From Help:

When using Open DataBase Connectivity (ODBC) with a 64-bit version of Windows, there are two versions of ODBC within Windows, 32-bit ODBC and 64-bit ODBC. If you have installed a 64-bit version of GenStat, the client menus will only work with the 64-bit ODBC control panel and drivers. If you concurrently also have a 32-bit version of GenStat installed, this will work with the 32-bit ODBC control panel and drivers.

Traditional breeding methods use the plant or animal’s physical characteristic (phenotype) observed in a set of environments to determine its breeding value (BV); this is often time consuming and inefficient. Many of the traits used to define a BV are only observable once the plant or animal has reached full maturity which will generate high costs with long time-to-market period.

As technology has moved on researchers were able to identify specific DNA regions (QTL) associated with a specific phenotypic trait. The locations of these QTLs were identified on linkage maps, and then the markers correlated, thus creating genetic markers. The problem with marker assisted technology is that for quantitative traits, it relies on a few QTLs to predict BV, and as such is limited in its predicting power since a lot of QTLs with small effects are all neglected.

The method of genomic selection to predict a genetic value attempts to circumvent some of these problems in defining B. It combines marker data, phenotypic and pedigree data to try and improve the accuracy for predicting breeding and genotypic values. This process should speed up the ability to identify genetic gains and hence bring seeds and new animal breeds to market quicker.

Given the importance of efficiency and accuracy required in scientific studies designed to provide predictions, ASReml has been recognised as one of the most appropriate pieces of software to assist in the calculations. Using ASReml, we can easily do the following analysis necessary for the application of GS:

1. Numerator Relationship Matrix (A)
2. Genomic Relationship Matrix (G)
3. Obtain G Matrix from Allele Frequencies
4. Regression Method to Obtain G Matrix
5. Normalized G matrix method (GN)
6. Calculating the Inverse of G
7. Combined Pedigree and Genomic Relationships (H)
8. Obtain G-1 matrix
9. Calculating the GBLUP Breeding Value

To read about methods of Genomic Selection see the following scholarly articles:

http://www.gsejournal.org/content/43/1/18 (Different models of genetic variation and their effect on genomic evaluation)

http://www.genetics.org/content/early/2012/01/23/genetics.111.137026 (Accuracy of Genomic \selection Methods in a Standard Data Set of Loblolly Pine (Pinus taeda L.)

1. The files contained in the zip file MUST be extracted prior to installing the AddIn.

2. Next, locate the GenStaAddIns folder – for your User profile. You can do this by simply selecting Tools | Options from the GenStat Menu bar. The default location of the GenStatAddIns folder for your profile will be shown.

Please Note: If you do not have Administrator Rights for your computer – you can change the location of the add-in folder using the Options menu. Locate it somewhere that you can access without requiring administrator rights.

3. To install the Add In files, first shut down GenStat if you are running it.

4. Go to the GenStatAddIns folder in the location you have just identified.

5. Then simply copy the extracted files into the AddIns folder.

6. Launch GenStat, you will find a new menu item ‘User’ (this may already be there if you have previously created AddIns). Click on ‘User’ and the CycDesigN applications are shown:

It is remarkable to think that we are now in our 9th year of providing free research tools to scientists and researchers who, through lack of funds, would not be able to access quality statistical analysis software. We also recently added ASReml to the Discovery Programme.

GenStat Discovery is provided absolutely free to scientists, researchers and teachers from government or educational establishments based in qualifying countries.

To request a trial of GenStat Discovery, make sure you meet the qualifying conditions, and then register here.

Whilst any and all of the various different types of weed can cause significant damage, and most will require careful management techniques to control, one specific type of weed – an invasive weed, can have devastating effects. An invasive species of weed is one which can be non-indigenous or natural to the local environment, but has a tendency to crowd out other plants or crops. This disruption can affect the local ecosystem significantly and cause economic implications and loss to farmers who find themselves over run by invasive weeds.

And yet there is only a limited amount of information on the population dynamics and ecology for invasive plants, and the same is true for the associated soil seed bank ecology or seed persistence: hence it has been difficult to formulate well planned strategies for the management or elimination of invasive weeds. The soil seed bank ecology and seed persistence of one particular species of invasive plant however has recently been studied by Australian researchers at the Alan Fletcher Research Station in Queensland. Lantana camara is now considered one of the most invasive species of weed in tropical and subtropical areas, and has been labelled as a Weed of National Significance in Australia, hence the researchers chose to try and understand the factors affecting seed survival and seedling emergence. Historically it has been a difficult weed to manage through biological measures or through introduction of specific pests to control its spread. Additionally as its seeds are bird-dispersed it is difficult to manage through the removal of seed sources, given the sources can be wide ranging. So one possible method of controlling or eliminating Lantana camara is through depleting its soil seed bank, and in order to do this a deeper understanding of the Lantana’s seed germination and survival is needed.

A detailed study was carried out over a number of years in Queensland using controlled sites with artificial irrigation and more natural sites using rainfall for watering. Information was taken on seed size, germination responses including monitoring the depth the seed was planted and the climatic conditions, seedling emergence and seed survival as well as the different environmental conditions.

The results from the study certainly back up the concept of Lantana being a difficult weed to control. As well as the seeds being bird-dispersed and thus requiring control measures for a wide area, the study showed that seedlings germinate in a variety of conditions, although are most successful in warmer and lighter conditions, suggesting that the closer the seeds are to the surface the more likely they will germinate and are most likely to germinate during the Summer months. The Lantana seedling emergence was affected by many factors, although it was lower for the more deeply buried seeds, suggesting that forms of control through seed suppression, such as mulching could be beneficial in controlling Lantana. Alternatively the seeds could be encouraged to the surface to stimulate emergence and then treat the weeds as they emerge. Through regression techniques in GenStat the researchers discovered that seedling emergence could continue for 3 – 11 years (depending on the depth of the seed) after weed management programmes have removed the actual Lantana plants. The details and information discovered on the ecology of the Lantana suggest that to effectively control or eliminate this weed farmers and agricultural producers would need to employ on-going management techniques and intensive follow-up on the weed management programmes, as the seed survival and persistence of the Lantana appears to be far greater than first thought.

Our thanks to Dr Gabrielle Vivian-Smith and her colleagues at Alan Fletcher Research Station for their help in producing this story.

The full paper is available here.

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

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.

VSNi is proud to announce the launch of GenStat for Teaching and Learning – a new free product based on the current Genstat 13, but amended to make it suitable for school and undergraduate students. The easy to use GenStat Menu system provides access to a wide range of standard statistical analyses, including: basic statistics, statistical tests (t, chi-square, nonparametric tests etc), regression (general models not just e.g. simple linear regression), generalized linear models, nonlinear models (standard curves and user-defined), analysis of variance, design of experiments and sample size, REML analysis of linear mixed models, multivariate analysis, six-sigma, survival analysis, time series and repeated measurements.

For full details of the key differences between GenStat for Teaching and Learning and the full commercial edition GenStat 13 please visit the website.

GenStat for Teaching and Learning is licenced at an institutional level, but individual trials are available by emailing support.

For researchers working with complex models and larger data sets, the Discovery Programme has been expanded to include ASReml Discovery. Recently launched ASReml Discovery is available to everyone involved in education, and also to non-commercial research organisations based in eligible countries. More details on ASReml Discovery and how to register for a copy can be found on the VSNI website.

Technical Tip – bootstrapping in GenStat

Bootstrapping is now a popular technique for estimating the variability of estimates from a given set of data while making no assumptions about the distribution of the data. The main assumption in bootstrapping is that the observations are exchangeable. In simple circumstances this means exactly that: so, under the “null hypothesis” of no differences between the treatments, the observed data values could have been allocated to any of the observed units. To bootstrap a data set, we draw a new set of observations from the exchangeable units by sampling with replacement from the original units. In each bootstrap sample, the original observations may be omitted or sampled one or more times.

With the launch of the GenStat Teaching and Learning Edition comes a new menu for bootstrapping, which will also be available in the 14th Edition later this year.

We can illustrate the use of the menu with data in the GenStat example spreadsheet Cane.gsh (in the GenStat Data folder). This contains yields of sugar cane under a range of levels of nitrogen fertilizer. The menu is shown in Figure 1.

Figure 1

We need to select the statistics to bootstrap from the choices in the drop-down list at the top of the menu; further boxes then appear to specify the data needed for that analysis. Here we have chosen to look at a linear regression between sugar yield and nitrogen. Running the analysis with the default options produces graphs showing the distributions of slopes and intercepts from the bootstrap resampling.

Figure 2

Figure 2 shows the distribution of the slope, and you can see that a slope of zero is outside the 95% confidence interval. So we can be confident that there is a relationship between sugar yield and the amount of nitrogen fertilizer. The menu produces the output below.

Other statistics available in the menu include means, medians, differences between means and medians, t-statistics and correlations.

The menu uses the BOOTSTRAP procedure, and you can look at the Input log to see how it does this if you want to use the procedure for more complicated analyses.

BOOTSTRAP needs a procedure RESAMPLE to do the analysis and calculate the statistics to be studied. This has a DATA option which passes a pointer containing the resampled data into the procedure. The STATISTICS parameter lists the estimates of the various for this sample, and the EXIT parameter returns a value of 1 if the estimation has failed and 0 otherwise.

The DATA pointer for RESAMPLE is created from the data structures that are listed for the DATA option of BOOTSTRAP. So DATA[1] contains the y-values (Yield), and DATA[2] contains the x-values (Nitrogen). The parameter of BOOTSTRAP lists names to be used to label the statistics in the output.

Figure 3

Figure 4

We can modify RESAMPLE if we want to use this as a template for another analysis, for example, an exponential regression. The easiest way to form the commands is to run the Standard Curves menu (under Stats | Regression Analysis) shown in Figure 3, and then set up the Save dialog (click the Save button) to save the estimated coefficients of the curve, as shown in Figure 4. After running this the Input log contains the following commands:

We can cut and paste this into the RESAMPLE procedure to bootstrap the analysis. We create a new text window (Ctrl+N), cut and paste the original RESAMPLE code to this window, and then paste the code above to replace the linear regression code. We need to make the following changes to exponential regression code we pasted in: change Yield to DATA[1] and Nitrogen to DATA[2], set the PRINT option of FITCURVE to * (unless you want output and graphs from the 100 bootstrap datasets!), return the 3 estimates in STATISTICS, and finally rename the parameters in the BOOTSTRAP statement to give the following amended program (with some additional editing to tidy up the code):

Running this program will now give bootstrap confidences limits for the exponential regression parameters. Figure 5 shows the bootstrap distribution of the parameter R.

Figure 5

So the new menu not only makes bootstrapping a straightforward process for many standard analyses, it also helps you to use the bootstrap for more complicated analyses. For more information you can either look at the on-line help for the Bootstrap menu, or the description of the BOOTSTRAP procedure in the GenStat Reference Manual 3 Procedure Library which can be opened by selecting the Procedure Library sub-option of the Reference Manual option of the Help menu on the menu bar.

Latest training courses

An Introduction to ANOVA and Design in GenStat is scheduled for 28th and 29th July in Pretoria, South Africa. More details can be found on the VSNi website.

Our training schedule is always being updated according to user requests and requirements, so do email support with suggestions and any specific requirements you have and check our website for new courses.

Out and about with VSNi

The 2011 events we are attending or supporting include: SUSAN at the University of Botswana, Gaborone, Botswana from 27 June – 1 July 2011, the Australian Applied Statistics Conference in Palm Cove, Tropical North Queensland from 12-15 July 2011. 58th ISI World Statistics Congress in Dublin, Ireland, from 21- 26 August 2011.

If you would like to meet with the VSNi staff at one of these events please email support 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 on our website.

Other News from VSNi

Success for the GenStat sponsored Holy Trinity Mariners U9′s football team! After a disappointing 2-0 defeat in the cup final, the Mariners returned to form with a resounding 4-0 victory in their last game of the season making them winners of the Chiltern Church U9′s League. Well done Mariners!

You can view a world map of our software being used in Conservation

View Conservation in a larger map

Sumatran tiger secrets unravelled using ASReml

Zoos have come a long way since the original Zoological Garden in  Regent’s Park, London. They are not just a collection of animals for  people to look and scientists to study; evolving into centres of  research and play a vital part in wildlife conservation and protection.  Certainly in the UK you would be hard pushed to find a zoo that doesn’t  have some form of conservation programme. Read more.

Capturing Nature’s Value

Conservation is becoming one of the most important, topical and  contentious issues of our day. Historically most people understand  conservation in the sense of an area set aside to protect local  wildlife. Typical examples of this would be the game reserves of Africa,  and other such national heritage sites across the world. But  conservation, as we are beginning to understand, is not so simple as  roping off an area to protect flora and fauna within it. This is perhaps  a simple way of protecting our environments, but we equally need to  protect privately owned areas of land.  A system known as payment for  ecosystem services (PES). Read more.

Elephants, bees and crop protection

The conservation of the African elephant provides tremendous opportunities for simultaneously conserving biodiversity and increasing benefits to local communities. Their role as a flagship species of global significance helps maintain biodiversity of the ecosystems they inhabit. In addition, the cultural and aesthetic values of elephants are also important, not only to African societies, but to the world at large. But this needs to be balanced against the fact that as the African elephant range becomes more and more fragmented and elephants get confined into smaller pockets of suitable habitat, humans and elephants are increasingly coming into contact and in conflict with each other. Read more…..

Can you imagine a world without elephants?

Elephants – their sheer size, incredible looks combined with intelligence, shyness and gentle nature are awe inspiring to most people; indeed is there a child in the world who is not wowed by them…so can you imagine a world without them? Read more……

GPS tags for tortoises

Giant Tortoises of the Galapagos Islands are the latest to project to benefit from the statistical analysis techniques available in GenStat. VSNi are delighted to be working with Stephen Blake and his colleagues in their study on the movement ecology of the Galapagos Tortoises. These amazing creatures are being fitted with GPS tags to track their movements as a part of a research programme and educational and outreach programme to increase our understanding of them, and to enable an appropriate conservations programme. For more information on this fascinating project and these beautiful creatures please visit www.gianttortoise.org More information on the part GenStat plays in this research project will be coming soon.