This paper investigates the impact of climate variability on maize yield in the Limpopo Basin of South Africa using the Generalized Maximum Entropy (GME) estimator and Maximum Entropy Leuven Estimator (MELE). Precipitation and temperature were used as proxies for climate variability, which were combined with traditional inputs variables (i.e., labor, fertilizer, seed, and irrigation). We found that the MELE fits the data better than the GME. In addition, increased precipitation, increased temperature, and irrigation have a positive impact on yield. Furthermore, results of the MELE show that the impact of precipitation on maize yield is stronger than that of temperature, meaning that the impact of climate variability on maize yield could be negative if the change increases temperature but reduces precipitation at the same rate and simultaneously. Moreover, the impact of irrigation on yield is positive but with a lower elasticity coefficient than that of precipitation, which supposes that irrigation may only partially mitigate the impact of reduced precipitation on yield.