Reinder De Jong

Changing growing season observed in Canada

It is theoretically interesting for climate change detection and practically important for agricultural producers to know whether climate change has influenced agroclimatic conditions and, if so, what the potential impacts are. We present analyses on statistical differences in means and variances of agroclimatic indices between three 30-year periods in the 20th century (i.e., 1911–1940, 1941–1970 and 1971–2000). We found many occurrences of statistically significant changes in means between pairs of the three 30-year periods. The findings consistently support agroclimatic trends identified from trend analysis as an earlier growing season start and an earlier end to spring frost (SF), together with an extended growing season, more frost-free days (FFD) and more available heat units were often found in the later 30-year periods as compared to the earlier ones. In addition, this study provides more detailed quantitative information than the trend signals for the practical interests of agricultural applications. Significant changes were detected for SF and FFD at a much larger percentage of stations between the latter two 30-year periods (1941–1970 vs. 1971–2000) as compared to the earlier two periods (1911–1940 vs. 1941–1970). In contrast, changes in variances of the selected agroclimatic indices were less evident than changes in their means, based on the percentage of stations showing significant differences. We also present new climate averages of the selected agroclimatic indices that can be useful for agricultural planning and management.

Impact of climate change scenarios on Canadian agroclimatic indices

Qian, B., De Jong, R., Gameda, S., Huffman, T., Neilsen, D., Desjardins, R., Wang, H. and McConkey, B. 2013. Impact of climate change scenarios on Canadian agroclimatic indices. Can. J. Soil Sci. 93: 243-259. The Canadian agricultural sector is facing the impacts of climate change. Future scenarios of agroclimatic change provide information for assessing climate change impacts and developing adaptation strategies. The goal of this study was to derive and compare agroclimatic indices based on current and projected future climate scenarios and to discuss the potential implications of climate change impacts on agricultural production and adaptation strategies in Canada. Downscaled daily climate scenarios, including maximum and minimum temperatures and precipitation for a future time period, 2040-2069, were generated using the stochastic weather generator AAFC-WG for Canadian agricultural regions on a 0.5°×0.5° grid. Multiple climate scenarios were developed, based on the results of climate change simulations conducted using two global climate models - CGCM3 and HadGEM1 - forced by IPCC SRES greenhouse gas (GHG) emission scenarios A2, A1B and B1, as well as two regional climate models forced by the A2 emission scenario. The agroclimatic indices that estimate growing season start, end and length, as well as heat accumulations and moisture conditions during the growing season for three types of field crops, cool season, warm season and over-wintering crops, were used to represent agroclimatic conditions. Compared with the baseline period 1961-1990, growing seasons were projected to start earlier, on average 13 d earlier for cool season and over-wintering crops and 11 d earlier for warm season crops. The end of the growing season was projected on average to be 10 and 13 d later for over-wintering and warm season crops, respectively, but 11 d earlier for cool season crops because of the projected high summer temperatures. Two indices quantifying the heat accumulation during the growing season, effective growing degree days (EGDD) and crop heat units (CHU) indicated a notable increase in heat accumulation: on average, EGDD increased by 15, 55 and 34% for cool season, warm season and over-wintering crops, respectively. The magnitudes of the projected changes were highly dependent on the climate models, as well as on the GHG emission scenarios. Some contradictory projections were observed for moisture conditions based on precipitation deficit accumulated over the growing season. This confirmed that the uncertainties in climate projections were large, especially those related to precipitation, and such uncertainties should be taken into account in decision making when adaptation strategies are developed. Nevertheless, the projected changes in indices related to temperature were fairly consistent.

Upscaling modelled crop yields to regional scale: A case study using DSSAT for spring wheat on the Canadian prairies

Huffman, T., Qian, B., De Jong, R., Liu, J., Wang, H., McConkey, B., Brierley, T. and Yang, J. 2015. Upscaling modelled crop yields to regional scale: A case study using DSSAT for spring wheat on the Canadian prairies. Can. J. Soil Sci. 95: 49–61. Dynamic crop models are often operated at the plot or field scale. Upscaling is necessary when the process-based crop models are used for regional applications, such as forecasting regional crop yields and assessing climate change impacts on regional crop productivity. Dynamic crop models often require detailed input data for climate, soil and crop management; thus, their reliability may decrease at the regional scale as the uncertainty of simulation results might increase due to uncertainties in the input data. In this study, we modelled spring wheat yields at the level of numerous individual soils using the CERES-Wheat model in the Decision Support System for Agrotechnology Transfer (DSSAT) and then aggregated the simulated yields from individual soils to regions where crop yields were reported. A comparison between the aggregated and the reported yields was performed to examine the potential of using dynamic crop models with individual soils in a region for the simulation of regional crop yields. The regionally aggregated simulated yields demonstrated reasonable agreement with the reported data, with a correlation coefficient of 0.71 and a root-mean-square error of 266 kg ha-1 (i.e., 15% of the average yield) over 40 regions on the Canadian prairies. Our conclusion is that aggregating simulated crop yields on individual soils with a crop model can be reliable for the estimation of regional crop yields. This demonstrated its potential as a useful approach for using crop models to assess climate change impacts on regional crop productivity.

Simulated crop productivity on different soils in Canada

Crop productivity on different soils across the Canadian Prairies was studied based on average yield of spring wheat simulated by the Cropping System Model in the Decision Support System for Agrotechnology Transfer (DSSAT). The simulations were conducted with estimated seeding dates, and both recommended nitrogen fertilizer application rates and unlimited nitrogen, for a comparison. The simulated yields represent the effect of soils on wheat productivity fairly well, although actual production might be different as local crop management practices, such as crop cultivars, fertilizer application rates and summerfallowing could have critical effects on final yields. Simulated yields with recommended and unlimited nitrogen fertilizer showed a very good relation, implying that the relative productivity on most soils was determined more by soil properties than nitrogen fertilizer application. The results also indicate that crop yield might be improved on most soils with higher nitrogen fertilizer application rates.