United Nations FAO (Food and Agricultural Organization) has predicted that food production must increase by 50% in 2030 and double by 2050 to meet the demands of a growing population. This has to be accomplished sustainably under the challenges of decreasing land availability (due to desertification, urbanization, and competition from biofuels) and water resources (depleting ground and surface water reserves, increasing salinity), low soil fertility and ineffective use of fertilizers, and climate change induced extreme events such as heat stress. Focus is on the root system of plants to achieve this. Rice, which requires nearly three times more water than other cereal crops such as wheat and maize, is India’s number one crop in terms of tonnage and one of the world’s top five major cereal crops. Here, we focus on investigating water and nutrient use efficiency of rice plants by studying it root system architecture (RSA).
The phenotype of RSA is influenced by its genotype and the biotic and abiotic environment. Advancements in imaging of roots from ex-situ, invasive methods like soil coring and shovelomics to in-situ methods like minirhizotrons, transparent gel-based growth system and hydroponics help us characterize the 3D architecture of the roots. Non-invasive imaging techniques such as X-ray Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are leveraged to obtain whole plant in-situ 3D RSA with micron scale resolution. A 3D root growth model combined with high resolution 3D imaging will help us understand the structure-functional relationship of the RSA. Understanding the root growth and function will be greatly beneficial for agricultural gains and food security.
The distribution of root water uptake in soil is influenced by spatio-temporal root distribution and soil properties. 1D root water uptake models, using simplified root growth and root distribution simulations, fail to capture the spatial heterogeneity of physiological processes in the root system [1, 2]. They also fail in modelling the horizontal water content variability that arises due to contrasting uptake by different root segments and negative feedbacks like inter-root competition for water and nutrients. The errors (arising due to the above-mentioned reasons) scale up resulting in inefficient model-based recommendations for water and nutrient use impacting crop yield. Hence, we need a 3D root water uptake model that considers the 3D RSA explicitly.
The dissertation will include the development of a 3D root growth model for rice and model simulations under resource constraints. Root growth models exist for specific plants such as maize and common beans. Since rice is a major crop in the Indian sub-continent, developing a 3D root growth coupled with 3D water and nutrient uptake models will be helpful for studying the root traits for resource acquisition under stress. This will help us in increasing the nutrient uptake efficiency of the roots, determine the optimal water and fertilizer input and cropping density to reduce root competition which can severely reduce the overall efficiency of the root system. This will be a step in the direction of achieving increased crop yield sustainably.
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