Crop yield sensitivity of global major agricultural countries to droughts and the projected changes in the future

Abstract

Understanding the potential drought impacts on agricultural production is critical for ensuring global food security. Instead of providing a deterministic estimate, this study investigates the likelihood of yield loss of wheat, maize, rice and soybeans in response to droughts of various intensities in the 10 largest producing countries. We use crop-country specific standardized precipitation index (SPI) and census yield data for 1961-2016 to build a probabilistic modeling framework for estimating yield loss risk under a moderate (-1.2 < SPI < -0.8), severe (-1.5 < SPI < -1.3), extreme (-1.9 < SPI < -1.6) and exceptional (SPI < -2.0) drought. Results show that there is >80% probability that wheat production will fall below its long-term average when experiencing an exceptional drought, especially in USA and Canada. As for maize, India shows the highest risk of yield reduction under droughts, while rice is the crop that is most vulnerable to droughts in Vietnam and Thailand. Risk of drought-driven soybean yield loss is the highest in USA, Russian and India. Yield loss risk tends to grow faster when experiencing a shift in drought severity from moderate to severe than that from extreme to the exceptional category, demonstrating the non-linear response of yield to the increase in drought severity. Sensitivity analysis shows that temperature plays an important role in determining drought impacts, through reducing or amplifying drought-driven yield loss risk. Compared to present conditions, an ensemble of 11 crop models simulated an increase in yield loss risk by 9%-12%, 5.6%-6.3%, 18.1%-19.4% and 15.1%-16.1 for wheat, maize, rice and soybeans by the end of 21st century, respectively, without considering the benefits of CO₂ fertilization and adaptations. This study highlights the non-linear response of yield loss risk to the increase in drought severity. This implies that adaptations should be more targeted, considering not only the crop type and region but also the specific drought severity of interest.

Keywords: Agricultural production; Climate change; Drought; Risk.

Graphical abstract

Fig. 1

Temporal changes in de-trended annual yield anomaly (blue line) and growing season SPI (red bar) during 1961–2016 for global (a) wheat, (b) maize, (c) rice and (d) soybeans. The correlation coefficient (R) and statistical significance (P-value) are given.

Fig. 2

Joint distribution function fitted for global yield anomaly of (a) wheat, (b) maize, (c) rice and (d) soybeans and drought index (i.e. SPI). Each red circle denotes a pair of observed yield anomaly and SPI, while the background colors represent the probability densities. There are 56 red circles for the period 1961–2016 in each subplot. The specific copula model selected for each crop-region combination can be found in Table 5.

Fig. 3

Conditional probability distribution of yield changes (%) relative to its long-term mean under moderate drought (red) and wet (blue) conditions for (a) wheat, (b) maize, (c) rice and (d) soybeans.

Fig. 4

The probability (%) of yield loss (i.e. yield dropping below historical average) when experiencing a moderate (blue bar), extreme (yellow bar), severe (magenta bar) and exceptional drought (red bar) for (a) wheat, (b) maize, (c) rice and (d) soybeans. The top 10 producing countries for each crop are selected for illustration. The colored background map indicates the gridded crop area percentage (Portmann et al., 2010), based on which weights are assigned to gridded climate for spatial aggregations.

Fig. 5

Projected changes in risk (%) of yield reduction in the future versus history as simulated by process-based crop models. Each dot represents the ensemble mean of risks simulated by 11 crop models under a given climate scenario, while the grey error lines indicate the corresponding uncertainties arising from crop models. The colors of dots represent the risk under various levels of drought severity. Here, five climate scenarios and four drought severity categories are considered, and there are 5 × 4 = 20 dots in each subplot.

Fig. 6

Uncertainties for the estimation of yield loss risk under droughts of various severity for each crop-region combination. The Markov chain Monte Carlo simulation technique is used within a Bayesian framework to derive the uncertainty based on the posterior distribution of copula parameters.

Fig. 7

SPI-based yield loss risk versus SPEI-based yield loss risk for (a) wheat, (b) maize, (c) rice and (d) soybean. The top 10 producing countries are selected for illustration with each dot denoting a country, while the color represents the drought severity. The moderate, severe, extreme and exceptional droughts are indicated with blue, green, magenta and red, respectively.