A 3D Model of Photosynthesis to Inform Breeding for Improved Rice Performance in a Changing Climate

Lead Research Organisation: University of Sheffield
Department Name: Animal and Plant Sciences


Increasing demands for global food production over the next decades will be a huge burden on the world's shrinking farmland. It is estimated that an increase of agricultural productivity by as much as 100% is required, yet it is widely acknowledged that present agronomic practices are on a collision course with environmental and sustainability goals. Added to this, global climate change is altering the environment in which crops grow, making it unlikely that present day plants will perform well in the future. These problems will affect the whole world, but the most acute problems will be felt by the poorest people, most of whom rely on one crop, rice, for their food. The aim of this project is to provide plant breeders with information that will allow them to produce a more efficient rice plant and, moreover, a plant that is ready to cope with the challenges that climate change is going to throw at us.

Breeding a new rice variety is a process which can take decades. Any procedure which shortens this process or more quickly identify the traits that farmers need to improve rice yield will have a significant affect on the lives of millions of people who depend on this crop. Moreover, due to the relative recent rapidity of climate change and the slowness of plant breeding, we need to start selecting new varieties of rice which will cope or even benefit from future elevated carbon dioxide levels (a driver of climate change) well in advance of those levels actually being reached. Exploiting the modern power of computational modelling provides the opportunity to do this.

Photosynthesis is the prime driver of food production for all crops, including rice. We have a very good understanding of the biochemistry of photosynthesis and computational models have been produced which can simulate the process, allowing us to predict how photosynthesis changes to altered level of particular enzymes. However, these models have a major drawback. They are 1-dimensional, treating photosynthesis as a process that occurs uniformly in a cell. In reality, photosynthesis occurs in leaves which contain many thousands of cells and the position and shape of each cell influence the efficiency of photosynthesis in each cell. The overall performance of a leaf actually reflects the performance of all the cells put together. The aim of this project is to create a 3D model of photosynthesis which takes into account the position and shape of each cell in a rice leaf. This will allow us to investigate the affect of altering the number, size and packing of cells on photosynthesis on a computer, without having to actually breed the plants. This would save plant breeders immense time and money, allowing them to more rapidly generate the next generation of rice plants required to tackle the problems described at the beginning of this section.

To achieve this aim we will use a combination of advanced imaging techniques to create 3-D leaf models and plant physiology and biochemistry techniques to measure leaf performance We will then use computational methods to model the entire process on a computer. We will then be able to ask questions such as: what pattern of cell division in the leaf is best for the efficiency of photosynthesis? Can we rationally design a rice leaf for improved performance?

We will then use the new model to explore how photosynthesis is likely to respond to the increased levels of CO2 in the atmosphere which are likely to occur over the next century and test these predictions using plants grown under elevated CO2, both in laboratory and field conditions. We will explore the model to predict which aspects of leaf structure are important for plants to maintain or increase photosynthesis under the various conditions predicted by climate change models. This information will allow breeders to start selecting plants now so that in 20-30 years time rice plants will still be able to generate sufficient food for the world population.

Planned Impact

The project will have the following impacts:

i) a new generation of systems model of leaf photosynthesis. The modelling approach will allow the exploration of leaf structure/function in silico, enabling rapid identification of novel traits for breeding from existing germplasm and/or the rationale design of new leaf structures. The potential savings in time and expense by allowing breeders/researchers to explore potential outcomes of altered leaf structure prior to experimentation will be a major long-term impact of this work. This impact will be facilitated by colleagues from The State Key Laboratory of Hybrid Rice, China (see letter of support).

ii) Although focussed on rice, the models generated will be generic and easily adapted to other crops. The potential impact thus extends to the wider agronomic community. The PIs and co-Is on the project have extensive established networks of contacts in the crop breeding community and these, coupled with our communication strategy outlined in the Management Plan, will ensure that the knowledge accumulated will be disseminated and exploited in a timely and appropriate manner.

iii) The project is based on systems biology and exploits rapid advances in computational approaches to biology. The success of this project will act as an exemplar of how experimental and theoretical approaches can be combined to tackle a fundamental problem of relevance to rice research. The development of new quantitative computational approaches will impact other researchers in the area, encouraging new research opportunities and engagement.

iv) The project will directly address the issue of rising CO2 levels, providing rice breeders the opportunity to identify traits that can be incorporated now into breeding schemes which may not reach fruition until mid-century, when we know that CO2 levels will have altered. By allowing breeders/researchers to take this into account, the research outputs will have the impact of contributing towards future-proofing the rice crop, an essential long-term goal with immense economic/social significance.

v) The proposal will be a great platform for enhancing UK-China collaboration in plant science, providing a basis for future impact in this area.


10 25 50
Description We have completed an analysis of the rice cultivar IR64, generating a range of anatomical, biochemical and physiological data on photosynthesis. These have been provided to our partners in Shanghai who have used the data to develop and validate a computational model of photosynthesis. This analysis has allowed us to focus on specific attributes of leaf cellular architecture which we are testing experimentally to see how they impact on photosynthetic performance. The results of the project are thus now coming to fruition. We have generated a validated new 3D model of photosynthesis (eLeaf) and these data have been submitted for publication. In addition, a paper describing the novel imaging methodology used during the project has been published in "Plant Methods". A paper describing the main results of the project is presently under review, as well as a publication reporting on a new analytical tool for photosynthesis.
Exploitation Route Modelling photosynthesis could be used as a tool by breeders to identify novel traits for selection for improved rice performance. Indeed, as a result of our findings we have opened up a new collaboration with a group at UPM in Malaysia where we will investigate whether our findings can be translated to near-field setting in South-east Asia.
Sectors Agriculture, Food and Drink,Digital/Communication/Information Technologies (including Software)

Description Basis for GCRF research actuvity in Malaysia
First Year Of Impact 2020
Sector Agriculture, Food and Drink
Description Breeding Rice Resilient to a High CO2 Future
Amount £471,081 (GBP)
Funding ID CHL\R1\180027 
Organisation The Royal Society 
Sector Charity/Non Profit
Country United Kingdom
Start 03/2019 
End 09/2021