Modelling shifts in agroclimate and crop cultivar response under climate change

Abstract

(i) to identify at national scale areas where crop yield formation is currently most prone to climate-induced stresses, (ii) to evaluate how the severity of these stresses is likely to develop in time and space, and (iii) to appraise and quantify the performance of two strategies for adapting crop cultivation to a wide range of (uncertain) climate change projections. To this end we made use of extensive climate, crop, and soil data, and of two modelling tools: N-AgriCLIM and the WOFOST crop simulation model. N-AgriCLIM was developed for the automatic generation of indicators describing basic agroclimatic conditions and was applied over the whole of Finland. WOFOST was used to simulate detailed crop responses at four representative locations. N-AgriCLIM calculations have been performed nationally for 3829 grid boxes at a 10 × 10 km resolution and for 32 climate scenarios. Ranges of projected shifts in indicator values for heat, drought and other crop-relevant stresses across the scenarios vary widely - so do the spatial patterns of change. Overall, under reference climate the most risk-prone areas for spring cereals are found in south-west Finland, shifting to south-east Finland towards the end of this century. Conditions for grass are likely to improve. WOFOST simulation results suggest that CO2 fertilization and adjusted sowing combined can lead to small yield increases of current barley cultivars under most climate scenarios on favourable soils, but not under extreme climate scenarios and poor soils. This information can be valuable for appraising alternative adaptation strategies. It facilitates the identification of regions in which climatic changes might be rapid or otherwise notable for crop production, requiring a more detailed evaluation of adaptation measures. The results also suggest that utilizing the diversity of cultivar responses seems beneficial given the high uncertainty in climate change projections.

Keywords: Adaptation; agroclimatic indicator; barley; crop simulation model; cultivar response diversity.

Figure 1

Barley cultivation, weather stations, major MTT official variety trial sites and Environmental Zones (EnZs) for Finland according to Metzger et al. (2005). Triangles indicate locations of MTT official variety trial sites for barley. Filled large squares indicate selected grid used for crop yield simulation in this study (small filled circles indicate long-term weather stations).

Figure 2

(A–B) Observed anomalies in average annual (A) air temperature and (B) precipitation and projected changes during the 21st century for Finland for SRES (Special Report on Emissions Scenarios (Nakicenovic et al. 2000) scenarios B1, A1B and A2 simulated with 11 GCMs selected for this study (stars, see Table S4) and for a larger ensemble of 24 GCMs (boxplots).

Figure 3

Projected changes for (A) sowing date (DelayS, deviations relative to fixed date 1st May) and three agroclimatic indicators: (B) early drought stress (RainAS), (C) specific heat stress (StressE), and (D) mean daily temperature accumulation rate at grain filling (TempHRAvg, higher value signals higher likelihood for yield reduction), for climate scenario IPSL-CM4/A2. The legend caption contains the abbreviation of the indicator (see Table 1) and the observed time period (e.g., DelayS1140 = sowing date expressed as deviation from May 1st for the time period 2011–2040).

Figure 4

Spatial patterns of the most risk prone areas for each of these indicators using pre-determined thresholds, as well as, the overlay of all three risk factors – IPSL-CM 4/A2 - for each of the three future time slices (2011–2040, 2041–2070 and 2071–2100).

Figure 5

Differences in precipitation sum 3–7 weeks after sowing (RainAS) in mm, and very high temperature stress (StressE) in days, between MIROC3.2(medres)/A1B and IPSL-CM4/A2 scenario. The two difference maps show the deviation values for MIROC3.2 relative to IPSL-CM4.

Figure 6

Changes in the median values of selected (10) agroclimatic indicators relative to the 1971–2000 reference period for (A) 2011–2040, (B) 2041–2070, (C) 2071–2100. Estimates based on five GCMs, that is, CSIRO-MK3.5/B1 (csiro), CCCMA-CGCM3.1(T63)/A1B (cccma), GISS-ER/B1 (giss), IPSL-CM4/ A2 (ipsl) and MIROC3.2(medres)/A1B. The key to site abbreviations given is as follows: TUR = Turku; JOK = Jokioinen; UTT = Utti, JYV = Jyväskylä, KUO = Kuopio, YLI = Ylistaro, OUL = Oulu, ROV = Rovaniemi (see, Fig. 1 for their location).

Figure 7

Simulated water limited grain yields (coloured lines) for three cultivar groups representing maturity classes late, medium and early (named Annabell (= late) Kustaa (= medium) and Kunnari (= early), respectively) and potential grain yield (for Annabell only) presented as 30-year moving averages under reference climate and scenario IPSL-CM4/A2 at (A) Jokioinen, (B) Utti, (C) Ylistaro, (D) Oulu for a clay loam and silty sand soil. The x-axis indicates the 30-year periods (1971–2000 till 2071–2100).

Figure 8

Probability density functions (PDFs) of simulated water limited grain yields for three cultivar groups (from late to early, Annabell, Kustaa and Kunnari) for a clay loam soil under reference climate (1971–2000) and alternative future climatic conditions (2071–2100) at Utti represented by 11 different climate scenarios (for details, see Table S4). PDFs for Baseline (1) and the two most contrasting future climates (2 and 3) are marked specifically. All PDFs assume normal distribution. Kolmogorov-Smirnov test for normality, performed on yield distributions based on crop simulation output for each year presented for each time period and climate scenario, confirmed this assumption. For this test, K-S (NORMAL) function in SPSS statistical software package (version 17.0) was applied.