Biometry and trends in Agricultural Research :   Some   Challenges and Opportunities     Girma Taye (PhD),   Ethiopian Ins...
Outline <ul><li>Introduction </li></ul><ul><li>Challenges of EIAR centers </li></ul><ul><li>Major themes of biometrics pro...
Outline (Cont…) <ul><li>Scientific software support </li></ul><ul><li>Advisory services </li></ul><ul><li>Support to unive...
Introduction <ul><li>Agriculture is Important to Africa </li></ul><ul><li>Most of the developed world seem to reduce metho...
Challenges of EIAR centers   <ul><li>A shift from on-station to on-farm trials;  </li></ul><ul><li>Different forms of on-f...
Challenges (cont.) <ul><li>From Isolated experiments to system modeling ( interaction among crops, tree and society ). </l...
Challenges (cont.) <ul><li>Studies of technology adoption, impact assessment and characterization of farming systems are s...
Major Themes of biometrics program at EIAR <ul><li>Blocks are often considered as a homogenous set and spatial information...
Major Themes (cont.) <ul><li>Statistical methods in plant protection experiments will be addressed.  </li></ul><ul><li>Dat...
Major Themes (cont.) <ul><li>Develop inferential methods for on-farm experiments.  </li></ul><ul><li>Statistical methods f...
Major Themes (cont.) <ul><li>Statistical methods for estimation and analysis of crop losses due to diseases, insect pests,...
Diversity of methodologies being served <ul><li>Study of relationship:  </li></ul><ul><li>Linear relationship:  continuous...
Diversity (cont.) <ul><li>Yield stability :   </li></ul><ul><li>Models and methods on stability and adaptability of variet...
Diversity (cont.) <ul><li>Research methods in cropping system:  intercropping ( maize with bean- bivariate ) and crop rota...
Diversity (cont.) <ul><li>Repeated measure analysis: ( Influence of soil water deficit on growth of haricot bean ) </li></...
Diversity (cont.) <ul><li>Complications arising from experiments  ( missing units, damaged, etc— covariance   )  </li></ul...
Diversity (cont.) <ul><li>Estimation of genetic parameters ( correlations, variances, diversity index, heritability, path-...
Diversity (cont.) <ul><li>Range of experimental designs ( IB, confounded, Systematic, nested, split,etc .) </li></ul><ul><...
Developments in Biometry <ul><li>Optimum designs ( some   requirements  ) </li></ul><ul><li>The Mixed Model Method of anal...
Biometric/informatics laboratory: Integration with Information Technology <ul><li>Trial data warehouse development for cro...
Integration (cont.) <ul><li>Using GIS facilities to augment spatial analysis and modeling </li></ul><ul><li>Preparation of...
Research methods and scientific software  training  workshops <ul><li>The biometrics and data processing services have gai...
Training workshops (cont.) <ul><li>Major category of training :  General and Specialized </li></ul><ul><li>Areas covered i...
Training workshops (cont.) <ul><li>Specialized:   </li></ul><ul><ul><ul><li>Designs (theories of designing experiments, lo...
Manuals and reference materials <ul><li>Technical manuals, short communications, training manuals and notes :  </li></ul><...
Scientific software support <ul><li>Packages used and area of emphasis: </li></ul><ul><li>SAS  ( all ),  GenStat ( for mod...
Support to Universities <ul><li>The BID of EIAR is attached to the Statistics Dept of AAU and Haramaya University to handl...
Advisory services to Scientists <ul><li>Review of the M&M part of research project proposals. </li></ul><ul><li>Advice to ...
Years of Biometric Support at EIAR paid off? <ul><li>A complete shift from MSTAT-C based analysis, advocated by CIMMYT yea...
Years of Biometric (cont.) <ul><li>Tests of hypothesis and interpretation of results have improved greatly over the years....
Years of Biometric (cont.) <ul><li>Researchers now have more reference materials prepared at home.  </li></ul><ul><li>Info...
Contribution of biometric support to IITA’s mission <ul><li>Current areas of IITA involve: </li></ul><ul><li>Developing im...
Major intervention areas <ul><li>Support on designing experiments and surveys </li></ul><ul><li>Support on providing analy...
Modeling & Analysis <ul><li>I will bring experience on the following: </li></ul><ul><li>Tailoring the mixed models to the ...
Modeling & Analysis <ul><li>Spatial modeling : help experiments to be more precise by avoiding systematic variations not c...
<ul><ul><ul><ul><ul><li>Thank you </li></ul></ul></ul></ul></ul>
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Biometry and trends in Agricultural Research: Some Challenges and Opportunities

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A presentation on biometry covering issues such as the diversity of methodologies being served, its development as well as its integration with information technology.

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Biometry and trends in Agricultural Research: Some Challenges and Opportunities

  1. 1. Biometry and trends in Agricultural Research : Some Challenges and Opportunities Girma Taye (PhD), Ethiopian Institute of Agricultural Research
  2. 2. Outline <ul><li>Introduction </li></ul><ul><li>Challenges of EIAR centers </li></ul><ul><li>Major themes of biometrics program at EIAR </li></ul><ul><li>Diversity of methodology being served at EIAR </li></ul><ul><li>Developments in Biometry at EIAR </li></ul><ul><li>Integration with Information Technology </li></ul><ul><li>Training workshops </li></ul><ul><li>Production of manuals and reference materials </li></ul>
  3. 3. Outline (Cont…) <ul><li>Scientific software support </li></ul><ul><li>Advisory services </li></ul><ul><li>Support to universities </li></ul><ul><li>Outcome of years of Biometric support at EIAR. </li></ul><ul><li>Contribution to IITA’s mission. </li></ul>
  4. 4. Introduction <ul><li>Agriculture is Important to Africa </li></ul><ul><li>Most of the developed world seem to reduce methodological development along this line. </li></ul><ul><li>The interest now is more on disease related and not on yield improvement. </li></ul><ul><li>Currently few countries work on biometry </li></ul><ul><li>This presentation uses EIAR as a model </li></ul>
  5. 5. Challenges of EIAR centers <ul><li>A shift from on-station to on-farm trials; </li></ul><ul><li>Different forms of on-farm experiments ( researcher designed-farmer managed, etc .) </li></ul><ul><li>Conventional breeding practices in crop and livestock are shifting towards the use of biotechnology </li></ul>
  6. 6. Challenges (cont.) <ul><li>From Isolated experiments to system modeling ( interaction among crops, tree and society ). </li></ul><ul><li>On-station trials rely on local control which is based on ‘visible’ criteria such as soil type, but several potential ‘inputs’ remain unknown or unmeasured. </li></ul><ul><li>A shift to adaptation to the changing climate </li></ul>
  7. 7. Challenges (cont.) <ul><li>Studies of technology adoption, impact assessment and characterization of farming systems are shifting from standard survey methods to participatory approach </li></ul>
  8. 8. Major Themes of biometrics program at EIAR <ul><li>Blocks are often considered as a homogenous set and spatial information is not incorporated in analysis of data. </li></ul><ul><li>Measurements taken over time on the same experimental unit are correlated and proper understanding and analysis required. </li></ul><ul><li>Mismatch between design and analysis ( multilevel experiments are wrongly analyzed ). </li></ul>
  9. 9. Major Themes (cont.) <ul><li>Statistical methods in plant protection experiments will be addressed. </li></ul><ul><li>Data collection and management procedures will be standardized. System and understanding on which data to obtain and use and how to store clarified. </li></ul>
  10. 10. Major Themes (cont.) <ul><li>Develop inferential methods for on-farm experiments. </li></ul><ul><li>Statistical methods for analysis of data with score and ranks </li></ul><ul><li>Selection of treatments and choice of site will use statistical aspects. </li></ul>
  11. 11. Major Themes (cont.) <ul><li>Statistical methods for estimation and analysis of crop losses due to diseases, insect pests, and weeds will be considered. </li></ul><ul><li>Foundation for research database for important commodities </li></ul><ul><li>Plot size, shape, and number of replication used in most crops are borrowed from other country’s experience and will be developed. </li></ul>
  12. 12. Diversity of methodologies being served <ul><li>Study of relationship: </li></ul><ul><li>Linear relationship: continuous response variable versus a factor ( yield prediction model for Enset ) </li></ul><ul><li>Non-linear : Curve fitting ( tree growth – inverse polynomial was used, lactation curve, etc .) </li></ul>
  13. 13. Diversity (cont.) <ul><li>Yield stability : </li></ul><ul><li>Models and methods on stability and adaptability of varieties over a given location. </li></ul><ul><li>( review of the methods done ) </li></ul><ul><li>Exploring ways of accounting for the GxE interactions ( on pulses, soya bean, teff, maize ). </li></ul>
  14. 14. Diversity (cont.) <ul><li>Research methods in cropping system: intercropping ( maize with bean- bivariate ) and crop rotation ( wheat and teff ) </li></ul><ul><li>Crop protection surveys: to estimate production and yield loss for major crops ( small area sampling ) </li></ul><ul><li>Assessment of past experiments: performances of past experiments assessed to take corrective measure ( a number of crops ) </li></ul>
  15. 15. Diversity (cont.) <ul><li>Repeated measure analysis: ( Influence of soil water deficit on growth of haricot bean ) </li></ul><ul><li>Design and analysis of multilevel experiments ( on-farm experiments, tree experiments, etc. ) </li></ul><ul><li>Use of multivariate techniques ( clustering of genotypes/environments, genetic distance, canonical correlations, principal component analysis, etc .) </li></ul>
  16. 16. Diversity (cont.) <ul><li>Complications arising from experiments ( missing units, damaged, etc— covariance ) </li></ul><ul><li>Selection of treatments VS replication ( A compromise ) </li></ul><ul><li>Choice of sites ( Statistical consideration ) </li></ul><ul><li>Statistical input to the “scaling-up” activity ( some analysis issues ) </li></ul>
  17. 17. Diversity (cont.) <ul><li>Estimation of genetic parameters ( correlations, variances, diversity index, heritability, path-coefficient ) </li></ul><ul><li>Design and Analysis of long-term chemical and organic fertilizer trials ( several sources of variations ) </li></ul><ul><li>Impact assessment & Technology adoption ( Generalized linear models ) </li></ul><ul><li>Non-parametric methods ( for distribution free analysis ): </li></ul>
  18. 18. Diversity (cont.) <ul><li>Range of experimental designs ( IB, confounded, Systematic, nested, split,etc .) </li></ul><ul><li>Several joint works done with researchers ( on enset, wheat, potato, cotton, socio-economics, agro-meteorology and modeling of data already collected ) </li></ul><ul><li>Molecular markers analysis </li></ul>
  19. 19. Developments in Biometry <ul><li>Optimum designs ( some requirements ) </li></ul><ul><li>The Mixed Model Method of analysis ( yield estimate for multi-location trials ) </li></ul><ul><li>Spatial modeling approaches ( splines, modified Papadakis ) </li></ul><ul><li>Optimum plot dimension and replication for variety trials ( Wheat, Potato and Cotton ). </li></ul><ul><li>“ Statistical monitoring and evaluation of agricultural experiments” ( for number of crops ) </li></ul>
  20. 20. Biometric/informatics laboratory: Integration with Information Technology <ul><li>Trial data warehouse development for crop, livestock, forestry, soils and related research areas. This helps: </li></ul><ul><li>[ i) efficiency of storage, access and processing, ii) “global” or meta analysis of the data ] </li></ul>
  21. 21. Integration (cont.) <ul><li>Using GIS facilities to augment spatial analysis and modeling </li></ul><ul><li>Preparation of scripts for selected analysis methods in statistical software ( SAS ). </li></ul><ul><li>Provided a review of selected software and cautions required when using them ( SAS, SPSS, Agrobase, MSTATC, GenStat, Mintab, Systat, Instat ) </li></ul>
  22. 22. Research methods and scientific software training workshops <ul><li>The biometrics and data processing services have gained a good experience at offering training courses of different magnitude over the years . </li></ul>
  23. 23. Training workshops (cont.) <ul><li>Major category of training : General and Specialized </li></ul><ul><li>Areas covered include: </li></ul><ul><li>General: </li></ul><ul><ul><ul><li>Data management </li></ul></ul></ul><ul><ul><ul><li>Designing experiments using basic designs (RCBD, split-plot, Latin, lattice, alpha-lattice) </li></ul></ul></ul><ul><ul><ul><li>Treatment structure </li></ul></ul></ul><ul><ul><ul><li>Statistical software ( SAS, SPSS, Stata, Excel, MSTAT-C, GenStat ) </li></ul></ul></ul><ul><ul><ul><li>Analysis, interpretation of results and presentation. </li></ul></ul></ul>
  24. 24. Training workshops (cont.) <ul><li>Specialized: </li></ul><ul><ul><ul><li>Designs (theories of designing experiments, locating blocks, designing multiple experiments, Systematic designs, etc.) </li></ul></ul></ul><ul><ul><ul><li>Analysis of data using software ( SAS, GenStat, Agrobase, STATA ) </li></ul></ul></ul><ul><ul><ul><li>Scientific writing </li></ul></ul></ul>
  25. 25. Manuals and reference materials <ul><li>Technical manuals, short communications, training manuals and notes : </li></ul><ul><li>Three technical manuals, 5 training manuals and three working papers were published. </li></ul><ul><li>Lessons from past advisory services were compiled and presented as reference. </li></ul><ul><li>A guideline for designing experiments. </li></ul>
  26. 26. Scientific software support <ul><li>Packages used and area of emphasis: </li></ul><ul><li>SAS ( all ), GenStat ( for modeling in general ), Agrobase ( for multi-location/year experiments ), SPSS ( for survey data management and analysis ), MSTAT-C ( for multi-location/year trials, where same location or different locations are used overtime ), S+ ( for spatial modeling ), ASRemel ( for spatial mixed models ), STATA ( for data management and general analysis ). </li></ul>
  27. 27. Support to Universities <ul><li>The BID of EIAR is attached to the Statistics Dept of AAU and Haramaya University to handle biometry/biostatistics. </li></ul><ul><li>in the last two years offered ‘topics in biometry”, “special topics” and “experimental Design“ courses, and supervised six statistics and two breeding masters thesis in a range of disciplines. </li></ul>
  28. 28. Advisory services to Scientists <ul><li>Review of the M&M part of research project proposals. </li></ul><ul><li>Advice to project leaders, </li></ul><ul><li>Advice to individual researchers on specific research methodology </li></ul><ul><li>Review statistical aspect of reports and manuscripts published in EIAR or collaborators. </li></ul><ul><li>Advice to students attached to the Institute </li></ul><ul><li>Advice the DG, DDG and directors on policy issues related to research methodology, scientific computing and research database. </li></ul>
  29. 29. Years of Biometric Support at EIAR paid off? <ul><li>A complete shift from MSTAT-C based analysis, advocated by CIMMYT years ago, to modern software ( SAS, GenStat, Agrobas, SPSS, STATA, etc .) </li></ul><ul><li>Commonly used designs like CRD, RCBD and split plot designs are no more a problem at EAIR. </li></ul>
  30. 30. Years of Biometric (cont.) <ul><li>Tests of hypothesis and interpretation of results have improved greatly over the years. </li></ul><ul><li>Researchers are successful in their postgraduate studies. </li></ul><ul><li>Researchers tend to publish several papers with minimum difficulty. The number and quality of publications has greatly increased in the last decade at EIAR. </li></ul>
  31. 31. Years of Biometric (cont.) <ul><li>Researchers now have more reference materials prepared at home. </li></ul><ul><li>Information on Optimum plot dimension, number of replication and block size is available for wheat, potato and cotton to be used in designing new experiments in order to improve quality of test results. </li></ul>
  32. 32. Contribution of biometric support to IITA’s mission <ul><li>Current areas of IITA involve: </li></ul><ul><li>Developing improved, drought tolerant, disease/rust resistant varieties </li></ul><ul><li>Biotechnology based breeding & selection </li></ul><ul><li>Survey of disease occurrence </li></ul><ul><li>Crop/genetic diversity </li></ul>
  33. 33. Major intervention areas <ul><li>Support on designing experiments and surveys </li></ul><ul><li>Support on providing analysis methods that fits the design </li></ul><ul><li>Contribute to impact assessment and technology adoption studies </li></ul><ul><li>Bio-informatics </li></ul><ul><li>Monitoring and evaluation of past trials </li></ul><ul><li>Training & material development </li></ul>
  34. 34. Modeling & Analysis <ul><li>I will bring experience on the following: </li></ul><ul><li>Tailoring the mixed models to the need of IITA </li></ul><ul><li>The methods of multivariate approach: classification, dimension reduction, etc. </li></ul><ul><li>Diversity analysis: distance, similarity, scaling, etc. </li></ul><ul><li>Help scientists analyze their data publish their papers </li></ul><ul><li>Sustained support on Statistical software </li></ul>
  35. 35. Modeling & Analysis <ul><li>Spatial modeling : help experiments to be more precise by avoiding systematic variations not captured by blocking. </li></ul><ul><li>Analysis of ordinal disease score </li></ul><ul><li>Analysis of molecular variance (AMOVA) </li></ul><ul><li>I can learn more quickly about IITA and provide expected support </li></ul>
  36. 36. <ul><ul><ul><ul><ul><li>Thank you </li></ul></ul></ul></ul></ul>

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