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
相关论文
共 50 条
  • [31] Mathematical modelling study for water uptake of steadily growing plant root
    Chu JiaQiang
    Jiao WeiPing
    Xu JianJun
    SCIENCE IN CHINA SERIES G-PHYSICS MECHANICS & ASTRONOMY, 2008, 51 (02): : 184 - 205
  • [32] Mathematical modelling study for water uptake of steadily growing plant root
    JiaQing Chu
    WeiPing Jiao
    JianJun Xu
    Science in China Series G: Physics, Mechanics and Astronomy, 2008, 51 : 184 - 205
  • [33] Effect of plant uptake strategy on the water-optimal root depth
    Guswa, A. J.
    WATER RESOURCES RESEARCH, 2010, 46
  • [34] Modelling the growth and water uptake function of plant root systems: a review
    Wang, EL
    Smith, CJ
    AUSTRALIAN JOURNAL OF AGRICULTURAL RESEARCH, 2004, 55 (05): : 501 - 523
  • [35] Reply to "Comment on 'Macroscopic Root Water Uptake Distribution Using a Matric Flux Potential Approach' "
    van Lier, Quirijn de Jong
    van Dam, Jos C.
    Metselaar, Klaas
    VADOSE ZONE JOURNAL, 2010, 9 (02): : 503 - 503
  • [36] Modeling Soil Water Dynamics Based on the Approach of Compensatory Root Water Uptake
    Li, Cong
    Hu, Zhengfeng
    Zhang, Kefeng
    4TH INTERNATIONAL CONFERENCE ON ADVANCES IN ENERGY RESOURCES AND ENVIRONMENT ENGINEERING, 2019, 237
  • [37] A neural network approach for estimating model parameters of rockfill materials
    Wang Jizhe
    Su Yingfeng
    CONSTRUCTION AND URBAN PLANNING, PTS 1-4, 2013, 671-674 : 167 - 170
  • [38] A Bayesian Neural Network approach to estimating the Energy Equivalent Speed
    Riviere, C
    Lauret, P
    Ramsamy, JFM
    Page, Y
    ACCIDENT ANALYSIS AND PREVENTION, 2006, 38 (02): : 248 - 259
  • [39] A neural network approach for estimating large K distribution parameters
    Smolíková, R
    Wachowiak, MP
    Zurada, JM
    Elmaghraby, AS
    IJCNN'01: INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-4, PROCEEDINGS, 2001, : 2139 - 2143
  • [40] ESTIMATING CONSTRUCTION PRODUCTIVITY - NEURAL-NETWORK-BASED APPROACH
    CHAO, LC
    SKIBNIEWSKI, MJ
    JOURNAL OF COMPUTING IN CIVIL ENGINEERING, 1994, 8 (02) : 234 - 251