Abstract
SummaryA statistical approach has been applied to study the nutrient composition of flowers in a peach orchard with 50 peach trees suffering from different degrees of Fe deficiency. Flowers from each individual tree were analysed in five crop seasons. The existence of a consistent nutrient balance over years in peach flowers, with nutrient concentrations depending to some extent on the levels of other nutrients, was revealed by a principal component-type analysis. The analysis produced a dominant first principal component, which explained 21% of the total variance. The second component explained 13% of the variance, with further components explaining less than 9%. A regression analysis revealed that the first principal component was closely related to chlorophyll (Chl) concentration determined in leaves 120 d after full bloom, as it explained 59% of average Chl concentration variance (between 40 and 52% in single year analysis), which is equivalent to a correlation coefficient of 0.77. A multiple regressi...
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Claims 5
Claim · model-written summary
Peach flowers showed a consistent nutrient balance across years, with some nutrient concentrations depending on other nutrients.
- Taxon
- peach
Prunus persicae0 - Organ / stage
- flowerse0
- Compound
- other nutrientse0
- Outcome
- nutrient concentrationse0
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The existence of a consistent nutrient balance over years in peach flowers, with nutrient concentrations depending to some extent on the levels of other nutrients, was revealed by a principal component-type analysis.”
Claim · model-written summary
The first principal component explained 21% of nutrient composition variance.
- Taxon
- peachPrunus persicat0
- Outcome
- total variancee0
- Value
- 21%e0
- Qualifier
- first principal componente0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The analysis produced a dominant first principal component, which explained 21% of the total variance.”
t0“peach”
Claim · model-written summary
The second principal component explained 13% of variance.
- Taxon
- peachPrunus persicat0
- Outcome
- variancee0
- Value
- 13%e0
- Qualifier
- second componente0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The second component explained 13% of the variance, with further components explaining less than 9%.”
t0“peach”
Claim · model-written summary
Further principal components each explained less than 9% of variance.
- Taxon
- peachPrunus persicat0
- Outcome
- variancee0
- Value
- less than 9%e0
- Qualifier
- further componentse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The second component explained 13% of the variance, with further components explaining less than 9%.”
t0“peach”
Claim · model-written summary
The first principal component explained 59% of mean leaf chlorophyll variance and correlated with chlorophyll at 0.77.
- Taxon
- peachPrunus persicat0
- Organ / stage
- leavese0
- Outcome
- Chl concentration variancee0
- Value
- 59%e0
- Qualifier
- between 40 and 52% in single year analysise0
Not reportedDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“A regression analysis revealed that the first principal component was closely related to chlorophyll (Chl) concentration determined in leaves 120 d after full bloom, as it explained 59% of average Chl concentration variance (between 40 and 52% in single year analysis), which is equivalent to a correlation coefficient of 0.77.”
t0“peach”