Estimating plant root water uptake using a neural network approach

被引:11
|
作者
Qiao, D. M. [1 ,2 ]
Shi, H. B. [3 ]
Pang, H. B. [1 ]
Qi, X. B. [1 ]
Plauborg, F. [4 ]
机构
[1] Chinese Acad Agr Sci, Farmland Irrigat Res Inst, Xinxiang 453003, Henan, Peoples R China
[2] Chinese Acad Agr Sci, Grad Sch, Beijing 100081, Peoples R China
[3] Inner Mongolian Agr Univ, Coll Water Conservancy & Civil Engn, Hohhot 010018, Inner Mongolia, Peoples R China
[4] Aarhus Univ, Fac Agr Sci, Dept Agroecol & Environm, DK-8000 Aarhus C, Denmark
关键词
Sunflower; Pedotransfer function; Water stress; Soil salinity; NONUNIFORM TRANSIENT SALINITY; HYDRAULIC CONDUCTIVITY; NUTRIENT COMPETITION; LENGTH DENSITY; SOIL; MODEL; RHIZOSPHERE; IRRIGATION; DISTRIBUTIONS; CALIBRATION;
D O I
10.1016/j.agwat.2010.08.017
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
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 100 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. Crown Copyright (C) 2010 Published by Elsevier B.V. All rights reserved.
引用
收藏
页码:251 / 260
页数:10
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