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LeasyScan: a novel concept combining 3D imaging and lysimetry for high-throughput phenotyping of traits controlling plant water budget.

Vadez V, Kholová J, Hummel G, Zhokhavets U, Gupta SK, Hash CT - J. Exp. Bot. (2015)

Bottom Line: Close agreement between scanned and observed leaf area data of individual plants in different crops was found (R(2) between 0.86 and 0.94).Similar agreement was found when comparing scanned and observed area of plants cultivated at densities reflecting field conditions (R(2) between 0.80 and 0.96).This new platform has the potential to phenotype for traits controlling plant water use at a high rate and precision, of critical importance for drought adaptation, and creates an opportunity to harness their genetics for the breeding of improved varieties.

View Article: PubMed Central - PubMed

Affiliation: ICRISAT-Crop Physiology Laboratory, Greater Hyderabad, Patancheru 502324, Telangana, India v.vadez@cgiar.org.

No MeSH data available.


Transpiration rate profile (in mg cm-2 min-1) as a function of thermal time (degree-days, with base temperature of 10ºC and optimal temperature of 25–35ºC) in (A) two genotypes of sorghum (VPD-insensitive R16 and VPD-sensitive S35), and (B) in two genotypes of pearl millet (VPD-insensitive H77/833-2 and VPD-sensitive PRLT-2/89-33). The insert in each figure represents a close-up of a 3 d period at 191–227 degree-days. Each data point is the mean (±SE) of six replicated sectors for each genotype.
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Figure 9: Transpiration rate profile (in mg cm-2 min-1) as a function of thermal time (degree-days, with base temperature of 10ºC and optimal temperature of 25–35ºC) in (A) two genotypes of sorghum (VPD-insensitive R16 and VPD-sensitive S35), and (B) in two genotypes of pearl millet (VPD-insensitive H77/833-2 and VPD-sensitive PRLT-2/89-33). The insert in each figure represents a close-up of a 3 d period at 191–227 degree-days. Each data point is the mean (±SE) of six replicated sectors for each genotype.

Mentions: The transpiration pattern of R16 and S35 over this period showed the usual transpiration rate peak around the midday period. Quite consistently across days, the transpiration rate of VPD-insensitive R16 was higher than in VPD-sensitive S35 (Fig. 9A). The Fig. 9A insert shows in more details the transpiration rate over three consecutive days (i.e. at 191–237 degree-days), and indeed shows a consistent pattern of having transpiration rate in R16 above that in S35 for about 2–3h during the midday period. Similar results are shown for the two pearl millet genotypes (Fig. 9B). Here also, the transpiration rate of VPD-insensitive H77/833-2 was consistently above that of VPD-sensitive PRLT. The Fig. 9B insert shows the details of the transpiration pattern over three consecutive days. These results then provide experimental support to the theoretical representation of that mechanism (see fig. 1 in Sinclair et al., 2005). Similar kind of data was also obtained in pairs of lines of cowpea and peanut (data not shown). Several QTLs for the capacity to restrict transpiration under high VPD have been mapped in pearl millet (Kholová et al., 2012), three of which were co-mapped with the yield-based terminal drought tolerance QTL found earlier (Yadav et al., 2002). This work was done by manually phenotyping plant transpiration and leaf area, with the limitation that the destructive measurement could only provide one snapshot of the plant response. LeasyScan provides the opportunity to access many more comprehensive details on the effects of VPD on both the response of the volumetric growth (leaf growth rate) and massic growth (transpiration, taken as a proxy for photosynthesis). We have shown the physiological significance of the transpiration response to VPD across crops (Vadez et al., 2014). The current LeasyScan setup therefore allows VPD response measurements at a very large scale in a seamless manner and in conditions that are close to field conditions. Optimization of the analytical scale data treatment is still needed to filter out erroneous transpiration data (for instance following rain or irrigation). Expansion in the analytical scale capacity is currently ongoing.


LeasyScan: a novel concept combining 3D imaging and lysimetry for high-throughput phenotyping of traits controlling plant water budget.

Vadez V, Kholová J, Hummel G, Zhokhavets U, Gupta SK, Hash CT - J. Exp. Bot. (2015)

Transpiration rate profile (in mg cm-2 min-1) as a function of thermal time (degree-days, with base temperature of 10ºC and optimal temperature of 25–35ºC) in (A) two genotypes of sorghum (VPD-insensitive R16 and VPD-sensitive S35), and (B) in two genotypes of pearl millet (VPD-insensitive H77/833-2 and VPD-sensitive PRLT-2/89-33). The insert in each figure represents a close-up of a 3 d period at 191–227 degree-days. Each data point is the mean (±SE) of six replicated sectors for each genotype.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License 1 - License 2
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Figure 9: Transpiration rate profile (in mg cm-2 min-1) as a function of thermal time (degree-days, with base temperature of 10ºC and optimal temperature of 25–35ºC) in (A) two genotypes of sorghum (VPD-insensitive R16 and VPD-sensitive S35), and (B) in two genotypes of pearl millet (VPD-insensitive H77/833-2 and VPD-sensitive PRLT-2/89-33). The insert in each figure represents a close-up of a 3 d period at 191–227 degree-days. Each data point is the mean (±SE) of six replicated sectors for each genotype.
Mentions: The transpiration pattern of R16 and S35 over this period showed the usual transpiration rate peak around the midday period. Quite consistently across days, the transpiration rate of VPD-insensitive R16 was higher than in VPD-sensitive S35 (Fig. 9A). The Fig. 9A insert shows in more details the transpiration rate over three consecutive days (i.e. at 191–237 degree-days), and indeed shows a consistent pattern of having transpiration rate in R16 above that in S35 for about 2–3h during the midday period. Similar results are shown for the two pearl millet genotypes (Fig. 9B). Here also, the transpiration rate of VPD-insensitive H77/833-2 was consistently above that of VPD-sensitive PRLT. The Fig. 9B insert shows the details of the transpiration pattern over three consecutive days. These results then provide experimental support to the theoretical representation of that mechanism (see fig. 1 in Sinclair et al., 2005). Similar kind of data was also obtained in pairs of lines of cowpea and peanut (data not shown). Several QTLs for the capacity to restrict transpiration under high VPD have been mapped in pearl millet (Kholová et al., 2012), three of which were co-mapped with the yield-based terminal drought tolerance QTL found earlier (Yadav et al., 2002). This work was done by manually phenotyping plant transpiration and leaf area, with the limitation that the destructive measurement could only provide one snapshot of the plant response. LeasyScan provides the opportunity to access many more comprehensive details on the effects of VPD on both the response of the volumetric growth (leaf growth rate) and massic growth (transpiration, taken as a proxy for photosynthesis). We have shown the physiological significance of the transpiration response to VPD across crops (Vadez et al., 2014). The current LeasyScan setup therefore allows VPD response measurements at a very large scale in a seamless manner and in conditions that are close to field conditions. Optimization of the analytical scale data treatment is still needed to filter out erroneous transpiration data (for instance following rain or irrigation). Expansion in the analytical scale capacity is currently ongoing.

Bottom Line: Close agreement between scanned and observed leaf area data of individual plants in different crops was found (R(2) between 0.86 and 0.94).Similar agreement was found when comparing scanned and observed area of plants cultivated at densities reflecting field conditions (R(2) between 0.80 and 0.96).This new platform has the potential to phenotype for traits controlling plant water use at a high rate and precision, of critical importance for drought adaptation, and creates an opportunity to harness their genetics for the breeding of improved varieties.

View Article: PubMed Central - PubMed

Affiliation: ICRISAT-Crop Physiology Laboratory, Greater Hyderabad, Patancheru 502324, Telangana, India v.vadez@cgiar.org.

No MeSH data available.