Identifying within-season cotton crop nitrogen status using multispectral imagery

 Jordan Romeo1, Patrick Filippi1, Jon Baird2, Anastasia Volkova3, Daniel K.Y. Tan1

1 The University of Sydney, Sydney Institute of Agriculture, School of Life and Environmental Sciences, Faculty of Science, Sydney NSW 2006,
2 NSW DPI, Australian Cotton Research Institute, Narrabri, NSW 2390,
3 FluroSat, 2/4 Cornwallis St, Eveleigh NSW 2015

Abstract:

Nitrogen (N) plays a key role in the growth and development of a cotton (Gossypium hirsutum) plant and the timing of N fertiliser application has a critical effect on yield. Under-application of N fertilizer reduces yield, while over-application encourages vegetative growth at the expense of reproductive growth, resulting in a higher production cost. In the Australian cotton industry, N fertiliser is typically applied at a uniform rate across a field at a high enough rate to ensure N is not limiting at any area of the field and this practice will not be cost effective with the rising cost of N. This study investigates the use of vegetation indices (VIs) derived from multispectral imagery from planes and satellites (Sentinel 2) to estimate plant nitrogen status to facilitate variable rate fertiliser application and also for predicting lint yield at time of harvest. Sicot 748B3F cotton was sown with six pre-season nitrogen rates 0, 37.5, 75, 112.5, 150 and 187.5 kg N/ha in three replicates in a randomised complete block design layout. Aerial multispectral imagery was collected on three dates across the growing season, as well as from satellites at 17 dates throughout the season. Imagery gathered were processed into VIs including Normalised Difference Vegetation Index (NDVI), Normalised Difference Red Edge Index, Canopy Chlorophyll Content Index (CCCI) and Modified Soil-Adjusted Vegetation Index (MSAVI). Plant tissue samples were collected for nitrogen concentration at 75 and 139 days after sowing, with coinciding aerial imagery. At plant maturity (143 DAS), the coefficient of determination values for N% were r2 = 0.34 for CCCI, r2 = 0.34 for NDVI and r2 = 0.27 for NDRE indicating a relatively low/moderate correlation between VIs and leaf N%. The best time to predict/forecast cotton lint yield using CCCI and NDRE was at 178 DAS, based on satellite imagery. This study suggests aerial and satellite imagery can provide potential for variable rate fertiliser application, and the possibility to replace the current method of N tissue testing.

 

Host

The Australian Society of Agronomy is the professional body for agronomists in Australia. It has approximately 500 active members drawn from government, universities, research organisations and the private sector.

Photo Credits

David Marland Photography david_marland@hotmail.com Graham Centre for Agricultural Innovation, Charles Sturt University

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