CHAMPAIGN, Illinois, May 22 (IPS) – When pioneering agronomist and father of the “Green Revolution” Norman Borlaug set out to breed a disease-resistant, high-yielding variety of wheat, he spent years laboriously planting and pollinating different specimens by hand. He manually catalogued every outcome until he landed on the variety that would transform farming and avert famine. The result was even greater than expected: it is estimated that he saved more than a billion people worldwide from starvation.
With the world facing the same existential need as during Borlaug’s time to transform agriculture to sustainably feed the global population, more efficient technologies and processes are critical. Computational biology and modeling offer tools that can guide scientists towards the most promising areas of emerging research and accelerate the breakthrough discoveries needed to make farming more equitable and sustainable. Combining data analysis, computer science and modelling, computational biology brings together these techniques to better understand biological systems.
An exciting possibility on the horizon for crop science is the early progress towards engineering cereal crops to source their own nutrients and reduce the need for fertilizer. Legumes like beans, peas and lentils already have this ability, but improving nutrient uptake and growth in non-legume plants would have a transformative impact on yields and sustainability.
Researchers, including those involved in the Engineering Nutrient Symbioses in Agriculture (ENSA) project working with funders like Gates Agricultural Innovations, are investigating plant interactions with a soil bacteria called rhizobia, as well as arbuscular mycorrhizal fungi (AMF), which provide the plant with nitrogen and phosphorus through biological processes.
Harnessing this ability would reduce the need for inorganic fertilizers to provide these key nutrients, ensuring multiple benefits. For one, fertilizer is often a big expense for farmers, especially given price volatility over the last several years. This can be a prohibitive cost for farmers in low-income countries or communities.
Furthermore, the overuse of fertilizers can cause negative environmental impacts. Nitrogen fertilizer production and use accounts for around five percent of greenhouse gas emissions and the nitrous oxide produced is 300 times more potent than carbon dioxide. Fertilizer run-off also causes dangerous algal blooms that develop in waterways, killing off aquatic biodiversity.
While the benefits of giving more plants the ability to source nutrients biologically are evident, it has not been clear until now what the exact effect of these nutrient symbioses would be on plants. More specifically, scientists know the interactions between soil bacteria or fungi and plants impact growth, but not by how much.
Recent research by my group has examined this for the first time using a metabolic model for maize. It analyzed the hypothetical growth rate of maize if it were to acquire the ability to interact with rhizobia, which it does not currently have. The model also assessed the growth rate when maize is associated with AMF.
Rhizobia aids in nitrogen fixation, pulling nitrogen from the air and sharing it with plants in exchange for carbon. AMF, instead, help plants access more nutrients in the soil beyond what can be accessed by their roots alone. The findings suggest that stacking these traits to allow for interactions with both rhizobia and AMF could more than double maize growth rates in nutrient-limited conditions. While the model does not predict changes in yield, it is reasonable to expect that higher growth rates under these conditions would also lead to higher yields.
The results of the modelling are particularly significant given the global importance of maize as a food security crop. For example, maize is one of the most important crops in sub-Saharan Africa, providing a third of all consumed calories, yet the region experiences chronically lower maize yields than other parts of the world. For an average smallholder maize farmer in sub-Saharan Africa with a two-hectare plot, doubling maize yields would equate to an additional $1000 each year.
Using a model that was developed and validated with experimental data, we were able to quantitatively highlight the potential of combining these two approaches, which may not have been prioritized otherwise. Without modeling, this kind of analysis would take years to collect, evaluate and classify, on top of the time needed to successfully engineer nitrogen-fixing maize, which does not currently exist.
Too often, modeling and experimental science are treated as separate and distinct from one another. And yet, when combined, the two offer enormous potential to accelerate crop science for the public good.
It does not take a vivid imagination to consider the many ways in which modeling can help validate and justify research priorities.
By uniting scientists across these disciplines at the Society of Experimental Biology’s annual conference later this year, I hope to ignite a conversation about how modeling can support and enhance translational experimental science. And by working together, we can compound the advances we are making towards more sustainable food systems for all.
Megan Matthews, a principal investigator with the Enabling Nutrient Symbioses in Agriculture (ENSA) project and Assistant Professor at the University of Illinois
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