- The ‘APH Wheat’ project is determining whether APH-quality wheat can be produced outside the current classification zones of Queensland and northern New South Wales
- A technique called compositing sees several grain samples for any variety ‘composited’ into one to reduce analytical costs and speed up testing processes
A keen student’s move into medical mathematics is helping to revolutionise the quality testing of Australian Prime Hard wheat and could one day expand the areas in which it can be grown
Daniel Tolhurst is applying his passion for mathematics to help fast-track the development of new crop varieties.
PHOTO: Paul Jones
Although parents are always on the lookout for what’s best for their children, their subtle tips and hints do not usually lead to anything particularly groundbreaking.
However, for University of Wollongong researcher and former honours student Daniel Tolhurst this is not the case, and agriculture is the beneficiary.
Daniel, who works on two key GRDC-supported projects, the ‘Australian Prime Hard (APH) Wheat’ project and the National Variety Trials, credits his mother with the direction in which his career has headed and flourished.
When Daniel left school he was keen to study veterinary science and began this journey by studying biology. Yet a couple of years into that degree he realised it was the statistics, not the biology, that really piqued his interest. And by chance his mother had come across a new degree being offered at the university – medical mathematics.
With a combination of mathematics, statistics and biology subjects, the degree is unique in Australia, and Daniel, with first-class honours, was the first to graduate in 2014.
Since then he has found a niche in statistics applied to agricultural sciences via his honours project under the supervision of mentor Professor Brian Cullis.
Today the bulk of his work is in experimental design and analysis for the ‘APH Wheat’ project with Dr Alison Smith.
This project aims to determine whether APH-quality wheat can be produced outside the current classification zones of Queensland and northern New South Wales, while exploring variety-by-environment interaction in wheat-quality traits by first accounting for that variation from field, laboratory or other non-genetic sources.
Daniel says the first phase is field experiments. “There are 24 experiments conducted annually throughout Queensland, NSW, Victoria, South Australia and Western Australia, and 12 of these are selected for quality testing on the basis of geography and receival standards.”
Then the unique laboratory phases begin. By using both composite and individual plot-replicate samples, all the information from the field experiments is included without blowing out the project’s budget and time constraints.
As an example, he says, a standard field trial of 20 wheat varieties (in 20 rows by three columns) would see 60 bags of grain produced. “Usually time and budget constraints mean you cannot put everything through the lab, so traditionally you would lose information from the field as only grain from column one, say, would be tested, or all samples for a variety would be combined into a single bag.
“So, to tackle that, we are using a statistical approach involving compositing. It means that all the samples are used but in different mixtures of individual and composite plot samples.”
The technique of compositing sees several grain samples for any variety ‘composited’ into one and that composited sample analysed. It is a technique that can reveal information that would otherwise require many more samples and, thus, substantially higher analytical costs.
Daniel explains: “For any variety, we put either two bags of grain together and leave another one, or three bags together to create one sample, or three separate bags stay as three samples. The key is that the combination of these different types of samples still allows us to model the variation in the field and help weed out any errors from that phase.”
While there is a set number of each sample type to composite (according to the total number of varieties at that field trial), Daniel has also developed an algorithm to decide what bags to mix across all trials. “It’s like a very large sudoku puzzle where I try to balance the sample types across both varieties and trials while maintaining set limits within trials. This prevents any particular variety from being processed as a single composite sample at all trials and so there will be relatively equal numbers of samples tested for every variety.”
Once the samples are sorted, testing in the lab also involves multiple phases for grain traits (such as screenings, test weight and protein), and for flour quality parameters such as milling, ash and protein, as well as dough traits, baking traits and noodle brightness. Replication in these phases is achieved by splitting a composite sample in two and processing these as separate samples.
The team has data from 2013 and 2014 and is now working on 2015 data.
Daniel says the data quality is great. “We have never had such high-quality data before and it is down to the statistical protocols we have established, including the model-based lab designs – they use revolutionary statistical techniques to integrate the compositing.
He says that while these quality-trait tests involve either two or three phases, both randomisation and replication are incorporated into every experimental phase. “So these designs are allowing the team to take into account any variation that happens in each phase of the lab as well as the field trial, making the findings very accurate.”
Although the work is still in its preliminary stages, it will help release information to the Australian wheat industry with more accuracy, he says.
“It will allow us to better understand sources of variation and genotype by variety interaction in wheat-quality traits. With this understanding we can release information more quickly.”
Daniel also works on the GRDC-supported National Variety Trials, work he began in a casual capacity while studying.
He says the work has led him from the office to the paddock: last year he represented Statistics for the Australian Grains Industry (SAGI) at the NVT spring field days in WA.
“It was a big eye-opener for me to see the trials themselves and talk to the growers about them. I definitely got a greater appreciation of what growers do. I had such a spring in my step in the office when I returned.”
As well as his mum, Daniel also credits Professor Cullis for helping him use his statistical passion. “I met Brian in my third year and he was so passionate about it and it got me even more passionate about it. I hope one day I can be half as influential on students as he is.”
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GRDC Project Code
North, South, West