A super weed commonly known as Palmer amaranth or Palmer pigweed (Amaranthus palmeri) may soon not be so super.
In first-of-its-kind research, a 10-member international team of scientists, led by Maor Matzrafi of the Hebrew University of Jerusalem, Ittai Herrmann from Ben-Gurion University of the Negev, and UC Davis agricultural entomologist Christian Nansen, used hyperspectral technologies to successfully predict the viability of the weed seeds and herbicide response.
Herbicide Resistance Info
The research, published in the current edition of Frontiers of Plant Science, (here) offers growers of cotton, soybean, corn, watermelon and other crops a new tool in their toolbox to thwart the growth of the herbicide-resistant Palmer amaranth, a fast-growing and highly aggressive weed which cripples crop yields.
The newly published research indicates that through hyperspectral technologies and analysis, growers may soon more accurately predict seed germination and response to the herbicide trifloxysulfuron-methyl (sold as Envoke and Monument). Correlation between leaf physiological parameters and herbicide response (sensitivity/resistance) was also demonstrated.
The weed, native to the desert regions of the southwest United States and northern Mexico, and now found throughout much of the world, has developed resistance in many areas to glyphosate, a broad-spectrum herbicide. The summer annual is especially troubling to cotton and soybean farmers in the southern United States. The weed can grow several inches a day and up to 8 to 10 feet in height. A single plant can produce between 100,000 and 500,000 seeds.
“Weed infestations in agricultural systems constitute a serious challenge to agricultural sustainability and food security worldwide,” the researchers wrote in their abstract, pointing out that weeds are responsible for more than 34 percent of crop yield losses throughout the world. “The ability to estimate seed viability and herbicide susceptibility is a key factor in the development of a long-term management strategy, particularly since the misuse of herbicides is driving the evolution of herbicide response in various weed species.”
In the research, they “demonstrated that hyperspectral reflectance analyses can provide reliable information about seed germination and levels of susceptibility in Amaranthus palmeri,” the researchers wrote. “The use of reflectance-based analyses can help to better understand the invasiveness of A. palmeri, and thus facilitate the development of target control methods. It also has enormous potential for impacting environment management in that it can be used to prevent ineffective herbicide applications.”
Their research showed high levels of accuracy. Using hyperspectral data, they successfully distinguished between germinating and non-germinating seeds, showing an accuracy of 81.9 percent and 76.4 percent, respectfully. Also, using a classification model that distinguishes between the three classes of herbicide response (sensitive, moderate response and resistant), they identified sensitive and resistant plants with high degrees of accuracy, 85.5 percent and 90.9 percent respectfully, from leaf hyperspectral reflectance profiles acquired prior to herbicide application.
Using less herbicide is a win-win situation: a win for the growers and a win for the environment.
Researcher Nansen, an assistant professor in the UC Davis Department of Entomology and Nematology, and also part of the State Key Laboratory Breeding Base for Zhejiang Sustainable Pest and Disease Control, Zhejiang Academy of Agricultural Sciences, Hangzhou, China, contrasted the seeds hyperspectral imaging system and analyzed the data.
Lead author Matzrafi, currently a post-doctoral researcher in the UC Davis Department of Plant Sciences, is with the Hebrew University of Jerusalem’s Robert H. Smith Faculty of Agriculture, Food and Environment as are co-authors Tom Kliper, Yotam Zait, and Baruch Rubin.
- Ittai Herrmann and Arnon Karnieli of the Remote Sensing Laboratory, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Beersheba, Israel
- Timea Ignat of the Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, Rishon LeZion, Israel
- Hanan Eizenberg, Department of Plant Pathology and Weed Research, Agricultural Research Organization, Newe Ya’ar Research Center, Newe Yaar, Israel
The study was funded in part by the Office of the Chief Scientist, Israel Ministry of Agriculture and Rural Development.
Nansen said his lab in the UC Davis Department of Entomology and Nematology is pursuing a wide range of applications of image-based classifications of “objects” – such as seeds, insects, and growing plants. “These applications are part of a growing appreciation for imaging at high spatial and spectral resolutions under controlled laboratory conditions–sometimes referred to as ‘proximal remote sensing’–to describe objects, he said. “As portions of the surface reflectance features penetrate into objects, this technology can be used non-invasively to quantify traits, which are associated with physiological stages – including viability of seeds and/or metabolic processes in growing leaves.”