A key user of GenStat is the IAMZ in Zaragoza, Spain, whose main aim is to train people in how to do research. The IAMZ stands (In Spanish) for the Mediterranean Agronomic Institute of Zaragoza, it is one of the 4 sites of the CIHEAM (International Centre for Advanced Mediterranean Agronomic Studies). The main purpose of the CIHEAM is “to develop cooperation between Mediterranean countries through postgraduate training and promotion of cooperative research in the field of agriculture and natural resources.” As a part of this the IMAZ develops Master programmes and advanced courses for professionals designed to update and further the training of researchers in agriculture and its related sciences.

Students come from all over the world to study for the Master programmes, some are straight from their first degree and wish to complete their education, and some are already at work and wish to update their knowledge and understanding of agricultural research. I spoke with Dr Ramzi Belkhodja from the IMAZ, who heads up the Master in plant breeding. The programme covers several areas, one of which being statistics and the use of statistics in plant breeding research. Dr Belkhodja and his colleagues recognised the need for anyone either in plant breeding research or planning on entering the field, to have a good understanding and grounding in statistical methods.

Currently the team teach their statistics course using GenStat. Given GenStat’s history in agricultural research it is perhaps no surprise that it is used as the phraseology and terminology reflects the language used in the biosciences. GenStat contains a huge range of statistical techniques; in fact you would be hard pushed to find a statistical test or routine not included in GenStat. However, it isn’t just GenStat’s inherent statistical strength or its history that makes it so useful to Dr Belkhodja and his colleagues.

GenStat’s straightforward Windows menu system means that the statistics is easy to teach and importantly easy for the students to pick up and understand, indeed Dr Belkhodja told me that the students are up and running with GenStat and statistical techniques within 3 teaching sessions. The self-explanatory menu system means that the statistical methods and concepts are easy to manage and understand. Combined with the speed and ease of importing any data from Excel (for example) into GenStat, and the lecturers at IMAZ can spend less time on the complicated syntax and more time on the analysis itself.

Given the students come from all over the world; GenStat also provides an added advantage for those coming from the developing world. When the students return, they can obtain a free copy of GenStat – as GenStat Discovery, to continue their research. So, in a world where “time is money”, students don’t need to learn a new package in their work environment; they can implement their learning and statistical understanding in a package they already know and trust.

With all the complexities facing the world from climate and environment changes to how we feed the world, one thing is clear - we need research in order to plan effectively. And research needs well trained researchers; people who understand agricultural issues and concerns, and who know how to research effectively. GenStat is known as a key research tool for many agricultural and other bioscience researchers, but we were delighted to find GenStat also has an important role to play in the teaching of these areas.

A key user of GenStat is the IAMZ in Zaragoza, Spain, whose main aim is to train people in how to do research. The IAMZ stands (in Spanish) for the Mediterranean Agronomic Institute of Zaragoza, it is one of the 4 sites of the CIHEAM (International Centre for Advanced Mediterranean Agronomic Studies). The main purpose of the CIHEAM is “to develop cooperation between Mediterranean countries through postgraduate training and promotion of cooperative research in the field of agriculture and natural resources.” As a part of this the IAMZ develops Master programmes and advanced courses for professionals designed to update and further the training of researchers in agriculture and its related sciences.

Students come from all over the world to study for the Master programmes, some are straight from their first degree and wish to complete their education, and some are already at work and wish to update their knowledge and understanding of agricultural research. I spoke with Dr Ramzi Belkhodja from the IAMZ, who heads up the Master in plant breeding. The programme covers several areas, one of which being statistics and the use of statistics in plant breeding research. Dr Belkhodja and his colleagues recognised the need for anyone either in plant breeding research or planning on entering the field, to have a good understanding and grounding in statistical methods.

Currently the team teach their statistics course using GenStat. Given GenStat’s history in agricultural research it is perhaps no surprise that it is used as the phraseology and terminology reflects the language used in the biosciences. GenStat contains a huge range of statistical techniques; in fact you would be hard pushed to find a statistical test or routine not included in GenStat. However, it isn’t just GenStat’s inherent statistical strength or its history that makes it so useful to Dr Belkhodja and his colleagues.

GenStat’s straightforward Windows menu system means that the statistics is easy to teach and importantly easy for the students to pick up and understand, indeed Dr Belkhodja told me that the students are up and running with GenStat and statistical techniques within 3 teaching sessions. The self-explanatory menu system means that the statistical methods and concepts are easy to manage and understand. Combined with the speed and ease of importing any data from Excel (for example) into GenStat, and the lecturers at IAMZ can spend less time on the complicated syntax and more time on the analysis itself.

Given the students come from all over the world; GenStat also provides an added advantage for those coming from the developing world. When the students return, they can obtain a free copy of GenStat - as GenStat Discovery, to continue their research. So, in a world where “time is money”, students don’t need to learn a new package in their work environment; they can implement their learning and statistical understanding in a package they already know and trust.

More information on GenStat is available on the VSNi website and for more details on the work and courses of the IAMZ look here.

Product News

ASReml, now at version 2, offers free add-ons for supported ASReml licenses, for S and R. Details on how to download this can be found on the VSNi website. Users will need to install their existing asreml.lic license key in C:\program files\Common Files\VSN International\Licenses which will enable the R and S add on to work, once downloaded. If they do not have this directory then they will need to install the asreml.lic file in the Bin directory within R and or S.

Technical tip - User Support

Whether you are installing a new license key for the first time or a replacement license key because you have changed your PC, you may come across the below error message:

The reason for this error message is generally due to the following:

Sometimes neither of the above has occurred, which means that your PC may be generating a new MAC address each time you reboot, which keeps changing the Host ID every time you use the machine.

The only way that users can overcome the problem if this keeps occurring is by sending us their machine details. The way to display these is by opening up a command Prompt and typing in ipconfig/all and selecting enter. This will display their machine details which they then will need to send us, the Host name and Physical address, for us to send them a license key. This will then solve the problem of the Host ID error message appearing regularly.

Out and about with VSNi

Another successful Australian GenStat User Conference is underway; this year the agenda covers a variety of presentations from developers and users alike based on “Biometrics in Primary Industries and the Environment”. Thank you to the organisers; and all those attending who will help to make a successful event.

We’re always looking for 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.

Latest training courses

The next training course is for Regression, nonlinear and generalized linear models in GenStat, scheduled for 5th and 6th March 2009 at The Paper Trail in Apsley, UK. To find out more or to book please look at the training page on our website.

As a part of our continued update and development of our courses, please let us know if you have any suggestions or topics for future training.

Don’t forget that we also offer specific on-site training if our current schedule doesn’t fit with your requirements. Please email the training team for prices and further information.

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.

As a leading supplier to agricultural research it’s fascinating to watch the stir being created by articles on GM crops and the use of pesticides, especially with the proposed EU legislation. But it’s not necessarily clear which route governments and the industry should be taking…

From fears of poisoning the earth with misuse of pesticides versus the potential reduction in Europe’s food production and food price increases if some pesticides are banned, to the ethical and moral issues over genetically modifying crops at one end of the spectrum to the real need and ability to provide crops that can withstand pests, diseases, droughts or other climatic conditions. And now recent research suggests that some GM crops may even protect neighbouring non GM crops (American
Association for the Advancement of Science (2008 September 19) “Genetically Modified Crops Protect Neighbors from Pests”). Each side of the argument seems compelling, so what should we do?

One thing is clear, sound reliable research is required for sensible future planning. And that is something we at VSNi can help with. By providing outstanding data analysis software, specialising in the biosciences, throughout the world we can assist researchers in each area to come up with results and projections based on solid data analysis.

VSNi software is firmly rooted in the biosciences and agriculture. GenStat, originally developed by statisticians at Rothamsted Research in the UK, is still extensively used there today. Projects range from developing strategies to reduce the use of pesticides, improving production and quality of crops or by using the earth’s natural biodiversity to help improve production or crop yields and
reduce the need for pesticides.

Improving yields covers the simple definition of “more” to the quality of the crop and the ability of the crop to adapt to different environments. For example, researchers in a plant breeding department work on developing hybrids, which then need testing in large multi-year, multi-locations trials. The purpose of these trials is to select the hybrids which have the highest yield potential, and the best adaptation to particular environments. Here it’s clear that whilst crop yields can be
improved, there is very little purpose in doing so if they do not adapt well to their targeted environments. In these analyses GenStat’s REML has been particularly useful for spatial analysis of trials conducted on uneven soils and for meta-analysis of large trial datasets; this allows researchers to evaluate genetic gain and the general and specific combining ability. The crucial issue in this
instance is the ability of the chosen data analysis package to provide complex statistical approaches, in an easy to use way; i.e. experimental design, analysis of variance and mixed model approaches (REML), but in a menu system.

“The experimental design tools of GenStat are really excellent and user-friendly. I think that it is the only statistical program that offers complex statistical approaches that are extremely useful in agricultural research in a friendly, easy-to-use way.”Abelardo de la Vega, Advanta Semillas

Another aspect of increasing yields is how herbicides are used. Researchers in Latvia are testing for the effectiveness of different herbicides to control weeds and maintain crop safety. One such research project centred around surveys on flora in five arable fields in five regions of Latvia aims to assess the effects of crop rotation and crop husbandry practices. These projects are aimed at providing scientifically based information to help agronomists and farmers determine the need for
weed control.

GenStat’s REML techniques were key as the number of contributing factors varied and using this technique allows for the analysis of unbalanced datasets, and produces output equivalent to the analysis of variance. Researchers were able to include over sixty species of weed, and analyse the effect of the use of different herbicides, on different crops including the effect previous crops have on the
trials.

Another customer of VSNi’s is The Organic Research Centre at Elm Farm in the UK. The centre is designed to look at providing solutions that develop and support sustainable agriculture and land use. Based upon organic principles to ensure the environment’s health is as protected as possible, the research programs are conducted at the farm in Berkshire and across 25 other farms in the
UK. GenStat is primarily used in the wheat breeding trials, looking at how Composite Cross Populations (rather than specific varieties) perform under fluctuating environmental conditions.

“The great aspect of this new analysis is that we can use data from all 12 experiments, i.e. 3 years over 4 sites, to work out which of the varieties and populations are both yielding and reliable,” says Sarah Clarke, ORC, “we can also split the experiments into those that are organic and those that are non-organic, to see if the populations differ between systems.”

The aim is to produce wheat that performs well year after year in differing environments. It is therefore vital that the tools used by these researchers are reliable and provide sound statistical analysis techniques, so that the results from the trials are as trustworthy as possible.

ASReml is another such trusted product. Again born out of agricultural research requirements, this time in the analysis of 12 years of data from over 1000 wheat variety trials, today ASReml is used across the world by plant and animal breeders to help solve some of the big puzzles found in these areas. Its ability to handle the large datasets so often found in agricultural research is one of its main
strengths; indeed the National Variety Trials in Australia, handling data from around 600 trials across Australia in over 250 distinct geographical locations, rely on the results from their trials using ASReml to give reliable predictions on genetic value of different crops in different locations.

“ASReml is the package that helps farmers, breeders and crop variety evaluators obtain the most reliable predictions of genetic value for a range of crops grown in different environments; farmers can get the best information available about performance of varieties in their own location and make an informed decision,” says Professor Brian Cullis, Research
Leader for DPI Biometrics and leader of the SAGI (Statistics for the Australian Grains Industry) project.

Another key strength of ASReml is the accuracy, speed and flexibility of the complex two-stage linear models used in these trials.

But it isn’t just the big Western companies that benefit from our software. Since 2003 we have provided GenStat Discovery, (now at edition 3) to research and teaching institutes across the developing world. As Stewart Andrews, VSNi CEO says “there is no justification for poorer countries to not have access to high quality research tools. Their need for research based on solid,
reliable data analysis tools is as great, if not greater than the West’s; assistance to these countries should come in the form of providing opportunities for self help and self determination, not just aid or gifts.”

GenStat Discovery has become a key tool in the researchers kit bag; relied on by many agricultural institutes throughout the developing world; many of these researchers would not otherwise have access to data analysis software, nor would they be able to share their results and ideas with colleagues around the world.

“I have spent money on other software packages that fall short of my needs.” Ugwu Kenneth Okonkwo, University of Nigeria, Nsukka

For the CGIAR centres this is important, as they work closely with institutes in the developing world such as ILRI, ICRISAT or ICRAF, and need to be able to collaborate on research activities. It is the concept of trust that is so important in these types of trials and research projects; something that VSNi prides itself on. Our software is and has been trusted by
researchers for over 30 years. Trusted because of our history in agriculture - we understand the types of data and the analysis needs within this industry and trusted because of the sound statistical principles on which the software is based.

Technical tip - User Support

Felix Grant’s recent review of GenStat 11th edition mentioned many new features, including the ability to carry out partial or full canonical correspondence analysis (CCA) using a menu based system. Included on the menu is an option to plot a biplot of the results, showing how site or species scores are related to one (or two) of the environmental variables.

Another option could be to run the command CRTRIPLOT to see a plot of species scores, site scores and biplot scores of the environmental variables in a single plot. The user has many options including being able to plot the scores in a three-dimensional environment, add convex hulls and other graphics for grouped data and set the dimensions to be plotted. See the help file for a full list of options.

Read Felix’s full review of GenStat 11th edition, or download your trial copy of the 11th edition.

Out and about with VSNi

As we enter the last quarter of the year, the travel doesn’t stop for VSNi. A key event we are looking forward to is the Australasian GenStat User Conference, from 2-5th December at Marysville, Victoria. A packed agenda on Biometrics in Primary Industries and the Environment, and with many of our developers attending, it’s your chance to share ideas and quiz us face to face. More information and to register is available at their website.

Roger Payne once again attended the Joint Annual Meeting of the GSA, SSSA, ASA, CSSA, GCAGS, and HGS, this year, held in Houston on 5 - 9th October; this year speaking on “A Guide to Analysing Counts and Proportions in Complex Situations”, and specfically covering the methods of analysing counts and proportions from the experiments often found in agriculture and biology. It described the types of biological investigation that have led to the development of methods such as generalized linear
mixed models and hierarchical generalized linear models. His talk also showed how these methods extend the more familiar generalized liner models to allow you to take account of additional sources of error variation. For wrap up information on this event go to the event website.

If you are involved in organising an event which may be of interest to VSNi and our users please let us know.

Latest training courses

The next training course is for REML and multi-level models in GenStat, scheduled for 14th November at The Paper Trail in Apsley, UK. To find out more or to book please look at the training page on our website.

As a part of our continued update and development of our courses, please let us know if you have any suggestions or topics for future training.

Correction

In the last Newsletter we focussed on Dr Brian Miller, of the IOM, and his work in epidemiology; VSNi would like to make a correction to the report number listed, which should have read:IOM research report TM/07/06. Read the article again “http://www.vsni.co.uk/case-studies/the-institute-of-occupational-medicine/”>here.

“GenStat is one of those packages with a reliable and road mapped programme of regular updates. GenStat upgrades have never been less than thorough, stable, significant and workmanlike progressions for one of the industry’s most serious packages; this is no exception.” So says Felix Grant.

He goes on to comment on the 26 new procedures in GenStat 11th Edition, highlighting those for Self Organising Maps (SOM) and canonical correspondence analysis, along with the directive level developments. Also highlighted are the new graphics enhancements.

You can read his full article here.

For the full list of the GenStat 11th Edition new features go here.

To download a trial copy or to obtain your upgrade of GenStat go here.

Contact Us

For further information about this course or on-site training, please email the training team or call them on +44 (0)1442-450230.

Anyone who loves Italian food is likely to have a strong opinion about the best texture of cooked pasta. But how can you gauge this? And therefore give real, practical cooking tips? Or even know what type of pasta you prefer? Particularly as there are so many different factors affecting the texture of cooked pasta: length of cooking time, length of time left standing, size and type of pasta, as well as the brand itself. I know when I was a student the best method was always considered to be throwing a strand of spaghetti at the wall and seeing if it stuck; but looking back I think this method was more for the food fights which usually followed. Thankfully the food manufacturers have developed a more scientific method for testing the firmness of pasta, but even this is fraught with complications.

Until recently using trained panellists was considered the best tool for measuring the cooking quality of pasta products; however this is open to bias; be it localised preferences, leading to the prevention of an internationally recognised standard for testing pasta texture, or just personal, individual preferences. Using panellists is also expensive, slow and needs a large sample. Over time various different methods and instruments have been developed to evaluate pasta. However, it seems that different cooking and instrument settings in different laboratories have influenced sample rankings within the tests. These issues became apparent to Dr Mike Sissons and his colleagues, who are part of the Durum Wheat breeding program at Tamworth Agricultural Institute, in Australia. One of Mike’s jobs is to test the firmness of the Durum wheat which has come from varieties trials. To do this he has to make the spaghetti, dry it and then cook it, but the firmness is very sensitive to many of the choices made in how to test the spaghetti firmness. How many strands to test, how are they arranged, what is the speed of the cut etc. The biggest factor is over and under cooking and the continued cooking of the spaghetti after it has been removed from the water.

Mike’s experiments were to determine the effect on firmness of changing each of these factors. Differences of up to 10% were noticed between the firmness readings at Tamworth and in a collaborating laboratory in Canada. These differences far exceed differences between varieties and a standard method of testing of spaghetti firmness was desired.

Happily a paper recently published at www.redorbit.com covers this very point. The study was designed to investigate the effect of process and instrument variables on the testing of the firmness of cooked pasta and from this to devise a standard procedure that can be replicated in laboratories so that true differences can be seen between similar examples of pasta.

Three commercial spaghetti samples were used, with different strand diameters and cooked in the same way; then the texture was analysed in such a way to provide 12 different measurements per sample. Different factors such as spaghetti weight, length of strand, cooling water temperature etc were varied; this allowed for researchers to find out the best conditions for testing the pasta’s firmness. The researchers then used the linear mixed models procedures available in ASReml to analyse the firmness of the spaghetti samples.

Responses of spaghetti firmness to most of the factors was linear or could be made linear with a transformation so the fitting of a model was very simple. However the advantage with ASReml is that you can add complex variance structures to a model which often cannot be done with other packages. In this simple case ASReml allows the researchers to estimate the variance for each level of factor in each experiment and determine if any of the levels had significantly lower variance. If the model suggested differing variances the level of factor with the lower variance was recommended as the setting to use. With both the Tamworth and Canadian laboratories using a standard method the correlations between laboratories was greatly improved and average differences between the laboratories was reduced.

This study has shown that there are ways of increasing the repeatability of the results, and hence make it easier to discriminate between textural differences in different pasta samples, and shows there is an alternative to sensory analysis. The impact for laboratories, the Durum trade and pasta manufacturing industry is huge; decisions on wheat variety, ingredients, recommended cooking times etc are based on science not taste buds.

As a footnote - throwing spaghetti at the wall is still used as a valid test of stickiness, for me personally this means I can happily continue trying to redecorate my kitchen!

In Australia the Grains Research and Development Corporation, in conjunction with the Australian Crop Accreditation System have set up the National Variety Trials (NVT) program. A program designed to provide information on newly released crop varieties to aid with crop variety selection decisions, based upon their individual growing conditions. Driven by the increasing commercial focus of plant breeding programs in Australia the NVT program conducts around 600 trials on the full range of commercially significant field crops including wheat, barley, triticale, oat, canola, lupin, lentil, field pea, faba bean and chickpea in over 250 geographic distinct locations.  This national program of comparative crop variety testing provides standardised trial management, data generation, collection and dissemination and is managed through an internet accessed database, that ensures a common approach and uniformity across the system.

The NVT program allows for single national analysis for each crop rather than state- based ones, and the results of the analysis are presented in terms of an estimated yielding ability for a specific environment. So whilst the data collected is nationally, providing more data and information and allowing for greater predictability, the results are now provided for individual areas and environmental conditions.

The large number of trials and consequently huge amounts of data (for example 210,000 records for wheat alone)  collected needs a data analysis system that can manage large datasets; and given that the results are used in choosing grain varieties, which will have an economic impact, such a tool must be highly accurate and trusted. The biometricians who run the analysis use ASReml.

The analysis model which is used is unique to ASReml as it involves the use of a two-stage linear mixed model in which the data from each trial is weighted according to its statistical and biological reliability. The linear mixed model includes terms which aim to model the variety by environment interactions in a plausible and interpretable manner.

”ASReml is the package that helps farmers, breeders and crop variety evaluators obtain the most reliable predictions of genetic value for a range of crops grown in different environments; farmers can get the best information available about performance of varieties in their own location and make an informed decision,” says Professor Brian Cullis, Research Leader for DPI Biometrics and leader of the SAGI (Statistics for the Australian Grains Industry) project, the project which run the analyses for the NVT program.  “ASReml is the only package we trust to run this type of analysis because of its proven accuracy, speed and flexibility for these types of complex two-stage models.”

ASReml is a vital tool in the NVT program; it has allowed for the analysis of huge amounts of data on the performance of different crops under different conditions. Without reliable analysis tools the reports would not have the credibility they have, and therefore Australian growers would not have the access to such useful reports to aid them in crop selection.

Of course, if we haven’t described your particular circumstances, you can still ask support the question.

Find out more about GenStat 11th Edition.

Find out more about GenStat Discovery Edition.

Find out more about GenStat for Teaching Edition.

Looking for ASReml? Find out more here.

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