Accelerating sugar beet breeding insights for SESVanderHave

Enhancing agronomic research through precise statistical analysis with Genstat

Deben Agronomy is dedicated to giving potato and vegetable growers the right guidance to grow better crops. They use their expert knowledge gained through independently generated trial data to provide actionable insights that boost crop yields and promote sustainable practices.  ### The challenge: Cultivating confidence In the pursuit of their research endeavours, Deben Agronomy faced a pressing challenge. They needed robust statistical analysis for their vegetable field trials data, specifically focusing on techniques like analysis of variance. Their primary goal was to instil confidence in the results derived from experimental trials—a critical factor for their decision-making and for their clients too.   For a field research scientist like those at Deben Agronomy, having confidence in their findings hinges on the ability to definitively attribute differences in results to specific treatments rather than mere chance. Achieving this level of accuracy was paramount in maintaining the integrity of their research and recommendations. To meet this challenge, they turned to Genstat.  ### Choosing the best tool: The decision process Finding the right solution involved a meticulous evaluation of available options. While considering various alternatives, Deben Agronomy's experts harkened back to their prior experience with Genstat. The intuitive nature and ease of use they had encountered in the past left a lasting impression. As a result, despite exploring other choices, Genstat's familiarity and reliability made it the clear choice.  ### Hello Genstat: Where stats become user friendly While acknowledging that they only scratched the surface of Genstat's capabilities, Deben Agronomy's scientists praised the software for its swiftness in generating intricate statistical analyses. This efficiency not only bolstered their results but also facilitated clearer explanations for their clients. The ability to present data-driven insights with clarity became an asset that set them apart.  _“Ease of use, combined with excellent support, has made our experience with Genstat truly satisfying. It generates statistical analyses quickly and easily to support my results, and the graphing capabilities allow me to explain these results clearly to clients.”_ **Angela Huckle, lead R & D agronomist at Deben Agronomy** ### Harvesting the results: Improved reputation and trust Using Genstat helped to transform Deben Agronomy’s research outputs. They gained a higher level of credibility and could confidently attribute variations to specific treatments where there was a response from either the crop or pest. This precision enhanced their reputation and fostered greater trust among clients who depend on their guidance.   Deben Agronomy's partnership with VSNi's Genstat has shown how reliable statistical analysis tools can reshape the landscape of agronomic research. They fortified their methodologies and elevated the calibre of insights provided, contributing to the growth and sustainability of the agricultural industry as a whole.  <table style="border-color:hsl(240, 75%, 60%);border-style:double;"><tbody><tr><td><strong>Ready to enhance your agronomic research with Genstat or have questions about our services?&nbsp;</strong><br><strong>Our experts are here to assist you. Feel free to contact us&nbsp;</strong> <a href="https://vsni.co.uk/contact-us">https://vsni.co.uk/contact-us</a></td></tr></tbody></table>

Uncovering disease control strategies: A Genstat case study on Maedi-Visna (MV) epidemiology in sheep

This case study focuses on the collaborative research of [Andrew Illius](https://www.ed.ac.uk/profile/andrew-illius), Emeritus Professor of Animal Ecology at the University of Edinburgh, and [Nick Savill](https://www.maths.ed.ac.uk/school-of-mathematics/events/math-biology/mathbiol/nick-savill), Senior Lecturer in the School of Biological Sciences at the University of Edinburgh. They have been investigating the epidemiology and control of maedi-visna (MV), a debilitating lentivirus disease that infects sheep, goats, and wild ruminants causing inflammatory lesions to build up in the lungs, mammary gland, brain and the synovial membrane of joints. MV is a significant concern for farmers due to its rapid and silent spread within flocks, leading to welfare and productivity issues. By the time clinical signs are evident, the disease is well-established, with perhaps 30-70% of the flock infected. Through the use of Genstat, Andrew and Nick analysed long-term unbalanced experimental data, revealing crucial insights into disease control strategies. ### Research objective For a farmer, the disease raises crucial questions: How quickly will symptoms develop, and will welfare and productivity suffer? How soon will it spread through the rest of the flock? Is living with the disease more expensive than trying to eradicate it? The primary aim of this research was to find ways to control MV by analysing data from long-term experiments. MV is caused by a small ruminant lentivirus (SRLV). However, despite a substantial amount of research on the pathology, immunology, and molecular biology of SRLVs, the team were surprised to find there was a lack of quantitative analysis on SRLV epidemiology. ### Modelling MV transmission The research team leveraged a mathematical model developed by Nick Savill based on infectious disease epidemiology, and the known and unknown aspects of SRLV biology. This model estimated the probability of seroconversion, shedding light on the disease's transmission patterns. The study was the first to estimate transmission parameters, revealing that MV is strongly associated with housing, and transmission is negligible among sheep kept at grass. This finding presented an opportunity for disease control exploiting the fact that transmission of the virus is too slow between grazing sheep to sustain the disease. ### Understanding disease spread Further analysis focused on understanding infectiousness changes during an individual's infection. The team examined whether ewes with long-standing infections posed the greatest risk to uninfected sheep, and also explored management strategies to slow disease spread. Genstat was instrumental to this analysis. ### Quantifying costs with Genstat To assess the costs of living with MV, a large dataset from [Lublin University of Life Sciences](https://up.lublin.pl/en/university/) was analysed. This dataset included lambing records from about 800 ewes of different breeds, with over 300 ewes present each year. Given the complex nature of the data (unbalanced design, multiple observations from individual ewes and rams over several years, and different breeds) the team utilised Genstat to analyse it. The team employed mixed models to distinguish random and fixed effects, making use of the latest release of Genstat, which included Akaike’s Information Coefficient (AIC) for model selection. The analysis addressed pre-weaning lamb mortality and showed that the probability of mortality was strongly affected by lamb birthweight and the SRLV status of the ewe. Lamb growth rate was also shown to be negatively related to the time since the dam had become infected. ### Genstat's role in the study Andrew Illius, with decades of experience using Genstat, praised the software for its crucial role in agricultural and ecological research.  “_The software's evolution from a Fortran-based system to a menu-driven environment on Windows significantly sped up analysis processes. Genstat's ability to handle unbalanced data and mixed models proved essential in this study. It’s a fantastic product, and a pleasure to use_.” ### Advancing scientific knowledge through Genstat The research provides evidence-based insights for controlling MV, suggesting targeted disease control measures and social distancing strategies for sheep. This Genstat case study demonstrates the software's versatility in analysing complex data and its importance in advancing scientific knowledge. By utilising Genstat, the collaborative research of Andrew Illius and Nick Savill contributes to understanding Maedi-Visna epidemiology, potentially leading to more effective disease control measures for the benefit of farmers and livestock alike. --- ### Example output from Genstat 

Enhancing genetic evaluation and selection for a corn seed company

Our client is a global agricultural seed company that specialises in the development of improved corn hybrids. With a diverse germplasm collection and extensive breeding programs, they aim to enhance yield, disease resistance, and other important traits in corn varieties. To leverage genetic data effectively and accommodate its future growth, they sought a comprehensive analysis solution. The aim is to incorporate existing genetic data and any future-generated data into integrated analysis models. #### Utilising ASReml-R for genetic parameterestimation To address their needs, our client implemented ASReml-R, a powerful statistical software package designed for genetic evaluation and mixed-effects modelling. ASReml-R was used to analyse the complex data structures of multiple generations andt rials of corn breeding, which included detailed phenotypic and genotypic information. #### Why they chose ASReml-R _Accurate genetic parameter estimation_: The mixed-effects modelling capabilities of ASReml-R enabled the team to estimate genetic parameters like heritability and genetic correlations with precision. This accuracy is crucial for assessing the potential of different traits for genetic improvement. Unlike other tools that struggle to fit thesecomplex models, ASReml-R excels in providing reliable estimates. _Empowering informed breeding decisions_: The estimated breeding values obtained through ASReml-R empowered our client to make more informed decisions regarding parent selection, hybrid combinations, and overall breeding strategies. By understanding the genetic potential of various traits, they could strategically focus on the most promising combinations and therefore increasing the likelihood of developing superiorcorn hybrids. _Handling complex pedigree structures_: ASReml's ability to handle intricate pedigree structures was particularly beneficial. Breeding programs often involve complex relationships between individuals, and ASReml-R effortlessly accounted for these complexities. By accurately modelling the genetic relationships within the population, the seed company gained valuable insights into the inheritance patterns of specific traits. _Accounting for environmental factors_: ASReml's flexible modelling capabilities allowed our client to account for environmental factors that influence trait expression. By incorporating these factors into their analyses, they could separate genetic and environmental effects accurately. This ability to disentangle the influences of genes and the environment contributed to more precise genetic evaluations. #### Tangible benefits - Improved performance and yield potential ASReml-R enabled our client to achieve several positive outcomes, including * _Improved breeding strategies_: The accurate estimation of genetic parameters helped to identify traits with high heritability, indicating their suitability for selection and improvement. This knowledge guided their breeding strategies, allowing them tofocus on traits that had a substantial impact on the performance of corn hybrids. * _Enhanced parent selection_: With a deeper understanding of genetic correlations and better breeding value estimation, the team could select parents that would likely produce progeny with the desired trait combinations. ASReml's predictions allowed them to manage their breeding population's genetic diversity and its potential, leading to superior long-term parent selection.  * _Efficient variety development_: By leveraging ASReml's analysis capabilities, our client streamlined their breeding efforts. They could identify the most promising hybrid combinations and allocate their resources more efficiently. This optimisation accelerated the development of superior corn varieties and reduced the time and cost associated with traditional breeding analytical methods. #### About ASReml-R - Statistical software for breeding ASReml-R is a comprehensive statistical software package specifically designed for genetic evaluation, mixed-effects modelling, multi-trait and genotype-environment interactions. Its specialised features, combined with expert knowledge, empower plant and animal breeders to perform robust statistical analyses, make informed breeding decisions, optimise yield potential, and develop superior varieties or populations more efficiently. ASReml's ability to handle complex data structures, accounting for pedigree relationships and incorporating environmental and design factors makes it an invaluable tool for any organisation involved in genetic evaluation and selection of outstanding genotypes.