Prediction and measurement of plant water availability variation across the landscape at field scale using surveying, remote sensing and modelling techniques to inform precision farming management for sustainable intensification.

The objective of this project is to develop a methodology to predict variation in plant water availability across the landscape at field scale using surveying, remote sensing and modelling techniques to inform precision farming management for sustainable intensification.
Crop growth depends on the availability of water and nutrients as well as favourable climatic conditions. Modern intensive farming techniques rely on chemical fertilizers to maximize harvested crop outputs. However, ensuring that nutrients are biologically available to both the crop and the rhizosphere biota depends on the soil water saturation levels. Balancing fertilizer input to meet crop needs while avoiding waste by leaching to ground and surface water and denitrification to nitrous oxide emissions is important. This is true both for farm economics and the environment and requires knowledge of the water holding capacity, plant soil interactions and the drainage of the soil. Precision farming technology allows automation of the spatial variation of fertilizer application based upon plant needs, however determining this rate currently depends on the interpretation of maps of previous harvest yields, soil nutrient surveys or optical sensors (tractor mounted, aerial or satellite). The effects of nutrient deficiency and water availability interact to make this interpretation an under-determined problem.
The aim of this project is to develop a work flow to use field scale remote sensing and soil physicochemical and hydrological property mapping coupled to soil–crop models such as DNDC, ECOSSE, MiscanFor, etc., to predict the fate of inputs and their impact on crop growth and yields. The models, to be run spatially at field scale, will use the survey information and optical sensor data as input and use spatial yield and agronomy data for parameterisation and validation using GIS techniques. Improvements of the crop growth and water stress modules as well as the integration of agronomy, hydrology, soil science and bio-informatics will be novel aspects of this work. The project will involve acquiring data from local arable farms using soil surveys and its analysis and remote sensing as well as desk based modelling.
The project will provide interdisciplinary training in the areas of soil and crop biology, agronomy, agricultural engineering, GIS and mathematical and computational modelling techniques. The student will benefit from iterations with the multidisciplinary ESF theme PhD cohort in UoA, the wide array of expertise in agronomy, soil and plant science in JHI and the environmental systems in Dundee.