4 0.8 7.2

(2040–2069) 6.8 8.3 4.1 10.6

(2040–2069) 7.3 8.8 4.6 10.7

(2040–2069) 6.3 7.8 3.7 9.8

(1961–1990) 334 382 404 968

(2040–2069) 371 424 465 1047

(2040–2069) 365 425 459 1015

(2040–2069) 366 442 449 1065

density, pH, field capacity and wilting point, and on site agricultural management included tillage, planting and harvest dates, and fertilizer application rate and timing. For historical simulations in western Canada, spring wheat was the predominant crop whereas corn was planted at the Harrow site in eastern Canada. A corn–corn–soybean–alfalfa–alfalfa–alfalfa rotation was also simulated to compare continuous corn production with corn in rotation. It is expected that future cropping practices will include the cumulative effects of changes in climate and agricultural management. For wheat production in western Canada and corn production in eastern Canada we simulated changes in a step-wise progression to observe effects of temperature and precipitation under no CO2 fertilization, CO2 fertilization with a change in growing season length, and change to a more favorable cultivar (Table 2). In simulations where growing season length was extended, we estimated the change in planting date based on the climate indices (Tables 3 and 4). Fertilizer application rate is dependent on expected crop production and level of N in the soil in the spring and we expect the quantity applied to change in accordance with future stresses. We assumed that farmers apply fertilizer at a rate that will impose some stress on crop production in order to optimize expected yields and input costs and to maximize economic gains. In DNDC simulations, we calibrated the fertilizer application rate for each scenario to impose a small average plant N stress (N stress is estimated by the model) that was consistent between historical and future simulations. Simulations were also performed to ascertain the effectiveness of the following cropping practices under future climate: fallow in rotation in western Canada as a moisture conserving strategy, winter wheat with less over-wintering damage, residue

removal for biofuel production, and irrigated corn in western Canada (Table 2). 3. Results and discussion There is much uncertainty in predicting the effects of climate change on cropping systems. To limit uncertainty in our estimates we have taken the approach of using measured data from experimental sites to parameterize and modify the DNDC model to better predict crop biomass production in Canada (Kröbel et al., 2011) and also to better predict inter-annual variations in yield as effected by historical climate (Grant et al., 2013). The model parameterization and validation work developed during the Grant et al. (2013) study was coordinated with model developments in this study to better characterize effects of climate on crop growth, improving the basis for extending projections into the future. Grant et al. (2013) demonstrated that DNDC95 was capable of predicting crop yields from long-term continuous wheat studies in western Canada with R2 relationships ranging from 0.51 to 0.70 and average relative error (ARE) ranging from −4.9% to +15.8%. 3.1. Climate indices All three climate scenarios (A1b, A2, and B1) using CGCM3 showed a moderate increase in average annual temperature with the most increase consistently being for the A2 scenario and the least being for the more environmentally friendly B1 scenario (Table 1). In Canada from 1948–2012 seasonal temperatures have warmed by 1.8 ◦ C, 1.4 ◦ C, 1.7 ◦ C, and 3.5 ◦ C during the spring, summer, autumn and winter, respectively (Environment Canada, 2012),

Table 2 Simulated changes in crop management for historical and SRES A1b, A2 and B1 climate scenarios at four research sites in Canada. Western sites (Swift Current, Lethbridge and Melfort)

Eastern site (Harrow)

Crop

Climate and management change

Crop

Climate and management change

Spring wheat Spring wheat Spring wheat Spring wheat

Historical climate Future climate, no change in atmospheric CO2 Future climate, elevated in CO2 Future climate, elevated CO2 , increased growing season length, fertilizer adjustment Future climate, elevated CO2 , increased growing season length, fertilizer adjustment, cultivar with higher GDD requirement Historical climate Future climate, elevated CO2 , increased growing season length, fertilizer adjustment Historical climate Future climate, elevated CO2 , increased growing season length, fertilizer adjustment

Continuous corn and rotational-corn Continuous corn and rotational-corn Continuous corn and rotational-corn Continuous corn and rotational-corn

Historical climate Future climate, no change in atmospheric CO2 Future climate, elevated CO2 Future climate, elevated CO2 , increased growing season length, fertilizer adjustment Future climate, elevated CO2 , increased growing season length, fertilizer adjustment, cultivar with higher GDD requirement Historical climate Future climate, elevated CO2 , increased growing season length, fertilizer adjustment Historical climate, 70% stover removal Future climate, 70% stover removal, elevated CO2 , increased growing season length, fertilizer adjustment

Spring wheat

Winter wheat Winter wheat Corn (irrigated) Corn (irrigated)

Spring wheat–fallow Spring wheat–fallow Spring wheat Spring wheat

Historical climate Future climate, elevated CO2 , increased growing season length, fertilizer adjustment Historical climate, 70% straw removal Future climate, 70% straw removal, elevated CO2 , increased growing season length, fertilizer adjustment

Continuous corn and rotational-corn

Winter wheat Winter wheat Continuous corn and rotational-corn Continuous corn and rotational-corn

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Table 3 Climate indices estimated from historical data for cool season crops and change in indices for future scenarios.

Climate scenario Swift current Historical (1961–1990) A1b (2040-2069) A2 B1 Lethbridge Historical A1b A2 B1 Melfort Historical A1b A2 B1 Harrow Historical A1b A2 B1 + *

Potential growing season length (d)

Last spring killing frost (JD)+

First fall killing frost (JD)

Average number cool days (d)

Average number heat days (d)

GDD (wheat)

GDD* (cool season)

SWD (mm)

175 +16 +26 +18

107 −8 −13 −7

292 +10 +15 +10

51 −12 −12 −9

13 +20 +24 +13

1311 +197 +230 +209

1608 +551 +611 +411

248 +107 +129 +77

183 +19 +32 +23

106 −10 −18 −8

292 +9 +14 +11

52 −16 −16 −13

12 +25 +25 +14

1302 +214 +291 +233

1638 +582 +631 +433

241 +111 +104 +41

162 +15 +22 +16

116 −7 −13 −8

288 +9 +15 +11

56 −12 −13 −10

7 +17 +18 +9

1224 +260 +285 +221

1437 +496 +532 +369

179 +57 +79 +57

205 +29 +29 +22

93 −24 −21 −14

324 +10 +8 +8

39 −17 −17 −14

8 +26 +33 +20

1578 +301 +215 +313

2043 +729 +771 +577

58 +64 +66 −24

Julian calendar day. Potential GDD for cool crops for entire season. GDD for wheat is limited to the period from planting to harvest date.

thus a 2–3 ◦ C increase in annual temperature from the 1961–1990 period to the 2040–2069 period is likely reasonable. Predicted increases in average annual precipitation for SRES scenarios were reasonably consistent and small across the three western sites (Table 1) but less precipitation increase was predicted for the A2 scenario at the eastern site. The increases in precipitation are also consistent with historical data in Canada whereby the departure from normal is +4.8% on average from 1991 to 2011. All three climate scenarios show marked differences in temperature-based indices for both cool season (Table 3) and warm season (Table 4) crops. At the three locations in the Canadian prairies estimated GSL of cool season crops increased by 2–3 weeks under the A1b and B1 climate scenarios and increased by about 3–4 weeks for the A2 scenario. At the eastern Canada site in Harrow, GSL was predicted to increase by about one month. Accordingly, the last spring killing frost occurred up to 3 weeks earlier, and the last fall killing frost occurred later across all locations. As expected the

average number of cool days during the growing season decreased and the average number of heat days increased for cool season crops. For warm season crops the average number of heat days increased only marginally, however, the average number of cool days decreased drastically by 25–41 days indicating that temperatures might be more favorable for growth of warm season crops in Canada in the future. Both the GDD of cool season crops and CHU of warm season crops increased substantially in the future scenarios. The changes in temperature-based indices estimated for the future climate scenarios are consistent with trends in historical climate in Canada whereby Qian et al. (2012, 2010b) found many occurrences of statistically significant changes in temperature-based climate indices between three 30 year time periods (1911–1940, 1941–1970 and 1971–2000). From the first to the third period, the analysis showed an earlier average last spring frost (≈11 days), a later average last fall frost (≈9 days), a longer average frost free period (≈20 days), and more available heat units. The differences

Table 4 Climate indices estimated from historical data for warm season crops and change in indices for future scenarios.

Climate scenario Swift current Historical (1961–990) A1b (2040–2069) A2 B1 Lethbridge Historical A1b A2 B1 Melfort Historical A1b A2 B1 Harrow Historical A1b A2 B1 +

Julian calendar day.

Potential growing season length (d)

Last Spring killing frost (JD)+

First Fall killing frost (JD)

Average number cool days (d)

Average number heat days (d)

CHU at 10 ◦ C

SWD (mm)

128 +17 +22 +14

132 −7 −8 −3

267 +9 +15 +12

95 −37 −36 −29

2 +8 +9 +3

2208 +699 +787 +536

221 +103 +132 +74

134 +20 +25 +17

128 −7 −9 −2

270 +6 +13 +10

104 −41 −41 −33

1 +9 +8 +2

2245 +723 +803 +573

217 +118 +117 +53

121 +15 +18 +12

135 −3 −4 −1

267 +9 +14 +11

100 −34 −33 −26

1 +3 +4 +1

2030 +653 +702 +497

160 +67 +85 +55

153 +34 +36 +28

122 −21 −21 −16

293 +18 +17 +15

62 −29 −30 −25

0 +3 +6 +2

2976 +1045 +1087 +854

123 +93 +94 +8

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were greater between the second and third period than between the first and the second periods. Our estimate of the commonly used water stress (SWD) index indicates that water stress might increase across most of Canada in the future, except under the B1 scenario at Harrow. When studying historical data, Qian et al. (2010b) found that the majority of the weather stations in Canada showed a significant positive trend in precipitation index from maximum 1 day and 10 day precipitation totals from 1995 to 2007, generally leading to a reduction in water deficit (SWD; PET-P). However, some stations showed a significant increase in water deficit, particularly in the semi-arid prairie ecozone. These historical findings differ from our SWD projections under the A1b and A2 scenarios but are somewhat consistent with our B1 predictions. An issue with the SWD index is that the potential effect of increased water use efficiency due to lower stomatal conductance in plants under elevated CO2 (Yu et al., 2004) concentrations is not considered. It has been observed that a decrease in ET occurs under elevated CO2 for both wheat (Hunsaker et al., 2000) and maize (Leakey et al., 2006) and the inclusion of this effect in the DNDC model is expected to reduce water stress in both C3 and C4 crops. 3.2. Effect of climate on crop yields To ensure that the DNDC model was predicting a suitable range of yields for the historical period (1961–1990) results were compared to average measured values from the four research sites (Table 5). Yield predictions were close to measurements for these periods except at Melfort where yields were over predicted by about 18%. Continuous wheat production at Melfort is hampered by weeds and disease (Zentner et al., 1990) and we did not simulate weeds or disease in this study. 3.2.1. Predicted change in crop yield at experimental sites in western Canada For the Lethbridge and Melfort research sites, DNDC simulations predicted similar trends in yield increase across all climate change scenarios (Fig. 1). Yields at Swift Current increased to a lesser extent, primarily due to a smaller projected increase in precipitation at Swift Current than at the other western sites (Table 1). Parry (2007) in a review suggested that current studies indicate that yield potential may be increased under climate change at mid and high-mid latitudes, which is consistent with results in our study. The same review also suggested that these regions are often more likely to have the economic means to pursue adaptation strategies. Wang et al. (2011) used the DSSAT model to predict yield and biomass changes in a currently available biofuel spring wheat cultivar under SRES scenarios for the same historical and future periods that we employed in our study. In their study, wheat yields were predicted to increase by 41–74% relative to the historical period which is considerably higher than our average projections of 24% yield increase at Swift current (assuming no change in cultivar). However, Wang et al. (2011) indicate that the effect of heat shock during anthesis was not yet included in the DSSAT model. There have been studies which indicate that climate change will result in unfavorable effects in many areas of the world. A study by Brisson et al. (2010) indicated that on-going climate change in Europe could be unfavorable for cereal yields due to increase temperature stress during anthesis and drought stress during stem elongation and that stagnation in yields is already occurring. Lobell et al. (2011) in their use of models to link yields to climate trends worldwide, determined that yields of wheat and corn would decline slightly on average when climate trends are considered. 3.2.1.1. Effect of future temperature and precipitation on spring wheat yield. A stepwise progression of changes in climate and

management was performed using the DNDC model to examine how each permutation affected model predictions. When only changes in temperature and precipitation were included in the simulations (no CO2 fertilization), wheat yields declined at Swift Current but increased marginally at the Lethbridge and Melfort sites (Fig. 1). In the future, GDD at Lethbridge and Melfort was projected to increase by about 18% (Table 3) and precipitation to increase by 13% whereas the increase was smaller at Swift Current at 15% and 10% for GDD and precipitation, respectively. At Swift Current the increase in temperature was offset by increased evaporative losses resulting in a small decline in yields. The added temperature stress effect on harvest index during anthesis in the DNDC code hampered yields more so in future than under historical weather (Table 6). This result was expected, with the reduction being more pronounced for the A1b and A2 scenarios and the reduction being greatest on winter wheat followed by spring wheat, then corn. The variable effect of temperature stress during anthesis on crop type is easily explained since the optimum growing temperatures for winter wheat, spring wheat and corn are set to 14 ◦ C, 18 ◦ C and 30 ◦ C, respectively in the DNDC model. As temperatures rise in western Canada, more stress is placed on crops that perform well under cooler temperatures, both during anthesis and to a lesser extent for the remainder of the growing season.

3.2.1.2. Effect of climate change including changes in CO2 fertilization and growing season length on spring wheat yield. When beneficial effects of increased CO2 concentrations, along with changes in weather and GSL (based on GSL changes in Table 3), were included in the simulations, average predicted yields increased substantially over the baseline historical yields (Fig. 1). The increase was less at Swift Current, again due to lower increases in GDD and precipitation in comparison to the other sites. Under increased CO2 fertilization, yield increases were associated with: (i) higher rate of photosynthesis for C3 crops (as indicated, values were updated in the model using results from FACE experiments), (ii) water stress was reduced due to more efficient water use with less water requirements for plant respiration, and (iii) the nitrogen use efficiency of spring wheat increased slightly. Additional tests indicated that the change in GSL with an earlier planting date had little effect on projected crop yields. The DNDC model tended to predict less water stress in the future, even when increased water use efficiency from CO2 fertilization was excluded from the simulation. The DNDC model predicted a sizable increase in PET, however, actual ET only increased marginally over historical values. Water leached to deeper depths and was not readily available for evapotranspiration.

3.2.1.3. Effect of climate change on yields of a new spring wheat cultivar (higher GDD requirements). When we evaluated a new crop cultivar (representing anticipated advances in breeding) with characteristics similar to varieties currently grown in mid-northern United States (except adapted to photoperiods in Canada), wheat yields increased substantially under climate change (Fig. 1). The new spring wheat cultivar required greater GDD0 (1600 vs. 2200) and thus a longer growing season to mature (increased by approximately 2 weeks), an optimum temperature of 20 ◦ C rather than 18 ◦ C and had a higher maximum potential biomass (assumed a 25% increase in max biomass production). It stands to reason that this scenario with the combined effects of CO2 fertilization, increased GSL and use of new cultivars is probably the most likely case for the future. Due to limitations in GDD during the growing season, the average wheat yields declined as expected at all three locations when the new cultivar was included in historical simulations. It is interesting to note that although yields were lower, total crop biomass was often greater under the historical simulation when

W.N. Smith et al. / Agriculture, Ecosystems and Environment 179 (2013) 139–150

145

Table 5 Measured and predicted grain yields and coefficients of variation for spring wheat, winter wheat and corn at four locations under historical and future climate scenarios. Grain yield Measured

Coefficient of variation SRES-A1b*

Historical

SRES-A2*

SRES-B1*

Measured

SRES-A1b*

Historical

(kg ha−1 ) Spring wheat–SC Spring wheat–Leth Spring wheat–Melf Winter wheat–SC Winter wheat–Leth Winter wheat–Melf Winter wheat–Harw Irrigated corn–SC Irrigated corn–Leth Irrigated corn–Melf Rainfed corn–Harw *

1319 1466 1813

1326 1492 2142 1460 1839 1515 3294 3463 3757 521 6343

6330

1666 2154 2846 2202 2952 3141 4435 10451 10941 9570 10941

SRES-A2*

SRES-B1*

36.9 34.4 20.5 43 37.1 57.2 22.8 5.7 5.4 4.3 21.4

30.2 29.7 20.7 34.5 43.7 64.4 21.6 5.2 6.5 6.1 24.4

(%) 1597 2112 2957 2354 3136 3154 4311 10554 11102 9913 10873

1685 2477 3007 2096 2884 2626 4557 9533 8996 7569 10228

43.0 49.9 44.6

30.4 42.2 26.3 46.2 48.3 78.7 12.8 14.5 13.9 19.2 22.7

22.7

33.7 33.6 24.0 42.4 44.8 58.2 23.6 6.5 8.5 4.7 20.7

Climate scenario simulations include increased CO2 fertilization, increased growing season length and no change in cultivar.

4000

(A) Swi Current

Historical SRES-A1b SRES-A2 SRES-B1 26 % 23 % 23 %

51 % 61% 44 % 61 % 71 % 57 %

30 %

49 % 50 % 65 %

5% 4% 22 %

2000

25 % 37 %

44 % 42 % 66 %

108 %* 95 %*

3000

1000

17 % 23 % 17 %

50 %* 76 %* 50 %*

26 % 20 % 27 %

(B) Lethbridge 91 %*

0

-5%

1000

-12 % -14%

2000

40 % 36 % 30 %

31 % 32 % 37 %

33 % 38 % 40 %

107 % 108 % 73 %

2000

63 %* 76 %*

(C) Melfort 3000

45 %*

0

4% 6% 7%

Average Annual Grain Yields (kg ha-1)

3000

1000 0 Spring wheat (current CO2)

Spring wheat (GSL , SRES-CO2)

Spring wheat (GSL , SRES-CO2) (new culvar)

WWF (GSL, SRES-CO2)

Winter Wheat (GSL, SRES-CO2)

Spring Wheat (GSL, SRES-CO2) (residue removal)

Fig. 1. Predicted effect of climate change on yield under SRES climate scenarios at (a) Swift Current, (b) Lethbridge and (c) Melfort research sites.

Table 6 Percent estimated reduction in grain yield due to temperature stress during anthesis. Spring wheat Hist

A1b

Winter wheat A2

B1

Hist

A1b

Corn A2

Decrease in yield due to inclusion of temperature effect at anthesis 16.2 25.8 26.7 21.1 24.9 41.5 41.8 Swift current 16.4 25.4 27 20.1 25.6 37.4 39.2 Lethbridge 6.2 12.6 13.6 9.1 20.1 23.7 29.5 Melfort Decrease in yield due to inclusion of temperature effect at anthesis in relation to historical simulations 9.4 10.1 5.2 16.5 16.9 Swift current 9 10.6 3.7 11.8 13.6 Lethbridge 5.1 6.7 3.2 3.6 9.4 Melfort

B1

Hist

A1b

A2

37.2 36.9 28.2

8.6 6.8 0

10.3 9.5 0.4

10.1 8.7 0.5

8.3 7.9 0.2

1.7 2.7 0.4

1.6 1.8 0.5

−0.2 1.1 0.2

12.3 11.3 8.1

B1

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W.N. Smith et al. / Agriculture, Ecosystems and Environment 179 (2013) 139–150 35 Historical SRES-A1b SRES-A2 SRES-B1

Frequency (100 years of simulaon)

30

25

20

15

10

5

0 0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Winter Wheat Grain Yields (kg ha-1) Fig. 2. Frequency distribution of winter wheat yield under historical, A1b, A2 and B1 SRES climate scenarios at the Swift Current research site.

a new cultivar was included. In many years the growing season length was not sufficient for grain filling to occur. 3.2.1.4. Effect of climate change on spring wheat yield in a wheat–wheat–fallow rotation. As expected, due to increased water and N availability from the fallow year, wheat following fallow in the historical simulations was of higher average yield than was continuous wheat in historical simulations. The predicted increase in yield under the projected climate change was, however, less than that of continuous wheat at the Swift Current and Lethbridge sites but similar at the Melfort site. The DNDC model indicates that the implementation of fallow as a moisture conserving practice on semi-arid soils may not be as critical in the future. Inclusion of fallow in rotation may however, still be required for reducing weed and disease problems. 3.2.1.5. Effect of climate change on winter wheat yield. Under future scenarios, winter wheat production is predicted to increase by 44% to 71% in Swift Current and Lethbridge without changing the cultivar or agricultural management (Fig. 1). An even greater response and increase in average yields due to climate change was predicted for the cooler Melfort region. Yield increases are generally projected under future scenarios, particularly in SRES A2 and A1b where CO2 concentration and temperature increase more than in the B1 scenario (Fig. 2). The greater increase in winter wheat growth in comparison to spring wheat can be attributed to less crop damages during over-wintering. 3.2.1.6. Effect of climate change on spring wheat yield under residue removal. Predicted yields for the historical period (1961–1990) were slightly lower when residues were removed than when they were retained. This was expected since C inputs were lower. The projected increase in yield under climate change was, however, similar to that of the spring wheat scenario with no residue removal. Based on the DNDC simulations, it is not expected that residue removal will further hamper crop performance in the future at the western study locations. 3.2.1.7. Effect on yield of irrigated corn due to climate change. We also assessed the likelihood of being able to successfully produce a cultivar of grain corn historically grown in eastern Canada (cultivar used in Harrow site simulations) in western Canada in the future. Based on the climate indices (Table 4) where the shift in growing season length, average number of cool days and CHU’s

indicated that the temperatures-based climate in western Canada may become similar to historical climate in eastern Canada, it was expected that we would be able to successfully grow corn cultivars that perform well in eastern Canada. The DNDC results supported this hypothesis as the grain corn cultivar that failed under historical climate in western Canada due to too few heat units performed very well under irrigation for future scenarios (Table 5). Greater yield projections occurred as expected for the A2 and A1b scenarios compared to the B1 scenario due to higher projected temperature and CHU. It is not expected that grain corn would perform well in western Canada in the future without irrigation unless there were substantial improvements in the water use efficiency of the cultivars. 3.2.2. Predicted change in crop yield at the Harrow research station in eastern Canada Greatly increased CHU’s at the Harrow research site in eastern Canada (Table 4) under climate change resulted in a general increase in corn yield, even without changes in CO2 concentration, GSL and cultivar (Fig. 3). The increase was in response to the high optimum temperature for growth of grain corn (30 ◦ C) and a high tolerance to temperatures below 40 ◦ C. The effect of temperature stress at anthesis on harvest index in the future only marginally affected corn yields in comparison to the influence on small grains. Increased precipitation (Table 1) resulted in similar or slightly less water stress in the DNDC simulations when a change in CO2 concentration was not included. When the effect of CO2 concentration was included in the simulations, yields increased further due solely to increased water and N use efficiency. Drury and Tan (1995) found that fluctuations in corn yield at Harrow, particularly in continuous corn, were more driven by water stress than temperature stress. Lobell et al. (2011) estimated that corn production in Canada could have declined since 1980 on average as a result of climate trends, however, it is our opinion that increases in corn heat units in eastern Canada, where the majority of corn production occurs, has resulted in increased yields. The effect of elevated CO2 on increased NUE and WUE, which was not included in the Lobell et al. (2011) study, could further contribute towards increased corn productivity. A corn cultivar with a GDD0 requirement of 3200 compared to the historical cultivar GDD0 of 2500 would produce higher yields in the future. This increased GDD0 requirement is comparable to parameterization for corn grown in the mid-United States latitudes. There was no influence of residue removal on corn yield increase under climate change. Note that the DNDC model predicted larger yield increases due to climate change for continuously grown grain corn than for corn in rotation at the Harrow site. DNDC does not simulate the increased likelihood of weeds/disease in a continuous cropping rotation along with any potential degradation in soil quality thus the benefit of cropping systems is not fully represented. In addition, historical rotational corn simulations had higher base yields than the continuous corn simulations and likely had less potential for realizing future yield increases due to the upper limits of the parameterization. The probability is that both continuous and rotational corn would respond similarly under climate change if weeds/disease were not a factor. 3.2.3. Predicted changes in inter-annual variations in yields Based on the climate indices (Tables 3 and 4), which largely indicated increased growing season length with more frost free days, higher GDD, higher CHU, and less cool stress, it was expected that the DNDC model might predict a general increase in yields under the climate change scenarios, particularly when including the effect of CO2 fertilization on WUE. It was, however, uncertain if the model would predict a greater level of variability.

W.N. Smith et al. / Agriculture, Ecosystems and Environment 179 (2013) 139–150

147

14000

75 % 57 %

SRES-B1

64 %

73 %

94 %* 69 %*

74 %*

96 %* 71 %

61 %

72 % 35 %

SRES-A2

29 %

SRES-A1b

6000 4000

2000 0

35 %

35 %

61 % 27 %

1%

8000

57 %

10000

41 %*

Rotaonal Corn 12000

29 %

Corn Grain Yields (kg ha-1)

8000

37 %

10000

74 %*

Historical

61 %

Connuous Corn 12000

6000

4000 2000 0

Current CO2

GSL , SRES-CO2

GSL , SRES-CO2 (new culvar)

GSL, SRES-CO2 (residue removal)

Fig. 3. Predicted effect of climate change on corn yield under SRES climate scenarios at the Harrow research site.

Changes in the quantity of extreme events (hot or cold periods and timing of killing frost) could influence both the magnitude and variation in yields. Results, however, showed that there was little indication that predicted yields were more variable under the future scenarios (Table 5). Projections of greater crop heat units, CO2 fertilization effects on biomass and a longer frost free growth period sometimes resulted in lower yield extremes. The extreme temperature events were not typically high enough to cause much stress on yield, particularly for corn, and the model indicated that there was less water stress in simulations of the future. There is a possibility that the AAFC weather generator may have under predicted the variability in extreme events (precipitation extremes and dry spells) in the future scenarios. Weather generators often underestimate inter-annual variability and this phenomenon is called overdispersion. Qian et al. (2008) did, however, find that the AAFC-WG did very well in reproducing extreme daily precipitation, and its capability in reproducing actual extreme daily temperatures was reasonably satisfactory. In a later study, Qian et al. (2010a) found that actual values of extremes from local scenarios may be more reliable for predicting extremes than those from direct GCM outputs which often did not reproduce realistic values. It is questionable whether or not extreme precipitation events have changed significantly in Canada up to now. In a study of historical changes in weather events and extremes Qian et al. (2012) found that there was often significant trends in agro-climatic indices, however, there were few stations with a significant difference in variances for most indices considered. We also need to keep in mind that the influence of pests and diseases and potential extreme events such as flooding are not taken into account in the DNDC model analysis. However, we could reason that agriculture in the Great Plains and Great Lakes areas of Canada may in the future become more productive but require more insect and disease control, similar to Northern areas of the United States.

3.3. Effect of climate scenarios and cropping strategies on nitrous oxide emissions Predicted N2 O emissions under historical climate were found to be in a realistic range in comparison to nearby measurements (Helgason et al., 2005; Rochette et al., 2008) with average emissions of 0.49 kg, 0.78 kg, and 1.98 kg N2 O–N ha−1 y−1 for Swift Current, Lethbridge, and Melfort under spring wheat production and 2.71 kg N2 O–N ha−1 y−1 under corn at Harrow. At the Swift Current site, Grant et al. (2013) found that estimates from the DNDC model of 275 g and 56 g N2 O–N ha−1 compared well to measurements of 217 g and 99 g N2 O–N ha−1 for the fertilized and unfertilized continuous wheat rotations, respectively. Across all sites and cropping scenarios, the quantity of N2 O emissions per unit area was predicted to double in the future. This was expected considering that substantially more fertilizer was assumed to be applied in anticipation of the higher potential crop yield. Several other studies have reported that N2 O emissions will likely increase in North America under climate change (XuRi et al., 2012; Del Grosso and Parton, 2012; Tian et al., 2012). Using the dynamic land ecosystem model (DLEM) Tian et al. (2012) projected that N2 O emissions in North America would increase by 157–227% compared to emissions in the 2000–2010 period, assuming no change in management, and that Canada would experience a faster rate of increase. A comparison of the N2 O emissions per ton of yield offers a value-based assessment (Table 7). Results indicate that emissions usually increased per ton of wheat yield in western Canada. However, at the eastern site emissions per ton of grain corn remained similar between historical and future periods. This is primarily because corn yields increased more so than did wheat yields thus a lower increase per ton of yield was incurred, but also, corn grain production in eastern Canada was not as adversely affected by the occurrence of extreme water stress whereby considerable quantities of excess N could be left in the soil after harvest.

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W.N. Smith et al. / Agriculture, Ecosystems and Environment 179 (2013) 139–150

Table 7 Estimates of N2 O emissions per ton of grain yield (Kg N2 O–N ha−1 y−1 ) at four locations in Canada. Swift current

Western Canada

Hist

A1b

Lethbridge A2

B1

Hist

Melfort

A1b

A2

B1

Hist

A1b

A2

B1

0.71 0.53 0.59 0.49 0.72 0.79 0.29

0.92 0.92 1.67 0.75 1.4 2.33 3.36

2.19 1.78 1.81 1.42 2.53 2.76 0.84

2.1 1.66 1.5 1.35 2.47 2.42 0.82

1.75 1.43 1.62 1.09 2.16 2.84 0.86

(kg N2 O–N ha−1 y−1 )

Scenarios Spring wheat—No CO2 increase Spring wheat—Increased GSL + SRES-CO2 Spring wheat—GSL + SRES-CO2 + new cultivar Spring wheat—Residue + GSL + SRES CO2 Wheat–wheat–fallow—GSL + SRES-CO2 Winter wheat—GSL + SRES-CO2 Irrigated-Cor— + GSL + SRES-CO2

0.37 0.37 0.58 0.31 0.60 0.60 0.27

0.92 0.74 0.80 0.63 1.00 0.80 0.42

1.00 0.79 0.73 0.66 1.03 0.85 0.48

0.68 0.56 0.59 0.47 0.90 0.53 0.28

0.53 0.53 0.83 0.48 0.53 0.52 0.27

0.89 0.7 0.63 0.6 0.87 0.84 0.47

1.03 0.76 0.71 0.68 0.92 0.83 0.51

Harrow—continuous corn

Harrow—rotational corn

Eastern Canada Scenarios

Hist (kg N2 O–N ha−1 y−1 )

A1b

A2

B1

Hist

A1b

A2

B1

Corn—no CO2 increase Corn—increased GSL + SRES-CO2 Corn—GSL + new cultivar Corn—residue + GSL + SRES-CO2 Winter wheat—GSL + SRES-CO2

0.43 0.43 0.58 0.39 0.21

0.61 0.48 0.51 0.43 0.94

0.6 0.45 0.5 0.46 0.81

0.56 0.46 0.46 0.44 0.72

0.56 0.56 0.97 0.54 –

0.59 0.49 0.52 0.48 –

0.6 0.49 0.54 0.48 –

0.67 0.58 0.65 0.57 –

Higher emission intensities were found for spring wheat in western Canada under the more severe A1b and A2 climate scenarios. This is not surprising considering that temperature increases less under the B1 scenario. Note that emission intensities for the new cultivars under historical climate were often high due to very low predicted yields for this scenario. 3.4. Effect of climate scenarios and cropping strategies on net GHG emissions The net total change in emissions of CO2 and N2 O in CO2 equivalents per ton of grain yield was estimated for the 1961–1990

SRES-A1b

55.0 45.0

(1976 mean) to the 2040–2069 (2055 mean) time period for locations in western Canada (Fig. 4). Although SOC was sequestered under climate change in some cases, such as for the improved spring wheat cultivar scenario, for winter wheat production in western Canada and for corn production at the eastern location, the relative contribution to change in net emissions from CO2 was generally small in comparison to the contribution from N2 O emissions. Our results suggest that increased N2 O emissions under climate change in western Canada might fully offset SOC gained through improved management (such as conversion from intensive- to no-tillage). Tian et al. (2012) projected that sizable increases in N2 O emissions due to climate change by the

SRES-A2

SRES-B1 N2 O CO2

(A) Swi Current

35.0 25.0

Mg CO2 equivalents ha-1 ton grain-1

15.0 5.0 -5.0

-15.0 45.0

(B) Lethbridge

35.0 25.0 15.0 5.0 -5.0

-15.0 45.0

(C) Melfort

35.0 25.0 15.0

Spring Wheat GSL, SRES-CO2 residue removal

Winter Wheat GSL, SRES-CO2

Wheat-Wheat-Fallow GSL, SRES-CO2

Spring Wheat GSL, SRES-CO2 new culvar

Spring Wheat GSL SRES-CO2

Spring Wheat Hist-CO2

Spring Wheat GSL, SRES-CO2 residue removal

Winter Wheat GSL, SRES-CO2

Wheat-Wheat-Fallow GSL, SRES-CO2

Spring Wheat GSL, SRES-CO2 new culvar

Spring Wheat GSL SRES-CO2

Spring Wheat Hist-CO2

Spring Wheat GSL, SRES-CO2 residue removal

Winter Wheat GSL, SRES-CO2

Spring Wheat GSL, SRES-CO2 new culvar

Spring Wheat GSL SRES-CO2

Spring Wheat Hist-CO2

-15.0

Wheat-Wheat-Fallow GSL, SRES-CO2

5.0 -5.0

Fig. 4. Estimates of change in net GHG emissions in CO2 equiv per ton of grain yield at Swift Current, Lethbridge and Melfort for three IPCC SRES climate scenarios.

W.N. Smith et al. / Agriculture, Ecosystems and Environment 179 (2013) 139–150

turn of the century might offset terrestrial CO2 sequestration by 47–166%. The total combined average change in GHG emissions for the A1b and A2 scenarios was about double that of the B1 scenario at western sites. The pattern of emissions between the scenarios was similar with the greatest change in emissions occurring for spring wheat with no CO2 fertilization and for the wheat–wheat–fallow rotation. At the Lethbridge site, where crop yields and N export in grain increased more so in the future than at the Swift Current and Melfort sites, there was a smaller increase in net GHG emissions under the A1b and A2 scenario and very little change in net GHG emissions under the B1 scenario. There was a larger increase in emissions at the Melfort site due to temperature increase driving the denitrification process. In eastern Canada the model predicted that there was very little change in net GHG emissions per ton of grain corn between historical and future climate (data not shown). As indicated earlier, N2 O emissions per ton of grain corn remained relatively stable and only small amounts of soil organic carbon was sequestered. High carbon inputs to the soil from increased corn production were for the most part offset by enhanced soil organic matter decomposition.

4. Conclusions The DNDC model was modified to include the effects of CO2 fertilization on crop production, water use efficiency, and N use efficiency based on some of the latest FACE data. The effect of temperature stress during anthesis on harvest index was also included in the model code. A validated version of the DNDC model was employed to estimate crop yields, SOC change and N2 O emissions under historical and future climate scenarios for four experimental sites in Canada. Historical trends in temperature, precipitation, and rising atmospheric CO2 concentration in Canada indicate that the climate is in general becoming more favorable for agricultural production and climate change projections based on IPCC SRES emission scenarios suggest that this trend is likely to continue. Temperature-based indices estimated in this study show that by the period 2040–2069 growing season length will increase, there will be less cool periods but more hot periods, and crop heat units and growing degree days will increase. The soil water deficit index indicated that there may be more water deficit for both warm and cool season crops in the future. The DNDC model, however, predicted lower water stress for crops due mostly to increased water use efficiency of crops under elevated CO2 . The DNDC model reasonably predicted inter-annual variations in historical crop yields in past work and in this study the model generally predicts that crop biomass production will increase under climate change. Assuming no change in cultivar, yields of spring wheat are predicted to increase by about 37% in western Canada from the 1961–1990 to the 2040–2069 period and the increase may double to 73% for new cultivars with higher GDD requirement. We should keep in mind that part of this estimated yield increase has already been realized since the 1961–1990 period. It was also found that the winter wheat crop should be more viable in the future and that irrigated corn production should be viable in western Canada where CHU’s are projected to increase to levels we currently experience in eastern Canada. The model indicates that grain corn production may increase significantly in eastern Canada, more so than wheat in western Canada, due primarily to a large projected increase in heat units that is favorable for C4 crop production especially when accompanied by an increase in water use efficiency under elevated CO2 . Inclusion of new algorithms in the DNDC model had both positive and negative influences on crop yields. Crop biomass and yields

149

were reduced though the inclusion of (i) lower CO2 fertilization effect on biomass than previously (based on FACE studies); (ii) the effect of high temperature stress at anthesis on harvest index and (iii) the effect of temperature stress on biomass production based on cardinal temperatures. On the other hand other additions had favorable effects on crop growth including (i) effect of CO2 fertilization on WUE and (ii) effect of CO2 fertilization on NUE. The DNDC model indicated that soil carbon should remain relatively stable in the future with small amounts of C sequestration occurring when biomass production and C inputs increase substantially such as for improved cultivars and winter wheat in western Canada and for grain corn at the eastern location. 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