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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. Keywords: Climate change,Droughts, South Africa, Crop modeling, Maize &nbsp

Smallholder farming systems in southern Africa are characterized by low-input management and integrated livestock and crop production. Low yields and dry-season feed shortages are common. To meet growing food demands, sustainable intensification (SI) of these systems is an important policy goal. While mixed crop-livestock farming may offer greater productivity, it implies trade-offs between feed supply, soil nutrient replenishment, soil carbon accumulation, and other ecosystem functions and services. Such settings require a detailed systems understanding to assess the performance of prevalent management practices and identify potential SI strategies. Models can evaluate different management scenarios on extensive spatio-temporal scales and help identify suitable management strategies. Here, we linked the process-based models APSIM for cropland and aDGVM2 for rangeland to investigate the effects of (i) current management practices, (ii) a SI scenario for crop production, and (iii) a scenario with separated rangeland and cropland management in two representative villages of the Limpopo province, South Africa, for the period 2000–2010. Village surveys informed the models of farming practices, livelihood conditions, and environmental circumstances. We found that modest SI measures (manure application, small fertilizer quantities, weeding, crop rotation) led to moderate yield increases but could cause increased water limitation effects in dry years. SI effects therefore strongly varied between years. Dry-season crop residue grazing substantially reduced feed deficits, but could not compensate severe feed deficits during the transition period from dry to wet season. Targeted irrigation or measures to improve water retention and soil water holding capacity may enhance SI efforts. Off-field residue feeding during the dry-to-wet season transition could further reduce feed deficits and reduce grazing pressure on rangeland during the early growing season. We argue that integrative modeling frameworks are needed to evaluate landscape-level interactions between ecosystem components, evaluate the climate resilience of landscape-level ecosystem services, and identify effective mitigation and adaptation strategies. Dedicated to the memory of our esteemed colleague Marian Koch.

The damaging competition between crops and parasitic weeds has a negative impact on agricultural productivity; however, the impact of disturbance on the soil’s microbial community has received less attention. Hence, this study investigates the microbial composition and diversity of the maize rhizosphere infected with Striga hermonthica using a shotgun sequencing approach from two maize-growing fields (Eruwa, Nigeria and Mbuzini, South Africa). The rhizosphere soil DNA was extracted from infested soil using a Nucleospin soil genomic DNA extraction kit and sequenced on an Illumina platform. The dominant phyla were Actinobacteria, Bacteroidetes, Deinococcus-Thermus, Acidobacteria, Chloroflexi, Cyanobacteria, Planctomycetes, Verrucomicrobia, Chlorobi, Proteobacteria, Firmicutes, Nitrospirae, Thermotogae, Synergistetes, Ascomycota, Euryarchaeota, and Crenarchaeota. Bacteria phyla were observed to be of higher proportion in the rhizosphere soil samples obtained from Striga-infested maize field in Eruwa (Es) than those recovered from Mbuzini (Ms). The alpha diversity of microbial communities indicated insignificance differences (p > 0.05) between the five taxonomical groups (phylum, class, order, family, and genus), while the beta diversity produced a significant (p = 0.01, R = 0.52) difference in the microbial diversity of the infested soil. In summary, the study sheds light on the diversity and composition of the microbiome of Striga hermonthica-infested soil, which influences the microbial functions in the management and sustenance of plant health against parasitic weeds.