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It is well known that photosynthetic health impacts the overall fitness of the mature plant. This study aims to determine photosynthetic vigour of spring wheat cultivars during field development as well as their biomass composition at maturity to determine which cultivars/varieties would be optimum for cellulosic ethanol production. Additionally, specimens were grown at non-acclimating (20˚C), cold acclimating (5˚C), non-acclimating high CO2 (20˚C/750 µmol mol-1 CO2) and cold-acclimating high CO2 (5˚C/750 µmol mol-1 CO2) to resolve photosynthetic responses to different environments. Plants were photoinhibited under high irradiance (5 fold growth irradiance) and low temperature (5˚C) while photochemical efficiency of PSII was monitored throughout using chlorophyll fluorescence imaging. Vegetative production was monitored using normalised difference vegetation index. De-epoxidation of xanthophyll photoprotective pigments were also recorded using HPLC and photochemical reflectance index. Additionally, carbon assimilation rate was recorded with infra-red gas analysis methods. It was discovered that no one wheat cultivar demonstrated any photosynthetic advantage in the field or under photoinhibitory conditions. However, photosynthetic differences were observed between wheat grown in different environments. Plants that were cold-acclimated or grown under high CO2 were more resilient to photoinhibitory stress. This was also reflected by most cold-acclimated cultivars having increased triose phosphate utilization, electron transport and zeaxanthin induction. Plants acclimated to high CO2 at room temperature also displayed increased electron transport and triose phosphate utilization but had decreased zeaxanthin induction. It is hypothesized increased excitation pressure in cold acclimated and high CO2 cultivars allowed for their increase in the development of photoinhibitory tolerance.

It is well known that photosynthetic health impacts the overall fitness of the mature plant. This study aims to determine photosynthetic vigour of spring wheat cultivars during field development as well as their biomass composition at maturity to determine which cultivars/varieties would be optimum for cellulosic ethanol production. Additionally, specimens were grown at non-acclimating (20˚C), cold acclimating (5˚C), non-acclimating high CO2 (20˚C/750 µmol mol-1 CO2) and cold-acclimating high CO2 (5˚C/750 µmol mol-1 CO2) to resolve photosynthetic responses to different environments. Plants were photoinhibited under high irradiance (5 fold growth irradiance) and low temperature (5˚C) while photochemical efficiency of PSII was monitored throughout using chlorophyll fluorescence imaging. Vegetative production was monitored using normalised difference vegetation index. De-epoxidation of xanthophyll photoprotective pigments were also recorded using HPLC and photochemical reflectance index. Additionally, carbon assimilation rate was recorded with infra-red gas analysis methods. It was discovered that no one wheat cultivar demonstrated any photosynthetic advantage in the field or under photoinhibitory conditions. However, photosynthetic differences were observed between wheat grown in different environments. Plants that were cold-acclimated or grown under high CO2 were more resilient to photoinhibitory stress. This was also reflected by most cold-acclimated cultivars having increased triose phosphate utilization, electron transport and zeaxanthin induction. Plants acclimated to high CO2 at room temperature also displayed increased electron transport and triose phosphate utilization but had decreased zeaxanthin induction. It is hypothesized increased excitation pressure in cold acclimated and high CO2 cultivars allowed for their increase in the development of photoinhibitory tolerance.

Proteases play a crucial role in plant defence mechanisms as well as acclimation to changing metabolic demands and environmental cues. Proteases regulate the development of a plant from germination through to senescence and plant death. In this thesis the role of proteases and their inhibitors in plant response to cold stress and CO2 enrichment were investigated. The activity and inhibition of cysteine proteases (CP), as well as degradation of their potential target proteins was investigated in transgenic tobacco plants expressing the rice cystatin, OC-I. Expression of OC-I caused a longer life span; delayed senescence; significant decrease in in vitro CP activity; a concurrent increase in protein content; and protection from chilling-induced decreases in photosynthesis. An initial proteomics study identified altered abundance of a cyclophilin, a histone, a peptidyl-prolyl cis-trans isomerase and two RuBisCO activase isoforms in OC-I expressing leaves. Immunogold labelling studies revealed that RuBisCO and OC-I is present in RuBisCO vesicular bodies (RVB) that appear to be important in RuBisCO degradation in leaves under optimal and stress conditions. Plants need to respond quickly to changes in the environment that cause changes in the demand for photosynthesis. In this study the effect of CO2 enrichment on photosynthesis-related genes and novel proteases and protease inhibitors regulated by CO2 enrichment and/or development, was investigated. Maize plants grown to maturity with CO2 enrichment showed significant changes in leaf chlorophyll and protein content, increased epidermal cell size, and decreased epidermal cell density. An increased stomatal index in leaves grown at high-CO2 indicates that leaves adjust their stomatal densities through changes in epidermal cell numbers rather than stomatal numbers. Photosynthesis and carbohydrate metabolism were not significantly affected. Developmental stage affected over 3000 transcripts between leaf ranks 3 and 12, while 142 and 90 transcripts were modified by high CO2 in the same leaf ranks respectively. Only 18 transcripts were affected by CO2 enrichment exclusively. Particularly, two novel CO2 -modulated serine protease inhibitors modulated by both sugars and pro-oxidants, were identified. Growth with high CO2 decreased oxidative damage to leaf proteins. / Thesis (PhD)--University of Pretoria, 2009. / Plant Science / unrestricted

Indiana University-Purdue University Indianapolis (IUPUI) / Drylands are the largest terrestrial biome on the planet, and the critically important systems that produce approximately 40% of global net primary productivity to support nearly 2.5 billion of global population. Climate change, increasing populations and resulting anthropogenic effects are all expected to impact dryland regions over the coming decades. Considering that approximately 90% of the more than 2 billion people living in drylands are geographically located within developing countries, improved understanding of these systems is an international imperative. Although considerable progress has been made in recent years in understanding climate change impacts on hydrological cycles, there are still a large number of knowledge gaps in the field of dryland ecohydrology. These knowledge gaps largely hinder our capability to better understand and predict how climate change will affect the hydrological cycles and consequently the soil-vegetation interactions in drylands. The present study used recent technical advances in remote sensing and stable isotopes, and filled some important knowledge gaps in the understanding of the dryland systems. My study presents a novel application of the combined use of customized chambers and a laser-based isotope analyzer to directly quantify isotopic signatures of transpiration (T), evaporation (E) and evapotranspiration (ET) in situ and examine ET partitioning over a field of forage sorghum under extreme environmental conditions. We have developed a useful framework of using satellite data and trend analysis to facilitate the understanding of temporal and spatial rainfall variations in the areas of Africa where the in situ observations are scarce. By using a meta-analysis approach, we have also illustrated that higher concentrations of atmospheric CO2 induce plant water saving and the consequent available soil water increases are a likely driver of the observed greening phenomena. We have further demonstrated that Leuning’s modified Ball-Berry model and RuBP limited optimization model can generally provide a good estimate of stomatal conductance response to CO2 enrichment under different environmental conditions. All these findings provide important insights into dryland water-soil-vegetation interactions.

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Soil carbon is the largest sink and source of the global carbon cycle and is disturbed by several natural, anthropogenic, and environmental factors. The global increase of atmospheric CO2 affects soil carbon cycling through varied biogeochemical processes. The first chapter is a compilation of current information on potential factors triggering soil acidification and weathering mechanisms under elevated CO2 and their consequences on soil inorganic carbon (SIC) pool and quality. Soil water content and precipitation were critical factors influencing elevated CO2 effects on the SIC pool. The second chapter examines a detailed column experiment in which six soils from the state of Mississippi, USA, representing acidic, neutral, and alkaline pH, were exposed to different CO2 enrichments (100%, 10%, and 1%) for 30 days. The leachates’ pH tended to attain an equilibrium state (neutral) with time under CO2 saturation. SIC increased under CO2 saturation, whereas cation exchange capacity (CEC) showed a decreasing pattern in all soils. In the third chapter, an eXplainable artificial intelligence (XAI) was performed to visualize the different forms of soil carbon variability across the Mississippi River Basin area. This model explains key insights and local discrepancies, suggesting a solution to the “Black-Box” issue. The best performing model, stack ensemble, showed improved RMSE (3 to 8%) and spatial variability for soil carbons than other ML models, especially after adding the residuals from regression analyses. Land cover type > soil pH > total nitrogen, > NDVI were identified as the top four crucial factors for predicting SOC when bulk density > precipitation, soil pH > mean annual temperature described SIC. The proposed automatic machine learning (AutoML) model with model agnostic interpolations might be a hallmark to mitigate the C loss under adverse climate change conditions and allow diverse knowledge groups to adopt a new interpretable ML algorithm more confidently. Findings from this study help predict the impact of elevated atmospheric CO2 on soil pH, acidification, and nutrient availability and develop strategies for sustainable land management practices under a changing climate.