A new study reveals how a computational model can accurately replicate the effects of nitrogen fertilization on maize, providing insights that can inform future breeding strategies that enhance nitrogen use efficiency.
Nitrogen is essential for plant growth and development and is a key component of enzymes involved in photosynthesis. It is directly correlated with crop yields and protein content in grains.
Generally, higher nitrogen fertilization results in increased crop yields. However, excessive nitrogen application can lead to leaching into water systems, causing eutrophication and harmful algal blooms, as well as increasing greenhouse gas emissions. High fertilizer usage also leads to increased costs to producers.
Therefore, the efficiency with which plants use nitrogen is particularly important.
Nitrogen use efficiency is determined by two main factors: the amount of nitrogen a plant can absorb from the soil (uptake) and how effectively the plant utilizes that nitrogen for growth and productivity (physiological use efficiency). Plant architecture, anatomy, physiology, genetics, and environmental conditions play significant roles in nitrogen use efficiency suggesting that changes in these areas could improve it. However, our understanding of the relative contributions of these traits to efficiency is currently limited. Gaining a better understanding is essential for identifying which traits to focus on in breeding programs aimed at improving nitrogen use efficiency.
Jie Lu, a Postdoctoral Researcher at Wageningen University and Research, and colleagues recently published an article in in silico Plants detailing their approach to quantifying the contributions of architectural and physiological traits to nitrogen use efficiency. In their study, they utilized a functional-structural plant model to simulate the effects of nitrogen fertilization on maize with varied traits.
Functional-structural plant models are advanced computational tools used to simulate a plant’s architecture and physiology allowing researchers to visualize and predict how plants develop under various conditions. By adjusting different factors in the model, scientists can explore scenarios that may be difficult or impossible to test in real life, such as extreme weather conditions or changes in specific traits. This helps them understand how plants respond to changes, leading to more effective strategies for improving growth and efficiency.
The authors expanded an existing functional-structural plant model by including plant and soil processes related to nitrogen uptake and physiological efficiency.
After confirming that the new model accurately predicted changes in the variables of interest (nitrogen uptake, yield, and physiological efficiency) across different cultivars and environmental conditions by comparing its predictions to measured values, the researchers used it to identify the physiological and architectural traits that affect those variables.
They achieved this by adjusting the values of fourteen traits one at a time and running simulations to see which traits had the greatest impact on the variables of interest under high and low nitrogen fertilization conditions. The traits they examined included root nitrogen transport, root diameter, root number, tissue density, the number of leaves, and photosynthesis.
The simulations indicated that the effects different traits had on the variables of interest were variable and complex. For instance, root architectural traits, such as root diameter and number, had a greater influence on nitrogen uptake than physiological traits like the root’s capacity for nitrogen uptake, particularly under low nitrogen conditions. Changes in the number of leaves had no impact on nitrogen uptake, yield, or physiological efficiency. Phyllochron, which is the time between leaf appearances, enhanced nitrogen uptake and physiological efficiency. It had no effect on yield under low nitrogen or on any of the variables under high nitrogen. Likewise, photosynthesis did not affect nitrogen uptake, yield, or physiological efficiency.
This model is the first step in identifying the traits that would be most effective for breeding to improve nitrogen use efficiency. However, more work is needed to better understand how the traits interact, which can be quite complex. For instance, increasing nitrogen uptake boosts photosynthesis, which in turn enhances biomass accumulation. This increase in biomass creates a larger nitrogen sink, as more nitrogen is stored in plant tissues, ultimately leading to further increases in nitrogen uptake. You can learn more by reading Source and Sink Mechanisms of Nitrogen Transport and Use by Tegeder and Masclaux-Daubresse.
Fortunately, these questions can be addressed in future versions of the model and with additional experimental data, further advancing efforts to develop maize with improved nitrogen use efficiency.
READ THE ARTICLE:
Jie Lu, Tjeerd Jan Stomph, Guohua Mi, Lixing Yuan, Jochem Evers, Identifying and quantifying the contribution of maize plant traits to nitrogen uptake and use through plant modelling, in silico Plants, Volume 6, Issue 2, 2024, diae018, https://doi.org/10.1093/insilicoplants/diae018
This model is freely available at https://git.wur.nl/lu068/cn-maize.
The post Exploring the Impact of Plant Traits on Nitrogen Use Efficiency in Maize appeared first on Botany One.
