Impact of climate extreme events and their causality on maize yield in South Africa

Abstract

Extreme climate events can have a significant negative impact on maize productivity, resulting in food scarcity and socioeconomic losses. Thus, quantifying their effect is needed for developing future adaptation and mitigation strategies, especially for countries relying on maize as a staple crop, such as South Africa. While several studies have analyzed the impact of climate extremes on maize yields in South Africa, little is known on the quantitative contribution of combined extreme events to maize yield variability and the causality link of extreme events. This study uses existing stress indices to investigate temporal and spatial patterns of heatwaves, drought, and extreme precipitation during maize growing season between 1986/87 and 2015/16 for South Africa provinces and at national level and quantifies their contribution to yield variability. A causal discovery algorithm was applied to investigate the causal relationship among extreme events. At the province and national levels, heatwaves and extreme precipitation showed no significant trend. However, drought severity increased in several provinces. The modified Combined Stress Index (CSIm) model showed that the maize yield nationwide was associated with drought events (explaining 25% of maize yield variability). Heatwaves has significant influence on maize yield variability (35%) in Free State. In North West province, the maize yield variability (46%) was sensitive to the combination of drought and extreme precipitation. The causal analysis suggests that the occurrence of heatwaves intensified drought, while a causal link between heatwaves and extreme precipitation was not detected. The presented findings provide a deeper insight into the sensitivity of yield data to climate extremes and serve as a basis for future studies on maize yield anomalies.

Figure 1

Historical maize yield (t ha⁻¹) in South Africa for the different provinces between the season of 1986/87 and 2015/16.

Figure 2

The trend (Z), the magnitude (β), and the corresponding frequency of Heat Magnitude Day (HMD) seasonally for (a) Free State, (b) KwaZulu-Natal, (c) Mpumalanga, (d) North West, (e) Others, and (f) South Africa, including (g) the spatial patterns of the 90th percentile of daily maximum temperatures (Tmax) from 1986/87 to 2015/16 maize growing season. The red line indicates the non-linear trend (LOESS) of heatwave magnitude. The linear regression lines for the inclination of sloping are shown in green. ns, symbolize no significant trend. The yellow contour lines on the map distinguish the spatial patterns.

Figure 3

The trend (Z) and the magnitude (β) of Standardized Precipitation Evapotranspiration Index (SPEI) seasonally for (a) Free State, (b) KwaZulu-Natal, (c) Mpumalanga, (d) North West, (e) Others, and (f) South Africa, including (g) the spatial patterns of mean SPEI-3 during maize growing season from 1986/87 to 2015/16. The orange lines indicate the non-linear trend (LOESS) of the drought index. The linear regression lines for the inclination of sloping are shown in green. ns, symbolize no significant trend. (*) denotes p-value ≤ 0.05. The red contour lines on the map distinguish the spatial patterns.

Figure 4

The trend (Z), the magnitude (β), and the corresponding frequency of Extreme Precipitation Modification (EPM) seasonally for (a) Free State, (b) KwaZulu-Natal, (c) Mpumalanga, (d) North West, (e) Others, and (f) South Africa, including (g) the 95th percentile of daily precipitation during maize growing season from 1986/87 to 2015/16. The purple line indicates the non-linear trend (LOESS) of the extreme precipitation index. The linear regression lines for the inclination of sloping are shown in green. ns, symbolizes no significant trend. The orange contour lines on the map distinguish the spatial patterns.

Figure 5

Province-based, namely: (a) Free State, (b) North West, and (c) National scale time-series maize yield anomalies (left-hand axis, grey bar) and Combined Stress Index (CSIm) (right-hand axis, red line) from 1986/87 to 2015/16 growing season.

Figure 6

Gaussian process regression of (a) EPM on HMD, (b) SPEI on HMD, (c) the scatter plot of residuals of EPM and SPEI, and (d) Causal discovery graph generated using PCMCI showing the relationships of HMD, SPEI, and EPM including the time lags (link labels) during maize growing season from 1986/87 to 2015/16. The node colors indicate the nonlinear auto-dependency of each variable (auto-MCI). The link colors indicate the interdependency strength (cross-MCI) between variables. The number between the link HMD-SPEI denotes the time lags (unit = 6 growing season periods of maize).