“UQ has people working across multiple domains from developing the genetics, understanding the agronomy, understanding the farming system to how do we deliver into the supply chain. “We want to support informed decision-making during the season – things like whether to add more nitrogen to increase the protein to get a particular outcome, or how much specialty noodle wheat we're going to produce in Australia and where are we going to sell it?
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“What we try to do as scientists is provide a whole series of tools that help plant breeders make better predictions about how to design a new variety that will work on that farm or work in that particular environment,” says Scott Chapman, Professor in Crop Physiology at The University of Queensland. While drawing upon precision data, predictive agriculture also integrates a vast array of agricultural, biological, climate, and hydrological data and sources into a full system model – using artificial intelligence and algorithms to predict outcomes, manage inputs, and plan for system shocks and changes decades into the future. Predictive agriculture differs from precision agriculture in its scope. However, in the digital era, decision-making is increasingly predictive - informed by a suite of data-driven tools that provide more precise information – such as genetic/genomic markers for breeding values or improving operational efficiencies using technologies such as automated planting and harvesting technology. A farmer must make predictions before planting crops or selecting animals for breeding.įor more than 10,000 years, experience and the human eye have been important to make these predictions and in the last 100 years, scientists have developed many means of measurement and mathematical insights about the climate and soil drivers of environment and the genetics and physiology of crops. Predictive agriculture has always been a part of agriculture.