Analysing trends in coffee production


Monday, 01 July, 2019


Analysing trends in coffee production

Coffee is a primary source of revenue and livelihood for millions of people across the world. Over 2 billion cups of coffee are consumed daily, with 90% of coffee beans sourced from developing countries.

Predicting the yield patterns for coffee plants presents a unique challenge to coffee breeders as the plants have unpredictable yield patterns. Years with high yields are followed by years with low yields, and vice versa.

Dubbed the ‘biennial effect’, this up-and-down pattern makes it difficult for coffee breeders to compare yields from different varieties of coffee. Accurate measures of yield are essential for coffee breeders to determine which varieties of coffee would be most useful to grow.

Indalécio Cunha Vieira Júnior, a researcher at the Federal University of Lavras in Brazil, compares it to physiological recovery. “Coffee plants need to ‘vegetate’ for a year to produce well the following year.”

A computational model that compensates for the biennial effect in coffee was outlined in a new study by Cunha and colleagues. The computational model reduces experimental error and increases the usefulness of data obtained from field trials, thereby improving the quality of coffee varieties supplied to farmers. By delving deeper into the biennial effect, the model could allow researchers to recommend the most productive varieties of coffee plants for farmers, with higher accuracy and lower costs.

“Ultimately, our findings could reduce the cost and time to launch a new coffee variety into the market by half,” Cunha said.

The computational model also generates data on biennial growth at the level of individual coffee plants, leading to improved coffee crop yields. Using information from the model, farmers could tailor cultivation strategies to individual plants.

The new model also allows researchers to determine why individual coffee plants may have high or low yields each year. Some coffee plants with high yields may belong to high-yielding varieties. However, the plants of high-yielding varieties may produce low yields during recovery years.

The study also yielded some unexpected results. Researchers discovered that the biennial effect in coffee doesn’t follow a well-defined pattern, as previously thought.

“Many researchers assumed that all coffee plants in an area would have similar yield patterns,” Cunha said. But, researchers found that some coffee plants can have reasonably stable yields across years. Other plants may have high yields for two years and reduced yields in the third.

“These findings will change how coffee breeding experiments are analysed,” Cunha said.

Cunha and colleagues used a computer simulation to test the effectiveness of their model. “The simulation allowed us to confirm our findings on real data,” Cunha said. It also helped researchers test conditions in which the model performed well and when it ran into difficulties.

In general, “simulation results showed the model could effectively determine individual biennial stages”, Cunha said. The new model was shown to be an improvement over older models.

Cunha is now trying to incorporate more genetic information into the current model. This would allow researchers to study genetic control of the biennial effect. Understanding the genetic basis of the biennial effect could allow breeders to identify coffee varieties with more uniform yields across multiple years.

Coffee isn’t the only crop to show biennial effects. Apple trees, for example, also exhibit biennial effects. Findings from Cunha’s work could also apply to these other crop varieties.

This research was supported by the National Council for Scientific and Technological Development (CNPq) and Empresa de Pesquisa Agropecuária de Minas Gerais (EPAMIG).

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