Practising precision II: Making precision pay

Hazel McInerney1, Peter McInerney1and Jon Medway2

1 3D-Ag Pty Ltd – 150 Dukes Road, Wagga Wagga, NSW, 2650,

Terrabyte Services Pty Ltd – 21 Turner Street, Wagga Wagga, NSW, 2650,


PIP (Precision in Practice) is an innovative new approach to identify and treat management zones.  PIP is a two phase process -Phase 1 enables farmers to accurately and cost effectively identify zones within paddocks or management units that are statistically different.  This is addressed in Practising precision I.  This paper addresses PIP Phase 2 – the agronomic and farm system implications of this tool to determine the optimal allocation of resources in the production system, so that both the soil resource and farm profit improve.

Using the zone and landscape information developed by PIP Phase 1, in conjunction with the experience of the land manager and their agronomist, PIP Phase 2 supports the development of a soil sampling plan by zone. Understanding that soil chemistry may not be the only issue, laboratory results are examined in the context the soil and landscape findings and where appropriate ameliorants, seed and fertilizer requirements can be accurately entered into controller maps for variable rate application.

The case study below demonstrates the potential for savings to be made.

An estimate of carrying capacity of land for ruminant livestock production across southern Australia, using gridded batch simulation modelling

Dean T. Thomas1, Eric J. Zurcher2, Gonzalo Mata1, Neville I. Herrmann2, Dave A. Henry3

1CSIRO Agriculture and Food, Centre for Environment and Life Sciences, Floreat, WA 6014,,
2CSIRO Agriculture and Food, Black Mountain Laboratories, Canberra, ACT 2601, 3CSIRO Agriculture and Food, Food Innovation Centre, Werribee, Vic. 3030


We present the first gridded modelling of grazing systems across southern Australia to estimate the carrying capacity of land for ruminant livestock production in relation to climate, soil and pasture species characteristics. A static Merino sheep trading enterprise was simulated using GrassGro™ software across a 5 km continental grid. A batch processing wrapper was developed to select inputs by geolocation and transfer these data to the simulation engine. The key output was a continental map of the optimised stocking rate per winter-grazed pasture hectare, as determined through maximising gross margin ($/ha), constrained by a supplementary feeding limit (< 20% of total energy intake) and a ground cover threshold (>50% cover for more than 90% of the time). We use the study to highlight some likely sources of error in existing methods, and how our method can help to compare livestock carrying capacity across a wider geographic region.


Online Farm Trials (OFT) – the past, present and future

Nathan Robinson1,2, Peter Dahlhaus1, Paul Feely1, Kate Light1, Andrew MacLeod1, Rob Milne1, Julie Parker1, Helen Thompson1, Judi Walters1, Ben Wills1

1 Centre for eResearch and Digital Innovation, Federation University Australia, Ballarat, Vic, 3350,,
2 Corresponding author: 


Online Farm Trials (OFT) ( is a free web-based resource and trial discovery system that contains more than 7,100 trials from 76 different organisations from across Australia. Since its inception in 2013, OFT has developed via a collaborative approach with grower groups, research organisations, agricultural experts and grains industry organisations. This ensures the outcomes are highly relevant, practical and beneficial for growers. Users can view, analyse and export grains research data as well as compare trials based upon historical, geographic and crop-specific search filters. Current developments include seasonally relevant collections of trials to highlight priority topics and aid on-farm decision making. To meet the future needs of industry stakeholders, system developments are planned to include expanded trial research information access, foster innovation through publishing and promoting active trials and enhance trial data standards and quality.

Improving the FAIRness of Australia’s grains research sector data

Ben Wills1,2, Julie Parker1, Nathan Robinson1, Megan Wong1

1 Centre for eResearch and Digital Innovation, Federation University Australia, Ballarat, Vic, 3350, Australia., 2 Corresponding author:


Across Australia’s arable landscapes, thousands of crop trials have been conducted to improve the profitability and sustainability of Australian grain production. Although there have been significant steps to make knowledge gained from trials available to users, there is the potential to further support the development of next generation data models and knowledge products by integrating trials from disparate sources by adhering to FAIR principles of data management. That is, making data: findable, accessible, interoperable and reusable. This research explores whether Online Farm Trials increase the FAIRness of agricultural grains trial datasets through a comparison of the trial data capture and handling practices of organisations whose datasets are not discoverable through Online Farm Trials (OFT) (N = 50) with the FAIRness of the datasets discoverable through OFT. The findings demonstrate that OFT is helping to make the results of Australia’s grains trials more FAIR to the users of trial data, and suggests a number of improvements to the FAIRness of trial datasets, foremost through the use of machine-readable metadata.

PAT: Accessible tools for precision agriculture data analysis

Christina Ratcliff 1*, David Gobbett 1, Rob Bramley 1

1 CSIRO Agriculture and Food, Locked Bag
2, Glen Osmond, South Australia, 5064 *,


PAT – Precision Agriculture Tools is a ‘plugin’ for QGIS, a free and open-source desktop geographic information system. PAT provides a suite of tools for processing and analysing precision agriculture (PA) datasets. The need for accessible and easy to use PA data processing tools has been demonstrated by survey data showing that whilst many growers have access to PA data, such as through yield monitors, a relatively small proportion of growers use these data in making management decisions. The PAT plugin is the culmination of many years of PA tool development, and includes tools for basic data preparation and map generation, as well as powerful tools for analysis of on-farm trials and experiments.

Practising precision I: A basis for making precision pay

Hazel McInerney1 Jon Medway2 and Peter McInerney1

1 3D-Ag Pty Ltd – 150 Dukes Road, Wagga Wagga, NSW, 2650,, 
Terrabyte Services Pty Ltd – 21 Turner Street, Wagga Wagga, NSW, 2650,


PIP (Precision in Practice) is a new tool enabling farmers to accurately and cost effectively identify management zones within paddocks or management units.  PIP is a two phase process with this paper addressing Phase 1.  PIP begins with developing an accurate set of farm maps, including total area, arable area and elevation.  Onto this base, historic satellite imagery is used to generate NDVI values and spatial statistics used to identify areas of difference (zones).  These are verified by in-field measurement and farmer feedback.  Phase 1 facilitates Phase 2 (see Paper 2) where targeted soil sampling by zone provides a source of information that can inform variable rate application.


Making more informed sensor decisions for better farm management

Glenn J Fitzgerald1, Eileen M Perry2

1 Agriculture Victoria, Department of Jobs, Precincts and Regions, 110 Natimuk Rd, Horsham, VIC, 3400,, 2 Agriculture Victoria, Department of Jobs, Precincts and Regions, Cnr. Midland Hwy and Taylor Street, Epsom, VIC, 3551


Given the plethora of sensors and platforms (including UAVs) available for detection and identification of crop characteristics, how does a user decide what to use, when to use it and whether it has value? Questions include, how do you choose the right sensor and platform?, what is an NDVI anyway?, are UAVs useful for farm management? The presentation will discuss some of the caveats of using remote sensing using the spectral NDVI as an example of some issues that need to be understood and considered when using sensing information with the intent of increasing understanding for better decision making. Growers, agronomists, scientists and other users will be able to ask more informed questions of their service providers and understand what questions to ask when they design projects to get better value from sensor technology.

A robust and rapid pollen viability test using impedance flow cytometry for high throughput screening of heat tolerant wheat (Triticum aestivum) germplasm

Anowarul I. Bokshi1, Daniel K.Y. Tan1, Richard M. Trethowan1

1The University of Sydney, Plant Breeding Institute, Sydney Institute of Agriculture, School of Life and Environmental Sciences, NSW 2006,


Pollen viability is an important physiological character for the screening of heat tolerant wheat germplasm. A robust and rapid analysis is essential to determine pollen thermotolerance. Our pre-breeding program assessed the Ampha Z32 impedance flow cytometer from Amphasys as a tool for rapid determination of pollen viability. The Ampha Z32 detects viable pollen based on the electric properties of cells which react to an applied alternating current, simultaneously generating information on cell size, membrane integrity and cytoplasmic conductivity. This study tested the hypothesis that pollen viability assessment using the conventional method by staining and counting under a microscope is comparable with the Ampha Z32 for rapid analysis of wheat pollen viability under high temperature stress. Pollen viability of three wheat genotypes was examined after heat stress during meiosis including an untreated control, using both the Ampha Z32 and conventional Lugol’s solution (KI)-stain-count. The cytometer provided comparable data and positive correlations with the conventional staining method.. The cytometer-generated data can enable much larger populations to be screened at lower cost, thus enhancing selection of wheat pollen for heat tolerance.


The Australian Society of Agronomy is the professional body for agronomists in Australia. It has approximately 500 active members drawn from government, universities, research organisations and the private sector.

Photo Credits

David Marland Photography Graham Centre for Agricultural Innovation, Charles Sturt University

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