<– Back to Projects



Global demand for food is increasing and modern agriculture must achieve high yields while becoming more sustainable. But there are many threats to his or her crops a farmer has to tackle. One of them is in the form of weeds
that compete with the crops for, e.g., nutrients and water. Modern agriculture, therefore, greatly relies on the effectiveness of synthetic
herbicides for weed control. However, the repetitive use of herbicides in modern cropping systems has led to the rise of herbicide-resistant weeds. As an example, blackgrass (Alopecurus myosuroides) is one of the most harmful weed species in many Western European countries. In England alone, the annual wheat yield loss due to herbicide resistance to just blackgrass is estimated at 0.8 million tons and the annual loss of farm income at 0.4 billion GBP (Varah et al., 2020).

Amongst the available tests for herbicide resistance, genetic resistance tests are the most scalable in terms of cost, turnaround time and throughput and are therefore the most promising basis to leverage testing strategies to reach such a high rate of informed decisions.

There are a variety of methods for genetic resistance testing that are currently being used. However, they all are either expensive, so the added value is low, or they have a long turnaround time or both. Therefore, knowledge on fields’ herbicide resistance status often is not available at a critical time point, treatments with low efficacy often are not prevented or the most effective treatment is not determined. In addition, preventive diagnosis is not the norm but would help to detect low-level resistance that has the potential to develop into serious cases in subsequent growing seasons. The result is economic and yield loss, unnecessary environmental pollution, and shortened product life with limited options in the future. To help solve these pressing problems, I have developed a new method for rapid and cost-effective genetic diagnosis of herbicide-resistant weeds.

Phase: Ideation
Area: Life Science / Ag Biotech
Goal/ Vision: The decision-making process for the best herbicide use should no longer be limited by a lack of genetic diagnostics. I want to improve the accessibility of genetic data and agricultural interpretations and provide them as a service to facilitate data- and knowledge-based herbicide management. Most importantly, I want to help make agriculture more sustainable.
Institute: Max Planck Institute for Biology
Contact: Dr. Ulrich Lutz
Max Planck Institute for Biology
Department of Molecular Biology
Max-Planck-Ring 9
72076 Tübingen, Germany
+49 7071 601 1405