Spectral variables, growth analysis and yield of sugarcane

Abstract

Spectral information is well related with agronomic variables and can be used in crop monitoring and yield forecasting. This paper describes a multitemporal research with the sugarcane variety SP80-1842, studying its spectral behavior using field spectroscopy and its relationship with agronomic parameters such as leaf area index (LAI), number of stalks per meter (NPM), yield (TSS) and total biomass (BMT). A commercial sugarcane field in Araras/SP/Brazil was monitored for two seasons. Radiometric data and agronomic characterization were gathered in 9 field campaigns. Spectral vegetation indices had similar patterns in both seasons and adjusted to agronomic parameters. Band 4 (B4), Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), and Soil Adjusted Vegetation Index (SAVI) increased their values until the end of the vegetative stage, around 240 days after harvest (DAC). After that stage, B4 reflectance and NDVI values began to stabilize and decrease because the crop reached ripening and senescence stages. Band 3 (B3) and RVI presented decreased values since the beginning of the cycle, followed by a stabilization stage. Later these values had a slight increase caused by the lower amount of green vegetation. Spectral variables B3, RVI, NDVI, and SAVI were highly correlated (above 0.79) with LAI, TSS, and BMT, and about 0.50 with NPM. The best regression models were verified for RVI, LAI, and NPM, which explained 0.97 of TSS variation and 0.99 of BMT variation. Key words: NDVI, vegetation index, biomass, field spectroscopy, temporal evolution VARIÁVEIS ESPECTRAIS E INDICADORES DE DESENVOLVIMENTO E PRODUTIVIDADE DA CANA-DE-AÇÚCARRESUMO: A informação espectral tem boa relação com variáveis agronômicas e pode contribuir com informações para o monitoramento, acompanhamento e previsão de safras. O presente trabalho descreve a análise multitemporal do comportamento espectral da variedade de cana-de-açúcar SP80-1842 e a relação com variáveis agronômicas como índice de área foliar (IAF), número de perfilhos por metro (NPM), produtividade (TCH) e biomassa total (BMT). Nas safras 2000/2001 e 2001/2002, um talhão comercial, localizada no município de Araras/SP foi monitorado em nove campanhas de coleta de dados radiométricos e agronômicos. O comportamento temporal das variáveis espectrais acompanhou o comportamento das variáveis agronômicas. A banda 4 (B4), o índice de vegetação da razão simples (SR), o índice de vegetação por diferença normalizada (NDVI) e o índice de vegetação ajustado ao solo (SAVI) aumentaram seus valores até o fim da fase de crescimento vegetativo, aproximadamente até os 240 dias após o corte, a partir do qual os valores se estabilizaram e diminuíram em função da entrada da cultura na fase de maturação. A banda 3 (B3) e o índice de vegetação da razão (RVI) tiveram queda em seus valores desde o início do ciclo, com posterior estabilização e aumento em seus valores devido ao aumento da quantidade de palha e da queda da biomassa foliar. As variáveis espectrais B3, RVI, NDVI e SAVI tiv...

Tropical agriculture and global warming: impacts and mitigation options

“…There is generally only a short fallow between ploughing out the old cane and re-planting. On the majority of farms in Brazil sugar cane is grown as a monoculture (Macedo, 1997;Simões et al, 2005). It is a highly flexible resource, offering alternatives for production of food, feed, fibre and energy.…”

“…it seems likely that when the burning ban is fully implemented steeply sloping land will go out of sugar cane production unless new harvesting methods are developed (Simões et al, 2005). By the return of crop residues to the soil surface the mechanical approach has indirectly favoured soil organic matter accumulation (Thorburn et al, 2001;Luca, 2002) and gas emission reduction when compared to the burning system (Andreae & Merlet, 2001).…”

Spatio-temporal variability of sugarcane fields and recommendations for yield forecast using NDVI

“…The few studies that have dealt with sugarcane were largely inspired by what was being done for cereal crops. Direct approaches that link crop biomass to multispectral measurements or vegetation indices were developed from field radiometry (Simoes et al 2005a), aircraft measurements (Schmidt et al 2000), high resolution satellite images (Almeida et al 2006, Ueno et al 2005, Krishna Rao et al 2002, and low resolution satellite images (Bastidas-Obando et al 2007, Schmidt et al 2000. Other indirect approaches that combine multispectral data with agrometerological, production or ecophysiological crop growth models (Rudorff et al 1990, Bastiaanssen et al 2003, Bappel et al 2005 have also been explored.…”

“…Although remote sensing is potentially very useful for sugarcane (see review by Abdel-Rahman et al 2008), Simoes et al (2005a) linked the paucity of publications of experimental results to the difficulty in collecting data for a large crop and to the length of the growing period. Almeida et al (2006) reported that predictions of sugarcane yield using remote sensing are rarely discussed in the specialized literature owing to the proprietary nature of past work, which has mainly been conducted by sugarcane industry research institutes.…”

Mapping Crop Planting Quality in Sugarcane from UAV Imagery: A Pilot Study in Nicaragua

“…The authors reported many successful studies, although this was not always the case, in particular when many confounding factors were present. It is worthwhile to note that studies conducted with hand-held spectrometers (i.e., [26,27]) achieved good results. Research carried out with spectrometers indicates that it is possible to retrieve nitrogen content [28] and the nitrogen/silicon ratio [23], which are critical data for fertilizing plans and yield forecasting.…”