Thankfully neither our livelihood or our next meal was dependent on the success of our vegetable crop, but seeing the speed and extent of the destruction caused by these caterpillars made me realise the very real problem that these creatures can cause farmers, whose livelihood does depend on the success of their crops. Without adequate pest control measures farmers can face either crop destruction, or tough decisions regarding the application of pesticides to their crops.  In our family instance our total crop (admittedly only around 10 Brassicas) was wiped out by the caterpillars, but not all pest infestations will have such a dramatic effect. In some instances plants can tolerate small “injuries” (physical harm by a pest) but no “damage” (monetary value lost as a result of pest injury, such as spoilage, or yield reduction etc), or will channel more energy into growth or fruiting. Therefore the effects of some pest populations may not cause enough of a loss to justify the time or expense of pest control operations. So the question is, at what point do farmers decide to employ some form of pest control measures?

Entomologists refer to a break-even point, or economic injury level (EIL) where the pest populations density is at a level where the cost to control it equals the amount of damage it inflicts. The EIL is usually expressed as the number of insects per unit area, or sampling unit. Often, due to the lag time in the implementation of a pest control strategy and its effects, pest control measures need to be put into place before a pest population reaches outbreak status: this moment is below the EIL and called the economic threshold. The economic threshold is the decision point for action to ensure the pest population does not reach the EIL.

Recent research carried out by Dr Rogers and Dr Brier at the Queensland Department of Primary Industries and Fisheries, in Australia, has looked at the effect of the cotton bollworm (Helicoverpa armigera) on soybean and dry bean.  The cotton bollworm is a major pest on both crops in Australia, Asia, India and Africa. However there are limited details on the EIL’s or economic thresholds for these plants. In the past, attempts to develop economic thresholds have used artificial defoliation or depodding techniques which may not accurately simulate the effect of the caterpillars’ eating, and hence lead to inaccurate results for EILs. This latest research has shown that the cotton bollworm does more than just eat the leaves; it eats the leaf veins, petioles, apical growing points and hence limits the plant’s ability to compensate for the damage caused by the caterpillar: this means that previous estimates for the economic threshold from research using artificial defoliation and depodding are artificially higher. Two papers were published in Crop Protection 29, detailing studies on pest damage relationships for the cotton bollworm on vegetative state (Crop Protection 20, 39-46) and on soybean and dry bean during pod fill (Crop Protection 29, 47-57).  The experiments were carried out at the Queensland DPI research station at Kingaroy, Queensland over 3 years. The collected data were analysed in GenStat using regression analysis; simple linear, multiple or split-line regressions were fitted depending on the relationship and data available.

“GenStat provided us with the security of knowing our statistical analysis was based on sound and reliable software,” says Dr Rogers, “We especially appreciated the support provided by GenStat staff.  They were able to provide us with an extension of the R2LINES procedure (a GenStat procedure that fits a model of 2 straight line segments to the data) that enabled us to get the most out of our data.  This was especially important to us as the data collection process in these trials was both painful and tedious – we spent months each year on our hands and knees in the dirt counting caterpillars so we wanted to get as much out of our data as we could!  With the enhanced R2LINES procedure we were able to model the variation in the data and produce parameter estimates that were biologically meaningful, both in terms of yield effects and how and when the plant’s compensatory responses cut in and for how long.”

The results of the studies have highlighted discrepancies with other studies where artificial defoliation has been used to mimic the behaviour of the caterpillar; the yield loss figures from Rogers’ and Brier’s studies are considerably lower than previous studies suggesting that the yield loss figures from previous studies are misleading. These studies have provided farmers with more accurate details on the effect of these caterpillars on soybean yield meaning more effective pest control measures can be employed.

Our thanks to Dr Rogers for his help in this piece.

Dr Rogers provides pesticide-efficacy data analysis services to the crop-protection industry and also assists authors with the preparation and submission of scientific publications.

Links to papers (abstracts):

Pest-damage relationships for Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae) on vegetative soybean

Pest-damage relationships for Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae) on soybean (Glycine max) and dry bean (Phaseolus vulgaris) during pod-fill

Students need to have a good grounding in statistics given how important it is many disciplines — scientific or otherwise. But it isn’t just the statistics that is important, it’s statistical software. Computer hardware and software developments together with the accessibility of data have accelerated the need for students to understand how their learning is used in the real world. It isn’t just a formula, it’s used by many through the world in their everyday life. In order to equip our young people with life skills we should be teaching them, not just the basics of statistics, but teaching them with tools they will encounter later in life.

By using a software system that is and always has been designed by everyday users of statistical analysis, we can show students the relevance of their learning. GenStat for Teaching is a menu driven, world class statistics system used by statisticians, scientists and researchers across the world.

Stewart Andrews will be attending ICOTS8 – 8th International Conference on Teaching Statistics.

Any farmer will tell you of the importance of vaccination in controlling disease, and this is no different for fish farmers, or specifically Atlantic salmon farmers.  Vaccinations against diseases such as furunculosis, vibriosis, cold-water vibriosis, winter ulcer and infectious pancreatic necrosis, which could otherwise decimate a farm, are vital in protecting the fish and farms and controlling these diseases in farmed Atlantic salmon.

However, vaccinations can cause reactions at the injection site such as adhesions and discoloration, reduced appetite and growth, and in severe cases a downgrading of the carcass at market. Not only can this have an economic impact on the salmon farms, but also may create a negative perception of the farms with the general public. As with all aspects of farming the public is concerned not just with the food in question, but also of the welfare of the production animals, which means that breeding companies need to be sure of the actual physical health and safety of their fish.

As with many aspects of farming and agriculture, all aspects of aquaculture are the subject of scientific research to enable better understanding and improved planning for the future, including prevention of disease and improved fish health. The results from challenge tests have indicated substantial genetic variation in disease resistance to a variety of bacterial and viral diseases (including some of those listed earlier), significant but relatively low genetic correlations among the different diseases and a high genetic correlation between the survival of non-vaccinated pre-smolts (smolts are young salmon as they migrate from freshwater to the sea) and the survival of older non-vaccinated full and half sibs under natural break-outs of the diseases. With this in mind Atlantic salmon breeding companies are looking at both the development of efficient vaccinations and the breeding of fish with higher disease resistance.

The results from the challenge test research may suggest that genetic variation in immune systems might lead to genetic variation in innate disease resistance and variation in reactions to the vaccinations. One question researchers in this area need an answer to is whether there is significant genetic correlation between vaccine reactions and innate resistance, as this could mean a change of vaccine dose or a breeding programme to produce fish with the lowest reaction rate to vaccines and the highest natural resistance to diseases.

And it is these issues that a recent project, undertaken by researchers from Nofima Marin, the Norwegian School of Veterinary Science and Aqua Gen AS and funded by the Research Council of Norway (published in the Elsevier journal Aquaculture 287 (2009) 52-58) has looked at.

Atlantic salmon were taken from farmed strains and from offspring of wild salmon, and reared at different farms. Data was collected on sex, size (at different stages), adhesion scores and melanin scores of fish at harvest size and survival data from challenge tests of their full- and halfsibs. The data collected was analysed using ASReml to fit a multivariate linear mixed animal model to obtain estimates for the variance and covariance components for the random effects of the studied traits. A single trait sire and dam model, which included the same fixed effects was used to obtain across year-class heritability estimates (using an alpha version of ASReml 3).

Results from the project indicate that the severity of vaccine injuries in Atlantic salmon could be reduced through selective breeding. Additionally the relatively high correlation that was found between adhesion scores and melanin scores show that there is unlikely to be a need to select against both of these traits. The researchers warned that including an additional breeding trait objective would reduce the genetic gain for other traits, and therefore recommended that other solutions to the vaccine injuries be found, such as improved vaccines or vaccination procedures. The results also indicated a negative genetic correlation between body weight and adhesion or melanin score; however, these estimates are most likely biased towards the negative end of the scale, since vaccine injuries are expected to reduce growth rate for non-genetic reasons.  The study also indicated “no genetic association between resistance to furunuculosis and ISA (infectious salmon anaemia) and vaccine injuries”, which suggest that the vaccine injuries are caused by other aspects of the immune system, not associated with the genetic variation in innate resistance to these diseases.

Research of this kind is extremely important to the aquaculture industry to enable it to continue to develop, expand and improve, not just the industry, but the procedures, processes and systems associated with it. ASReml is widely respected and used in situations such as this; the recently released ASReml 3 provides users with even greater ability to analyse large datasets or complex models, quickly and efficiently.

Our thanks to Dr Bjarne Gjerde of Nofima Marin for his help with this story.

And there can be no doubt of the importance of such programmes to the Mongolian Przewalski horse, which for around 30 years was bred in captivity under a project led by the Institute of Zoology at the Zoological Society London, and has been successfully reintroduced into the wild. Without the captive breeding programme this wild horse would have become extinct, indeed for many years was classed as such.

Since the 1970’s captive breeding programmes within zoos have become more important as wildlife populations decreased and the shipping of endangered animals became more restricted. Zoos needed to be able to successfully breed their own animals to maintain animal stocks, leading to the creation of “studbooks” listing information on the captive species and enabling zoos to recommend pairings and help maintain genetic diversity by avoiding “in-breeding”. This data can be very detailed, with birth, sex, parentage, source and the animal’s movement between institutions, as well as size and weight information.

Despite the increasing importance of captive breeding programmes to manage zoo stocks and animal conservation, very few studies have been carried out to understand selection and adaption to captivity. Admittedly quantitative genetic studies can be labour intensive which may help to explain this, however a recent study published in “Trends in Ecology and Evolution” suggests that the use of zoological records obtained from captive bred animals (such as data found in the “studbooks”) may be useful in studying selection and adaptation to captivity. The researchers concentrated on using a statistical technique known as the “animal model” -  a mixed effects model and a form of linear regression, where the explanatory terms are both fixed and random.

The animal model is already widely used in studies for the genetic improvement of livestock, and is increasingly being used in evolutionary studies, so conservation is the next logical step.

What is the animal model?

The animal model allows a phenotypic trait to be split into its additive genetic and other non-genetic variance components; it requires data that already exists or is easy to obtain for captive populations, for example information on the pedigree and phenotypic data on quantitative traits, such as body weight. Additional factors which highlight the suitability of this approach include the ability of the animal model to analyse unbalanced datasets, common with captive populations due to missing phenotypic data or pedigree links.  Using the data in existence and appropriate statistical tools could lead to a far greater understanding of the effects of captivity on the genetic variation of different traits. Although there are a host of other issues of interest to evolutionary biologists and conservationists, in terms of a captive breeding programme the heritability of reproductive traits, such as litter size or birth mass would be of vital importance.

As an example the researchers looked at the heritability estimate for the litter size in Sumatran tigers. Using ASReml, they were able to deduce that environmental factors appeared not to play a part in litter size; hence if female tigers producing large litters are bred by zoos, then it’s likely that large litters will continue to be produced in captivity, because this is a heritable trait.

To take this to the next step and achieve the goals of the reintroduction of endangered species to the wild, scientists need to be able to understand which traits are beneficial to both captive and wild populations. The use of existing data and statistics means that we could be closer to this. Certainly the researchers behind this study suggest that fine-tuning the data collection for studbooks could assist with very detailed analyses and understanding of genetic traits within captive populations, leading to more successful captive breeding programmes.

ASReml is a highly respected animal model software package and is perfectly suited to researchers working in genetics and animal breeding; it is already widely used in animal breeding programmes, due to its flexibility, speed and accuracy. As well as being specifically designed for the fitting of linear mixed models using the Residual Maximum Likelihood (REML), it is ideally suited to large datasets with complex variance methods. Please email VSNi with your name and organisation details quoting reference ZOOCB09 to obtain a free 60 day trial.

Our thanks to the researchers, especially Fanie Pelletier for their help in this piece. For more information on their research see the pages at the Université de Sherbrooke.

The original paper can be accessed from Trends in Ecology & Evolution, Volume 24, Issue 5, 263-270, 06 March 2009.

Our plans for 2010 and beyond are to continue to provide outstanding software for the biological sciences, across the world, and specifically to assist in areas where funding is limited – the developing world and education. As the Copenhagen talks come to a close we are mindful of all our responsibilities to support scientists and researchers working to understand and suggest solutions to the issues, by providing the best and most appropriate data analysis software.

To find out more about our software, and how it assists scientists and researchers visit our webpages, or email us for a free 60 day trial at support, with your name and organisation quoting reference VISION17CB, and we will arrange one for you.

We wish you all a very happy Christmas and successful New Year. This year we have opted not to send any Christmas cards, and to support the World Food Programme, Gift of Hope .

Please note our offices will be closed from 25th December to 28th December 2009, and on 1st January 2010 – we look forward to meeting and talking with you in the new year.

Best wishes,

Stewart Andrews
CEO, VSN International

A study, published in the Journal of Tropical Animal Health and Production, has been looking at determining the true reproductive and productive potential of the Matou goat, with a greater understanding of the reproductive parameters. The paper “Reproductive performance of Matou goat under sub-tropical monsoonal climate of Central China”, by researchers in China and Pakistan estimated the reproductive performance of the Matou goat to evaluate a meat breed, with the help of GenStat.

Goats are one of the oldest domesticated animals, providing milk, meat, hair and skins, plus the possible use of their dung as fertilizer. Their importance to local agriculture and communities has made them a popular animal in many developing countries, so to be able to evaluate and then recommend particular breeds for particular climates could be a great advantage to goat herders.

The Matou goat, indigenous to China, has a reputation for rapid growth rate, big build and good meat quality; it is therefore very highly prized. Its reproductive performance is dependent on both genetic and environmental factors, with environmental issues, such as the seasonal variety of feed, playing an important role.

The researchers gathered data from Matou goat herds (averaging 25 goats in each herd) from the main Matou goat production area of Shiye city in Hubei province. The does were checked daily, and their age at puberty, kidding, litter size and survival rate until puberty recorded, as was each kid’s weight within 24 hours of birth.

The data was analysed in GenStat using regression techniques and descriptive statistics. GenStat’s descriptive statistical techniques, which automatically calculate, maximum, minimums and the mean when data is read into GenStat, allow researchers to easily see some simple quantitative descriptions. The world-class regression techniques in GenStat allowed the researchers to explain or identify relationships between variables.

The results indicated that the litter size increased with the number of pregnancies, until the fourth kidding, and then the litter size dropped; however as litter size increased so the survival rate of kids decreased. Mortality rates among kids is an important factor determining the productivity of a herd, so to understand and be able to identify the optimum litter size and number of pregnancies could be important in promoting one breed of goat over another.

The information and results of the statistical analysis of this study indicate that the Matou goat is an important breed and even in these early stages of study could be recommended to other parts of China and other areas of the world having a similar climate.

Information such as this could be vital for goat herders across China and other similar areas; the ability to pick appropriate breeds for specific areas, and reducing some of the worries associated with animal breeding. The confidence in the results comes to some extent from a confidence in the software being used: GenStat is a tried a tested data analysis software package used throughout the world by biological scientists.

For more information on GenStat and its capabilities go to the VSNi webpages or to read the original research paper in the Tropical Animal Health and Production (2008) 40:17-23.

On a recent trip to Vietnam Stewart Andrews, our CEO, visited Vinh University, a trip which coincided with their prestigious 50th anniversary celebrations.  The trip came about after Stewart attended the ABIC conference in Thailand and met Professor Tran Ngoc Lan and Nguyen Tai Toan, from the Faculty of Agriculture, Forestry and Fisheries at Vinh University. Their discussions at ABIC made each realise the common goals and objectives they shared, and it quickly became apparent that VSNi could support the university via our Discovery programme.

“The developing world often has to “make do” with a patchwork of free or open source tools.  We are delighted that Vinh University can now analyse their data all within one system. GenStat’s intuitive menu system enables the researcher or student to understand simple tasks right through to advanced analysis with ease”. Says Stewart.

During his visit, Stewart attended the Biodiversity Workshop organised by the Professor and presented GenStat Discovery. The presentation, and the software, was so well received that Stewart eventually delivered the Discovery workshop twice!

Discussions in Vietnam, and on his return, have now led VSNi and Vinh into the development of a collaborative working agreement.

“Yesterday I used Genstat program to analyse my data, it was very
superexcellence. Thank you very much.” – Nguyen Thi Thanh, Entomologist, Vinh University

Already widely used by scientists and researchers in the biosciences the new procedures and functionality added at version 3 will further extend ASReml’s ability to analyse large datasets and complex statistical models. Since its original launch ASReml has enabled scientists to investigate and understand relevant factors in breeding and genetics leading to for example, the identification of superior grain varieties for farmers in Australia (based on the National Variety Trials system) and generating herd heritability estimates (based on research at the Pennsylvania State University).

For full details see the main ASReml page.

ASReml 3, the outstanding data analysis software designed for fitting linear mixed models is now available for download.

Already widely used by scientists and researchers in the biosciences the new procedures and functionality added to version 3 further extend ASReml’s ability to analyse large datasets and complex statistical models. Since its original launch ASReml has enabled scientists to investigate and understand relevant factors in breeding and genetics leading to for example, the identification of superior grain varieties for farmers in Australia (based on the National Variety Trials system) and generating herd heritability estimates (based on research at the Pennsylvania State University).

ASReml is vital for researchers with very large datasets, who may need to apply complex statistical models. Unlike other software systems ASReml can easily and efficiently handle large datasets. ASReml is unique in that it uses the Average Information (AI) algorithm for the fitting of linear mixed models and it is optimised for working with genetics data and hence performs considerably faster than other generic data analysis software. The superior range of variance models are specifically suited to the analysis of data arising from genetic applications in animal and plants, so ASReml is chosen by researchers and scientists who need these specific analysis tools provided in ASReml. It also has an in-built link with R for scientists and researchers who prefer to use the r programming environment rather than more generic windows based systems.

It was and continues to be developed by researchers working in bioscience, so the statistics are sound, valid and most importantly relevant to the needs of bioscientists. Users are supported by VSNi (if they have a support contract), a range of tutorials and user guides and by an active user community through the ASReml Forum, available through the VSNi webpages.

For a free 60 day trial of ASReml please email support with your name and organisation quoting reference VISION16CB, and we will arrange one for you.

For more details on ASReml, look at the new features.

Technical tip – New supported platforms for ASReml 3

ASReml has now been updated to run on both 32 bit and 64 bit applications of Linux both as the stand alone command driven ASReml and as the ASReml-R add on. Additionally Apple Macintosh users can run also run ASReml in a batch mode.

Windows (32 and 64 bit) users can use ASReml 3 as the command driven version, Windows interface or as an add-on to R.

For a more detailed explanation see the ASReml webpages.

Latest training courses

VSNi run training courses throughout the year on a variety of topics, and we are currently planning the schedule for 2010. Please email support with any specific training requests or go to the training pages on our website to keep updated on the latest courses.

Out and about with VSNi

From 29th November to 3rd December, the GenStat developer Dr David Baird will be presenting at the International Biometric Society Australasian Region Conference. Dr Alison Smith, a keen ASReml user, based at Wagga Wagga Agricultural Institute is also presenting her work on grain quality testing with ASReml at the conference.

If you would like to meet with VSNi staff at one of these events please email Carey Biggs to arrange the details.

We’re always updating the list of events we can support and sponsor – so please send us details of any events you are organising or involved in, and as we decide on more events for the future we’ll list them here.

KWS is one of the world’s leading suppliers of seeds to the farming industry and it is therefore no surprise that research into plant breeding and seeds is a crucial part of their work. The research and development teams at KWS focus on yield, seed quality, resistance to disease and pests, enhancing the nutrient quality and improved processing capability of the plants.

Recently the researchers at KWS decided to change their data analysis software portfolio by introducing GenStat. During the last 2 years main emphasis was put on establishing GenStat for statistical analysis of phenomic data from their field.

GenStat was chosen, because it was possible to analyse data from trials with large Alpha Designs with the REML techniques. Plus, GenStat’s REML algorithm dealt with missing values in a more superior way than the previous software.  REML itself can be used to analyse models with several types of error variation (multi-level models) and to fit models to correlated data like repeated measurements. GenStat’s powerful command language streamlined the data analyses, reducing the calculation time by at least 70%.

Additionally GenStat’s efficient data handling meant that far less data pre-processing was needed. GenStat allows the full integration into automated analysis pipelines, thus  decreasing calculation times by 30%. This allows the flexible addition of new statistical methods into the pipelines.

KWS researchers use a wide variety of different factors in their trials, using Block Designs, Alpha Designs, Lattice, Split Plot, Multi factorial Experiments. GenStat allows the researchers to easily design and analyse these and other types of experiments. GenStat also effortlessly handles large and complex data sets, which will be essential for the analysis of genomic data and their association with phenomic informations. Combine these factors with GenStat’s ability to easily export and import data from other databases and the plant breeder has very powerful analysis tools.

As well as the extensive technical capabilities of GenStat, KWS chose GenStat because of its history within agricultural science and research; it was originally designed by statisticians working on agricultural research at Rothamsted Research; where Fisher, Yates and Nelder (to mention but 3) developed statistical techniques that are central to modern statistics. GenStat’s connection with Rothamsted continues today.  Developments in each new version of GenStat reflect the needs of the agricultural scientist, including ANOVA, design of experiments a host of multivariate analysis techniques and linear mixed model analysis. A huge variety and complexity of data analysis is possible in GenStat, and an understanding of the requirements of experiments in this area is reflected in the terminology and thinking behind GenStat. But GenStat’s history not only shows its suitability to any form of agricultural research but also shows the stability of its performance; it has been tried and tested for over 30 years.

Coupled with the power and background of GenStat, VSNi were able to provide a bespoke consultancy service to KWS to assist with their specific needs; this is possible because our developers have been working with agricultural researchers and statistics for years. The VSNi statisticians were able to talk to the researchers at KWS, in their language to find more efficient ways to run their analyses.

In a subject area that demands precision and security GenStat provides the data analysis solution that removes the unknowns surrounding the research. You can trust the statistics within GenStat, because it is developed by people who know and understand the issues in agricultural research today.