Typescript (photocopy). / Digitized by Kansas Correctional Industries

Typescript (photocopy). / Digitized by Kansas Correctional Industries

Climate change threatens global food security by increasing extreme-weather shocks and reducing the productivity of major global crops. While recent research has highlighted the risk of rising extreme heat, comparatively little is known about how the intensity distribution of rainfall, and rainfall’s interactions with heat, influence global crops. Further, as the broader climate transition gains momentum, the industrial activities needed to mitigate and adapt to climate change will emit CO₂. These emissions remain unquantified and largely ignored in research and policy, and thus present an under-assessed risk to crops and society at large.This thesis advances the understanding of present and future agricultural risks from two aspects of hydroclimatic complexity: hourly rainfall intensity and temperature-moisture (T-M) couplings. Both aspects are expected to shift under climate change, with highly uncertain crop impacts. It further simulates the adaptation and mitigation emissions embedded in the broader climate transition, illuminating a previously under-appreciated benefit of enhance climate ambition. Climate warming is expected to intensify rainfall, decreasing the frequency of drizzle while boosting heavy and extreme events. I show that surprisingly heavy rainfall is optimal for US maize and soy yields, with yield loss due to drizzle and very extreme downpours. As a result, the future concentration of rainfall into fewer, heavier hourly events will benefit crop yields 2-3%, partly offsetting larger damages from warming. T-M couplings arising from land-air interactions and atmospheric circulation may shift under 21st Century warming, altering the likelihood of concurrent heat and drought extremes, with uncertain risks to crops. I demonstrate that maize and soy grown in regions with strong T-M couplings historically suffered enhanced crop sensitivity to heat. These couplings will strengthen over most of global croplands this century, worsening the impact of warming on crops by 5% globally, with large regional variations. The energetic demands of the broader climate transition – such as steel for wind turbines, and concrete for coastal barriers – will initially be satisfied by fossil fuels. I show that simulated mitigation and adaptation will emit 185GtCO₂ by 2100 under a transition path consistent with current policies (~2.7°C warming by 2100), equivalent to half the remaining carbon budget for 1.5°C. However, these emissions can be reduced by 90% under a 1.5°C transition path, a previously unidentified co-benefit of enhanced climate ambition.

South Africa is the largest producer of sugar in Africa and one of the ten largest sugarcane producers in the world. Sugarcane in South Africa is grown under a wide range of agro-climatic conditions. Climate has been identified as the single most important factor influencing sugarcane production in South Africa. Traditionally, sugarcane mill committees have issued forecasts of anticipated production for a region. However, owing to several limitations of such committee forecasts, more advanced technologies have had to be considered. The aim of this study has been to develop, evaluate and implement a pertinent and technologically advanced operational sugarcane yield forecasting system for South Africa. Specific objectives have included literature and technology reviews, surveys of stakeholder requirements, the development and evaluation of a forecasting system and the assessment of information transfer and user adoption. A crop yield model-based system has been developed to simulate representative crops for derived Homogeneous Climate Zones (HCZ). The system has integrated climate data and crop management, soil, irrigation and seasonal rainfall outlook information. Simulations of yields were aggregated from HCZs to mill supply area and industry scales and were compared with actual production. The value of climate information (including climate station networks) and seasonal rainfall outlook information were quantified independently. It was concluded that the system was capable of forecasting yields with acceptable accuracy over a wide range of agro-climatic conditions in South Africa. At an industry scale, the system captured up to 58% of the climatically driven variability in mean annual sugarcane yields. Forecast accuracies differed widely between different mill supply areas, and several factors were identified that may explain some inconsistencies. Seasonal rainfall outlook information generally enhanced forecasts of sugarcane production. Rainfall outlooks issued during the summer months seemed more valuable than those issued in early spring. Operationally, model-based forecasts can be expected to be valuable prior to the commencement of the milling season in April. Current limitations of forecasts include system calibration, the expression of production relative to that of the previous season and the omission of incorporating near real-time production and climate information. Several refinements to the forecast system are proposed and a strong collaborative approach between modellers, climatologists, mill committees and other decision makers is encouraged. / Thesis (Ph.D.)-University of KwaZulu-Natal, Pietermaritzburg, 2005.