Soil Moisture Deficit as a predictor of the trend in soil water status of grass fields

A. Kerebel, R. Cassidy, P. Jordan, N. M. Holden

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Nutrient loss from agricultural land following organic fertilizer spreading can lead to eutrophication and poor water quality. The risk of pollution is partly related to the soil water status during and after spreading. In response to these issues, a decision support system (DSS) for nutrient management has been developed to predict when soil and weather conditions are suitable for slurry spreading. At the core of the DSS, the Hybrid Soil Moisture Deficit (HSMD) model estimates soil water status relative to field capacity (FC) for three soil classes (well, moderately and poorly drained) and has potential to predict the occurrence of a transport vector when the soil is wetter than FC. Three years of field observation of volumetric water content was used to validate HSMD model predictions of water status and to ensure correct use and interpretation of the drainage classes. Point HSMD model predictions were validated with respect to the temporal and spatial variations in volumetric water content and soil strength properties. It was found that the HSMD model predictions were well related to topsoil water content through time, but a new class intermediate between poor and moderate, perhaps 'imperfectly drained', was needed. With correct allocations of a field into a drainage class, the HSMD model predictions reflect field scale trends in water status and therefore the model is suitable for use at the core of a DSS.
LanguageEnglish
Pages419-431
JournalSoil Use and Management
Volume29
Issue number3
DOIs
Publication statusPublished - 1 Sep 2013

Fingerprint

soil moisture
soil water
grass
decision support system
field capacity
water content
prediction
drainage
nutrient loss
soil strength
topsoil
slurry
trend
eutrophication
temporal variation
spatial variation
soil
agricultural land
fertilizer
water quality

Keywords

  • Soil moisture deficit
  • volumetric water content
  • soil strength
  • drainage
  • decision support system

Cite this

Kerebel, A. ; Cassidy, R. ; Jordan, P. ; Holden, N. M. / Soil Moisture Deficit as a predictor of the trend in soil water status of grass fields. 2013 ; Vol. 29, No. 3. pp. 419-431.
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Soil Moisture Deficit as a predictor of the trend in soil water status of grass fields. / Kerebel, A.; Cassidy, R.; Jordan, P.; Holden, N. M.

Vol. 29, No. 3, 01.09.2013, p. 419-431.

Research output: Contribution to journalArticle

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T1 - Soil Moisture Deficit as a predictor of the trend in soil water status of grass fields

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AU - Cassidy, R.

AU - Jordan, P.

AU - Holden, N. M.

PY - 2013/9/1

Y1 - 2013/9/1

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AB - Nutrient loss from agricultural land following organic fertilizer spreading can lead to eutrophication and poor water quality. The risk of pollution is partly related to the soil water status during and after spreading. In response to these issues, a decision support system (DSS) for nutrient management has been developed to predict when soil and weather conditions are suitable for slurry spreading. At the core of the DSS, the Hybrid Soil Moisture Deficit (HSMD) model estimates soil water status relative to field capacity (FC) for three soil classes (well, moderately and poorly drained) and has potential to predict the occurrence of a transport vector when the soil is wetter than FC. Three years of field observation of volumetric water content was used to validate HSMD model predictions of water status and to ensure correct use and interpretation of the drainage classes. Point HSMD model predictions were validated with respect to the temporal and spatial variations in volumetric water content and soil strength properties. It was found that the HSMD model predictions were well related to topsoil water content through time, but a new class intermediate between poor and moderate, perhaps 'imperfectly drained', was needed. With correct allocations of a field into a drainage class, the HSMD model predictions reflect field scale trends in water status and therefore the model is suitable for use at the core of a DSS.

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