Spatial Variability Mapping of Sugarcane Qualitative Attributes

Abstract

Spatial variability evaluation of qualitative attributes can be used as an excellent strategy to design forms of intervention that result in better crop profitability for some agricultural crops, for example, sugarcane. Based on the assumption that qualitative attributes of sugarcane present spatial variability and their distributions along the stems are uniform in different varieties, this study aimed to evaluate the distributions of the qualitative parameters of different sugarcane varieties and the spatial variability of these attributes in a commercial field. Samples of nine varieties were collected for laboratory quality analysis, and the Brix parameter was analyzed by a digital refractometer. The analysis of variance, the Tukey test, and geoestatistcs were the statistical analyses applied to the dataset. The maps were generated using 91 sample results from the laboratory analysis of the 16.6 ha field. It was found that, in the harvest period, there was no significant difference in Brix content along the sugarcane stems. Therefore, we can conclude that the Brix content along the sugarcane stems does not change in the harvesting period, and the ideal sampling size to better represent the factors affecting sugarcane qualitative attributes is six points per hectare.

Spatial Analysis of Soil Available Potassium and Plant Brix Content for Site Specific Nutrient Management in Sugarcane

“…Although this study was conducted on the scale of plots, this pattern may also occur on the scales of fields. K in sugarcane soils and Brix contents varies spatially (Dongare, Reddy, Kharche, & Ramteke, 2022;Ferraz, de P. Corrêdo, Wei, & Molin, 2019). The quantification of spatial variation is, therefore, necessary, and techniques have been available, which is often called spatial interpolation.…”

Proximal spectroscopy sensing for sugarcane quality prediction and spatial variability mapping

“…On a first view, there is a visual spatial similarity between TRS (Figure 7) and measured Pol maps (Figure 5c). This was expected due to the presence of a high correlation between these attributes (FERRAZ et al, 2019). The Both attributes are obtained as a function of the pol of juice and reducing sugars of juice, respectively, and as a function inversely proportional to the Fibre content.…”

Quantification and classification of coffee fruits with computer vision

“…That is, it is possible to conduct guided sampling to explore the possible factors that may have led to these results. This information could, consequently, support decision-making related to agricultural management practices (FERRAZ et al, 2019), especially in the scope of delimiting management zones with specificities of each location. Once the management zones are defined, new actions can be taken.…”