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Estimating Pearl Millet Leaf Area and Specific Leaf Area
Author(s) -
Payne W. A.,
Wendt C. W.,
Hossner L. R.,
Gates C. E.
Publication year - 1991
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.2134/agronj1991.00021962008300060004x
Subject(s) - pennisetum , specific leaf area , leaf area index , dry weight , pearl , mathematics , agronomy , horticulture , biology , botany , photosynthesis , geography , archaeology
Leaf area and specific leaf area (SLA) are important parameters in many agronomic and ecological processes, but can be difficult and expensive to measure. This study was made to test simplified methods of estimating pearl millet [ Pennisetum glaucum (L.) R. Br.] leaf area and SLA. Leaf length, maximum width, area, and dry mass data were obtained at 2‐wk intervals from plants grown in 75‐L pots. Pots contained 85 kg of acidic, P‐deficient Betis sand (sandy, silicious, thermic Psammentic Paleustalf) and were treated with four P levels and two water treatments (stressed and nonstressed). Individual leaf area was estimated non‐destructively with the following equations: Leaf area = 0.68 ✕ (leaf length ✕ maximum width) −0.114 ( R 2 = 0.955) and Ln(leaf area) = 2.08 ✕ Ln(length) −3.53 ( R 2 = 0.939). Individual leaf area and whole plant leaf area were calculated from leaf dry mass by the following linear and nonlinear equations: Leaf area = 133.6 ✕ Leaf mass + 22.69 (R 2 = 0.900), and Leaf area = 162.84 ✕ Leaf mass 0.687 (R 2 = 0.973). Residual errors indicated that the nonlinear equation was more accurate for area estimation of small leaves (≤0.20 g), and that leaf area data were heteroscedastic. Leaf dry mass was also used to calculate SLA by the nonlinear equation SLA = 176.7 × Leaf mass −0.216 ( R 2 = 0.918), which gave excellent fit to experimental data independent of harvest date, P level and watering treatment. Our results demonstrate that pearl millet leaf area and SLA can be accurately estimated and easily simulated from simple regression equations.

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