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

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.

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.

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.

Review by John A. Wass, Ph.D.

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The Getting Started manual describes this software as “…a complete and comprehensive statistics package…” but, actually, there is a lot more to it. Most packages these days have a variety of statistical tests with diagnostics and options, graphics, data handling capacity, a quality (QA) module and an experimental design (DOE) module. This package seems to have been designed with the life scientist in mind (not surprising, as it comes from a spin-off of Rothamsted Research and the company, VSNi, is subtitled ‘software for bioscientists’). As such, it offers several significant extras. For example: meta analysis, data mining algorithms, time series analysis and microarray analysis. I stressed this last feature in the title, as microarray analysis represents a very sophisticated and specialized area of bioinformatics, which is usually only had as highly specialized and expensive commercial software, or freeware with extensive programming requirements.

Read his full article. (Opens a new browser window to Scientific Computing website)

Full list of the GenStat 11th Edition new features.

Download a trial copy or to obtain your upgrade of GenStat.

“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.

Chad Dechow, an ASReml user from the Department of Dairy and Animal Science at The Pennsylvania State University, studied herd heritability estimates in over 20,000 herds. He and his colleagues compared covariance parameter estimates in subsets of data, pooled from herds with high, medium or low individual herd heritability estimates. The individual herd heritability estimates, generated from a regression model using ASReml, applied to milk yield, fat yield, protein yield and somatic cell score. These regression techniques stratified the herds by heritability; additive genetic variance increased progressively and permanent environmental variance decreased as herd heritability increased. Future work by the same authors demonstrated that herds with low heritability had a high rate of parent misidentification and that such herds were poor candidates for genetic testing herds.

“We used ASReml,” says Chad, “because it is a very flexible mixed models program that is capable of analyzing large datasets in a very efficient manner.”

ASReml was used to generate individual herd heritabilities; the model and results can be seen in the original paper published in The Journal of Dairy Science 2008. 91:1648-1651

It’s important for breeders to know which factors are genetic and which are affected by environment, for example health events have low heritability indicating that improved management would be important in improving animal health, rather than genetic selection. Understanding which factors can be changed by genetic selection and which through environmental influences is vital as it means that breeders and farmers can plan and manage their herds more effectively; as they understand which aspects can be changed through a breeding programme and which through environmental factors. As heritability differs so significantly between different traits it’s again vital to understand which aspects are most likely to be changed through a breeding program; the higher the heritability a trait is, the more quickly it can be changed through genetic selection; again helping breeders and farmers to plan better, as it can indicate timescales for any genetic changes to take place.

There is no doubt that, as with many things in life, the better understood something is the more effective the planning will be. Research, such as Chad’s is vital to help cattle breeders and farmers breed and manage their herds more effectively. And all good research needs support and suitable tools; ASReml is such a tool. Software that was developed by researchers in animal and crop breeding, it is highly suited to this purpose providing users with the ability to obtain reliable predictions of genetic values. Research needs trustworthy and reliable tools and ASReml is a great fit for the job.

Thanks to Chad Dechow for his help on this piece. The full paper is available here.

Also of interest:

Chad is the editor of The Specht Report – a report on breeding performance.

The Western District of Victoria is known for its heavy clay soils, that are prone to short periods of waterlogging for 2-3 months a year in the Winter; but with the right pasture species livestock producers could increase pasture production during the Summer by utilising this stored soil moisture. Additionally a species that can use out of season rainfall and moisture stored in the soil, would reduce groundwater recharge. One such plant is Summer-active tall fescue.

The experiment, which began in November 2004 with the establishment of the grass, is taking place at the DPI EverGraze research site at Hamilton;  4 grazing system treatments were imposed in a randomised design in September 2006. Since then the plots have been grazed at different levels and herbage mass measured monthly to calculate herbage accumulation.

GenStat’s REML technique was used to model the repeated measurements over time.

This enabled seasonal changes in pasture productivity and quality to be detected, and indicated how the plant responds to climatic events, such as the summer rainfall that is a common characteristic of the research site. Stocking rate, and major husbandry practises, such as lambing and calving, could then be timed to coincide with pasture availability. Within each time period, GenStat was used to determine the effects of grazing treatment on pasture persistence, productivity and quality.  Grazing management was then adjusted, based on the results of the GenStat analysis, to achieve maximum consumption of high quality pasture by livestock, whilst also ensuring the long term survival of the pasture.  Throughout this research, efforts were made to reduce error, and increase the accuracy of the results.  This was achieved by using repeated measurements from fixed quadrants, and also by using GenStat’s graphics capabilities to generate residual plots, which were used to check the normality of the data, and, where needed, impose data transformations.

The study found that the accumulation rate and nutritional value of the Summer-active tall fescue are closely related with its growing conditions. Specific grazing systems and the environmental conditions had an effect on the herbage accumulation and nutritional value. Research, such as Margaret’s is vital in being able to provide sound advice and reliable observations to landowners, and because GenStat was originally developed by statisticians working in agricultural research, it is the most suitable data analysis tool for agricultural research. Couple GenStat’s understanding of the agricultural researchers issues with the reliable and proven statistics, and ethics of good statistical practice and you know your analysis results can be trusted.

Details of Margaret’s project and more information is available at EverGraze. An action sheet based on Margaret’s work on summer active Tall Fescue is here.

And yet, thanks to developments in agricultural research, studying all aspects of the problems and issues faced in agricultural production, farmers and agricultural producers are able to plan more effectively and manage their businesses using far more informed decisions than in the past.

In relation to the issues facing potato growers, research conducted by The Nematology and Entomology Research Group at the Crop and Environment Research Centre at the Harper Adams University College in the UK, into the relationship between the potato cyst nematode Globodera rostochiensis (G. rostochiensis) and the diseases caused by Rhizoctonia solani (R. solani) has indicated there are links between the two problems. Both problems can cause severe damage to a potato crop, with deformed or malformed plants that are not marketable as well as producing reduced yields or even potatoes with a lower nutrient value. It’s estimated that potato cyst nematodes could cause annual losses in the region of 300 million Euros to potato producers in the European Community alone; so a better understanding of the problem, which could lead to a way of controlling this threat, could mean huge cost savings and increased production.

The trial, undertaken by Dr. Matthew Back, Placement Manager (Agriculture) / Lecturer in Plant Pathology and the team at Harper Adams University College in 2000 and 2001, was the first to look at the issues under field conditions, rather than in a controlled environment. By looking at the nematodes and the fungus in natural conditions, researchers are able to determine far better how significant the interaction is between the two problems. Two sites were used, and planted with the cultivar Desiree and the plants were monitored for diseases related to the R. solani.

The results were analysed in GenStat. Regression analysis (again in GenStat) showed there were relationships between the density of the nematodes and the incidence and severity of disease caused by R. solani. Likewise simple linear regression analysis showed a significant linear relationship between the tuber yield and the infection of runners with R. solani. Multiple regression analysis showed that interactions between the nematode root invasion and the runner infection had a significant effect on the final tuber yield. The study has shown that there are clear interactions between this nematode and the fungus. There were positive relationships between the nematode densities and infection on all potato parts, but specifically between the invasion of potato roots by juvenile nematodes and the infection of runners with R. solani. It seems that the interaction is indirect given that each problem affects different parts of the potato. Interestingly it seems that the nematode plays an important role in the development of R.solani diseases, but these infections may reduce nematode development.

Studies such as these are vital in planning for future agricultural development. By understanding the relationship between different pests and pathogens, more precise and hopefully more productive growing strategies can be developed.

Full paper published in: European Journal of Plant Pathology, Issue Volume 114, 2 / February, 2006, pages 215-223.