Leaf thickness to predict plant water status

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Plant-based techniques to measure crop water status offer advantages over soil-based methods. The objective of this study was to quantify the relationship between leaf thickness measurements, as a promising plant-based technique, with leaf relative water content (RWC) and assess the model across different species and leaf positions. The relationship between RWC and relative thickness (RT) was determined on corn (Zea mays L.), sorghum (Sorghum bicolor (L.) Moench), soybean (Glycine max (L.) Merr.), and fava bean (Vicia faba L.). RWC was calculated as measured leaf water content/leaf water content at full turgor, and RT as measured leaf thickness/leaf thickness at full turgor. Two leaves from the top, middle, and bottom of five plants of each species were collected at 60 days of age. Leaf samples brought to full turgor were left to dehydrate in a lab. Leaf thickness was measured using a magnetic field sensor and water content using weight loss. The RWC-RT relationship showed a distinct breakpoint, which we hypothesise coincides with the turgor loss point. Linear piecewise modelling was used to regress RWC versus RT, resulted in models explaining 86–97% of the variations. The precision was improved by including leaf position on the plant in the model. The piecewise model parameters were related to salt tolerance of the species, which is also an indicator of drought resistance. Generally, the species with greater drought and salinity tolerance had a larger RT at the breakpoint.

Original languageEnglish (US)
Pages (from-to)148-156
Number of pages9
JournalBiosystems Engineering
Volume156
DOIs
StatePublished - Apr 1 2017

Fingerprint

Water content
Water
water content
leaves
turgor
water
Vicia faba
Drought
Sorghum
Droughts
Soybeans
Zea mays
sorghum
Thickness measurement
Salt-Tolerance
Salinity
drought resistance
Magnetic Fields
Crops
salinity tolerance

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Food Science
  • Animal Science and Zoology
  • Agronomy and Crop Science
  • Soil Science

Cite this

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abstract = "Plant-based techniques to measure crop water status offer advantages over soil-based methods. The objective of this study was to quantify the relationship between leaf thickness measurements, as a promising plant-based technique, with leaf relative water content (RWC) and assess the model across different species and leaf positions. The relationship between RWC and relative thickness (RT) was determined on corn (Zea mays L.), sorghum (Sorghum bicolor (L.) Moench), soybean (Glycine max (L.) Merr.), and fava bean (Vicia faba L.). RWC was calculated as measured leaf water content/leaf water content at full turgor, and RT as measured leaf thickness/leaf thickness at full turgor. Two leaves from the top, middle, and bottom of five plants of each species were collected at 60 days of age. Leaf samples brought to full turgor were left to dehydrate in a lab. Leaf thickness was measured using a magnetic field sensor and water content using weight loss. The RWC-RT relationship showed a distinct breakpoint, which we hypothesise coincides with the turgor loss point. Linear piecewise modelling was used to regress RWC versus RT, resulted in models explaining 86–97{\%} of the variations. The precision was improved by including leaf position on the plant in the model. The piecewise model parameters were related to salt tolerance of the species, which is also an indicator of drought resistance. Generally, the species with greater drought and salinity tolerance had a larger RT at the breakpoint.",
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Leaf thickness to predict plant water status. / Afzal, Amin; Duiker, Sjoerd W.; Watson, John E.

In: Biosystems Engineering, Vol. 156, 01.04.2017, p. 148-156.

Research output: Contribution to journalArticle

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