Drought patterns: their spatiotemporal variability and impacts on maize production in Limpopo province, South Africa.

Due to global climate change, droughts are likely to become more frequent and more severe in many regions such as in South Africa. In Limpopo, observed high climate variability and projected future climate change will likely increase future maize production risks. This paper evaluates drought patterns in Limpopo at two representative sites. We studied how drought patterns are projected to change under future climatic conditions as an important step in identifying adaptation measures (e.g., breeding maize ideotypes resilient to future conditions). Thirty-year time horizons were analyzed, considering three emission scenarios and five global climate models. We applied the WOFOST crop model to simulate maize crop growth and yield formation over South Africa's summer season. We considered three different crop emergence dates. Drought indices indicated that mainly in the scenario SSP5-8.5 (2051-2080), Univen and Syferkuil will experience worsened drought conditions (DC) in the future. Maize yield tends to decline and future changes in the emergence date seem to impact yield significantly. A possible alternative is to delay sowing date to November or December to reduce the potential yield losses. The grain filling period tends to decrease in the future, and a decrease in the duration of the growth cycle is very likely. Combinations of changed sowing time with more drought tolerant maize cultivars having a longer post-anthesis phase will likely reduce the potential negative impact of climate change on maize.

Potential occurrence of Puccinia sorghi in corn crops in Paraná, under scenarios of climate change.

In the face of climate change scenarios, it is important to evaluate the possibility of an increase in the incidence of corn crop diseases and to promote studies aimed at creating mitigation measures. This paper aims to study the impacts that regional climate changes may have on the potential occurrence of corn common rust (Puccinia sorghi), in the region of Castro, Paraná (Brazil). The Eta climate model was driven by the global model CanESM2. We use the Historical simulation of the EtaCanESM2 model from 1981 to 2005, and future projections from 2046 to 2070 to simulate the occurrence of common rust. The criteria was adopted to simulate the common rust disease favored in environments with the minimum temperature lower than 8 °C, the maximum temperature higher than 32 °C, average temperature between 16 and 23 °C, and relative humidity higher than 95%. In Brazil, there are two different seasons for corn crop (Normaland Safrinha). Results show that relative humidity and minimum temperature simulated by the model presented good skills, approaching the observed data. Compared to the Historical simulation, the projections show a tendency to increase of maximum and minimum temperature in the future, and a tendency to decrease relative humidity. There is an increase in the number of days with the potential for the occurrence of the disease. The distribution of days with favorable conditions to rust disease tends to change in the future. In the Normaland Safrinhaseasons, there is a tendency to increase the number of days with favorable conditions to common rust occurrence. The influence of planting time is greater in Historical simulation when compared to future scenarios. The Safrinhaseason may present more days with the potential for the occurrence of common rust in the future than the Normalseason.