1622™: helping sugarcane farmers protect the Great Barrier Reef

1622™: helping protect the Great Barrier Reef by enabling sugarcane growers make better nitrogen fertiliser decisions

What is the problem 1622™ was helping solve?

Nitrogen losses from sugarcane cropping are a major threat to the Great Barrier Reef’s health, and optimising nitrogen fertiliser management will reduce these losses. Despite substantial investment in improving agricultural management practices, the improvement in water quality is not yet on track to meet ecological targets for protecting the health of the Reef’s ecosystems. A new approach was needed so we started developing a suite of apps, called 1622™, which combine data from diverse sources to help sugarcane farmers optimise their crop management, reduce nitrogen losses and help protect the Reef.

How could 1622™ help the sugarcane industry?

Sugarcane farmers in the wet tropics region of north Queensland have been using the water quality component, 1622™WQ, since its launch in January 2020. It means that for the first time, farmers have real-time information on key factors for growing sugarcane.

Water quality: The 1622™WQ app displays nitrogen losses at multiple sites within an agricultural catchment. Growers can see information over a defined time period and can compare multiple locations within a catchment. It allows farmers to see, for example, the influence of recent rainfall on water quality, how water quality differs between locations, or whether management actions such as recent fertilising has affected nitrogen losses. They can also see other aspects of water quality, like creek height and turbidity.

1622™WQ also displays data from both Bureau of Meteorology weather stations and local on-farm weather stations to provide farmers a more accurate picture on rainfall variability.

Other 1622™ apps we developed to various stages are:

Crops: Our drone-based LiDAR system aimed to help farmers use less nitrogen-based fertiliser without affecting their profits. The aim was to show information on sugarcane crop growth from a range of data sources including satellite and drone imagery, and crop modelling. Information was available through the growing season, so farmers could compare different management strategies in real time through the season.

What if?: The ‘What if?’ function of 1622™ aimed to allow farmers to evaluate the risks and benefits of changing nitrogen fertiliser applications on crop performance and environmental impact. For example, ‘what if I change my fertiliser rate, harvest date and/or fertilising date and how would that affect my crop yields and nitrogen losses?’

Following an independent evaluation, we’ve found that our 1622 apps could bring $20.4m – $62.9m in economic benefits from 2021 – 2030, mainly in cost savings from reduced fertiliser use. Read more about the evaluation.

Acknowledgements

Some data used by 1622™ are sourced from the Queensland Government, Bureau of Meteorology, and NESP project 2.1.7. UAV features developed in conjunction with Hovermap.

How can I access 1622™WQ?

Although the project is complete, growers and the public can still use the tool by visiting 1622.farm.

Where can I get more information on 1622™?

See our media release, article in ECOS magazine or follow us on Twitter using #1622app.

Watch team members Yuri Shendryk and Peter Baker present some of the science behind 1622™.

Read our select publications:

Shendryk Y, Sofonia J, Garrard R, Rist Y, Skocaj D, Thorburn PJ (2020) Fine-scale prediction of biomass and leaf nitrogen content in sugarcane using UAV LiDAR and multispectral imaging. International Journal of Applied Earth Observation and Geoinformation 92, 102177. doi:10.1016/j.jag.2020.102177

Vilas MP, Thorburn PJ, Fielke S, Webster T, Mooij M, Biggs JS, Zhang YF, Adham A, Davis A, Dungan B, Butler R, Fitch P (2020) 1622WQ: A web-based application to increase farmer awareness of the impact of agriculture on water quality. Environmental Modelling and Software 132, 104816. doi:10.1016/j.envsoft.2020.104816

Zhang YF, Fitch P, Thorburn PJ (2020) Predicting the trend of dissolved oxygen based on the kPCA-RNN model. Water 12, 585. doi:10.3390/w12020585

Shendryk I, Rist Y, Ticehurst C, Thorburn P (2019) Deep learning for multi-modal classification of cloud, shadow and land cover scenes in PlanetScope and Sentinel-2 imagery. ISPRS Journal of Photogrammetry and Remote Sensing 157, 124-136. doi:10.1016/j.isprsjprs.2019.08.018

Sofonia J, Shendryk I, Phinn S, Roelfsma C, Kendoul F, Skocaj D (2019) Monitoring sugarcane growth response to varying nitrogen application rates: A comparison of UAV SLAM LiDAR and photogrammetry. International Journal of Applied Earth Observation and Geoinformation 82, 101878. doi:10.1016/j.jag.2019.05.011

Zhang YF, Fitch P, Vilas M, Thorburn PJ (2019) Applying multi-layer artificial neural network and mutual information to get prediction of trends in dissolved oxygen. Frontiers in Environmental Science 7, 46. doi:10.3389/fenvs.2019.00046

Zhang YF, Thorburn PJ, Fitch P (2019) Multi-task temporal convolutional network for predicting water quality sensor data. pp. 122-130 in Gedeon T, Wong K, Lee M (eds), ‘Neural Information Processing. ICONIP 2019.’ Communications in Computer and Information Science, vol 1142. (Springer: Cham). doi:10.1007/978-3-030-36808-1_14

Zhang YF, Thorburn PJ, Xiang W, Fitch P (2019) SSIM – a deep learning approach for recovering missing time series sensor data. The IEEE Internet of Things Journal 6, 6618-6628. doi:10.1109/JIOT.2019.2909038

Keating BA, Thorburn PJ (2018) Modelling crops and cropping systems – evolving purpose, practice and prospects. European Journal of Agronomy 100, 163-176. doi:10.1016/j.eja.2018.04.007

Meng R, Dennison P, Shao F, Shendryk I, Rickert A, Hanavan RP, Cook BD, Serbin SP (2018) Mapping canopy defoliation by herbivorous insects at the individual tree level using bi-temporal airborne imaging spectroscopy and LiDAR measurements.  Remote Sensing of Environment. 215, 170-183.  doi:10.1016/j.rse.2018.06.008

Puntel LA, Sawyer JE, Barker DW, Thorburn PJ, Castellano MJ, Moore KJ, VanLoocke A, Heaton EA, Archontoulis SV (2018). A systems modeling approach to forecast corn economic optimum nitrogen rate. Frontiers in Plant Science. 9, A 436. doi:10.3389/fpls.2018.00436

Schaffelke B, Fabricius K, Kroon F, Brodie J, De’ath G, Shaw R, Tarte D, Warne M, Thorburn PJ (2018). Support for improved quality control but misplaced criticism of GBR science. Marine Pollution Bulletin 129, 357-363. doi:10.1016/j.marpolbul.2018.02.054

Seidel SJ, Palosuo T, Thorburn PJ, Wallace D (2018). Towards improved calibration of crop models – where are we now and where should we go? European Journal of Agronomy 94, 25-35. doi:10.1016/j.eja.2018.01.006

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