In 2022, CRDC invited its key partners to provide feedback on the health of the partnership with CRDC via stakeholder survey, to identify what's working effectively and to highlight the opportunities for strengthening the partnership. This report outlines the findings. This process will be repeated in 2025 to ensure continuous improvement.

Background

The project ‘Feasibility study of managed aquifer recharge [MAR] for improved water productivity for Australian cotton production’ is investigating the potential to implement MAR at a regional scale in established and emerging irrigated cotton growing regions of Australia. The broad aim of the project case studies was to evaluate how MAR might be feasible for irrigated cotton production and associated cropping systems in the focus regions, and make recommendations on further work to evaluate local hydrogeological conditions, plan the necessary site-specific infrastructure, and establish the legal, social and organisational conditions for implementation of MAR.

The focus of this report is the second case study of the MAR feasibility project, the Gilbert River Agricultural Precinct (GRAP) in the Gilbert River catchment. The study has concentrated on identifying how potential interventions that might provide timely and reliable water yields from the alluvial bed sands adjacent to the Gilbert River might work, and what information would be needed to support investment in the sustainable active management of bed sands.

The broad approach taken was to draw on evidence from a holistic feasibility assessment to scope the most promising opportunities (“scenarios”) for MAR, within an active management paradigm, and to test and refine these scenarios with local stakeholders and state government stakeholders. The key questions we have addressed in the context of seven feasibility criteria are:

1. How might potential strategies (e.g., leaky weirs, recharge weirs, infiltration basins) be applied in the Gilbert River to maximise storage, top up and/or slow down drainage from the bed sands to provide water for irrigation when needed?

2. What are the policy and implementation implications of active management of bed sands? What environmental, social and/or hydrological impacts would need to be tracked?

3. How could an active management approach operate? What governance and information sharing arrangements might be needed?

In addressing these questions, we have identified limitations in the current conceptualisations used in the models underpinning Water Resource Plans (WRP) for the Gulf catchments and, from this, opportunities to improve conceptual understanding of the bed sands.

The initial phase of .the project was devoted to the selection and testing of analytical techniques for assessing total nitrogen and leaching losses, and gaseous losses of nitrogen by ammonia volatilization and denitrification following the application of urea to cotton. Total nitrogen loss was measured in microplots by determining the amounts of labelled fertilizer nitrogen recovered in plants and soil, and subtracting this from the amount originally applied. Ammonia loss was determined directly by a micrometeorological technique which determines the ammonia concentration gradient in the air above the cotton crop. Denitrification loss was calculated by subtracting the measured ammonia loss from the total nitrogen loss. The movement of 15N down the soil profile gave an indication of the importance of leaching loss.

The 2020-21 year marked the third year under the CRDC Strategic RD&E Plan 2018–23. The strategic RD&E investments that CRDC made in 2020-21 under this plan are helping to continue to drive the Australian cotton industry towards a future of innovation, increased commercialisation and digital transformation.

In 2020-21, Australian cotton growers and the Australian Government co-invested $16.9 million through CRDC into cotton RD&E, across 188 projects and in collaboration with 85 research partners. The investments were made in the five key areas identified in the Strategic RD&E Plan: increasing productivity and profitability on Australian cotton farms; improving cotton farming sustainability and value chain competitiveness; building the adaptive capacity of the Australian cotton industry; strengthening partnerships and adoption; and driving RD&E impact.

In the Annual Report, we bring you an update on our progress towards our strategic goals – our investments, our innovations, and our intended impacts – three years into our Strategic RD&E Plan 2018–23.

Research was undertaken to develop a machine vision-based weed spot sprayer for the Australian sugar and cotton industries. Competition from weeds causes significant loss in production that is estimated at $70M annually to the Australian sugar industry. Commercially-available weed spot sprayers have ability to control weeds growing between the rows. However, weeds growing in the row cannot be easily controlled as the current technologies do not discriminate weeds from sugarcane. Machine vision-based weed detection has potential to reduce herbicide use and runoff by selectively spraying weeds.

This project aimed to integrate machine vision-based weed discrimination algorithms with commercial spray control systems from a spray equipment manufacturer, to develop a field-ready, optimised, precision weed detection system. The project followed from SRA project NCA011 (2010/011) in which proof-of-concept algorithms for discriminating Guinea Grass from sugarcane were developed.

The video data set of sugarcane and Guinea Grass was extended in the current project using a dedicated over-the-row data collection unit, consisting of colour and colour/depth cameras. The data collection device was sent to Cairns in 2016 where video imagery was recorded across nine field trials. Collected video was used to refine and optimise Guinea Grass detection under a range of conditions. Evaluations indicated that colour cameras were more robust and cost-effective than colour/depth cameras.

A commercialisation agreement was developed with the aim of making this technology commercially available to the sugar industry, and a generic sensor module compatible with the commercial partner was developed. An SRA sprayer instrumented with generic sensor modules was evaluated in field trials at Cairns in 2018. Agronomic field trials consisted of recording hit and miss rates during a real-time detect and spray operation using the machine vision-based weed spot sprayer. The machine vision-based spot sprayer achieved average hit rates of 96%, 88% and 67% with less than 1% false triggers on big, medium and small Guinea Grass, respectively, in agronomic field trials for large cane. The spot sprayer was inconsistent in agronomic trials for low cane; however, weed detection accuracy of 89% accuracy was achieved during post-processing analysis that involved image analysis parameter adjustment and recorded field images being replayed through the spot sprayer equipment in a desktop study.

Simultaneous to research in the sugar industry, development of the machine vision-based spot sprayer also occurred for application in the cotton industry. Volunteer cotton is an emerging weed problem in the Australian cotton industry, and there are currently six grasses and two broadleaf weeds with herbicide resistance in Australian cotton systems.

Initially, the project focussed on weed discrimination algorithms using a colour/depth camera, leading to development of novel algorithms. However, the colour/depth camera was limited in application given it required a shade hood and controlled lighting. Subsequently, algorithm development focussed on colour cameras that could operate in daylight and on a free-standing boom that was consistent with industry use cases. The algorithms were implemented on the generic sensor module and a ute-mounted spot spray boom was designed and manufactured for data collection using the generic sensor module to validate developed algorithms.

Video imagery of weeds was collected at six cotton field sites using the ute-mounted spot spray boom, a Phantom drone and a handheld phone/gimbal apparatus. Post-processing analysis was applied to video data collected at five of the cotton farm sites to evaluate weed management strategies using the machine vision-based weed spot sprayer.

Traditional practices of broadacre weed management and the use of the machine vision-based spot spray system were compared by calculating the volumes of herbicide used for both potential approaches. The volume of herbicide required was significantly reduced using the machine vision-based weed spot sprayer. Potential herbicide cost savings were calculated as up to $8 per hectare for a glyphosate-based strategy, and $30 to $40 per hectare for alternate modes of action.

Commercial trials will be required to continue during 2019 for extended field trials including spray efficacy, for both sugar and cotton industries.

Newer export destinations for Australian cotton have demanded larger volumes of traditionally ‘lower’ grade, i.e., shorter staple Middling and Strict Middling types for bulk knit fabrics that like higher grades are still required to dye brightly and consistently. In order to demonstrate the suitability of Australian cotton the CRDC sponsored a small project to demonstrate the yarn quality, dyeing ability and knitted fabric appearance of Australian cotton measured against competing growths in these markets.

The project was proposed after interactions with Bangladeshi spinners in 2016 and 2017; where comparisons were drawn between the dyeing-ability of Australian cotton and other growths used by Bangladeshi mills. A controlled mill trial was proposed to provide a comparison of ‘lower grade’ Australian cotton with other export growths, with which it might compete in this market.

Bales of shorter staple, Strict Middling type cotton from seven countries (Australia, USA, Brazil, China, Uzbekistan, West Africa and India) were supplied by the Australian Cotton Shippers Association (ACSA). Each bale was spun at CSIRO’s industry-scale pilot mill into carded and combed medium-fine count yarn for evaluation. Bales were assessed on fibre and yarn properties, spinning performance, dyed colour and fabric pilling properties.

The results showed Australian cotton was best overall in terms of fibre, yarn and fabric properties. The Australian cotton was whiter (brighter) and produced relatively cleaner, more even and stronger yarn. These fibre and yarn properties translated into a bright, clean fabric with good (bright) dyed colour and better pilling resistance than the other growths. Whilst not representative in a proper sampling sense, the trial highlighted the widely accepted consistency of Australian cotton quality and the excellent efficacy with which it can be transformed into higher quality dyed yarn and fabric.