With pressure mounting on farmers to measure and reduce their impact on water quality, Trinity AgTech has launched an enhanced module within their natural capital navigation tool, Sandy, to specifically support farmers in optimizing nitrogen use and reducing nitrate leaching in real-time.
Utilizing more than 300 data points, the module has the potential to protect water through a substantial reduction in nitrate leaching and reduce costs by analyzing each farm and field’s nitrogen uptake efficiency on a daily basis.
“Sandy’s dynamic water protection module will help all farmers to manage and reduce their impact on water quality while also saving money through a reduction in nitrate wastage,” explains Dr. Milad Toolabi, director of artificial intelligence-machine learning (AI-ML) advanced analytics at Trinity AgTech.
“Sandy’s forecasting and scenario analysis capabilities can alert farmers to the optimum time and quantity of nitrogen to apply. This allows a precision approach to applications, contributing towards a cost saving and a reduction in water pollution risk.”
Having already been adopted by farms across Europe, UK farm trials have indicated improvements not only in terms of financial savings and efficiency but also in farm sustainability.
“Reducing nitrogen usage also reduces carbon dioxide equivalent emissions from fertilizer applications, which significantly improves a farm’s sustainability or carbon score,” says Dr. Toolabi.
He adds that Sandy connects information already gathered from the farm, such as soil data, regionality, crop information, application, and management practices, alongside external metrics, including weather data, to enable farmers to make real-time, evidence-based decisions.
“Integrating with farm management software such as Gatekeeper and Muddyboots, farmers can use Sandy to map out the financial and environmental impact of immediate and future farm decisions,” he says.
“Whether farmers are in the business of growing grass or arable crops, the tool can provide them with the right insights to support environmental and financial gains. Ultimately, it will help farmers profit from their decision-making, which is key for a sustainable, resilient farm business.
“Despite the model being based on complex science, artificial intelligence, and machine learning engineering, it’s very easy to use. It’s a useful tool for farmers in NVZs, and for those who are looking to improve the efficiency of farm inputs and save money while also capitalizing on the environmental gains of improved water quality.”
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