New method to measure soybean rhizobia
Soil-borne rhizobia bacteria form a mutually beneficial relationship with legumes, during which the rhizobia convert atmospheric nitrogen into plant-available nitrogen in exchange for carbohydrate energy from the plant. This process is called biological nitrogen fixation and is a major player in the global nitrogen cycle, facilitating greater agricultural productivity with less fertilizer input.
Soybean, a legume planted on nearly 30 million ha annually in the United States, can fulfill most of its nitrogen requirements via biological nitrogen fixation. Many commercial, seed-applied rhizobia inoculants are available to soybean producers to encourage biological nitrogen fixation and increase yield, but scientists have found mixed results regarding the effectiveness of these products.
To begin researching the predictability of a positive yield response to seed inoculation, a team of scientists at the University of Wisconsin–Madison determined a new method for the quantification of soybean-associated rhizobia in the soil. The method is described in the November–December 2010 issue of Crop Science.
Soil samples were collected in April of 2009, and the number of rhizobia was estimated with the most probable number (MPN) technique. This method involves inoculation of soybean plants with a dilution series of the soil sample. Based upon the number of plants that become infected with rhizobia, an estimate of the population size in the soil can be made according to previous research.
This method has been widely used for more than 50 years but requires large amounts of time to process one sample (up to six weeks). To improve efficiency, the scientists at the University of Wisconsin–Madison have taken a genetic approach.
From the soil, they are able to quantify a gene that is specific to the rhizobia associated with soybean using quantitative polymerase chain reaction (qPCR).
“PCR technology was conceived approximately 25 years ago and has many medical, food safety, and research applications,” explains Branden Furseth, the graduate student who conducted the study. “This is just one more way in which the technique can be used to further our understanding of the world around us.”
The same samples were analyzed using qPCR, and the two methods were highly correlated with one another, allowing the qPCR technique to be used as a predictor of the MPN estimation. The qPCR technique is very high throughput and has accelerated the team’s field research during the past two seasons. By investigating how soybean yield responds to the population of rhizobia in the soil, the researchers hope to reveal a response threshold for the use of inoculants, which could be used for product use recommendations and diagnostic testing for producers.
“Based on soil sample analysis, we want to have the ability to predict the soybean yield response to seed inoculation with rhizobia,” Furseth says. “A pre-plant soil rhizobia test for producers would increase the efficiency of using these products.”
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