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A mechanistic model for nitrogen-limited plant growth

AbstractBackground and AimsNitrogen is often regarded as a limiting factor to plant growth in various ecosystems. Understanding how nitrogen drives plant growth has numerous theoretical and practical applications in agriculture and ecology. In 2004, Göran I. Ågren proposed a mechanistic model of plant growth from a biochemical perspective. However, neglecting respiration and assuming stable and balanced growth made the model unrealistic for plants growing in natural conditions. The aim of the present paper is to extend Ågren’s model to overcome these limitations.MethodsWe improved Ågren’s model by incorporating the respiratory process and replacing the stable and balanced growth assumption with a three-parameter power function to describe the relationship between nitrogen concentration (Nc) and biomass. The new model was evaluated based on published data from three studies on corn (Zea mays) growth.Key ResultsRemarkably, the mechanistic growth model derived in this study is mathematically equivalent to the classical Richards model, which is the most widely used empirical growth model. The model agrees well with empirical plant growth data.ConclusionsOur model provides a mechanistic interpretation of how nitrogen drives plant growth. It is very robust in predicting growth curves and the relationship between Nc and relative growth rate.
- Sun Yat-sen University China (People's Republic of)
- University of Alberta Canada
- Sun Yat-sen University China (People's Republic of)
Nitrogen, Plant Development, Biomass, Plants, Zea mays, Ecosystem
Nitrogen, Plant Development, Biomass, Plants, Zea mays, Ecosystem
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).5 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
