biometry and trends in agricultural research: some challenges and opportunities

Biometry and trends in Agricultural Research: Some Challenges and Opportunities

Girma Taye (PhD), Ethiopian Institute of Agricultural

Research

Outline

Introduction Challenges of EIAR centers Major themes of biometrics program at EIAR Diversity of methodology being served at EIAR Developments in Biometry at EIAR Integration with Information Technology Training workshops Production of manuals and reference materials

Outline (Cont…)

Scientific software support Advisory services Support to universities Outcome of years of Biometric support at

EIAR. Contribution to IITA’s mission.

Introduction

Agriculture is Important to Africa Most of the developed world seem to reduce

methodological development along this line. The interest now is more on disease related

and not on yield improvement. Currently few countries work on biometry This presentation uses EIAR as a model

Challenges of EIAR centers

A shift from on-station to on-farm trials; Different forms of on-farm experiments

(researcher designed-farmer managed, etc.)

Conventional breeding practices in crop and livestock are shifting towards the use of biotechnology

Challenges (cont.)

From Isolated experiments to system modeling (interaction among crops, tree and

society). 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.

A shift to adaptation to the changing climate

Challenges (cont.)

Studies of technology adoption, impact assessment and characterization of farming systems are shifting from standard survey methods to participatory approach

Major Themes of biometrics program at EIAR

Blocks are often considered as a homogenous set and spatial information is not incorporated in analysis of data.

Measurements taken over time on the same experimental unit are correlated and proper understanding and analysis required.

Mismatch between design and analysis (multilevel experiments are wrongly analyzed).

Major Themes (cont.)

Statistical methods in plant protection experiments will be addressed.

Data collection and management procedures will be standardized. System and understanding on which data to obtain and use and how to store clarified.

Major Themes (cont.)

Develop inferential methods for on-farm experiments.

Statistical methods for analysis of data with score and ranks

Selection of treatments and choice of site will use statistical aspects.

Major Themes (cont.)

Statistical methods for estimation and analysis of crop losses due to diseases, insect pests, and weeds will be considered.

Foundation for research database for important commodities

Plot size, shape, and number of replication used in most crops are borrowed from other country’s experience and will be developed.

Diversity of methodologies being served

Study of relationship:

Linear relationship: continuous response variable versus a factor (yield prediction model for Enset)

Non-linear : Curve fitting (tree growth –inverse polynomial was used, lactation curve, etc.)

Diversity (cont.)

Yield stability:

Models and methods on stability and adaptability of varieties over a given location. (review of the methods done)

Exploring ways of accounting for the GxE interactions (on pulses, soya bean, teff, maize).

Diversity (cont.)

Research methods in cropping system: intercropping (maize with bean-bivariate) and crop rotation (wheat and teff)

Crop protection surveys: to estimate production and yield loss for major crops (small area sampling)

Assessment of past experiments: performances of past experiments assessed to take corrective measure (a number of crops)

Diversity (cont.)

Repeated measure analysis: (Influence of soil water

deficit on growth of haricot bean)

Design and analysis of multilevel experiments (on-farm experiments, tree experiments, etc.)

Use of multivariate techniques ( clustering of genotypes/environments, genetic distance, canonical

correlations, principal component analysis, etc.)

Diversity (cont.)

Complications arising from experiments (missing units, damaged, etc—covariance )

Selection of treatments VS replication (A compromise )

Choice of sites (Statistical consideration)

Statistical input to the “scaling-up” activity (some analysis issues)

Diversity (cont.)

Estimation of genetic parameters (correlations, variances, diversity index, heritability, path-coefficient)

Design and Analysis of long-term chemical and organic fertilizer trials (several sources of variations)

Impact assessment & Technology adoption (Generalized linear models)

Non-parametric methods (for distribution free analysis):

Diversity (cont.)

Range of experimental designs (IB, confounded, Systematic, nested, split,etc.)

Several joint works done with researchers (on enset, wheat, potato, cotton, socio-economics, agro-meteorology and modeling of data already collected)

Molecular markers analysis

Developments in Biometry

Optimum designs (some requirements ) The Mixed Model Method of analysis (yield

estimate for multi-location trials) Spatial modeling approaches (splines, modified

Papadakis) Optimum plot dimension and replication for

variety trials (Wheat, Potato and Cotton). “Statistical monitoring and evaluation of

agricultural experiments” (for number of crops)

Biometric/informatics laboratory: Integration with Information Technology

Trial data warehouse development for crop, livestock, forestry, soils and related research areas. This helps:

[ i) efficiency of storage, access and processing, ii) “global” or

meta analysis of the data]

Integration (cont.)

Using GIS facilities to augment spatial analysis and modeling

Preparation of scripts for selected analysis methods in statistical software (SAS).

Provided a review of selected software and cautions required when using them (SAS, SPSS, Agrobase, MSTATC, GenStat, Mintab, Systat, Instat)

Research methods and scientific software training workshops

The biometrics and data processing services have gained a good experience at offering training courses of different magnitude over the years.

Training workshops (cont.)

Major category of training: General and Specialized Areas covered include:

General: Data management Designing experiments using basic designs (RCBD, split-plot, Latin, lattice, alpha-

lattice) Treatment structure Statistical software (SAS, SPSS, Stata, Excel, MSTAT-C,

GenStat)

Analysis, interpretation of results and presentation.

Training workshops (cont.)

Specialized: Designs (theories of designing experiments, locating

blocks, designing multiple experiments, Systematic designs, etc.)

Analysis of data using software (SAS, GenStat, Agrobase, STATA)

Scientific writing

Manuals and reference materials

Technical manuals, short communications, training manuals and notes:

Three technical manuals, 5 training manuals and three working papers were published.

Lessons from past advisory services were compiled and presented as reference.

A guideline for designing experiments.

Scientific software support

Packages used and area of emphasis:

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).

Support to Universities

The BID of EIAR is attached to the Statistics Dept of AAU and Haramaya University to handle biometry/biostatistics.

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.

Advisory services to Scientists

Review of the M&M part of research project proposals.

Advice to project leaders, Advice to individual researchers on specific

research methodology Review statistical aspect of reports and

manuscripts published in EIAR or collaborators. Advice to students attached to the Institute Advice the DG, DDG and directors on policy issues

related to research methodology, scientific computing and research database.

Years of Biometric Support at EIAR paid off?

A complete shift from MSTAT-C based analysis, advocated by CIMMYT years ago, to modern software (SAS, GenStat, Agrobas,

SPSS, STATA, etc.)

Commonly used designs like CRD, RCBD and split plot designs are no more a problem at EAIR.

Years of Biometric (cont.)

Tests of hypothesis and interpretation of results have improved greatly over the years.

Researchers are successful in their postgraduate studies.

Researchers tend to publish several papers with minimum difficulty. The number and quality of publications has greatly increased in the last decade at EIAR.

Years of Biometric (cont.)

Researchers now have more reference materials prepared at home.

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.

Contribution of biometric support to IITA’s mission

Current areas of IITA involve: Developing improved, drought tolerant,

disease/rust resistant varieties Biotechnology based breeding & selection Survey of disease occurrence Crop/genetic diversity

Major intervention areas

Support on designing experiments and surveys Support on providing analysis methods that fits the

design Contribute to impact assessment and technology

adoption studies Bio-informatics Monitoring and evaluation of past trials Training & material development

Modeling & Analysis

I will bring experience on the following: Tailoring the mixed models to the need of IITA The methods of multivariate approach: classification,

dimension reduction, etc. Diversity analysis: distance, similarity, scaling, etc. Help scientists analyze their data publish their

papers Sustained support on Statistical software

Modeling & Analysis

Spatial modeling: help experiments to be more precise by avoiding systematic variations not captured by blocking.

Analysis of ordinal disease score Analysis of molecular variance (AMOVA) I can learn more quickly about IITA and

provide expected support

Thank you