Prediction of Sugarcane Yield Based on NDVI and Concentration of Leaf-Tissue Nutrients in Fields Managed with Straw Removal

Abstract

The total or partial removal of sugarcane (Saccharum spp. L.) straw for bioenergy production may deplete soil quality and consequently affect negatively crop yield. Plants with lower yield potential may present lower concentration of leaf-tissue nutrients, which in turn changes light reflectance of canopy in different wavelengths. Therefore, vegetation indexes, such as the normalized difference vegetation index (NDVI) associated with concentration of leaf-tissue nutrients could be a useful tool for monitoring potential sugarcane yield changes under straw management. Two sites in São Paulo state, Brazil were utilized to evaluate the potential of NDVI for monitoring sugarcane yield changes imposed by different straw removal rates. The treatments were established with 0%, 25%, 50%, and 100% straw removal. The data used for the NDVI calculation was obtained using satellite images (CBERS-4) and hyperspectral sensor (FieldSpec Spectroradiometer, Malvern Panalytical, Almelo, Netherlands). Besides sugarcane yield, the concentration of the leaf-tissue nutrients (N, P, K, Ca, and S) were also determined. The NDVI efficiently predicted sugarcane yield under different rates of straw removal, with the highest performance achieved with NDVI derived from satellite images than hyperspectral sensor. In addition, leaf-tissue N and P concentrations were also important parameters to compose the prediction models of sugarcane yield. A prediction model approach based on data of NDVI and leaf-tissue nutrient concentrations may help the Brazilian sugarcane sector to monitor crop yield changes in areas intensively managed for bioenergy production.

Assessing Multiple Years’ Spatial Variability of Crop Yields Using Satellite Vegetation Indices

“…Plant et al [40] explained that NDVI is sensitive to canopy reflectance decreasing its correlation with cotton yield over a small field area. Yields of wheat, maize, rice, sugarcane, and soybean were also successfully estimated by means of NDVI [67,78,79]. After positive achievements with NDVI, other VIs proved also effective in the estimation of different crop yields [15,24,41,42,51,80,81].…”

Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique

“…In addition, the type of input predictors for both models were evaluated to fit the predictive models using high-resolution data. The study demonstrates the potential of using spectral bands as an alternative approach to the common crop yield forecast based on VIs [23,[54][55][56]. The spatial variability of the fields was mapped for the range of yield data values across all fields over the sugarcane growing seasons.…”

“…Abdel-Rahman et al [22] listed different applications of RS techniques for sugarcane, such as disease detection, crop health status, and nutrition scouting by identifying patterns in spectral data throughout its phenological stages and orbital images. Lisboa et al [23] found a relationship between the (Normalized Difference Vegetation Index) NDVI and the concentration of leaf-tissue nutrients when monitoring sugarcane yield, and they observed changes in light canopy reflectance according to the concentration of leaf-tissue nutrients. Rahman and Robson [24] developed a time-series approach through orbital images from the Sentinel-2 satellite to estimate sugarcane yield at the individual block level.…”

Prediction of Ratoon Sugarcane Family Yield and Selection Using Remote Imagery

“…Spectral imaging has been used in several crop species, including sugarcane, to evaluate yield, nutritional status, and crop health [12][13][14][15][16], and can be used to identify spectral traits linked to yield for selection [17]. If the image is acquired aerially, this method also has the advantage of covering large areas easily.…”