Assessing the impacts of climate variability on agricultural productivity at regional, national or global scale is essential for defining adaptation and mitigation strategies. We explore in this study the potential changes in spring wheat yields at Swift Current and Melfort, Canada, for different sowing windows under projected climate scenarios (i.e., the representative concentration pathways, RCP4.5 and RCP8.5). First, the APSIM model was calibrated and evaluated at the study sites using data from long term experimental field plots. Then, the impacts of change in sowing dates on final yield were assessed over the 2030-2099 period with a 1990-2009 baseline period of observed yield data, assuming that other crop management practices remained unchanged. Results showed that the performance of APSIM was quite satisfactory with an index of agreement of 0.80, R2 of 0.54, and mean absolute error (MAE) and root mean square error (RMSE) of 529 kg/ha and 1023 kg/ha, respectively (MAE = 476 kg/ha and RMSE = 684 kg/ha in calibration phase). Under the projected climate conditions, a general trend in yield loss was observed regardless of the sowing window, with a range from -24% to -94% depending on the site and the RCP, and noticeable losses during the 2060s and beyond (increasing CO2 effects being excluded). Smallest yield losses obtained through earlier possible sowing date (i.e., mid-April) under the projected future climate suggested that this option might be explored for mitigating possible adverse impacts of climate variability. Our findings could therefore serve as a basis for using APSIM as a decision support tool for adaptation/mitigation options under potential climate variability within Western Canada.
Adapting agricultural systems and mitigating the potential adverse effects of anticipated, future climate variability has become more important than ever in the context of global climate changes. Decision support tools are often used for agricultural/agroclimate risk management. They generally rely on modelling approach including biophysical process-based crop growth models (field to regional or global scale models) [
Canada is one of the top wheat producers in the world, with a production of circa 37.5 million metric tons in 2013 [
Although evaluating the impacts of climate variability on agricultural production at global scale is essential [
Two sites located in the Province of Saskatchewan in Western Canada [Swift Current (107˚48'W, 50˚17'N), and Melfort (104˚36'W, 52˚52'N)] were considered. The yield data of the selected sites originated from Agriculture and Agri-Food Canada (AAFC)’s experimental plots at these locations. Data spanned a >20-year period at each site over 1946-2009 (
Historical climate data (i.e., daily minimum and maximum temperatures, and precipitation) originated from weather stations and were provided by Environment Canada and other partner institutions through the Drought Watch program (http://www.agr.gc.ca/pfra/drought/index_e.htm). The data period (referred to as historical period) was set according to the available wheat yield data (1949-2009 and 1961-2009 for Swift Current and Melfort, respectively). Daily solar radiation over these periods was generated using the SolarCalc model [
Climate scenarios from the Canadian fourth generation Regional Climate Model (CanRCM4) driven by the second generation Canadian Earth System Model (CanESM2) at 0.22˚ horizontal grid resolution (approximately 25 km) were considered in this study. The CanRCM4 model is a “limited-area version” of the CanAM4 (Canadian Atmospheric global climate Model), developed by the CCCma/EC. A detailed description of its underlying physical processes can be found in [
Site | Soil Type | Wheat Cultivar | Data Period |
---|---|---|---|
Swift Current | Orthic Brown Chernozem | Marquis | 1949-1965 |
Neepawa | 1966-1997 | ||
AC Barrie | 1998-2009 | ||
Melfort | Orthic Black Chernozem | Marquis | 1946-1964 |
Neepawa | 1965-1973 and 1995-2000 | ||
AC Barrie | 2001-2009 |
A two-step procedure was used to test the APSIM model. This included 1) a sensitivity analysis to determine wheat parameters requiring most careful definition, and 2) a robustness test using reported yield data at the study sites. The calibration of the crop parameters was carried out based on the default values of an Australian wheat cultivar (i.e., Hartog). Data from a 4-year experimental study (1998-2001) near Swift Current [
A range of values of each of the crop parameters above and below those reported in the literature was run in APSIM, while keeping parameters other than the one being tested held constant. The most sensitive crop parameters were found to be thermal times during the vegetative, flowering and grain filling periods, and sensitivities to vernalisation and photoperiod. These parameters were then fine-tuned using data derived from experimental plots [
All the simulations over the study period (historical and future) were performed using the previous settings at the two sites. In order to exclude the “carry-over” effects from previous seasons, soil N and water contents were reset every year in autumn (i.e., 31 October). The main differences in configuration settings between sites included the soil physico-chemical properties, soil plant water available capacity (PAWC), nitrogen content in soil layers (
In order to assess what might be the effects on the future climate variability at each of the two sites, the impacts of varying sowing windows on grain yield were explored. The sowing date has been reported as one the sensitive parameters influencing spring wheat grain yield in the study region [
Physical Parameters | Chemical Parameters | Soil Water-Related Parameters for Wheat | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Depth (cm) | BD (g/cc) | LL15 (mm/mm) | DUL (mm/mm) | SAT (mm/mm) | Sand (%) | Silt (%) | Clay (%) | Fbiom | Finert | EC (1:5 dS/m) | pH | LL (mm/mm) | PAWC (mm/mm) | KL (day−1) | |
Swift Current | 0 - 15 | 1.22 | 0.09 | 0.29 | 0.34 | 32 | 58 | 10 | 0.04 | 0.4 | 0.1 | 6.9 | 0.09 | 30.0 | 0.06 |
15 - 30 | 1.30 | 0.09 | 0.28 | 0.33 | 30 | 52 | 18 | 0.02 | 0.5 | 0.1 | 6.9 | 0.09 | 28.5 | 0.06 | |
30 - 60 | 1.40 | 0.12 | 0.28 | 0.33 | 23 | 56 | 21 | 0.01 | 0.7 | 0.3 | 6.9 | 0.12 | 48.0 | 0.04 | |
60 - 90 | 1.58 | 0.12 | 0.32 | 0.37 | 31 | 47 | 22 | 0.01 | 0.9 | 0.4 | 7.0 | 0.12 | 60.0 | 0.04 | |
90 - 120 | 1.76 | 0.12 | 0.33 | 0.34 | 31 | 47 | 22 | 0.01 | 0.9 | 0.2 | 7.5 | 0.12 | 63.0 | 0.03 | |
Melfort | 0 - 15 | 0.94 | 0.10 | 0.35 | 0.35 | 14 | 60 | 26 | 0.04 | 0.4 | 0.1 | 6.8 | 0.2 | 23.7 | 0.06 |
15 - 30 | 1.36 | 0.10 | 0.31 | 0.31 | 12 | 52 | 36 | 0.02 | 0.5 | 0.1 | 7.0 | 0.27 | 31.4 | 0.06 | |
30 - 45 | 1.56 | 0.11 | 0.30 | 0.30 | 8 | 33 | 58 | 0.02 | 0.5 | 0.3 | 8.0 | 0.29 | 17.4 | 0.04 | |
45 - 60 | 1.60 | 0.16 | 0.40 | 0.40 | 4 | 23 | 73 | 0.02 | 0.5 | 0.3 | 8.0 | 0.32 | 11.7 | 0.04 | |
60 - 120 | 1.68 | 0.18 | 0.37 | 0.37 | 4 | 23 | 73 | 0.02 | 0.7 | 0.3 | 8.0 | 0.35 | 10.8 | 0.03 |
by three latest sowing dates, including the initial sowing window (as used for model training), were considered for assessing the impacts of changing sowing windows on grain yield under the future climate scenarios. The possible earliest sowing dates were 20 April, 26 April, 2 May and 9 May, whereas the possible latest sowing dates were 24 May, 31 May and 7 June. Simulations were performed using each of the two RCPs over the 2030-2099 period. Trends in future simulated spring wheat yields were then assessed over three periods [i.e., the 2040s (2030-2049), 2060s (2050-2069), and 2080s (2070-2099)] using a 20-year baseline period (1990-2009) of observed yields.
The statistical indicators used for evaluating the performance of APSIM included the coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE) and the Willmott index of agreement (d; [
The MAE measures the average magnitude of the errors in a set of forecasts, without considering their direction. It was calculated as follows:
where n is the number of observations, Pi is the predicted yield or biomass, and Oi is the observed yield or biomass.
The RMSE is one of the most widely used errors measures. It gives the weighted variations in errors between the predicted and observed values. It was calculated as follows:
The d-index is a descriptive measure and has values ranging from 0 to 1. The higher the index value the better the model performance. Its formula is as follows:
where
Regarding the total rainfall during the cropping season (
Simulated grain yield, total above-ground biomass, and anthesis and physiologic maturity dates were compared
to the observed ones at Swift Current during the 1998-2001 period (
Anthesis and physiologic maturity dates were satisfactorily captured by the model depending on the year. Differences between dates ranged from 1 day to 11 days, and 4 days to 9 days, respectively for anthesis and physiologic maturity. Moreover, the comparison between predicted and observed grain yield and biomass resulted in R2 > 0.80 in both crop variables. The spring wheat yield was simulated with RMSE of 684 kg/ha and MAE of 476 kg/ha. Based on simulations including five sites in Saskatchewan (including Swift Current) and Manitoba, Mkhabela and Bullock (2012) [
A robustness test of APSIM was then carried out based on long time series data of spring wheat yield at Swift
Anthesis Date (Days after Planting, DAP) | Physiologic Maturity Date (DAP) | Statistical Indicators | |||||
---|---|---|---|---|---|---|---|
Year | Observed | Simulated | Observed | Simulated | Yield | Biomass | |
1998 | 73 | 62 | 103 | 96 | |||
1999 | 69 | 73 | 101 | 110 | R2 | 0.86 | 0.87 |
2000 | 70 | 69 | 101 | 105 | MAE (kg/ha) | 476 | 1146 |
2001 | 66 | 60 | 100 | 94 | RMSE (kg/ha) | 684 | 1373 |
Current and Melfort. Comparisons between observed and predicted yields resulted in MAE, RMSE and d values of 492 kg/ha, 1000 kg/ha and 0.74, respectively, for Swift Current. The values were 603 kg/ha, 1196 kg/ha and 0.75, respectively for MAE, RMSE, and d at Melfort.
When data from the two sites were pooled, the overall robustness was quite satisfactory (
be explained by the differences in cultivars over the period considered (
A probabilistic forecasting tool, the Integrated Canadian Crop Yield Forecaster (ICCYF, which integrates agroclimate variables and remote sensing indices), has been developed for generating yield forecasts at the seasonal and regional-scale of major grain crops in Canada [
Under the projected climate conditions and the criteria defined for simulations, simulated sowing dates were almost in the same range for a given fixed earliest possible date and varying latest possible date, irrespective of the period considered (
sowing occurring during 9 May and 7 June. An increasing trend in yield loss (>10% on average) was observed when the possible earliest sowing date was fixed late in the season (i.e., from 20 April to 9 May, at a weekly time step), regardless of the period at both sites. However, for a fixed earliest possible sowing date the latest possible sowing date generally did not affect the percentage of change (
Possible Latest Sowing Date | Possible Earliest Sowing Date | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Melfort, RCP4.5 | |||||||||||||
2030-2049 | 2050-2069 | 2070-2099 | |||||||||||
26 Apr | 20 Apr | 2 May | 9 May | 26 Apr | 20 Apr | 2 May | 9 May | 26 Apr | 20 Apr | 2 May | 9 May | ||
31 May | −27 | −24 | −30 | −35 | −43 | −39 | −50 | −54 | −60 | −55 | −64 | −67 | |
24 May | −28 | −24 | −30 | −35 | −43 | −39 | −50 | −54 | −60 | −55 | −65 | −67 | |
7 Jun | −28 | −25 | −30 | −36 | −43 | −39 | −50 | −54 | −60 | −55 | −64 | −67 | |
Melfort, RCP8.5 | |||||||||||||
2030-2049 | 2050-2069 | 2070-2099 | |||||||||||
26 Apr | 20 Apr | 2 May | 9 May | 26 Apr | 20 Apr | 2 May | 9 May | 26 Apr | 20 Apr | 2 May | 9 May | ||
31 May | −32 | −30 | −35 | −35 | −49 | −43 | −59 | −59 | −81 | −75 | −86 | −86 | |
24 May | −32 | −30 | −35 | −42 | −49 | −43 | −59 | −63 | −81 | −75 | −86 | −90 | |
7 Jun | −32 | −30 | −35 | −42 | −49 | −43 | −59 | −63 | −81 | −75 | −86 | −90 | |
Swift Current, RCP4.5 | |||||||||||||
2030-2049 | 2050-2069 | 2070-2099 | |||||||||||
26 Apr | 20 Apr | 2 May | 9 May | 26 Apr | 20 Apr | 2 May | 9 May | 26 Apr | 20 Apr | 2 May | 9 May | ||
31 May | −34 | −31 | −38 | −46 | −69 | −62 | −72 | −78 | −71 | −64 | −74 | −77 | |
24 May | −34 | −31 | −38 | −46 | −69 | −62 | −72 | −78 | −71 | −65 | −74 | −77 | |
7 Jun | −34 | −31 | −38 | −46 | −69 | −62 | −72 | −78 | −71 | −64 | −74 | −77 | |
Swift Current, RCP8.5 | |||||||||||||
2030-2049 | 2050-2069 | 2070-2099 | |||||||||||
26 Apr | 20 Apr | 2 May | 9 May | 26 Apr | 20 Apr | 2 May | 9 May | 26 Apr | 20 Apr | 2 May | 9 May | ||
31 May | −51 | −46 | −55 | −62 | −71 | −62 | −79 | −83 | −86 | −82 | −91 | −94 | |
24 May | −51 | −46 | −55 | −61 | −71 | −62 | −79 | −82 | −86 | −82 | −91 | −94 | |
7 Jun | −51 | −46 | −55 | −62 | −71 | −62 | −79 | −83 | −86 | −82 | −91 | −94 |
ranges were 1% to 3% and 12% to 20% over the same periods. The northern site was thus more vulnerable to changes in climate conditions. The combined effects of lower rainfall and relatively low temperatures, compared to the southern site (i.e., Swift Current), may adversely impact the final grain yield.
At both sites, the benefit of increased rainfall during the cropping season may have been offset by factors such as the evaporative demand [
Our simulations were based on only one general circulation climate model (CanESM2), and the results should be interpreted accordingly as results from ensemble climate models may be preferred for such studies (broad simulation context and model uncertainties taken into account in ensemble models). Nevertheless, the trend of the impact of changes in future climate on wheat production in Western Canada was reasonably captured. Across Canada, projected climate changes would affect negatively the productivity of major crops including spring wheat [
A performance analysis of the APSIM model was conducted at two sites in the Province of Saskatchewan in Canada based on spring wheat yield data from long term experimental plots. Although the period involved wheat cultivars with different traits, reasonable grain yield simulations could be obtained with a model calibration based on the latest cultivar (RMSE = 684 kg/ha; MAE = 476 kg/ha). The robustness test of the model based on long time series of observed data resulted in MAE = 529 kg/ha and RMSE = 1043 kg/ha (Willmott index of agreement = 0.80). Relying on this evaluation, a sensitivity analysis of the impact of change in sowing windows on final yield was then performed under projected climate conditions (RCP4.5 and RCP8.5) over the period 2030-2099. A general trend in spring wheat yield loss from a baseline of 1990-2009 was observed regardless of the sowing window and the RCP, with a range from −24% to −94% depending on the site. Smallest yield losses could be obtained through earlier possible sowing date (i.e., mid-April) under the projected future climate conditions. Although the simulations were based on only one general circulation climate model (the CanESM2 model), the trend in spring wheat yield changed was well captured by APSIM under projected climate conditions at both sites. Our findings could therefore serve as a basis for using APSIM as a decision support tool for adaptation/mitigation options under possible climate variability across Western Canada.
We thank the Growing Forward II Program and Sustainability Metrics Project of Agriculture and Agri-Food Canada (AAFC) and the National Science and Engineering Council of Canada (NSERC)’s Visiting Fellows in Government Laboratories Program for providing funding support. We also thank Dr. Hong Wang and Dr. Brian McConkey (AAFC-Swift Current) for providing the long-term crop rotation experimental data.
LouisKouadio,NathanielNewlands,AndriesPotgieter,GregMcLean,HarveyHill, (2015) Exploring the Potential Impacts of Climate Variability on Spring Wheat Yield with the APSIM Decision Support Tool. Agricultural Sciences,06,686-698. doi: 10.4236/as.2015.67066