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Wheat Special Report No.8
Management and Use of� International Trial Data�
for Improving Breeding Efficiency�
Ciudad Obregon, Sonora, Mexico
January 21-22, 1992
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Wheat Special Report No.8
Management and Use of .� International Trial Data�
for Improving Breeding Efficiency�
Ciudad Obrygon, Sonora, Mexico
January 21-22, 1992
P.N. Fox and G.P. Hettel� Editors�
Correct Citation: Fox, P.N., and G.P. Hettel, eds. 1992. Management and Use of International Trial Data for Improving Breeding Efficiency. Wheat Special Report No.8. Mexico, D.F.: CIMMYT.
Contents
iii Preface
iii Acronyms/Abbreviations Used in this Report
iv Workshop Participants
1 Initial Discussion
2 Shaping the Second Revolution--an Historical Perspective on International Trials
6 Statistical Analysis of International Yield Trial Data in ICARDA's Cereal Program
9 AMMI Analysis of Yield Trials
13 Long-Term Similarity of Environments in the ISWYN
15 Using the Shifted Multiplicative Model (SHMM) to Identify Subsets of Environments without Genotypic Rank Change: II. Clustering Method
23 Analysis of International Nursery Data--Results and Implications for International Nursery Design
29 Update on the Data Managment System (DMS) for the Wheat Program
33 Use of Relational Data Bases in Wheat Breeding
37 Yield Trial System!lnternational Nursery Logistics
39 General Discussion/Conclusions/Actions to Be Taken
42 Appendix I--The Wheat Program's Data Management Sysem (DMS)
46 Appendix 2--Proposed New Versions of E, GE, and G Data 46 A. General Notes to Be Taken (E Data) 48 B. New Trait List (GE Data) 50 C. PMS Characteristics (G Data)
53 Appendix 3--1989 Position Paper on CIMMYT International Wheat Nurseries
62 Appendix 4--SuIVey of CIMMYT Staff on International Nurseries Reporting
66 Appendix 5--Cooperating Stations Involved with CIMMYf International Nurseries
66 A. Country Abbreviations from the International Standards Organization 70 B. CIMMYf Wheat Program Cooperating Locations
84 Appendix 6--Genotypes Used in ISWYNs
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Preface
The Germplasm Improvement Subprogram organized a workshop and think-tank on management and use of international trial data for increasing breeding efficiency at Ciudad Obregon, Mexico on January 21-22, 1992. Participants included scientists from INIFAP (Mexican National Institute of Forestry, Agriculture, and Livestock Research), Cornell University, the University of Queensland (Australia), India, ICARDA, and CIMMYT.
The workshop explicitly recognized that data associated with germplasm are valuable resources that enhance the worth of germplasm for both plant scientists and farmers of the developing world. The Wheat Program's planned Data Management System embraces this principle and will organize information around the germplasm to which it refers, rather than by the source of the information (e.g., a specific trial).
Selected papers presented during the workshop and included in this Special Report provide a point of departure or bench mark for our future breeding strategies and planning as we strive to improve both CIMMYT and national wheat breeding programs. Selected discussion notes are included after each paper. Appendices 1-4 provide additional background and details about CIMMYT International Wheat Nurseries and the Data Management System that may not be detailed in the selected papers. Appendices 5 and 6 provide listings of cooperating locations for all nurseries and ISWYN genotypes of which we would appreciate assistance in making corrections.
This Wheat Special Report on the management and use of international trial data emphasizes the importance of shared knowledge. But please keep in mind the information in this report is disseminated with the understanding that it is not published in the sense of a refereed journal.
S. Rajaram Leader, Germplasm Improvement Subprogram
Acronyms/Abbreviations Used in this Report
AMMI--Additive Main Effects and Multiplicative Interaction. ANOVA--Analysis of variance. CD-ROM--Compact disk with read only memory. CIANO--Centro de Investigaciones Agricolas del Noroeste (Northwestern Agricultural Research Center), Mexico. CIMMYT--International Maize and Wheat Improvement Center. CV--Coefficient of Variation. DBMS--Database management system. DD--Data dictionary. DMS--Data Management System (proposed by CIMMYT). EDYT--Elite Durum Yield Trial. ESWYT--Elite Selection Wheat Yield Trial. ICARDA--International Center for Agricultural Research in the Dry Areas. INIFAP--Instituto Nacional de Investigaciones Forestales y Agropecularias (Mexican National Institute of Forestry, Agriculture, and Livestock Research). ISWYN--International Spring Wheat Yield Nursery. ISO--International Standards Organization.
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Acronyms/Abbreviations Continued
ITYN--International Triticale Yield Nursery.� ITSN--International Triticale Screening Nursery.� LISA·-Laboratory for Information Science in Agriculture (Colorado State University).� LSD--Least significant difference.� NARS--National agriculture research system.� PCA--Principal component analysis.� RAPID--Rapid Analysis Program for International Data.� RDB--Relational database.� SCS--Systems and Computing Services of CIMMYT.� SGYS--Small Grains Yield System.� SGSS--Small Grains Screening System.� SHMM--Shifted Multiplicative Model.� WANA--West Asia and North Africa.� WGB--Wheat Germplasm Bank.� WINS--Wheat International Nursery System.� WPMS--Pedigree Management System of CIMMYT.�
Workshop Participants� Osman Abdalla, Head, Durum Wheat Section� Max Alcala, CIMMYT Wheat International Nurseries� Miguel Camacho e., INIFAP, Mexico� John Corbett, CIMMYT Geographer� Jose Crossa, CIMMYT Biometrician� Ian DeLacy, University of Queensland, Australia� R.A. Fischer, Director, CIMMYT Wheat Program Paul Fox, CIMMYT Wheat International Nurseries Guillermo Fuentes, CIMMYT Pathologist Hugh Gauch, Cornell University, USA Maarten van Ginkel, CIMMYT Bread Wheat Breeder Arturo Hernandez J., INIFAP, Mexico Gene P. Hettel, CIMMYT Information Services K.B.L. Jain, IARI, India Jesus Martinez S., INIFAP, Mexico M.e. Jesus Naro S., INIFAP, Mexico Wolfgang Pfeiffer, Head, Triticale Section, CIMMYT Sanjaya Rajaram, Leader, Germplasm Improvement
Subprogram and Head, Bread Wheat Section, CIMMYT Juan M. Ramirez D., INIFAP, Mexico Rodrigo Rascon, CIMMYT Experiment Stations P. Roger Rowe, CIMMYT Deputy Director General, Research Mario Salazar G., INIFAP, Mexico Hector Sanchez, CIMMYT Systems and Computing Services Ken Sayre, CIMMYT Wheat Agronomist Henrik Schou, CIMMYT Systems and Computing Services Ravi P. Singh, CIMMYT Wheat GeneticistlPathologist Bent Skovmand, Head, CIMMYT Wheat Germplasm Bank S.K. Yau, ICARDA International Nurseries
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Initial Discussion
Rajaram: From a breeders' perspective, analysis of yield data is certainly required, but how much is too much? Historically, ingredients of the CIMMYT bread wheat breeding recipe have been:
• Shuttle breeding. • Knowledge of parents, adaptability, disease resistance, quality, etc. • Stability of performance over time and space. • Combining ability. • Average Coefficient of Infection for rust diseases. • Intuition. • Simple experimental designs and quick analyses.
Fischer: What has been the influence of notes coming back from the international� screening nurseries and yield trials on breeding decisions across mega-environments?� Rajaram: The yield nurseries have been the best. The ISWYN, for example, has� educated us on where our lines stand. In recent years the ESWYT has been helpful, but� without the ISWYN, we would have no idea of what breeders would need in Zimbabwe,� for example.�
Fischer: Have the international trials been more or less important over time?� Rajaram: Importance has remained about the same. Information related to yield has� always been very important. The major importance of the screening nurseries has been� the distribution of gennplasm to national programs.�
Abdalla: How do you feel about the mega-environment concept?� Rajaram: It is a natural evolution. That is not to say that gennplasm will not cross ME� boundaries.�
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Shaping the Second Revolution--an Historical Perspective on International Trials
P.N. Fox� Head, International Wheat Nurseries�
CIMMYT�
Summary
A revolution in wheat breeding occurred with germplasm exchange. The second revolution will exchange information related to germplasm, not only adding value to the germplasm, but also strengthening bonds between institutions and between scientists. We are on threshold of dramatic advances, limited only by our imaginations in using them. Positive dynamic feedback between genetics and environmental information will provide unprecedented insights into crop adaptation.
Introduction
Norman Borlaug spoke of a revolution in wheat breeding brought about by germplasm exchange. I foreshadow a second revolution driven by exchange of information relating to germplasm. The international nursery system will provide the cornerstone of this revolution, which will increase breeding efficiency, with the major beneficiaries being farmers in developing countries. Further expansion of the nursery network is unlikely, but major advances in efficiency must occur.
The international nursery system is user-driven with CIMMYT managing, on behalf of cooperators, the associated data that are freely available and will become increasingly accessible with CD-ROM and other technologies.
Small Leaps Forward
Mechanization, computerization, and robotics Although this workshop focuses on data exploration, it would be unsound to forget the related areas of experimental design and mechanization of nursery preparation, including robotics, in the overall international nursery scenario.
International feedback loop International nursery data provide feedback to CIMMYT breeders. There is an element of recurrent selection in the process of distributing elite germplasm from Mexico and recrossing or discarding this material in subsequent cycles, on the basis of international performance. Anyone who has planned crosses in Obregon will attest to the fact that overall yields in international testing and less formal data, such as notes from outreach staff, are used extensively. One of the challenges before us is to make the feedback loop, to CIMMYT and to others, more efficient. This will occur through:
• Better data exploration. • Quicker turnaround of data. • Better access to raw data, results of analyses and interpretative summaries.
Data exploration and breeding An extensive battery of techniques is available for data exploration and speakers will detail some. Such techniques complement but in no way replace the intimate field experience breeders have for their germplasm. I have a profound respect for breeders' knowledge of their germplasm, including an uncanny ability to identify superior crosses. In tandem to these skills, newer statistical methods are useful in identifying subtle
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differences among sister lines and in rapid assessments, from wide testing, of adaptation of new germplasm. Seri 82 appears the most broadly-adapted, high-yielding spring bread wheat and international data exploration forewarns breeders of the Achilles' Heel of such outstanding widely sown germplasm in a way that is impossible with national testing. For example, Seri 82's leaf rust resistance depends on Lr23 and Lr26 and its superiority is tending to decline in locations with heavy infection of BYD, septoria tritici blotch and Helminthosporium species. This warning allows timely initiation of corrective backcrossing and other measures.
Because different major stresses with time may be detected from the results of international nurseries, as a result of many random processes in play, we must be ready to use what surfaces. For example, identification of a cohesive group of locations subject to heat stress in ESWYT-9 allowed inferences about superior germplasm for such conditions, but different stresses emerged in the next year.
Time-lag problem The ESWYT-lO bulletin provides some of the flavors to come in the area of data exploration. These analyses, however, do not attack the time-lag problem, but a joint ITYN-21 and -22 bulletin is planned for this year to bring the dissemination of the second year's analysis forward, while, at the same time, providing a prototype for future desk-top publishing initiatives for international nurseries.
Short-term versus long-term analyses To date, the lead in newer analyses in the ESWYTs has given year-specific results, but moving from short-term considerations to long-term ones is the theme of a major analysis of ISWYN data, to be presented by another speaker. Long-term relationships among locations will be important in refining the definitions of mega-environments.
Analysis or pathological data Multivariate exploration of disease data has been largely ignored and correction of foliar scores for height and maturity to better identify genetic sources of resistance has been infrequent.
Adding value to seed through data Where possible, CIMMYT should distribute existing data with nurseries to aid in the selection efficiency of clients. For example, there is much information on bread making quality generated before germplasm is distributed in an ESWYT.
Impact and singing for our supper CIMMYT will increasingly have to sing for its supper. To this end, the next impact study for germplasm will be facilitated by arranging our proposed databases to store the origins of wheats (especially with respect to CIMMYT's contribution) according to the system used in the recent impact study.
Great Leaps Forward
I have spoken of small improvements of an evolutionary nature in nursery analysis and reporting. However, relational database technology heralds the great leap on which the Wheat Pedigree Management System (WPMS) is based and on which the Data Management System (OMS) will follow. I refer to other speakers and Appendix 1, which outlines OMS and its query structure.
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Removing barriers to association WPMS overcame barriers to association by uniquely identifying wheat germplasm. The line Siete Cerros was represented no less than 14 different ways in ISWYN bulletins and one had to know a lot about wheat (and a little Spanish) to realize that all the associated data related to the same wheat.
Crossing data frontiers WPMS overcomes such ambiguities and redundancies and lays the foundation for DMS, which will integrate information from different sources around the germplasm to which it pertains. DMS will make possible many powerful associations between genetic information and performance data. This interface was seldom crossed because of problems in association of data from different sources. Detailed genetic information generated in laboratory studies was seldom coupled to field performance data for several reasons. One was a different scale of operations. Thousands of lines might be evaluated in the field with the resultant data eventually finding their way to a dingy end on a series of flat files on magnetic tapes. The results from the more intensive laboratory test on a limited number of genotypes might be committed to paper. Even if the researchers involved in the separate efforts were aware of each other's work, combining the data was never considered because there would have been so many gaps in the intensive laboratory information if combined with the field data in a flat file. Relational databases make cross referencing these types of information feasible and efficient. Today, decisions on which types data should be stored are less critical. As data storage becomes increasingly cheaper relative to data generation, the question becomes: on which characteristics do we require rapid querying?
The scope of DMS will ultimately embrace data from international trials, national trials, the Wheat Germplasm Bank, quality and pathology laboratories, and research in molecular biology, as well as hopefully interfacing to a recent Canadian initiative for a directory of elite germplasm for studies of plant mineral nutrition.
Environmental
Data
G Data
(genotypic adaptation, genetic information, genes)
Figure 1. Environmental data and genotypic performance data will provide dynamic positive feedback.
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Genes and the environment: dynamic feedback� Here is an example of how data integration might function. If we knew the genetics of� boron tolerance of only one or two entries in our database, but could extract international� performance data on them, implications about the distribution of boron related problems� could be made. Conversely, better knowledge of the environment facilitates typing� germplasm (Figure 1).�
Integration of isolated rust research� Other powerful new associations may be possible by enhanced integration of rust data.� The existing international nursery data system accommodates Modified Cobb Scales and� for this reason has under-utilized a wealth of data generated on seedling reactions, many� of which relate to known races. McIntosh in Australia and Smit and co-workers in South� Africa regularly return such meticulously recorded information and they are generated by� CIMMYT's own pathologists. While imputing rust genes from field scores may be� possible by multivariate analysis of field scores alone, by extrapolation from responses of� known genotypes to responses of uncharacterized germplasm, integrating such trends� with seedling reactions will strengthen the process.�
Discussion Notes�
van Ginkel: Is it possible to identify 10 key locations so that we can get data back� quickly?� Fox: We should not be getting data back from key sites only. We are not bogged down� necessarily on the return of data, but on how CIMMYT processes it.�
Rajaram: We can't blame delays on the cooperators. We take 2 years to process the� data.� Fox: I agree, that's what I'm saying. We are going to desktop publish some key nurseries� as a prototype and thus circumvent the old bulletin generation system.�
van Ginkel: The best cooperators will send the best data early. What do we gain by� waiting?� Fox: The Southern Hemisphere is 11 months behind anyway.�
Abdalla: Bulletins are not the only means of sending information to cooperators. There� are other ways, such as visitations, etc. Maybe we need to consider other means.� Fox: Send information, such as quality data, with the seed.�
Yau: How can we go about getting better disease data? Cooperators use different scales� and even then can one trust the data?� Fox: This is not much of a problem anymore--at least for CIMMYT--since our Outreach� staff has been working hard to gain standardization across national programs.�
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Statistical Analysis of International Yield Trial Data in ICARDA's Cereal Program
S.K. Yau� International Center for Agricultural Research�
in the Dry Areas (ICARDA)�
This presentation provides: 1) a brief introduction about ICARDA's international/regional cereal nurseries, especially yield trials, and 2) an explanation of the approach and procedures used by ICARDA to extract information on entry response and adaptability and site similarity.
When ICARDA was established in 1977, an international nursery system based on CIMMYT's experience was already in place. Initially, there were segregating populations, observation nurseries, and yield trials. In 1978, the crossing blocks were added. Recently, trait-specific nurseries and gerrnplasm pools for disease resistance were established. These six types of nurseries cover the requirements of most national programs.
In 1978, there were only three yield trials: 1) barley, 2) bread wheat, and 3) bread and durum wheats for rainfed areas. A yield trial for durum wheat was added in 1979. ICARDA now has a joint mandate with CIMMYT for wheat improvement in West Asia and North Africa (WANA). As experience has accumulated, it became apparent that the WANA is a heterogeneous region. So for the benefits of the national programs, the yield trials were gradually divided and targeted to one of the region's three mega-environments (Le., high altitude, lowland with low rainfall, and lowland with moderate rainfall). In 1991, there were four barley, three durum wheat, and two bread wheat yield trials.
About two-thirds (more for wheat, less for barley) of the yield trials are sent upon request to cooperators in the WANA. Asia and Mediterranean Europe receive some trials, while the rest of the world receives a few. Following the recent agreement between CIMMYT and ICARDA, the joint wheat yield trials will not be sent outside WANA in the coming season. The total number of sets sent to national programs increased from 99 in 1978 to 572 in 1986. Since then the numbers have declined to 443 in 1991 as seed requests are now better scrutinized to ensure that a nursery is appropriate for the area making the request.
All of the yield trials were/are of the same design--lattice (RCB before 1990), three replicates (four before 1988), six rows of 2.5 m spaced 30 em apart, and 24 entries (including a local check, a long-term check, and two or three other checks). Many entries and growing sites are different each year. There is no individual randomization for each site.
Although the international nursery system was established in 1977, it was not until 1984 that a scientist was placed in charge of the operation full-time. The main emphasis of that first scientist was to computerize the annual reporting and the development of computer programs. When I acquired the position in the summer of 1986, I felt that my challenge would be to provide better statistical analysis for each year's nurseries/trials and to conduct in-depth analysis of the vast amount of data collected over the years.
Since 1987, the following analyses have been conducted and reported on in the annual nursery reports: 1) nonparametric adaptability, 2) relative yield, 3) regression, and 4) clustering.
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.For nonparametric adaptability analysis, we use a visual display consisting of locations (rows) and entries (columns). In 'each row, entries with a rank of five or less for grain yield are highlighted. The total number of times the entry is among the top five yielders is shown at the bottom of the display and provides an indication of the adaptability of the entry. Since the locations are grouped by regions, one can also observe whether an entry has specific adaptation to a particular region or not.
The exercise is repeated to show which entries are significantly higher yielding than the long-term, improved and local checks at each location. The comparison with the local check is of particular importance. It draws attention to those locations where the local check performs very well or very poorly. Investigation of these locations is suggested.
The use of entry mean yield across locations with contrasting yields often gives bias to entries that perform well in high-yielding location. However, using relative yield, i.e., site mean coding by expressing entry yield relative to location mean yield give equal weight to each location and converts the simple variance of entry yield across locations from a biological to an agronomic type of stability measure (Yau 1991). So in the nursery reports, mean relative yields and standard deviations (based on relative yields) of entries are calculated for each region. High relative mean yields and low standard deviations indicate good adaptability.
The use of nonparametric analysis and relative yield is simple and fast for indicating adaptability of the test entries. It would be useful for routine analyses of multienvironment trials conducted by plant breeders.
Although the joint regression analysis is carried out for each trial, the usefulness of the method for highly selected entries is doubtful. Analyses over four seasons of the Regional Bread Wheat Yield Trial on rainfed locations showed that heterogeneity of regression accounted for less than 3% of the interaction variance and was not significant, but deviation from regression was highly significant.
For better understanding of the GxE interaction, a multi-variate technique is needed. After reviewing different multi-variate procedures, I chose cluster analysis. Since 1987, we have clustered trial entries and locations on mean entry yields for each location. Yields are standardized within a location before cluster analysis is done. Centroid linkage was used in the early years, but the group-average linkage is being used at the moment.
Examples of the usefulness of cluster analysis were presented on entry as well as location grouping in Yau et al. (1989, 1991).
The Additive Main-Effect and Multiplicative Interaction (AMMI) model has been suggested to be a powerful tool for analyzing GxE interaction. The postdictive model has been tried on some data sets, but the results were not encouraging. The predictive mode will be tried as soon as we obtain the program.
Besides the routine analyses carried out for each year's yield trials, greater use of the international nurseries as a research tool for more penetrating analyses of GxE interaction is being attempted. The possibilities to work collaboratively with CIMMYT, advanced institutions and scientists working on agro-ecological characterization are being explored.
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References Cited
Yau, S.K. 1991. Variance of relative yield as an agronomic type of stability measure. In pages 297-306, Proceedings of the Eighth Meeting of the EUCARPIA Section on Biometrics on Plant Breeding, July 1-6, 1991. Brno, Czechoslovakia.
Yau, S.K., G. Ortiz-Ferrara, and J.P. Srivastava. 1989. Cluster analysis of wheat lines based on grain yield from different sites. Rachis 8(2):31-35.
Yau, S.K., G. Ortiz-Ferrara, and J.P. Srivastava. 1991. Classification of diverse bread wheat growing environments based on differential yield responses Crop Sci. 31(3):571576.
Discussion Notes
Schou: What program do you use for analysis?� Yau: We have one program for analysis and another for management, but the software is� old and not adequate for the new analyses we want to do.�
Fox: What is your rate of return of data from sets sent to cooperators?� Yau: We get an initial 60-70% return; we send out a letter to obtain an additional 10%.�
van Ginkel: How do ICARDA breeders use the data?� Yau: They use the data to help confirm their own subjective ideas.�
Fischer: At least one genotype (Nesser) coming out of the CIMMYT/ICARDA bread� wheat program does well in Syria. What does your data say about it?� Yau: The data are being analyzed�
Fischer: How do you go about setting checks?� Yau: The long-term check never changes; the second check changes over time.�
Rajaram: I think we need to compare relevant crosses simultaneously in Mexico and� Aleppo.�
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AMMI Analysis of Yield Trials
Hugh G. Gauch, Jr. Cornell University Ithaca, New York
In order for Additive Main Effects and Multiplicative Interaction (AMMI) to be applicable to a given data set, two structural requirements must be met:
• The data must be organized in a two-way table, with or without replication.
• The data matrix must contain one kind of measurement and units, such as kg/ha.
The usual application in agriculture is a regional yield trial organized as a genotypes by environments table, where the "environments" are actually site-year combinations. Basic AMMI modeling is possible without replication, but rigorous assessments of predictive accuracy (using data splitting and validation) and of postdictive accuracy (using F-tests) require replication. An expectation maximization algorithm, EM-AMMI, can tolerate and impute missing data. Also, the smallest allowable matrix size is 3x3, but genuinely interesting results require a larger size of at least 5x5 or preferably 5xlO. It is not allowed for different matrix rows (or columns) to contain different units, such as soil nutrient concentrations, average monthly temperatures, and average annual rainfall. Also the management must be quantitative, not qualitative (such as the flower colors white, pink, and purple).
In order for AMMI to be truly useful, two additional conditions are required:
• The data structure must conform, to a substantial degree, with the AMMI model. When data exhibit significant main effects and significant interaction effects-which is the most common case encountered in yield trials--then AMMI is usually effective.
• Research purposes must call for parameters, estimates, error reduction, tables, graphs, or insights of the sort provided by AMMI. This condition is almost always met because AMMI serves a remarkably rich variety of purposes.
Violation of one more of the four conditions discussed above means that AMMI is not appropriate and some other statistical model should be used. AMMI is not applicable to data in a one-way factorial, nor in a three-way or higher factorial design. For example, a set of genotypes tested in one site for 1 year constitutes a one-way factorial design. However, if these genotypes have an underlying two-way design arising from, say, a diallel cross, then AMMI is applicable. Likewise, a single trial could have an underlying factorial structure in its environmental component, such as a single genotype tested in a single site and year, but with fertilizers formulated in a two-way factorial design having five levels of nitrogen and five levels of phosphorus. On the other hand, an experiment most naturally regarded as a three-way or higher factorial can often be structured or decomposed into one or more two-way subproblems. The most common instance is an experiment most naturally regarded as a genotypes by sites by years three-way factorial design, structured as a genotypes by environments two-way design, where environments are site and year combinations. Especially when experimental sites change from year to year, this can be a fruitful approach because the original three-way perspective would generate an enormous number of missing cells.
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For a data matrix with different units in each row (or column), the additive portion of AMMI is meaningless, so other analyses are required. Popular candidates involve first standardizing each matrix row to mean zero and variance one, and then applying Principal Component Analysis (PCA), factor analysis, or some other clustering procedure such as incremental sums of squares. More fundamentally, agricultural experiments often generate data structures other than a single two-way matrix. Sometimes multiple observations are made for each cell of a two-way design, such as a genotypes by environments design with measurements of yield, height, and insect infestation. AMMI can analyze each measurement separately, but exploration of their joint structure requires other methods. Sometimes, agricultural experiments generate two or more two-way matrices, most commonly a genotypes by environments matrix of yields and a factors by environments matrix of environmental factors (such as soil nutrient concentrations, temperatures, and rainfall). The joint structure between the two matrices must be explored by other multivariate methods, such as canonical correlation analysis and redundancy analysis. However, AMMI may be useful to summarize the matrix of environmental data in terms of a few vectors, and likewise standardized PCA may summarize the matrix of environmental data. Then simple methods like multiple regression can explore relationships between these two sets of vectors.
The AMMI literature has emphasized the usefulness of the AMMI biplot for model diagnosis (Bradu and Gabriel 1978; Technometrics 20:47-68). If only the additive effects are significant or sizeable, then the analysis of variance (ANOVA) submodel is diagnosed. Likewise, if only the interaction is significant, then the PCA submodel is diagnosed. If the interaction has some special, simple structure--such as FinlayWilkinson linear regressions, or an even simpler joint regression (concurrence)-inspection of the biplot makes the diagnosis evident. If the main effects or the interaction of both has some cluster structure, this will also be apparent. Hence, even when AMMI is not the most appropriate model, nevertheless an initial AMMI analysis may offer the easiest means for diagnosing the appropriate other model or submodel.
If the best research purpose is to classify the sites (or genotypes) into, say, five groups, then it may be best to use a classification method instead of AMMI. Nevertheless, particularly when the inherent variability of the material is continuous rather than naturally clustered, an informal subdivision of the material based upon an AMMI biplot may work about as well as anything.
Although invented in 1952, the AMMI analysis is relatively new to agriculturists. It will take time to assess its usefulness for various kinds of yield trials and various research purposes, particularly in comparison to other available analyses. Nevertheless, a few comments seem in order.
Yield trials with relatively elite genotypes tested in relatively favorable (highly productive) environments typically have most of the sum of squares in environments, then interaction, and finally genotypes. More diverse material tested in more diverse environments tends to have the interaction predominate. In either situation, both of the main effects and also the interaction are all significant, so AMMI is usually effective.
Interaction and main effects present the breeder with very different problems and decisions. Exactly for this reason, there is an implicit wisdom in the venerable tradition of distinguishing and separating main effects from interaction. Interaction disturbs genotype rankings from environment to environment; main effects do not. Hence, it is helpful to have an AMMI biplot show main effects along one axis, and interactions along another axis, thereby showing whether any two genotypes (or two environments) differ in main effects, interaction effects, both, or neither. In other words, the usual decomposition
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of the data into genotypes, environments, and genotype-environment interaction is not only a nice statistical model, but also matches up with distinguishable agricultural problems and opportunities. Of course, a slight modification of many other statistical analyses could allow them to make this same distinction. For example, besides applying a clustering alogrithm to the original yield data, one might also analyze the interactions.
Increasing selection gains and containing research costs both demand experimental efficiency. AMMI often gives free extra replications of 1 to 3 times the actual physical replications, whereas blocking typically gives 0.2 to 0.4, so this neglected opportunity to partition the treatment design into a model and residual is often more effective than is the usual analysis of the experimental design. However, since the treatment and experimental designs are orthogonal, both strategies can be employed. These offer remarkably costeffective means for gaining accuracy, which in turn improves selection gain.
A deeper understanding of interactions can clarify the target mega-environments, reveal the implications of interaction for breeding gains, and show the consequences (intended or not) of past breeding policies for the present populations.
The entire subject of heritability, from definitions to analyses, is based upon simplistic additive or linear models. Something new might emerge from revisiting this subject with thinking that better handles interaction.
Without interaction, a single wheat or maize variety would flourish planet-wide. In many ways, interaction is the problem that breeders face. In retrospect, it may seem that breeders' greatest successes (apart from disease resistances) largely concern favorable situations (mega-environment one) in which interaction is mostly sidestepped. But further progress may have more to do with handling interaction head-on.
Summary
Agronomists and breeders frequently collect yield data for a number of genotypes in a number of environments (site-years), resulting in a two-way data table--usually replicated, but sometimes not. The AMMI model combines regular ANOVA for additive main effects with peA for multiplicative structure within the interaction (i.e., within the residual from ANOVA).
AMMI is effective for:
• Understanding genotype x environment (GxE) interaction, particularly with a biplot graph showing both main effects and interaction effects for both genotypes and environments.
• Improving the accuracy of yield estimates, offering an orthogonal and additional error-control strategy to the usual strategy of replicating and blocking.
• Increasing the probability of successfully selecting genotypes with the highest yields, as quantified through order statistics calculations.
• Imputing missing data with the expectation maximization algorithm, EMAMMI.
• Increasing the flexibility and efficiency of experimental designs.
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Ultimately, these advantages imply larger selection gains in breeding research and more reliable recommendations in agronomic research. AMMI is ordinarily the statistical method of choice when main effects and interaction are both important, which is the most common case encountered in yield trial research.
The calculation for AMMI can be done conveniently by the MATMODEL program (which resides in the CIMMYT VAX computer; see Dr. Jose Crossa for details). For information on this program as well as citations of recent literature, see Gauch and Furnas (1991, Agronomy Journal 83:916-920).
Discussion Notes
Fox: How do we divide resources between better design and better analysis?� Gauch: Generally, one should have fewer reps and more treatments. In an extreme� example, with CIMMYr's connections to a few large centers and many smaller sites, 5�10% of the trial could get you 80-90% of the information.�
Pfeiffer: We gain more if we consider the environment and have the appropriate number� of reps accordingly...also change the size of the plot.� Gauch: It is true that we gain accuracy by enlarging plot size or increasing reps, however� AMMI is an extraordinarily cost-effective way to gain accuracy with fewer reps and� smaller plot sizes.�
Fischer: A lot of our yield performance is based on information from one site in 1 year.� We are confident that performance at CIANO is representative, but do we know that it is� the best predictor of the overall ISWYN mean? In other words, is it the best site?� Gauch: You should be able to use AMMI to find out.
Fischer: Perhaps we should do yield tests at other sites as advanced yield trials, for example, two reps at CIANO and two reps somewhere else in Mexico--would this mean anything to the rest of the world? Gauch: The key word is perhaps.
Fischer: Noise is more than error, in fact it can be biologically useful. For example, say we have 60 sites, but one site has a unique boron problem--this would re-rank the genotypes and AMMI would drop the atypical site out.
12�
Long-Term Similarity of Environments in the ISWYN
Ian DeLacy� University of Queensland�
Australia�
Table 1 presents preliminary results of a manuscript in preparation for Euphytica. Here the locations are divided into 15 relatively homogeneous groups, based on their longtenn genotypic performance. The final study will be conducted on standardized deviations from location mean yield, such that each location-year combination has a mean of zero and phenotypic standard deviation of one. Such a data manipulation ensures that clustering depends largely on genotypic rank changes among environments.
Within each of the 15 groups, locations have been re-ordered in numerical order with respect to their new proposed identifiers. The alternative would have been to leave all location identifiers in their order from the complete dendrogram, i.e., the untruncated dendrogram extended to the level at which each of the locations was a single member group. Either way, advantages of the new location numbering system become apparent. For example, the 23 locations in group 265 are all from Africa, as shown by the fact that their identifiers commence with the digit one. Similarly, the six members of group 313 are from Europe (including the former USSR), indicated by the first digit being a three. Group 339, however, comprises members from Africa, Asia (start with 2), Europe, the North American Continent (4), Oceania (5) and South America (6).
Table 1. Membership at the 15 group level for the classification of the 178 locations used three or more times in the first 26 ISWYNs based on deviations from location mean yield of the entries grown in them.
Gr~ NUllber "'-.berl of gr~ NlIIle in
group
95 1 314101 265 23 100021 100031 100041
122071 122091 123011 100071 123021
100121 127011
105021 127021
111011 127031
111031 122031 127041 128021
122061 128031
128091 140011 147011 308 8 194021 202031 209011 212011 212041 610021 610031 614011 313 6 301041 312011 316021 351011 353011 374011 316 5 204011 315051 354041 453011 635031 325 6 411051 421031 601021 601041 630021 630051 330 3 601281 610011 632011 333 8 190041 191011 191021 192071 202011 203012 203041 612041 334 4 212061 216021 225041 226011 336 21 171021
241051 190011 250021
191061 283021
220021 285011
222071 354031
222091 371021
222141 400011
224031 225011 411031 601011
226071 612031
635081 337 24 202021 212051 220011 222061 222081 226021 226041 226081 270061 281011
283011 311011 317011 317021 330011 332011 350011 354131 371011 560111 610051 610091 612021 631011
338 36 192011 222101
194011 241081
200021 270241
202051 205021 207021 210051 211021 212021 317041 334101 354051 354091 400021 401041
222051 410031
410051 410071 410081 411021 420021 420101 421011 421211 550011 550061 560101 601031 610041 612011 615011 635021
339 23 190031 333011
191031 375011
193031 200031 400031 401021
203011 410061
205011 410091
210021 211011 211031 411011 413071 420042
316011 550021
550081 600011 635011 340 8 207011 216011 226031 334051 354081 401011 410011 420041 341 2 191071 410021
13�
All location numbers except one in Table 1 terminate with the digit "1", which represents standard or default management conditions. The terminating digit "2" signifies that more than one management system was used at the location indicated.
Readers are referred to Appendix 5, which details all locations which have participated in the ISWYNs, organized according to the proposed system. From this appendix, the specific identities (along with latitude, longitude and elevation) of the 178 locations in Table 1 can be determined. Comments on the proposed system are solicited and readers are especially urged to inform the first named editor of this working paper of any errors detected. Please send the complete record as it appears and the complete corrected record. Your assistance will be greatly appreciated.
Discussion Notes
DeLacy: The correlation between the yield of a set of adapted lines in a single yield trial and the long-term performance in the farmer's field is essentially "zero"--no matter what you do.
Singh: Should we do analysis on a regular basis or just every 10 years or so? DeLacy: Add new data as they come in to the historical set and see where they fit.
Fischer: There is something about the optimum environment that gives good predictability (Obregon, Giza, Israel, etc.). We were really lucky that we were making all of our selections in Obregon.
14�
Using the Shifted Multiplicative Model (SHMM) to Identify Subsets of� Environments without Genotypic Rank Change:�
II. Clustering Method�
J. Crossa, CIMMYT; P.L. Cornelius, University of Kentucky; M. Seyedsadr, Wayne State University; and P. Byrne, CIMMYT
Note: Based on paper presented at the 1991 ASA Meetings in Denver, Colorado.
Introduction
In plant breeding, genotype x environment interaction is critical only if it involves significant crossover interaction (COl) (Baker 1990), i.e., significant reversal in genotypic rank across environments. Gregorius and Namkoong (1986) defined "separability of genotypic effects from environmental effects". Such separability implies absence of significant genotypic rank-change. The shifted multiplicative model (SHMM), developed by Seyedsadr and Cornelius (1991b) provides a powerful analytical tool for studying COL The general relationships between the multiplicative terms of the SHMM model and Gregorius and Namkoong's definition of separability are described by Cornelius and Seyedsadr (1991). Conditions considered sufficient for the absence of statistically significant COl were:
• SHMM1 is an adequate model for fitting the data.
• "Primary effects" of environments have the same sign.
Cornelius and Seyedsadr (1991) used an exploratory method for identifying subsets of environments that possess separability of genotypic effects.
In most crop breeding trials, genotype x environment interaction is complex and separability of genotypic effects may not occur. In this situation, it would be useful to have a methodology for finding subsets of environments in which separability of genotypic effects holds. The objectives of this study were to present a cluster methodology for finding subsets of environments with separability of genotypic effects, and to examine the success of the cluster method in reducing the number of significant COl within each final group of environments.
Materials and Methods
Yield data (kglha) from an international maize variety trial conducted in 1987 were analyzed. The trial had eight genotypes grown in 59 locations.
SHMM statistical analysis The SHMM model with t multiplicative terms (SHMMt) is:
Yij = S + ~tk=l "'kaikYjk + eij (Seyedsadr and Cornelius 1991b)
where Yij is the yield of the ith genotype in the jth environment,
S is the shift parameter,
Ak is the singular value for the axis k,
15
ai1 and Yj 1 are the "primary effects" of the ith genotype and the jth environment,
ai2 and Yj2 are their "secondary effects", and eij is a random error.
When the SHMM1 values are plotted against primary effects of environments or genotypes, the diagram shows a set of concurrent regression lines which intersect at yield level y = 13. If SHMM1 is an adequate model and the phenotypic values are located either all to the left or all to the right of the point of concurrence, then genotypic effects are "separable" from environmental effects. Otherwise, there is rank-change on opposite sides of the concurrence point and effects are "nonseparable". If SHMM1 is inadequate, statistically significant secondary effects are an almost sure indicator of nonseparability even if primary effects have the desired pattern.
Finding subsets of sites for which SHMMt fits adequately:� clustering method, distance measures, and dichotomous split procedure� Distance measure (d) between two sites is defined as:�
d = residual sum of squares, RSS(SHMM 1) if the fitted� SHMM1 does not show genotypic crossover and otherwise�
d= min[~gi=1(Yi1-Y.1)2, ~gi=1(Yi2 - Y.2)2].
The latter chooses the RSS of the better of the two possible SHMM1 solutions constrained to not show rank-change, namely, either 13 = Y.1 or 13 = Y.2.
Once d is obtained, clustering is done by the complete linkage method. SHMM analyses are then done on the last two cluster groups (end of the dendrogram) to determine if SHMM1 without genotypic crossover is an adequate model. If this fails for either cluster, this cluster is dichotomously split into two smaller clusters suggested by the next lower branch of the tree in its portion of the dendrogram. This process continues until satisfactory clusters are identified.
Assessing the adequacy of SHMMt Adequacy of SHMM1 for modelling a subset of data was assessed using the following tests:
• Approximate tests, F1 and FGHl, against a pooled error (Cornelius et al. 1992).
• The Seyedsadr and Cornelius (1991a) (SC) test.
• The Seyedsadr and Cornelius/Schott-Marasinghe (SCISM) test (Cornelius et al. 1992).
• A test constructed by analogy to the test of Yochmowitz and Cornell (1978) (YC) for Additive Main effects and Multiplicative Interaction (AMMI) models.
Statistical tests for assessing significance of COl The following tests (Cornelius et al. 1992, Crossa et al. 1992) were used to assess COl among and within subsets:
• Test 1, which requires calulating differences between all possible pairs of genotypes in all possible pairs of environments.
• Test 2, which uses a t-test with a comparison-wise error rate (a) of 0.05.
16
• Test 3, which is a t-test with a defined interaction-wise Type I error rate, i.e., error rate per 2 x 2 subtable tested for COls.
Results and Discussion
Grouping sites with separable genotypic effects SHMMI is not an adequate model for the entire data set because secondary and tertiary effects were significant (Table 1), so we proceed to look for satisfactory subsets of sites. SHMM1 is also an inadequate model in both of the two major groups of sites (A and B) identified in the dendrogram (Figure 1), since secondary and tertiary effects are large and significant in both of these groups (Table 2). The next split fonns groups A1, A2, B1, and B2. SHHM1 is adequate for Al (nonsignificant secondary effect) but not for A2, which is then subdivided into A3 and A4 (Figure 1). SHMM1 is adequate for group A3, but not for group A4 where mean squares owing to secondary and tertiary effects (172 and 141, respectively) are significant by the F1 test. Because site 33 joins the rest of the sites late in the dendrogram, its exclusion from A4 to form group AS improves the fit of SHMM1 (mean square of secondary effects is 111 and not significant). Site 33 is left as an ungrouped site. SHMM1 is satisfactory for fitting group B1 due to the large value of the pooled error variance (301; mean square error of sites 6 and 17 were 263 and 339, respectively). SHMM1 is satisfactory for group B2 (only primary effects are significant).
The cluster analysis has identified five final groups of sites (A1, A3, AS, Bl, and B2) for which a SHMMI that does not display genotypic crossover (Figure 2) is an adequate model.
Significant COl among and within groups Table 3 gives all possible 2 x 2 interactions (I), crossover interactions (COl), and significant COls obtained by Test 1, Test 2, and Test 3 for sites in different groups and for sites in the same final group. The number of all possible interactions between any two genotypes with any two environments is 47,908; 41% of them (19,713) had a COl pattern, Le., with genotypic rank change (Table 3). Of the possible within-group interactions, 34% (4,339) had a COl pattern, while 44% (15,374) of the possible amonggroup interactions had a COl pattern (Table 3).
The percentage of COl that was significant by Test 3 (the most liberal one) was 8% (352) within groups and 16% (2,482) among groups. As percentages of all possible interactions, these are 2.8% and 7.1 %, respectively. These results indicate the effectiveness of the distance measure used in the cluster analysis in reducing the number of significant COls\ with genotypic rank changes within the final groups of sites.
Conclusions
The cluster analysis based on the proposed distance measure appears to do an excellent job of allowing the user to identify groups of environments in which genotypic rankchange interactions are negligible relative to those between groups. This procedure can be effectively used for grouping environments with genotypic separability.
References
Azzalini, A., and D.R. Cox. 1984. Two new tests associated with the analysis of variance. J. Royal Statist. Soc. 46:335-343.
17
Baker, R.J. 1990. Crossover genotype-environmental interaction. In: Genotype-ByEnvironment Interaction and Plant Breeding (Ed. Manjit S. Kang). Louisiana State University Agricultural Center, Baton Rouge.
Cornelius, PL., and M. Seyedsadr. 1991. Using the Shifted Multiplicative model (SHMM) to identify subsets of environments without genotypic rank change. I Exploratory method. Agronomy Abstracts.
Cornelius, P.L., M. Seyedsadr, and J. Crossa. 1992. Using the shifted multiplicative model to search for separability in crop cultivar trials. Theor. Appl. Genet. (in press).
Crossa, J., P.L. Cornelius, M. Seyedsaur, and P. Byrne. 1992. Multiplicative model cluster analysis for grouping environments without genotypic rank change. Theor. App. Genet. (in press).
Gregorius, H.R., and G. Namkoong. 1986. Joint analysis of genotypic and environmental effects. Theor. Appl. Genet. 72:413-422.
Seyedsadr, M., and P.L. Cornelius. 1991a. Hypothesis testing for components of the shifted multiplicative model for a nonadditive two-way table. University of Kentucky Dept. of Statistics Tech. Rept. 315.
Seyedsadr, M., and P.L. Cornelius. 1991b. Shifted multiplicative models for nonadditive two-way tables. Communications in Statistics (in press).
Yochmowitz, M.G., and R.G. Cornell. 1978. Stepwise tests for multiplicative components of interaction. Technometrics 20:79-84.
Discussion Notes
Crossa: SHMM analysis can be used for any number of genotypes and sites.�
Corbett: CIMMYT sends out nurseries on request so we have a large group of similar� environments. If subsetting environments, how do too many similar environments present� a bias? What is the viability of doing a cluster anaysis on a subset?� PfeitTer: The difference in genotypes can be very small.�
Fischer: Should we run an AMMI first before doing a SHMM or is that redundant?�
DeLacy: I think SHMM has potential--It is the crossover that matters.�
18�
c ----------------,
~l i
Figure 1. Dendrogram resulting from cluster analysis of 59 sites. Final groups are marked with an arrow.
8000
7000
y I 6000 E L 0
5000 I N
K 400J G /HA
3000
2000 I
1000 ~___r_--r--........-___.,r---....,...............,..-..,...._____r_-~--,--___r-_r_----.--.,.___,______,-....,._____';_JJ o. 195 0.200 o 205 o.2 10 o 215 0.220 0.225
PRI~ARY EFFECT OF 51 TE (SHM~I)
Figure 2. SHMMl fitted model to final group AI. Numbers refer to individual genotypes.
19
Table 1. Analysis of variance (ANOVA) and SHMM analysis for grain yield (kg/ha).
Source of variation df MS
ANOVA
Model's corrected total 471 2194 Genotype (G) 7 2947 a Site (S) 58 16324 a G X S 406 162 b
Pooled error 1239 111
SHMM analysis
Model's corrected total 471 2194 Primary effects 98.20 9862 1 2 3 4 5 Secondary effects 82.23 196 4 5 Tertiary effects 70.45 160 5 Remainder 220.10 170
a Significant (P<0.05) when tested against the interaction.
b Significant (P<O.OS) when tested against the pooled error.
1 Significant (P<0.05) by the YC test.
2 Significant (P<0.05) by the SCiSM test.
3 Significant (P<0.05) by the SC test.
4 Significant (P<O.05) by the FGHI test.
5 Significant (P<O.OS) by the Fl test.
20�
Table 2. Mean squares of SHMM analyses for various site groups.
Group A+ Group Al Group A2
1 2 3 4 5 123 4 5 1 2 3 4 5Component 1 8476 6385 7775 4 5 4 5Component 2 156 133 202 5 4 5Component 3 168 135 227
Remainder 147 118 122 Error 109 111 106 SHMM1 residuals 153 126 170
Group A3 Group A4 Group AS
1 2 3 4 5 1 2 3 4 5 1 2 3 4 5Component 1 4620 5496 4628 5Component 2 103 172 111 5Component 3 170 141 116
Remainder 13 134 118 Error 121 102 98 SHMM1 residuals 115 147 115
Group B Group Bl Group B2
Component Component Component
1 2 3
7761 1 255 1 170 5
2 2
3 3
4 4
5 5
878 3 312
4 5 6189 112 121
1 2 3 4 5
Remainder 133 129 Error 115 301 91 SHMM1 residuals 179 312 122
1 Significant (P<0.05) by the YC test.
2 Significant (P<0.05) by the SCISM test.
3 Significant (P<0.05) by the SC test.
4 Significant (P<0.05) by the FGHl test.
5 Significant (P<0.05) by the FI test.
+ Site composition of the final groups:
Group A1 = 1,2,3,4,7,8,10,14,16,19,20,21,22,23,24,25,28,35 48,54,55,56,59;
Group A3 = 9,15,30,40; Group A5 = 11,12,13,29,31,36,37,42,45,47,50,53,57,58; Group B1 = 6, 17; Group B2 = 5,18,26,27,32,34,38,39,41,43,44,46,49,51,52.
21
Table 3. All possible 2 x 2 interactions (I), crossover interactions (COl), and significant crossover interactions obtained by test 1, test 2, and test 3 for sites in different final groups and for sites in the same final group.
Allocation of sites r cor Test 1+ Test 2 Test 3
Total 47980 19713 1 282 2834
Among final groups 35140 15374 1 276 2482
within final groups 12768 4339 0 6 352
Group Al 7084 2550 0 6 213
Group A3 168 50 0 0 0
Group AS 2548 677 0 0 71
Group B1 28 9 0 0 2
Group B2 2940 1053 0 0 66
+ Test 1 is the Azzalini and cox test; Test 2 is the simple t-test; Test 3 is the t-test with the joint 0.05 significant protection.
22�
Analysis of International Nursery Data--�Results and Implications for International Nursery Design�
Wolfgang H. Pfeiffer� CIMMYT Wheat Program�
Introduction
"... virtually all phenotypic effects are not related to the gene in any simple way. Rather they result from a chain of physico-chemical reactions and interactions initiated by genes but leading through complex chains of events, controlled or modified by other genes and the external environment, to the final phenotype" (Allard 1960).
The nearly infinite number of these genotype x environment (GE) interactions constitutes a major challenge for plant breeders; selection of high yielding germplasm with consistent performance is based largely on phenotypic traits, without knowledge of the causal biochemical, physiological and morphological relationships. Identification of elite gerrnplasm is further complicated by the dependency of genotypic and environmental effects, and scientist are "... plagued ... by the disturbing feeling that each genotype may have its own characteristic response" (Sprague 1955).
Although fluctuations in performance caused by GE are "masking" genetic breeding values and force breeders to evaluate germplasm over a range environments, the capitalization on specific GE interactions and resulting response pattern in crop improvement is a major source of genetic progress, particularly if the underlying biological factors of GE can be identified. Consequently, the critical importance of GE in crop enhancement has been emphasized. However, opinions have widely differed regarding the methods to evaluate GE, the level of knowledge on biochemical and physiological "pathways" and associated traits required to make "real" progress, and the application of trait/marker oriented versus empirical approaches in breeding programs. A wider acceptance of integrated crop improvement strategies that consider biological and practical, empirical approaches as complementary, rather than alternative, reflects a new trend in breeding and increased knowledge and availability of tools for both approaches.
Since CIMMYT's cereal breeding programs have gradually shifted towards integrated approaches, the role of CIMMYT's International Nurseries in this altered scenario needs to be revised.
Objectives
This paper discusses different types of international yield nursery analysis in the context of applied breeding and the integration of different types of analysis in CIMMYT's breeding concept and strategy.
GE-Interaction Concepts and Approaches
CIMMYT's breeding operations focus on the development of high-yielding, widelyadapted gerrnplasm with built-in spatial, temporal, and system-independent yield stability. Wide adaptation is a key issue in breeding efforts, since this prerequisite facilitates the access and transfer of genetic systems to local germplasm or a direct release. In this context, adaptation can be defined as the ability of a variety to produce consistent high yields over a range of environments (wide adaptation), or over a specific, narrowly defined environment (specific adaptation). In CIMMYT's breeding hypothesis, adaptation is considered as a quantitative character related with yield per se; yield
23�
stability can be defined for spatial, temporal, and system dependent yield fluctuations of adapted genotypes.
The most important prerequisite of adaptation on the genotype level is yield per se and its components. Change in components related with yield per se will, in general, not alter adaptation. Other components of adaptation such as tolerance or resistance to biotic (e.g., resistance to single/multiple pathogens and races of a pathogen) and abiotic stresses (e.g., tolerance to drought and thermal stress) affect environmental yield potential, but also adaptation and yield stability. Incorporation or modification of such a buffering mechanism can increase the realized environmental yield potential as well as yield stability and the range of adaptation. Hence, increasing specific adaptation can increase wide adaptation and vice versa. In contrast to photoperiod insensitivity, which contributes to wide adaptation, photoperiod sensitivity contributes to both yield potential and specific adaptation, but decreases the range of adaptation.
Hence, the information required by breeders involves both genotypes and environments: the evaluation and quantification of genetic yield potential, adaptation, and yield stability of genotypes, as well a characterization of similarities among test sites as defined megaenvironments. Once such mega-environments are defined in terms of GE, the underlying biotic and abiotic determinants should be identified. With stresses recognized, traitoriented selection could reinforce breeding for both broad and specific adaptation.
Based on this concept, data from multilocational international yield nurseries have been analyzed in the past to generate information for the various themes. However, the nurseries were not specifically designed for research purposes and served as germplasm distribution tools.
ISWYN Analysis--Results and Implications
Genotypes The early, more common statistical approach entailed an evaluation of the "total" environment, by analysis of variance of yield data, without identifying the specific environmental factors which determine GE. This approach provided relevant results on adaptive pattern, spatial, and temporal yield stability, genotypic response to increasing levels of productivity, similarities among genotypes and was used to compare locally developed and so-called high yielding germplasm (Pfeiffer 1984, Pfeiffer and Braun 1989). Further, the results permitted conclusions on underlying biological factors of germplasm performance particularly if biotic stresses were involved. The conditionality of the results, Le., their association with the particular set of genotypes and test sites included in the nursery, caused a certain bias and difficulties in their interpretation. Estimates for regression response, stability, repeatability of stability parameters, heritability estimates, and components of variance in the stability analysis are likely to be overestimated due to the extreme diversity of the genotypes and test sites included in the ISWYN. The linear approach largely failed to differentiate among genetically closely related germplasm, e.g., sister lines from the same cross. Problems of a more statistical nature, such as very low portions of "explainable" GE, overestimation of deviations from the regression due to similar or identical site means, etc. are discussed in detail by Freeman (1973) and Pfeiffer (1984).
Test sites Other objectives of the ISWYN analysis were:
• The description and quantification of the selection ability (SA) of test sites--test sites with high SA allow discrimination among genotypes are representative for a
24�
given target area, and repeatable in different growing cycles in terms of their environmental conditions.
• The grouping of the test sites into agroecological zones.
• The identification of key sites with high SA on a global level and within agroecological zones--based on the assumption that selection at those locations should maximize genetic gains.
• Identification of the underlying biological factors of SA and agroecological groupings.
The results and methods of this study have been published by Braun (1983) and Braun et a1. (1992). The regression technique used to estimate genotypic response was used in a reverse manner: the regression of the entry grain yields at a test site on the entry means across test sites can be used to describe the discrimination among entry mean yields--the higher the regression coefficient, the better the discrimination. The deviations from this regression can be used to describe the accuracy and should be small. Other parameters used to describe SA are the heritability of a test site, and the correlation between the entry grain yields at a test site and the entry mean yields across test sites for all test sites included or subgroups of test sites. All these parameters can be combined in one measure (Vtz 1972, Wricke 1971). Since this analysis was based on the same linear model and data as the analysis to estimate parameters for genotypes, the restrictions, problems, and limitations remained the same. The estimates provided a fairly rough picture of SA, clusters of test sites, and underlying biological factors, however, lack of orthogonality and the small number of test sites within the different clusters allowed detailed analysis only for a limited number of major groupings. In general, it was not possible to obtain applicable results for groups of marginal abiotic stress environments with low grain yields in absolute terms, low variability among entry grain yields, low heritabilities, and high error variances. The generation of data with a certain accuracy at such environments may have required a different experimental design and larger plot size; however, the problem of similar site means would have remained. Another major problem encountered was the grouping of locations itself. By using several parameters--correlations among test sites, site mean yields, regression slopes of test sites, disease scores, and heritability, variance and error estimates--a meaningful grouping of the test sites was possible. The procedure and the interpretation of the results are too time consuming for general application--e.g., correlation matrices for a single year can reach dimensions of 80 x 80, with 3160 correlation coefficients to be grouped in an unknown number of clusters or predefined number of clusters.
IWWPN Analysis--Results and implications
Based on the experience with the ISWYN data, multivariate analysis appeared to be the more appropriate technique to analyze 17 years of IWWPN (International Winter Wheat Performance Nursery) data (Peterson and Pfeiffer 1989). Multivariate analysis, such as cluster and factor analysis, consider genotypic response as multidimensional, with each dimension representing a test environment. Conversely, they can characterize similarities among environments in terms of performance of genotypes grown. Thus, both genotypes and environments can be described in terms of GE (Peterson and Pfeiffer 1989, Pfeiffer and Fox 1991).
A parallel study using cluster analysis resulted in very similar groupings. Further, the� grouping of test sites into regional and subregional divisions was facilitated by the� relatively high orthogonality of test sites over years.�
25
Discussion
CIMMYT's Wheat Program distributes different types of nurseries with different objectives:
• Germplasm distribution nurseries for early segregating (F2) and intermediate products (F3, F4) without request to return data.
• Unreplicated screening nurseries primarily for germplasm dissemination with a secondary function as germplasm screening mechanism (e.g., IBWSN, IDSN, ITSN).
• Replicated yield nurseries primarily to quantify adaptive pattern of genotypes with a secondary germplasm dissemination function (e.g., ISWYN, ESWYT, IDYN,ITYN).
• Specially designed, replicated investigative yield nurseries, such as the International Drought Trial have been distributed occasionally on a irregular base.
Some of the suggestions that resulted from the ISWYN analysis have been implemented: 1) the shift from RCB to lattice design for international yield trials; 2) the use of improved, new methods for analysis such as AMMI, cluster, and factor analysis, which are easily interpretable by breeders and provide additional information on environmental similarities and adaptive pattern of the genotypes; and 3) focus on mega-environments by separate screening and yield nurseries for different agroecological zones with a set of genotypes adapted to the environmental conditions of the mega-environment and standards common to different nurseries.
However, the information potential of international nurseries has not been exploited. The inclusion of differentials for particular abiotic (e.g., trace element deficiency or toxicity) or biotic stress (e.g., nematodes) in screening nurseries with specific instructions for note taking could be used to detect actual or potential production constraints. Once such environmental constraints have been identified, they can be quantified in specifically designed investigative nurseries with plot size, for example, adjusted to environmental variability. The International Drought Trial may have yielded more information for moisture stress situations than several years of ISWYN data: it was distributed to relatively few selected cooperators who recorded additional information on genotypes and environmental parameters; additional information was generated in line source testing and breeders trials. Hence, this allows scientists at CIMMYT base to evaluate the representativity of their selection and testing environments, modify testing environments and methodologies accordingly, and target basic research. Further, expanded multilocational yield testing during intermediate and/or final stages of germplasm development in Mexico maYI result in higher selection gains and substitute for part of the international yield testing with higher overall resource efficiency. Past experience has shown that international cooperators adopt CIMMYT's methodologies and procedures, e.g., new experimental designs. Hence, investigative nurseries can play an important role in human resource development in developing countries. Further, efforts from CIMMYT to distribute germplasm together with data and information, e.g., on milling and baking quality or leaf rust genes, will facilitate selection and increase efficiency in NARCs.
26�
References
Allard, R.W. 1960. Principles of Plant Breeding. John Wiley and Sons, Chichester.
Braun, H.-J. 1983. Untersuchungen ueber die Selektionseignung von Orten fuer die Zuechtung von Sommerweizen im Tropisch- Subtropischen Bereich. Dissertation Universitaet Hohenheim, Federal Republic of Germany.
Braun, H.-J., W.H. Pfeiffer, and W.G. Pollmer. 1992. Environments for selecting widely adapted spring wheats. Crop Sci. (in press).
Comstock, R.E., and R.H. Moll. 1963. Genotype-environment interactions. In pages 164196, Statistical Genetics and Plant Breeding, Nat. Acad. Sci.--Nat. Res. Council Pub!. 982.
Freeman, G.H. 1973. Statistical methods for the analysis of genotype-environment interactions. Heredity 31:339-354.
Peterson, c.J. and W.H. Pfeiffer. 1989. International winter wheat evaluation: Relationship among test sites based on cultivar performance. Crop Sci. 29:276-282.
Pfeiffer, W.H. 1984. Ertragsleistung, Ertragsstabilitaet und Adaptation von Sommerweizen auf regionaler und globaler Ebene --Analyse einer Serie von internationalen Sortenversuchen ueber 15 Jahre und 973 Umwelten. Dissertation Universitaet Hohenheim, Federal Republic of Germany.
Pfeiffer, W.H., and H.-J. Braun. 1989. Yield stability in bread wheat. In pages 157-174, J.R. Anderson and P.B. Hazell, eds., Variability in Grain Yields: Implications for Agricultural Research and Policy in Developing Countries.
Pfeiffer, W.H., and P.N. Fox. 1991. Adaptation of triticale. In pages 54-59, Proceedings of 2nd International Triticale Symposium, Passo Fundo, Rio Grande do SuI, Brazil, 1-5 Oct. 1990.
Sprague, G.F. 1966. Quantitative genetics in plant improvement. In pages 315-354, KJ. Frey, ed., Plant Breeding--a symposium held at Iowa State University, Ames.
Utz, H.F. 1972. Die Zerlegung der Genotyp x Umwelt-Interaktionen. EDV in Medizin und Biologie 3:52-59.
Wricke, G. 1971. Eine orthogonale Aufteilung der Interaktion fuer ein eingeschraenktes. Model. Rundschr. Arb-Gem Bioun. DLG 1.
Discussion Notes
Rajaram: In the screening nurseries, maybe we should reduce the load on cooperators by asking them to provide information only on lines that are useful to them. Pfeiffer: Cooperators should take notes for all entries. They should report what lines they selected as soon as possible...Perhaps we should supply data with seed. Singh: Ifwe follow through on Rajaram's suggestion, we would not know why a particular line was not selected--it would be interesting to know if a disease was involved.
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Gauch: When is GLY significant statistically?� Pfeiffer: Almost always.�
DeLacy: There is no year effect when testing in many locations around the world. On� average, the worldwide year mean is the same each year. Similarly GY is usually not� significant under these testing conditions.� Pfeiffer: I agree.�
Fischer: Is the correlation still there when looking at highly selected material--as� opposed to the early years when there was a lot of junk in the nurseries?�
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Update on The Data Management System (DMS) for the Wheat Program
Henrik Schou and Hector Sanchez� Systems and Computing Services�
Note: For additional background on the Wheat Program's Data Management System, See Appendices 1 and 2.
This paper reports on the findings of a DMS survey conducted by SCS concerning problems and deficiencies involving the management of evaluation data in the Wheat Program. The aim of this study was to identify:
• Current user procedures. • Deficiencies to overcome. • Goals of the new system. • Scope of the DMS project. • Acceptable scenarios for completing the project. • Suggestions for a possible project plan.
Current User Procedures
In this section, we list current user procedures, i.e., the daily operations performed by Wheat Program staff. The aim of analyzing these procedures is not to get a complete picture of the Wheat Program's way of working, but to get a feel for the work flow so that appropriate decisions can be made relative to setting up a functional and userfriendly data management system.
Below is a list of the day-to-day operations done in various sections of the Wheat Program. The codes mean the use of the following:
• S = Special package. • WPFBS = The field book system. • M =No system. • WINS = The WINS system. • WGB = The wheat germplasm bank system. • WSGSS/WSGYS = WSGSS and WSGYS. • FIBOS =The FIBOS system. • EXTRACT =The extract program.� • * = Operations to be considered by the DMS project.�
1) Scientist designs a field book. FIBOS
2) Scientist requests a field book. The Field Book System generates the book containing crosses and selection histories. WPFBS, FIBOS
3) * Scientist collects evaluation data. These data are recorded on a sheet. M
4) * Wheat technician captures evaluation data. The recorded evaluation data are converted to an ASCII format. M
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5) * Scientist loads the converted evaluation data into LOTUS or MSTAT. A data analysis is extracted. S
6) Secretary drafts an international nursery trial. A draft book with crosses and selection histories is produced. M
7) A nursery sheet is drafted, which is then converted to an international nursery sheet with the same information. M
8) Secretary sends an international trial to a cooperator. WINS
9) Secretary sends a list of available international trials to the CIMMYT cooperators. WINS
10) Secretary receives request from cooperator for an international trial. WINS
11) A decision is made on how to distribute the nurseries. Secretary sends international trials requested by cooperators through the mail. WINS
12) Secretary receives a filled-in nursery sheet from a cooperator. M
13) * Secretary checks nursery field book for errors, which if found, an error list is mailed to the cooperator stating the error(s) and requesting the cooperator to correct the error(s). M
14) * Production department filters the nursery data. Values are checked to see that they all are within tolerated ranges; if they are not, a decision is made on how to correct the error(s). WSGSS/WSGYS
15) * Data on returned nursery sheets are converted to ASCII format. A nursery file is generated. M
16) * An international nursery bulletin is produced. The converted nursery sheet is used as input to a program which extracts the International nursery bulletin. WSGSS/WSGYS
17) * The ASCII nursery file is transformed to special file format.
18) * A scientist analyzes international nursery data. The special file is loaded into a standard package and reports are extracted and then combined with WSGSS/WSGYS output. EXTRACT
19) Head of Wheat Germplasm Bank enters passport data. Passport data about new collections are entered into the Bank. WGB
20) Head of Wheat Germplasm Bank prints field books from the Bank. Accessions with the desired properties are extracted from the Bank and used as entries in the field book. WGB
21) Head of Wheat Germplasm Bank transfers regeneration data to the Bank. The Bank accessions are updated with the regeneration information and addresses are allocated in the Bank. WGB
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Data Management System
In this section current deficiencies and new system goals for operations 3, 4, 5, 13, 14, 15, 16, 17, and 18 above are discussed.
Current deficiencies to overcome Deficiencies of the current system expressed by members of the user community in the survey are listed below:.
1) Evaluation data are not always stored.
2) It is not possible to retrieve information.
3) In order to facilitate research on wheat data, a lot of "donkey work" to organize the data must be done.
4) New traits can not be analyzed.
5) It is tedious to make a LOTUS or MSTAT report because the data needed are only available on tape.
New system goals Operations 3, 4, 5, 13, 14, 15, 16, 17, and 18 are supposed to be affected by DMS and the new system goals include the following:
1) All the wheat data should be accessible.
2) Evaluation data must be stored in a format that facilitates researching.
3) It should be possible to add traits to evaluation data.
Acceptable scenarios A brief description is presented here of two possible systems that satisfy the new goals-VAX-PC integration and a VAX-PC network. It is important to realize that goal #1 implies a connection of the three Vax databases--the Pedigree Management System (PMS), Wheat Germplasm Bank (WGB), and the Wheat Program Field Book System (WPFBS)--and the evaluation database that has to be constructed as part of goal #2.
The VAX-PC integrated solution. The three systems--PMS, WGB, and WFPBS--that reside on the VAX and would be interconnected. The Evaluation database would also be VAX-based and connected to the others. Facilities to export data to the PCS for use in MSTAT, Lotus, or other packages would be available. Data would be entered on the VAX or on a PC and would reside in the common data repository on the VAX. Filtering would take place on the VAX.
The VAX-PC network solution. The three systems residing on the VAX would be interconnected, but the evaluation database would be PC-based and not physically connected to the VAX databases. The link to the VAX-based systems would maintained through cross and selection history.
Development, operational, and maintenance costs For either scenario, it is estimated that the whole project will take 4 programmer/analyst years. The Wheat Program will need to allocate resources for the acceptance test,
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acceptance test generation, and conversion of evaluation data. For the network solution, an additional network database package would have to be licensed and installed.
Operational costs are estimated to be less than those currently, but will include entry of evaluation data from about 1500 books per year, hardware maintenance, and costs of using standard software packages.
The network solution would have additional costs of keeping two separate databases consistent and of operating a network database.
Maintenance costs will involve fine tuning the system, including correction of minor errors.
Suggested Development Plan
SCS suggests that the project be broken down into units with each unit consisting of analysis, design, and implementation. Each product has to be accepted by the Wheat Program and upon delivery be able to generate acceptance tests and execute acceptance test cases.
Conclusion
The scope of the DMS project is limited to evaluation data from field books and international nurseries. A large part of the project will be the interconnection of PMS, WPFBS, and WGB. The other part concerns the design of a new database containing the evaluation data.
We suggest that a steering committee consisting of wheat scientists and SCS analyst/programmers agree on the steps of the development plan. Ideally, the project should be broken down into smaller units and as each unit is delivered, it should be approved and accepted by the Wheat Program.
Discussion Notes
Sanchez: The PMS conversion is the bottleneck--the Bank system cannot be linked to PMS until the PMS conversion is completed.
Fox: Certain tapes in SCS should not be put on the system--either too old or too expensive to convert.
Fischer: There is not much need to convert historical data. Conversion of new data will be an accomplishment.
DeLacy: Data for potential inclusion in the system should be treated on a case-by-case basis.
van Ginkel: Is it possible to include data from previous years in the fieldbooks? Schou: As long as the data are there, yes.
Fischer: Even if you have all the data at your fingertips, is it humanly possible to use it? It boggles the mind!
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Use of Relational Databases in Wheat Breeding
John D. Corbett� CIMMYT Geographer�
"Data is a resource in much the same way that employees, products, natural resources, finances, and other materials are resources" (Relational Databases: Concepts, Design, and Administration, 1991, McGraw Hill)
Wheat breeders generate volumes of data for which the major limitation to use is access. Information access and information exchange offer mechanisms to enhance the efficiency of breeding efforts, if only through the better management of the data a breeder generates. There are two parts to this paper: First, some general characteristics of relational databases (RDB) and how such data management systems might directly affect CIMMYT. Second, I will present a practical or prototype example of how a relational database works (the interface) using data from the International Spring Wheat Yield Nursery (ISWYN). This example will be summarized through a simple listing of the example queries.
Introduction
For breeders, better efficiency can be found in expediting the access to annual trials and in access to historical data as well as germplasm data from other sources (namely PMS and the germplasm bank). One way in which CIMMYT contributes to its cooperators is in the form of seed (nurseries). We need these data. We also need to make the feedback to cooperators faster, more thorough, and more convenient and, thus, these data should be managed in a relational database environment. One possible effect of more timely feedback is the improvement of trial data returned from cooperators.
An untapped source of information is the academic community worldwide. If supplied with our data, the contributions from these off-CIMMYT sources may prove valuable.
A relational database means access to your data and ease of query of these same data.
Goals of the Relational Data Base:
• Access to "all" data--international trials, the germplasm bank, Pedigree Management System, genetic information, environmental data (climate, soils, meteorology), output from crop models, etc. Because there is no positional dependency between the relations, requests do not have to reflect any preferred structure and therefore can be nonprocedural.
• Simplicity: The end user is presented with a simple data model. Requests are formulated in terms of the information content and do not reflect any complexities caused by system-oriented aspects. A relational data model is what the users see, but it is not necessarily what will be implemented physically.
• Application program independence. Data which are accessed by a relational database can be extracted so that it is in a form suitable for many applications (e.g., SAS and other statistical package input files, ASCII text, etc.).
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• Consistency through use of same data by all researchers.
• Reduction in application development, storage, and processing costs.
CIMMYT can be thought of as an information-driven enterprise. Access to and use of our data is paramount to the success of our enterprise.
Potential users must know what data exists! Thus CIMMYT is actively seeking ways to publish our data in such ways as to maximize utility. CD-ROMs (Read Only Access) offer great potential.
Part 1: The Relational Database
RDBs link "entities": An entity is a uniquely identifiable thing such as a person, place, genotype, or concept about which we desire to record information.
An entity is a logical object, whereas a table is a relational object that can be mapped by a database into physical datasets stored on disk.
An entity can be thought of as a ROWand each entity has some unique identifier (primary key) and attributes (columns, also called fields) and through the keys, some relation with other data.
The attributes of the logical entities will be implemented in relational tables as COLUMNS. Each column must be identified. One or more columns in each table will consist of the unique identifier, or primary key. Relationships and the corresponding foreign keys will also be implemented as columns in tables. Primary and foreign keys should be the only columns that appear in more than one table because they represent the only attributes that appear with more than one entity.
An attribute (also called a field and which appear as columns) is a data element.
A relation is a group of attributes.
Attributes are related through a key and describe some entity of interest.
The RDB begins with the translation of each entity into a relational table.
The RDB can be defined as a collection of interrelated data items that can be processed by one or more application systems. The RDB permits common data to be integrated and shared between corporate functional units and provides flexibility of data organization. It facilitates the addition of data to an existing ROB without modification of existing application programs. This data independence is achieved by removing the direct association between the application program and physical storage of data.
Final comments Our data become an asset that we can make available to NARSs and universities.
CIMMYT will have a distributed database system. Each group has direct control over its data (more efficient data processing =increased data integrity). A network allows for the sharing of such data and the relational database management system (DBMS) allows access to the data.
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Note that for calculated fields, an ROB will not store the calculated field (Lotus and other spreadsheets do) as the numeric fields that created it are sufficient and are created at retrieval time. Scripts can be created which store the algorithm and these are easily accessed.
The data dictionary (DO) is the second most important tool in the ROB environment (after the DBMS itself). The DD is used to record facts about objects or events in the database environment in order to facilitate communication and provide a permanent record. The DO is based on giving information about the database itself, its contents, and its structure. The DO focuses on data related components: data elements or attributes; data groups, rows, tables; data structure; databases. CIMMYT will need to put resources into the development and design of the data dictionary.
In an ROB environment, the DO is a very important tool for systems design, documentation, data management, and data security. It can be used as a repository to collect the user requirements which are used as the primary input to the logical design phase of database development.
A note on data entry. The relational database and the DBMS use forms to assist with the entry of data. In this way, each bit of information entered into the database is checked for a range of possible values. Thus, yields would be flagged if entered as 10000 kg/ha.
Part 2: An example of possible queries: ISWYN
1) Select all data (germplasm type, rep, yield, disease scores) for all trials in which rainfall was less than 200 mm and yields were greater than 5000 kg/ha.
2) Select all data for cases where disease scores on stripe rust and head rust are high.
3) Select for all cases where Nand P fertilizer were used and yields were less than 3000 kg/ha.
4) Use the cross number for Bluebird (II23584) and find all occurrences of this cross and when grown in an ISWYN nursery.
5) Calculate the average yield of the repetitions for a given site in each ISWYN where it occurs.
6) Count the number of times a select germplasm is used.
7) Select stations used more than eight times and which occur in South America. Provide the latitude, longitude, and elevation of these stations.
Conclusion
The development of a relational database system at CIMMYT will require effort to clean historical data and to change the way in which we view the data we generate. In Appendices 5 and 6, we ask for your input to this process. Appendix 5 lists cooperating stations. Please update any errors you can identify. Appendix 6 provides the common name, a unique identifier, the cross and selection 10 (when known) for all germplasm that has been grown in the ISWYN trials. Please send any corrections to the first named editor. Provide a complete copy of the data in error along with a complete note on your corrections.
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Discussion Notes
Corbett: Access to our own data should not be an issue...The Paradox package is very� user-friendly. We will be able to come up with a computer screen that looks like the� cooperators' forms. This will speed-up data input by secretaries and enable them to get� new data into the system as quickly as it is sent in by cooperators.� Fischer: That is the key. The breeders need all the new information (up to yesterday) at� crossing time--50% of the demand will be during this period.�
Sanchez: What about security?� Corbett: No problem, Paradox allows the files to be easily locked.�
36�
Discussion on the Yield Trial System/� Inte~national Nursery Logistics�
B. Skovmand� Head, Wheat Germplasm Bank�
The present system for managing yield trials, ESCAM, is now outmoded and needs to be replaced. However, development of a new yield trial system is not foreseen until the development of the Data Management System (DMS) is completed. The yield trial system is inflexible in the sense that it is Randomized Complete Block Design (RCB) with number of entries and replications fixed. Further, the system cannot be integrated with our Wheat Pedigree Management System (WPMS), with the result that breeding programs cannot be totally converted.
A small change in program management of the yield trials can minimize the effect of not being able to connect the ESCAM to WPMS. Two types of fieldbooks are being produced in relation to yield trials:
• The actual randomized yield trial.
• A Parcella Chica (PC or small plot) book.
Traditionally, the yield trial books are produced first and the PC books generated from this list, which means that we have to get a list from WPMS, transfer this to the old fieldbook system (FlBGS) and in the end convert the PC list to WPMS. This is cumbersome and time consuming. If the breeding programs were to generate the PC list first and then use this list as input for producing the yield trial list, we could avoid the conversion back to WPMS and ESCAM could be utilized for the time being without causing extra work.
Discussion Notes
Rajaram: In bread wheat, there would be a problem to generate the PC list first.�
Fox: Regarding new experimental designs, the breeders will have to push for these or� there will be no sense of urgency.� DeLacy: Beware of the fact that you may end up with a system that can't handle the new� designs that will be coming along in the next 5-6 years.�
Rajaram: What is the cost of switching from RCB to alpha-lattice?� Fox: Major labor component is adding sticky labels to seed envelopes for the lattice.�
Rajaram: Smaller plots mean at least 15% higher yields and their use may mean we'll� start losing information--especially if there are germination problems like this year.� Jain: In India, we prefer 5 meters because 2.5 meters requires us to hand plant.� Yau: ICARDA, after some discussion, has decided to keep rows at 2.5 meters.� DeLacy: In Australia, we custom fit seed amounts to what cooperators want. This results� in better data, however, trying to fit local designs may be a bit radical for CIMMYT.� Fox: We need a larger sample of cooperator input before we make any changes.� Fischer: Then we need to do a cooperator survey. A concern I have is: are cooperators� following our instructions to trim the edge rows?�
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Alcala: We sent 7.5 tons of seed to cooperators in 1991-92, averaging 12 grams/envelope...Seed washing for KB currently takes 3.5 months.
Abadalla: What is our current seed washing policy? Fischer: We want to reduce KB teliospores to the lowest level possible. We have extended the policy to Central Mexico. Until we find a location without teliospores in Mexico or can prove that the teliospores do not present a risk, we will have to continue to wash the seed. We are currently installing washing equipment on the International Nurseries bodega roof at El Batan.
Rajaram: 40% of the seed weight is currently with the Yield Trials. For the total weight to remain the same, we must reduce the number of entries and sets if we add any new nurseries.
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General Discussion/Conclusions/Actions to Be Taken
Discussion
Rajaram: There is some risk in selecting crossing material from the PYT and YTs...Give� F2s to cooperators who request them, but reserve the right to regulate when necessary� what we give them.�
Fischer: Is there anything to be gained by testing in two locations in the Yaqui Valley?� How well do the artificial environments fit the targeted ones?� Sayre: ENET is a potential data base for determining if more locations in Mexico are� needed.� DeLacy: Before making a change in such <Lsuccessful system, you need the data to show� that a significant improvement is likely.�
Fox: As I understand it, the policy is that the SNs are our major germplasm distribution� tool; the yield nurseries (YNs) for research. The YNs are the only ones that distribute� non-CIMMYT material; could such material be distributed in the SNs?�
van Ginkel: Screening nurseries (SNs) provide the major flow of information for the� crossing programs.� Rajaram: We are looking primarily at disease resistance of ±250 lines; not all that� interested in yield in the SNs.� Delacy: I think that yield data in the SNs are useful and should be recorded even though� 2-row unreplicated.� Singh: I also agree that yield data are useful in the SNs.� Fischer: Looks like there are a number of votes that yield in the SNs is useful.�
Crossa: Suggest going to an alpha lattice design.� Fox: All five YNs will be going to this design soon.� Fischer: What are the additional costs of such a move?� Fox: We'll need some kind of machine to attach the sticky labels...The important thing is� that we must know now if we are to move to lattice for the next cycle as we prepare the� seed shipments� Fischer: Before we decide we need to involve the national programs in the decision.� We've gone long enough without any feedback from them. Perhaps delay the lattice� decision for a year during which time we do cooperator survey.� Fox: National programs are very passive--not all that many complain about any changes� we make. It is almost frightening how cooperators accept the way we do things and the� responsibility implied...However, I believe the lattice design will be an important� technology transfer mechanism for national programs to test their own varieties.� CIMMYT is often the only contact for such technology transfer. Most national programs� are not keeping up with developments in the literature.� Fischer: CIMMYT needs to develop some sort of user-friendly training manual for yield� testing.�
Fox: Perhaps distribute data with seed if DMS turns out to be the efficient and rapid tool� it is purported to be.� Fischer: There is a possibility that too much data will distract our attention.� Fox: Is there a risk in going from three to two reps? We need to decide if we are going to� do it--it will cut the number of envelope by one-third.� DeLacy: More varieties and fewer reps is the way to go for selection. Newer designs� (lattice, AMMI) are more efficient and only require two reps. However, telling� cooperators you are going to two reps is not the same as convincing them.�
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Gauch: Two vs three reps regarding AMMI: take historical data using two reps and use the third rep to validate.
Workshop Conclusions
Although we have had tremendous impact in the past with the old system, changes are coming. Broad adaptation across several environments will not carry us too much further into the future, especially with shrinking resources. We will be moving to specific environments with more analysis.
Some major points:
• Yield testing within Mexico--no consensus that there be any major changes at the moment. Although we should consider using smaller plots, testing on beds to save resources, and analyzing the ENET.
• Screening nurseries--Consensus that the SNs are important for disease resistance; no consensus on whether yield data are important. Need to look at Australian infonnation on the subject. Need to streamline data collection and speed up its turnaround.
• Yield trials--consensus on the following:
1) Go from three to two reps--notwithstanding the problems in the national� programs.� 2) Do not increase site numbers, perhaps even reduce number of sites due to� the quality of data from certain sites.� 3) Upgrade the statistical analyses (to AMMI, etc.) being used.�
• Infonnation for NARSs--CIMMYT has important technology transfer and training roles. Need to design guidelines for experimental design and yield testing, e.g., include a chapter on biometrics in the Training Manual being put together by Villareal and Rajaram and develop a training manual for yield testing. Need to consult with NARSs on anticipated changes.
• Data Management System--After seeing what can be done during the demonstrations at this workshop, the breeders agree on its value. However, DMS is only part of the challenge--how to extract the wisdom is the rest of the challenge. There is a lot of infonnation on how to do it, but not so much on why to do it.
Specific Actions to Be Taken
The following actions are planned in the near future:
• In 1993-94, ISWYN, ESWYT, EDYT, IDYN, and ITYN will be distributed internationally with two replicates instead of three. This is possible because of gains from the alpha-lattice design. The savings may allow a modest increase in numbers of entries in subsequent years when new protocols for seed washing become routine and automated.
• The CIMMYT Wheat Program and the Biometrics Unit will fund a consultancy to address Spatial Analysis of field experiments and trial design.
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• The scope of coverage of general nurseries, such as ISWYN, will not change, but others will be more specifically targeted.
• The first product of the Wheat DMS Project will focus on fast feedback to CIMMYT breeders for making crossing decisions.
41�
Appendix i--The Wheat Program's Data Management System (OMS)
P.N. Fox� International Wheat Nurseries�
February 1992�
"What is needed here [nursery reportsJ is the compilation of international trial data in a desktop-computer-database file that can be provided instead of the hardcopy and allow for query and data retrieval according to the user's whims." A. Blum, Volcani Institute.
Introduction
Norman Borlaug once said that international testing "broke down a psychological barrier which had tended to keep the efforts of each wheat researcher isolated. The new testing led to an unexpected acceleration of wheat breeding around the world." This revolution brought about by germplasm sharing will be paralleled by another revolution driven by information exchange and lead by OMS.
Objectives of OMS
A.� To provide a secure, flexible system for the storage of important data on wheat, triticale and barley germplasm.
B.� To provide an integrated set of user-friendly tools for entering, filtering, transforming and accessing data.
Justification
Efficiency, Research, Serving NARSs and Policy Making.
A.� The Wheat Program handles data each year from international trials as well as breeders' trials in Mexico and from activities in laboratories, greenhouses and the Germplasm Bank. Important genetic information is also generated outside of CIMMYT operations. Because much of these data are not stored in an organized fashion, integration of laboratory and field data, for example, is impossible. The increased use of PCS, more decentralized research programs, and personnel changes raise the risk of a fragmented database, part of which may become lost or inaccessible in the future without a more systematic approach to data storage.
B.� Wheat Program scientists require a more accessible database. DMS would furnish convenient read-only data access to program scientists, while . guaranteeing the integrity of the master database.
C.� International trial data coordinated by CIMMYT is under-utilized. Storing agroclimatic and crop management information together with genetic information and plot data will facilitate partitioning and aggregation of data in new and potentially revealing ways.
O.� OMS is viewed as part of a larger system that will include flexible options for trial design, statistical analysis and reporting, and graphic presentation. Together these components will result in more timely and better quality information for CIMMYT scientists and cooperators.
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Some History
International nursery bulletins are produced using software written in FORTRAN in the early 1970s, when CIMMYf wheat staff with the computing system division of the RCA company in Mexico City and Dr. Abel Mexas from the Ford Foundation designed and implemented RAPID (Rapid Analysis Program for International Data). Modifications and additions were found to be extremely difficult. However, in 1978-79 LISA (Laboratory for Infonnation Science in Agriculture) from Colorado State University reviewed the system and made some limited modifications to accommodate new requirements of the rapidly expanding testing program. Since that time, the software has been used without significant changes. The resulting software systems (SGYS and SGSS) were never designed as data management tools. Their function was solely generation of international nursery bulletins and they only became a type of default data management system, when the program EXTRACf was written by Neal Bredin to access data from SGYS/SGSS.
. However, data from trials conducted with advanced lines in Mexico, the stage before lines enter international nurseries, are not included. EXTRACf is unwieldy and requires intervention of SCS personnel.
A system for the "corporate data", maize and wheat, was assigned a high priority at a special meeting of the MAC on 12 June 1989.
During 1990, "Data Management System: System Requirements," aimed at a common relational database system for Maize and Wheat was produced.
It was considered by the Wheat Program that, at least initially, agronomic trials would not be considered by DMS. We will prime DMS with international nurseries data, adding data from other field, laboratory and Gennplasm Bank operations later (Appendix 2 covers the status of definition of traits, as well as genetic and environmental descriptors, to be stored or accessed by DMS).
In 1991, a Memorandum of Understanding was signed by the CIMMYf Maize Program and Michigan State University (MSU) for software development. The MSU option was not chosen by the Wheat Program for a number of reasons:
• SCS has delivered the Pedigree Management System (PMS) and has an intimate appreciation of the Wheat Program. By contracting outside, we would lose this considerable in-house expertise, placing all strategic Wheat systems in jeopardy. PMS is the core of the Wheat Program's systems strategy.
• Because of this expertise and the good working relations established between Wheat and SCS, we believe we will get a better return on every dollar invested inhouse compared with outside.
• Problems of communication over distance.
• The Maize Program is seeking much more than a Data Management System. The Wheat Program has many of these additional elements available through PMS.
The Wheat and Maize Programs will remain in contact on the progress of their separate systems. It may prove possible to share certain specific operational functions peripheral to DMS, such as nursery shipment control, automatic generation of phytosanitary certificates and spatial analysis of field trials.
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Pedigree Management System (PMS)
This section draws from Neal Bredin's "PMS/NWFB Consultancy Report" of December 1990. PMS and associated functions have been developed in SYSTEM 1032 on a VAX computer under the VMS operating system.
The benefits realized via PMS can be attributed to:
• Unique identification of germplasm and
• Accessible pedigree relationships.
Why is unique identification important? If one wishes to share information about something, one has to identify it. Although this sounds simple, it is not always straight forward. Consider how germplasm is identified at CIMMYT. A cross name and selection history constitute primary identifiers. These identifiers are not always unique, the same germplasm may be known by several different names (e.g., VEE#5, KVZ/BUHO/IKAL/BB, CM33027-F-15M-500Y-OM, etc.). It is not obvious that one of these names may be equivalent to another.
This 'failure to associate' is a barrier to effective information sharing. This barrier has prevented the Wheat Program from developing more than a collection of isolated software systems supporting specific tasks. SGYS/SGSS, Breeders Seed Inventory System and the old Field book System (FIBOS) are examples of systems that are self contained, serve a useful function and will never be able to effectively interchange the information they manage.
Information has been managed by how and where it was collected rather than by identifying the germplasm to which it is associated. To illustrate this consider how one would request information about a specific li ne. The questioning procedure in a source oriented system proceeds as follows:
• Which data collection systems record the type of data in which I'm interested (e.g., SGSS, SGYS)?
• Within the confines of that system, which trials (information sources) are potentially interesting?
• Did the line in which I'm interested appear in any of those trials?
Were we to associate information directly to the germplasm to which it relates, the search for information would proceed as follows:
• In what trials (information sources) did the line occur?
• Of these, which are potentially interesting?
In the first scenario, we were faced with the task of reducing the number of places to look even before establishing that the line of interest is in one of those places. Consequently many potential sources of information are overlooked.
In the second scenario, we started from a position of knowing what data were available;� we only had to choose those sources that were potentially interesting.�
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The data-germplasm association provides a more effective information system than a source-data system. The key to developing a data-germplasm association is the ability to uniquely identify the germplasm:
PMS implements a system to identify germplasm uniquely. The cross identifier (CID) uniquely identifies each cross. The selection identifier (SID) uniquely identifies among the progeny of a cross. Together the CID and SID uniquely identify any germplasm. Already, the 'ability to associate' of PMS has proved a powerful and versatile tool in unexpected areas. For example, the cytoplasmic diversity of CIMMYT bread wheats was examined. From PMS we have the potential to develop a highly integrated data management system, linked to the Wheat Germplasm Bank. DMS will enhance the 'ability to associate', providing the capacity to associate data on germplasm with combinations of latitude, altitude, diseases, country, mega-environment, available moisture and other environmental parameters.
Some examples of what DMS will and won't do
DMS could ask for:
• A list of all sites where international bread wheat, durum wheat and triticale trials have been grown in the same season with less than 350 rom of available water.
• A list of instances where variety X, to be released by a national program, occurs in the database and a second sublist in which all instances of susceptible reactions to five major diseases for the variety are reported.
DMS will not:
• Produce balanced data sets from unbalanced ones, by estimating missing variety by site combinations.
• Provide yield data for all trials sprayed with ILOXAN.
Final Comment
CIMMYT itself must become an exemplary cooperator in the network and take the lead� in returning data.�
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Appendix 2--Proposed New Versions orE, GE, and G Data
P.N. Fox� International Wheat Nurseries�
February 1992�
A. General Notes to Be Taken (E Data)
We are asking for more than in the past. We must see the changes from the cooperators' point of view and guard against undue complexity, which could prove counter-productive in efforts to manage data more usefully.
It has been suggested that the proposed detail on water availability is out of proportion with for instance, crop stand where we solicit "poor, fair or good". However, crop stand is also considered as a per plot observation under GE data. Cooperators should also be encouraged to score characters 16 to 21 (below) on a per plot basis when observations are in the moderate to heavy range.
The question of temperature data was raised. Our thinking was that temperature, as opposed to rainfall, is relatively stable across years. Nonetheless, extreme deviations from the norm could be accommodated in 23, a general comment on weather. Screening nurseries may require less ennvironmental data than replicated yield nurseries. There is opportunity to revise these questions.
1. COUNTRY
2. STATE, PROVINCE OR DEPARTMENT
3. LOCATION NAME
4. STATION NAME
5.� LATITUDE degrees-minutes-N/S
6.� LONGITUDE degrees-minutes-EIW
7. ALTITUDE ABOVE SEA LEVEL IN METERS
8.� USE OF FIELD IN YEAR PRECEDING TRIAL SOWING crop (please specify)/natural or improved pasture/ weed-free fallow
9.� SOWING date dd-mm-yy germination delayed by dry seed bed?
yes/no
10. DATE HARVESTED dd-mm-yy
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11.� UNITS OF FERTILIZER APPLIED kg ha- 1/other (please specify) N-P205-K20-other (please specify)
12.� CROP STAND poor/fair/good
13. SCALES USED AND DATES ON WHICH AGRONOMIC AND DISEASE NOTES WERE TAKEN including height and test weight�
trait-scale used-dd-mm-yy� trait-scale used-dd-mm-yy�
" 10 times
14.� YIELD SCALE USED kg plot-1/other (please specify)
15.� LOCAL CHECK name-crop
16.� FOLIAR DISEASE DEVELOPMENT none/slight/moderatelheavy
17.� ROOT DISEASE DEVELOPMENT none/slight/moderatelheavy
18.� INSECf DAMAGE none/sl ight/moderatelheavy
19.� WEED PROBLEM none/slight/moderatelheavy major species, if serious
20.� BIRD DAMAGE none/slight/moderatelheavy
21.� HERBICIDE DAMAGE none/slight/moderatelheavy
22. OTHER COMMENTS AND OBSERVATIONS
23.� WEATHER general comment
24. WATER AVAILABLE TO CROP IN MILLIMETERS (mm) A. estimate of stored plant-available moisture in
full root zone at sowing in mm B. precipitation (ppn.) in mm in the 12 months
(mo) up to month of harvest ppn. 11th mo before harvest month name "10th " " "9th " " "8th " " "7th " " "6th " "
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5th II II
4th II 11
3rd II II
2nd " " 1st " "
month of harvest " total
C. irrigated? yes/no If yes,
number pre-sowing irrigations number post-sowing irrigations estimate of total water applied by
irrigation (mm)
25. SOIL classification (if available) surface texture
sandy/sandy loam/loam/clay loam/silty clay/clay/other (please specify)
pH unknown/<4/4-5.5/5.5-7/>7/actual value
% organic matter aluminum toxicity?
yes/no if yes, surface/subsoillboth/unknown
any root barrier? yes/no/unknown if yes, depth (cm) if no, depth of root zone (cm)
26. PLOT DIMENSIONS Sown Harvested No. of rows A No. of rows D Length of row (m) B Length of row (m) E Space btn rows (cm) Area (m2) =
C Space btn rows (cm) Area (m2) =
F
(AxBxC/100) (DxExF/100)
B. Trait List (GE Data)
We do not expect all traits to be recorded by all cooperators. Currently international nursery fieldbooks list the following traits to be recorded: the three rusts, powdery mildew, loose smut, net blotch, scald, BYD, days to heading, height, yield and "others". The current software, Small Grain Yield System (SGYS) and Small Grain Screening System (SGSS) can, however, manipulate 80 fixed traits. Should we remove this biasing factor towards given traits in fieldbooks and stress that cooperators return data on the traits important at their sites? * DENOTES KEY TRAITS.
PATHOLOGY TRAIT CROP SCALE Stem rust ALL MCobb Leaf rust ALL MCobb Stripe rust on leaf ALL MCobb
48�
Septoria tritici blotch Septoria nodorum blotch Helminthosporium sativum Helminthosporium tritici-repentis Fusarium nivale on leaf Xanthomonas stripe Powdery mildew Scald Net blotch General foliar blights Yellow rust in spike Covered smut Loose smut Fusarium scab H. sativum in spike S. nodorum in spike Barley stripe Barley yellow dwarf Barley stripe mosaic virus Black point (H. sativum,
Alternaria, S. nodorum) Kamal Bunt (grain infection)� Other bunts (grain infection)� Scab (grain infection)�
QUALITY TRAIT� ALVEOGRAM W VALUE*� ALVEOGRAM PIG OR P/L VALUE*� TEST WEIGHT*� FLOUR SEDIMENTATION INDEX*� YELLOW BERRY*� LOAF VOLUME*� GRAIN PROTEIN *� FLOUR PROTEIN *� FLOUR YIELD*� FALLING NO.� FLOUR PIGMENT� SEED HARDNESS� SEED TYPE� SEMOLINA PIGMENT� SEMOLINA PROTEIN� DOUGH MIXING TIME� COOKIE SPREAD FACfOR� GLUTEN CONTENT�
AGRONOMIC TRAIT� GRAIN YIELD*� CROP STAND� 1000 GRAIN WEIGHT*� GRAINS/SPIKE� SPIKES/M2� CHECKMARK*� DAYS TO HEADING (DC 55)*� DAYS TO ANTHESIS (DC 65)*�
BWDUTC 00-99� BWDUTC 00-99� ALL 00-99� ? 00-99� ALL 00-99� ? 00-99� ALL 00-99� BA 00-99� BA 00-99� ALL 00-99� ALL 0-9� BA 0-9� ALL 0-9� ALL 0-9� ? 0-9� BWDUTC 0-9� BA 0-9� ALL 0-9� BA 0-9� ALL 0-9�
ALL 0-9� BWDUTC 0-9� BWDUTC 0-9�
CROP SCALE� BWTC 0-999� BWTC 0.0-99.9� ALL kglhl� ALL c.c.� DU %� BWTC c.c.� ALL %� ALL %� BWTC %� BWTC sec.� DU p.p.m.� BWTC %� ALL 1-4� DU p.p.m.� DU %� BWTC min.� BWTC 0-3� TC %�
CROP SCALE� ALL kglha� ALL ?� ALL g� ALL� ALL� ALL yes/no� ALL days� ALL days�
49�
DAYS TO MATURITY (DC 85)* PLANT HEIGHT* LODGING % AREA* LODGING ANGLE TO VERTICAL FORAGE YIELD (FRESH WEIGHT) ACID SOIL TOLERANCE HARVEST INDEX SHATTERING % NECK BREAK ABOVE GROUND BIOMASS COLD TOLERANCE STAY GREEN EARLY VIGOR DAMAGE* (SPECIFY TYPE)
C. PMS Characteristics (G Data)
PMSTRAIT CID SID 1R(1B) TRANSLOCATION OTHER TRANSLOCATIONS & SUBSTNS ALUMINIUM TOLERANCE GENES BORON INSENSITIVITY GENES MANGANESE INSENSITIVITY GENES GRAIN COLOR HMW SUBUNITS LRGENES SRGENES YRGENES SLOW RUSTING Pdp GENES Vrn GENES RhtGENES SPROUTING TOLERANCE LEAF WAXINESS COMMERCIAL VARIETY YEAR OF RELEASE COUNTRY OF ORIGIN ORIGIN (as in impact study)
Pathology Notes
ALL days ALL em ALL % ALL 1-3 TC kg/ha ALL 1-5 ALL % ALL % BA % ALL kg/ha ALL 1-5 ALL 1-5 ALL 1-5 ALL %
CROP ALL ALL BW ALL ALL ALL ALL BWDUTC BW BWDUTC BWDUTC BWDUTC ALL BWDUTC BWDUTC BWDUTC ALL ALL ALL ALL ALL ALL
Key pathological traits have to be defined. Also lacking are transformations for the Modified Cobb Scale and the 00-99 scale. These are required for averaging across replicates and over sites. The 00-99 scale is described in Septoria disease manual (Eyal et al. 1987, page 24). The first digit 0 to 9 is the percent height of the plant, up to where infection has occurred (flag leaf height = 9), and the second digit 0 to 9 estimates the percentage of the foliar area covered with the disease within that height. A new version of "Instructions for the management and reporting of results for the CIMMYT wheat program international nurseries"--including all Latin names for each disease--is required for implementation of DMS.
50�
0-9 Scale for spike diseases and/seedborne viruses infecting whole plant.
0= 1% infected spikes/or plants 1= 1 to 10% infected spikes/or plants 2= 11 to 20 3= 21 to 30 4= 31 to 40 5= 41 to 50 6= 51 to 60 7= 61 to 70 8= 71 to 80 " 9= 81 to 100% "
A second digit (0-9) is suggested for H. sativum, S. nodorum, and Fusarium scab to estimate the severity of infection on the spike.
0-9 Scale for Grain Infections due to black point (due to H. sativum, Alternaria, S. nodorum), Kamal Bunt, other bunts and scab
0= No visible infected grain 1= 1 to 10% infected grains 2= 11 to 20 " " 3= 21 to 30 " " 4= 31 to 40 " " 5= 41 to 50 " " 6= 51 to 60 " " 7= 61 to 70 " " 8= 71 to 80% infected grains 9= 81 to 100% infected grains
The following diseases and insects should be included in comments or as % damage: S. nodorum on node none/slight/moderatelheavy H. sativum on node "� Root rot "� Eye spot "� Insect damage "� Aphid incidence "� Soilborne Wheat Mosaic Virus "�
AGRONOMY NOTES Grain yield: corrected to 12% or other specified�
moisture� Days to heading: days from sowing to when 50% of culms�
have half of the spike visible (DC55)� Days to anthesis:� days from sowing to when 50% of spikes�
have flowered i.e. at least 1 anther� dehisced (DC65)�
Days to maturity:� days from sowing to physiological� maturity when 50 percent of the� peduncles are yellow (approx. DC86)�
Plant height:� measured from ground level to tip of� the average spike, excluding awns at� maturity�
Lodging area:� as percent of effective plot area i.e. %� the harvest area�
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Harvest index: Shattering %: Neck break: Early vigor: Damage:
measured at 0% moisture percent per plot percent per plot scored at five leaf stage a generic term covering birds, worms, root rots, aphids, snails, rodents, suni pest, hail, frost, grazing and any other damage a cooperator might want to include on a % damage per plot basis.
52�
Appendix 3--1989 Position Paper on CIMMYT International Wheat Nurseries
P.N. Fox� International Wheat Nurseries�
We at CIP learned fast that if we do not gather relevant data to set priorities someone else will gladly assume that role.
--Robert E. Rhoades, Potatoes and Our Fragile Earth
Agroecological Research at CIP
... the panel recommends that: * CIMMYT overhauls its international testing program of nurseries to take better advantage of new technology and trial designs and thus make more relevant the experimental and analytical components of this important effort. Greater economy and efficiency should be possible from the testing programme if it were concentrated in selected sites in each mega-environment. ...
--Report of the third external program review of CIMMYT, 1988.
Aim of this Paper
This document was written in 1989 to provoke, rather than to propose definite solutions and to stimulate thinking on the future use of International Nurseries. Flaws in the system are mentioned only to describe where we are, before addressing where we are going.
Summary
Readers should consider the following points:
• There is danger of throwing away the baby with the bath water in a "key site" approach because the chance of accumulating genes for undiagnosed stresses is minimized. A key site system must complement and link to the existing nursery network. Any initiative involving key sites should strengthen the efficiency and increase the relevance of our major research tool--the current network.
• "Aberrant sites" may be more useful than "key sites", especially when underlying biological explanations for abnormal behavior are revealed.
• Large data sets risk including unreliable sites that are, at worst, random noise. Our experience shows that inclusion of such "unreliable" sites hardly influences across-site analyses of yield data. Discarding such sites, however, may result in the loss of valuable information.
• International nursery yield data are notoriously noisy, but methods, especially the Additive Main Effects and Multiplicative Interactive (AMMI) analysis, are currently being applied to this problem at CIMMYT.
• International yield nursery data are under-rated as a research tool. However, integration of results and feedback to cooperators must be improved. The quality
53�
of the tool will be improved with a data management system and current implementation of more efficient trial designs.
• Any trial design improvements for yield data analysis should have positive spinoffs for disease as well. Analysis of disease data requires in-depth revision and more staff input.
• CIMMYT could become a center of excellence for analysis of large biological data sets. Our comparative advantage is in the huge data bases collected and awaiting analysis, combined with in-house biological experience for interpretation.
• CIMMYT must move rapidly towards giving cooperators the choice of receiving field books on diskette or paper and must be ready to receive data from cooperators on diskette.
• International Nurseries must be linked to the Pedigree Management System (PMS) to take full advantage of its management capabilities and to input valuable information to the proposed and urgently required Data Management System (OMS).
• International nursery bulletins are still produced by software from the early 19708. Bulletin formats desperately need overhauling and the best option appears to be building an interface between OMS and an off-the-shelf statistical package.
• Implementation of the recommendations discussed in this position paper will require a strong and explicit commitment from the Wheat Program and a dynamic Systems and Computing Services (SCS).
Sites, Sacraments, and Serendipity
A science fiction film revolved round the theme of election of the u.S. President. Instead of the unjustifiable expense of today's electoral process, the one person who most closely represented the feeling of the entire U.S. population was identified to individually select the President. Perhaps the theme has elements in common with the "key site" approach to germplasm testing.
It is beyond dispute that CIMMYT's wheat breeding efforts would benefit from more detailed characterization of environments. Judicious selection of sites for this process could form a subset of II key sites" to improve understanding of genotype by environment interaction. However, such a system must complement and link to the ISWYN/ESWYT/lDYN/EDYT/lTYN network so that we know some points in the environmental spectrum covered by international trials with greater accuracy than the remainder of sites. This should facilitate extrapolation, with respect to limiting environmental factors, from key sites to other testing sites. Any "key site" initiative should strengthen the efficiency and increase the relevance of the existing International Nurseries network and not be an independent thrust. Similarly, special research efforts, like the three crop stress trial distributed in 1987, should link to the major network. Unusual results (referred to below in the section on discarding sites from analyses) from the ISWYN21 at Quito were interpretable mainly because an acid soil indicator set of varieties was also sown. This provides an example of the general principle that detailed physiological or environmental observations or special trials and regular international nurseries, should complement each other.
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Key sites should strengthen the current system's scope and not restrict it. Against a global current of more sites and less replication it appears somewhat ironical that forces seem to be pushing CIMMYT in the opposite direction. Efficiency aside, the potential political repercussions of denying more cooperators yield nurseries cannot be overlooked.
The word "key" has become somewhat emotionally charged in the English language. The media also love this star player of the world of scientific grantsmanship. Once the label is attached, "key" information is superior to other information.
Analysis of scientists' conceptions of what constitutes a key site generally indicates environmental conditions which are stable over years. The factors responsible for discrimination of germplasm are repeatable and thus more readily identified than in highly unpredictable sites. Examples of key sites include:
• CIANO (Mexico) and some locations in the Punjab (India and Pakistan) for yield potential and leaf rust resistance,
• Quito (Ecuador) for stripe rust, and
• Passo Fundo (Brazil) for acid soils.
The information may be no more accurate than from any other site, but interpretation is neater. However, the idea that key sites will provide the same information as extensive wide testing is an illusion in which generally large genotype by site by year interactions are not considered. These interactions may preclude finding key locations for such elusive traits as drought tolerance.
One thrust for moving towards a key site approach may be the fact that we did relatively little that was innovative or adventurous in reporting the huge volume of International Nurseries results to cooperators. At the same time a shotgun distribution approach which served CIMMYT breeders fairly well was not coherently and rationally defended.
Inexplicably, external pressure to reduce the scale of international testing by focusing on "key sites" seems stronger than pressure to use available data more intelligently. Maybe this pressure stems from scientific reductionism which is an approach also commonly used by some nuclear physicists to tackle complex problems in their field. If it is difficult to study complex interactions between subatomic particles, they often decided to take on only one or two dimensions of the problem at a time. As Mitchell J. Feigenbaum said in describing the flaws of such methodology: "There's a fundamental presumption in physics that the way you understand the world is that you understand the stuff that you think is truly fundamental. Then you presume that the other things that you don't understand are details. The assumption is that there are a small number of principles that you can discern by looking at things in their pure state--this is the true analytic notion-and then somehow you put these things together in more complicated ways when you want to solve more dirty problems. If you can. In the end you have to change gears. You have to reassemble how you conceive of the important things that are going on." Is genetics any different? It is relatively straightforward to interpret varietal performance when dominated by reactions to stripe rust, but not as the function of complex interactions between genes for dwarfing, photoperiod, vernalization, undetected soilborne diseases, several foliar diseases and abiotic stresses which occur unpredictably at different stages of the growing cycle.
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The international testing system is perhaps conceived of, especially with pre-computer thinking, as too large, too random and too difficult to manage. It is true that careful scrutiny of data from five sites is more useful than cursory review of data from 50. So why not just look at a small manageable "key site" data set? This attitude comes through loud and strong in Abraham Blum's book Plant Breeding for Stress Environments. Blum affirms that "while CIMMYT may perform some 90 wheat variety trials throughout the world in 1 year, no one will take seriously a unified biometrical analysis of all these tests." However, it must be acknowledged that at least "Heredity" took one such analysis seriously, with the analysis of ISWYNlO data by Byth et al. becoming a landmark article on multivariate characterization of locations and genotypes. While it is not clear what constitutes a unified biometrical analysis, Blum does have a point about generalization over an extremely large environmental range. However, Byth's pattern analysis approach addresses exactly this point in attempting to subdivide the ecological spectrum into more homogeneous subgroups. Pattern analysis of large data sets may be seen as a tool for generating hypotheses which can be tested, perhaps at key sites using more conventional, and possibly more rigorous, statistics.
A strict "key site" approach to germplasm testing may unwittingly discard screening sites which expose germplasm to unrecognized stresses. Experience with tolerance to two nematode species which attack wheat in Australia is pertinent in this context. Heterodera avenae, the "eelworm" nematode, whose economic implications were almost ignored until the 19705, has probably been in southern Australia since the last century. The variety 'Festiguay' had become popular in some areas of South Australia through its moderate resistance to stem rust during epidemics in 1973 and 1974. When fields cropped to Festiguay in 1974 and 1975 were resown to wheat in 1978 after rotation, spectacular cleaning effects were recognized through failure of the eelworm to reproduce on Festiguay during the previous wheat cycle, reports South Australian breeder Tony Rathjen. The fortuitous resistance of Festiguay to eelworm resulted in this variety representing 50% of grain deliveries to some silos in the early 1980s, despite inherently low yield. It is now believed that eelworm was responsible for average production losses of about 15% in South Australia. CIMMYT lines 'Pitic 62' and 'Mexico 120' both have a high degree of tolerance to this pest. Data presented as examples of intractable genotype x environment interactions were subsequently explainable by tolerance introduced unknowingly to Australian breeding programs in 1963. By 1978 the tolerance was common in advanced lines and is now a feature of some varieties. In the state of Queensland, testing sites which had been regarded as "aberrant" because of their unexplained variability were found to be heavily infested with the nematode Pratylenchus thomei and are now considered important screening sites for the pest. CIMMYT line 'Potam' is a source of tolerance.
An "aberrant site" approach could be the other side of the "key site" coin. Maybe the next breakthroughs will come from "aberrant" rather than "key" sites. The very aberrance may be due to important but still unrecognized factors. It should be underlined that this phenomenon has already occurred for CIMMYT. Before aluminium tolerance was recognized as an important constraint, Passo Fundo appeared to be a miserable location characterIzed by high Coefficients of Variation (CVs) and low heritability. With accumulation of higher levels of aluminium tolerance in the gene pool, it became a key location for screening aluminium tolerance. E.J. Kahn Jr. in The Staffs of Life describes how Portuguese Jesuits persevered with wheat in the highly unfavorable environment of colonial Brazil to produce communion wafers. Today's legacy is a number of key sites and levels of aluminium tolerance which are useful in many parts of the world.
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Discarding Sites from Analyses
In general for large data sets, the risk of including unreliable sites which are at worst random noise is less than the risk of discarding them. Our experience shows that inclusion of "unreliable" sites barely influences across-site analyses. However, discarding such sites may result in loss of valuable information, once an underlying biological explanation for the abnormal behavior is found. ISWYN21 data from Quito, Ecuador, were nearly excluded from reports, until the differential genotypic response could be associated with soil acidity. CIMMYT Mexico was uneasy with the pattern of yields exemplified by 'Thornbird' and 'Siete Cerros', 5.2 tlha and 37 kg/ha, respectively. At the opposite extreme, New Zealand data have been questioned as being atypically high for Australasia, even though New Zealand held world spring wheat yield records in the 50s.
The examples from New Zealand and Ecuador highlight pitfalls of discarding trials on subjective bases. However, objective methods may cause more problems. The time honored CV, the ratio of the square root of the error mean square to the trial mean, is a poor criterion for discarding trial data. Environments with low yields and large error terms are unduly penalized, although often providing valuable discrimination of germplasm. Disease and other stresses may produce high CVs--and very useful information. For plant breeding purposes, the per experiment heritability or the statistical significance of the genotypic variation is more meaningful than the CV.
The following table provides an extreme example of how genetic parameters may be biased by site selection.
ISWYN 17 No. of sites a 2g
2 age
54 7 46 33* 17 37
* excluding sites from nthn. USA and nthn. Europe
Sites from northern USA and northern Europe may differentiate germplasm in a different manner than sites in developing countries (for which CIMMYT is primarily working). Being generally high yielding and with fewer environmental constraints to differentiate germplasm, these northern sites reduced the apparent genetic variance. They increased the genotype by environment interaction largely by the interaction between those sites as a group ve~us the rest of the world as another group. While this explanation may be debated, the effect of the subdivision on the genetic parameters is irrefutable and alarming.
Trial Design and Analytical Methods for Yield
International yield data are notoriously noisy, but methods, especially AMMI analysis, are currently being applied to this problem at CIMMYT. Other statistical approaches, including Neighbor methods, may also be useful in reducing noise. Generalized Lattices should result in more useful information through greater trial accuracy. For this reason, ITYN21 for 1989/90 was distributed using a Generalized Lattice design and independent randomizations at each location. However, the inflexibility of the existing nursery reporting system will come home to roost with a vengeance when the output from lattice analyses has to be spliced into a bulletin. Immediate decisions have to be taken on such
57�
issues and the policy determined for 1990/91 international yield nurseries. Rowand Column and other designs may be considered in the future.
The recent initiative of ICARDA to improve across-site analysis, using cluster analysis is to be commended. While CIMMYf is considering similar options, we are largely hamstrung by the inflexibility of the processing software for international data. Simple, coherent explanations of newer analytical techniques are essential to make them useful to cooperators. For example, a well-explained dendrogram of inter-site relationships should allow some cooperators to begin to make their own inferences over time about their environment with respect to other regions of the world.
To increase the utility of international nursery data, the question of long term checks should be addressed.
Analysis of Disease Data
Processing and analysis of disease data is complex and trial design improvements should have positive spin-offs for disease screening. As disease notes are to a degree subjective, judgements on cooperators' scoring ability are inevitable and make the issue somewhat delicate. Our ability to analyze disease data lags behind that for yield, but indications are that more filtering and manipulation is needed. Leon van Beuningen's studies on correction of foliar blight scores for height and maturity provide a useful starting point. The Global Disease Monitoring Project will complement this work. The justification for a "key site" approach is stronger for disease screening, where unreliable data are more influential, than for yield.
Biotechnology and Upstream International Nurseries
If biotechnology plays a role in wheat breeding, it will probably have the biggest impact in the highly reliable conditions which characterize "key sites". For instance, known genes to match known environmental conditions could be monitored by RFLP techniques. If this thesis is accepted, it follows that traditional statistically oriented testing should concentrate more on marginal and unpredictable environments at the expense of key sites, which become increasingly the gene jockeys' domain.
In passing upstream, CIMMYf could become a center of excellence for analysis of large biological data sets. Our competitive advantage is in having huge data bases already collected, combined with on-tap biological experience required for interpretation. Computing hardware facilities would generally be sufficient if the current tempo of upgrading were maintained. Maybe the time is now ripe to consider at least Postdoctoral positions in analysis of international wheat data.
The relationship between observed phenotypic diversity for adaptation and its underlying genetic base is largely unexplored, except for more simple examples where adaptation is grossly affected by disease susceptibility. The scope of the CIMMYf testing programme combined with modem cytogenetic and transformation techniques, for single gene insertion, might provide powerful insights.
Germplasm Distribution Mechanism, Research Tool and Catalyst
Norman Borlaug once said that international testing "broke down a psychological barrier which had tended to keep the efforts of each wheat researcher isolated. The new testing led to an unexpected acceleration of wheat breeding around the world."
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It is perceived that screening nurseries have been a much more potent agent for gennplasm distribution than replicated yield nurseries, which have provided a generally under-rated research tool. Some of the studies which have been conducted largely in isolation are listed below. All but the penultimate were conducted on yield nursery data.
Refereed Scientific Journals:
Byth et ale (1976) ISWYN4 Heredity 37:215-230 Fox et ale (1990) ITYN Euphytica (in press) Laing & Fischer (1976) ISWYN Euphytica 26: 129-139 Menz (1980) ISWYN Field Crops Res. 3:33-41
Other Presentations:
Abdalla & Varughese ITYN Eucarpia 1987 Braun ISWYN Thesis Univ. of Hohenheim Byth et ale ISWYN10 Unpubl. report to CIMMYT Crossa et ale ESWYT8 submitted to TAG Fox & Skovmand ITYN ASA Washington 1983 Fox et ale ISWYN/ITYN ASA Chicago 1985 Pfeiffer ISWYN Thesis Univ. of Hohenheim Pfeiffer & Braun ISWYN In Variability in grain
yields (J.R. Anderson & P.B.R. Hazell)
Phung et ale ISWYN7 Unpubl. report to CIMMYT Skovmand et ale ITYN 6IWGS Kyoto 1983 Skovmand et ale ITSN Eucarpia Clermont-Ferr.1984 Worrall et ale ISWYN 3rd Int. Wheat Conf.
Madrid, 1980
The ISWYN10 study, which suggested a dramatic narrowing in the genetic base since ISWYN4, was not well received by CIMMYT. The authors themselves acknowledge that they perhaps ignored the influence of discarding very poor germplasm between ISWYNs 4 and 10. All parties involved would have benefitted from a more open discussion of the findings.
The research function of yield nurseries will be significantly improved with improved trial design and analyses. However, priority setting will be required to affect such changes and tradeoffs will have to be made. Use of CIMMYT-managed international data by others should be actively encouraged. Slow response to requests for data amounts to passive discouragement. Independent analyses can be safeguards against CIMMYT biases. More importantly, the more heads working on international data the better, and the greater the chance of detecting trends with implications for breeders.
Electronic Media
CIMMYT must move rapidly towards giving cooperators the choice of receiving field books on disk or paper and must be ready to receive data from cooperators on disk. Our more advanced cooperators, who have the greatest capacity to return results of more sophisticated genetic and other analyses, are frustrated by CIMMYT's adherence to the paper medium. The International Nurseries data base should be freely available on COROM to stimulate its use. Because of the size of the data base, other fonns of distribution are unwieldy.
59
Glasnost in Reporting
A degree of "glasnost ll in international nursery reporting should be encouraged. For example, germination problems associated with new seed dressings could be shared openly with cooperators, as could discussion of different sowing rates used by CIMMYT and ICARDA in international testing. Cooperators would also benefit from guidance from CIMMYT for issues such as in which circumstances they might expect problems from sticky dough associated with the 1B/1R translocation.
Bulletin Formats
International nursery bulletins are produced using software written in the early 1970s, when CIMMYT wheat staff with the computing system division of the RCA company in Mexico City and Dr. Abel Mexas from the Ford Foundation designed and implemented RAPID (Rapid Analysis Program for International Data). Modifications and additions were found to be extremely difficult. However, in 1978-79 LISA (Laboratory for Information Science in Agriculture) from Colorado State University reviewed the system and made some limited modifications to accommodate new requirements of the rapidly expanding testing program. Since that time, the software has been used without significant changes. The extremely rapid growth of the nursery network in the 70s precluded detailed attention to the finer points of reporting. The immediate issues were to pack and despatch increasing quantities of seed and to input incoming data, whose growth paralleled the growth in seed tonnage. In 1988, the CIMMYT wheat program shipped 2961 sets of seed.
CIMMYT now suffers some degree of credibility problem from the reporting format. Close cooperators may accept shortcomings, but the wider scientific community will become increasingly critical. It is time for improvements. For example:
• Sometimes, CIMMYT sites do not report sowing and harvest dates as well as other basic information. Immediate attention should be paid to this.
• LSDs and CVs are produced for sites that return unreplicated data.
• Many latitudes are incorrect e.g. 28°N for Cordoba, Spain.
• Correlations greater than one sometimes appear.
• Many small details, in themselves not major, collectively detract from the bulletins. For instance, 'BYD', not 'BYDV', should be used for field symptoms and many place names are misspelled.
One wonders why there is so little reaction from cooperators to less trivial errors. Their silence is worrying and begs the question "Are the reports read?"
The international botanical system for dates, day/month/year with the day and year in arabic numerals and the month in roman numerals, should be considered to avoid confusion between the commonly used date conventions of Latin America and Europe on one hand and USA and Canada on the other.
Refinements in headings of individual location tables could be made. The almost universal "cereal staff' in the tables is not useful. Abbreviations N, P20S and K for fertilizer applications could reduce cluttering, as would a default convention for seasonal
60
conditions (whereby II normal conditions II would disappear from most tables, with only atypical factors reported).
Ancillary Environmental Data
It would be useful to maintain a detailed data base for sites, bringing together many valuable ancillary data, such as soil tests. This would not be published in bulletins, but would be available for research.
The Next Step
The Pedigree Management System (PMS) will become the core of the Wheat Program's computerized plant breeding and germplasm management systems in Mexico. International Nurseries must also be linked to PMS to take full advantage of its management capabilities and to input valuable infonnation to the proposed DMS-envisaged as an extension of PMS. PMS was designed basically to manage the bookkeeping in breeding, but has also shown its worth as a powerful and versatile research tool in unexpected areas. For example, it was determined extremely rapidly using PMS that the majority of CIMMYT bread wheats have one of three dominant cytoplasms.
Of possible strategies for overhauling nursery bulletin format, the best appears to be through an interface from DMS to an off-the-shelf package such as SAS or GENSTAT for bulletin generation. The question is, does CIMMYT have available expertise to implement this option rapidly? The most optimistic estimate is that DMS will be at least one and a half years in development.
It would be fruitful to consider how other international centers handle reporting and analysis of data.
Acknowledgment
Helpful commentary from Hans Braun was appreciated in the preparation of this position paper.
61�
Appendix 4--Survey of CIMMYT Staff on International Nurseries Reporting
In 1989, twenty-one scientific staff--less than 50% of the Wheat Program--responded,� though of these, not all responded to all questions. Eleven replies came from outreach.�
Ql: Is more, or less, information about the origin of entries required, for instance� the name and type of the institution producing each entry?� Four respondents felt that no more infonnation was required, while 13 suggested� additions, the most common being credit for the breeding institution. It was also� suggested that for large countries it is useful to know the region of origin.�
Q2: Is the complete pedigree for all entries required?� YES, 15; NO, 2. '�
Q3: In ISWYN21, one site reported protein and another falling number. Would� more information on quality from the CIMMYT laboratory, for instance� sedimentation values from Obregon and Toluca samples, be useful?� Two thought that more information would not be useful and fourteen thought to the� contrary. However, it was suggested that depending on the heritability of the characters� involved, some parameters might be misleading for certain locations. Two people� suggested that the earlier practice of providing a general description of quality and� agronomic characteristics had been most useful. Greater effort to obtain and compile� more quality data generated by cooperators was advocated. Pigment content, protein� percentage and microsedimentation values for durums should be considered.�
Q4: For whom should CIMMYT be producing nursery reports? Cooperators? The� wider scientific community? CIMMYT staff? Other? Why?� The majority felt we should be addressing principally cooperators and CIMMYT staff.� Six expressed the need also to cover the wider scientific community. Additionally, two� people replied that for targets beyond CIMMYT staff and cooperators, a different� reporting medium should be considered.�
Q5: Do you find them useful?� Thirteen indicated finding the reports useful, while two suggested limited value for� reasons including generally high CVs, inadequate synthesis of data and failure to explain� abnormal yields.�
Q6: Is it necessary to repeat the variety or cross in each table?� YES, 12; NO, 6.�
Q7: Is it necessary to repeat the origin of each entry in each table?� YES, 3; NO, 15.�
Q8: In your opinion which of the variables being returned can be considered as� reliable data and which are unreliable?� This question elicited a strong and useful response. There was general agreement that� much useful data is returned, qualified by the idea that direct measurements were more� reliable than readings, for instance for diseases. Within the disease field, several felt that� rust scores are usually reliable, but foliar blight scores are more doubtful, depending to a� large degree on the cooperators and the representativeness of locations. The need for case� by case evaluation was a common response, with a plea from one staff member for better� communication between International Nurseries and outreach. Two suggested that the� variability of traits should guide us with respect to their utility, although this would be� difficult for unreplicated data. Interestingly, no one stated explicitly that yield data were�
62�
unreliable, although the opinion was expressed that the relative variety yields were more useful than the yields themselves. One reply doubted the utility of yield data from screening nurseries. Another took the ISWYN21 variables and rated days to maturity, stripe rust in the head, lodging %, scab %, falling no., protein %, tan spot and BYD as unreliable. In retrospect, this may have been an easier way for everyone to respond. Other responses supported this reply with respect to maturity and lodging. Careful examination of disease data was recommended as was the recording of the date of observation. One reply suggested that yield components were often badly taken.
Q9: Should we be more discriminating with respect to which data are included in across-site summary statistics? YES, 12; NO, 2. If so, how? This had been handled to a degree in the previous question. Of three suggesting generally more screening of data, one explicitly advocated "pre-screening" in the appropriate outreach offices. Two suggested a minimum level of infection for disease data. LSDs and CVs for yield data were again mentioned twice as was the idea that summaries from a very few sites are a waste. More training of cooperators was mentioned along with tact to avoid offence to cooperators when discarding data.
QI0: Is the information presented at the top of tables for individual locations insufficient, adequate or excessive? The majority opinion (13) was adequate, but some of these were qualified by "adequate if all information supplied correctly" and the question was asked why CIANO and Toluca are generally the worst offenders in this respect. Three suggested that the information was insufficient, in one case in relation to soil fertility and in the others with respect to moisture supply. The necessity for a clear statement of both precipitation and irrigation, preferably including number of irrigations, was highlighted. Otherwise, it appears that Kafr EI Sheikh grew 6 tons of wheat on 52 mm moisture!
Qll: Could the above information (QI0) be presented in another part of the bulletin? YES, 7; YES, BUT INADVISABLE, 2; YES IF COMPRISES SUMMARY ADDITIONAL TO RESULTS PRESENTED FOR EACH SITE, 1; NO, 6.
Q12: Should we solicit data on pH, soil depth and soil texture from cooperators? Eleven replies were in favour of seeking this additional information, with two suggesting that the data might aid in mega-environment definition. One answer suggested that we only solicit such information from reliable cooperators, another suggested key sites only. Adding which chemicals are applied as well as identifying problem weeds was suggested. It was pointed out that the information we may seek is not always available. Three other replies recommended requesting pH, while another favoured soil texture. Three staff felt we should not solicit these data, with one suggesting the effort would better directed to obtaining more important meteorological data.
Q13: Would more across-site statistical analyses be useful? YES, 14; YES BUT ONLY FOR INTERNAL USE, 1; NO, 2. If so, which kind? Why? Five specifically mentioned cluster analysis, usually in the context of grouping like sites and sometimes like genotypes. Others mentioned the need to group like sites without suggesting a method. Some suggested multivariate methods in general. The need to analyze across sites with similar stresses was mentioned along with the necessity for some across-year analysis. Three indicated that stability analyses should be employed. Some felt that across-region analyses would be useful, while others expressed
63
reservations about the present regional groupings and the use of clustering was advocated� to derive biologically meaningful site groups, for which different gene pools could be� developed. One suggested weighting sites by their error terms before clustering. Parallel� analyses across regions and also across mega-environments was suggested. Identification� of key sites was given as a goal of across-site analysis. One reply specifically drew� attention to the need for more across-site analysis for disease data.�
Q14: Would more within-site statistical analyses be useful?� YES, 8; NO, 7.� If so, which kind? Why?� Suggestions embrace better qualitative and quantitative description of disease� development, broad sense heritabilities, a 7 x 7 lattice design for the ISWYN, more� attention to randomization to overcome interplot interference and presentation of yields� as percent of the top yield and percent of local check.� Should the local check and check varieties of other species be included in the� analyses?� YES, 3; NO, 2; SAME SPECIES YES & OTHERS NO, 2. One reply also indicated that� other species should not be included in the analyses of variance, but yield could be� expressed as a percentage of other species when they represent the top yield. One reply� questioned the practice of including more than one species in a trial.�
Q15: Are the correlations presented for each location useful and valid?� YES, 4; NO, 3; DOUBTFUL, 5; IF DATA GOOD CORRELATIONS USEFUL, 2. One� of these last two replies included the comment that correlations should not be presented� automatically and especially not when there a large number of missing or zero values.�
Q16: Are the regional summary tables useful?� YES, 12; NO, 2; BETTER DEFINITION REQUIRED, 3 (2 SUGGESTING MEGA�ENVIRONMENTS). Many replies referred to question 13.�
Q17: Should we differentiate individual location tables, regional summaries and� overall summaries using different colored paper?� YES, 7; NO, 8; IF INEXPENSIVE, 4.�
Q18: With increasing mechanization, will we, or should we, have to prepare seed for cooperators who wish to sow non-standard plot lengths? YES, 6; NO, 10. One reply suggested that we supply the amount of seed for each entry that the cooperator requires along with empty sets of envelopes for sowing. Another had serious doubts concerning edge effects and our standard yield plot.
Q19: Any other specific comments on entries, layout or analyses?* Include more basic information such as quality characteristics, Rht genes and�
resistance genes for entries.� * CVs are too high. Can we use Nearest Neighbor analyses? * CIMMYT managed locations should be exemplary, not the least well documented. * Large tables should be laser printed for better reproduction. * Some disease scales need modification. * Indicate which lines are not significantly different from the top yielder. * Implement new designs such as Nearest Neighbor and lattices. * Plot yields of each entry across locations. * Make more use of ranking, as in ICARDA reports. * Produce bulletins faster to increase their utility.
64
Q20: BROADER ISSUES RAISED FOR INPUT. We suggest that there is a fundamental conflict between producing rapid superficial analyses for mass circulation and answering more detailed questions relating to CIMMYT's breeding and selection methodology.
Currently international nurseries fulfill two quite separate functions, namely to distribute germplasm worldwide and to obtain yield, agronomic and disease data for feedback to CIMMYT. The reliability of data and the question of whether the two functions in one trial are compatible should be addressed.
There is a trade-otT to be examined between large global data sets with little reliable environmental data and detailed yield, disease and physical data collection at key locations where more sophisticated experimental design may be feasible.
It was suggested that: * analysis of key locations and the broader data set is quite feasible. Better analysis
will serve both goals as well as widen the audience range. * The ISWYN be discontinued and the IBWSN improved with a subset of entries
maintained for three years. Base staff to brainstorm the question and advise outreach.
* More discussion is needed. * ISWYN could be upgraded and distributed to "good" or "useful" locations, with
germplasm dissemination left to screening nurseries. * Data returned is better than 10 years ago. * Questions related to CIMMYT's methodology are not relevant for bulletins. * International Nurseries staff should know "mega-environments", stations and�
statistical procedures.� * Key locations can grow special as well as general trials. More sophisticated testing
should be conducted in addition to, not instead of, routine testing. * Rapidly disseminated data can also be more exhaustively analyzed for�
methodological purposes.� * Meteorological and disease data must be improved. * Additional data from mega-environments using (specially?) selected germplasm is
required.* ISWYN is useful for initial observations, but special material and areas selected for
CIMMYT long range breeding strategy are needed. * Multivariate analyses of both physical and biological data are required to�
characterize test sites and mega-environments.�
65�
Appendix S--Cooperating Stations Involved with CIMMYT International Nurseries
A. Country Abbreviations from the International Standards Organization (ISO) and New Numbers, March 1992
AFRICA (AF)� Southern� AF-S S. AFRICA ZAF 100�
East�
Central�
West�
AF-S BOTSWANA BWA 101� AF-S COMOROS COM 102� AF-S LESOTHO LSO 103� AF-S MADAGASCAR MDG 104� AF-S MALAWI MWI 105� AF-S MAURITIUS MUS 106� AF-S MOZAMBIQUE MOZ 107� AF-S NAMIBIA NAM 108� AF-S SWAZILAND SWZ 109� AF-S ZAMBIA 2MB 110� AF-S ZIMBABWE ZWE 111�
AF-E BURUNDI BDI 120� AF-E DJIBOUTI DJI 121� AF-E ETHIOPIA ETH 122� AF-E KENYA KEN 123� AF-E RWANDA RWA 124� AF-E SEYCHELLES SYC 125� AF-E SOMALIA SOM 126� AF-E SUDAN SDN 127� AF-E TANZANIA TZA 128� AF-E UGANDA UGA 129�
AF-C ANGOLA AGO 140� AF-C CAMEROON CMR 141� AF-C CENTRAL AFRICAN REP. CAF 142� AF-C CHAD TCD 143� AF-C CONGO COG 144� AF-C EQUATORIAL GUINEA GNQ 145� AF-C GABON GAB 146� AF-C NIGERIA NGA 147� AF-C ZAIRE ZAR 148�
AF-W BENIN BEN 160� AF-W BURKINA FASO HVO 161� AF-W COTE D'IVOIRE CIV 162� AF-W GAMBIA GMB 163� AF-W GHANA GHA 164� AF-W GUINEA GIN 165� AF-W GUINEA BISSAU GNB 166� AF-W LIBERIA LBR 167� AF-W MALI MLI 168� AF-W MAURITANIA MRT 169� AF-W NIGER NER 170� AF-W SENEGAL SEN 171�
66
AF-W SIERRA LEONE SLE 172� AF-W TOGO TGO 173� Western Islands AF-WI CAPE VERDE CPV 174� AF-WI REUNION REU 175� AF-WI SAO TOME STP 176� AF-WI ST. HELENA SHN 177� North AF-N ALGERIA DZA 190� AF-N EGYPT EGY 191� AF-N LIBYA LBY 192� AF-N MOROCCO MAR 193� AF-N TUNISIA TUN 194�
ASIA (AS)
West AS-W AFGHANISTAN AFG 200� AS-W BAHRAIN BHR 201� AS-W CYPRUS CYP 202� AS-W IRAN IRN 203� AS-W IRAQ IRQ 204� AS-W JORDAN JOR 205� AS-W KUWAIT KWT 206� AS-W LEBANON LBN 207� AS-W OMAN OMN 208� AS-W QATAR QAT 209� AS-W SAUDI ARABIA SAU 210� AS-W SYRIA SYR 211� AS-W TURKEY TUR 212� AS-W UNITED ARAB EMIRATES ARE 213� AS-W YEMEN ARAB REPUBLIC YEM 214� AS-W YEMEN DEMOCRATIC YMD 215� AS-W ISRAEL ISR 216� South AS-S BANGLADESH BGD 220� AS-S BHUTAN BTN 221� AS-S INDIA IND 222� AS-S MALDIVES MDV 223� AS-S MYANMAR (BURMA) BUR 224� AS-S NEPAL NPL 225� AS-S PAKISTAN PAK 226� AS-S SRI LANKA LKA 227� Southeast AS-SE PHILIPPINES PHL 240� AS-SE SINGAPORE SGP 241� AS-SE THAILAND THA 242� AS-SE VIETNAM VNM 243� AS-SE BRUNEI BRN 250� AS-SE INDONESIA IDN 251� AS-SE KAMPUCHEA REPUBLIC KHM 252� AS-SE KIRIBATI KIR 253� AS-SE LAOS LAO 254� AS-SE MALAYSIA MYS 255�
67�
AS-SE NAURU AS-SE NIUE AS-SE PAPUA NEW GUINEA North AS-N P.R. CHINA AS-N HONG KONG AS-N JAPAN AS-N KOREA (N) AS-N S. KOREA AS-N MACAU AS-N TAIWAN
EUROPE & USSR (80) East EU-E USSR (WESTERN PART) EU-E USSR (EASTERN PART) EU-E ALBANIA EU-E BULGARIA EU-E CZECHOSLOVA EU-E GERMANY EU-E HUNGARY EU-E POLAND EU-E ROMANIA EU-E YUGOSLAVIA West EU-W AUSTRIA EU-W BELGIUM EU-W ENGLAND EU-W FRANCE EU-W GERMANY EU-W IRELAND EU-W LUXEMBOURG EU-W NETHERLANDS EU-W SWITZERLAND South EU-S GREECE EU-S ITALY EU-S MALTA EU-S PORTUGAL EU-S SPAIN North EU-N DENMARK EU-N FINLAND EU-N GREENLAND EU-N ICELAND EU-N NORWAY EU-N SWEDEN
NORTH AMERICA (NA)� North� NA-N CANADA (WESTERN PART)� NA-N CANADA (EASTERN PART)� NA-N U.S.A. (WESTERN PART)�
NRU NIU PNG
CHN HKG JPN PRK KOR MAC TWN
SUN SUN ALB BGR CSK DEU HUN POL ROM YUG
AUT BEL GBR FRA DDR IRL LUX NLD CHE
GRC ITA MLT PRT ESP
DNK FIN GRL ISL NOR SWE
CAN CAN USA
68�
256 257 258
270 280 281 282 283 284 285
300 301 310 311 312 313 314 315 316 317
330 331 332 333 334 335 336 337 338
350 351 352 353 354
370 371 372 373 374 375
400 401 410
NA-N U.S.A. (CENTRAL PART) NA-N U.S.A. (EASTERN PART) NA-N U.S.A. (ALASKA)
NA-N U.S.A. (HAWAII) NA-N MEXICO (NORTH + 23LAT) NA-N MEXICO (SOUTH - 23LAT) central America NA-CA BELIZE NA-CA COSTA RICA NA-CA EL SALVADOR NA-CA GUATEMALA NA-CA HONDURAS NA-CA NICARAGUA NA-CA PANAMA Caribbean (CARB) NA-CARB ANTIGUA NA-CARB BAHAMAS NA-CARB BARBADOS NA-CARB BERMUDA NA-CARB CAYMAN ISLANDS NA-CARB CUBA NA-CARB DOMINICA NA-CARB DOMINICA REPUBLIC NA-CARB GRENADA NA-CARB GUADELOUPE NA-CARB HAITI NA-CARB JAMAICA NA-CARB MARTINIQUE NA-CARB MONTSERRAT NA-CARB NETHERLANDS ANTILLES NA-CARB PUERTO RICO NA-CARB ST. CHRISTOPHER & NEVIS NA-CARB ST. LUCIA NA-CARB ST. PIERRE & MIQUELON NA-CARB ST. VINCENT GRENADINE NA-CARB TRINIDAD & TOBAGO NA-CARB UK VIRGIN ISLANDS NA-CARB US VIRGIN ISLANDS
OCEANIA (OC)
Islands OC-IS AMERICAN SAMOA OC-IS COOK ISLANDS OC-IS FIJI OC-IS FRENCH POLYNESIA OC-IS GUAM OC-IS NEW CALEDON OC-IS NORFOLK ISLAND OC-IS SAMOA OC-IS SOLOMON ISLANDS OC-IS TAHITI OC-IS TOKELAU OC-IS TONGA OC-IS TUVALU
USA USA USA USA MEX MEX
BLZ CRI SLV GTM HND NIC PAN
ATG BHS BRB BMU CYN CUB DMA DOM GRD GLP HTI JAM MTQ MSR ANT PRI
SPM VCT TTO VGB VIR
ASM COK FJI PYF GUM NCL NFK WSM SLB
TKL TON TUV
411 412 413 414 420 421
450 451 452 453 454 455 456
470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492
510 511 512 513 514 515 516 517 518 519 520 521 522
69�
OC-IS VANUATU VUT 523� OC-IS WALLIS & FATUNA ISLANDS WLF 524� West� OC-W AUSTRALIA AUS 550� OC-W NEW ZEALAND NZL 560�
SOUTH AMERICA (SA)� Southern Cone (SC)� SA-SC BRAZIL (NORTH PART) BRA 600�
Andean Region (AR)�
SA-SC BRAZIL (SOUTH PART) BRA 601� SA-SC ARGENTINA ARG 610� SA-SC CHILE CHL 612� SA-SC FALKLAND ISLANDS FLK 613� SA-SC PARAGUAY PRY 614� SA-SC URUGUAY URY 615�
SA-AR BOLIVIA BOL 630� SA-AR COLOMBIA COL 631� SA-AR ECUADOR ECU 632� SA-AR FRENCH GUIANA GUF 633� SA-AR GUYANA GUY 634� SA-AR PERU PER 635� SA-AR SURINAME SUR 636� SA-AR VENEZUELA VEN 637�
B. CIMMYT Wheat Program Cooperating Locations, March 1992
The list of cooperating locations beginning on page 71 uses country abbreviations accepted by the ISO system. The list represents CIMMYT's first attempts to convert the old location system file (COOP.LIS) to a new sortable system that refers to geographic locations alone without considering management factors and other location descriptions. Such information, however, will be linked to location codes in DMS (e.g., see Table 1 on page 13).
The first digit indicates one of six major world zones. Together, the first three digits uniquely indentify countries. The fourth and fifth digits then identify locations, defined by latitude and longitude, within countries.
Duplication and errors remain. Please advise the first named editor of errors. Please supply the complete record as it appears in the list and then how it should appear. We would also greatly appreciate hearing of the multiple station numbers that refer to what should be considered one single location.
70�
Cnt Hew state City Lat. Long. Elev. Abb. stat
ZAF ZAF ZAF ZAF ZAF ZAF ZAF ZAF ZAF ZAF
10001 10002 10003 10004 10006 10016 10005 10007 10008 10009
TRANSVAAL ( 2 ) OFS (2) TRANSVAAL (1) CAPE PROVo ( 6) CAPE PROVo ( 6) CAPE PROVo (6 ) CAPE PROVo ( 5 ) CAPE PROVo (9 ) CAPE PROVo ( 4 ) OFS ( 3 )
LICHTENBURG SENSAKO BETHLEHEM ROODEPLAAT STELLENBOSCH - MARIENTAL WELGEVALLEN ELSENBURG WITDAM TYGERHOEK LANGGEWENS 5ENSAKO
026 028 025 033 033 033 033 034 033 028
105 12S 35S 50S 52S 51S 45S 09S 30S 18S
026 028 028 018 018 018 018 019 018 028
10E 12E 21E 50E 42E 50E 25E 54E 40E 10E
1340 1687 1164
152 152 176 100 168
91 1631
ZAF 10010 NATAL MAKATINI 027 25S 032 10E 74 ZAF ZAF
10011 10012
CAPE PROVo ( 1) TRANSVAAL (3)
CRADOCK GROBLERSDAL
032 025
08S 255
025 029
38E OOE
660 950
ZAF 10013 OFS (1) BETHLEHEM 028 lOS 028 18E 1631 ZAF 10014 TRASVDAI BURGES HALL 025 27S 030 50E 660 ZAF ZAF ZAF ZAF ZAF ZAF
10015 10017 10018 10019 10020 10021
CAPE PROVo CAPE PROVo CAPE PROVo CAPE PROVo CAPE PROVo RUSTENBURG
( 7 ) (3 ) (2 ) (10 ) ( 8 )
OUTENIAGUA STELLENBOSCH - SENSAKO STET SENSAKO NAPIER SENSAKO STELLENBOSCH - 5TELLENBOSCH TOBACCO RES. I.
033 033 033 034 033 025
55S 20S 185 285 565 40S
022 021 018 019 018 027
25E 08E 59E 54E 51E 14E
204 99
200 180
91 1234
BWA 10101 GOOD HOPE BAROLONG 025 28S 025 30E 1000 BWA BWA
10102 10103 MOSHU
MOGOBANE 024 020
55S 03S
025 023
45E 12E
1000 1000
. BWA 10104 NAGAMILAND WEST ESTMA 019 07S 022 18E 900 LSO 10301 MASERU 029 18S 027 30E 1510 LSO 10302 THABA TSEKA 029 315 028 36E 2250 MDG 10401 TANANARIVE FIFA NANOR 020 OOS 047 OOE 1500 MWI 10501 CENT. REG. CHITEDJE 014 155 033 39E 1100 MWI 10502 NTCHEU DIS. TSANGANO 015 l1S 034 37E 1600 MWI 10503 CENT. REG. BEMBEKE 014 lOS 034 26E 1560 MOZ 10701 MAPUTO UMBELUZ1 026 03S 032 32E 12 MOZ 10702 NIASSA (1) LICHINGA 013 18S 035 14E 1356 MOZ 10703 NIASSA (1) MATAMA 013 17S 035 20E 1200 MOZ 10704 GAZA GUIJA-CHOKWE 024 31S 033 OOE 33 2MB 11001 STHN. PROVo MAZABUKA 015 51S 027 45E 1049 2MB 11002 LUSAKA UNIVERSITY 015 25S 028 19E 1282 2MB 11003 NTHN. PROVo MBALA 008 53S 031 22E 1668 2MB 11005 NTHN. KATITO MBALA 009 08S 031 20E 1673 2MB 11004 CENTRAL MOUNT MAKULU 015 32S 028 15E 1213 2MB 11006 CENTRAL GOLDEN VALLEY 015 OOS 028 30E 1200 2MB 11007 WSTN. PROVo KALABO 014 57S 022 42E 1051 ZWE 11101 HARARE (2) GWEBI 017 41S 030 52E 1448 ZWE ZWE ZWE
11102 11105 11103
HARARE-(4) HARARE-(4) HARARE (1)
CHIREDZI (UNI CHIREDZI (UNI CHISIPITE
OF OF
ZWE) ZWE)
021 021 017
01S 01S 40S
031 031 031
35E 35E 14E
430 1468 1300
ZWE 11104 HARARE (3) HARARE 017 45S 031 05E 1506 ZWE 11106 NYANGA NYANGA 018 17S 032 45E 1830 ZWE 11107 NYANGA NYANGA 018 17S 032 45E 1830 ZWE 11108 MUTARE MUTARE 019 OOS 032 45E 1070 ZWE 11109 RATTRAY ARN CHISIPITE 017 45S 030 15E 1300 BDI 12001 MURAMVYA NYAKARARO 003 31S 029 36E 2200 BDI 12002 MURAMVYA KISOZI 003 33S 029 41E 2090 ETH 12201 ARSI ( 1 ) KULUMSA 008 OON 039 09E 2180 ETH 12202 SHOA (6) MELKA WERER 009 16N 038 42E 750 ETH 12203 SHOA (1) KOKATE 006 55N 037 50E 2100 ETH 12204 ASMARA ERITREA 015 16N 038 52E 2340 ETH 12205 SHOA (2) HOLETTA - A 008 01N 040 07E 1700 ETH 12206 SHOA (3) DEBRE ZEIT 008 55N 038 58E 1860 ETH 12207 SHOA (5) HOLETTA - B 009 OON 038 30E 2390 ETH 12208 ARSI (2 ) NEGHALLIE 009 OON 038 30E 1800 ETH KEN
12209 12301
SHOA RIFT
(4) VALLEY (3)
AMBO MOLO
(SHEWA) 008 000
57N 15S
038 035
07W 44E
2225 2804
71�
Cnt Hew state City Lat. Long. Elev. Abb. stat
KEN KEN KEN KEN KEN KEN KEN KEN KEN
12302 RIFT VALLEY (5 ) 12303 RIFT VALLEY ( 6) 12304 RIFT VALLEY (7 ) 12305 RIFT VALLEY ( 8) 12306 RIFT VALLEY (2 ) 12307 RIFT VALLEY (4) 12308 RIFT VALLEY(10) 12309 RIFT VALLEY (1) 12310 RIFT VALLEY (9)
NJORO ELDORET KENYA BREWERIES NGORE ALJORO N.P.B.S. ENDEBERS TlMAU NAROK
000 000 000 001 000 000 008 000 001
25S 30N 40S OOS 05S 20S OON 05N OOS
036 035 035 035 036 035 034 035 035
OOE 20E 56E 30E 20E 56E 45E 25E 30E
2165 2100 2790 1830 2200 2166 1920 2700 1830
SOM SDN SDN SDN SDN SDN SDN TZA TZA TZA TZA TZA
12601 12701 12702 12703 12705 12706 12704 12801 12802 12803 12804 12805
LR SHABELE GEZlRA HIDEIBA KASSALA KASSALA KASSALA SHAMBAT MOSHI (3 ) MOSHI (2 ) IRINGA MBEYA (1) MBEYA (2)
AFGOY WAD MEDANI ED DA'EIM KHASHM ELGIRBA KHASHM ELGIRBA KHASHM ELGIRBA KHARTOUM KILlMANJARO LYAMUNGA TANWAT UYOLE U.A.C.
(NEW HALFA)
002 014 017 015 015 015 015 003 003 009 008 009
06N 24N 35N 08N 08N 08N 37N 23S 14S 17S 55S 40S
045 033 033 035 035 035 032 037 037 034 033 034
11E 29E 27E 45E 45E 45E 32E 17E 53E 46E 22E 40E
100 411 353 400 400 440 375
1250 1280 1980 1800 2250
TZA 12806 ARUSHA (2) MONDULI TBL 003 30S 036 30E 1846 TZA TZA TZA
12807 MOSHI ( 1 ) 12808 ARUSHA (1) 12809 ARUSHA (3)
W. KILlMANJARO TARO AG. TARO SELIAN
002 003 004
55S l1S 30S
037 036 035
41E 51E 10E
1950 1372 1768
UGA AGO
12901 14001
ENTEBBE HUAMBO
KAMPALA CHIANGA
000 012
05N 44S
032 015
30E 50E 1695
CMR 14101 WASSANDE NGAOUNDERE 007 05N 014 03E 1400 CMR TCD NGA
14102 14301 14701
NORTH WEST
KADAWA
IRA-BAMBUI FORT LAMY KANO
006 015 012
OON 05N OON
010 012 008
OOE 10E 36E
1982
1000 NGA NGA
14702 14703
SAMARU LAKE CHAD
ZARIA MAl DUGURI
011 011
01N 55N
007 013
30E 37E
686
NGA ZAR ZAR ZAR
14704 14801 14802 14803
GAMBORU KINSHASA KIVU (2) KIVU (1)
KINSHASA LOHOTU NDHlRA
12 004 001 000
30N 30S 01S 16S
14 015 029 029
50E 17E 20E 10E
310 280
2100 2190
ZAR 14804 SHABA (2) KOLWEZI 010 43S 025 30E 1380 ZAR 14805 SHABA (3) KIPUSHI HAUT 011 44S 027 14E 1310 ZAR 14806 SHABA (1) FUNGURUME 010 04S 026 04E 1165 HVO 16101 MOGTEDO MOGTEDO 012 30N 001 OOW 270 HVO 16102 SOUROU SOUROU 013 06N 003 25W 250 GHA 16401 ACCRA ACCRA 005 35N 000 25W GHA 16402 KUMASI KUMASI 007 OON 001 40W 282 GHA 16403 UPPER REG. TONO 010 51N 180 MLI 16801 BAMAKO 012 SON 008 OOW NER SEN
17001 17101
IMRAN DAKAR
KOLLO NIAMEY DAKAR
013 014
18N SON
002 017
21E 32W
210 10
SEN 17102 DU FEEUVE VILLAGE GUEDE 016 30N 014 SOW 10 DZA DZA
19001 ALGER 19002 ALGIERS
QUED SHAR BARAKI
036 036
43N 05N
003 003
84E 10E
24 10
DZA 19003 GUELMA GUELMA 036 30N 007 30E 263 DZA 19004 SETIF SETIF 036 UN 005 25E 1033 DZA 19007 SETIF SETIF 036 09N 005 21E 1033 DZA 19005 CONSTANTINE EL KHROUB 036 16N 006 42E 640 DZA 19006 SIDI-BEL-ABBES SIDI-BEL-ABBES 035 UN 000 37W 450 EGY 19101 EL GEMMEIZA EL GARBIA 030 48N 031 07E 8 EGY 19102 GIZA CAIRO 030 02N 031 13E 21 EGY 19103 SAKBA KAFR EL SHEIKH 031 07N 030 57E 6 EGY EGY EGY
19104 19109 19105
EL MATANA U.E. (KENA) SHANDAWELL
UPPER EGYPT EL MATANA SONAG
(KENA) 025 025 026
30N 30N 36N
032 032 031
35E 35E 40E
89 89 57
EGY 19106 SIDS BENI-SUEF 029 04N 031 06E 28
72�
Cnt New stat. city Lat. Long. Elev. Abb. stat
EGY 19107 BAHTIM KALOBIA 030 13N 031 12E 21 EGY 19108 SERW .S. DONIAT 031 14N 031 39E 2 EGY 19110 MALLAWI EL MINIA 027 42N 030 45E 39 EGY 19111 FAUME TAMIA 029 07N 030 06E 28 EGY 19112 FAUME TAMAI EGY 19113 KOMOMBO ASWAN EGY 19114 GIZA GIZA 015 08N 35 45E 440 EGY 19115 BAHTIM BAHTIM 031 OON 030 OOE 21 LBY 19201 TRIPOLI SIDI 032 SON 013 05E 10 LBY 19202 TRIPOLI TAJOURA 032 53N 013 24E 11 LBY 19203 PENDUNTE 032 28N 020 53E 210 LBY 19204 EL MARJ ARC EL JABEL AL AKHDAR 030 30N 020 52E 310 LBY 19205 FEZ ZAN SEBHA 027 SON 014 40E 465 LBY 19206 SE. DESERT EL SARIR 026 28N 021 22E 300 LBY 19207 KUFRA KUFRA 025 OON 023 OOE 415 LBY 19208 GAREAN JILEALEA 032 ION 013 OOE 725 LBY 19209 AL-ZAHERA ZAWIA AL-ZAHRA 032 40N 012 50E 50 LBY 19210 MISURATA TOMENA 032 45N 012 45E 32 LBY 19211 AL-ZAHERA ZAWIA AL-ZAHRA 032 45N 012 45E 15 LBY 19212 ZORDA BENGHAZI ZORDA BENGHAZI 032 OON 023 OOE 350 MAR 19301 DEROUA MEN. BENI MELLAL 032 20N 006 17W 500 MAR 19302 MENARA MARRAKECH 031 48N 008 07W 464 MAR 19303 RABAT (3) MARCHOUCH 033 33N 006 24W 500 MAR 19304 KENITRA SIDI KACEM 034 13N 005 43W 84 MAR MAR MAR
19305 RABAT (I) 19306 RABAT (I) 19307 SETTAT
GUlCH (DEBBAGH) DEBBAGH (GUlCH) SIDI EL AYDI
033 033 036
59N 59N 34N
006 006 012
52W 52W 52W
25 25
450 MAR 19308 RABAT (2) INRA 034 59N 006 SOW 65 TUN 19401 ARIANA TUNIS 036 48N 010 12E 10 TUN 19407 TUNIS ARIANA 036 48N 010 12E 10 TUN 19402 BEJA BEJA 036 44N 009 08E 150 TUN 19403 BOU SALEM KOUDIAT 036 35N 009 OOE 134 TUN 19404 MATEUR MATEUR 037 03N 009 42E 120 TUN 19405 KHAROAN HINDI ZITOUN 035 SON 000 OOE 350 TUN 19406 BOULIFA LE KEF 036 38N 008 50E 350 AFG 20001 FIROZ KALU HERAT 034 24N 062 13E 964 AFG 20002 SHISHAM BAGH JALALABAD 034 25N 070 27E 552 AFG 20003 DARUL AMAN KABUL 034 33N 069 12E 1803 AFG 20004 KUNDUZ 036 SON 068 SSE 403 AFG 20005 MAZAR-I-SHAR 036 42N 131 67E 378 AFG 20006 BAGHLAN 036 06N 068 39E 51 AFG 20007 TAKHAR 044 30E 036 49N 804 AFG 20008 BALKH 067 12N 036 42 350 CYP 20201 NICOSIA (2) NICOSIA 035 06N 033 28E 153 CYP 20202 NICOSIA (I) LAXIA 035 04N 033 20E 200 CYP 20203 NICOSIA (3) ATHALASSA 035 08N 033 24E 142 CYP 20204 DROMOLAXIA LARMACA 034 52N 033 36E 25 CYP 20205 PRASTIO SYNGRASST 035 06N 032 25E 50 IRN 20301 AHWAZ KHUZESTAN 031 17N 048 40E 20 IRN 20302 CHEPAN IRN 20303 SAFIABAD DEZFUL 032 16N 048 25E 83 IRN 20304 ARAGHEE MOH GORGAN 036 SIN 054 25E 132 IRN 20305 KARAJ TEHRAN 035 SON 050 58E 1321 IRN 20306 FARS{l) DARAB 028 45N 054 33E 1100 IRN 20307 FARS(2) DARAB-HASSAN-ABAD 029 OON 055 OOE 1100 IRN 20308 CHALOOS KELARDASHT 036 OON 050 OOE 1200 IRN 20309 GORGAN MAZANDARAN 036 55N 054 20E 5 IRN 20310 MAZANDARAN (1) DASHT NAZ 035 47N 30 IRN 20311 KHORASAN (I) MASHHAD 036 13N 059 40E 985 IRN 20312 KHORASAN (2) FARUBROMAN NISHABOO 036 37N 058 60E 1350 IRN 20313 ZABOL SISTAN & BALOCHESTAN 030 53N 061 41E 49 IRN 20314 GACHSARAN KOHGILOOYEH & BOVAIR AHMAD 030 20N 050 OOS 71 IRN 20315 ZARGAN FARS 029 46N 052 43E 160
73�
Cnt Hew state City Lat. Long. Elev. Abb. stat
IRQ IRQ IRQ IRQ IRQ IRQ IRQ JOR JOR JOR JOR JOR JOR JOR JOR JOR JOR JOR JOR LBN LBN LBN QAT QAT SAU SAU SAU SAU SAU SYR SYR SYR SYR SYR SYR SYR TUR TUR TUR TUR TUR TUR TUR TUR TUR TUR TUR TUR
20401 20402 20403 20404 20405 20406 20407 20501 20512 20502 20503 20504 20505 20506 20507 20508 20509 20510 20511 20701 20702
108 20901 20902 21001 21002 21003 21004 21005 21101 21102 21103 21104 21105 21106 21107 21201 21202 21203 21204 21205 21207 21210 21206 21208 21209 21211 21212
ABU'GBRAIB BAKRAJo NINlVEH FAR QADISSIA ARBIL NINEWA KUFA JORDAN VALLEY JORDAN VALLEY AMMAN IRBID (2) AMMAN IRBID (3) RABBAH MAAN MADABA MOSHAGAR IRBID (1) MARU TERBOL TEL AMARA NTH COASTAL PL RAWDAT HARM WADI EL-AREIG RIYADH (2) AL KHARJ JIZAN ONAIZAH RIYADH (1) IZRAA A.R.S ALEPPO (1) ALEPPO (2) DAMASCUS ( 1 ) JELLIN-ACSA EL ZIERCH DAMASCUS (2) CUKUROVA SAKARYA ANKARA E .A.R. I. LA.G.P.B. IZMIR (4) IZMIR DIYARBAKIR TRAKYA IZMIR (1) ADANA (2) KASTAMONU
BAGHDAD SULAIMANYA MOSUL F.C.R.S. ARBIL HAMMAN AL ALILE KUFA DEIR ALLA UNIV. FARM JUBEIHA RAMTHA WADI YABIS IRBID KERAK SHOUBAK MOUSHAKER MOSHAGAR MARROW MARU BEKA'A VALLEY BEKA'A VALLEY
DOHA DOHA (RAWDAT HARM) RIYADH AL KHARJ HAKMA AL GASSIM DIRAB DERRA MOUHAPHAZA BREDA TEL HADYA KARAHTA DARAA LATTAKIA DAMASCUS ADANA ADAPAZARI C.A.I. ESKISEHIR IZMIR EGE RARI AEGEAN DIYARBAKIR EDIRNE E.B.Z.A. CUKUROVA UNIV. ORZA
033 035 036 031 036 036 037 032 032 030 032 035 035 035 033 031 036 032 034 033 033 038 025 025 024 024 017 026 024 032 035 036
032 035 033 036 040 039 039 038 038 038 037 041
037
20N 34N 20N 59N 11N 01N OON 12N 12N 01N 34N 36N SIN 45N 30N 34N 12N 33N 32N 55N 55N 35N 48N 48N 37N 15N 55N 55N 20N SIN 56N 05N
45N 28N 34N SON 47N 40N 46N 27N 40N 35N 55N 40N
19N
044 045 043 044 044 043 040 035 035 035 036 032 032 031 032 035 039 035 035 035 035 040 051 051 046 047 055 044 046 036 037 036
035 036 036 035 030 032 030 027 027 027 040 026
035
24E 23E 09E 59E OOE 14E OOE 36E 37E 52E OlE 24E 32E 16E 35E 48E OOE 51E 51E 28E 28E OOE 18E 18E 45E 10E 43E 59E 37E 15E 11E 55E
59E 03E 37E 20E 25E 39E 31E 15E 04E 05E 12E 34E
15E
34 750 223
20 414 305
24 -224 -224
980 520 200 555 970
1400 785 785 618 620 950 950
10 50 50
700 540
92 724 600 575 300 282
421 50
617 20 30
1044 801
28 10 20
660 48
90
TUR 21213 SAMSUN 041 18N 036 20E 10 YEM YEM YEM
21401 21402 21403
AL-MINJEDAH TAIZ (1) BODEIDAH
SANA'A AUSSEFERA SURDUD
015 013 015
23N OON 15N
044 042 043
13E 30E 15E
2350 1350
170 YEM YEM
21404 21405
CENT. HIGHLAND TAIZ (2)
DHAMAR 014 013
36N 57N
044 044
21E 11E
2330 1750
YEM YMD
21406 21501
TAIZ (2 ) HAORAMOUT
ARA SEIYUN
013 016
58N OON
044 049
12E OOE
1700 600
YMD 21502 BADRAMOUT SEIYUN 016 OON 049 OOE 600 YMD 21503 STHN UPLAND TAIZ 013 42N 044 OOE 1350 ISR 21601 KIRYAT-GAT MIVHOR FARM 031 37N 034 47E 120 ISR 21602 BET DAGAN VOLCANI 032 OON 034 48E 30 ISR 21605 BET DAGAN 032 OON 034 17E 30 ISR 21603 NVE-YA AR STET R.E.S. 032 05N 035 OOE 60 ISR 21604 REHOVOT WEIZMANN INST. 031 90N 034 80E 50
74�
Cnt Hew state City Lat. Long. Elev. Abb. stat
BGD BGD BGD BGD BGD BGD BGD BGD BGD BGD BGD BTN
22001 22002 22003 22007 22004 22005 22006 22008 22009 22010 22011 22101
JOYDEBPUR PABNA JAMALPUR (1) JAMALPUR (2) DHAKA JESSORE MYMENSINGH DINAJPUR (2) DINAJPUR (1) CHITAGONG RAJSHAHI WANDIPHODRANG
BARI ISHURDI JAMALPUR JAMALPUR DHAKA JESSORE BAU CAMPUS RAJABARI NASHIPUR HATHAZARI RAJSHAHI WANDIPHODRANG
023 024 024 024 023 023 024 025 025 022 025 027
46N 25N 25N 56N 06N 13N 46N 67N 29N 30N OON 31N
090 089 089 089 090 089 090 088 088 091 089 089
23E OOE 07E SSE 04E 13E 24E 70E 25E 47E OOE 49E
8 8 8 8 7 8
19 29 29 13 18
1300 BTN IND IND
22102 22201 22202
BHUR BIHAR ( 1) GUJARAT( 1)
BHUR PATNA JUNAGADH
027 025 021
35N 33N 30N
089 085 070
SSE 09E 28E
460 51
137 IND IND IND IND IND IND IND IND IND IND IND IND IND
22221 22203 22204 22205 22206 22207 22218 22208 22209 22210 22211 22212 22213
GUJARAT( 1) HARYANA(3) KASHMIR M.P.(2) MAHARASHTRA NEW DELHI NEW DELHI PUNJAB RAJASTHAN U.P.(5) M.P.(l) KARNATAKA(l) U.P.(3)
? JAMMU POWARKHEDA NIPHAD IARI IARI LUDHIANA DUNGARPUR PANTNAGAR INDORE MYSORE BANARAS
021 029 032 022 020 028 028 030 026 029 022 012 026
30N 38N 43N 44N 06N 35N 35N 56N 58N OON 37N 22N 27N
070 075 074 077 074 077 077 075 075 079 075 076 080
28E 06E 54E 42E 06E 12E 12E 52E 48E OOE 50E 40E 25E
60 215 314 299 549 228 228 247 450 243 600 638 150
IND IND IND
22214 22215 22216
BIHAR(2) KARNATAKA(2) WEST BENGAL
PUSA DHARWAR MALDA
025 015 025
52N 42N 02N
085 076 088
48E 07E 06E
51 638
IND IND IND
22217 22219 22220
A.P. HARYANA( 1) u.P.(S)
RAJENDRANAGAR KARNAL FAIZABAD MASADHA
017
026
19N
47N
078
082
38E
12E
542
113 IND IND
22222 22223
GUJARAT(2) U.P.(4)
UIJAPUR KANPUR
023 026
OON 28N
072 080
OOE 24E
125 123
IND IND IND
22224 22225 22226
KARNATAKA(3) HARYANA(2) RAJASTHA
UGAR HISSAR BARKHERA KOTA
016 029 025
SON ION 13N
071 075 075
07E 46E 25E
555 215 258
IND 22227 U.P.(2) KANPUR - AZAD UNIV. 015 30N 060 75E 4061 IND BUR
22228 22401
U.P.(I) SHAN
DAVRALA HEHO
MEERUT 020 45N 090 50E 1140
BUR 22402 PYNMANA YEZIN 019 SIN 096 07E 100 BUR 22403 YE-U SAGAIN PANGON 023 02N 095 28E 120 BUR 22404 SAGAING ZALOKE FARM 022 02N 095 02 24 NPL 22501 RUPANDEHI(2) BHAIRAHWA 027 30N 083 25E 105 NPL 22502 DHANUSHA ( 1 ) JANAKPUR 1 027 48N 085 58E 90 NPL 22503 DHANUSHA(2) JANAKPUR 2 026 48N 085 58E 90 NPL 22504 LALITPUR KHUMALTAR 027 SON 085 20E 1369 NPL 22505 DHANKUTA PAKHRIBAS 027 05N 087 20E 1933 NPL 22506 CHITWAN RAMPUR 027 37N 084 24E 228 NPL 22507 DOLAKHA JIRI 026 CON 086 OOE 1700 NPL NPL
22508 22509
BARA GANDAKI
PARWANIPUR TANAHU
(NARAYANI) 028 03N 084 04E 580
NPL 22510 KABRE KABRE 027 06N 086 03E 1700 NPL NPL
22511 22512
RUPANDEHI(l) KHAJURA
BHAIRAHWA NEPALJUNG
(I.A.A.S.) 027 06N 082 04E 105 182
NPL 22513 LAMJUNG SUNDAR BAZ 720 NPL 22514 JHAPA TARAHARA 026 45N 087 20E 20 PAK 22601 NWFP PESHAWAR 032 33N 068 OOE 340 PAK 22602 PUNJAB BAHAWALPUR 029 25N 071 40E 170 PAK PAK
22603 22609
FAISALABAD FAISALABAD
FAISALABAD FAISALABAD
(LYALLPUR) 031 031
30N 30N
073 073
10E 10E
213 213
75�
Cnt Hew state City Lat. Long. Elev. Abb. stat
PAK 22611 FAISALABAD FAISALABAD (AYUB) 031 30N 073 10E 213 PAK 22604 SIND TANDOJAM 025 02N 063 38E 19 PAK 22605 ISLAMABAD (C.D.R.I. ) 033 41N 073 07E 526 PAK 22606 BALUCHISTAN SARIAB QUETTA 030 11N 066 57E 1730 PAK 22607 ISLAMABAD ISLAMABAD 033 39N 073 05E 683 PAK 22608 NWFP PIRSABAK 034 OON 072 OOE 288 PAK 22610 SILSIT N. PEKORRA 036 20N 073 50E 2120 PAK 22612 PUNJAB BARANI PAK 22613 NWFP SERAI NAURUNG (BANNU) 034 OON 071 OOE PAK 22614 NWFP DERA ISMAIL KHAN 031 OON 070 00 171 PAK 22615 SIND KARACHI 024 SIN 067 02E 8 LKA 22701 UPPER UVA BANDARAWELA 006 SON 080 53E 1219 LKA 22703 UPPER UVA BANDARAWELA 006 SON 080 53E 1219 LKA 22702 UVA MONERAGALA 006 SIN 081 20E 120 THA 24101 CHAING MAl ( 3 ) SANPATONG 018 46N 099 02E 300 THA 24102 CHIANG MAl (I) PANG DA 018 15N 098 36E 820 THA 24103 NAKHON RAT. SUWAN FARM 014 40N 102 OOE 300 THA 24104 CHIANG MAl (2 ) SAMOENG 018 17N 098 36E 820 THA 24105 CHIANG MAl (3) UNIV. 018 47N 098 58E 314 THA 24106 CHIANG RAI (I) CHIANG RAI 019 52N 099 47E 403 THA 24107 CHIANG RAI (I) CHIANG RAI 019 52N 099 47E 403 THA 24108 CHIANG MAl (3) FANG 019 57N 099 10E 430 THA 24109 LAMPANG ARTC 018 21N 099 36E 275 THA 24110 CHIANG RAI ( 2 ) BOONRRAWD FARM 019 52N 099 47E 450 THA 24111 SURIN SURIN 014 53N 103 30E 146 VNM 24212 HANOI (2) VAN DIEN 021 OlN 105 48E 5 VNM 24213 HANOI (1) HANOI 021 OON 105 OOE 5 PHL 25001 MANILA QUEZON CITY 014 38N 121 03E 45 PHL 25002 LAGUNA LOS BANOS 014 ION 121 15E 21 PHL 63 ISABELA SAN MATEO 017 08N 121 53E 69 PHL 25003 ISABELA SAN MATEO 017 08N 121 53E 69 PHL 25004 BAGUIO BENGUET 016 40N 120 60E 1330 PHL 25005 CAGAYAN CAGAYAN 018 21N 121 37E 4 IDN 25201 WEST JAVA ( 2 ) BOGOR 006 405 106 45E IDN 25202 WEST JAVA ( 3 ) KUNINGAN 006 595 108 50E 545 IDN 25203 WEST JAVA ( 1 ) SUKAMANDI 006 295 107 39E 15 ION 25204 W SUMATRA SUKARAMI R.I. 001 005 100 30E 925 CHN 27001 BEIJING BEIJING 039 48N 116 28E 50 CHN 27016 BEIJING BEIJING 040 OON 116 OOE 50 CHN 27019 BEIJING BEIJING 039 59N 116 16E 54 CHN 27021 BEIJING BEIJING (CAAS) 039 48N 118 24E 31 CHN 27023 BEIJING BEIJING (AG. UNIV.) 039 56N 116 20E 44 CHN 27002 FUJIAN FUZHOU 026 OON 119 23E 5 CHN 27003 YUNNAN (3) KUMMING 025 07N 102 43E 1916 CHN 27004 HEILONGJIANG-3 NAN HU 048 31N 114 OOE 29 CHN 27005 JIANGSU (4) SUZHOU 034 19N 117 22E 34 CHN 27006 JIANGSU (I) NANJING 032 OON 118 48E 9 CHN 27029 JIANGSU (I) NANJING 032 03N 118 47E 67 CHN 27007 GUANGDONG GUANGZHOU 023 06N 113 18E 8 CHN 27008 ZHEJIANG HENGZHOU 030 12N 120 19E 8 CHN 27009 JIANGSU (2) NANJING 032 25N 119 25E 12 CHN 27010 HEILONGJIANG-2 KESHAN 048 04N 125 52E 1234 CHN 27011 IN. MONGOLIA-2 KESHI KETENGGI 042 54N 117 36E 1235 CHN 27012 SHANGHAI SHANGHAI ACADEMY 031 13N 121 19E 4 CHN 27013 HUBEI WUHAN 030 38N 114 04E 23 CHN 27014 SICHUAN (1) CHENGDU 030 40N 104 OlE 506 CHN 27015 HEILONGJIANG-5 HEIHEI 050 15N 127 27E 168 CHN 27017 QINGHAI 036 38N 101 38E 2309 CHN 27025 QINGHAI XINING 036 45N 101 38E 2309 CHN 27026 QINGHAI DA YOU SHAN 036 44N 101 52E 2560 CHN 27018 JIANGSU (3 ) 033 23N 120 09E 19 CHN 27020 SHAN XI TAIYAN 037 47N 112 33E 800 CHN 27022 YUNNAN (I) FUKIN 024 27N 118 24E 63
76�
Cnt Hew state City Lat. Long. Elev. Abb. stat
CHN 27024 IN. MONGOLIA-1 CHN 27027 NING XIA CHN 27028 HEIBEl CHN 27030 JILIN CHN CHN CHN
27031 27033 27032
SICHUAN (2) SICHUAN (2) YUNNAN (2)
CHN 27034 HEILONGJIANG-1 CHN 27035 HIELONGJIANG-4 CHN 27036 NING XIA JPN 28101 KITAMI JPN 28102 FUKUOKA JPN 28103 KANAGAWA JPN 28104 SHIMANE JPN 28105 IBARAKI KOR 28301 CHUNAM (1) KOR KOR
28302 28303
CHUNAM HONAM
(3)
KOR 28304 YEONGNAM KOR KOR
28305 28306
CHUNAM (2) GYEONGGI PROVo
TWN 28501 SUN 30001 ABKHAZIAN SUN 30102 SFSR SUN 30103 UKRAINE SUN 30104 UKRAINE SUN 30105 KRUMEX ALB 31001 BGR BGR
31101 31102
DOBRUDJA CHIRPAN
(2 )
BGR 31103 SADOVA PLOVDIV BGR 31104 DOBRUDJA (1) BGR 31105 SOFIA CSK 31201 BOHEMIA (3) CSK CSK
31202 31203
BOHEMIA BOHEMIA
(2) (1)
CSK 31204 SLOVAKIA CSK HUN
31205 31410
BOHEMIA (4) MARTONVASAR
HUN 31411 KECSKMET HUN 31412 SZEGED POL 31501 LASKI POL 31502 PUSTKOW POL 31503 CENTRAL POL 31504 ULHOWEK POL 31505 WARSAW POL 31506 HENRYKOW POL 31507 POZNAN POL 31508 KRAKOW POL 31509 ELBLAG POL 31510 KRAKOW POL 31511 RADOM POL 31512 LESZNO POL 83 HENRYKOW 2 ROM 31601 FUNDULEA ROM 31602 TURDA ROM 31603 BUCHAREST ROM 31604 TIMISOARA ROM 31605 PRASOV YUG 31701 CROATIA YUG 352 NOVISAD YUG 31702 NOVISAD YUG 31703 MACEDONIA
HU HEHAOTE YONGNING HAN DAN
GUAN XIAN XIN DU DAFONG SIT ME DO HARBIN (AC. AGR. SCS.) JIU SAN AGR. INS. GU YAAN DIST. AGR. INST. HOKKAIDO KYUSHU N.I.A.S. S.A.E.S. YATABE MOKPO KWANGJU CHUNBUK MILYANG MUAN SUWON TAICHUNG ABKHAZSKAYA ASSR LENINGRAD POLTAVA ODESSA KYACREOGAP LUSHNJE TOLBUKHIN CHIRPAN I.I.P.R. GEN. TOSHEVO SOFIA STUPICE PRAHA RUZYNE KLATOVY MIROVKA PIESTANY KAMENICNA MARTONVASAR V.C.R.I. SZEGED RADOM WARKA WARSAWA ZAMOSC RADZIKOW WROCLAW NAGRADOWICE KRAKOW DEBINA GRODKOWICE DANKOW CHORYN WROCLAW CALARASI CLUJ BUCHAREST TIMISOARA PRASOV CROATIA VOYVODINA VOYVODINA MACEDONIA
040 038 036 043 030 030 024 045 048 036 043 033 035 035 036 034 035 035 035 034 036 026 042 059 049 046 045 040 043 042 042 043 042 050 050 049 048 050 047 046 046 051 050 052 050 052 050 052 050 054 049 051 052 052 044 046 044 045 045 045 045 045 043
49N 111 41E 1041 14N 106 15E 1118 36N 114 30E 57 54N 125 13E 237 59N 103 40E 600 56N 103 91E 514 32N 100 19E 1659 41N 126 37E 172 52N 125 17E 288 OON 106 15E 1753 47N 143 42E 196 12N 130 30E 10 OON 139 OOE 20 20N 132 43E 10 01N 140 08E 23 47N 126 20E 25 08N 126 56E 43 55N 126 57E 8 29N 028 45E 12 47N 126 23E 53 17N 126 59E 39 57N 120 32E 15 22N 057 OOE 7 56N 030 18E 22N 033 10E 104 27N 030 42E 34 OON 038 55E 37 57N 019 42E 40N 028 10E 236 12N 025 20E 172 42N 023 18E 40N 028 10E 156 OON 028 OOE 562 05N 014 25E 270 03N 014 20E 350 23N 013 17E 430 37N 017 50E 162 05N 016 30E 474 21N 018 49E 150 55N 019 43E 122 o N 020 OOE 80 45N 021 15E 100 55N 016 48E 200 14N 021 OOE 100 25N 023 25E 270 12N 020 38E 90 39N 017 OlE 220 20N 017 10E 85 OON 020 OOE 220 ION 019 OOE 59N 020 16E 220 42N 020 44E 155 30N 017 30E 72 18N 019 06E 64 27N 026 32E 66 35N 023 48E 428 29N 026 07E 46N 021 15E 85 42N 025 33E 496 49N 015 59E 177 OON 019 OOE 84 OON 019 OOE 84 OON 022 ODE 250
77�
Cnt Rew state City Lat. Long. Elev. Abb. stat
YUG 31704 BOSNIA 1ST SOKOLAC 043 56N 018 48E 860 YUG 31705 BOSNIA 2ND SOKOr...p.C 043 56N 018 48E 860 AUT 33001 LOWER AUSTRIA GROSS ENZERSZDORF 048 12N 016 34E 153 BEL 33101 GEMBLOUX GEMBLOUX 050 28N 004 53E 160 BEL 33102 GEMBLOUX GEMBLOUX 050 34N 004 40E 160 BEL 33103 LINTER NEERHESPEN 050 47N 005 04E 45 GBR 33201 CAMBRIDGE P.B.I. 052 ION 000 06E 17 GBR 33202 NORFOLK MILN MARSTERS LTD. 052 53N 000 40E 79 GBR 33203 LINCOLNSHIRE ELSOMS SEEDS 052 48N 000 07W 1 GBR 33204 WILTSHIRE HARTHAM PARK 051 25N 002 11W 400 GBR 33205 LINCOLNSHIRE NICKERSON 053 90N 000 lOW 60 GBR 92 WALES PLAS GOGGERDAN 052 26N 004 01W 33 FRA 33301 SEINE ET MARNE MONTCEAUX 048 21N 002 57E 53 FRA 33302 PUY DE DOME CLERMONT-FERRAND 045 47N 003 06E 340FRA 33303 NORD (1) CANADA SEMENCES 048 01N 002 SOW 100 FRA 33304 HERAULT MONTPELLIER 043 35N 003 54E 18 FRA 33305 PARIS PARIS 048 52N 002 20E FRA 33306 YVELINES VERSAILLES 048 46N 002 09N FRA 33307 EURE ET LOIRE-1 CARGILL 048 5 N 001 08E 130 FRA 33308 OISE C.A.C.B.A. 049 ION 002 30W 120 FRA 33309 TARN LA COURTADE 043 59N 002 OOE 183 FRA 33310 NORD (3) FLORIMOND DESPREZ 050 34N 003 05E 50 FRA 33311 NEW CALEDONIA NESSADIOU 021 30S 002 45E 0 FRA 33312 GERS CLAEYS-POUY 044 OON 001 08 150 FRA 33313 EURE ET LOIRE-2 BOISSAY (TOURY) 049 OON 002 OOE 130 FRA 33314 ILE DE FRANCE-2 ORGERUS YVELINES 048 SON 001 43E 101 FRA 33315 ILLE ET VlLAINE LE RHEU 048 04N 001 44W 40 FRA 33316 MORENVAL PICARDIE 049 OON 003 OOE 10 FRA 33317 ILE DE FRANCE-1 BENOIST 048 SON 001 42 12 FRA 33318 CHER (CENTER) LE CLOS LAN 17 FRA 71 NORD (2) PERONN 050 OON 001 OOE 59 DEU 31301 BAVARIA FREISING-WEI 048 24N 011 44E 467 DDR 33401 BAVARIA FREISING-WEI 048 24N 011 44E 467 DEU 31302 LWR SAXONY (2 ) WETZE 051 08N 009 09E 125 DDR 33402 LWR SAXONY (2) WETZE 051 08N 009 09E 125 DEU 31303 MAGDEBURG (1) LANGENSTEIN 051 42N 011 OOE 202 DDR 33403 MAGDEBURG (1) LANGENSTEIN 051 42N 011 OOE 202 DEU 31304 SCHWERIN GUELZOW GUESTROW 053 48N 012 12E 12 DDR 33404 SCHWERIN GUELZOW GUESTROW 053 48N 012 12E 12 DEU 31305 BADEN-WURT. (2 ) HOHENHEIM 048 43N 009 13E 407 DDR 33405 BADEN-WURT (2) HOHENHEIM 048 43N 009 13E 407 DEU 31306 LWR SAXONY (1) BERGEN 049 OON 013 OOE 70 DDR 33406 LWR SAXONY (1) BERGEN 049 OON 013 OOE 70 DEU ·31307 MAGDEBURG (3) HADMERSLEBEN 052 OON 011 OOE 87 DDR 33407 MAGDEBURG (3) HADMERSLEBEN 052 OON 011 OOE 87 DEU 31308 BADEN-WURT. ( 1) NURTINGEN 048 39N 009 23E 310 DDR 33408 BADEN-WURT (1) NURTINGEN 048 39N 009 23E 310 DEU 31309 MAGEDBURG (2) GATERSLEBEN 051 49N 011 11E 110 DEU 106 HOLSTEIN SCHLESWIG 051 25N 010 36E 5 DEU 31310 HOLSTEIN SCHLESWIG 051 25N 010 36E 5 IRL 33501 KILDARE BACKWESTON 053 30N 006 40W 50 IRL 33502 LYONS LYONS 053 30N 006 35W 65 NLD 33701 WAGENINGEN WAGENINGEN 051 58N 005 40E 5 NLD 33702 LELYSTAD CEBECO 052 30N 005 30E -4 NLD 33703 GRONINGEN GEERTSEMA ZADEN 053 13N 006 35E 0 NLD 33704 RILKAND VAN DE HAVE 51 27N 4 20E -4 CHE 33801 NYON NYON 046 32N 006 40E 430 CHE 33802 ZURICH ZURICH 047 29N 008 32E 445 GRC 35001 THESSALONIKI-1 EPANOMI P.B.I. 040 38N 022 57E 10 GRC 35004 THESSALONIKI-1 EPANOMI P.B.I. 040 38N 022 57E 10 GRC 35002 PLATY PLATY 040 39N 022 31E 10 GRC 35003 THESSALONIKI-2 AGIOS MAMAS 040 14N 023 20E 14 GRC 35005 THESSALONIKI-3 CEREAL INST 040 40N 022 60E 10
78�
Cnt Hew state City Lat. Long. Elev. Abb. stat
GRC GRC
35006 35007
VERMIO GIANITSA
VERMIO (MOUNTAIN) GIANITSA
040 37N 022 30E 140
ITA ITA
35101 35102
ROME (4) CASSACCIA FOGGIA
041 041
54N 28N
012 015
29E 31E
20 76
ITA ITA ITA
35103 35104 35105
ROME (5) ROME (1) PIACENZA
RIETI PONTE GALERIA PIACENZA
041 041 044
54N 48N 56N
012 012 012
29E 41E 27E
402 3
79 ITA ITA ITA
35106 35107 35108
ANCONA ROME (3) ENEA & ITA
CASACCIA RIETI MARC HE
043 041 043
30N 54N 15N
013 012 013
10E 29E 30E
30 -400
229 ITA ITA
35109 35110
ROME ROME
(2) (2)
INVIOLATELLA INVIOLATELLA
041 041
53N 53N
012 012
29E 29E
75 75
ITA 35111 ROVIGO BADIA POLESINE 045 05N 011 30E 1 ITA ITA
35112 35113 ENEA & CERM
FOGGIA MACERATA
041 043
27N 15N
003 013
02E 30E
76 190
ITA 35114 VITERBO SEHAG-AZIENDA ITA 35115 POLICORO MATERA 040 ION 016 40E 5 ITA 35116 POLICORO MATERA 040 ION 016 40E 5 ITA 35117 POLICORO MATERA 040 ION 016 40E 5 ITA ITA
35118 35119
TARQUINIA VITERBO
VITERBO UNIV. TUSCIA
042 042
13N 24N
011 012
46E 08E
8 40
MLT 35220 VALLETTA 035 53N 014 31E PRT PRT
35301 35302
ALENTEJO BEJA
ELVAS BEJA
038 038
54N 01N
007 007
09W 52W
208 272
PRT 35303 LISBON LISBON 038 43N 009 08W 77 PRT 35304 VILLA POUCA LU.T.A.D. 041 30N 007 39W 750 PRT 35305 TRAS-OS-MONTES VILLAREAL 041 19N 007 44W 400 PRT 35306 ELVAS CASAS VELHAS ESP ESP ESP ESP ESP
35401 35402 35403 35404 35405
MADRID (3) CORDOBA (2) ZARAGOZA (1) MADRID (2) CADIZ
EL ENCIN MAHISA COGULLADA LN.LA. JEREZ LA MERCED
040 037 041 040 036
33N 53N 38N 26N 43N
003 004 000 003 006
19W 46W 54W 51W 09W
600 220 202 490
20 ESP ESP ESP
35406 35407 35408
SEVILLA CORDOBA CORDOBA
( 3 ) ( 1 ) ( 4 )
TOMEJIL LAS ALFAYATAS EL ENCINAR
037 037 038
24N SON OON
005 000 004
35W 59W 20W
72 200 180
ESP ESP
35409 35425
SEVILLA SEVILLA
(4 ) (4)
LA LA
RINCONADA RINCONADA
037 037
30W 30N
005 005
57W 57W
20 20
ESP 35410 VALLADOLID ZAMADUENAS 041 42N 004 43W 700 ESP 35411 ZARAGOZA (2) MONTANANA 041 43N 000 50W 225 ESP ESP
35412 35413
BADAJOZ CORDOBA
( 3 ) (3)
LA ORDEN ALAMEDA DEL OBISPO
038 037
49N 53N
006 004
39W 47W
200 110
ESP 35414 LLEIDA (1) L URGELL 041 33N 000 55E 250 ESP ESP
35416 35415
LLEIDA (1) SEVILLA (5)
PALUAU D ANGLEFOLA CRUZ DEL CAMPO
041 037
33N 35N
000 006
55E OOW
250 12
ESP 35417 BARCELONA BERGUS-CARDONA 041 55N 001 40E 625 ESP ESP
35418 35419
BADAJOZ SEVILLA
(1) (6)
CAMPILLOS DE EL PEDROSO
LLERENA 038 037
28N 50N
005 005
50W 45W
520 500
ESP 35420 LLEIDA (2) BOLDU 041 34N 001 OlE 250 ESP ESP ESP
35421 35422 35423
MADRID LLEIDA LLEIDA
(1) (3) (4)
SEMILLAS AGRIC. FINCA LA CARRERADA GIMENELLS
040 041 041
OON 35N 40N
003 004 000
40W 25W 25E
490 360 250
ESP ESP ESP
35424 35426 35427
GRANADA SEVILLA HUELVA
(2) (1)
GRANADA EL PALMAREJO EL ROCIO
037
037
21N
08N
003
006
35W
30W
650
2 ESP ESP
35428 35429
BADAJOZ GRANADA
( 2 ) ( 1 )
LOBERAS GUADIX
038 037
47N 21N
006 003
43W 05W
30 95
ESP 35430 SORIA GOMARA 041 40N 002 12W 998 ESP 35431 GUADALAJARA EL LLANO 040 ION 001 lOW 430 FIN 37101 HELSINKI 060 05N 024 54E 38 FIN 37102 HYRLA HANKKIJA 060 20N 025 02E 38 NOR 37401 AAS VOLLE BEKK 060 OON 011 OOE 90 NOR 37404 AAS VOLLE BEKK 059 40N 010 47E 90
79�
Cnt Hew stat. City Lat. Long. Elev. Abb. stat NOR 37402 STANGE STAUR 061 OON 011 OOE 200 NOR NOR
37403 79
OSTRE OSLO
TOTEN APELSVOLL JORDET
060 059
42N 05N
010 011
52E OOE
264 58
SWE SWE SWE
37501 37502 37503
LANDS KRONA LANDS KRONA SVALOV
(2) (1)
SVALOV WEIBULLSHOL SVALOV
056 055 055
OON 55N 35N
013 OOE 012 50E 013 06E
50 5
50 SWE 37504 BJERTOP BJERTOP 058 16N 013 06E 84 CAN CAN CAN CAN CAN
40001 40002 40003 40004 40005
ALBERTA (3) MANITOBA SASKATCHEWAN-3 SASKATCHEWAN-2 ALBERTA (4)
ELLERSLIE WINNIPEG SASKATOON WATROUS BEAVER LODGE
053 049 052 051 055
34N 38N ION SON 12N
113 31W 097 07W 106 41W 105 SOW 119 24W
677 235 501 576 745
CAN CAN CAN
40006 40007 40008
ALBERTA (2) SASKATCHEWAN-l ALBERTA (1)
OLDS SWIFT CURRENT LACOMBE
051 050 051
47N 17N 47N
114 107 113
OOW SOW 40W
914 825 838
CAN 40101 P.E.I. CHARLOTTETOWN 046 20N 063 OOW 15 CAN CAN
40102 40103
QUEBEC (1) ONTARIO (1)
LAVAL UNIV. GUELPH
046 043
47N OON
071 080
18W 25W
80 333
CAN 40104 ONTARIO (2) ELORA 043 39N 080 25W 380 CAN USA USA
40105 41001 41002
QUEBEC (2) ARIZONA (4) ARIZONA (2)
SAINTE MESA YUMA
FOY 046 033 032
48N 25N 44N
071 III 114
18W 52W 36W
50 375
46 USA USA USA
41003 41004 41005
CALIFORNIA (4) CALIFORNIA (3) COLORADO (2)
DAVIS FRESNO FORT COLLINS
038 036 041
32N 43N OON
121 46W 119 48W 105 OOW
18 108
1518 USA USA
41006 41007
COLORADO IDAHO
(1) FORT COLLINS ABERDEEN
040 042
35N 56N
105 112
OOW SOW
1575 1341
USA USA USA
41008 41009 41010
MONTANA WASHINGTON CALIFORNIA
( 1 ) ( 6)
BOZEMAN PULLMAN TULELAKE
045 046 041
04N 42N 58N
III 117 121
09W 08W 28W
1456 768 129
USA 41011 OREGON (1) CORVALLIS 044 34N 123 12W 68 USA USA
41012 41013
WASHINGTON OREGON (2)
(2 ) ROYAL SLOPE MADRAS
046 045
55N 15N
119 121
15W 48W
365 1235
USA 41014 OREGON (3) PENDLETON 045 30N 118 26W 454 USA USA USA
41015 41016 41017
CALIFORNIA (1) ARIZONA (1) ARIZONA (3)
IMPERIAL VALLEY PIONEER HI-BRED MARICOPA
EL CENTRO 032 032 033
SON 38N 15N
115 114 III
34W 45W 45W
9 23
363 USA USA
41018 41019
CALIFORNIA CALIFORNIA
(2) (5)
ARCO SEED WOODLAND NORTHRUP KING
036 038
40N 41N
121 121
36W 48W
20 21
USA 41020 CALIFORNIA (7) STOTTENBERG 056 SIN 120 27W 150 USA 41021 UTAH LOGAN 041 45N III 49W 1371 USA USA
41022 149
WASHINGTON WASHINGTON
(4 ) (3)
RITZVILLE LIND
047 046
6 N 118 58N 117
25W 08W
488 462
USA USA
41101 41102
NTH. DAKOTA (1) NTH DAKOTA (4)
CASSELTON LANGDON
046 048
45N 45N
097 098
15W 20W
208 492
USA 41103 STH DAKOTA BROOKINGS 044 20N 096 SOW 591 USA USA
41104 41105
MINNESOTA MINNESOTA
(7) (5)
MOORHEAD ST. PAUL
047 044
54N 57N
096 093
48W 05W
274 260
USA 41106 INDIANA BROOKSTON 040 25N 086 56W 215 USA 41107 MICHIGAN EAST LANSING 042 40N 084 30W USA 41108 MISSOURI COLUMBIA 039 OON 092 40W 228 USA USA
41109 41110
NEBRASKA OHIO
( 1 ) LINCOLN WOOSTER
040 040
48N 47N
096 081
41W 56W
360 315
USA USA
41111 41112
NEBRASKA (2) LOUISIANA
MEAD A.E.S.
041 030
ION OON
096 SOW 091 OOW
1170 30
USA USA USA USA USA USA USA USA
41113 41114 41115 41116 41117 41118 41119 41120
TEXAS (3) TEXAS (4) TEXAS (1) TEXAS (2) NTH. DAKOTA (2 ) NTH. DAKOTA (3 ) TEXAS (5 ) MINNESOTA (6)
ERICKSON MCGREGOR BEEVILLE UVALDE AMENIA N.A.P.B. OVERTON MINNEAPOLIS
030 031 028 029 047 047 032 045
35N 23N 23N 14N OON 30N OON OON
096 20W 097 30W 097 46W 099 45W 097 13W 096 SOW 095 OOW 93 lOW
110 210
28 278 293 152 110 294
80�
Cnt Hew state City Lat. Long. Elev. Abb. stat
USA USA USA USA USA USA USA
41121 41122 41201 41202 41203 41204 41205
MINNESOTA (4 ) MINNESOTA (3) ALABAMA (2) GEORGIA (2) ALABAMA (1) GEORGIA (1) KENTUCKY
EDEN PRAIRIE STANTON ALABAMA GEORGIA A.M.U. GEORGIA LEXINGTON
044 044 034 031 030
038
49N 29N 39N 28N 30N
OON
93 93 086 083 088
084
27w 01W 46W 30W OOW
30W
277 278 190 370
304 USA USA MEX MEX MEX MEX MEX MEX MEX MEX MEX
41206 41307 42001 42002 42003 42004 42016 42005 42006 42007 42008
NEW YORK ALASKA COAHUILA (3) NUEVO LEON ( 1 ) SINALOA SONORA (1) SONORA (1) COAHUILA (2) CHIHUAHUA SONORA (3) TAMAULIPAS
ITHACA PALMER ZARAGOZA MONTERREY LOS MOCHIS CIANO CIANO BUENAVISTA DELICIAS NAVOJOA RIO BRAVO
45 061 028 025 025 027 027 025 028 027 026
5 N 76 34N 149 33N 100 05N 100 48N 109 20N 109 20N 109 22N 101 11N 105 02N 109 OON 098
5 w 16W ssw 36W OOW 54W 54W OOW 30W 25W 13W
305 61
350 537
15 38 38
1785 1170
40
MEX MEX MEX MEX
42009 42010 42011 42012
B.C.N. (2 ) NUEVO LEON (2 ) SONORA (2) DURANGO (1)
MEXICALI LA LEGANA NAVIDAD HERMOSILLO VALLE DEL GUADIANA
031 025 028 024
40N 40N 36N 01N
114 100 111 104
45W 36W 12W 40W
25 1895
60 1889
MEX MEX MEX
42013 42014 42015
B.C.N. (3 ) B.C.N. (1 ) CAESTOD
CAEMEXI CAECOEN CD. CONSTITUCION
032 031 024
31N 35N 57N
115 116 111
26W 61W 42W
6 720
48 MEX MEX
42017 42018
DURANGO (2) COAHUILA (1)
FCO. I. MADERO CAESIA 025 13N 100 35W 2140
MEX 42019 MICHOACAN (4) VILLA MADERO MEX 42020 MICHOACAN (5) POTZUMARAN MEX 42101 GUANAJUATO (2) EL BAJIO 020 32N 100 49W 1765 MEX 42102 JALISCO (2) ZAPOPAN 020 40N 1550 MEX MEX MEX
42103 42104 42105
TOLUCA MICHOACAN TLAXCALA
(2 ) ATIZAPAN PATZCUARO HUAMANTLA
019 019 019
16N 30N 19N
099 101 097
51W 40W 56W
2640 2040 2553
MEX 42106 NAYARIT SANTIAGO 021 20N 105 20W MEX 42107 VERACRUZ POZA RICA 020 34N 097 26W 60 MEX 42108 AGUASCAL. PABELLON 022 09N 102 17W 1912 MEX 42109 COLIMA LA POSTA 019 27N 104 37W MEX MEX MEX MEX
42110 42111 42112 42113
GUANAJUATO ( 1 ) GUANAJUATO (3 ) GUANAJUATO (4) JALISCO (3)
CORTAZAR ROQUE SAN JOSE ITURBIDE TEPATITLAN
020 020 021 020
29N 34N OON 42N
100 100 100 102
58W SOW 24W 42
1750 1765 1870 1960
MEX 42114 TAMAULIPAS LAS HUASTECAS 022 33N 098 31W 40 MEX 42115 MICHOACAN (1) CUTZITAN 019 20N 101 40 2350 MEX 42116 S.L.P. EBANO 022 12N 098 23W 65 MEX 42117 CAEVAMEX CHAPINGO 019 17N 098 53W 2249 MEX 42118 HIDALGO LAGUNILLA 020 32N 100 49W 1764 MEX 42119 JALISCO (1) JESUS MA. 020 37N 102 11W 2110 MEX 42120 MICHOACAN (3) BRISENAS 020 15N 102 33W 1517 MEX 42121 TEXCOCO EL BATAN IRRIGATED 019 31N 098 SOW 2249 CRI 45101 ALAJUELA POASITO 009 SON 084 02W 1600 CRI 45102 ALAJUELA (1) FABIO BAUDRIT MORENO 010 01N 084 16W 840 CRI CRI CRI
45103 45104 45105
CARTAGO (2) ALAJUELA (3) CARTAGO (1)
DURAN FRAIJANES ALTURA
009 010 009
56N 15N 56N
083 084 083
52W 15W 52W
2337 1650 2200
CRI SLV
45106 45201
ALAJUELA (2) SL SALVADOR
NARANJO EL SALVADOR
010 013
06N 35N
084 089
22W lOW
900
GTM 45301 QUEZALTENANGO LABOR OVALLE 014 52N 091 30W 2407 GTM 45302 CHIMALTENANGO-1 CHlMALTENANGO 014 39N 090 49W 1790 GTM 45303 CHIMALTENANGO-2 PATZUN 014 39N 090 49W 2035 HND 45401 SAN PEDRO SULA SAN PEDRO SULA 015 31N 088 01W HND 45402 TEGUCIGALPA TEGUCIGALPA 014 12N 087 09W
81�
Cnt • ew state City Lat • Long. Elev. Abb. stat
NIC 45501 MANAGU MANAGUA 012 ION 086 21W NIC 45502 MATAGALPA ESTANCIA CORA 013 OON 085 OOW 200 NIC 45503 JINOTEGA JINOTEGA 013 17N 086 11W 1000 CUB 47501 SIBONEY HAVANA 023 08N 082 21W DOM 47701 SANTIAGO (2 ) SANTIAGO 019 34N 070 46W DOM 47702 SANTIAGO ( 1 ) QUINIGUA 019 31N 071 46W 148 DOM 47703 AZU AZUA 018 23N 070 SOW 25 JAM 48101 KINGSTON HOPE GARDEN 018 OON 076 46W JAM 48102 LAWRENCEFIELD LAWRENCEFIELD 017 SON 000 77W 15 PRI 48501 MAYAGUEZ 018 17N 067 08W TTO 49001 ST. AUGUSTINE NCL 51501 BOURAIL 021 OOS 167 OOE THI 51901 PAPARA 017 305 149 30E 2 AUS 55001 SA (2) ADELAIDE 034 58S 138 38E 123 AUS 55002 QLD TOOWOOMBA 027 30S 151 58E 666 AUS 55003 VIC (2) WERRIBEE 037 54S 144 38E 23 AUS 55004 SA (1) ROSEWORTHY 034 32S 138 40E 60 AUS 55005 NSW (1) TAMWORTH 031 05S 151 56E 600 AUS 55006 NSW (3) WAGGA WAGGA 035 07S 147 22E 22 AUS 55007 VIC (1) HORSHAM 036 45S 142 15E 138 AUS 55009 VIC (1) HORSHAM 036 43S 142 12E 138 AUS 55008 NSW (2) CASTLEHILL 033 44S 150 10E 122 NZL 56010 MANAWATU PALMER5TON NTH 040 23S 175 37E 15 NZL 56011 CANTERBURY LINCOLN 043 38S 172 30E 11 NZL 56012 SOUTHLAND GORE 046 07S 168 54E 123 BRA 60001 DF BRASILIA 015 45S 047 47W 1000 BRA 60003 DF BRASILIA 015 35S 047 42W 1000 BRA 60002 SALVADOR BAHIA 013 OOS 038 38W 3 BRA 60004 M. GERAIS SAO GOTARDO 019 30S 046 OOW 1100 BRA 60005 SAO PAULO (2 ) GUAlRA (FAZENDA MATEIRO) 020 20S 048 18W 490 BRA 60006 SAO PAULO (1) CRUZ ALTA BRA 60007 5TA. CATAR. ( 1 ) CAMPOS NOVOS 000 34S 051 13W 920 BRA 60101 RGS (3) CRUZ ALTA 028 38S 053 36W 473 BRA 60119 RGS ( 3 ) CRUZ ALTA 028 38S 053 36W 473 BRA 60102 RGS (1) PASSO FUNDO 028 15S 052 24W 684 BRA 60112 RGS (1) PASSO FUNDo 028 15S 052 24W 684 BRA 60124 RGS (1) PASSO FUNDO 028 15S 052 24W 684 BRA 60129 RGS (1) PASSO FUNDO 028 15S 052 24W 684 BRA 60103 SAO PAULO (3) CAMPINAS 022 53S 047 04W 663 BRA 60104 PARANA (1) LONDRINA 023 22S 051 lOW 540 BRA 60110 PARANA (1) LONDRlNA 023 22S 051 lOW 540 BRA 60105 PARANA (6) PONTA GROSSA 025 06S 050 lOW 868 BRA 60116 PARANA (6) PONTA GROSSA 025 30S 050 20W 900 BRA 60106 RGS (8) BAGE 031 20S 054 05W 214 BRA 60107 RGS (6) PORTO ALEGRE 030 08S 051 08W 6 BRA 60108 PARANA (4) PALOTINA (OCEPAR) 024 17S 053 50W 300 BRA 60117 PARANA (4) PALOTINA 024 18S 053 SSW 310 BRA 60109 PARANA (5) CASCAVEL (OCEPAR) 024 56S 053 26W 760 BRA 60111 MS DOURADOS 022 14S 054 4'9W 452 BRA 60113 SAO PAULO (4) CAPAO BONITO 024 OOS 048 20W 710 BRA 60114 RGS (4) SAO BORJA 028 39S 056 OOW 96 BRA 60115 PARANA (2) FAXINAL 024 OOS 051 21W 730 BRA 60118 PARANA (7) MORRETES 025 30S 048 49W 59 BRA 60120 RGS (2) VACARIA 028 30S 050 56W 955 BRA 60122 RGS (2) VACARIA 028 30S 050 56W 955 BRA 60121 PARANA (3) GUAlRA 024 05S 054 15W 230 BRA 60123 RGS (7) DA PALMA 031 OOS 052 30W 127 BRA 60125 STA. CATAR. ( 2) MAFRA 026 lOS 049 80W 800 BRA 60126 PEDRAS ALTAS 031 40S 053 35W 300 BRA 60127 PEDRAS ALTAS 031 41S 053 45W 250 BRA 60128 RGS (5) JULIO DE CASTILHO 029 13S 053 40W 516 ARG 61001 BA (4) BALCARCE 037 45S 058 18W 130 ARG 61002 BA (7) LA DULCE 038 20S 059 40W 72
82�
Cnt Hew state City Lat. Long. Elev. Abb. stat
ARG ARG ARG
61014 61003 61004
BA (7) BA (1) CORDOBA
LA DULCE (BUCK) PERGAMINO MARCOS JUAREZ
038 033 032
205 565 425
059 060 062
OOW 33W 07W
72 65
110 ARG 61005 TUCUMAN SAN MIGUEL DE TUCUMAN 026 485 065 12W 460 ARG ARG ARG
61006 61007 61008
BA (5) CHACO (1) BA (8)
BORDENAVE R.S. PENA TRES ARROYOS
037 026 038
515 525 235
063 060 060
01W 27W 16W
212 91
109 ARG 61009 PARANA ENTRE RIOS 031 50S 060 31W 110 ARG ARG
61010 61011
BA LA
(3) PAMPA
PLA (CRIADERO KLEIN) ANGUIL
035 036
01N 30S
060 063
15W 59W
55 165
ARG ARG ARG
61012 61013 61015
BA (6) BA (9) CHACO (2)
TRES ARROYAS (CHACRA) NECOCHEA (CARGILL) COL. BENITEZ
038 038 027
lOS 29S 255
058 058 058
OOW 49W 56W
50 20 54
ARG ARG CHL CHL CHL CHL CHL CHL
61016 61017 61201 61202 61203 61204 61205 61206
BA (10) BA (2) CAUTIN (1) NUBLE SANTIAGO (3 ) SANTIAGO ( 1) SANTIAGO ( 2 ) RANCAGUA
BAHIA BLANCO (ACA) CASTELAR TEMUCO CHILLAN (QUlLAMAPU) HUELQUEN LA PLATINA PIRQUE GRANEROS
038 034 038 036 033 033 033 034
295 405 41S 31S 51S 27S 40S 03S
061 058 072 071 070 070 070 070
54W 26W 25W 55W 41W 38W 36W 42W
159 22
200 217
39 629 654 479
CHL 61210 RANCAGUA GRANEROS 034 03S 070 42W 500 CHL CHL
61207 61208
CAUTIN (2) VALLENAR
GORBEA (BAER ) HDA LAS VENTANAS
039 028
12N 34S
072 071
80W OOW
90 348
CHL 61209 HIDANGO 034 07S 071 44W 120 CHL 61211 LA PAMPA OSORNO 040 52S 073 12W PRY PRY
61401 61403
CORDILLERA CORDILLERA
CAACUPE CAACUPE
(IAN) 025 025
355 24S
057 057
32W 06W 228
PRY PRY PRY
61402 61404 61405
ITAPUA (2) ITAPUA (1) ALTO PARANA
CAP. MIRANDA CAP. MESA (LAPACHO) HERANDORIAS
027 026
17S 265
055 054
49W 41W
200 294
PRY 61406 CAAGUAZU CAMPO A. PRY 61407 SAN PEDRO VOLENDAM URY 61501 COLONIA LA ESTANZUELA 034 20S 057 41W 81 URY URY
61502 61503
SALTO SALTO
(2) (1)
SALTO SALTO
034 031
20S 20S
057 058
41W OOW
81 50
URY 61504 MONTEVIDEO SAYAGO 034 45S 056 lOW 0 URY URY
61505 61506
RIO RIO
NEGRO NEGRO
(1) (2)
YOUNG (LA ESTAN. ) MENAFRA
032 033
40S lOS
057 058
41W lOW
50 50
BOL 63001 POTOSI BETANZOS 019 40S 065 30W 3450 BOL BOL BOL
63002 63003 63004
COCHABAMBA SANTA CRUZ POTOSI
( 3 ) (1 )
SAN BENITO ABAPO IZOGZOG CHINOLI
(CORGEPAI) 017 016 019
30S 30S 34N
066 068 065
06W 08W 24W
2730
3450 BOL BOL BOL BOL COL
63005 63006 63007 63008 63101
SANTA CRUZ ( 3 ) COCHABAMBA (2 ) SANTA CRUZ (2 ) COCHABAMBA (1) CUNDINAMARCA
ABAPO IZOGZOG PAlRUMAN I SAAVEDRA LA VIOLETA TIBAITATA
(CORGEPAI) 018 017 017 017 004
39S 21S 14S 205 04N
063 066 063 066 074
01W 19W lOW 33W 12W
386 2584
320 2680 2550
COL 63102 NARINO OBONUCO 001 13N 077 16W 2710 COL 63103 META 004 07N 073 30W 330 COL 63104 CESAR MOTILONIA 010 02N 073 13W 130 COL 63105 NARINO lMUES ECU ECU
63201 63202
PICHINCHA AZUAY
( 1 ) STA. CATALINA CUENCA
000 002
225 50S
078 078
33W 50W
3050 2370
ECU 63203 CARCHI EL ANGEL 000 40N 077 50W 3110 ECU ECU
63204 63205
PICHINCHA PICHINCHA
( 2 ) STA. CATALINA COTOPAXI
(EL PUGRO) 000 22S 078 33W 3200
GUY 63401 KINGSTON GEORGETOWN 006 46N 058 lOW PER 63501 ANCASH ANCASH 009 15S 077 40W 2600 PER 63502 JUNIN HUANCAYO 012 05N 075 08W 3245 PER 63503 LIMA (2) LA MOLINA 012 05S 076 57W 251 PER 63504 CAJAMARCA (2 ) CAJAMARCA 007 08S 078 39W 2600 PER 63505 PUNO SALCEDO 015 535 070 OOW 3835
83�
Cnt Hew stat. City Lat. Long. Elev. Abb. stat
PER 63506 CUSCO (2) ANDENES 013 27S 072 16W 3391 PER 63507 AREQUIPA SANTA RITA 016 30S 072 lOW 610 PER 63508 CUSCO (1) TARAY 013 27S 072 16N 2910 PER 63509 AYACUCHO 013 13S 072 12W 2720 PER 63510 CAJAMARCA ( 1 ) CAJABAMBA PER 63511 LIMA (1) HUARAL DONOSO 011 28S 017 14W 182 VEN 63701 ARAGUA (1) CAGUA 010 llN 067 30W 444 VEN 63702 ARAGUA (2) 010 15N 067 36W 450
Appendix 6--Genotypes (Sorted by Common Name) Used in ISWYNs, LISting as of February 1992
Many selection histories and crosses were unavailable from ISWYN reports. We will use WPMS to check the list beginning on page 85 for errors and to extract additional stored information, including synonyms and missing selection histories where possible. Abbreviations will be checked against the approved WPMS list. However, as historical information is incomplete, we would appreciate notification of errors and receiving missing information. Please send these to the first named editor of this document.
84�
Appendix 6••Genotypes Used in ISWYNs
Uniq. Abbr.
• 1267 5149
209 11IS9 185 340013 210 521/69 211 715/70
1001 77 .13 1002 79.218 1003 25IS21
159 m789
87 ARZ
1114 ATA
240 AGB3
1005 AINT 1229 AKBAR
1006 tel7
1083 ALMR
1155 ALD
1157 ALD Sl
1156 ALD S2
1153 durum5
1007 Altar 1227 ALUBUC
1004 ALVA 305 ANA
1132 AGS
1259 ANT
21 ANZA
191 ARI 213 ARV
1154 AUFN
89 AZ
1008 BL1022 1009 BL1044 1059 BL1049 1264 BR15
1150 BR16
192 BT2281
193 BT2288
Common HIUIl• Cross Selection History
(tel)/Anza*8 6A250(tel)/ANZA*8 S149-3-15-3-5-3-5-2-0(lR)
309 3400-1-3 521/69 715/70 77-13 79-218 793-3402 7C//TOB/NAPO 7C//TOB/NAP063
CM789-21-1A-2Y-OM ARZ MAY054E/LR64//TAC/3/LR64//TZPP/Y54
II21419-288 ATA 81 KVZ/CUT75
SE1066-9S-1S-6S-0S-7KE-OKE Abu-Ghraib #3 SN64/KLRE
IIl9975 Aintree Akbar RON/TOB
CM7705-3M-IY-2M-2Y-OY Alamos 83 (tel) ABN"R" /MIA
X24SS1 Almansor 1 E4870/C306//M5392-66.5/3/BB//CC/
INIA66 Alondra "S" D6301/NAI60//WRM/3/CNO*2/CHR
CM11683 Alondra "SOIA D6301/NAI60//WRM/3/CNO*2/CHR
CM11683-A-1Y-1M-3Y-2M-OY Alondra 4546 D6301/NAI60//WRM/3/CNO*2/CHR
CM11683 Altar 84 (durum) RUFF/FG//MEXI75/3/SHWA
CD22344 Altar INIAP Alubue IAS58/IAS55//ALD/3/MRNG/4/ALD/
CM74362-(1-10)M-07Y IASS8.103A//ALD/5/BUC 026M-SY-1B-OY
Alva Anahuac 75 1I12300//LR64/81S6/3/NOR
II30842 Angus Th/2*Supreza/3/Fn//Kenya58/N/7/
pembina/Fn/5*Th/6/Mida//Kenyal17A/ 2*Th/3/Fn/4*Th/4/MNIII584/5/ Kenya58/N//3*Le
Antizana CNO/GLL II27829-19Y-2M-4Y-OM
Anza LR/N10B//3*ANE II8739-4R-1M-1R�
Ariana 66 K338/EDCH� Arvand� Aurifen 908/FN*2//4160/3/YT54/N10B/4/2*C14�
CH7817-3P-4P-1P-2P-1P� Azteea 67 PI/CHR//SN64�
II199S7-18M-1Y-3M-9Y� BL 1022� BL 1044� BL 1049� BR 15 AS54*2/TOKAI80//PF6913�
P73387-1P-37F-OF-OR-1F-OR� BR 16 PAT70402/ALONDRA//PAT72160/ALONDRA�
B19789-H-508M-1Y-10F-701Y-1F-700Y� BT 2281 TZPP/SN64//LR64/SN64/3/�
SON64A/SKE*3/AN� BT 2288 TACUARI/PJ62�
85�
Uniq.
•� 1010 1219
1075
1225
703
1221
90
139
1086 1232
1011 160
1171
362
1159
1244
126
157
135
94
161
162
164
1183
1184
1188
1185
199
46
47
93
1170
1
Abbr.
BT501 m58766
cl9268
BCN
tc13
BAU
BJ66
BAJ66S
BALA BYA
BANKS BAN
BAN83
BART
BTU
durum3
BB#l
BB#2
BB#3
BB#4
BB PR1
BB PR2
BB S#7
BOW
BOW Sl
BOW S2
BOW#l
BON
BZA55
BZA63
BCH
BUC
BMAN
COJlllDOn Rame Cross Selection History
BT 501 BUC/PVN BUC/PAVON
CM58766-l8Y-3M-5Y-2M-OY BUCKY/TOB/CNO BUCKY//TOB/CN067
CID19268 no selection history Bacanora 88 JUP/BJY//URES
CM67458-4Y-1M-3Y-1M-5Y-OB Bacum (tcl) MAYA II/ARM
X2832-24N-3M-7N-4M-OY Bagula TTR/JUN
CM59123-3M-1Y-3M-1Y-3M-OY Bajio 66 SN64//TZPP/NAI60
1I18889-4M-1Y-1M-3Y Bajio 66 "s" SN64//TZPP/NAI60
1I18889-4M-1Y-3Y Balaka E5557/H 845 Balanya 80 WE/GTO//KAL/BB
CM8288 Banks Barani 70 PI62/GB55//C271
PK50-18A-4A-13A Barani 83 BB/GLL/3/GTO/7C//BB/CNO
CM32347-3M-1Y-1M-1Y-1B-OA Barkat BB/GLL//CARP/3/PVN
CM33483-C-7M-1Y-OM Baturira TZPP*2/AN64//INIA66/3/CNO/JAR//KVZ
CM21335-C-9Y-3M-1Y-1Y-IY-OB Bittern "s" (durum) JO/AA//FG
CM9799-126M-1M-4Y-OY-OM Bluebird #1 CNO//SN64/KLRE/3/8156
1I23584-15Y-6M-OY Bluebird #2 CNO//SN64/KLRE/3/B156
1I23584-26Y-2M-1Y-OM Bluebird #3 CNO//SN64/KLRE/3/8156
1I23584-26Y-2M-2Y-OM Bluebird #4 CNO//SN64/KLRE/3/8156
1I23584-26Y-2M-3Y-2M-OY Bluebird Pak Res1 CNO//SN64/KLRE/3/8156
1I23584-303M-OY-11A-8A-2304 Bluebird Pak Res2 CNO//SN64/KLRE/3/8156
1I23584-303M-OY-11A-1A Bluebird Sam #7 CNO//SN64/KLRE/3/8156
1I235B4-102M-OY-6M-OY-1T-OT Bobwhite AU//KAL/BB/3/WOP
CM33203 Bobwhite "s" AU//KAL/BB/3/WOP
CM33203-F-4M-4Y-1M-1Y-OM Bobwhite "5"2 AU//KAL/BB/3/WOP
CM33203-K-9M-2Y-1M-1Y-2M-OY Bobwhite #1 AU//KAL/BB/3/WOP
CM33203-K-IOM-7Y-3M-2Y-1M-OY Bonanza PI/CHR//SN64
II19957-18M-1Y-3M-(?) Bonza 55 Y50/KT48 1I2254-2p-l1lb-4b-lb
Bonza 63 RN/2*BZA55 VI-36-2-30b-2t-1b
Brochis "5" CNO/BB//CDL/4/7C/3/LR64/INIA66// CM5872-C-1Y-IM-3Y-OM INIA66/BB
Buck Buck BY/MAYA/4/BB//HD832.5.5/0N/3/CNO/PJ CM31678-R-4Y-2M-24Y-1M-OY
Buck Manantial RMAG/BQQ
86�
Uniq.
•� 1074
1161
1076
175
231 166 167
62 63 64
1133 1012 1149
313 367 375
104
103
1234
35 1013 1201
95
152
1014 96
31 194
1015 39
1087 1182
168
1123 65
215
1279
1016
Abbr.
BMAP
BOMB
BPAT
BUL
BTT C271 C273 C306 C518 C591 C7659 C8055 CEP14
CGT700 CGT705 rn1221
rn8943
i30793
rn83271
CT244 te15 CAETES
CAL
CAL A
te14 CAN
CZHO CRT
CTAN CTFN
CH74 CHAT
CHB
CHENAB CHL
CHRC
te16
CHIV
COllDDOn Raae Selection History
Buek Mapuehe
Buek Ornbu
CM26339-E-4Y-IY-2Y-IM -15Y-3LD
Buek Pataeon
Bulbul PK2858-7A-3A-4A-OA
Butte C271 C 273 C 306 C 518 C 591 C 7659 C 8055 CEP 14 Tapes
B13374-3Z-1A-6A-2A-OS CGT 700 CGT 705 CM1221
CM1221-57M-3Y-OM-KOOO CNO/7C//CNO/INIA/3/SX
CM8943-F-1M-4Y-OM CNO/PJ62/GLL
II30793-1M-2R-2M-OR CNO/PRL
CM83271-18Y-1B-5Y-1B-OY CT 244 Caborea 79 (tel) Caete "s"
CM39992-8M-3Y-1M-OY Calidad
II22429-16M-IY-IM-OY Calidad S A
II22429-11M-IY-IM-OY Cananea (tel) Canario "s"
CM2097-31M-IY-IM-4Y-OM Carazinho Carthage
II28071-7M-3Y-7M-OY Castan centrifen
CM7920-9R-1M-2R Chapingo 74 Chat
CM33090-M-4M-2Y-5M-OY Chenab 70
PK146-12A-4A-IA Chenab 79 Chhoti Lerma
II15929-1M-4Y-2M Chiroea
CM8963-A-IM-IY-IM-5Y6M-OY
Chiva (tel) X24551-8Y-3M-IY-OM
Chivito
Cross
BUCK PANGARE/3/RAFAELA MAG/BUCK PAMPERO//BUCK RELEN
CNO/GLL/TOB/BMANI/BB/4/TXPP/ SON64//TZPP*2/AN
CAEREN 2.4.2/3/RAFAELA MAG/ B. PAM//BAGE/K. PETISO
PI/FROND/3/PI/MZ//MXP
C230/NP165 C519/C209 RGN 1974/CHZC3//2*C591/3/P19/C281 PB TYPE 8A/PB TYPE 9 PB TYPE 8B/PB TYPE 9 UNKNOWN
PEL72 3 80 / ATR71
CAN/JAR66 TOB/CI CNO//NAD63/CHR/3/S0N/KLRE//EN
CNO/7C//CNO/INIA/3/SX
CNO/PJ62/GALLO
CN079/PRL
JUP/BJY
TZPP/SON64A/TZPP//ANE
Tzpp/sON64A/TZPP//ANE
HK/38MA//2*RFN/90S/3/FN/4/YR
COLONISTA/FRONTANA NAPO/TOB//S156
YAQUI53/N10B//2*LR
FRATERNAL SELECTION KVZ/TI//TITO
C271/WILE//SN64
PK3563/cH70 LR64/HUAR
SD648.5/8156/3/CHR//SN64/KLRE/41 BB/CAL/5/ZBZ
ABN"R" /MIA
87�
Uniq. Abbr. Common Hame Cro•• Selection History•
99 CLI Choli 21931/CH53//2*LR64/3/8156/4/NAR59 1I21515-1P-lP-3P-5M-OY
1194 CVA Chova GLL/YR RESEL(B)/3/AU//KAL/BB CM34603-A-1M-3Y-3M-1Y-1M-OY
202 CHR Chris FRONTANA/3*THATCHER/3/KEN58/ NEWTHATCH//2*THATCHER
1017 CHUM18 Chuan Mai 18 1158 CKR Chukar PTS/3/TOB/CTFN//BB/4/BB/HD832.5.5/0N
CM20769-A-8Y-1M-2Y-5M-1Y-OM 98 CN067 Ciano 67 PI/CHR//SN64
I119957-18M-1Y-3M-9Y 131 CN067A ciano 67 "SUA PI/CHR//SN64
1119957 200 CN067B Ciano 67 "SUB PI/CHR//SN64
II19957-18M-2Y-2M-1Y-7M-1Y 97 CN067C Ciano 67 "s"c PI/CHR//SN64
1I19957-18M-2Y-6M-1Y-2M-1Y 317 CN079 Ciano 79 BY/MAYA/4/BB//HD832.5.5/0N/3/CNO/PJ
CM31678-R-4Y-2M-21Y-OM 100 CGN Ciguena SN64*2//TZPP/TZPP/Y54/3/AN64A/4/
1I21406-6-2-300Y-301M-OY 2*FR//Y/KT 1275 CISI Cisne INIA KVZ/CJ
SWM1430-4Y-3Y-OM 101 CLEO Cleopatra 74 BB/3/2*INIA66/NAPO//II20350/4/F2
CF258-30R-4M-OT 114 COC Cocoraque 75 I112300//LR64/8156/3/NOR
II30842-58R-1M-4Y-OM 1018 durum2 Cocorit (durum) RAE/4*TC60//STW63/3/AA
D27617 369 COOK Cook 217 COW Cowbird KLAT//INIA66/BB/4/NP876/PJ//CAL/3/BB
CM16716-M-3M-2Y-3M-OY 48 CPO Crespo 63 FR/4/N/3/2*MT/K//BAGE/5/GB60
11l1263-3T-1B-2T-1B-1T 201 CR1M crim THATCHER/3/THATCHER*3/KLEIN
T1TAN//KENYA 58/NEWTHATCH 1205 CROW CrownS" FR316/3/MCM/KT//Y50/4/ZA75/S/BJY
CM40457 1193 CZI Cruz Alta 1NTA KVZ/BON/7/2193/CH53//AN/3/GB/4/
CM33729-A-1M-2Y-1Y-3Y PJ/5/S0TY/6/YR RESEL -OM-1J-OS-?
230 HMN Cuckoo "S"A 7C/ON//1NIA66/BMAN 1I28428-8Y-1M-1Y-1M
228 CUC RS Cuckoo ReSel 7C/ON//INIA66/BMAN II28428-8Y-1M-OM-(1-16B)
1207 CUPE Cucurpe S 86 HIM/COC//NAC CM41195-A-13M-2Y-3M-1Y-1M-OY
1208 CUPE S Cucurpe S 86 "S" HIM/COC//NAC CM41195-J-7M-1M-OM
1073 CUVA Cukurova 86 BB/KAL CM9160-11M-5Y-6M-OY
1222 CUMPAS Cumpas T88 A PRL/VEE#6 CM64624-2Y-1M-4Y-OM-16Y-OM
1282 CUMPAB Cumpas T88 B PRL/VEE#6 CM64624-2Y-1M-4Y-OM-97Y-OM
1091 DWR39 DWR 39 HD(M)1508/S308 2 DTE Diamante INTA SN64/TZPP//NA160/3/NAR59
II21872-9T-2B-3T-2B 195 DGA Dougga KLPE/RAF//2*8156(R)
1I23997-4Y-100M-300Y 3 ELGA El Gaucho SINVALOCHO MA//RICCO/LIN CALEL MA
88�
Uniq.
•� 107
204
1215
242 1144
1273
1211
1218
222
1238
1239
323
1090 23
4 1191
1186
1175
55 56 57 58
241 1181
221 243
1019 66
1095
1092 1094 1093
1096
1097 1098 1089
1085 1177
Abbr.
EMU
ERA
ESDA
EDAK FAN1
wm4578
FSD85
FALKE
FLK SA
FLK SB
FLK SC
FLK SD
G162 GB GTO GALVEZ
GARA
GEN
GZ139 GZ144 GZ150 GZ155
GLE GLEN
GLI74 i41593
HB501 HD1941 HD2172
HD2177 HD2236 HD2278
HD2281
HD2329 HD2402 HI977
HP1209 HUW206
Common Hame Cross Selection History
Emu TOB/NAPO//NO/ERA/3/BB/GLL CM8327-9M-1Y-1M-1Y-1M-OY
Era II-50-10/4/PEMBINA/II-52-329/3/II-5338/II-58-4//II-53-5EN
Minn 11-62-64 Esmeralda 86 BUC/BJY
CM49641-9Y-1M-1Y-5Y-OM Estanzuela Dakaru LEE/ND34 FAN #1
-33B-OY FLN/ACC//ANA FLN/ACC//ANA
SWM4578-56M-3Y-3M-2Y-OM Faisalabad 85 MAYA/MON//KVZ/TRM
CM44083-N-3Y-1M-1Y-1M-1Y-OB Falke BUC/4/TZPP//IRN46/CN067/3/PRT
CM56744-7Y-2Y-1M-1Y-OM Flicker "S"A WE//CNO/NO/3/ZBZ
CM8954-B-7M-1Y-1M-OY Flicker "S"B WE//CNO/NO/3/ZBZ
CM8954-B-7M-1Y-1M-1Y-OM Flicker "S"B ReSel WE//CNO/NO/3/ZBZ
CM8954-B-7M-1Y-1M-1Y-OM-(1-18B) Flicker "S"D WE//CNO/NO/3/ZBZ
CM8954-B-7M-1Y-1M-1Y-OM-2PTZ-OY G 162 GUIZHOU LARGE HEAD NO.7/YANGMAI NO.3 Gabo BOBIN W39//BOBIN W39/GAZA Gaboto BAGE 2018//F44/SVL/3/BAGE 1971.37 Galvez 87 BB/GLL//CARP/3/PVN
CM33483-C-7M-1Y-OM-5B-OY Gara AU//KAL/BB/3/WOP
CM33203-K-9M-19Y-3M-3Y-OM Genaro 81 KVZ/BUHO//KAL/BB
CM33027-F-12M-1Y-6M-OY Giza 139 HINDI 90/KENYA B 256 Giza 144 REGENT/2*GIZA 139 Giza 150 MIDA/CADET Giza 155 REGENT/2*GIZA 139//MIDA/CADET/
2*HINDI 62 Glenlea PEMBINA*2/BAGE//CB100 Glennson 81 KVZ/BUHO//KAL/BB
CM33027-F-8M-1Y-8M-1Y-2M-OY Gloria 74 H/2*RAF2 H/2*RAF2
II41593-1R-3M-1S-2M-OS HB 501 HD 1941 E5477/SN64 HD 2172 HD2160/5/TOB/CNO//BB/2/NAI*2//
TT/SN64/3/HD1954 HD 2177 HD1962//E4870/K65/3/HD1593 HD 2236 HD2119/HD1981 HD 2278 HD2119/HD1912/HD1592//3/
HD1962//E4870/K65 HD 2281 HD2160/HD1912/HD1592//3/
HD1962//E4870/K65 HD 2329 HD2252/UP262 HD 2402 HD2267/HD2236 HI 977 GLL/AUST II 61.157//CNO/N066/3/
Y50E/3*KAL HP 1209 E4871/PJ62 HUW 206 KVZ/BUHO//KAL/BB
CM33027-F-15M-4Y-4M-2Y-2M�
89�
Uniq.
•� 1108 1084 1020 1099
1192
1246
82
79
370 364 218
108
1237
366
40 1112 1113 1077 1101
226 165
109
169
81
34 1064 1249
1130
1104
187 188
86 41
1115 1202
1252
1134 32
1168
1021 1022
Abbr.
HUW37 HUW55 HW135 c19343
HAHN
HAVIK
HZ1l2
HZ2152
HZ806 HZ695 HORK
HORK A
HORK B
HUAC
HQN i 78112 IA7846 IA7837 IAC24 IA554 i30565
i35953
i26478
l1I511
121520 17I58 241538
251523
251541
51531� 51544� 5I549� 81513�
91537 CAETE
IA18
IA6 IA520 5ARA
ILNZ IBB
Common Kame CroBB Selection History
HUW 37 KAL/5331//H01982 HUW 55 E4870/H01982//INIA66/3/H02189 HW 135 HY5LOP/PVN HYSLOP/PVN
CI019343 no selection history Hahn CBC148/KA/7/HK/MOA38/4/4777/3/
CM33682-L-IY-IY-4M REI//Y/KT/5/YR/6/TUC 4Y-I00B-501Y-500B-OY
Havik VEE/H499.71A//4*JUP CMH80A.383-1B-IY-IB-IY-IB-IY-OB
Hazera 112 21931/CH53//ANOE5/3/GA56/4/AN64 1I2985-5H-2H-IIH
Hazera 2152 YT54A 3*/NI0B II8474-A-8T-12B
Hazera 806/1976 Hazera 895 Hork HOP/RON//KAL
CM8874-K-IM-IY-IM-2Y-OM Hork "5"A HOP/RON//KAL
CM8874-K-IM-1Y-OM Hork 5 Re5el HOP/RON//KAL
CM8874-K-IM-IY-OM-(1-356Y)-(l-200B) Huacamayo YR//5N64/NT5207.85/3/CNO/7C//GTO
CM8671-B-IM-IY-IM-IY-IM-OY Huelquen MO/MCM//EXCHANGE IA 78112 KVZ//CNO/PJ IA 7846 KVZ/3/CN067/CHR//ON IA 7873 CNO/GLL lAC 24 IA551/4/5N64/Y50E//GTO/3/2*CNO IA5 54 IA516/4/Y53/NI0B//Y50/3/KT54B 1I30565 CC/INIA66/3/CNO//ELGA/5N64
II30565-22M-OY-OT 1I35953 INIA66/NAR59/3/CIN/PJ62
1I35953-2R-5M-4R-OM INIA/BB INIA/BB
1I26478-74M-OY-4T-IT-OT 15WYNll Ell SN64/TZPP/3/NAI60//ST464/BZA/4/ not in PMS 21931/3/CHA53/AN//GB55/5/AN64
ISWYN12 E20 ISWYN17 E8 ISWYN24 E38 BUHO/5/SN64/TZPP//Y50/3/NAPO E-II-75-1935-1E /4/LAC617(67A) 9E-IE-IE-IE-OE
ISWYN25 E23 TG//BJ66/INIA66/3/15WRN74-141 not in PMS
ISWYN25 E41 INIA66/5/ELGA/SN64/4/ not in PMS TG/3/5N64//TZPP/NAI60
ISWYN5 E31 L 1418-3463L 1231 x 23L1274-111 (L) ISWYN5 E44 36896/2*CJ54//YT54 A(H) ISWYN5 E49 4265/HD3/4/MO//K/Y/3/WIS/SUP F6 ISWYN8 E13 MARA//(SUPREMO-MENTANA-MCM) ? not in PMS
ISWYN9 E37 LEE/NI0B//GB55/3/GB56 Iapar 17 Caete JUP/BJY
CM39992-8M-7Y-OM Iapar 18 Marumbi PF72640/PF7326//PF7065/ALONORA
Fl1933-0-500M-5n3Y-502M-500Y-lG-OG Iapar 6 Tapejara UNKNOWN CROSS Iassul 20 COLONIAS//FN/K58 Icta Sara 82 TAST/4/TP//CNO/NO/3/CNO/7C/5//JUP
CM3089-G-IY-4M-IY-3M-IY-OY� Iliniza� Imbabura�
90�
Uniq. Abbr.
• 1169 IMU
1122 IND66
110 INIA66
1255 ITP25
1196 ITP30
115 m4900
301 JAH
17 JAR SA
6 JAR SB
5 JAR SC
112 JAR
1121 JM4058 1190 JUN
113 JUP
1212 JUR
205 IN
1265 PBTI0
7 c6552
123 KALBB
116 KALBBR
1136 BAW28 1224 KAUZ
1223 KAUZ S
1272 KVK
1023 K61063 1024 KKI
85 KLD
1138 KNN
1025 KPAA 1110 KTUM
1139 KYB79 1131 KITT
CORlOn Hame Cross Selection History
Imuris T79 BY/MAYA/4/BB//HD832.5.5/0N/3/CNO/PJ CM31678
Indus 66 PJ/GB55 II8156
Inia 66 LR64/SN64 II19008-83M-I00Y-I00M-I00Y-I00C
Itapua 25 PI/3/LR64//TZPP/KNTT2 III8790-1R-IT-2Y-IC
ltapua 30 JUP/ALD CM36867-18Y-21M-3Y-OM-OE
JP12867/PL J9128.67/PALOMA CM4900-5Y-IM-3Y-OM
Jahuara 77 TZPP/PL//7C CM5287-J-IY-2M-IY-OM
Jaral "S"(A) SN64//TZPP/NAl60 II18889
Jaral "S"(B) SN64//TZPP/NAl60 IlI8889-6T-4T-2T-IT-2B
Jaral "S"(C) SN64//TZPP/NAl60 II18889-101M-IR-3C-IT-2B-OY
Jaral 66 SN64//TZPP/NAI60 lII8889-101M-IR-3C-4Y
Jinmai 4058 OFN//HUADONG#5/ABD Junco BB/GLL//CARP/3/PVN
CM33483-C-7M-IY-OM-20B-OY-2PTZ-OY Jupateco 73 III2300//LR64/8156/3/NOR
II30842-31R-2M-2Y-OM Juriti lAC5/ALDAN
CM46961-16M-113PR-IT-OT Justin THATCHER/KENYA
FARMER//LEE/MlDA/3/CONLEY CI13462
KI34/VEE KI34(60)/VEE PBTI0-IA-3A-OA
KLPE/RAF KLPE/RAF ClD6552 no selection history
Kalyan Bluebird KAL/BB ll26992-30M-IY-IM-3Y-OM
Kalyan Bluebird ReSel KAL/BB II26992-30M-IY-IM-3Y-OM(I-208B)
Kanchan UP301/C306 Kauz JUP/BJY//URES
CM67458-4Y-IM-3Y-IM-3Y-OB Kauz ItS" JUP/BJY//URES
CM67458-4Y-IM-3Y-IM-2Y-OB-3Y-OY Kavko "5" KVZ/3/CNO/CHR//ON
SE375-12S-3S-0S Kenya 6106.3 Kenya Kifaru Kenya Leopard LAGEADlNHO/3*K354P//CII2632/3/3*K354P
1346 A.2 A.l Kenya Nungu WlS245/lI.50.17//Cl8154/2*
FR/3/2*TOB66 Kenya Paa Kenya Tumbili
KTB/GIZAI55//NAD63/T238.1.5.8.17.10//KL ATL/TOB66//CFN/BB Khyber 79 WREN//CNO/GLL/3/C271 Kitt TH/2*SUPREZA/3/FN//K58/N/7/
PEMBINA/FN/5/TH/6/MD//KI17A/2/ ... ?
91�
Uniq.
•� 1260
8
60 1198
1111 170
118
1026 1137
80 1027
1028 117
373 181
1261
1209
1029 999
1030 182
1189
1164
1116 225
1031 1080 1033 1102 1034 1035 1213
9
233
1036 1037
36 37
705 10
223
102
24
Abbr.
KLCB
KLRE
WM1353 KHN
Ij2484 6IS25
i19008
LU265 LV 115682 LKH757
LAP286 LR64A
LIE LMP LINEA
LIRA
LIZ LOCCK LMAI10 LU
m33254
m29251
MLKS11 MN7083 MN7086 MN7357 MN7529 MN7663 m82128 MRSIM MCU
MG41
MGP
MHS18 MLB MITU MITUIS tell MJI
MAYA
MAYA S
MDS
CM43903-H-4Y-1M-1Y-3M-3Y-OB Liz Local Check Long Mai #10 Lundi MARA//LEE/SELKIRK
S595-A1-A6-B2 MAI/PJ62//EMU MAIPO/PJ62//EMU
CM33254-T-1M-1Y-6M-3Y-OM
COJllmOn BaJIl8 Selection History
Klein Chamaeo KL B103-71-20Y8M-llOOYK ?
Klein Rendidor
Kloka WM 1353 Kohinoor
CM37987-I-1Y-5M-OY LAJ 2484 LR/3*P4160
not in PMS LR64/SN64
II19008-52M-6Y-7M-101C LU 265 Lagoa Vermelha Lakhish Line #1568/2 Lakhish Line #757 not in PMS
Lap 286 Lerma Rojo 64A
II8724-8Y-1C-1Y Liesbeck Limpopo Linea E 1979
MSN-F6-79-3E-OE Lira
MAYA/MON CM29251-3M-17Y-4M-OY
MLKS-11 MN 7083 MN 7086 MN 7357 MN 7529 MN 7663 MN 82128 MR SIM Macuta
CM47207-6M-103PR-2T-OT Magnif 41
Magpie CM20668-D-4Y-4M-1Y-OY
Mahissa 18 Malabadi Manitou Manitou Insensitive Mapache (tel) Marcos Juarez Inta
Cross
RELEN/BAGE//KLPE/5/BB/8156/4/ 12300//MASS 5/GTO/3/JAR
KLEIN COMETA/KLEIN33 AG//SINVALOCHO MA/KL H33 AG
OREF1158/FDL//MFO/2*TIBA63/3/COC
KVZ//CNO/PJ LR/3*P4160
LR64/SN64
VERANAPOLIS*2//MARROQUIT/NEWTHATCH
YSO/N10N//L52/3/2*LR
CNO/INIAen SN64//TZPP/NAI60/3/TOKWE
KVZ/TRM/ /PTM/ANA
II19975-68Y-1J-6Y-1J-4Y-1J-OB Maya 74 CNO/GLL
1127829 Maya 74"S" CNO/GLL
II27829-19Y-1M-500Y-501M-OY Mendos EUREKA/CI 12362//2*GABO/3/MENTANA/
6*GABO/4/SPICA/KODA//GABO
MAYA74/MONCHO
MULTILINE OF KALANSONA
CRIM/2*ERA//MN69235
MN6988/MN69169
IAS64/ALDAN
LINEA G/SINVALOCHO MA//SINVALOCHO MA/MAGNIF MG
JAR/NAPO/3/LR64//TZPP/3*ANE/4/ BB/NOR//CNO/7C/3/CAL
TH*7/FN//CANTHATCH/3/TH*6/PI170925
SN64/KLRE
92�
Uniq. Abbr. COllllDOn Mame Cross Selection History•
25 MENG Mengavi 421 durum1 Mexicali 75 (Ourum) GOOVZ469/3/TO//61.130/LOS
CM470-1M-3Y-OM 179 MEX Mexicano 1481 YT54/NI0B 126.1C
1I7064 26 MX Mexico 120 YT54/NI0B
1I7064 78 MX226 Mexico 226 SN64/KLRE
1I19975-68Y-IJ-6Y-3J-2Y 42 MFN Mexifen SN64//6*SKE/3*ANE
1II8903-17M-2R-3C 1197 MNV Minivet"S" BB/CNO//INIA66/S0TY/3/SPRW/4/PVN
CM37705 1148 MN082 Minuano 82 TRINTANI*2/SELKIRK FL33//LINHAGEM
B12598-0A-10A-IA-OA 1100 MTC Mitacore lAS 50/JARAL
120 MO Mochis 73 SON64/KLRE//BB 1I26502-8Y-3M-1T-1M-5S-0M
121 MON Mancha WE/GTO//KAL/BB CM8288-A-3M-7Y-OM
1284 MON SA Mancha S A WE/GTO//KAL/BB CM8288-A-3M-6Y-5M-1Y-1M-OY
1233 MON SB Mancha S B WE/GTO//KAL/BB CM8288�
67 MOTI Mati YT54/NI0B//NP852� 43 c6753 NAR*2IPJ NAR*2/PJ�
CI06753 1118 N0610 NO 610 N0526/KITT//BTT 1038 n51817 NK 7751817 1039 NL410 NL 410 1040 NL459 NL 459
68 NP824 NP 824 W245:44-25-7-5/NP770//C518/NPI65 69 NP832 NP 832 E145 (KENYA)/PISSI LOCAL 70 NP852 NP 852 E1915/NP761//NP761 71 NP880 NP 880 C281/NP790 72 NP881 NP 881
1220 NR861 NR86-1 TTR/JUN CM59123-3M-IY-2M-IY-2M-2Y-OM
1274 NR86II NR86-I1 GOK16/0GA//AU/JTS179/3/VEE SWM12779-BN-BK-6N
1041 n14.13 NS 14.13 1072 n51.28 NS 51.28 AU/7C 1105 n54.17 NS 54.17 JARAL/BEZOSTAJA 1
150 NAC Naeozari 76 TZPP/PL//7C� CM5287-J-IY-2M-2Y-3M-OY�
232 NAC SA Nacozari S A TZPP/PL//7C� CM5287-J-1Y-2M-IY-IM-OY�
151 NAC SB Nacozari S B TZPP/PL//7C� CM5287-J-1Y-2M-IY-4M-OY�
122 NAI60 Nainari 60 SPO/MTA//GB-KEN/3/TH/Q//KENYA/� P4160-6H-3Y-2C MTA/4/GB-KEN
1043 NJG484 Nanjing 4840 1285 NJ2049 Nanjing 82049
224 NAFN Naofen PCH/4/2*KT54A/N10B//KT54B/3/NAR59 T2494-14T-4T-IV
49 NAP0 Napa 63 FR//FN/Y48/3/NAR 1I9314-22T-4B-IT
50 NAR Narino 59 FCR/MCM//KT48/Y48 1I4777-2B-1B-I0T-IT-?
171 NAYAB Nayab 70 1204 NKT A Neelkant "S"A HOI220/3*KAL//NAC
CM40454-11M-4Y-IM-IY-44M-IY-OB
93�
Uniq. Abbr. Common Rame Cross t Selection History
1167 NKT B Neelkant "S"B HD1220/3*KAL//NAC CM40454-1M-4Y-2M-3Y-OM
1044 NG8201 Ning 8201 NINGMAI N04/0LSEN//ALD/YANGMAI N03 1119 NG8319 Ning 8319 NINGMAI NO.4/0LESON//ALD/YANGMAI NO.3 1141 NG8331 Ning 8331 YANGMAI NO.4/NING 7840 1045 NG8401 Ning 8401 1262 NKCH Norkin Churrinche ERA/POLK//TOB
N7568-9A-1G-1G-OA-OG 124 NO Noroeste 66 LR64/SN64
1I19008-52M-4Y-4M-2Y 125 NOR Norteno 67 LR64/SN64
1I19008-52M-6Y-3M-2Y 44 NFN Novafen "s" RULOFEN*2//FKN/N10B
CH10593-2P-3P-6P-1P 1280 tel2 Novojoa "S" (tel) MAYAll/ARM
X2802-38N-3M-6N-5M-OY 1245 OASIS Oasis 86 AGATHA/3*YR
CMH77A.485-8B-5Y-1B-1Y-OB 178 OLN Ollanta
1120 OML Olmill NORIN 721NORIN 12 1203 OPATA Opata 85 BJY/JUP
CM40038-6M-4Y-2M-1Y-2M-1Y-OB 1088 ORSO Orso FUNO/PRODUTTORE
22 OXY Oxley PJ62/GB56//TZPP/NAI60/3/2*WW15 1163 PAI4 PAl 4 INIA66/CAL//INIA66/CC
CM28647-67Y-1M-1M-OY 1046 p73121 PAT 73121 1047 f70100 PF 70100 1048 PF8237 PF 8237 1199 PFAU PFAU HORK/YMH//KAL/BB
CM38212 1200 PFAUA PFAU "s" HORK/YMH//KAL/BB
CM38212-1-7Y-2M-1Y-3M-2Y-OM 176 PK594 PJ62/GB55//NAI60 PJ62/GB55//NAI60
PK594-80A-1A-OA 1049 PR3 PR 3
174 PAK20 Pak 20 21931/3/CH53/AN//GB56/4/AN64 II20985-511-2II-1I1�
172 Pk5725 Pakistan 5725� 173 Pk5747 Pakistan 5747�
51 PAL Palmira 1 TH/STC//FR 1I5962-4T-2B-1T-2B
1254 PPAI Pampa Inta JAR/CHR//CC/JAR H2139-1P-4B-2P-1P-OP
1216 PGO Papago 86 BUC/PVN CM52359-2M-3Y-1Y-2M-1Y-OM
1147 PARA2 Parana 2 2. 71-74T-1T-1T-2T-OY
11 PAR68 Parana 68/1116 216 PAT19 Pat 19 SK/J9281. 67
B530-0J-52C-1C-OC 12 PATOB Pato (B) TZPP/SON64A//NAR59
II21974 13 PATO Pato Argentino TZPP/SON64A//NAR59
1I21974-4R-4M-2B-OY-OP-OY 327 PVN Pavon 76 VCM//CNO/7C/3/KAL/BB
CM8399-D-4M-3Y-1M-1Y-1M-OY 1236 PVNRS Pavon ReSel VCM//CNO/7C/3/KAL/BB
CM8399-D-4M-3Y-1M-OY-(1-26B)-OY 127 PVN1 Pavon Sl VCM//CNO/7C/3/KAL/BB
CM8399-D-4M-3Y-1M-1Y-OM 328� PVN2 Pavon S2 VCM//CNO/7C/3/KAL/BB�
CM8399-D-4M-3Y-1M-OY�
94�
Uniq.
•� 329
330
1235
128
1214
371
14 1160
130
372 91
133
206
1266
1050 1051 1052
363
1257
1231
1253
1140 134
180
212
1053 1128
1270
1054 16
177
138
207
1082 73
Abbr.
PVN3
PVN4
PVN5
PJ
PDZ
PHO
PTS m21692
PI
PLNK PLO
PTM
PRT
PJB81
QT4081 QT4083 QFG2 QZ75
QUM
RABE
RTNI
ROMI RQ
S1103
JIT43
SA75 m40392
s83125
SD2968 c6781
6IS24
i18892
I84690
SADO SL
COJlllllOn Name Cross Selection History
Pavon 53 VCM//CNO/7C/3/KAL/BB CM8399-D-4M-2Y-2M-3Y-1M-OY
Pavon 54 VCM//CNO/7C/3/KAL/BB CM8399-D-4M-3Y-OM-OBK
Pavon 55 VCM//CNO/7C/3/KAL/BB CM8399
Penjamo 62 FKN/N10B II7078-1R-6M-IR-IM
Perdiz IAS58/BJY//BNQ CM47971-A-4M-I05PR-1T-OT
Phoebe CAL/KVZ//TRM CM30831-D-4Y-2M-1Y-OM
Piamontes INTA TH//LA ESTANZUELA/RAF 6MA/3/FN Pionero Inta PV18A/CNO//JAR
CM21692-1B-4B-1B-3B-OB-? pitic 62 YT54/NIOB
II7064-1Y-1H-1R-2M Planinka Pollo BB/GLL/3/CHO/SN64//BB
II35129-26Y-2M-IY-1M-IY-OM
CID6781 no selection history 5N64/C271 SN64/C271
not in PMS SN64/KNTT A SN64/KNTT A
II18892-2M-3Y-5M-2Y SOTY/3*JUSTIN SN64//TZPP/Y54/3/3*JUSTIN
II84690-2Y-6C-6Y-4C-2Y-1C Sado DGA/SN64 Safed Lerma Y50//N10B/L52/3/LR64
IIl5444-118Y-2C
Potam 70 II22402-6M-4Y-IM-1Y-OM
Protor II24908-30M-3Y-3M-OY
punjab 81 PK6841-2A-2A-IA-OA
QT 4081 QT 4083 Qian Feng #2 Quetzal 75
II786-1X-1X-3X-OX-? Quimori
II23584 Rabe "s"
CM79516-025Y-4M03Y-01M-2Y-OB
Retacon Inta H2138-12P-1B-2P-IP-OP
Romi Roque 73
II18883-6M-6R-I1C-1Y S1103
S1103 S331/NOR
JIT-43-2L SA75 SAP/MON
CM40392 5C 83125
SC83125-1H-IH SD 2968 SN//SKE/LR
INIA66/NAP063
TOB/CNO
INIA/3/SN64/P4160(E)//SN64
CNO//SN64/KLRE/3/8156
GOV/AZ//MUS/3/R37/GHL121// KAL/BB/4/ANI
JAR/CHR//CN067
Y50E/8156//KAL/3/TOB/CNO SN64//6*SKE/3*ANE
JARAL/3/MARA//LEE/SK
S331/NOR67
NAI/CB151//S984 SAP/MON
SN64//SKE/LR64A
95�
Uniq.
•� 52
229
1187
38 1180
1142
1146
316 302 136
1217
1055
197
74
361 360
1258
137
140
1277
1056 1057 1109
142
1058 1145
1143
189 1124
1125
196 59
1226
1268
29 30
149
20 190
Abbr.
SAK68
SAP
SRHD
SK SERI
SHA4
SHA5
STA STM 7C
SIND83
SIPA
SOL
SKA
SOND SGL SKI
SN64
SPRW
STAR
11B .15 SUN278 SNLG SX
SU220 SUZ1
SUZ8
SRX T80l7
T8020
T64.2W i22964
m20106
575110
TR236 TR380 i19025
TAC TAlC
CM33027-F-15M-500Y-OM-87B-OY
SWM7215-2Y-2Y-OY-2Y-OY-41M-OY
TOB/CC//PTO/3/BB/GLL TOB/CC//PATO/3/BB/GLL CM7l-20106-10E-15E-4E-1E
TOK*3/S111LAI TOK*3/S111LA1 S75110-2-3
TR 236 IRN 59.111//3*GABO/CHARTER TR 380 TZPP/AN64 TZPP/AN64
1I19025-l00M-101Y-100C-2Y Tacuari MASSAUX#5/GABOTO Taichung 31 SHOAWASE/SAITAMA
COlllDen Naae Selection History
Samaea 68 1I13022-9B-lT-2B-1T
Sapsucker BR69-1Y-3M-OY
Sarhad 82 CM33203-K-9M-24Y-OM
Selkirk Seri 82
Shanghai #4 -14B-OY
Shanghai #5 -7B-OY
Shasta Shortim siete Cerros
1I8156-1M-2R-4M Sindi 83
CM5287 Sipa INIA
CM30697-2M-14Y-OM Soltane
1I19975-68Y-1J-6Y-1J-3Y Sonalika
IIl8427-4R-1M Sonderend Songlen Sonka Inia
1I26502-8Y-6M-2Y-OM Sonora 64
1I8469-2Y-6C-4C-2Y-1C Sparrow
CM2182-5M-1Y-2M-3Y-OM Star
Sun 11B-15 Sun 278 Sunelg Super X
II8156-lM-2R-4M Suweon 220 Suzhoe #1
-5B-OY Suzhoe #8
-31B-OY Syrimex T 8017
T 8020
T-64-2-W TOB/8156
1I22964-3Y-5M-OY
Cross
BZA/2*AFM
INIA66/S0TY//CZHO
AU//KAL/BB/3/WOP
MCMURACHY/EXCHANGE//3*REDMAN KVZ/BUHO//KAL/BB
INIA66/ANZA
PJ/GB55
TZPP/PL/l7c
SIS/PVN
SN64/KLRE
1I54-388/AN/3/YT54/N10B//LR64
SN64/KLRE//BB
YT54/N10B//2*Y54
FN/MD//Kl17A/3/2*COFN/4/SN64/ KLRE/3/CNO//2*LR64/SN64
LFN/SDY//PVN
KITE*4//CS3D/AG#14 PJ/GB55
YT54A/N10B//3*LR64 ROLLO/MAGNIF/4/S0N/TZPP//
NAI/3/MOYSTAD ROLLO/MAGNIF/4/S0N/TZPP
//NAI/3/MOYSTAD K338/ECDH//KOUDIAT 17 KT Y TOB/8156
96�
uniq. Abbr.
• 1165 TAN
143 TI
1060 TEJO 925 barley
III TES
1250 THB
1251 THB S
1166 TSH
1195 TSH S
53 TIBA63
27 TG
307 TTM
145 TOB
147 TOL73
1276 TONI
45 TQFN
148 TRM
368 TOW
1061 TRIG01 28 TDK
1162 TRI
105 TRI A
1228 TUI
1248 TNG
1240 TURA
1241 TURB
1242 TURC
184 TRP
1206 TRT
1135 UP1109 1063 UP201 1126 UP262
75 UP301
76 UP310 219 JIT35
COllllllOn Hame Cross Selection History
Tanager SIS/PVN CM30697-2M-8Y-1M-1Y-1B-OY
Tanori 71 SN64/CNO//INIA66 1I25717-11Y-3M-1Y-OM
Tejo Tequila MINN126/CM67
CMB-72-189-11Y-3B-1Y-OB Tesopaco 76 INIA66/S0TY//CZHO
BR69-1Y-3M-3Y-OM Thornbird IAS63/ALD//GV/LV
F11915-A-502M-1Y-3F-701Y-15F-OY Thornbird "s" IAS63/ALD//GV/LV
Fl1915-A-502M-1Y-3F-701Y-SF.700Y Thrush BJY/GJO/4/MAI/3/BB//TOB/CNO
CM34742-E-2M-SY-3M-500Y-100B-501Y-OM Thrush "S" BJY/GJO/4/MAI/3/BB//TOB/CNO
CM34742 Tiba 63 FN/3/T48/MAY054//MENKEMEN
II10668-4T-2B-1T Timgalen AGUILERA/KENIA//MAROQUI/SUPREMO
/3/GB/4/WINGLEN Titmouse PL/3/INIA66/CNO//CAL/4/BJY
CM30136-3Y-1Y-OM Tobari 66 TZPP/SON64A
II19021-4M-3Y-102M-100Y-101C Toluca F73 INIA66/NAPO//CNO
II28036-111M-1R-2M-1T-OM Toniehi 81 CAR422/ANA
SWM4610-2Y-20M-OM Toquifen "S" 90S/FN*2//4160/3/YT54/N10B/4/2*C14
CH7817-3P-4P-1P-2P-1P Torim 73 BB/INIA
1I26591-1T-7M-OY-5SY-OM Towhee CLI//RQ/SOTY/3/SIS/PVN
CM34709-G-15M-5Y-OM Trigo 1 Triple Dirk URUGUAY 1084/NX DIRK 48 Trisa INIA CNO/INIA66//BB
CM28339-17Y-4M-2Y-OM Trisa INIA A CNO/INIA66//BB
CM28339-17Y-1M-1Y-OM Tui HER/ SAP / /VEE
CM74849-2M-2Y-3M-2Y-OB-48M-OY Tungurahua AMZ/5/FR*2/FN//3*Y/3/2*4777
E-II-685512-7E-OE-6E-OY /4/FR//MYS4*2/4777 Turaeo "S"A CN079*2/PRL
CM90312-A-2B-12Y-1B-OY Turaco "S"B CN079*2/PRL
CM90312-A-2B-3Y-3B-OY Turaeo "S"C CN079*2/'PRL
CM90312-D-3B-8Y-6B-OY Turpin 7 MY54/N10B//P4160
IIS715-7Y-1C Tyrant DGA/BJY
CM40610-25Y-4M-1Y-1M-1Y-OB UP 1109 UP262/UP368 UP 201 UP 262 S308/BAJIO 66 UP 301 LR64/SN64
1I19008 UP 310 KLPE/RAF//LR64/2*SN64 UP3011/SN/PI62 UP301//SN64/PI62
JIT-35-2L
97�
Uniq.
•� 1243
1174
106S 1066 1067 1068 1069 1070 1172
303
1179
1178
1173
1176
155
83 1152
365
312
1107
1117
1230
1129 1127 1278
1081 61
158
203
1071 156
1106
239
339
1269
IS
183
Abbr.
m90351
URES
V1130 V1287? V5648 V79143 V79353 V878 VEE#4
VEE#5
VEE#5S
VEE#7
VEE#8
VEE#7S
VCM
VI VTI
VIREO
VUL
W84/11
W84/14
WL2265
WL410 WL711 WBD911
WHT XJ
i3518
yw0890
YMI6 Y50
durum4
YRT S
YRT
ZA/ZP
ZAF
ZBZ
Coaaon Raae Cross Selection History
URES*2/PRL URES*21PRL CM90315-A-2B-2Y-1B-OY
Ures 81 KVZ/BUHO//KAL/BB CM33027-F-12M-1Y-4M-2Y-2M-OY
V 1130 V 1287.GII V 5648 V 79143 V 79353 V 878 Veery #4 KVZ/BUHO//KAL/BB
CM33027-F-12M-1Y-10M-1Y-3M-1Y-OM Veery #S KVZ/BUHO//KAL/BB
CM33027-F-1SM-SOOY-OM Veery #S"S" KVZ/BUHO//KAL/BB
CM33027-F-1SM-SOOY-OM-110B-OY Veery #7 KVZ/BUHO//KAL/BB
CM33027-F-15M-4Y-4M-3Y-2M-1Y-OM Veery #8 KVZ/BUHO//KAL/BB
CM33027-F-12M-1Y-1M-1Y-1M-OY Veery 7"S" KVZ/BUHO//KAL/BB
CM33027-F-15M-4Y-4M-2Y-1M-1Y-OM Vicam 71 INIA66/NAP063
II22398-39M-1R-OY-101M-OY Victor I FN//K58/N/3/II-SO-3S/4/3*MARA Victoria Inta JAR//BB/CNO
Cl191-7B-3B-1T-3P-OP Vireo INIA66/0N//INIA66/BB/3/COC
CM28235-2Y-6Y-OM Vulture YR RESEL(B)/TRF//RSK/TRM
CM3604-A-1M-5Y-OM W84/11
K20/MENG/5/SN64//TZPP/NAI60/3/DROM/4/SKE W84/14 NAD/LR//BB/3/IBWSN264
BE2S93-BU1-BUl-SEl-TM WL 2265 CARTHAGE//Kal/BB
CM7806 WL 410 SON63/S326//KAL WL 711 S309/CHRIS//KAL Westbred 911
Sln male-sterile facil. Rec. Sln. Wheaton CRIM/2*ERA//BUI/GALLO Xelaju FKN/GB56
II8325-4M-2R-2M Y50E/3*KAL Y50E/3*KAL
II3518-5M(F1)-31Y-OM-8M-OY YW00890 ELGAU/SN64//7C/NAPOEN
YW00890-2S-11-12-21-11-2B Yang Mai 6 Yaqui 50 NEWTHATCH/MARROQUI 588
II120-3C-(9-11C)-24C Yavaros (durum) JO/AA//FG
CM9799 Yecorata "S" INIA66/CNO//CAL/3/BB#2 RESEL
II40041-5M-2R-2M-4S-0M Yecorato 77 INIA66/CNO//CAL/3/BB#2 RESEL
II40041-9M-2R-6M-4S-0M ZA75/ZP ZA75/ZP
SA79014-9-11 Zaafrane SN64/KLRE
II1997S-68Y-IJ-1Y-1J-5Y-1T Zambezi 8156//LEE/ND74
SS9S-A1-A6-B2
98�
Uniq. Abbr. COllllDOn Kame Cross Selection History•
220 ZA75 Zaragoza 75 MENG/8156 1I22364-1Y-6C-1Y-1C-4Y-3C-2R-100Y
119 ZA75 A Zaragoza 75A MENG/8156 1I22364-1Y-6C-1Y-1C-4Y-3C-1Y-2B-300Y-OY
376 ZA75 B Zaragoza 75B MENG/8156 1I22364-1Y-1C-1Y-IC-4Y-3C-IOOY
1247 ZAGU Zaraguro "S" E-II-3958-2E-OE-OE-9E
54 ZIPA Zipa 68 F/Y48//2*AFM 1I12924-20B-3T-2B-IT
99�
CIMMYT Wheat Special Report Series� (As of April 1, 1992)�
No.1. Russian Wheat Aphid Research at CIMMYT, 27 pages.�
No.2. Wheat and Wheat Breeding in China, 14 pages.�
No.3. Impact of Crop Management Research in Bangladesh, 15 pages.�
No.4. Wheat Cultivar Abbreviations (In Press).�
No.5. A Guide to the CIMMYT Bread Wheat Breeding Program, 43 pages.�
No.6. Wheat Production and Grower Practices in the Yaqui Valley,� Sonora, Mexico, 75 pages.�
No.7. Kamal Bunt Research at CIMMYT (In Press).�
No.8. Management and Use of International Trial Data for Improving� Breeding Efficiency, 100 pages.�
No.9. Durum Wheats: Challenges and Opportunities (In Press).�
100�
CIO L DE MEJOAAMIENTO DE MAIZ TRIGO N l M Z AND WHEAT IMPROV ME T CENTER Apart~do Postal 6 6 1 06600 Mil ico. D. F. e Ie
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