SARDI scientist Rhiannon Schilling showcases a demonstration application created by using paddock data and machine learning models. PINPOINTING the cause of paddock-yield variability using large data sets and innovative machine-learning models is the focus of a project led by the University of Adelaide and funded by the Grains Research and Development Corporation (GRDC). South Australian Research and Development Institute (SARDI) agriculture scientist Rhiannon Schilling gave an update of the project at ABARES Outlook online this week. Ms Schilling said the research looked at the challenge of working out what is behind variability in crop growth and yield across paddocks. "Often there has been a focus on improving grain yields of our varieties; but when we drive around and have a look at our paddocks, we can see that we are not always achieving uniform crop growth and yield across our paddocks," Ms Schilling said.

Embedding big data and machine learning into everyday farming could soon help Australia significantly boost its food production to meet growing demands without degrading soil and water quality or overusing fertilisers. That's the vision a University of Sydney research team, led by associate professor Thomas Bishop, will put to the test as they look for better ways to harvest information literally in the field by finding novel approaches to precision farming. The individual datapoints might be small, but the vision is large with agriculture taking up 56 percent of Australia's land surface and generating a wealth of data in the process - much of it currently unconnected. To get a much clearer picture of agricultural performance, researchers will investigate combining disparate data sets surrounding crop yield, weather, and management practices to better predict crop volumes and quality. This will guide researchers and the agricultural industry on how best to apply fertilisers to maximise grain output and quality, without increasing the amount of chemical runoff into waterways.

On October 1, Stephen Paddock killed 58 people and wounded 546 more, firing multiple rifles from a hotel room in Las Vegas overlooking an outdoor concert. No one knows why he did it. As part of the attempt to figure that out, The New York Times and others report, the Clark County Coroner's office is sending Paddock's brain to the Stanford University lab of Hannes Vogel, a neuropathologist. Vogel (who, at the request of Stanford's communications office, is not speaking to the press) will perform both visual and microscopic examinations of Paddock's brain, looking for abnormalities, tumors, degenerative illnesses, or anything else that might suggest why an otherwise unassuming video poker player would turn his extensive gun collection on innocent people. Nobody thinks it's going to work.

Why? What could possibly have motivated Stephen Paddock, a retired accountant, to open fire from a balcony above a Las Vegas music festival, killing at least 59 people and injuring more than 500? Police found 23 guns in Paddock's hotel room, but have not discovered any connection to international terrorism, despite a claim of involvement from so-called Islamic State. President Donald Trump described the act as "pure evil" and some investigators say there is reason to believe the gunman, 64, had a history of psychological problems. Meanwhile, searches have uncovered explosives at Paddock's home in a retirement community in the small town of Mesquite, north east of Las Vegas. There is a second house in northern Nevada which Swat teams are due to check for booby-traps before carrying out a search. The authorities have yet to confirm the identities of any of the people killed, but Jordan McIldoon, 23, from British Columbia in Canada, has been named as a victim of the attack by CBC News.

An angry, recalcitrant ram enters the paddock from the southwest gate and charges directly at you at a constant speed. I'm especially on the hunt for Riddler Jr. problems -- puzzles that can stoke the curiosity and critical thinking of Riddler Nation's younger compatriots. The problem's submitter, Po-Shen Loh, noted that the bounds on the length of the optimal journey can be found by using the graph theory concept of a minimum spanning tree, which connects all vertices in a graph (Pokéstops in this case) in the most efficient way. The length of the optimal journey is bounded between the weight of the minimum spanning tree of the graph created by the Pokéstops, and two times its weight -- it's possible you'd need to double-back, after all, retracing your steps.