Indexing
All Issues
Submission
Author Guidelines
Reviewers
Editorial Members
Publication Fee
Vol.2 , No. 3, Publication Date: Sep. 14, 2017, Page: 21-25
 of sweet cherry orchards is crucial for diagnosis about their production potential. The objective of this research was to develop simple, precise and non-destructive methods for estimating LA/tree (and based on that the LAI, dividing LA/tree by the area allocated to each tree) in orchards trained as tatura-trellis. Therefore, four models were evaluated: two simple linear regression models using as explanatory variables the Trunk Cross-Sectional Area (TCSA; dm<sup>2</sup>) (30 cm above the floor) or the Wood Volume of the Central Leader (WVCL; dm<sup>3</sup>) (considering the trunk as a cone: WVCL = π•r<sup>2</sup>•h/3) and two multiple regression models combining Mean Leaf Area (MLA; dm<sup>2</sup>) with the two previously mentioned variables. In all cases, LA/tree was the dependent variable. The TCSA and WVCL of 27 ‘Sweetheart’/’SL64’ trees conducted as tatura-trellis were registered, covering a wide range of tree sizes. At full canopy, all leaves of each tree were counted and 2% of them were randomly taken as a sample, from which the MLA was estimated. Leaf Area (LA) per tree was calculated multiplying the number of leaves by MLA. The best models to explain LA/tree variability were multiple linear regression equations combining WVCL and MLA (LA/tree = -9.18 + 37.31 ∙ MLA + 0.83 ∙ WVCL), or TCSA and MLA (LA/tree = -11.23 + 37.2 ∙ MLA + 15.91 ∙ TCSA), with R<sup>2</sup> of 0.83 and 0.84, respectively. The R<sup>2</sup> of simple linear regression models utilizing WVCL or TCSA were also high (0.69 and 0.70, respectively). However, the non-inclusion of MLA in these models, would limit their applicability in cultivars with different leaf morphology. The multiple regression equation including WVCL and MLA as explanatory variables seems to be the most robust model to be applied in other situations, because it considers two dimensions of the trunk and the leaf morphology.&picture=http://www.aascit.org/aascit/decorators/img/journal/805.jpg)
| [1] | Yamil Jorge Balul, CORFO, Gobierno de la Provincia de Chubut, Rawson, Argentina; UCAR, Ministerio de Agroindustria de la Nación, Rawson, Argentina. |
| [2] | Eduardo Daniel Cittadini, Estación Experimental Agropecuaria Chubut, Centro Regional Patagonia Sur, INTA, Trelew, Argentina. |
Knowledge of the Leaf Area Index (LAI) of sweet cherry orchards is crucial for diagnosis about their production potential. The objective of this research was to develop simple, precise and non-destructive methods for estimating LA/tree (and based on that the LAI, dividing LA/tree by the area allocated to each tree) in orchards trained as tatura-trellis. Therefore, four models were evaluated: two simple linear regression models using as explanatory variables the Trunk Cross-Sectional Area (TCSA; dm2) (30 cm above the floor) or the Wood Volume of the Central Leader (WVCL; dm3) (considering the trunk as a cone: WVCL = π•r2•h/3) and two multiple regression models combining Mean Leaf Area (MLA; dm2) with the two previously mentioned variables. In all cases, LA/tree was the dependent variable. The TCSA and WVCL of 27 ‘Sweetheart’/’SL64’ trees conducted as tatura-trellis were registered, covering a wide range of tree sizes. At full canopy, all leaves of each tree were counted and 2% of them were randomly taken as a sample, from which the MLA was estimated. Leaf Area (LA) per tree was calculated multiplying the number of leaves by MLA. The best models to explain LA/tree variability were multiple linear regression equations combining WVCL and MLA (LA/tree = -9.18 + 37.31 ∙ MLA + 0.83 ∙ WVCL), or TCSA and MLA (LA/tree = -11.23 + 37.2 ∙ MLA + 15.91 ∙ TCSA), with R2 of 0.83 and 0.84, respectively. The R2 of simple linear regression models utilizing WVCL or TCSA were also high (0.69 and 0.70, respectively). However, the non-inclusion of MLA in these models, would limit their applicability in cultivars with different leaf morphology. The multiple regression equation including WVCL and MLA as explanatory variables seems to be the most robust model to be applied in other situations, because it considers two dimensions of the trunk and the leaf morphology.
Keywords
TCSA, Wood Volume, Mean Leaf Area
Reference
| [01] | Cittadini E. D., Van Keulen H., Peri P. L. and De Ridder N. 2006. Designing a “target-tree” for maximizing gross value of product in Patagonian sweet cherry orchards. International Journal of Fruit Science 6 (3): 3-22. |
| [02] | Flore J. A. and Layne D. R. 1999. Photoassimilate production and distribution in cherry. HortScience 34: 1015-1019. |
| [03] | Proebsting E. L. 1990. The interaction between fruit size and yield in sweet cherry. Fruit Varieties Journal 44: 169-172. |
| [04] | Roper T. R. and Loescher W. H. 1987. Relationships between leaf area per fruit and quality in ‘Bing’ sweet cherry. HortScience 22: 1273-1276. |
| [05] | Whiting M. D. and Lang G. A. 2004. “Bing” sweet cherry on the dwarfing rootstock “Gisela 5”: Thinning affects fruit quality and vegetative growth but not net CO2 exchange. Journal of the American Society for Horticultural Science 129: 407-415. |
| [06] | Cittadini E. D., Peri P. L., De Ridder N. and Van Keulen H. 2008. Relationship between mean fruit weight and the ratio of fruit number to leaf area, at spur and whole-tree level, for three sweet cherry varieties. Acta Horticulturae 795: 669-672. |
| [07] | Cittadini E. D., Vallés M. B., Rodríguez M. J., Van Keulen H., De Ridder N. and Peri P. L. 2008. Effect of Fruit Number to Leaf Area Ratio on Fruit Quality and Vegetative Growth of ‘Bing’ Sweet Cherry Trees at Optimal LAI. Acta Horticulturae 795: 677-680. |
| [08] | Cittadini E. D. 2007. Sweet cherries from the end of the world: Options and constraints for fruit production system in South Patagonia, Argentina. PhD Thesis, Graduate School PE&RC, Wageningen University. Wageningen, The Netherlands. 134 p. |
| [09] | Szlápelis S. and Cittadini E. D. 2008. Cuantificación del efecto de la relación “número de frutos por unidad de área foliar” sobre el rendimiento and la calidad de fruta en el cultivo de cerezos, en el valle de Sarmiento. XXXI Congreso Argentino de Horticultura. Mar del Plata, 30 Sep. - 3 Oct. 2008. p. 148. |
| [10] | Raffo M. D and Iglesias N. 2004. Efecto de la intercepción and distribución de la radiación fotosinteticamente activa en manzano cv. Fuji bajo cuatro sistemas de conducción de alta densidad. RIA (INTA) 33 (2): 29-42. |
| [11] | Gil Salaya G. F. 1999. Fruticultura: El potencial productivo. Crecimiento vegetativo and diseño de huerto and viñedos. Alfaomega. México, DF. 342 pp. |
| [12] | Flore J. A. and Layne D. R. 1990. The influence of tree shape and spacing on light interception and yield in sour cherry (Prunus cerasus cv. Mont. morency). Acta Horticulturae 285: 91-96. |
| [13] | Wagenmakers P. 1994. Light relations in orchard systems. Ph. D. Thesis. Wageningen Agricultural University, Wageningen, The Netherlands. 151 p. |
| [14] | Patten K. D., Patterson M. E. and Proebsting E. L. 1986. Factors accounting for the within-tree variation of fruit quality in sweet cherries. HortScience 11 (3): 356-360. |
| [15] | Mora M., Ávila F., Carrasco-Benavides M., Maldonado G., Olguín-Cáceres J. and Fuentes S. 2016. Automated computation of leaf area index from fruit trees using improved image processing algorithms applied to canopy cover digital photographs. Computers and Electronics in Agriculture 123: 195-202. |
| [16] | Poblete-Echeverría C., Fuentes S., Ortega-Farias S., Gonzalez-Talice J. and Yuri J. A. 2015. Digital Cover Photography for Estimating Leaf Area Index (LAI) in Apple Trees Using a Variable Light. |
| [17] | Machado Guimarães M. J., Coelho Filho M. A., Pereira Peixoto C., De Assis Gomes Junior F. and Machado De Oliveira V. V. 2013. Estimation of leaf area index of banana orchards using the method LAI-LUX. Water Resources and Irrigation Management 2 (2): 71-76. |
| [18] | Rich P. M. 1990. Characterizing plant canopies with hemispherical photographs. In: Goel N. S. and J. M. Norman (Eds.). Instrumentation for studying vegetation canopies for remote sensing in optical and thermal infrared bands. Remote Sensing Reviews, Harwood Academic Publishers, London, England. 13-29 p. |
| [19] | Zárate-Valdez J. L., Whiting M. L., Lampinen B. D., Metcalf S., Ustin S. L. and Brown P. H.. 2012. Prediction of leaf area index in almonds by vegetation indexes. Computers and Electronics in Agriculture 85: 24-32. |
| [20] | Liu C., Kang S., Li F., Li S. and Du T. 2013. Canopy leaf area index for apple tree using hemispherical photography in arid region. Scientia Horticulturae 164: 610-615. |
| [21] | Arnó J., Escolà A., Vallès J. M., Llorens J., Sanz R., Masip J., Palacín J. and Rosell-Polo J. R. 2013. Leaf area index estimation in vineyards using a ground-based LiDAR scanner. Precision Agriculture 14 (3): 290-306. |
| [22] | Villalobos F. J., Orgaz F. and Mateos L. 1995. Non-destructive measurement of leaf area in olive (Olea europaea L.) trees using a gap inversion method. Agricultural and Forest Meteorology 73 (1-2): 29-42. |
| [23] | Holland D. A. 1968. The estimation of total leaf area on tree. Annual Report of the East Malling Research Station for 1967, p. 101-107. |
| [24] | Barlow H. W. B. 1969. The relation of leaf area to stem cross section. Report of East Malling Research Station for 1968, p. 117-119. |
| [25] | Palmer J. W. 1987. The measurements leaf area on apple tree. Journal of Horticultural Science 62: 5-10. |
| [26] | Angelocci L. R. and Valancogne C. 1993. Leaf area and water flux in apple trees. Journal of Horticultural Science 67 (2): 299-307. |
| [27] | Tersoglio E. 2001. Perspectivas del cultivo del cerezo en la República Argentina. Asociación Cooperadora de la EEA Mendoza INTA Argentina. p. 70. |
| [28] | Marshall J. D and Waring R. H. 1986. Comparison of Methods of Estimating Leaf-Area Index In Old-Growth Douglas-Fir. Ecology 67 (4): 975-979. |
| [29] | Hochmaier V., San Martino L. and Cittadini E. D. 2014. Non-destructive estimation of leaf area index in vase-shape sweet cherry trees. Acta Horticulturae 1020: 209-215. |
| [30] | Cittadini E. D. and Peri P. L. 2006. Estimation of leaf area in sweet cherry using a non-destructive method. RIA (INTA) 35 (1): 143-150. |
