A Multivariate Approach to Determine the Economic Profitability of Sugarcane Production Under Diverse Climatic Conditions in Brazil

Abstract The prominent position of sugarcane as a source of renewable and sustainable energy resulted in the expansion of its production into regions under limiting climatic conditions, thus affecting patterns related to growth, ripening, and profitability. This study provides an assessment of the f...
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Autor*in:	Cardozo, Nilceu Piffer [verfasserIn] Bordonal, Ricardo de Oliveira [verfasserIn] Panosso, Alan Rodrigo [verfasserIn] Crusciol, Carlos Alexandre Costa [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	spp. Biomass production Industrial quality Cluster analysis Strategic planning

Übergeordnetes Werk:	Enthalten in: Sugar tech - Neu Delhi : Springer India, 1999, 22(2020), 6 vom: 02. Juli, Seite 954-966
Übergeordnetes Werk:	volume:22 ; year:2020 ; number:6 ; day:02 ; month:07 ; pages:954-966

Links:	Volltext

DOI / URN:	10.1007/s12355-020-00854-7

Katalog-ID:	SPR041681231

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520			\|a Abstract The prominent position of sugarcane as a source of renewable and sustainable energy resulted in the expansion of its production into regions under limiting climatic conditions, thus affecting patterns related to growth, ripening, and profitability. This study provides an assessment of the factors that compose the economic return of sugarcane production using a multivariate approach. Monthly data, including total recoverable sugars, price, productivity (for an average of five mechanized harvesting), and rainfall during seven harvest seasons (2011/2012–2017/2018), were used to perform the multivariate statistical analyses considering the climatic conditions of four regions in the state of São Paulo, southeastern Brazil (Araçatuba, Assis, Ribeirão Preto, and Piracicaba). The chosen techniques were hierarchical and non-hierarchical (k-means) cluster analysis and principal component analysis. The data indicated the existence of three groups of months that exhibited different performances: Groups I, II, and III with intermediate, high, and low gross economic returns, respectively. Although group organization presented regional variations, July, August, September, and eventually October (Group II) generally exhibited the best gross economic returns (R$5065.1 $ ha^{−1} $). December and November (Group III) exhibited the lowest economic returns (R$4731.5 $ ha^{−1} $), and April, May, and June (Group I) exhibited intermediate returns that were close to the annual average (R$4829.6 $ ha^{−1} $). Given the territorial extent of Brazil and the significant variations in environmental conditions, the adaptation of sugarcane cultivation and harvesting strategies to the characteristics of each producing region is fundamental for the rational and sustainable exploitation of the crop in the country.
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700	1		\|a Crusciol, Carlos Alexandre Costa \|e verfasserin \|4 aut
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912			\|a GBV_ILN_2088
912			\|a GBV_ILN_2093
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912			\|a GBV_ILN_2107
912			\|a GBV_ILN_2108
912			\|a GBV_ILN_2110
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allfields	10.1007/s12355-020-00854-7 doi (DE-627)SPR041681231 (SPR)s12355-020-00854-7-e DE-627 ger DE-627 rakwb eng 630 640 ASE Cardozo, Nilceu Piffer verfasserin aut A Multivariate Approach to Determine the Economic Profitability of Sugarcane Production Under Diverse Climatic Conditions in Brazil 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The prominent position of sugarcane as a source of renewable and sustainable energy resulted in the expansion of its production into regions under limiting climatic conditions, thus affecting patterns related to growth, ripening, and profitability. This study provides an assessment of the factors that compose the economic return of sugarcane production using a multivariate approach. Monthly data, including total recoverable sugars, price, productivity (for an average of five mechanized harvesting), and rainfall during seven harvest seasons (2011/2012–2017/2018), were used to perform the multivariate statistical analyses considering the climatic conditions of four regions in the state of São Paulo, southeastern Brazil (Araçatuba, Assis, Ribeirão Preto, and Piracicaba). The chosen techniques were hierarchical and non-hierarchical (k-means) cluster analysis and principal component analysis. The data indicated the existence of three groups of months that exhibited different performances: Groups I, II, and III with intermediate, high, and low gross economic returns, respectively. Although group organization presented regional variations, July, August, September, and eventually October (Group II) generally exhibited the best gross economic returns (R$5065.1 $ ha^{−1} $). December and November (Group III) exhibited the lowest economic returns (R$4731.5 $ ha^{−1} $), and April, May, and June (Group I) exhibited intermediate returns that were close to the annual average (R$4829.6 $ ha^{−1} $). Given the territorial extent of Brazil and the significant variations in environmental conditions, the adaptation of sugarcane cultivation and harvesting strategies to the characteristics of each producing region is fundamental for the rational and sustainable exploitation of the crop in the country. spp. (dpeaa)DE-He213 Biomass production (dpeaa)DE-He213 Industrial quality (dpeaa)DE-He213 Cluster analysis (dpeaa)DE-He213 Strategic planning (dpeaa)DE-He213 Bordonal, Ricardo de Oliveira verfasserin aut Panosso, Alan Rodrigo verfasserin aut Crusciol, Carlos Alexandre Costa verfasserin aut Enthalten in Sugar tech Neu Delhi : Springer India, 1999 22(2020), 6 vom: 02. Juli, Seite 954-966 (DE-627)570507685 (DE-600)2433394-3 0974-0740 nnns volume:22 year:2020 number:6 day:02 month:07 pages:954-966 https://dx.doi.org/10.1007/s12355-020-00854-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 22 2020 6 02 07 954-966
spelling	10.1007/s12355-020-00854-7 doi (DE-627)SPR041681231 (SPR)s12355-020-00854-7-e DE-627 ger DE-627 rakwb eng 630 640 ASE Cardozo, Nilceu Piffer verfasserin aut A Multivariate Approach to Determine the Economic Profitability of Sugarcane Production Under Diverse Climatic Conditions in Brazil 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The prominent position of sugarcane as a source of renewable and sustainable energy resulted in the expansion of its production into regions under limiting climatic conditions, thus affecting patterns related to growth, ripening, and profitability. This study provides an assessment of the factors that compose the economic return of sugarcane production using a multivariate approach. Monthly data, including total recoverable sugars, price, productivity (for an average of five mechanized harvesting), and rainfall during seven harvest seasons (2011/2012–2017/2018), were used to perform the multivariate statistical analyses considering the climatic conditions of four regions in the state of São Paulo, southeastern Brazil (Araçatuba, Assis, Ribeirão Preto, and Piracicaba). The chosen techniques were hierarchical and non-hierarchical (k-means) cluster analysis and principal component analysis. The data indicated the existence of three groups of months that exhibited different performances: Groups I, II, and III with intermediate, high, and low gross economic returns, respectively. Although group organization presented regional variations, July, August, September, and eventually October (Group II) generally exhibited the best gross economic returns (R$5065.1 $ ha^{−1} $). December and November (Group III) exhibited the lowest economic returns (R$4731.5 $ ha^{−1} $), and April, May, and June (Group I) exhibited intermediate returns that were close to the annual average (R$4829.6 $ ha^{−1} $). Given the territorial extent of Brazil and the significant variations in environmental conditions, the adaptation of sugarcane cultivation and harvesting strategies to the characteristics of each producing region is fundamental for the rational and sustainable exploitation of the crop in the country. spp. (dpeaa)DE-He213 Biomass production (dpeaa)DE-He213 Industrial quality (dpeaa)DE-He213 Cluster analysis (dpeaa)DE-He213 Strategic planning (dpeaa)DE-He213 Bordonal, Ricardo de Oliveira verfasserin aut Panosso, Alan Rodrigo verfasserin aut Crusciol, Carlos Alexandre Costa verfasserin aut Enthalten in Sugar tech Neu Delhi : Springer India, 1999 22(2020), 6 vom: 02. Juli, Seite 954-966 (DE-627)570507685 (DE-600)2433394-3 0974-0740 nnns volume:22 year:2020 number:6 day:02 month:07 pages:954-966 https://dx.doi.org/10.1007/s12355-020-00854-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 22 2020 6 02 07 954-966
allfields_unstemmed	10.1007/s12355-020-00854-7 doi (DE-627)SPR041681231 (SPR)s12355-020-00854-7-e DE-627 ger DE-627 rakwb eng 630 640 ASE Cardozo, Nilceu Piffer verfasserin aut A Multivariate Approach to Determine the Economic Profitability of Sugarcane Production Under Diverse Climatic Conditions in Brazil 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The prominent position of sugarcane as a source of renewable and sustainable energy resulted in the expansion of its production into regions under limiting climatic conditions, thus affecting patterns related to growth, ripening, and profitability. This study provides an assessment of the factors that compose the economic return of sugarcane production using a multivariate approach. Monthly data, including total recoverable sugars, price, productivity (for an average of five mechanized harvesting), and rainfall during seven harvest seasons (2011/2012–2017/2018), were used to perform the multivariate statistical analyses considering the climatic conditions of four regions in the state of São Paulo, southeastern Brazil (Araçatuba, Assis, Ribeirão Preto, and Piracicaba). The chosen techniques were hierarchical and non-hierarchical (k-means) cluster analysis and principal component analysis. The data indicated the existence of three groups of months that exhibited different performances: Groups I, II, and III with intermediate, high, and low gross economic returns, respectively. Although group organization presented regional variations, July, August, September, and eventually October (Group II) generally exhibited the best gross economic returns (R$5065.1 $ ha^{−1} $). December and November (Group III) exhibited the lowest economic returns (R$4731.5 $ ha^{−1} $), and April, May, and June (Group I) exhibited intermediate returns that were close to the annual average (R$4829.6 $ ha^{−1} $). Given the territorial extent of Brazil and the significant variations in environmental conditions, the adaptation of sugarcane cultivation and harvesting strategies to the characteristics of each producing region is fundamental for the rational and sustainable exploitation of the crop in the country. spp. (dpeaa)DE-He213 Biomass production (dpeaa)DE-He213 Industrial quality (dpeaa)DE-He213 Cluster analysis (dpeaa)DE-He213 Strategic planning (dpeaa)DE-He213 Bordonal, Ricardo de Oliveira verfasserin aut Panosso, Alan Rodrigo verfasserin aut Crusciol, Carlos Alexandre Costa verfasserin aut Enthalten in Sugar tech Neu Delhi : Springer India, 1999 22(2020), 6 vom: 02. Juli, Seite 954-966 (DE-627)570507685 (DE-600)2433394-3 0974-0740 nnns volume:22 year:2020 number:6 day:02 month:07 pages:954-966 https://dx.doi.org/10.1007/s12355-020-00854-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 22 2020 6 02 07 954-966
allfieldsGer	10.1007/s12355-020-00854-7 doi (DE-627)SPR041681231 (SPR)s12355-020-00854-7-e DE-627 ger DE-627 rakwb eng 630 640 ASE Cardozo, Nilceu Piffer verfasserin aut A Multivariate Approach to Determine the Economic Profitability of Sugarcane Production Under Diverse Climatic Conditions in Brazil 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The prominent position of sugarcane as a source of renewable and sustainable energy resulted in the expansion of its production into regions under limiting climatic conditions, thus affecting patterns related to growth, ripening, and profitability. This study provides an assessment of the factors that compose the economic return of sugarcane production using a multivariate approach. Monthly data, including total recoverable sugars, price, productivity (for an average of five mechanized harvesting), and rainfall during seven harvest seasons (2011/2012–2017/2018), were used to perform the multivariate statistical analyses considering the climatic conditions of four regions in the state of São Paulo, southeastern Brazil (Araçatuba, Assis, Ribeirão Preto, and Piracicaba). The chosen techniques were hierarchical and non-hierarchical (k-means) cluster analysis and principal component analysis. The data indicated the existence of three groups of months that exhibited different performances: Groups I, II, and III with intermediate, high, and low gross economic returns, respectively. Although group organization presented regional variations, July, August, September, and eventually October (Group II) generally exhibited the best gross economic returns (R$5065.1 $ ha^{−1} $). December and November (Group III) exhibited the lowest economic returns (R$4731.5 $ ha^{−1} $), and April, May, and June (Group I) exhibited intermediate returns that were close to the annual average (R$4829.6 $ ha^{−1} $). Given the territorial extent of Brazil and the significant variations in environmental conditions, the adaptation of sugarcane cultivation and harvesting strategies to the characteristics of each producing region is fundamental for the rational and sustainable exploitation of the crop in the country. spp. (dpeaa)DE-He213 Biomass production (dpeaa)DE-He213 Industrial quality (dpeaa)DE-He213 Cluster analysis (dpeaa)DE-He213 Strategic planning (dpeaa)DE-He213 Bordonal, Ricardo de Oliveira verfasserin aut Panosso, Alan Rodrigo verfasserin aut Crusciol, Carlos Alexandre Costa verfasserin aut Enthalten in Sugar tech Neu Delhi : Springer India, 1999 22(2020), 6 vom: 02. Juli, Seite 954-966 (DE-627)570507685 (DE-600)2433394-3 0974-0740 nnns volume:22 year:2020 number:6 day:02 month:07 pages:954-966 https://dx.doi.org/10.1007/s12355-020-00854-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 22 2020 6 02 07 954-966
allfieldsSound	10.1007/s12355-020-00854-7 doi (DE-627)SPR041681231 (SPR)s12355-020-00854-7-e DE-627 ger DE-627 rakwb eng 630 640 ASE Cardozo, Nilceu Piffer verfasserin aut A Multivariate Approach to Determine the Economic Profitability of Sugarcane Production Under Diverse Climatic Conditions in Brazil 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The prominent position of sugarcane as a source of renewable and sustainable energy resulted in the expansion of its production into regions under limiting climatic conditions, thus affecting patterns related to growth, ripening, and profitability. This study provides an assessment of the factors that compose the economic return of sugarcane production using a multivariate approach. Monthly data, including total recoverable sugars, price, productivity (for an average of five mechanized harvesting), and rainfall during seven harvest seasons (2011/2012–2017/2018), were used to perform the multivariate statistical analyses considering the climatic conditions of four regions in the state of São Paulo, southeastern Brazil (Araçatuba, Assis, Ribeirão Preto, and Piracicaba). The chosen techniques were hierarchical and non-hierarchical (k-means) cluster analysis and principal component analysis. The data indicated the existence of three groups of months that exhibited different performances: Groups I, II, and III with intermediate, high, and low gross economic returns, respectively. Although group organization presented regional variations, July, August, September, and eventually October (Group II) generally exhibited the best gross economic returns (R$5065.1 $ ha^{−1} $). December and November (Group III) exhibited the lowest economic returns (R$4731.5 $ ha^{−1} $), and April, May, and June (Group I) exhibited intermediate returns that were close to the annual average (R$4829.6 $ ha^{−1} $). Given the territorial extent of Brazil and the significant variations in environmental conditions, the adaptation of sugarcane cultivation and harvesting strategies to the characteristics of each producing region is fundamental for the rational and sustainable exploitation of the crop in the country. spp. (dpeaa)DE-He213 Biomass production (dpeaa)DE-He213 Industrial quality (dpeaa)DE-He213 Cluster analysis (dpeaa)DE-He213 Strategic planning (dpeaa)DE-He213 Bordonal, Ricardo de Oliveira verfasserin aut Panosso, Alan Rodrigo verfasserin aut Crusciol, Carlos Alexandre Costa verfasserin aut Enthalten in Sugar tech Neu Delhi : Springer India, 1999 22(2020), 6 vom: 02. Juli, Seite 954-966 (DE-627)570507685 (DE-600)2433394-3 0974-0740 nnns volume:22 year:2020 number:6 day:02 month:07 pages:954-966 https://dx.doi.org/10.1007/s12355-020-00854-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 22 2020 6 02 07 954-966
language	English
source	Enthalten in Sugar tech 22(2020), 6 vom: 02. Juli, Seite 954-966 volume:22 year:2020 number:6 day:02 month:07 pages:954-966
sourceStr	Enthalten in Sugar tech 22(2020), 6 vom: 02. Juli, Seite 954-966 volume:22 year:2020 number:6 day:02 month:07 pages:954-966
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	spp. Biomass production Industrial quality Cluster analysis Strategic planning
dewey-raw	630
isfreeaccess_bool	false
container_title	Sugar tech
authorswithroles_txt_mv	Cardozo, Nilceu Piffer @@aut@@ Bordonal, Ricardo de Oliveira @@aut@@ Panosso, Alan Rodrigo @@aut@@ Crusciol, Carlos Alexandre Costa @@aut@@
publishDateDaySort_date	2020-07-02T00:00:00Z
hierarchy_top_id	570507685
dewey-sort	3630
id	SPR041681231
language_de	englisch
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author	Cardozo, Nilceu Piffer
spellingShingle	Cardozo, Nilceu Piffer ddc 630 misc spp. misc Biomass production misc Industrial quality misc Cluster analysis misc Strategic planning A Multivariate Approach to Determine the Economic Profitability of Sugarcane Production Under Diverse Climatic Conditions in Brazil
authorStr	Cardozo, Nilceu Piffer
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topic_title	630 640 ASE A Multivariate Approach to Determine the Economic Profitability of Sugarcane Production Under Diverse Climatic Conditions in Brazil spp. (dpeaa)DE-He213 Biomass production (dpeaa)DE-He213 Industrial quality (dpeaa)DE-He213 Cluster analysis (dpeaa)DE-He213 Strategic planning (dpeaa)DE-He213
topic	ddc 630 misc spp. misc Biomass production misc Industrial quality misc Cluster analysis misc Strategic planning
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title	A Multivariate Approach to Determine the Economic Profitability of Sugarcane Production Under Diverse Climatic Conditions in Brazil
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title_full	A Multivariate Approach to Determine the Economic Profitability of Sugarcane Production Under Diverse Climatic Conditions in Brazil
author_sort	Cardozo, Nilceu Piffer
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author_browse	Cardozo, Nilceu Piffer Bordonal, Ricardo de Oliveira Panosso, Alan Rodrigo Crusciol, Carlos Alexandre Costa
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title_sort	multivariate approach to determine the economic profitability of sugarcane production under diverse climatic conditions in brazil
title_auth	A Multivariate Approach to Determine the Economic Profitability of Sugarcane Production Under Diverse Climatic Conditions in Brazil
abstract	Abstract The prominent position of sugarcane as a source of renewable and sustainable energy resulted in the expansion of its production into regions under limiting climatic conditions, thus affecting patterns related to growth, ripening, and profitability. This study provides an assessment of the factors that compose the economic return of sugarcane production using a multivariate approach. Monthly data, including total recoverable sugars, price, productivity (for an average of five mechanized harvesting), and rainfall during seven harvest seasons (2011/2012–2017/2018), were used to perform the multivariate statistical analyses considering the climatic conditions of four regions in the state of São Paulo, southeastern Brazil (Araçatuba, Assis, Ribeirão Preto, and Piracicaba). The chosen techniques were hierarchical and non-hierarchical (k-means) cluster analysis and principal component analysis. The data indicated the existence of three groups of months that exhibited different performances: Groups I, II, and III with intermediate, high, and low gross economic returns, respectively. Although group organization presented regional variations, July, August, September, and eventually October (Group II) generally exhibited the best gross economic returns (R$5065.1 $ ha^{−1} $). December and November (Group III) exhibited the lowest economic returns (R$4731.5 $ ha^{−1} $), and April, May, and June (Group I) exhibited intermediate returns that were close to the annual average (R$4829.6 $ ha^{−1} $). Given the territorial extent of Brazil and the significant variations in environmental conditions, the adaptation of sugarcane cultivation and harvesting strategies to the characteristics of each producing region is fundamental for the rational and sustainable exploitation of the crop in the country.
abstractGer	Abstract The prominent position of sugarcane as a source of renewable and sustainable energy resulted in the expansion of its production into regions under limiting climatic conditions, thus affecting patterns related to growth, ripening, and profitability. This study provides an assessment of the factors that compose the economic return of sugarcane production using a multivariate approach. Monthly data, including total recoverable sugars, price, productivity (for an average of five mechanized harvesting), and rainfall during seven harvest seasons (2011/2012–2017/2018), were used to perform the multivariate statistical analyses considering the climatic conditions of four regions in the state of São Paulo, southeastern Brazil (Araçatuba, Assis, Ribeirão Preto, and Piracicaba). The chosen techniques were hierarchical and non-hierarchical (k-means) cluster analysis and principal component analysis. The data indicated the existence of three groups of months that exhibited different performances: Groups I, II, and III with intermediate, high, and low gross economic returns, respectively. Although group organization presented regional variations, July, August, September, and eventually October (Group II) generally exhibited the best gross economic returns (R$5065.1 $ ha^{−1} $). December and November (Group III) exhibited the lowest economic returns (R$4731.5 $ ha^{−1} $), and April, May, and June (Group I) exhibited intermediate returns that were close to the annual average (R$4829.6 $ ha^{−1} $). Given the territorial extent of Brazil and the significant variations in environmental conditions, the adaptation of sugarcane cultivation and harvesting strategies to the characteristics of each producing region is fundamental for the rational and sustainable exploitation of the crop in the country.
abstract_unstemmed	Abstract The prominent position of sugarcane as a source of renewable and sustainable energy resulted in the expansion of its production into regions under limiting climatic conditions, thus affecting patterns related to growth, ripening, and profitability. This study provides an assessment of the factors that compose the economic return of sugarcane production using a multivariate approach. Monthly data, including total recoverable sugars, price, productivity (for an average of five mechanized harvesting), and rainfall during seven harvest seasons (2011/2012–2017/2018), were used to perform the multivariate statistical analyses considering the climatic conditions of four regions in the state of São Paulo, southeastern Brazil (Araçatuba, Assis, Ribeirão Preto, and Piracicaba). The chosen techniques were hierarchical and non-hierarchical (k-means) cluster analysis and principal component analysis. The data indicated the existence of three groups of months that exhibited different performances: Groups I, II, and III with intermediate, high, and low gross economic returns, respectively. Although group organization presented regional variations, July, August, September, and eventually October (Group II) generally exhibited the best gross economic returns (R$5065.1 $ ha^{−1} $). December and November (Group III) exhibited the lowest economic returns (R$4731.5 $ ha^{−1} $), and April, May, and June (Group I) exhibited intermediate returns that were close to the annual average (R$4829.6 $ ha^{−1} $). Given the territorial extent of Brazil and the significant variations in environmental conditions, the adaptation of sugarcane cultivation and harvesting strategies to the characteristics of each producing region is fundamental for the rational and sustainable exploitation of the crop in the country.
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title_short	A Multivariate Approach to Determine the Economic Profitability of Sugarcane Production Under Diverse Climatic Conditions in Brazil
url	https://dx.doi.org/10.1007/s12355-020-00854-7
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author2	Bordonal, Ricardo de Oliveira Panosso, Alan Rodrigo Crusciol, Carlos Alexandre Costa
author2Str	Bordonal, Ricardo de Oliveira Panosso, Alan Rodrigo Crusciol, Carlos Alexandre Costa
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doi_str	10.1007/s12355-020-00854-7
up_date	2024-07-03T23:10:52.072Z
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This study provides an assessment of the factors that compose the economic return of sugarcane production using a multivariate approach. Monthly data, including total recoverable sugars, price, productivity (for an average of five mechanized harvesting), and rainfall during seven harvest seasons (2011/2012–2017/2018), were used to perform the multivariate statistical analyses considering the climatic conditions of four regions in the state of São Paulo, southeastern Brazil (Araçatuba, Assis, Ribeirão Preto, and Piracicaba). The chosen techniques were hierarchical and non-hierarchical (k-means) cluster analysis and principal component analysis. The data indicated the existence of three groups of months that exhibited different performances: Groups I, II, and III with intermediate, high, and low gross economic returns, respectively. Although group organization presented regional variations, July, August, September, and eventually October (Group II) generally exhibited the best gross economic returns (R$5065.1 $ ha^{−1} $). December and November (Group III) exhibited the lowest economic returns (R$4731.5 $ ha^{−1} $), and April, May, and June (Group I) exhibited intermediate returns that were close to the annual average (R$4829.6 $ ha^{−1} $). 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A Multivariate Approach to Determine the Economic Profitability of Sugarcane Production Under Diverse Climatic Conditions in Brazil

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