Estimating plant root water uptake using a neural network approach

D.M. Qiao, H.B. Shi, H.B. Pang, X.B. Qi and F. Plauborg

Agricultural Water Management, 2010, vol. 98, issue 2, 251-260

Abstract: Water uptake by plant roots is an important process in the hydrological cycle, not only for plant growth but also for the role it plays in shaping microbial community and bringing in physical and biochemical changes to soils. The ability of roots to extract water is determined by combined soil and plant characteristics, and how to model it has been of interest for many years. Most macroscopic models for water uptake operate at soil profile scale under the assumption that the uptake rate depends on root density and soil moisture. Whilst proved appropriate, these models need spatio-temporal root density distributions, which is tedious to measure in situ and prone to uncertainty because of the complexity of root architecture hidden in the opaque soils. As a result, developing alternative methods that do not explicitly need the root density to estimate the root water uptake is practically useful but has not yet been addressed. This paper presents and tests such an approach. The method is based on a neural network model, estimating the water uptake using different types of data that are easy to measure in the field. Sunflower grown in a sandy loam subjected to water stress and salinity was taken as a demonstrating example. The inputs to the neural network model included soil moisture, electrical conductivity of the soil solution, height and diameter of plant shoot, potential evapotranspiration, atmospheric humidity and air temperature. The outputs were the root water uptake rate at different depths in the soil profile. To train and test the model, the root water uptake rate was directly measured based on mass balance and Darcy's law assessed from the measured soil moisture content and soil water matric potential in profiles from the soil surface to a depth of 100Â cm. The 'measured' root water uptake agreed well with that predicted by the neural network model. The successful performance of the model provides an alternative and more practical way to estimate the root water uptake at field scale.

Keywords: Sunflower; Pedotransfer; function; Water; stress; Soil; salinity (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:eee:agiwat:v:98:y:2010:i:2:p:251-260

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