Authors:
Eric KALY
, Daouda NGOM
, Sékouna DIATTA2
, Abdoul Aziz DIOUF
and Raymond MALOU ,Senegal
|
Abstract:
Techniques based on proximal remote sensing combined with linear regression models allowed
to estimate the biomass production and grain yield of rice during the crop cycle. With the
availability of several photos, post-classification comparisons allowed to make a spatio-temporal
monitoring of the canopy cover of the different stages of crop development. Data observed in
field were used to set up simple models for estimating and/or forecasting yields. The canopy
cover rate increases significantly from the 30th day after sowing at the flowering stage (CCx =
19% on the 30th day and 76% on the 78th day). From the senescence to the maturity, it decreases
considerably (59% at maturity). Statistical tests (Fisher statistic, coefficient of determination and
p-value) permit to show that the Power model (Y = ? + b1X + b2X2 +… .. + bnXn with X the
explanatory variable) would be the most appropriate than the usual linear model for estimating
and/or forecasting production in irrigated rice growing in the delta. This approach of the spatiotemporal assessment of green canopy coverage by remote sensing techniques was tedious but
specific and required the recourse of photo processing software. The use of simple linear
regression models suitable for the estimation of the biomass and grain yield of the paddy
produced satisfactory results. |
