Spatial and temporal patterns of agrometeorological indicators in maize producing provinces of South Africa

Abstract

Climate change impacts on maize production in South Africa, i.e., interannual yield variabilities, are still not well understood. This study is based on a recently released reanalysis of climate observations (AgERA5), i.e., temperature, precipitation, solar radiation, and wind speed data. The study assesses climate change effects by quantifying the trend of agrometeorological indicators, their correlation with maize yield, and analyzing their spatiotemporal patterns using Empirical Orthogonal Function. Thereby, the main agrometeorological factors that affected yield variability for the last 31 years (1990/91–2020/21 growing season) in major maize production provinces, namely Free State, KwaZulu-Natal, Mpumalanga, and North West are identified. Results show that there was a significant positive trend in temperature that averages 0.03–0.04 °C per year and 0.02–0.04 °C per growing season. There was a decreasing trend in precipitation in Free State with 0.01 mm per year. Solar radiation did not show a significant trend. Wind speed in Free State increased at a rate of 0.01 ms−1 per growing season. Yield variabilities in Free State, Mpumalanga, and North West show a significant positive correlation (r > 0.43) with agrometeorological variables. Yield in KwaZulu-Natal is not influenced by climate factors. The leading mode (50–80% of total variance) of each agrometeorological variable indicates spatially homogenous pattern across the regions. The dipole patterns of the second and the third mode suggest the variabilities of agrometeorological indicators are linked to South Indian high pressure and the warm Agulhas current. The corresponding principal components were mainly associated with strong climate anomalies which are identified as El Niño and La Niña events.

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

“…This was in line with our prior study where the agrometeorological indicators, such as temperature, precipitation, solar radiation, and wind speed, showed no correlation with maize yield in KwaZulu-Natal. In contrast with Mpumalanga, only precipitation and wind speed were correlated with maize yield 90 .…”

Comprehensive assessment of climate extremes in high-resolution CMIP6 projections for Ethiopia

“…We used the Mann-Kendall (MK) method (Mann, 1945;Kendall, 1962;Pohlert, 2020) to test the trends in the respective climate extreme indices for both the baseline climate and future projections. The MK is a non-parametric test (i.e., the data does not have to meet the normality assumption) and widely used method because of its simplicity (Cattani et al, 2018;Esayas et al, 2018;Afuecheta and Omar, 2021;Li et al, 2021;Simanjuntak et al, 2022). The MK test determines the presence of monotonic (i.e., consistent) increasing or decreasing tendency of data in a given time.…”