Japan tests new satellite on robotic tractors in Riverina

PHOTO: A drone hovers over the Japanese robotic tractor trialled at Rice Research Australia in south-west NSW. (Laurissa Smith)

How would you feel about leaving a tractor to drive itself in one paddock, while you work in another ?

To the busy farmer, struggling to find local labour, it's an appealing concept.

Around the world, manufacturers, engineers and researchers are now trying to turn that into a reality.

In Japan, they've designed a self-steering robotic tractor which can sow, plough and spray crops.

An advanced positioning signal is transmitted from Japan's Quasi-Zenith Satellite System to control the tractor's movements.

The Japanese Government is funding trials to test the tractor on crops at Rice Research Australia near Jerilderie in south-west New South Wales.

Engineering firm Hitachi Zosen, machine manufacturer Yanmar, Hokkaido University and several other Australian universities are working together on the project.

Phil Collier, research director with Australia's Co-operative Research Centre for Spatial Information, hopes the technology can help farmers run their equipment with more accuracy.

"The satellites in the sky determine the position of the tractor in a global frame of reference," he said.

"The additional information that comes from the QZSS Satellites brings the precision down from several metres to two centimetres.

"The whole objective is to bring down the precision to a reliable level and a consistent level to allow that tractor to navigate its way down the rows of crops so things aren't getting run over."

If the trials prove successful, people in rural and remote Australia will have access to precise positioning, without having to rely on the mobile network.

At the moment, the robotic tractor is being tested on rice crops and paddocks late at night and into the early hours of the morning, when the satellite is passing over Australia.

The boundary of the field, the tractor's path and the start and end point of where it can turn are all programmed on a computer inside its cab.

This is to ensure the tractor doesn't veer off into a fence or an irrigation channel.

The CRC's Phil Collier says the technology's application won't be limited to precision farming.

"From mining to automated guidance of cars, anything where there's a level of machine automation required that's outside, then this technology has got that ability to solve that problem.

"My prediction, if I can be so bold, is that this sort of technology will move from sophisticated installations in machines like this to mobile phones in due course and people will have it in their back pocket."

The Japanese Government intends to deploy another three satellites in the near future, which will give Australia 24 hour coverage of the advanced positioning signals, once the technology is commercialised.

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Source: ABC

Statistical model predicts performance of hybrid rice

Long-grain rice

Genomic prediction, a new field of quantitative genetics, is a statistical approach to predicting the value of an economically important trait in a plant, such as yield or disease resistance. The method works if the trait is heritable, as many traits tend to be, and can be performed early in the life cycle of the plant, helping reduce costs.

Now a research team led by plant geneticists at the University of California, Riverside and Huazhong Agricultural University, China, has used the method to predict the performance of hybrid rice (for example, the yield, growth-rate and disease resistance). The new technology could potentially revolutionize hybrid breeding in agriculture.

The study, published online in the Proceedings of the National Academy of Sciences, is a pilot research project on rice. The technology can be easily extended, however, to other crops such as maize.

"Rice and maize are two main crops that depend on hybrid breeding," said Shizhong Xu, a professor of genetics in the UC Riverside Department of Botany and Plant Sciences, who co-led the research project. "If we can identify many high-performance hybrids in these crops and use these hybrids, we can substantially increase grain production to achieve global food security."

Genomic prediction uses genome-wide markers to predict future individuals or species. These markers are genes or DNA sequences with known locations on a chromosome. Genomic prediction differs from traditional predictions in that it skips the marker-detection step. The method simply uses all markers of the entire genome to predict a trait.

"Classical marker-assisted selection only uses markers that have large effects on the trait," Xu explained. "It ignores all markers with small effects. But many economically important traits are controlled by a large number of genes with small effects. Because the genomic prediction model captures all these small-effect genes, predictability is vastly improved."

Without genomic prediction, breeders must grow all possible crosses in the field to select the best cross (hybrid). For example, for 1000 inbred parents, the total number of crosses would be 499500.

"It is impossible to grow these many crosses in the field," Xu said. "However, with the genomic prediction technology, we can grow only, say, 500 crosses, then predict all the 499500 potential crosses, and select the best crosses based on the predicted values of these hybrids."

Xu noted that genomic prediction is particularly useful for predicting hybrids because hybrid DNA sequences are determined by their inbred parents.

"More cost-saving can be achieved because we do not need to measure the DNA sequences of the hybrids," he said. "Knowing the genotypes of the parents makes it possible to immediately know the genotype of the hybrid. Indeed, there is no need to measure the genotype of the hybrid. It is fully predicted by the model."

When the researchers incorporated "dominance" and "epistasis" into their prediction model, they found that predictability was improved. In genetics, dominance describes the joint action of two different alleles (copies) of a gene. For example, if one copy of a gene has a value of 1 and the other copy has a value of 2, the joint effect of the two alleles may be 4, indicating that the two alleles are not additive. In this case, dominance has occurred. Epistasis refers to any type of gene-gene interaction.

"By incorporating dominance and epistasis, we took into account all available information for prediction," Xu said. "It led to a more accurate prediction of a trait value."

Genomic prediction can be used to predict heritable human diseases. For example, many cancers are heritable and genome prediction can be performed to predict disease risk for a person.

Xu was joined in the research by Qifa Zhang and his student Dan Zhu at Huazhong Agricultural University, China.

Next the research team, led by Xu and Zhang, will design a field experiment to perform hybrid prediction in rice.

Source: University of California - Riverside