Journal of Plant Studies; Vol. 1, No. 2; 2012
ISSN 1927-0461
E-ISSN 1927-047X
Published by Canadian Center of Science and Education
Estimating the Leaf Area of Irvingia Gabonensis (Aubry-Lecomte Ex
O’ Rorke) Baill from Linear Measurement
Eucharia Uzokwe1, Temitope Borokini2, Ibrahim Lawal1, Ibrahim Audu3 & Adamson Adams3
1
Forestry Research Institute of Nigeria (FRIN), Ibadan, Nigeria
2
National Centre for Genetic Resources and Biotechnology (NACGRAB), Ibadan, Nigeria
3
Federal College of Forestry, Ibadan, Nigeria
Correspondence: Temitope Borokini, National Centre for Genetic Resources and Biotechnology (NACGRAB),
Ibadan, Nigeria. Tel: 234-8054-506-902. E-mail: tbisrael@gmail.com
Received: April 10, 2012
doi:10.5539/jps.v1n2p68
Accepted: May 15, 2012
Online Published: August 10, 2012
URL: http://dx.doi.org/10.5539/jps.v1n2p68
Abstract
Leaf area measurements are of great value in physiological and agronomic studies. Leaf area is important for
plant light interception, growth analysis, photosynthesis, leaf area index estimation among others. This study
focused on estimating the leaf of Irvingia gabonensis using a linear (non-destructive) measurement. A total of 90
leaves were selected randomly from nursery and field which represented different leaf sizes ranging from small
(3 cm width), medium (5 cm width) and large (7 cm width). The lamina length (L) and lamina width (W) were
measured with a ruler, while the leaf area (LA) were measured using a software model WinRHizo PRO2005,
installed on computer system with an attached scanning machine. The best fit model was selected based on F test
and coefficient of determination R2. Correlation of all parameters is significant at 1% level. Product of square
length and width [(LW)2] correlated best with leaf area having a correlation coefficient (r) of 0.99. Quadratic and
linear regression of the data produced thirteen leaf area models. The best five models were derived from single
dimensional measurement product and sum of length and width of either lamina length or width. The five top
models were recommended for leaf area estimation of Irvingia gabonensis but for ease of application, model 13
(Y = 0.2309x2- 2.5694x + 67.268: R2 = 99%) was preferred for its simplicity. The models developed and
recommended in this study can be adopted for rapid and accurate estimation of adaxial leaf area of I. gabonensis
under field conditions.
Keywords: determination coefficient, leaf area, linear measurement, non-destructive, Irvingia gabonensis
1. Introduction
Leaf area is an important variable for most ecophysiological studies in terrestrial ecosystems concerning light
interception, evapotranspiration, photosynthetic efficiency, fertilizers, and irrigation response and plant growth
(Blanco & Folegatti, 2005). Plant physiologists require leaf area measurements for studying primary production
in plants (Bleasdale, 1984). Ecologists use leaf area relations for elucidating competition among different plant
species (Harper, 1977). Leaf area estimate is valuable in studies of plant nutrition, plant competition,
plant-soil-water relations, plant protection measures, respiration, light reflectance, and heat transfer in plants, and
thus it is an important parameter in understanding photosynthesis, light interception, water and nutrient use, and
crop growth and yield potential (Smart 1974; Williams, 1987). Leaf area is important for crop light interception
and therefore has a large influence on growth (Boote, Jones, & Hoogenboom, 1998), transpiration (Enoch &
Hurd, 1979) and growth rate (Leith, Reynolds, & Rogers, 1986). Leaf area production is essential for energy
transference and dry matter accumulation processes in crop canopies. It is also useful in the analysis of canopy
architecture as it allows for the determination of leaf area index, which is important for light interception,
radiation use efficiency, plant growth among others.
Leaf area measuring instruments are very expensive and often not available in developing countries and remote
research stations. When available, these instruments are prone to large errors as a result of incorrect use that may
lead to inconclusive results (de Jesus, Dovale, Coelho, & Costa, 2001). These problems have been recognized by
many researchers, who have developed less expensive and/or alternative, indirect methods (Ma, Gardner, &
Selamat, 1992). Indirect methods are based on the assumption that mass and size dimensions of different plant
68
www.ccsenet.org/jps
Journal of Plant Studies
Vol. 1, No. 2; 2012
parts are allometric (Gardner, Pearce, & Mitchell, 1985). These indirect methods may increase precision of leaf
area determination where sample or leaf size are difficult to handle (Ma et al., 1992) and can reduce the overall
sampling effort necessary to estimate leaf area (Lieth et al., 1986). Indirect methods of measuring leaf area can
be classified as non-destructive and destructive methods. In non-destructive methods, leaf area is usually
estimated by measuring the number, width or length of plant parts or whole plant. These measurements can be
undertaken without cutting the plants. Non-destructive methods have been successfully applied for various crops
such as cotton and castor (Wendt, 1967), sorghum (Shih, Gascho, & Rahi, 1981), soybean (Lieth et al., 1986),
pearl millet (Payne, Wendt, Hossner, & Gates, 1991), maize (Stewart & Dwyer, 1999) and sunflower (Bange,
Hammer, Milroy, & Rickert, 2000). Non-destructive estimation of leaf areas offers researchers reliable and
inexpensive alternatives in plant research studies. Non-destructive leaf-area or plant growth measurements are
often desirable because continued use of the same plant over time can reduce variability in experiment when
compared with destructive sampling (Nesmith, 1991). The determination of leaf area using this method involves
measuring lengths, widths and areas of leaf samples and calculating the several possible regression coefficients
or leaf factors to estimate areas of subsequent samples (Silva, Ferreira, Fontes, & Cardoso, 1998; Gutienez &
Larvin, 2000; Astegiano, Favaro, & Bouzo, 2001; Guo & Sun, 2001; Jayeoba, Oluwasemire, Abiola, Uzokwe, &
Ogunbajo, 2006).
Irvingia gabonensis is a member of the family Irvingiaceae and locally known as “Ogbono” in Southwestern
Nigeria. It is naturally distributed in several West African countries such as Ghana, Togo, Cameroon, Senegal,
Benin, and Nigeria among many others. Its bark is an excellent medicinal product (Van Dijk, 1999), the
mesocarp of its fruit is consumed fresh and the kernels extracted from the fruit are used as basic soup ingredients.
A type of oil is extracted from the hot almond paste and can be used as a cosmetic product, as food or in
pharmacopoeia (Awono, Djouguep, Zapfack, & Ndoye, 2009). Its seed (kernel) serves as soup thickener which
provides carbohydrate, oil and protein so enhancing health and nutrition. There has been a massive depletion of
its stocks in African forests, while natural regeneration of the species has also been constrained by wild animals.
Consequently due to a combination of these constraints, the available stocks in the nursery are usually guided
jealously. Therefore all physiological and agronomic study of this plant had to be conservative in design. This
justified the need for the development of leaf factors for non-destructive leaf area measurement of the plant.
2. Materials and Methods
2.1 Plant Material
Leaf samples from selected seedlings of Irvingia gabonensis in the tree crop nursery of Forestry Research
Institute of Nigeria (FRIN), Jericho, Ibadan were used for the study. Various leaf sizes (30 each of small-sized,
medium-sized and large-sized) were selected from the nursery stock and matured stands of I. gabonensis over a
period of three months. The study was carried out at the nursery of the Forestry Research Institute of Nigeria
(FRIN), Ibadan, Nigeria (7º 26’N; 3º 26’E).
2.2 Methodology
The leaf area (LA) of the leaves was measured using a software model WinRH120 PRO 2005, installed on
computer system with an attached scanning machine. The lamina length (L) and lamina width (W) were
measured from the lamina tip to the point of petiole intersection along the lamina midrib and edge to edge at the
widest part of the lamina respectively with the aid of a well graduated meter rule. The data generated were filled
to linear and quadratic regressions of L and W. Actual leaf area was taken as the dependent variable (Y) and the
combinations of L and W as the independent variable (X), correlation analysis of the leaf area (LA) and of the
independent variables L, W, L2, W2, L+W, and LW were calculated. Statistical criteria for model selection were
based on correlation coefficient (r) and coefficient of determination (R2).
3. Results
3.1 Correlation Analysis
The results showed that there is a strong significant relationship between leaf area and all the parameters studied
at 1% level (Table 1).
69
www.ccsenet.org/jps
Journal of Plant Studies
Vol. 1, No. 2; 2012
Table 1. Correlation matrix of leaf area and linear leaf measurement of Irvingia gabonensis
L (cm)
W (cm)
L (cm)
LW (cm2)
L+W (cm)
0.980061 0.979554 0.795894 1
0.994193 0.972979 0.793986 0.9923551
L2 (cm2)
0.994132 0.937165 0.806261 0.9852850.9884281
2
2
W (cm )
L+L (cm)
W+W (cm)
LW (
cm2)
L+W
(cm)
L2
(cm2)
L
W
L
(cm)
(cm)
(cm)
1
0.942482 1
0.799451 0.750913 1
W2
(cm2)
L+L
(cm)
W+W
(cm)
(LW)2
(cm2)
(L+L)2
(cm2)
(W+W)2 LA
(cm2)
(cm2)
0.935244 0.993391 0.759617 0.9839050.9658520.9393031
1
0.942482 0.799451 0.9800610.9941930.9941320.9352441
0.942482 1
0.750913 0.9795540.9729790.9371650.9933910.9424821
(LW)2 (cm2) 0.944283 0.942572 0.791352 0.98248 0.9557340.9676920.9663380.9442830.9425721
(L+L)2 (cm2) 0.994132 0.937165 0.806261 0.9852850.9884281
0.9393030.9941320.9371650.967692 1
(W+W)2 (cm2) 0.935244 0.993391 0.759617 0.9839050.9658520.9393031
2
LA (cm )
0.9352440.9933910.966338 0.939303 1
0.976497 0.960744 0.808377 0.9882540.9838370.9805990.9631140.9764970.9607440.970455 0.980599 0.9631141
Table 1 shows the correlation analysis of the parameters studied against Leaf Area (LA).
Key: L = Length; W = Width (breadth); LA = Leaf area.
3.2 Generation and Testing of Models
Table 2 shows all the possible equations generated for the estimation of leaf area in I. gabonensis, all of which
are significant at 1%. The graphical representations of the linear models tested were shown in Figures 1-9. The
relationship between LA and LW was observed to be linear and significant at 1% with the correlation coefficient
(r) of 0.93 (Figure 1). Similar trends of linear and significant relationship were observed in the relationship
between LA and L+W with the correlation coefficient (r) of 0.96 (Figure 2), between LA and L2 with the
correlation coefficient (r) of 0.92 (Figure 3); and between LA and (L+L) with the correlation coefficient (r) of
0.96 (Figure 4), between LA and W with the correlation coefficient (r) of 0.99 (Figure 5), between LA and
(W+W) with the correlation coefficient (r) of 0.93 (Figure 6), between LA and L with the correlation coefficient
(r) of 0.97 (Figure 7), between LA and W2 with the correlation coefficient (r) of 0.93 (Figure 8) and between LA
and (LW)2 with the correlation coefficient (r) of 0.99 (Figure 9).
However, it should be noted that model 13 for the relationship between L and [(LW)2] gave the best fit at R2 =
99.4% (Figure 2), while models developed for the relationships between LA and L+W, LA and W, LA and LW as
well as LA and L with the corresponding R2 values of 97.9%, 97.8%, 97.7% and 97.2% respectively were also
tested to be good and can also be applied as well (Table 2).
Table 2. Equations for possible estimation of Irvingia gabonensis leaf area
Parameters
LW
L+W
L2
L+L
W
W+W
L
L
L
W2
(LW)2
(LW)2
(LW)2
Equations/Model
Y= 6E-06x2 + 0.4672x – 0.0231
Y=0.0002x2+0.1215x + 4.8975
Y=0.0003x2 + 1.0902x – 0.4566
Y=0.0003x2 + 0.1682x + 6.855
Y= -5E-05x2+0.0353x + 1.491
Y=4.1021Ln(x)-9.625
Y=0.0535x + 4.5815
Y=4.3573Ln(x)-9.6335
Y=0.0002x2 + 0.0826x + 3.4765
Y=0.2108x-0.1909
Y=42.113x-1625.4
Y=149.85e 0.0241x
Y=0.2309x2-2.5694x + 67.268
R2 Value
0.977
0.979
0.962
0.965
0.978
0.959
0.964
0.965
0.972
0.928
0.952
0.954
0.994
The equations/models generated for the estimation of the leaf area of Irvingia gabonensis.
70
www.ccsenet.org/jps
Journal of Plant Studies
Vol. 1, No. 2; 2012
Figure 1. Relationship between leaf area
(LA) and the product of Lamina leaf length and
width (LW) of Irvingia gabonensis
Figure 2. Relationship between leaf area
(LA) and summation of lamina leaf length and
width (L+W) of Irvingia gabonensis
Figure 3. Relationship between leaf area
(LA) of leaf and square lamina leaf length (L2) of
Irvingia gabonensis
Figure 4. Relationship between leaf area
(LA) and summation of lamina leaf length and
length (L+L) of Irvingia gabonensis
71
www.ccsenet.org/jps
Journal of Plant Studies
Figure 5. Relationship between leaf area
(LA) and lamina width (W) of Irvingia
gabonensis
Vol. 1, No. 2; 2012
Figure 6. Relationship between leaf area
(LA) and summation of lamina width and width
(W+W) of Irvingia gabonensis
Figure 7. Relationship between leaf area
(LA) and lamina leaf length (L) of Irvingia
gabonensis
Figure 8. Relationship between leaf area
(LA) and square lamina leaf Width (W2) of
Irvingia gabonensis
72
www.ccsenet.org/jps
Journal of Plant Studies
Vol. 1, No. 2; 2012
Figure 9. Relationship between leaf area (LA) and the product of square lamina leaf length and width
[(LW)2] of Irvingia gabonensis
4. Discussions and Conclusion
The models developed from this study will enable crop scientists including tree breeders, agronomists and
horticulturist that may be interested in the study of growth and development of Irvingia garbonensis either in the
field or at the nursery stage without destroying the leaves thereby compromising on precision and accuracy. The
linear relationship between the LA and [(LW)2] gave the best bit and the model, Y = 0.2309x2 – 2.5694x +
67.268: R2 = 99% is recommended for predicting leaf area. Furthermore, models developed for the relationships
between LA and L+W, LA and W, LA and LW, LA and L were also good. The models developed and
recommended in this study can be adopted for rapid and accurate estimation of adaxial leaf area of I. gabonensis
leaves under field conditions. Furthermore, non-destructive determination of leaf area by the model assures
repeated measurement of leaves in growth monitoring experiments, while the model serves as a good substitute
where leaf area meter or sophisticated expertise is not available.
Similar results were obtained between LA and W, LA and W2, and LA and LW in cabbage (Olfati, Peyvast,
Shabani, & Nosratie-Rad, 2010) and shea butter (Ugese, Baiyeri, & Mbah, 2008). Pandey and Singh (2011)
reported that the non-destructive estimates of leaf area by graphical method are as good as those obtained
destructively by leaf area meter method.
References
Astegiano, E. D., Favaro, J. C., & Bouzo, C. A. (2001). Estimacion del area foliar en distintos culltivares de to
mate (Lycopersicon esculentum Mill) Utilizando medidas foliaies lineals. Investigation Agraria Produccion
Proteccion vegetables, 10(2), 249-256.
Awono, A., Djouguep, A., Zapfack, L., & Ndoye, O. (2009). The potential of Irvingia gabonensis: Can it
contribute to the improvement of the livelihoods of producers in southern Cameroon? International Journal
of Social Forestry, 2(1), 67-85.
Bange, M. P., Hammer, G. L., Milroy, S. P., & Rickert, K. G. (2000). Improving estimates of individual leaf area
of sunflower. Agron. J, 92, 761-765. http://dx.doi.org/10.2134/agronj2000.924761x
Blanco, F. F., & Folegatti, M. V. (2005). Estimation of leaf area for greenhouse cucumber by linear
measurements under salinity and grafting. Agricultural Science, 62(4), 305-309.
Bleasdale, J. K. A. (1984). Plant Physiology in Relation to Horticulture (2nd edition). London, UK: Macmillan
Press.
Boote, K. J., Jones, J. W., & Hoogenboom, G. (1998). Simulation of crop growth: CROPGRO Model. In: Peart,
R.M. and Curry, R.B. (eds.) Agricultural Systems Modeling and Simulation, Marcel Dekker, Inc., New York,
73
www.ccsenet.org/jps
Journal of Plant Studies
Vol. 1, No. 2; 2012
pp. 651-692.
De Jesus, W. C., Dovale, F. X. R., Coelho, R. R., & Costa, L. C. (2001). Comparisons of two methods for
estimating
leaf
area
index
on
common
bean.
Agron.
J,
93,
989-991.
http://dx.doi.org/10.2134/agronj2001.935989x
Enoch, H. Z., & Hurd, R. G. (1979). The effect of elevated CO2 concatenation in the atmosphere on plant
transpiration and water use efficiency: A study with potted carnation plants. Int. J. Biometer, 23, 343-351.
http://dx.doi.org/10.1007/BF01553106
Gardner, F. P., Pearce, R. B., & Mitchell, R. L. (1985). Physiology of crop plants. Ames, IO: Iowa State Univ.
Press. pp. 187-208.
Gutierrez, T. A., & Larvin, A. A. (2000). Mediciones Lineales en la hoja para estimacion no destructive del area
filiar en vides CV. Chardonnay. Agricultural tecnica, 60(1), 69-73.
Guo, D. P., & Sun, Y. Z. (2001). Estimation of leaf area of stem lettuce (Latuca sativa var angustaria) from
linear measurement. Indian Journal of Agricultural Sciences, 71(7), 483-486.
Harper, J. L. (1977). Population Biology of Plants. Oxford, UK: Academic Press.
Jayeoba, O. J., Oluwasemire, K. O., Abiola, I. O., Uzokwe, N. E., & Ogunbajo, O. R. (2006). Leaf area
prediction models for Treculia africana using linear measurement. Savanna Journal of Agriculture, 1,
27-31.
Leith, J. H., Reynolds, J. P., & Rogers, H. H. (1986). Estimation of leaf area of soybeans grown under elevated
carbon dioxide levels. Field Crops Res, 13, 193-203. http://dx.doi.org/10.1016/0378-4290(86)90021-3
Ma, L., Gardener, F. P., & Selamat, A. (1992). Estimation of leaf area from leaf and total mass measurements in
peanut. Crop. Sci, 32, 461-471. http://dx.doi.org/10.2135/cropsci1992.0011183X003200020036x
Nesmith, D.S. (1991). Non destructive leaf area estimation of rabbiteye blueberries. Hortscience,
26(10),1332-1339.
Olfati, J. A., Peyvast, Gh., Shabani, H., & Nosratie-Rad, Z. (2010). An Estimation of Individual Leaf Area in
Cabbage and Broccoli Using Non-destructive Methods. J. Agr. Sci. Tech, 12, 627-632.
Pandey, S. K., & Singh, H. (2011). A Simple, Cost-Effective Method for Leaf Area Estimation. Journal of
Botany. http://dx.doi.org/10.1155/2011/658240
Payne, W. A., Wendt, C. W., Hossner, L. R., & Gates, C. E. (1991). Estimating pearl millet leaf area and specific
leaf area. Agron. J, 83, 937-941. http://dx.doi.org/10.2134/agronj1991.00021962008300060004x
Shih, S. F., Gascho, G. J., & Rahi, G. S. (1981). Modeling biomass production of sweet sorghum. Agron. J, 73,
1027-1032. http://dx.doi.org/10.2134/agronj1981.00021962007300060028x
Silva, N. F., Ferreira, F. A., Fontes, P. C. R., & Cardoso, A. A. (1998). Modelos para estimar agrea foliar de
abobora por meio de medidas lineares. Revista ceres, 45(259), 287-291.
Smart, R. E. (1974). Photosynthesis by grapevine canopies. Journal of Applied Ecology, 11(3), 997-1006.
http://dx.doi.org/10.2307/2401759
Stewart, D. W., & Dwyer, L.M. (1999). Mathematical characterization of leaf shape and area of maize hybrids.
Crop. Sci, 39, 422-427. http://dx.doi.org/10.2135/cropsci1999.0011183X0039000200021x
Ugese, F. D., Baiyeri, K. P., & Mbah, B. N. (2008). Leaf area determination of shea butter tree (Vitellaria
paradoxa C.F. Gaertn.). Int. Agrophysics, 22, 167-170.
Wendt, C. W. (1967). Use of a relationship between leaf length and leaf area to estimate the leaf area of cotton.
Agron. J, 59, 484-486. http://dx.doi.org/10.2134/agronj1967.00021962005900050034x
Williams, L. E. (1987). Growth of “Thompson Seedless” grapevines: I. Leaf area development and dry weight
distribution. Journal of American Society and Horticultural Science, 112(2), 325-330.
Van Dijk, J. F. W. (1999). “Non Timber Forest Products in the Bipindi Akom II. A Socio-Economic and
Ecological Assessment.” Tropenbos Cameroun Serie 1. p. 197.
74