Project No. 2467
Dr Eleftheria Stavridou – NIAB East Malling
Prof Tiina Roose – University of Southampton
Dr Carlota Gonzalez Nogue – NIAB East Malling
The current global food production system has a significant negative impact on the environment, with agriculture consuming 70% of global water demand and contributing to pollution through pesticide and fertilizer runoff. Intensive fruit production in the UK is important economically, but it needs to reduce its environmental footprint while satisfying local berry demand and decreasing reliance on imports.
Conventional nutrient application practices are costly and result in excessive nutrient use. Unbalances between crop demand and water and mineral transfers in the substrate are responsible for large saline variations of concentrations around the roots, which stress the plants and give rise to large production losses. This stress on plants results in substantial production losses and prompts growers to discharge water and nutrients into the environment.
To address this, we aim to develop dynamic mechanistic models for plants and substrates to optimize nutrient and water management. Emphasis will be on the development of simulation models for plant nutrient and water relationships. Experimental studies as well as modelling will be performed to develop a model that simulates the dynamics and heterogeneity of water and mineral flows in the coconut coir substrate in dependence on root absorption and convective and diffusive transfers in the substrate. The substrate model will be linked to the crop model to predict the strategy of fertigation to meet the demand.
We are looking for a highly motivated, independent and enthusiastic doctoral researcher with the following:
• Applicants should possess a degree in Biological, Environmental sciences, Engineering or any or any related field (geo-ecology, agronomy, hydrology, geology, material science, numerical modelling, etc.)
• Interest in developing and conducting experimental hands-on work in lab and field conditions.
• Interest in test and develop models
• The applicant should enjoy math and quantitative approaches in science and have some affinity with modelling and or computer programming.