Multivariate analysis of the temporal variability of sugarcane ripening in south-eastern Brazil
Nilceu P. Cardozo A , Paulo C. Sentelhas B E , Alan R. Panosso C and Antonio S. Ferraudo DA Sugarcane Research Center, CTC, Piracicaba, SP, Brazil.
B Department of Biosystems Engineering, ESALQ, University of São Paulo, Piracicaba, SP, Brazil.
C Department of Mathematics, FEIS-UNESP, Ilha Solteira, SP, Brazil.
D Department of Exact Sciences, FCAV-UNESP, Jaboticabal, SP, Brazil.
E Corresponding author. Email: pcsentel.esalq@usp.br
Crop and Pasture Science 65(3) 300-310 https://doi.org/10.1071/CP13160
Submitted: 8 May 2013 Accepted: 20 February 2014 Published: 14 April 2014
Abstract
Sugarcane ripening is a process controlled by cultivar characteristics and the interaction of genotypes with local climate. The objective of this study was to characterise the temporal variation of sugarcane ripening by assessing the multivariate structure contained in sugarcane quality data, and by correlating the results with local climatic conditions. Eight sugarcane cultivars were evaluated from March to October in Piracicaba, São Paulo State, Brazil. Characteristics related to the quality of raw sugarcane juice were submitted to statistical analysis by ANOVA, hierarchical and non-hierarchical (k-means) clustering methods, and principal components, in order to classify the cultivars into groups for each month of sampling. The ANOVA showed a clear difference (P < 0.001) among harvesting months for all sugarcane quality variables, which was reinforced by the cluster analysis for the whole dataset that selected groups according to the month of harvest. By analysing the quality variables by months, patterns of similarity among sugarcane cultivars were identified, which allowed three ripening groups to be established: early, middle and late. As the harvesting season progressed, the variations within each group, as well as among groups, were explained mainly by local soil-water availability conditions. The early ripening cultivars showed polarisable sugar (Pol) values >13% in early May, whereas these values were reached by the middle cultivars in July, and the late ones in August–September. However, the differences among groups tended to decrease through the harvest season, as expressed by the Euclidean distance, which decreased from 5.62 in March to 1.82 in September, when the water deficit reached the maximum accumulated value, totalling >130 mm. The non-hierarchical analyses (k-means) and principal components methods agreed, resulting in the identification of the same three main cultivar groups. The approach proposed for cultivar classification in this study is more complete than the usual analysis of Pol variation over time, since it allowed all of the variability contained in the sugarcane quality dataset to be analysed in an integrated way, providing a better understanding of the differences observed in the ripening of different cultivars.
References
Alexander AG (1973) ‘Sugarcane physiology: a comprehensive study of the Saccharum source-to-sink system.’ (Elsevier Scientific: Amsterdam)Allen RG, Pereira LS, Raes D, Smith M (1998) ‘Crop evapotranspiration: guidelines for computing crop water requirements.’ Irrigation and Drainage Paper, 56. (FAO: Rome)
Ariyo OJ (1993) Genetic diversity in West African Okra (Abelmoschus caillei (A. Chev.) Stevels)—Multivariate analysis of morphological and agronomic characteristics. Genetic Resources and Crop Evolution 40, 25–32.
| Genetic diversity in West African Okra (Abelmoschus caillei (A. Chev.) Stevels)—Multivariate analysis of morphological and agronomic characteristics.Crossref | GoogleScholarGoogle Scholar |
Beiragi MA, Sar BAS, Geive HS, Alhossini MN, Rahmani A, Gharibdoosti AB (2012) Application of the multivariate analysis method for some traits in maize. African Journal of Agricultural Research 7, 1524–1533.
Cardozo NP (2012) Modeling sugarcane ripening as function of meteorological variables. MSc Thesis, Universidade de São Paulo, Piracicaba, SP, Brazil. Available at: www.teses.usp.br/teses/disponiveis/11/11131/tde-14032012-080359/
Cardozo NP, Sentelhas PC (2013) Climatic effects on sugarcane ripening under the influence of cultivars and crop age. Scientia Agricola 70, 449–456.
Clements HF (1962) The ripening of sugar cane. Sugar y Azúcar 57, 29–78.
CONSECANA (2006) ‘Manual de instruções.’ (Conselho dos Produtores de Cana-de-açúcar, Açúcar e Álcool do Estado de São Paulo: Piracicaba, SP, Brasil)
Fernandes AC (2011) ‘Cálculos na agroindústria da cana-de-açúcar.’ (STAB: Piracicaba, SP, Brasil)
Ferraro DO, Rivero DE, Ghersa CM (2009) An analysis of the factors that influence sugarcane yield in Northern Argentina using classification and regression trees. Field Crops Research 112, 149–157.
| An analysis of the factors that influence sugarcane yield in Northern Argentina using classification and regression trees.Crossref | GoogleScholarGoogle Scholar |
Ferreira EA, Aspiazú I, Concenço G, SIlva AF, Silva AA, Galon LL, SIlva DV (2011) Evaluation and grouping of sugarcane genotypes in agreement with their physiologic characteristics types. Revista Trópica – Ciências Agrárias e Biológicas 5, 30–38.
Hair JF, Black B, Babin B, Anderson RE, Tatham RL (2005) ‘Multivariate data analysis.’ 6th edn (Prentice Hall: Englewood Cliffs, NJ)
Hartingan JA (1975) ‘Clustering algorithms.’ (Wiley: New York)
Horii J (2004) A qualidade da matéria-prima, na visão agrícola. Visão Agrícola. 1, 91–93.
Informa Economics FNP (2012) ‘AGRIANUAL 2012: Anuário da Agricultura Brasileira.’ (Informa Economics FNP: São Paulo)
Inman-Bamber NG (2004) Sugarcane water stress criteria for irrigation and drying off. Field Crops Research 89, 107–122.
| Sugarcane water stress criteria for irrigation and drying off.Crossref | GoogleScholarGoogle Scholar |
Jackson PA, Hogarth DM (1992) Genotype × environment interactions in sugarcane. I. Patterns of response across sites and crop-years in north Queensland. Australian Journal of Agricultural Research 43, 1447–1459.
| Genotype × environment interactions in sugarcane. I. Patterns of response across sites and crop-years in north Queensland.Crossref | GoogleScholarGoogle Scholar |
Jackson PA, Mcrae T, Hogarth DM (1995) Selection of sugarcane families across variable environments II. Patterns of response and association with environmental factors. Field Crops Research 43, 119–130.
| Selection of sugarcane families across variable environments II. Patterns of response and association with environmental factors.Crossref | GoogleScholarGoogle Scholar |
Johnson RA, Wichern DW (2001) ‘Applied multivariate statistical analysis.’ 5th edn (Prentice Hall: Englewood Cliffs, NJ)
Kaiser HF (1958) The varimax criterion for analytic rotation in factor analysis. Psychometrika 23, 187–200.
| The varimax criterion for analytic rotation in factor analysis.Crossref | GoogleScholarGoogle Scholar |
Lavanholi MGDP (2008) ‘Qualidade da cana-de-açúcar como material-prima para produção de açúcar e álcool.’ In ‘Sugarcane’. (Eds LL Dinardo-Miranda, ACM Vasconcelos, MGA Landell) (Instituto Agronômico: Campinas)
Lawes RA, Lawn RJ (2005) Applications of industry information in sugarcane production systems. Field Crops Research 92, 353–363.
| Applications of industry information in sugarcane production systems.Crossref | GoogleScholarGoogle Scholar |
Lawes RA, McDonald LM, Wegener MK, Basford KE, Lawn RJ (2002) Factors affecting cane yield and commercial cane sugar in the Tully district. Australian Journal of Experimental Agriculture 42, 473–480.
| Factors affecting cane yield and commercial cane sugar in the Tully district.Crossref | GoogleScholarGoogle Scholar |
Lawes RA, Wegener MK, Basford KE, Lawn RJ (2004) The evaluation of the spatial and temporal stability of sugarcane farm performance based on yield and commercial cane sugar. Australian Journal of Experimental Agriculture 55, 335–344.
| The evaluation of the spatial and temporal stability of sugarcane farm performance based on yield and commercial cane sugar.Crossref | GoogleScholarGoogle Scholar |
Legendre BL (1975) Ripening of sugarcane: effects of sunlight, temperature, and rainfall. Crop Science 15, 349–352.
Maji AT, Shaibu AA (2012) Application of principal component analysis for rice germplasm characterization and evaluation. Journal of Plant Breeding and Crop Science 4, 87–93.
Mamet LD, Galwey NW (1999) A relationship between stalk elongation and earliness of ripening in sugarcane. Experimental Agriculture 35, 283–291.
| A relationship between stalk elongation and earliness of ripening in sugarcane.Crossref | GoogleScholarGoogle Scholar |
Marin FR, Jones JW, Royce F, Suguitani C, Donzeli JL, Pallone Filho WJ, Nassif DSP (2011) Parameterization and evaluation of predictions of DSSAT/CANEGRO for sugarcane Brazilian production systems. Agronomy Journal 103, 304–315.
| Parameterization and evaluation of predictions of DSSAT/CANEGRO for sugarcane Brazilian production systems.Crossref | GoogleScholarGoogle Scholar |
Rahman MM, Hussain A, Syed MA, Ansari A, Mahmud MAA (2011) Comparison among clustering in multivariate analysis of rice using morphological traits, physiological traits and simple sequence repeat markers. American-Eurasian Journal of Agriculture and Environmental Science 1, 876–882.
Rao CP, Rahman MA, Rao PN, Reddy JR (1985) Genetic divergence analysis in sugarcane. Genetica Agraria. 39, 237–247.
Robertson MJ, Donaldson RA (1998) Changes in the components of cane and sucrose yield in response to drying-off of sugarcane before harvest. Field Crops Research 55, 201–208.
| Changes in the components of cane and sucrose yield in response to drying-off of sugarcane before harvest.Crossref | GoogleScholarGoogle Scholar |
Rudorff BFT, Aguiar DA, Silva WF, Sugawara LM, Adami M, Moreira MA (2010) Studies on the rapid expansion of sugarcane for ethanol production in São Paulo State (Brazil) using landsat data. Remote Sensing 2, 1057–1076.
| Studies on the rapid expansion of sugarcane for ethanol production in São Paulo State (Brazil) using landsat data.Crossref | GoogleScholarGoogle Scholar |
Scarpari MS, Beauclair EGF (2004) Sugarcane maturity estimation through edaphic-climatic parameters. Scientia Agricola 61, 486–491.
| Sugarcane maturity estimation through edaphic-climatic parameters.Crossref | GoogleScholarGoogle Scholar |
Scarpari MS, Beauclair EGF (2009) Physiological model to estimate the maturity of sugarcane. Scientia Agricola 66, 622–628.
| Physiological model to estimate the maturity of sugarcane.Crossref | GoogleScholarGoogle Scholar |
Sneath PH, Sokal RR (1973) ‘Numerical taxonomy: The principles and practice of numerical classification.’ (W.H. Freeman: San Francisco, CA)
Stuppiello J (1987) Cana-de-açúcar como matéria-prima. In ‘Cana-de-açúcar cultivo e utilização’. pp. 761–804. (Fundação Cargill: Campinas, SP, Brazil)
Thornthwaite CW, Mather JR (1955) The water balance. In ‘Publications in climatology VIII’. pp. 1–104. (Drexel Institute of Climatology: Centerton, NJ)
UNICA (2012) Dados e cotações – estatísticas, Produção Brasil. UNICA (Sugarcane Industry Association Brazil). Available at: www.unica.com.br/dadosCotacao/estatistica
Venkataramana S, Mohan Naidu K, Singh S (1991) Invertases and growth factors dependent sucrose accumulation in sugarcane. Plant Science 74, 65–72.
| Invertases and growth factors dependent sucrose accumulation in sugarcane.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK3MXit1Gkt74%3D&md5=66937f5eaa551f26bd6f62bcd7f97bbeCAS |
Viana JMS, Cardoso AA, Cruz CDR, Regazzi AJ, Del Giudice RM (1991) Genetic divergence in sugarcane (Saccharum spp.) clones. Brazilian Journal of Genetics. 14, 753–763.
Wagih ME, Ala A, Musa Y (2004) Evaluation of sugarcane varieties for maturity earliness and selection for efficient sugar accumulation. Sugar Technology 6, 297–304.
| Evaluation of sugarcane varieties for maturity earliness and selection for efficient sugar accumulation.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2MXhtFSktbbP&md5=85c55cee216eec4ac24cd5e3f4041ba2CAS |
