The famous cereal 'green revolution' of the 1960s/1970s increased crop yields, averted famine and fed a growing world population. Green Revolution Varieties (GRVs) of rice and wheat were the genetic foundation of the green revolution. GRVs carry mutant growth regulatory genes that confer dwarfism, and this dwarfism increases yield because it reduces loss due to 'lodging' (flattening of plants by wind and rain), hence causing the yield increases of the green revolution. However, the mutant growth regulatory genes also cause GRVs to be less efficient in assimilating the nitrogen (N) supplied to them in the form of fertilizer. As a result, N that is not assimilated by GRVs is dissipated into the wider environment, where it causes severe damage to terrestrial and aquatic ecosystems, together with atmospheric greenhouse-gas pollution that precipitates climate change. Because today's high-yielding crop varieties still depend upon the mutant dwarfing genes for their high yields, it is necessary to find ways of developing new crop varieties that retain the benefits of GRV dwarfism but that are more efficient in their use of N fertilizers (have improved N use efficiency, NUE). Here we propose to exploit the rapid genetics and molecular biology of the genetic model Arabidopsis to make discoveries that will enable future enhancement of GRV NUE. The GRV dwarfing genes cause accumulation of a class of growth inhibitory proteins called DELLAs, and DELLAs also accumulate in the dwarf Arabidopsis GRV mutant model gai. Accumulated DELLAs inhibit the action of another class of regulatory proteins, the PIFs (or Phytochrome Interacting Factors). Our recent preliminary evidence from studies of Arabidopsis suggest that the inhibitory effect of DELLAs on PIFs may explain the reduced NUE of GRVs, and it is this novel and exciting finding that we exploit in this proposal. We will therefore first further test our working hypothesis that interactions between DELLAs and PIFs affect the assimilation of N: that the DELLAs accumulated in GRVs and gai oppose PIF function, thus reducing N assimilation. If this hypothesis is correct, modulation of the DELLA-PIF relationship may provide a novel route towards improving GRV NUE. We have the following objectives: A. Obtain an in-depth understanding of PIF-regulation of Arabidopsis and rice N assimilation - essentially performing genetic tests of the role of PIFs in regulation of N metabolism and assimilation in Arabidopsis and rice. B. Determine how the DELLA-PIF interaction regulates the abundance of mRNA encoding nitrate reductase (NR), a key enzyme in N assimilation - this an exploration of how the DELLA-PIF interaction controls the expression of the gene encoding that enzyme. C. Determine if the DELLA-PIF interaction also directly affects the abundance and/or specific enzymatic activity of the NR enzyme itself. D. Determine if NUE can be increased despite retaining yield-enhancing dwarfism. This is important because it could lead to the development of crops which retain the high yields of current GRVs, but at reduced environmental cost. First, we will determine if increasing PIF activity might confer such benefits. However, because increasing the activity of PIFs themselves in GRVs might have additional unwanted consequences, we will additionally explore other routes (downstream of PIFs) to improving GRV NUE whilst retaining yield-enhancing dwarfism. Inherent in our strategy is initial translation of findings from Arabidopsis model to crop (rice), exploiting our long-standing combined expertise in DELLA biology, model-crop translations, and whole genome sequence analysis. Our long-term aim (future proposals) is to use the fundamental understanding gained here in the development of rice and wheat GRVs having enhanced NUE, thus enhancing global food security and reducing agricultural environmental degradation.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

China

Karst landscapes cover a large proportion of the Earth's land surface: ~ 12% They represent an important C store on land, and also are considered to play an important role in climate regulation by consuming atmospheric CO2 during chemical weathering. However, we cannot be certain how effective this sink is if we do not know how efficiently the rivers draining karst landscapes remobilise weathered C to the atmosphere as CO2. Further, although there is a large body of research on how much C is carried away from weathered karst systems, researchers have not yet quantified by direct measurement the C return to the atmosphere from rivers. We propose to measure this CO2 degassing from rivers in karst systems and using chemical tracers that can distinguish between different source of carbon in the rivers, quantify how much C comes from the different sources and how these contributions change with different land use change, or as season and river flow change. We will do this by undertaking measurements in a karst catchment in China. This field location is key to the research as there is much known about C loss by weathering and how different land use in the catchment (e.g. bare rock vs. paddy soils) affects the weathering process. In the first year of the research we will undertake fieldwork to measure CO2 efflux from springs and surface waters, ensuring we capture natural variation in efflux due to differences in river flow and seasonal temperature. In year 2 based on our detailed understanding of the catchment controls on CO2 efflux, we will use specialised tracers of the source of the C (isotopes) to quantify how much C in the CO2 degassed comes from the different sources. With this knowledge we will produce a quantitative process-based model to estimate CO2 efflux, that relies on descriptors of catchment and river characteristics. Finally we will test this model in other catchments to assess how well it describes more widely CO2 efflux from karst drainage systems, and from this testing refine the model / identify how it can be improved. The model is important as it will support up-scaling of CO2 efflux from karst drainage systems and this information is valuable to those modelling the global C cycle and trying to understand how changes in atmospheric CO2 concentration are affected by fluxes to the atmosphere from the Earth's surface.