Improving mid-season nitrogen management of rice

(DPI) The Crown in right of the State of New South Wales acting through the Department of Primary Industries within Regional NSW

  • Project code: PRJ-011058

  • Project stage: Closed

  • Project start date: Wednesday, August 1, 2018

  • Project completion date: Monday, December 6, 2021

  • Journal Articles From Project: Modeling Mid-Season Rice Nitrogen Uptake Using Multispectral Satellite Data Remote Sensing (Issue: 11 on 6/8/2019), Predicting panicle initiation timing in rice grown using water efficient systems Field Crops Researcch (Issue: 239 on 26/5/2019), IMPACT OF UAV TIME-OF-FLIGHT ON RICE NITROGEN UPTAKE MODELS IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium (Issue: pp. 4355-4358 on 1/7/2020), Rice nitrogen status detection using commercial-scale imagery International Journal of Applied Earth Observations and Geoinformation (Issue: 21 on 27/11/2021)

  • National Priority: RIC-Agronomy and farming systems


This project will improve mid-season nitrogen management of rice leading to increased grain yield and quality, water productivity, nitrogen use efficiency and profitability.



Research Organisation

(DPI) The Crown in right of the State of New South Wales acting through the Department of Primary Industries within Regional NSW

Objective Summary

1) Develop scripts required to enable red edge sensor imagery to be automatically processed and embedded in an online delivery system, allowing growers to access spatial NDRE maps of their rice fields at PI.
2) Refine algorithms for predicting PI nitrogen uptake from NDRE for Koshihikari and YRK5 for red edge sensors suitable for providing accurate spatial PI nitrogen topdressing recommendations for these varieties to growers.
3) Utilise phenology data to develop improved models for prediction of PI date for aerial, drill and delayed permanent water (DPW) rice sowing methods. Extend the modelling to include prediction of anthesis date and work towards the prediction of rice maturity date.
4) Research current and new satellite, aerial and drone based red edge and hyperspectral sensors as they become available to improve prediction of PI N uptake in rice without the need for physical crop sampling for all varieties.
5) Maintain the NIR instrument and rice calibrations and ensure growers receive accurate nitrogen topdressing recommendations from the NIR Tissue Test Service. Ensure research projects have accurate and cost effective sample analysis results.