Ground reference data for sugarcane biomass estimation in São Paulo state, Brazil

In order to make effective decisions on sustainable development, it is essential for sugarcane-producing countries to take into account sugarcane acreage and sugarcane production dynamics. The availability of sugarcane biophysical data along the growth season is key to an effective mapping of such dynamics, especially to tune agronomic models and to cross-validate indirect satellite measurements. Here, we introduce a dataset comprising 3,500 sugarcane observations collected from October 2014 until October 2015 at four fields in the São Paulo state (Brazil). The campaign included both non-destructive measurements of plant biometrics and destructive biomass weighing procedures. The acquisition plan was designed to maximize cost-effectiveness and minimize field-invasiveness, hence the non-destructive measurements outnumber the destructive ones. To compensate for such imbalance, a method to convert the measured biometrics into biomass estimates, based on the empirical adjustment of allometric models, is proposed. In addition, the paper addresses the precisions associated to the ground measurements and derived metrics. The presented growth dynamics and associated precisions can be adopted when designing new sugarcane measurement campaigns. Design Type(s) observation design • time series design Measurement Type(s) plant structure • leaf area index • plant matter • water-based rainfall Technology Type(s) data collection method Factor Type(s) Sample Characteristic(s) Saccharum hybrid cultivar RB867515 • Saccharum hybrid cultivar SP80-3280 • Piracicaba Mesoregion • cropland biome Design Type(s) observation design • time series design Measurement Type(s) plant structure • leaf area index • plant matter • water-based rainfall Technology Type(s) data collection method Factor Type(s) Sample Characteristic(s) Saccharum hybrid cultivar RB867515 • Saccharum hybrid cultivar SP80-3280 • Piracicaba Mesoregion • cropland biome Machine-accessible metadata file describing the reported data (ISA-Tab format)

[1]  Bernardo Friedrich Theodor Rudorff,et al.  Yield estimation of sugarcane based on agrometeorological-spectral models , 1990 .

[2]  Clement Atzberger,et al.  Object Based Image Analysis and Data Mining applied to a remotely sensed Landsat time-series to map sugarcane over large areas , 2012 .

[3]  R. Gomathi,et al.  Physiological Studies on Ratoonability of Sugarcane Varieties under Tropical Indian Condition , 2013 .

[4]  H. Franco,et al.  Fertigated Sugarcane Yield and Carbon Isotope Discrimination (Δ13C) Related to Nitrogen Nutrition , 2016, Sugar Tech.

[5]  A. R.,et al.  Review of literature , 1969, American Potato Journal.

[6]  J. Privette,et al.  VALERI: a network of sites and a methodology for the validation of medium spatial resolution land satellite products , 2003 .

[7]  J. Allison,et al.  Effects of treatments potentially influencing the supply of assimilate on its partitioning in sugarcane. , 2002, Journal of experimental botany.

[8]  Guangnan Chen,et al.  In-field measurement and sampling technologies for monitoring quality in the sugarcane industry: a review , 2014, Precision Agriculture.

[9]  Nicolas Baghdadi,et al.  Multitemporal Observations of Sugarcane by TerraSAR-X Images , 2010, Sensors.

[10]  H. Franco,et al.  Biomass and Nutrient Content by Sugarcane as Affected by Fertilizer Nitrogen Sources , 2016 .

[11]  Rubens Augusto Camargo Lamparelli,et al.  GROWTH INDICES AND PRODUCTIVITY IN SUGARCANE , 2005 .

[12]  Simoes,et al.  Orbital Spectral Variables, Growth Analysis And Sugarcane Yield [variáveis Espectrais Orbitais, Indicadoras De Desenvolvimento E Produtividade Da Cana-de-açúcar] , 2009 .

[13]  P. Zimba,et al.  DISCRIMINATION OF SUGARCANE VARIETIES WITH PIGMENT PROFILES AND HIGH RESOLUTION , HYPERSPECTRAL LEAF REFLECTANCE DATA , 2009 .

[14]  R. Wiedenfeld.  Water stress during different sugarcane growth periods on yield and response to N fertilization. , 2000 .

[15]  L. Dendooven,et al.  Effects of Partial Defoliation on Sucrose Accumulation, Enzyme Activity and Agronomic Parameters in Sugar cane (Saccharum spp.) , 2004 .

[16]  Robert A. Gilbert,et al.  Sugarcane Response to Mill Mud, Fertilizer, and Soybean Nutrient Sources on a Sandy Soil , 2008 .

[17]  Rubens Augusto Camargo Lamparelli,et al.  Growth indices ans productivity in sugarcane , 2005 .

[18]  Ricardo Augusto de Oliveira,et al.  ÁREA FOLIAR EM TRÊS CULTIVARES DE CANA-DE-AÇÚCAR E SUA CORRELAÇÃO COM A PRODUÇÃO DE BIOMASSA , 2007 .

[19]  S. Poni,et al.  Transcriptional Responses to Pre-flowering Leaf Defoliation in Grapevine Berry from Different Growing Sites, Years, and Genotypes , 2017, Front. Plant Sci..

[20]  Nicolas Baghdadi,et al.  Potential of SAR sensors TerraSAR-X, ASAR/ENVISAT and PALSAR/ALOS for monitoring sugarcane crops on Reunion Island , 2009 .

[21]  E. Yuliwati,et al.  A Review , 2019, Current Trends and Future Developments on (Bio-) Membranes.

[22]  Pecan.  Sugar Cane , 1898, The American Naturalist.

[23]  Elfatih M. Abdel-Rahman,et al.  The application of remote sensing techniques to sugarcane (Saccharum spp. hybrid) production: a review of the literature , 2008 .

[24]  Hui Lin,et al.  Monitoring Sugarcane Growth Using ENVISAT ASAR Data , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[25]  H. Sandhu,et al.  Relationships among Leaf Area Index, Visual Growth Rating, and Sugarcane Yield , 2012 .

[26]  H. Franco,et al.  Nitrogen in sugarcane derived from fertilizer under Brazilian field conditions , 2011 .

[27]  E. E. Sano,et al.  Imagens multipolarizadas do sensor Palsar/Alos na discriminação das fases fenológicas da cana‑de‑açúcar , 2012 .

[28]  Rubens Augusto Camargo Lamparelli,et al.  Spectral variables, growth analysis and yield of sugarcane , 2005 .

[29]  P. Thorburn,et al.  Prioritizing Crop Management to Increase Nitrogen Use Efficiency in Australian Sugarcane Crops , 2017, Front. Plant Sci..