Project overview

Modern cotton farming generates vast amounts of data, yet most on-farm trials still rely on small plots and limited replication. The Spatially Distributed Experimentation (SDE) project tested new approaches to make large-scale, whole-paddock trials more practical and accurate. 

By bringing together researchers, technology providers, and growers, the project explored how digital tools, precision agriculture data, and statistical models can be combined to interpret yield maps, soil variability, and treatment effects in real production environments. 

The project demonstrated that robust experimentation can happen within commercial fields, allowing growers to test management strategies across real variability — a key step towards smarter, data-driven decisions that reduce risk and improve profitability. 

Outcomes and achievements

• Hosted the Cotton SDE Workshop (2023) bringing together national experts and growers
• Delivered a framework for field-scale experimentation using yield maps and spatial analytics
• Identified technologies that support scalable trials
• Helped build industry consensus on the value of spatial experimentation for adaptive management
• Informed CSD on how these approaches can support its variety trial evaluations for delivering better value for grower
• Key contributors and partners: Cotton Seed Distributors (CSD), Cotton Research and Development Corporation, AgEcon, participating growers and agronomists. 

Benefits and features 

• Real-time water monitoring: Growers see exactly how water they have and where it’s located- whether in storage, in the soil, or allocated
• Informed irrigation decisions: By knowing exactly how much water is available, growers can irrigate based on actual needs rather than guesswork
• Planning and forecasting: The dashboard can help estimate water availability for the remainder of the season, assisting in crop planning and infrastructure decisions
• Traceability and ‘origin of water’: Track water from source to field, offering insights into water movement and usage across the property
• Benchmarking: Potential to standardise water-use metrics across different farms and regions. 

The interface has been engineered to be sensor-agnostic, though the initial version is focused on GoannaAg technology to allow for streamlined testing, with plans to broaden compatibility in later versions and iterations. 

Project status

Spatially Distributed Experimentation was completed in 2023.

Resources

Media article – Australian Cottongrowers December 2022

Spatially Distributed Experimentation Final Report

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