Browsing by Author "Mokonoto, Ofentse."

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Assessing climate change impacts on productivity of sugarbeet and sugarcane using aquacrop.
(2018) Mokonoto, Ofentse.; Kunz, Richard Peter.; Mabhaudhi, Tafadzwanashe.
Globally, the use of biofuels has grown over the years and their importance in helping to reduce a) dependency on fossil-based fuels and b) greenhouse emissions has been widely recognised. Various feedstocks are used for biofuels, viz. sugar-based crops for bioethanol production and oil from vegetable crops for biodiesel production. The research presented in this study focused on sugar crops such as sugarcane and sugarbeet. The sugarcane industry is widely established in South Africa, whereas sugarbeet is still a new crop and hence, there is little information on its water use efficiency (WUE) and potential yields under South African growing conditions. Overall, there is a need to better understand the agricultural potential and water use requirements of these feedstocks, in order to grow the biofuels industry in South Africa in a sustainable manner. Furthermore, climate change poses a threat to global food security as well as to biofuel feedstock production. There are uncertainties regarding the potential impacts of climate change on the yield and WUE of agricultural crops. One of the main objectives of this study was to calibrate the AquaCrop crop model for sugarcane and sugarbeet using experimental datasets. This study then followed a modelling approach to estimate dry yields and WUEs of these two sugar feedstocks to add to the existing knowledge base for potential biofuel production in South Africa. Sugarbeet was planted at the Ukulinga research farm and field equipment was used to collect data for the calibration of the crop model to better estimate attainable yield and WUE. Growth and yield datasets were provided by the South African Sugarcane Research Institute to calibrate the model for sugarcane, as well as validate AquaCrop for both feedstocks. The performance of the crop model was tested using various statistical methods. The model’s performance was satisfactory after calibrating it for sugarcane. However, the calibration process was compromised by the lack of sufficient leaf area index data. For sugarbeet, AquaCrop simulated the canopy cover, yield and WUE well, but tended to over-estimate observations. For the validation process, simulations closely matched the observed yields for both feedstocks. However, the model’s ability to simulate soil water content at Ukulinga was considered unsatisfactory. The calibrated AquaCrop model was used for long term assessments of yield and WUE. Baseline simulations were undertaken using 50 and 30 years of climate data and the results indicated that the 30 years of data could adequately estimate the long-term attainable productivity of sugarcane and sugarbeet. According to the literature, an ensemble approach to climate change modelling reduces uncertainty in long-term assessments. Hence, climate projections from several global climate models (GCMs), that were downscaled using dynamical and statistical approaches, were obtained and used to assess the potential impacts of climate change on yield and WUE of the selected feedstocks. An increase in yield and WUE of both feedstocks is projected in the distant future. The statistically downscaled GCMs projected higher increases compared to the dynamically downscaled GCMs. Increases in future WUE are much higher compared to yields projections. The so-called “CO2 fertilisation” effect largely benefits C3 crops (sugarbeet) with regards to yield improvements. However, the results also show that C4 crops (sugarcane) also benefit from improved WUE. Both sugarcane and sugarbeet will benefit from the anticipated climate change when planted in February and May, respectively. However, it is recommended that other planting dates should be studied for sugarcane.