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Why is active carbon storage being ignored by modelers?

Active carbon pools need to be included for accurate simulation of plant growth patterns under fluctuating environments.
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The classic view that carbon storage is a passive process, where accumulation only occurs when supply from photosynthesis exceeds metabolic demand from sink organs, has been challenged numerous times. In reality, carbon storage is a dynamic and active process that competes with metabolic demand for carbohydrates in response to environmental stresses. A new paper illustrates why crop modelers should include active carbon pools in their simulations and propose a more accurate modelling framework.

Plants are faced with a constantly changing environment which affects their growth. For example, throughout the day they experience abrupt changes in temperature and light when a cloud passes overhead. Different processes occur during the day and night. On a longer timescale, plants are exposed to long bright days in the summer and short dark days in the winter.

To cope with these changes, plants actively manage carbon storage to optimize their growth. Despite the important role of active carbon storage in plants, most crop models still include carbon storage as a passive process that occurs whenever there is an excess of carbon from photosynthesis relative to the demand of carbon for metabolism.

It is likely that active carbon storage has been under-represented in crop models because of the difficulty for simulating it. “Simulating carbon storage response to light and temperature fluctuations at a whole-plant level would require models to include complex interactions between circadian signals, environmental cues, and metabolic signals,” according to Graduate student Ana Cristina Zepeda and her colleagues at Wageningen University and Research. They challenge modelers to include active carbon storage in their simulations in a new paper published by in silico Plants.

In the paper, the authors reviewed experimental evidence that carbon accumulation and remobilization in plants is continually changing in response to the carbon status of the plant, which is in turn highly dependent on the environmental factors such as temperature or light. They highlighted two key physiological mechanisms for active carbon storage:

The partitioning of assimilates between soluble sugars and starch. On a diel scale, assimilates are partitioned into sucrose for the immediate demands during the day and starch to fulfil carbon demand during the following night.

The degradation and remobilization of starch. On a diel scale, during the night starch is degraded to soluble sugars to sustain metabolism and growth. On a seasonal scale, carbohydrate reserves in storage pools (e.g., roots) are mobilized for grain filling or in the spring for the formation of new tillers or resprouting.

The authors then reviewed the physiological mechanisms which allow plants to accumulate and remobilize carbon and the ways that carbon storage is currently included in crop growth models. From this, they identified knowledge gaps that need to be filled to accurately represent carbon storage in crop simulation models. The authors made the following recommendations:

  • To simulate a dynamic active carbon storage pool, place the carbon input from photosynthesis on a temporary storage pool (an extra state) which will later be partitioned among different sink organs like leaves, fruits or storage itself. This is opposed as the classical structure where growth is the net result of daily carbon input from gross photosynthesis minus carbon loss in respiration  
  • To accurately include a dynamic carbon pool, couple biochemical models describing soluble sugars and starch metabolism to whole-plant growth models.
  • To accurately model growth, include the effect of temperature and light fluctuations on sink activity (not just photosynthesis).
A flow chart with many arrows and coloured zones connecting carbon dioxide, light intensity and temperature to photosynthesis.
Schematic diagram of the inclusion of a physiology-based dynamic carbon storage pool and how it is influenced by day length, circadian clock and temperature.

Finally, the authors demonstrated how the inclusion of a dynamic carbon pool in growth models offers a more realistic representation of carbon allocation and growth patterns under stress conditions by comparing the output of three models that differ only in the inclusion of a carbon pool. This exercise illustrated that the inclusion of a dynamic carbon pool in growth models offers a more realistic representation of carbon allocation in abiotic stress conditions (for example, a higher allocation to storage during sink-limited conditions), and a more realistic effect on the growth patterns.

Zepeda concludes that “Including separated starch and sucrose pools to represent an ‘active’ pool in growth models can increase the level of detail and robustness of the models. This is particularly important because simulations can be used to guide crop improvement under fluctuating environmental conditions, such as climate change.”

READ THE ARTICLE:

Ana Cristina Zepeda, Ep Heuvelink, Leo F M Marcelis, Carbon storage in plants: a buffer for temporal light and temperature fluctuations, in silico Plants, 2022, diac020, https://doi.org/10.1093/insilicoplants/diac020

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