Simple legumes, complex analyses.

Since starting work at VSNi it’s no surprise to find so many applications of our software, be it GenStat or ASReml, are working within some aspect of agricultural research; projects that range from development of new plants to new herbicides or pesticides. But this isn’t just a race to find the best, most productive type of grain, or the most effective pesticide, in so many cases this is linked to the welfare of the local ecology. More and more agricultural producers can now see a benefit of using the earth’s natural resources to assist in crop production.

Researchers at the New South Wales Department of Primary Industry in Australia highlight the complexities of farming using one of the more traditional methods of crop production. Southern Australian farms have long used annual legumes in the pasture phase of crop rotation methods. These act as natural disease breaks and restore soil structure and nitrogen in the soil, as well as providing valuable, high quality feed for livestock. Fantastic! In the panic of today’s world on pesticide poisoning this seems an ideal solution, and has certainly been a good one – here we are using the earth’s natural resources and traditional crop rotation methods to help increase production, and as a bonus create livestock feed…however it is not that straightforward.

The problem with annual pastures and crops, is that they are often shallow rooted and therefore use less rainfall than the deeper rooted perennial species. This has led to a significant increase in groundwater recharge with higher water tables and dry land salinisation in parts of Australia where the agricultural production has meant a change from perennial deep-rooted species to annual crops and pastures. Simple answer! Surely we just swap the annual pastures and crops for perennial ones… but which one? Australia is a big country with a diverse range of climates. How then do you determine which perennial legumes and herbs are best suited to Australian conditions, with so much variation between regions?

A team of researchers headed by Guangdi Li took on the challenge. The purpose of their study was to evaluate a wide range of herbaceous perennial species as potential new species for incorporation into the farming system of Southern Australia. Guangdi Li’s team needed to identify species with an ability to adapt to a broad range of environments, and species which are particularly suited to specific soil conditions, environments and climatic patterns. They evaluated 91 perennial legumes and herbs (entries) at 10 sites across Southern Australia. There were originally 17 sites but 7 were abandoned because of weeds or poor establishment leaving 4 in Western Australia, 1 in South Australia and 5 sites in New South Wales. Only a subset of the 91 was sown at each site, local conditions not being conducive to the successful establishment of many of the varieties. In fact only a handful of the entries were sown at all the sites. Some of the sites were chosen because they posed environmental constraints to plant growth and hence could be used to identify alternate species that may be better adapted to these more demanding environments. Those constraints were saline or waterlogged soils, although drier than usual conditions minimised the effects of waterlogging. Over the three years of the experiment there were a number of occasions that herbage mass and plant frequency was assessed across the ten sites, creating 67 and 21 ‘environments’. Each environment provides information about the performance of the varieties, but of course successive samplings from the same site are not independent of each other, and a high correlation was expected.

ASReml was chosen to analyse this data because it can handle the estimation of the genetic correlations between pairs of environments. The analysis involves a mixed model in which the variance-covariance matrix of the entries in different environments is modelled using a factor analytic (FA) structure. The FA model facilitates the accurate prediction of entry means for individual environments using best linear unbiased prediction. Selection across a range of environments, be it the saline or waterlogged sites or even for those samplings taken in Summer for instance, is easily achieved by using the Predict facility in ASReml. Entries that showed potential in saline or waterlogged conditions can be targeted for inclusion in future breeding programs.

The complexities of this study very much reflect the complexities faced by the agricultural producers.  In farming there are often no straightforward, obvious answers to the questions posed and the problems faced. This is why research and studies such as Guandi Li’s are so important; they give evidence based on sound science and statistical analysis techniques which show the importance of specific types of plants; in this instance Lucerne, which performed well over a broad range of environments. This in itself suggests projects are needed to fully exploit this plant so that its limiting factors (susceptibility to acid conditions and heavy grazing) can be overcome. Additionally Guangdi Li and his team showed that there is a range of deep-rooted perennial legumes and herbs which could be used in the Southern Australian farming system, rather than relying on a narrow range. The impact of the results of this and future trials are huge, it is yet another step closer to even better planned farming policies and methods, and hence to a true understanding of how we best feed ourselves and the world.

There is no doubt that sound scientific research is needed on which to base future crop, farming methods or policy decisions; research that is built using proven scientific tools. VSNi are a key provider of these tools; we know this because many agricultural scientists rely on us to continue providing high quality, relevant statistical analysis software built by statisticians who understand the complexities of agricultural research and data.


Comments are disallowed for this post.

Comments are closed.