Remote estimation of chlorophyll on two wheat cultivars in two rainfed environments
Davide Cammarano A F G , Glenn Fitzgerald B , Bruno Basso C E , Deli Chen A , Peter Grace D E and Garry O’Leary BA Department of Resource Management and Geography, School of Land and Environment, The University of Melbourne, Parkville, Vic. 3010, Australia.
B Department of Primary Industries, Primary Industries Research, Victoria, PB 260, Horsham, Vic. 3401, Australia.
C Department of Crop Systems, Forestry and Environmental Sciences, University of Basilicata, Via Ateneo Lucano 10, Potenza 85100, Italy.
D Institute for Sustainable Resources, Queensland University of Technology, D Level 3, Gardens Point, Qld 4001, Australia.
E Kellogg Biological Station, Michigan State University, Hickory Corners, MI, USA.
F Current address: Institute for Sustainable Resources, Queensland University of Technology, D Level 3, Gardens Point, Qld 4001, Australia.
G Corresponding author. Email: davide.cammarano@qut.edu.au
Crop and Pasture Science 62(4) 269-275 https://doi.org/10.1071/CP10100
Submitted: 22 March 2010 Accepted: 8 March 2011 Published: 19 April 2011
Abstract
For this study we hypothesise that the use of canopy chlorophyll content index (CCCI) and crop greenness will be useful in assessing crop nutritional status and provide a robust management tool by growth stage DC30 for fertiliser application across multiple sites without being confounded by soil and biomass differences. The objectives of this study were: (i) to study the robustness of the CCCI and greenness as a measure of crop N content at two different locations, and (ii) to validate the model developed for crop nitrogen (N) determination. Data were collected from two rain-fed field sites cropped to wheat, one in Southern Italy (Foggia) and the other in the south-eastern wheat belt of Australia (Horsham). Data collection was conducted during the growing season in 2006–07 (December–June) for the Italian site and during the 2006 and 2007 (June–December) growing seasons for the Australian site. Measurements included crop biophysical properties (leaf area index (LAI), biomass, crop N concentration), hyperspectral remote sensing data, and SPAD (chlorophyll meter) determination. An independent dataset including SPAD, biomass, and remotely sensed data from Horsham (Australia) was used to test the validity of the model developed. Results showed that there is good correlation between SPAD and crop N content. The relationship between greenness (measured as LAI*SPAD) and CCCI was fitted with an exponential model and was not affected by biomass accumulation or soil reflectance (r2 = 0.85; y = 15.1e4.5424x; P < 0.001). When this model was tested on the independent dataset it yielded good results for the estimation of greenness (y = 1.22x − 54.87; r2 = 0.90; P < 0.001; root mean square error 32.2; relative error 15%). In conclusion, SPAD measurements combined with LAI could be used as a crop nutritional management tool by DC30 for fertiliser application across multiple sites.
Additional keywords: canopy chlorophyll content index (CCCI), rainfed, remote sensing, SPAD, spectral.
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