Existing wireless technology and the emergence of low-cost IoT sensors in agriculture offer the possibility to gather relevant data from the soil-plant-atmosphere continuum in real time for irrigation scheduling and to have this information available remotely online. Integration of these separate technologies with automated control structures for irrigation to develop smart automated irrigation systems has the potential to reduce labour costs and increase water productivity within the cotton industry. With the final objective of developing a relatively low-cost, smart and fully automated irrigation system in collaboration with automation providers such as Padman Stops, research in this preliminary project was focused on the sensing component that such a smart automated system would have. The project showed that sensing weather, soil and crop data in real-time by means of a wireless sensor network that periodically uploads data to internet by means of low-energy and low-cost data loggers (WiField logger) offers large opportunities to optimise irrigation management through automation. In this research, data from the in-field sensors and a remotely sensed crop vegetation index (Sentinel-2 NDVI images of each bay obtained from Google Earth Engine) were ingested and analysed in Google Cloud Platform. The processed data (ETc, soil tension, etc.) plus 7-day weather forecast and soil moisture predictions were then presented in a dashboard available online using Google Data Studio in an easy to interpret manner. For the soil moisture prediction, Lasso, Decision Tree, Random Forest and Support Vector Machine modelling methods were trialled. Random Forest models gave consistently good results (mean 7-day prediction error from 8.0 to 16.9 kPa). Linear regression with two of the most important predictor variables (the square of cumulative crop evapotranspiration minus rainfall and the square of growth degree days) was not as accurate, but allowed extraction of an interpretable model. The methodology developed in this project could be used as part of a closed-loop sensing and irrigation automation system. Future research will be focused on exploring new low-cost soil and plant-based sensors, refining the algorithms developed for soil moisture prediction and the integration of such system with IoT irrigation control structures to develop a fully automated irrigation platform. With such platform, the current challenges for the widespread adoption of automated irrigation within the Australian cotton industry are likely to be significantly reduced and higher rates of adoption occur. Adoption of the proposed sensing and automation irrigation systems by cotton irrigators would result in reduced labour costs associated with irrigation water management and improvements water productivity.

The RMIT research project funded by CRDC on “The economic benefits of composting textile waste: process mapping and optimal location” focused on two distinct issues: • Identification of potential methods of transforming 100% cotton textile wastes into organic compost. • Determining the optimal location within Australia for waste processing by minimizing the transportation cost, which is reflected by minimizing the integration of cargo volume-distance between collection areas and the processing facilities, and subsequently to agricultural farms. The analysis is done through examination of existing literature and the project was divided into five operational stages as mentioned below. Stage 1 analysis demonstrates that end-of-life (EOL) consumer clothing waste in Australia can be collected via donations from households and strategic locations. Collection methods encompass direct consumer engagement, city council collaboration, and partnerships with private entities and NGOs. Additionally, pre-consumer textile and clothing waste can be acquired either directly from overseas industries or through local brokers. Stage 2 analysis confirms that textile sorting primarily depends on labels, with alternative methods like burning tests necessary when labels are absent. Sorting can be manual or automatic, the latter being more efficient through the integration of machinery, artificial intelligence (AI), and technologies like Near Infrared (NIR) spectroscopy, Fourier Transform Infrared (FTIR), and Radio Frequency Identification (RFID) sorting. Contaminants are eliminated pre-composting via industrial washing/cleaning of textile waste. Post-cleaning, textiles can be shredded manually, mechanically, or chemically. The study found automatic technologies like double shaft or Aduro shredders to be highly efficient in garment shredding. Stage 3 analysis identified three main composting methods: aerobic, anaerobic, and vermicomposting. The study found vermicomposting, utilizing earthworms, to be the most effective and nutrient-rich method for composting cotton textiles. This method yields compost higher in nitrogen, phosphorus, and potassium (NPK) compared to other counterparts. Vermicompost also retains nutrients longer than traditional compost and provides essential macro and micronutrients, including vital NPK to plants more rapidly

Riparian ecosystems support biodiversity, provide ecosystem functions and deliver various ecosystem services vital for humans (Naiman et al. 2005). Riparian vegetation is particularly important in driving the composition, structure, and function of terrestrial and aquatic systems at a local and landscape scale - influencing fish, insects, birds, mammals and soil microbiota (Capon et al. 2016). Also providing many critical services to the broader landscape such as nutrient cycling, providing fresh water and carbon sequestration. In addition, riparian vegetation dynamics are often sensitive to changed hydrological regimes, with river regulation often resulting in ecological shifts (Jansson et al., 2000).

The 2011-2012 Annual Report for the Cotton Catchment Communities CRC. 

The 2010-2011 Annual Report for the Cotton Catchment Communities CRC. 

The 2008-2009 Annual Report for the Cotton Catchment Communities CRC. 

In 2025, CRDC invited its key partners to provide feedback on the health of the partnership with CRDC via a 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 2028 to ensure continuous improvement.

The CRDC Annual Report 2024-25 outlines progress against the second year of the CRDC Strategic Plan 2023-28, Clever Cotton.

The project aimed to improve the efficiency of aerial chemical defoliation in the Australian cotton industry. With newer cotton varieties developing denser canopies, achieving complete defoliation in fewer passes has become increasingly difficult. This results in higher chemical usage, increased operational costs, and a greater risk of spray drift. The research focused on understanding the aerodynamic and environmental factors affecting spray penetration to optimize application efficiency and improve defoliation outcomes.

Using Computational Fluid Dynamics (CFD) analysis, the study identified turbulent kinetic energy (TKE) and particle z-velocity as key factors influencing spray deposition. TKE was found to be the dominant driver of deposition at the top of the canopy, and optimizing for TKE, despite slightly reducing particle z-velocity, still led to an overall improvement in spray effectiveness. The study highlighted that spray boom configurations, particularly nozzle settings, had the greatest impact on canopy penetration, making them a more effective focus for optimization than pilot-controlled flight adjustments.

For the AT802A aircraft, specific nozzle configurations, including increasing flat fan angles, using smaller orifice nozzles, and incorporating a 12° deflection angle, were shown to significantly enhance canopy penetration and deposition. The study recommends validating these changes using the USDA droplet model before implementation to ensure their effectiveness in real-world conditions.

Despite challenges in modelling a 3D plant canopy, the research validated current aerial application practices and confirmed the reliability of existing spray models for optimization. It also showcased the potential of multiphase CFD for future advancements in aerial spray techniques.

CRDC undertakes an annual survey of cotton growers to gather information about farming practices and growers’ views on research, development and extension. This information helps to inform CRDC about the benefits of the research it invests in. Change in industry practice can be quantified by comparing information across the surveys conducted over the past 20 years.