Use of modelling tools to assess climate change impacts on smallholder oil seed yields in South Africa.

Oil seed crops are the second most important field crops after cereals in the agricultural economy globally. The use and demand for oilseed crops such as groundnut, soybean and sunflower have grown significantly, but climate change is expected to alter the agroecological conditions required for oilseed crop production. This study aims to present an approach that utilizes decision-making tools to assess the potential climate change impacts on groundnut, soybean and sunflower yields and the greenhouse gas emissions from the management of the crops. The Decision Support Tool for Agrotechnology Transfer (DSSAT v4.7), a dynamic crop model and the Cool Farm Tool, a GHG calculator, was used to simulate yields and estimate GHG emissions from these crops, respectively. Four representative concentration pathways (RCPs 2.6, 4.5, 6.0, and 8.5), three nitrogen (0, 75, and 150 kg/ha) and phosphorous (0, 30 and 60 P kg/ha) fertilizer rates at three sites in Limpopo, South Africa (Ofcolaco, Syferkuil and Punda Maria) were used in field trials for calibrating the models. The highest yield was achieved by sunflower across all crops, years and sites. Soybean yield is projected to decrease across all sites and scenarios by 2030 and 2050, except at Ofcolaco, where yield increases of at least 15.6% is projected under the RCP 4.5 scenario. Positive climate change impacts are predicted for groundnut at Ofcolaco and Syferkuil by 2030 and 2050, while negative impacts with losses of up to 50% are projected under RCP8.5 by 2050 at Punda Maria. Sunflower yield is projected to decrease across all sites and scenarios by 2030 and 2050. A comparison of the climate change impacts across sites shows that groundnut yield is projected to increase under climate change while notable yield losses are projected for sunflower and soybean. GHG emissions from the management of each crop showed that sunflower and groundnut production had the highest and lowest emissions across all sites respectively. With positive climate change impacts, a reduction of GHG emissions per ton per hectare was projected for groundnuts at Ofcolaco and Syferkuil and for sunflower in Ofcolaco in the future. However, the carbon footprint from groundnut is expected to increase by 40 to 107% in Punda Maria for the period up to 2030 and between 70-250% for 2050, with sunflower following a similar trend. We conclude that climate change will potentially reduce yield for oilseed crops while management will increase emissions. Therefore, in designing adaptation measures, there is a need to consider emission effects to gain a holistic understanding of how both climate change impacts on crops and mitigation efforts could be targeted.

A decision support system for institutional support to farmers in the face of climate change challenges in Limpopo province.

Smallholder farmers in South Africa continue to be affected by the changing climate despite the existence of support to improve their adaptive capacity. This study focused on the institutional support systems and support types available to farmers in agro-ecological zones of Limpopo Province and assessed support types best suited to each area. Six hundred farmers were purposively sampled across the agro-ecological zones of Limpopo and interviewed. Support types looked at included monetary, machinery, seeds, educational support and others (irrigation scheme, animals, fertilizer, pesticides). Supporting institutions included Agro finance institutions, DAFF, Banks, and NGOs. Results showed that 70.01% of farmers received support from DAFF 25.60% from NGO's and 4.39% from Agro finance institutions. The most number of support received was two types 33.3% of the farmers. The result from the ANOVA showed that there were no significant differences in the level of difficulty experienced by farmers in accessing the various support institutions across the agro-ecological zones. In terms of the various support types received, there was a statistically significant difference in seeds (p = 0.002 < α = 0.05) and educational (p = 0.0001 < α = 0.05) support received between the different areas. Furthermore, the support needs varied across zones with farmers in arid-zone needing machinery, education, seeds and lastly monetary support while the semi-arid zone needed machinery, education, others, seeds, monetary and the humid, machinery, education, others, money and seeds. It is therefore recommended that support for farmers should be location-specific in order to enhance the adaptive capacity of an area and not be based only on the availability of certain support. There is a need for proper coordination between institutions in their aim to assist farmers to cope with climate change.