Maize (Zea mays L.) is an essential crop in South Africa serving as a staple food; however, agricultural drought threatens its production, resulting in lower yields. This study aimed to assess the impact of agricultural drought on maize yield in the major areas (Bethlehem, Bloemfontein, and Bothaville) that produce maize in the Free State Province from 1990 to 2020. The study used the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) to examine drought occurrences and severity during the maize growing season (October–March). The Standardized Yield Residuals Series (SYRS), Crop Drought Resilient Factor (CDRF), Spearman’s Rank Correlation (r_s), and yield loss rate were employed to emphasize agricultural drought impact on maize yield. The results based on the SPI and SPEI show that drought frequently occurred in Bethlehem, followed by Bloemfontein and Bothaville. Drought severity indicated that moderate droughts were prevalent in Bethlehem, while severe droughts were in all areas (Bethlehem, Bloemfontein, and Bothaville) and extreme droughts in Bloemfontein. The agricultural drought’s impact on maize varied across growth seasons and areas. Notably, the lowest SYRS value of −2.38 (1991/92) was observed in Bethlehem. An extremely strong significant correlation (_{rsSPEI-6 vs SYRS} = 0.83, p = 1.07 × 10⁻⁸) was observed between the SPEI and SYRS in Bloemfontein during the October–November–December–January–February–March (ONDJFM) season. The CDRF indicated that maize yield was severely non-resilient (CDRF < 0.8) to drought in Bethlehem (CDRF = 0.27) and Bloemfontein (CDRF = 0.33) and resilient (CDRF = 1.16) in Bothaville. The highest maize yield loss of −88.62% was observed in Bethlehem due to extreme agricultural drought. The results suggest that, historically, agricultural drought was a threat to maize production in the studied areas, particularly in Bethlehem and Bloemfontein. This underscores the implementation of sustainable agricultural practices, such as drought-resistant varieties in these areas, to mitigate the impacts of climate change, especially drought, and ensure food security. This is a step toward achieving Sustainable Development Goal 2 (Zero Hunger).

Soybean is one of the significant legume crops in South Africa, yet genotype × environment interactions (GEI) complicate reliable cultivar selection. Despite this, GEI, performance, and stability of soybean genotypes have not been comprehensively evaluated across South Africa’s major soybean–producing provinces (Mpumalanga, North–West and Free State). Therefore, this study assessed GEI, performance, and stability of soybean genotypes across key agronomic traits using multi–environment trials (METs) over three growing seasons. Additive main effects and multiplicative interaction (AMMI) and genotype plus genotype–by–environment (GGE) biplot approaches were used to quantify trait variation, identify best genotypes, and characterizing environmental relationships. Stability and performance were further assessed using multi–trait stability index (MTSI), weighted average of absolute scores (WAASB), and weighted average of absolute scores and yield (WAASBY), while chord diagrams visualized inter–trait correlations. Results indicated that environmental effects dominated trait variation across all traits. Which–Won–Where patterns revealed that no genotype excelled across all environments, but G5 (LG60260IPRO) was the best genotype across all evaluated traits, particularly in cool (E1) and moderate (E2) environments across seasons. Vector analyses showed positive correlations among environments within the same location and negative correlations among contrasting environments. WAASB highlighted G8 (PAN1521R) as the most stable and high–performing genotype across all evaluated traits with dominant performance in moderate environment. WAASBY identified G5 as a superior genotype across all traits, with superiority particularly evident in cool and moderate environments. MTSI identified G2 (DM59I60 RSF IPRO) as a consistently stable genotype across all evaluated environments. Chord diagrams revealed predominantly positive inter–trait correlations across all traits in E2 and mixed correlations in E1 and E3 (warm environment). Overall, the identified superior, high–performing, and stable genotypes are promising candidates for cultivar recommendation and soybean breeding programmes targeting productivity, stability, and adaptation across South Africa’s diverse environments.