Microbial Biodiversity Research Programme

Data collection in several model systems is successfully complete. The data analyses, which are based on refined or newly developed bioinformatic methods, are well advanced. Examples of these are: A site-specific differentiation of soil microbiomes is feasible; Pseudomonads that are effective against plant diseases possess potential for use in leaf microbiomes; Fatty whey metagenomes have been sequenced down to strain level. The upshot of all this work has been an expansion of knowledge concerning the functions of organisms in agriculturally and nutritionally important ecosystems.

Website of the Agroscope Microbial Biodiversity Research Project

Documentary on Microbial Biodiversity (in German)


Bee Pollination also Important for Field Crops

A study on the importance of pollination by honey- and wild bees in Switzerland revealed that, in addition to fruits and berries, pollinator-dependent field crops are grown on 14% of the country’s arable land. The estimated value of the yield for all crops achieved through pollination is CHF 341 million per year. There are probably not enough honey bees available everywhere to accomplish this pollination, although coverage is good on average. In view of these large figures, the protection of honey bees and wild bees is essential.


Perennial Ryegrass for Clover-Grass Mixtures

Modern forage production in this day and age would be unthinkable without perennial ryegrass. Agroscope’s comparative variety trials offer support for agricultural practitioners faced with the challenge of keeping track of the many varieties of this forage plant, which are bred worldwide. Based on the trial results, out of 62 tested varieties Agroscope now recommends ‘Artonis’, ‘Soronia’, ‘Araias’, ‘Koala’ and ‘Praetorian’ for forage production in Switzerland. The first four varieties stem from the Agroscope breeding programme.


Identifying the Right Crop Variety for the Right Place

Large-scale prediction of the performance of genotypes is crucial for predicting genotypic performance in specific environments and increasing our knowledge in order to develop future crop varieties. Agroscope has started applying predictive algorithms to mine historical datasets. High prediction accuracies for certain sites and genotypes applying algorithms based on ridge regression and deep learning showed promise for the difficult task of identifying the right variety for the right place.