Spatial variability of the nutritional status and the leaf chlorophyll index of from rubber tree

Abstract

The development of crops is related to their nutritional status and the leaf chlorophyll apparent index. The objective of this study was to use fuzzy classification to determine the degree of membership (fuzzy index -FI) of macronutrientes and leaf micronutrients classified as low, adequate and high, quantify the chlorophyll index and to determine the spatial variability of these attributes for the rubber tree (Hevea brasiliensis) (Fx 3864) at the initial stage of development, aiming at the definition of management zones. Sampling grid regulates at 6x7 m spacing was built to determine the attributes: chlorophyll a and b, macro and leaf micronutrients, totaling 100 sample points. The fuzzy classification and geostatistical was used to map the nutritional status of the rubber tree. In the fuzzy analysis the macronutrient map shows the degree of membership 0.50<FI<0.75, in more than 75% of the area, indicating high nutritional status.The limiting factor was the concentration of P in the lowest percentage of points (58%) in the appropriate class. Micronutrients have 0.25<FI<0.50, for more than 75% of the area, therefore, average nutritional status. Leaf Cu presented 48% of the data in low sufficiency. Therefore, it is recommended to analyze macronutrients and leaf micronutrients separately to define management zones for foliar fertilization. Chlorophyll a, b and a/b leaf index do not correlate with macronutrients and fuzzyficated micronutrients. The leaf indices of chlorophyll a and b have a very high correlation (r = 0.89

Identification of Male and Female Parents for Hybrid Rice Seed Production Using UAV-Based Multispectral Imagery

“…In the field of remote sensing, there are hundreds of vegetation indices in use at present [19][20][21]. Based on the vegetation indices that have been researched more fully and with higher precision, six visible vegetation indices and four multispectral vegetation indices were selected in this study: ExR (Excess Red index) [22], ExG (Excess Green index) [23], ExB (Excess Blue index) [24], ExGR (Excess Green minus excess Red index) [25], G (Green coordinate) [26], CIVE (Color Index of Vegetation Extraction) [27], NDVI (Normalized Difference Vegetation Index) [28], LCI (Leaf Chlorophyll Index) [29], NDRE (Normalized Difference Red Edge Vegetation Index) [30] and GNDVI (Green Normalized Difference Vegetation Index) [31]. The calculation formulas and theoretical ranges of six visible light vegetation indices and four multispectral vegetation indices are shown in Table 2.…”