Previous Page Table of Contents Next Page


Aastveit, A.H. & Martens, H. 1986. ANOVA interactions interpreted by partial least squares regression. Biometrics, 42: 829-844.

Abou-El-Fittouh, H.A., Rawlings, J.O. & Miller, P.A. 1969. Classification of environments to control genotype by environment interactions with an application to cotton. Crop Sci., 9: 135-140.

Acevedo, E. & Fereres, E. 1993. Resistance to abiotic stresses. In M.D. Hayward, N.O. Bosemark & I. Romagosa, eds. Plant breeding: principles and prospects, p. 406-421. London, Chapman & Hall.

Afifi, A.A. & Clark, V. 1984. Computer-aided multivariate analysis, p. 258-309. London, Lifetime Learning Publications.

Allard, R.W. & Bradshaw, A.D. 1964. Implications of genotype-environment interactions in applied plant breeding. Crop Sci., 4: 503-508.

Almekinders, C.J.M., Louwaars, N.P. & de Bruijn, G.H. 1994. Local seed systems and their importance for an improved seed supply in developing countries. Euphytica, 78: 207-216.

Annicchiarico, P. 1992. Cultivar adaptation and recommendation from alfalfa trials in northern Italy. J. Genet. Breed., 46: 269-278.

Annicchiarico, P. 1997a. Joint regression vs AMMI analysis of genotype-environment interactions for cereals in Italy. Euphytica, 94: 53-62.

Annicchiarico, P. 1997b. Additive main effects and multiplicative interaction (AMMI) of genotype-location interaction in variety trials repeated over years. Theor. Appl. Genet., 94: 1072-1077.

Annicchiarico, P. 1997c. STABSAS: a SAS computer programme for stability analysis. It. J. Agron., 1: 7-9.

Annicchiarico, P. 1998. Yield gain from recommendation of lucerne varieties based on mega-environment identification by AMMI analysis. In Proc. Fifth Eur. Soc. Agron. Cong., Vol. 2, p. 121-122. Slovak Agric. Univ., Nitra, Slovakia.

Annicchiarico, P. 2000. Variety x location interaction and its implications on breeding of lucerne: a case study. In Proc. XIII Eucarpia Medicago spp. Group Meeting, p. 35-43. Univ. of Perugia, Perugia, Italy.

Annicchiarico, P. 2002. Defining adaptation strategies and yield stability targets in breeding programmes. In M.S. Kang, ed. Quantitative genetics, genomics, and plant breeding, p. 365-383. Wallingford, UK, CABI.

Annicchiarico, P. & Mariani, G. 1996. Prediction of adaptability and yield stability of durum wheat genotypes from yield response in normal and artificially drought-stressed conditions. Field Crops Res., 46: 71-80.

Annicchiarico, P. & Perenzin, M. 1994. Adaptation patterns and definition of macro-environments for selection and recommendation of common-wheat genotypes in Italy. Plant Breed., 113: 197-205.

Annicchiarico, P. & Pecetti, L. 1998. Yield vs. morphophysiological trait-based criteria for selection of durum wheat in a semi-arid Mediterranean region (northern Syria). Field Crop Res., 59: 163-173.

Annicchiarico, P. & Piano, E. 1994. Interference effects in white clover genotypes grown as pure stands and binary mixtures with different grass species and varieties. Theor. Appl. Genet., 88: 153-158.

Annicchiarico, P., Bertolini, M. & Mazzinelli, G. 1995. Analysis of genotype-environment interactions for maize hybrids in Italy. J. Genet. Breed., 49: 61-68.

Annicchiarico, P., Chiari, T., Bellah, F., Doucene, S., Yallaoui-Yaïci, N., Bazzani, F., Abdellaoui, Z., Belloula, B., Bouazza, L., Bouremel, L., Hamou, M., Hazmoune, T., Kelkouli, M., Ould-Said, H. & Zerargui, H. 2002a. Response of durum wheat cultivars to Algerian environments. I. Yield. J. Agric. Environ. for Int. Develop. (in press)

Annicchiarico, P., Chiari, T., Bellah, F., Doucene, S., Yallaoui-Yaïci, N., Bazzani, F., Abdellaoui, Z., Belloula, B., Bouazza, L., Bouremel, L., Hamou, M., Hazmoune, T., Kelkouli, M., Ould-Said, H. & Zerargui, H. 2002b. Response of durum wheat cultivars to Algerian environments. II. Adaptive traits. J. Agric. Environ. for Int. Develop. (in press)

Annicchiarico, P., Chiari, T., Doucene, S., Yallaoui-Yaïci, N., Delli, G. & Bellah, F. 2002c. Response of durum wheat cultivars to Algerian environments. IV. Implications on a national breeding strategy. J. Agric. Environ. for Int. Develop. (in press)

Anon. 1993. Scelta delle varietà di frumento tenero. L'Informatore Agrario, 49(32) Suppl.: 1-66.

Anon. 1994. Scelta delle varietà di frumento tenero. L'Informatore Agrario, 50(32) Suppl.: 1-50.

Anon. 1995. Scelta delle varietà di frumento tenero. L'Informatore Agrario, 51(33) Suppl.: 1-50.

Anon. 1996. Scelta delle varietà di frumento tenero. L'Informatore Agrario, 52(33) Suppl.: 1-50.

Ashby, J.A., Gracia, T., del Pilar Guerrero, M., Quiròs, C.A., Roa, J.I. & Beltrán, J.A. 1995. Institutionalising farmer participation in adaptive technology testing with the CIAL. Network Paper 57. Cali, Colombia, CIAL-CIAT.

Atlin, G.N. & Frey, K.J. 1989. Predicting the relative effectiveness of direct versus indirect selection for oat yield in three types of stress environments. Euphytica, 44: 137-142.

Atlin, G.N. & McRae, K.B. 1994. Resource allocation in Maritime cereal cultivar trials. Can. J. Plant Sci., 74: 501-505.

Baker, R.J. 1988. Tests for crossover genotype-environmental interactions. Can. J. Plant Sci., 68: 405-410.

Balfourier, F., Oliveira, J.A., Charmet, G. & Arbones, E. 1997. Factorial regression analysis of genotype-by-environment interaction in ryegrass populations, using both isozyme and climatic data as covariates. Euphytica, 98: 37-46.

Bänzinger, M., Bertrán, F.J. & Lafitte, H.R. 1997. Efficiency of high-nitrogen selection environments for improving maize for low-nitrogen target environments. Crop Sci., 37: 1103-1109.

Barah, B.C., Binswanger, H.P., Rana, B.S. & Rao, G.P. 1981. The use of risk aversion in plant breeding; concept and application. Euphytica, 30: 451-458.

Basford, K.E. & Cooper, M. 1998. Genotype x environment interactions and some considerations of their implications for wheat breeding in Australia. Aust. J. Agric. Res., 49: 153-174.

Basford, K.E. & Tukey, J.W. 2000. Graphical analysis of multiresponse data: illustrated with a plant breeding trial. Boca Raton, FL, Chapman & Hall/CRC Press.

Basford, K.E., Williams, E.R., Cullis, B.R. & Gilmour, A. 1996. Experimental design and analysis of variety trials. In M. Cooper & G.L. Hammer, eds. Plant adaptation and crop improvement, p. 125-138. Wallingford, UK, CABI.

Becker, H.C. 1984. Analysis of genotype x environment interaction with partitioning of environmental effects into effects of locations and years. Vortr. Pflanzenzüchtg., 7: 209-218.

Becker, H.C. 1987. Zur Heritabilität statistischer Maßzahlen für die Ertragssicherheit. Pflanzenzüchtg. 12: 134-144.

Becker, H.C. & Léon, J. 1988. Stability analysis in plant breeding. Plant Breed., 101: 1-23.

Biarnès-Dumoulin, V., Denis, J.B., Lejeune-Hénaut, I. & Etévé, G. 1996. Interpreting yield instability in pea using genotypic and environmental covariates. Crop Sci., 36: 115-120.

Bidinger, F.R., Hammer, G.L. & Muchow, R.C. 1996. The physiological basis of genotype by environment interaction in crop adaptation. In M. Cooper & G.L. Hammer, eds. Plant adaptation and crop improvement, p. 329-347. Wallingford, UK, CABI.

Blum, A. 1988. Plant breeding for stress environments. Boca Ratoon, FL, CRC Press.

Bowman, D.T. 1989. Statistical procedures to measure population variation. In H.T. Stalker & C. Chapman, eds. Scientific management of germplasm: characterization, evaluation and enhancement, p. 65-73. Rome, IBPGR.

Bowman, D.T. & Watson, C.E. 1997. Measures of validity in cultivar performance trials. Agron. J., 89: 860-866.

Bradley, J.P., Knittle, K.H. & Troyer, A.F. 1988. Statistical methods in seed corn product selection. J. Prod. Agric., 1: 34-38.

Bradu, D. & Gabriel, K.R. 1978. The biplot as a diagnostic tool for models of two-way tables. Technometrics, 20: 47-68.

Bramel-Cox, P.J. 1996. Breeding for reliability of performance across unpredictable environments. In M.S. Kang & H.G. Gauch, eds. Genotype-by-environment interaction. p. 309-339. Boca Raton, FL, CRC Press.

Bramel-Cox, P.J., Barker, T., Zavala-Garcia, F. & Eastin, J.D. 1991. Selection and testing environments for improved performance under reduced-input conditions. In D. Sleper, P.J. Bramel-Cox & T. Barker, eds. Plant breeding and sustainable agriculture: considerations for objectives and methods. p. 29-56. CSSA Special Publ. 18. Madison, WI, ASA, CSSA, SSSA.

Brancourt-Hulmel, M., Biarnès-Dumoulin, V. & Denis, J.B. 1997. Points de repère dans l'analyse de la stabilité et de l'interaction génotype-milieu en amélioration des plants. Agronomie, 17: 219-246.

Braun, H.J., Pfeiffer, W.H. & Pollmer, W.G. 1992. Environments for selecting widely adapted spring wheat. Crop Sci. 32: 1420-1427.

Braun, H.J., Rajaram, S. & van Ginkel, M. 1996. CIMMYT's approach to breeding for wide adaptation. Euphytica, 92: 175-183.

Brennan, P.S. & Byth, D.E. 1979. Genotype c environmental interactions for wheat yields and selection for widely adapted wheat genotypes. Aust. J. Agric. Res., 30: 221-232.

Brennan, P.S., Byth, D.E., Drake, D.W., DeLacy, I.H. & Butler, D.G. 1981. Determination of the locations and number of test environments for a wheat cultivar evaluation program. Aust. J. Agric. Res., 32: 189-201.

Brown, K.D., Sorrels, M.E. & Coffman, W.R. 1983. A method for classification and evaluation of testing environments. Crop Sci., 23: 889-893.

Byerlee, D. & Husain, T. 1993. Agricultural research strategies for favoured and marginal areas - the experience of farming system research in Pakistan. Expl. Agric., 29: 155-171.

Byerlee, D. & Morris, M. 1993. Research for marginal environments. Are we under-invested? Food Policy, 18: 381-394.

Byrne, P.F., Bolanos, J., Edmeades, G.O. & Eaton, D.L. 1995. Gains from selection under drought versus multilocation testing in related tropical maize populations. Crop Sci., 35: 63-69.

Burdon, R.D. 1977. Genetic correlation as a concept for studying genotype-environment interaction in forest tree breeding. Silvae Genet., 26: 168-175.

Byth, D.E. & Mungomery, V.E. 1981. Interpretation of plant response and adaptation to agricultural environments. Brisbane, Aust. Inst. Agric. Sci.

Calhoun, D.S., Gebeyehu, G., Miranda, A., Rajaram, S. & van Ginkel, M. 1994. Choosing evaluation environments to increase wheat grain yield under drought conditions. Crop Sci., 34: 673-678.

Calinski, T. 1960. On a certain statistical method of investigating interaction in serial experiments with plant varieties. Polish Acad. of Sci. Bull. (Cl. II), 8: 565-568.

Calinski, T., Czajka, S. & Kaczmarek, Z. 1987. A model for the analysis of a series of experiments repeated at several places over a period of years. I. Theory. Biuletyn Oceny Odmian, 17-18: 7-33.

Carmer, S.G. & Walker, W.M. 1988. Significance from a statistician's viewpoint. J. Prod. Agric., 1: 27-33.

Ceccarelli, S. 1989. Wide adaptation: How wide? Euphytica, 40: 197-205.

Ceccarelli, S. 1994. Specific adaptation and breeding for marginal conditions. Euphytica, 77: 205-219.

Ceccarelli, S. 1996. Positive interpretation of genotype by environment interaction in relation to sustainability and biodiversity. In M. Cooper & G.L. Hammer, eds. Plant adaptation and crop improvement. p. 467-486. Wallingford, UK, CABI.

Ceccarelli, S. & Grando, S. 1991. Environment of selection and type of germplasm in barley breeding for low-yielding conditions. Euphytica, 57: 207-219.

Ceccarelli, S., Acevedo, E. & Grando, S. 1991. Breeding for yield stability in unpredictable environments: single traits, interaction between traits, and architecture of genotypes. Euphytica, 56: 169-185.

Ceccarelli, S., Valkoun, J., Erskine, W., Weigand, S., Miller, R. & Van Leur, J.A.G. 1992. Plant genetic resources and plant improvement as tools to develop sustainable agriculture. Expl. Agric., 28: 89-98.

Ceccarelli, S., Grando, S. & Impiglia, A. 1998. Choice of selection strategy in breeding barley for stress environments. Euphytica, 103: 307-318.

Ceccarelli, S., Grando, S., Tutwiler, R., Baha, J., Martini, A.M., Salahieh, H., Goodchild, A. & Michael, M. 2000. A methodological study on participatory barley breeding. I. Selection phase. Euphytica, 111: 91-104.

Chapman, S.C., Hammer, G.L., Podlich, D.W. & Cooper, M. 2002. Linking bio-physical and genetic models to integrate physiology, molecular biology and plant breeding. In M.S. Kang, ed. Quantitative genetics, genomics, and plant breeding, p. 167-187. Wallingford, UK, CABI.

CIMMYT. 1989. Towards the 21st century: CIMMYT's strategy. El Batan, Mexico, CIMMYT.

Clawson, D.L. 1985. Harvest security and intraspecific diversity in traditional tropical agriculture. Econ. Botany, 39: 56-67.

Cochran, W.G. & Cox, G.M. 1957. Experimental designs, p. 553-566. Second edition. New York, J. Wiley & Sons.

Conway, G. 1998. The doubly green revolution. Food for all in the 21th century. London, Penguin.

Cooper, M. & Byth, D.E. 1996. Understanding plant adaptation to achieve systematic applied crop improvement - A fundamental challenge. In M. Cooper & G.L. Hammer, eds. Plant adaptation and crop improvement, p. 5-23. Wallingford, UK, CABI.

Cooper, M. & DeLacy, I.H. 1994. Relationships among analytical methods used to study genotypic variation and genotype-by-environment interaction in plant breeding multi-environment experiments. Theor. Appl. Genet., 88: 561-572.

Cooper, M. & Hammer, G.L. (eds). 1996a. Plant adaptation and crop improvement. Wallingford, UK, CABI.

Cooper, M. & Hammer, G.L. 1996b. Synthesis of strategies for crop improvement. In M. Cooper & G.L. Hammer, eds. Plant adaptation and crop improvement, p. 591-623. Wallingford, UK, CABI.

Cooper, M., Woodruff, D.R., Eisemann, R.L., Brennan, P.S. & DeLacy, I.H. 1995. A selection strategy to accommodate genotype-by-environment interaction for grain yield of wheat: managed-environments for selection among genotypes. Theor. Appl. Genet., 90: 492-502.

Cooper, M., DeLacy, I.H. & Basford, K.E. 1996a. Relationships among analytical methods used to study genotypic adaptation in multi-environment trials. In M. Cooper & G.L. Hammer, eds. Plant adaptation and crop improvement, p. 193-224. Wallingford, UK, CABI.

Cooper, M., Brennan, P.S. & Sheppard, J.A. 1996b. A strategy for yield improvement of wheat which accommodates large genotype by environment interactions. In M. Cooper & G.L. Hammer, eds. Plant adaptation and crop improvement, p. 487-511. Wallingford, UK, CABI.

Cooper, M., Stucker, R.E., DeLacy, I.H. & Harch, B.D. 1997. Wheat breeding nurseries, target environments, and indirect selection for grain yield. Crop Sci. 37: 1168-1176.

Cooper, M., Podlich, D.W., Jensen, N.M., Chapman, S.C. & Hammer, G.L. 1999. Modelling plant breeding programmes. Trends Agron., 2: 33-64.

Cornelius, P.L. 1980. Functions approximating mandel's tables for the means and standard deviations of the first three roots of a Wishart matrix. Technometrics, 22: 613-616.

Cornelius, P.L. 1993. Statistical tests and retention of terms in the additive main effects and multiplicative interaction model for cultivar trials. Crop Sci., 33: 1186-1193.

Cornelius, P.L., Seyedsadr, M.S. & Crossa, J. 1992. Using the shifted multiplicative model to search for "separability" in crop cultivar trials. Theor. Appl. Genet., 84: 161-172.

Cornelius, P.L., Crossa, J. & Seyedsadr, M.S. 1996. Statistical tests and estimators of multiplicative models for genotype-by-environment interaction. In M.S. Kang & H.G. Gauch, eds. Genotype-by-environment interaction, p. 199-234. Boca Raton, FL, CRC Press.

Crossa, J. 1990. Statistical analyses of multilocation trials. Adv. Agron., 44: 55-85.

Crossa, J. & Cornelius, P.L. 1997. Sites regression and shifted multiplicative model clustering of cultivar trial sites under heterogeneity of error variances. Crop Sci., 37: 406-415.

Crossa, J., Gauch, H.G. & Zobel, R.W. 1990. Additive main effects and multiplicative interaction analysis of two international maize cultivar trials. Crop Sci., 30: 493-500.

Crossa, J., Fox, P.N., Pfeiffer, W.H., Rajaram, S. & Gauch, H.G. 1991. AMMI adjustment for statistical analysis of an international wheat yield trial. Theor. Appl. Genet., 81: 27-37.

Crossa, J., Cornelius, P.L., Seyedsadr, M.S. & Byrne, P. 1993. A shifted multiplicative model cluster analysis for grouping environments without genotypic rank range. Theor. Appl. Genet., 85: 577-586.

Crossa, J., Cornelius, P.L., Sayre, K. & Ortiz-Monasterio, R.J. 1995. A shifted multiplicative model fusion method for grouping environments without cultivar rank change. Crop Sci., 35: 54-62.

Cruz Medina, R. 1992. Some exact conditional tests for the multiplicative model to explain genotype-environment interaction. Heredity, 69: 128-132.

Dagnelie, P. 1975a. Theorie et méthodes statistiques. Vol. 2. Second edition. Gembloux, Belgium, Les Presses Agronomiques.

Dagnelie, P. 1975b. Analyse statistique a plusieurs variables. Gembloux, Belgium, Les Presses Agronomiques.

Decoux, G. & Denis, J.B. 1991. INTERA. Logiciels pour l'interpretation statistique de l'interaction entre deux facteurs. Versailles, France, Biométrie INRA.

DeLacy, I.H., Eisemann, R.L. & Cooper, M. 1990. The importance of genotype-by-environment interaction in regional variety trials. In M.S. Kang, ed. Genotype-by-environment interaction and plant breeding, p. 287-300. Baton Rouge, LA, Louisiana State Univ.

DeLacy, I.H., Fox, P.N., Corbett, J.D., Crossa, J., Rajaram, S., Fischer, R.A. & van Ginkel, M. 1994. Long-term association of locations for testing spring bread wheat. Euphytica, 72: 95-106.

DeLacy, I.H., Basford, K.E., Cooper, M., Bull, J.K. & McLaren, C.G. 1996a. Analysis of multi-environment data - An historical perspective. In M. Cooper & G.L. Hammer, eds. Plant adaptation and crop improvement, p. 39-124. Wallingford, UK, CABI.

DeLacy, I.H., Basford, K.E., Cooper, M. & Fox, P.N. 1996b. Retrospective analysis of historical data sets from multi-environment trials - Theoretical development. In M. Cooper & G.L. Hammer, eds. Plant adaptation and crop improvement, p. 243-267. Wallingford, UK, CABI.

DeLacy, I.H., Ratnasiri, W.G.A. & Mirzawan, P.D.N. 1996c. Retrospective analysis of historical data sets from multi-environment trials - Case studies. In M. Cooper & G.L. Hammer, eds. Plant adaptation and crop improvement, p. 269-290. Wallingford, UK, CABI.

Denis, J.B. 1980. L'analyse de régression factorielle. Biom. Praxim., 10: 1-34.

Denis, J.B. 1988. Two-way analysis using covariates. Statistics, 19: 123-132.

Denis, J.B. & Vincourt, P. 1982. Panorama des méthodes statistiques d'analyse des interactions génotype x milieu. Agronomie, 2: 219-230.

Denis, J.B., Piepho, H.P. & van Eeuwijk, F.A. 1997. Modelling expectation and variance for genotype by environment data. Heredity, 79: 162-171.

Dickerson, G.E. 1962. Implications of genetic-environmental interaction in animal breeding. Anim. Prod. 4: 47-63.

Donald, C.M. 1968. The breeding of crop ideotypes. Euphytica, 17: 385-403.

Donald, C.M. & Hamblin, J. 1983. The convergent evolution of annual seed crops in agriculture. Adv. Agron., 36: 97-145.

Dowker, B.D., Jackson, J.C. & Phelps, K. 1978. Variation studies in carrots as an aid to breeding. VI. Genotype-environment interactions in contrasting field environments. J. Hort. Sci., 53: 131-137.

Draper, N.R. & Smith, H.S. 1981. Applied regression analysis, p. 305. Second edition. New York, Wiley & Sons.

Dyke, G.V., Lane, P.W. & Jenkyn, J.F. 1995. Sensitivity (stability) analysis of multiple variety trials, with special reference to data expressed as proportions or percentages. Expl. Agric., 31: 75-87.

Ebdon, J.S. & Gauch, H.G. 2002. AMMI analysis of national turfgrass performance trials: II. Cultivar recommendations. Crop Sci. (in press)

Eberhart, S.A. & Russell, W.A. 1966. Stability parameters for comparing varieties. Crop Sci., 6: 36-40.

Eberhart, S.A. & Russell, W.A. 1969. Yield and stability for a 10-line diallel of single-cross and double-cross maize hybrids. Crop Sci., 9: 357-361.

Edmeades, G.O., Bolaños, J., Lafitte, H.R., Rajaram, S., Pfeiffer, W. & Fischer, R.A. 1989. Traditional approaches to breeding for drought resistance in cereals. In F.W.G. Baker, ed. Drought resistance in cereals, p. 27-52. Wallingford, UK, CABI International.

Eisemann, R.L., Cooper, M. & Woodruff, D.R. 1990. Beyond the analytical methodology - better interpretation and exploitation of genotype-by-environment interaction in breeding. In M.S. Kang, ed. Genotype-by-environment interaction and plant breeding, p. 108-117. Baton Rouge, LA, Louisiana State Univ.

Ekbohm, G. 1981. A test for the equality of variances in the paired case with incomplete data. Biometrical J., 23: 261-265.

Engledow, F.L. 1925. The economic possibilities of plant breeding. In Proc. Imperial Botanical Conf., p. 31-40. London, F.T. Brooks.

Eskridge, K.M. 1990. Selection of stable cultivars using a safety-first rule. Crop Sci., 30: 369-374.

Eskridge, K.M. & Mumm, R.F. 1992. Choosing plant cultivars based on the probability of outperforming a check. Theor. Appl. Genet., 84: 494-500.

Evans, L.T. 1993. Crop evolution, adaptation, and yield. New York, Cambridge Univ. Press.

Evans, L.T. 1998. Feeding the ten billion: plants and population growth. New York, Cambridge Univ. Press.

Everitt, B. 1980. Cluster analysis. London, Heinemann Educational Books.

Eyzaguirre, P. & Iwanaga, M. (eds). 1996. Participatory plant breeding. Proceedings of a workshop on participatory plant breeding, 26-29 July 1995, Wageningen, the Netherlands. Rome, IPGRI.

Falconer, D.S. 1989. Introduction to quantitative genetics, p. 318-355. Third edition. New York, Longman.

Falconer, D.S. 1990. Selection in different environments: effects on environmental sensitivity (reaction norm) and on mean performance. Genet. Res. Camb., 56: 57-70.

FAO. 1996. Food requirements and population growth. Technical Background Document No. 4. Rome.

FAO. 1999. Recent developments in biotechnology as they relate to plant genetic resources for food and agriculture, by C. Spillane. Background Study Paper 9. Rome.

Federer, W.T. & Scully, B.T. 1993. A parsimonious statistical design and breeding procedure for evaluating and selecting desirable characteristics over environments. Theor. Appl. Genet., 86: 612-620.

Finlay, K.W. & Wilkinson, G.N. 1963. The analysis of adaptation in a plant-breeding programme. Aust. J. Agric. Res., 14: 742-754.

Fischer, K.S. 1996. Research approaches for variable rainfed system. Thinking globally, acting locally. In M. Cooper & G.L. Hammer, eds. Plant adaptation and crop improvement, p. 25-35. Wallingford, UK, CABI.

Flintham, J.E., Börner, A., Worland, A.J. & Gale, M.D. 1997. Optimizing wheat grain yield: effects of Rht (giberellin-insentitive) dwarfing genes. J. Agric. Sci., Camb., 128: 11-25.

Fox, P.N. & Rosielle, A.A. 1982a. Reducing the influence of environmental main-effects on pattern analysis of plant breeding environments. Euphytica, 31: 645-656.

Fox, P.N. & Rosielle, A.A. 1982b. Reference sets of genotypes and selection for yield in unpredictable environments. Crop Sci., 22: 1171-1175.

Freeman, G.H. 1973. Statistical methods for the analysis of genotype-environment interaction. Heredity, 31: 339-354.

Frensham, A.B., Barr, A.R., Cullis, B.R. & Pelham, S.D. 1998. A mixed model analysis of 10 years of oat evaluation data: use of agronomic information to explain genotype by environment interaction. Euphytica, 99: 43-56.

Gail, M. & Simon, R. 1985. Testing for qualitative interactions between treatment effects and patient subsets. Biometrics, 41: 361-372.

Gallais, A. 1992. Adaptation et adaptabilité en amélioration des plantes. Sel. Fr., 42: 55-57.

Gauch, H.G. 1988. Model selection and validation for yield trials with interaction. Biometrics, 44: 705-715.

Gauch, H.G. 1990. Full and reduced models for yield trials. Theor. Appl. Genet., 80: 153-160.

Gauch, H.G. 1992. Statistical analysis of regional yield trials: AMMI analysis of factorial designs. Amsterdam, Elsevier.

Gauch, H.G. 1998. MATMODEL Version 2.1: AMMI and related analyses for two-way data matrices. Ithaca, NY, Microcomputer Power.

Gauch, H.G. & Furnas, R.E. 1991. Statistical analysis of yield trials with MATMODEL. Agron. J., 83: 916-920.

Gauch, H.G. & Zobel, R.W. 1990. Imputing missing yield trial data. Theor. Appl. Genet., 79: 753-761.

Gauch, H.G. & Zobel, R.W. 1996a. AMMI analysis of yield trials. In M.S. Kang & H.G. Gauch, eds. Genotype-by-environment interaction, p. 85-122. Boca Raton, FL, CRC Press.

Gauch, H.G. & Zobel, R.W. 1996b. Optimal replication in selection experiments. Crop Sci., 36: 838-843.

Gauch, H.G. & Zobel, R.W. 1997. Identifying mega-environments and targeting genotypes. Crop Sci., 37: 311-326.

Ghaderi, A., Everson, E.H. & Cress, C.E. 1980. Classification of environments and genotypes in wheat. Crop Sci., 20: 707-710.

Giauffret, C., Lothrop, J., Dorvillez, D., Gouesnard, B. & Derieux, M. 2000. Genotype x environment interactions in maize hybrids from temperate or highland tropical origin. Crop Sci., 40: 1004-1012.

Gilmour, A.R., Cullis, B.R., Welham, S.J. & Thompson, R. 1999. ASREML reference manual. Biometric Bulletin No. 3. Orange, New South Wales, NSW Agriculture.

Gollob, H.F. 1968. A statistical model which combines features of factor analytic and analysis of variance techniques. Psychometrika, 33: 73-115.

Gower, J.C. 1966. Some distance properties of latent root and vector methods used in multivariate analysis. Biometrika, 53: 325-338.

Gupta, S.S. 1965. On some multiple decision (selection and ranking) rules. Technometrics, 7: 225-246.

Hadjichristodoulou, A. 1987. Stability of performance of cereals in low-rainfall areas as related to adaptive traits. In J.P. Srivastava, E. Porceddu, E. Acevedo & S. Varma, eds. Drought tolerance in winter cereals, p. 191-200. Chichester, UK, Wiley.

Hammer, G.L. & Vanderlip, R.L. 1989. Genotype-by-environment interaction in grain sorghum. III. Modeling the impact in field environments. Crop Sci., 29: 385-391.

Hardwick, R.C. & Wood, J.T. 1972. Regression methods for studying genotype-environment interaction. Heredity, 28: 209-222.

Hartley, H.O. 1950. The maximum F-ratio as a short cut test for heterogeneity of variance. Biometrika, 37: 308-312.

Hayes, P.M., Liu, B.H., Knapp, S.J., Chen, F., Jones, B., Blake, T., Franckowiak, J., Rasmusson, D., Sorrels, M., Ulrich, S.E., Wesenberg, D. & Kleinhofs, A. 1993. Quantitative trait locus effects and environmental interaction in a sample of North American barley germplasm. Theor. Appl. Genet., 87: 392-401.

Hazell, P.B.R. 1982. Application of risk preference estimates in firm-household and agricultural sector models. Am. J. Agric. Econ., 64: 384-390.

Helms, T.C. 1993. Selection for yield and stability among oat lines. Crop Sci., 33: 423-426.

Henderson, C.R. 1975. Best linear unbiased estimation and prediction under a selection model. Biometrics, 31: 423-447.

Hildebrand, P.E. & Poey, F. 1985. On-farm agronomic trials in farming systems research and extension. Boulder, CO, Lynne Rienner Publ.

Hill, J. 1975. Genotype-environment interactions - A challenge for plant breeding. J. Agric. Sci., Camb., 85: 477-493.

Hill, R.R. & Rosenberger, J.L. 1985. Methods for combining data from germplasm evaluation trials. Crop Sci., 25: 467-470.

Hoogenboom, G., White, J.W., Acosta-Gallegos, J., Gaudiel, R.G., Myers, J.R. & Silbernagel, M.J. 1997. Evaluation of a crop simulation model that incorporates gene action. Agron. J., 89: 613-620.

Horner, T.W. & Frey, K.J. 1957. Methods for determining natural areas for oat varietal recommendations. Agron. J., 49(3): 313-315.

Hühn, M. 1990. Nonparametric measures of phenotypic stability. Part 1: theory. Euphytica, 47: 189-194.

Hühn, M. 1997. Weighted means are unnecessary in cultivar performance trials. Crop Sci., 37: 1745-1750.

Hunt, L.A., Pararajasingham, S., Jones, J.W., Hoogenboom, G., Imamura, D.T. & Ogoshi, R.M. 1993. GENCALC: software to facilitate the use of crop models for analyzing field experiments. Agron. J., 85, 1090-1094.

Hussein, M.A., Bjornstad, A. & Aastveit, A.H. 2000. SASG x ESTAB: a SAS program for computing genotype x environment stability statistics. Agron. J., 92: 454-459.

Jackson, P., Robertson, M., Cooper, M. & Hammer, G. 1996. The role of physiological understanding in plant breeding; from a breeding perspective. Field Crops Res., 49: 11-37.

Jalaluddin, M. & Harrison, S.A. 1993. Repeatability of stability estimators for grain yield in wheat. Crop Sci., 33: 720-725.

Jinks, J.L. & Connolly, V. 1973. Selection for specific and general response to environmental differences. Heredity, 34: 401-406.

Jolliffe, I.T. 1986. Principal component analysis. New York, Springer-Verlag.

Kang, M.S. 1988. A rank-sum method for selecting high-yielding, stable corn genotypes. Cereal Res. Comm., 16: 113-115.

Kang, M.S. (ed.) 1990. Genotype-by-environment interaction and plant breeding. Baton Rouge, LA, Louisiana State Univ.

Kang, M.S. 1993. Simultaneous selection for yield and stability in crop performance trials: consequences for growers. Agron. J., 85: 754-757.

Kang, M.S. 1998. Using genotype-by-environment interaction for crop cultivar development. Adv. Agron., 62: 199-252.

Kang, M.S. (ed.) 2002. Quantitative genetics, genomics, and plant breeding. Wallingford, UK, CABI.

Kang, M.S. & Gauch, H.G. (eds). 1996. Genotype-by-environment interaction. Boca Raton, FL, CRC Press.

Kang, M.S. & Gorman, D.P. 1989. Genotype x environment interaction in maize. Agron. J., 81: 662-664.

Kang, M.S. & Magari, R. 1995. STABLE: a BASIC program for calculating stability and yield-stability statistics. Agron. J., 87: 276-277.

Kang, M.S. & Pham, H.N. 1991. Simultaneous selection for high yielding and stable crop genotypes. Agron. J., 83: 161-165.

Kataoka, S. 1963. A stochastic programming model. Econometrika, 31: 181-196.

Kempthorne, O. 1952. The design and analysis of experiments. New York, J. Wiley & Sons.

Kempton, R.A. 1984. The use of biplots in interpreting variety by environment interactions. J. Agric. Sci., 103: 123-135.

Koutsos, T., Koutsika-Sotiriou, M. & Fasoulas, A.C. 1992. The impact of genotype x soil texture interaction on the efficiency of selection for yield in maize (Zea mays L.). Euphytica, 61: 61-65.

Kroonenberg, P.M. 1995. Introduction to biplots for G x E tables. Centre for Statistics Research Report No. 51. Brisbane, Univ. of Queensland.

Lentner, M. & Bishop, T. 1986. Experimental design and analysis. Blacksburg, VA, Valley Book.

Léon, J. & Becker, H.C. 1988. Repeatability of some statistical measures of phenotypic stability - Correlations between single year results and multi years results. Plant Breeding, 100: 137-142.

Lin, C.S. & Binns, M.R. 1988. A method for analyzing cultivar x location x year experiments: a new stability parameter. Theor. Appl. Genet., 76: 425-430.

Lin, C.S. & Binns, M.R. 1991. Genetic properties of four types of stability parameter. Theor. Appl. Genet., 82: 505-509.

Lin, C.S. & Butler, G. 1988. A data-based approach for selecting locations for regional trials. Can. J. Plant Sci., 68: 651-659.

Lin, C.S. & Poushinsky, G. 1983. A modified augmented design for an early stage of plant selection involving a large number of test lines without replication. Biometrics, 39: 553-561.

Lin, C.S., Binns, M.R. & Lefkovitch, L.P. 1986. Stability analysis: where do we stand? Crop Sci., 26: 894-900.

Lin, C.S., Butler, G. Hall, I. & Nault, C. 1992. Program for investigating genotype-environment interactions. Agron. J., 84: 121-124.

Lorenzetti, R. 2000. The Wheat Science. The Green Revolution of Nazareno Strampelli. J. Genet. Breed. (special publ.), p. 40. Rome.

Loss, S.P. & Siddique, K.H.M. 1994. Morphological and physiological traits associated with wheat yield increases in Mediterranean environments. Adv. Agron., 52: 229-276.

Ludlow, M.M. & Muchow, R.C. 1990. A critical evaluation of traits for improving crop yields in water-limited environments. Adv. Agron., 43: 107-153.

Lynch, M. & Walsh, B. 1998. Genetics and analysis of quantitative traits. Sunderland, MA, Sinauer Associates.

Magari, R. & Kang, M.S. 1997. SAS-STABLE: stability analyses of balanced and unbalanced data. Agron. J., 89: 929-932.

Mandel, J. 1971. A new analysis of variance model for non-additive data. Technometrics, 13: 1-18.

Mariani, B.M., Novaro, P. & Stefanini, R. 1983. Efficiency of linear and multi-phase regression methods to evaluate genotype-environment interaction for grain yield and protein content in Italian durum wheat varieties. Z. Pflanzenzüchtg., 90: 56-67.

McCown, R.L., Keating, B.A., Probert, M.E. & Jones, R.K. 1992. Strategies for sustainable crop production in semi-arid Africa. Outlook on Agric., 21: 21-31.

McGuire, S., Manicad, G. & Sperling, L. 1999. Technical and institutional issues in partecipatory plant breeding - done from a perspective of farmer plant breeding. Working Document No. 2. Cali, Colombia, CIAT.

McLaren, C.G. 1996. Methods of data standardization used in pattern analysis and AMMI models for analysis of international multi-environment variety trials. In M. Cooper & G.L. Hammer, eds. Plant adaptation and crop improvement, p. 225-242. Wallingford, UK, CABI.

Milliken, G.A. & Johnson, D.E. 1984. Analysis of messy data. Vol. I: Designed experiments. Belmont, CA, Lifetime Learning Publ.

Mock, J.J. & Pearce, R.B. 1975. An ideotype of maize. Euphytica, 24: 613-623.

Muir, W., Nyquist, W.E. & Xu, S. 1992. Alternative partitioning of the genotype-by-environment interaction. Theor. Appl. Genet., 84: 193-200.

Mungomery, V.E., Shorter, R. & Byth, D.E. 1974. Genotype x environment interactions and environmental adaptation. I. Pattern analysis - Application to soya bean populations. Aust. J. Agric. Res., 25: 59-72.

Muñoz, P., Voltas, J., Araus, J.L., Igartua, E. & Romagosa, I. 1998. Changes over time in the adaptation of barley releases in north-eastern Spain. Plant Breed., 117: 531-535.

Nageswara Rao, R.C., Williams, J.H. & Singh, M. 1989. Genotypic sensitivity to drought and yield potential of peanuts. Agron. J., 81: 887-893.

Nassar, R. & Hühn, M. 1987. Studies on estimation of phenotypic stability: tests of significance for nonparametric measures of phenotypic stability. Biometrics, 43: 45-53.

van Oosterom, E.J., Kleijn, D., Ceccarelli, S. & Nachit, M.M. 1993. Genotype-by-environment interactions of barley in the Mediterranean region. Crop Sci., 33: 669-674.

Paterson, A.H., Damon, S., Hewitt, J.D., Zamir, D., Rabinowitch, H.D., Lincoln, S.E., Lander, E.S. & Tanksley, S.D. 1991. Mendelian factors underlying quantitative traits in tomato: comparison across species, generations, and environments. Genetics, 127: 181-197.

Patterson, H.D. 1997. Analysis of series of variety trials. In R.A. Kempton & P.N. Fox, ed. Statistical methods for plant variety evaluation, p. 139-161. London, Chapman & Hall.

Pecetti, L. & Annicchiarico, P. 1993. Grain yield and quality of durum wheat landraces in a dry Mediterranean region of northern Syria. Plant Breed., 110: 243-249.

Pederson, D.G. & Rathjen, A.J. 1981. Choosing trial sites to maximize selection response for grain yield in spring wheat. Aust. J. Agric. Res., 32: 411-424.

Perkins, J.M. & Jinks, J.L. 1968. Environmental and genotype-environmental components of variability. III. Multiple lines and crosses. Heredity, 23: 339-356.

Perrin, P.K., Winkelmann, D.L., Moscardi, E.R. & Anderson, J.R. 1976. From agronomic data to farmer recommendations: an economics training manual. El Batan, Mexico, CIMMYT.

Peterson, C.J., Moffatt, J.M. & Erickson, J.R. 1997. Yield stability of hybrid vs. pureline hard winter wheats in regional performance trials. Crop Sci., 37: 116-120.

Pham, H.N. & Kang, M.S. 1988. Interrelationships among and repeatability of several stability statistics estimated from international maize trials. Crop Sci., 28: 925-928.

Piano, E., Annicchiarico, P. & Pecetti, L. 2001. Aspetti metodologici di una proposta di revisione dei criteri di iscrizione delle varietà di specie foraggere al Registro Nazionale. Sementi Elette 47(1-2): 21-29.

Piepho, H.P. 1994a. Best linear unbiased prediction (BLUP) for regional yield trials: a comparison to additive main effects and multiplicative interaction (AMMI) analysis. Theor. Appl. Genet., 89: 647-654.

Piepho, H.P. 1994b. A comparison of the ecovalence and the variance of relative yield as measures of stability. J. Agron. and Crop Sci., 173: 1-4.

Piepho, H.P. 1995. Robustness of statistical tests for multiplicative terms in the additive main effects and multiplicative interaction model for cultivar trials. Theor. Appl. Genet., 90: 438-443.

Piepho, H.P. 1996. Analysis of genotype-by-environment interaction and phenotypic stability. In M.S. Kang & H.G. Gauch, eds. Genotype-by-environment interaction, p. 151-174. Boca Raton, FL, CRC Press.

Piepho, H.P. 1997. Tests for equality of dispersion in bivariate samples - Review and empirical comparison. J. Stat. Comput. Simul., 56: 353-372.

Piepho, H.P. 1998. Methods for comparing the yield stability of cropping systems - A review. J. Agron. Crop Sci., 180: 193-213.

Piepho, H.P. 1999. Stability analysis using the SAS System. Agron. J., 91: 154-160.

Piepho, H.P., Denis, J.B. & van Eeuwijk, F.A. 1998. Predicting cultivar differences using covariates. J. Agric. Biol. Environ. Stat., 3: 151-162.

Pinstrup-Andersen, P., Pandya-Lorch, R. & Rosegrant, M.W. 1999. World food prospects: critical issues for the twenty-first century. Washington, IFPRI.

Podlich, D.W., Cooper, M. & Basford, K.E. 1999. Computer simulation of a selection strategy to accommodate genotype-environment interactions in a wheat recurrent selection programme. Plant Breed., 118: 17-28.

Pollak, L.M. & Corbett, J.D. 1993. Using GIS datasets to classify maize-growing regions in Mexico and Central America. Agron. J., 85: 1133-1139.

Pooni, H.S. & Jinks, J.L. 1980. Non-linear genotype x environment interactions. II. Statistical models and genetic control. Heredity, 45: 389-400.

Prabhakaran, V.T. & Jain, J.P. 1994. Statistical techniques for studying genotype-environment interactions. New Delhi, South Asian Publ.

Ramey, T.B. & Rosielle, A.A. 1983. HASS cluster analysis: a new method of grouping genotypes or environments in plant breeding. Theor. Appl. Genet., 66: 131-133.

Rasmusson, D.C. 1987. An evaluation of ideotype breeding. Crop Sci., 27: 1140-1146.

Roberts, W.S. & Swinton, S.M. 1996. Economic methods for comparing alternative crop production systems: a review of literature. Am. J. Alt. Agric., 11: 10-17.

Romagosa, I. & Fox, P.N. 1993. Genotype x environment interaction and adaptation. In M.D. Hayward, N.O. Bosemark & I. Romagosa, eds. Plant breeding: principles and prospects, p. 373-390. London, Chapman & Hall.

Romagosa, I., Fox, P.N., Garcìa del Moral, L.F., Ramos, J.M., Garcìa del Moral, B., Roca de Togores, F. & Molina-Cano, J.L. 1993. Integration of statistical and physiological analyses of adaptation of near-isogenic barley lines. Theor. Appl. Genet., 86: 822-826.

Rosenzweig, C. & Hillel, D. 1998. Climate change and the global harvest. New York, Oxford Univ. Press.

Rotili, P. & Zannone, L. 1975. Principaux aspects d'une méthode de sélection de la luzerne basée sur des dispositifs qui utilisent la concurrence entre les plantes. Ann. Amélior. Plantes, 25: 29-49.

Russell, G., van Gardingen, P. & Wilson, G.W. 1993. Using physiological information about varieties: the way forward? Aspects Appl. Biol., 34: 47-56.

Saeed, M. & Francis, C.A. 1984. Association of weather variables with genotype x environment interactions in grain sorghum. Crop Sci., 24: 13-16.

Saindon, G. & Schaalje, G.B. 1993. Evaluation of locations for testing dry bean cultivars in western Canada using statistical procedures, biological interpretation and multiple traits. Can. J. Plant Sci., 73: 985-994.

Schut, J.W. & Dourleijn, C.J. 2000. Prediction of barley progeny performance in the presence of genotype-environment interaction. Plant Breed., 119: 47-50.

Searle, S.R. 1987. Linear models for unbalanced data. New York, J. Wiley & Sons.

Searle, S.R., Casella, G. & McCulloch, C.E. 1992. Variance components. New York, J. Wiley & Sons.

Seif, E., Evans, J.C. & Balaam, L.N. 1979. A multivariate procedure for classifying environments according to their interaction with genotypes. Aust. J. Agric. Res., 30: 1021-1026.

Shaner, W.W., Philipp, P.F. & Schmehl, W.R. 1982. Farming systems research and development - guidelines for developing countries. Boulder, CO, Westview Press.

Shorter, R., Byth, D.E. & Mungomery, V.E. 1977. Genotype x environment interactions and environmental adaptation. II. Assessment of environmental contributions. Aust. J. Agric. Res., 28: 223-235.

Shukla, G.K. 1972a. Some statistical aspects of partitioning genotype-environmental components of variability. Heredity, 29: 237-245.

Shukla, G.K. 1972b. An invariant test for the homogeneity of variances in a two-way classification. Biometrics, 28: 1063-1072.

Simmonds, N.W. 1979. Principles of crop improvement. London, Longman.

Simmonds, N.W. 1991. Selection for local adaptation in a plant breeding programme. Theor. Appl. Genet., 82: 363-367.

Singh, R.B. 2000. Environmental consequences of agricultural development: a case study from the Green Revolution state of Haryana, India. Agric. Ecosyst. Environ., 82: 97-103.

Singh, S.P., Gutierrez, J.A., Urrea, C.A., Molina, A. & Cajiao, C. 1992. Location-specific and across-location selections for seed yield in populations of common bean, Phaseolus vulgaris L. Plant Breed., 109: 320-328.

Singh, M., Ceccarelli, S. & Grando, S. 1999. Genotype x environment interaction of cross-over type: detecting its presence and estimating the crossover point. Theor. Appl. Genet., 99: 988-995.

Sleper, D.A., Barker, T.C. & Bramel Cox, P.J. (eds) 1991. Plant breeding and sustainable agriculture: considerations for objectives and methods. CSSA Special Publ. 18. Madison, WI, ASA, CSSA, SSSA.

Smith, M.F. & Gauch, H.G. 1992. Effects of noise on AMMI and hierarchical classification analyses. S. Afr. Stat. J., 26: 121-142.

Smith, M.F. & Smith, A. 1992. The success of the AMMI model in predicting lucerne yields for cultivars with differing dormancy characteristics. S. Afr. J. Plant Soil, 9: 180-185.

Snedecor, G.W. & Cochran, W.G. 1967. Statistical methods, p. 155. Sixth edition. Ames, IA, Iowa Univ. Press.

Sneller, C.H. & Dombek, D. 1995. Comparing soybean cultivar ranking and selection for yield with AMMI and full-data performance estimates. Crop Sci., 35: 1536-1541.

Sneller, C.H., Kilgore-Norquest, L. & Dombek, D. 1997. Repeatability of yield stability statistics in soybean. Crop Sci., 37: 383-390.

Sprague, G.F. & Federer, W.T. 1951. A comparison of variance components in corn yield trials. 2. Error, year x variety, location x variety, and variety components. Agron. J., 43: 535-541.

Steel, R.G.D. & Torrie, J.H. 1960. Principles and procedures of statistics, p. 173-446. New York, McGraw-Hill.

Summerfield, R.J., Ellis, R.H. & Craufurd, P.Q. 1996. Phenological adaptation to cropping environments. From evaluation descriptors of times to flowering to the genetic characterization of flowering responses to photoperiod and temperature. Euphytica, 92: 281-286.

Talbot, M. 1984. Yield variability of crop varieties in the U.K. J. Agric. Sci., Camb., 102: 315-321.

Talbot, M. & Wheelwright, A.V. 1989. The analysis of genotype x environment interactions by partial least squares regression. Biuletyn Oceny Odmian, 21/22: 19-25.

Tesemma, T. & Bechere, E. 1998. Developing elite durum wheat landrace selections (composites) for Ethiopian peasant farm use: raising productivity while keeping diversity alive. Euphytica, 102: 323-328.

Theobald, C.M., Talbot, M. & Nabugoomu, F. 2002. A Bayesian approach to regional and local-area prediction from crop variety trials. J. Agric. Biol. Environ. Stat., 7: 12-28.

Tigerstedt, P.M.A. 1994. Adaptation, variation and selection in marginal areas. Euphytica, 77: 171-174.

Turesson, G. 1922. The genotypical response of the plant species to the habitat. Hereditas, 3: 211-350.

Ud-Din N., Carver, B.F. & Clutter, A.C. 1992. Genetic analysis and selection for wheat yield in drought-stressed and irrigated environments. Euphytica, 62: 89-96.

van Eeuwijk, F.A. 1992. Interpreting genotype-by-environment interaction using redundancy analysis. Theor. Appl. Genet., 85: 89-100.

van Eeuwijk, F.A. 1995. Linear and bilinear models for the analysis of multi-environment trials. I. An inventory of models. Euphytica, 84: 1-7.

van Eeuwijk, F.A. & Elgersma, A. 1993. Incorporating environmental information in an analysis of genotype by environment interaction for seed yield in perennial ryegrass. Heredity, 70: 447-457.

van Eeuwijk, F.A., Keizer, L.C.P. & Bakker, J.J. 1995. Linear and bilinear models for the analysis of multi-environment trials. II. An application to data from the Dutch Maize Variety Trials. Euphytica, 84: 9-22.

van Eeuwijk F.A., Denis, J.B. & Kang, M.S. 1996. Incorporating additional information on genotypes and environments in models for two-way genotype by environment tables. In M.S. Kang & H.G. Gauch, eds. Genotype-by-environment interaction, p. 15-49. Boca Raton, FL, CRC Press.

Vargas, M., Crossa, J., Sayre, K., Reynolds, M., Ramìrez, M.E. & Talbot, M. 1998. Interpreting genotype x environment interaction in wheat by partial least squares regression. Crop Sci., 38: 679-689.

Vargas, M., Crossa, J., van Eeuwijk, F.A., Ramìrez, M.E. & Sayre, K. 1999. Using partial least squares regression, factorial regression, and AMMI models for interpreting genotype x environment interaction. Crop Sci., 39: 955-967.

Villegas, D., Aparicio, N., Nachit, M.M., Araus, J.L. & Royo, C. 2000. Photosynthetic and developmental traits associated with genotypic differences in durum wheat yield across the Mediterranean basin. Aust. J. Agric. Res., 51: 891-901.

Virk, D.S., Virk, P.S., Mangat, B.K. & Harinarayana, G. 1991. Weighted regression analysis for comparing varietal adaptation. Theor. Appl. Genet., 81: 559-561.

Wallace, D.H., Zobel, R.W. & Yourstone, K.S. 1993a. A whole-system reconsideration of paradigms about photoperiod and temperature control of crop yield. Theor. Appl. Genet., 86: 17-26.

Wallace, D.H., Baudoin, J.P., Beaver, J., Coyne, D.P., Halseth, D.E., Masaya, P.N., Munger, H.M., Myers, J.R., Silbernagel, M., Yourstone, K.S. & Zobel, R.W. 1993b. Improving efficiency of breeding for higher crop yield. Theor. Appl. Genet., 86: 27-40.

Walsh, B. 2002. Quantitative genetics, genomics, and the future of plant breeding. In M.S. Kang, ed. Quantitative genetics, genomics, and plant breeding, p. 23-32. Wallingford, UK, CABI.

Watson, S.L., DeLacy, I.H., Podlich, D.W. & Basford, K.E. 1996. GEBEI: an analysis package using agglomerative hierarchical classificatory and svd ordination procedures for genotype x environment data. Centre for Statistics Research Report No. 57. Brisbane, Univ. of Queensland.

Weber, W.E. & Westermann, T. 1994. Prediction of yield for specific locations in German winter-wheat trials. Plant Breed., 113: 99-105.

Weltzien, E., Smith, M., Meitzner, L.S. & Sperling, L. 1999. Technical and institutional issues in participatory plant breeding - from the perspective of formal plant breeding: a global analysis of issues, results and current experience. CGIAR Systemwide Program on Participatory Research and Gender Analysis for Technology Development and Institutional Innovation. Working Document No. 3. Cali, CIAT.

Westcott, B. 1986. Some methods of analysing genotype-environment interaction. Heredity, 56: 243-253.

Whan, B.R., Anderson, W.K., Gilmour, R.F., Snelling, K.L., Regan, N.C. & Turner, N.C. 1991. A role for physiology in breeding for improved wheat yield under stress. In E. Acevedo, A.P. Conesa, P. Monneveux & J.P. Srivastava, eds. Physiology-breeding of winter cereals for stressed Mediterranean environments, p. 179-194. Versailles, France, INRA-ICARDA.

Williams, E.J. 1952. The interpretation of interactions in factorial experiments. Biometrika, 39: 65-81.

Williams, W.T. (ed.) 1976a. Pattern analysis in agricultural science. Amsterdam, Elsevier.

Williams, W.T. 1976b. Hierarchical agglomerative strategies. In W.T. Williams, ed. Pattern analysis in agricultural science, p. 84-90. Amsterdam, Elsevier.

Witcombe, J.R. 1988. Estimates of stability for comparing varieties. Euphytica, 39: 11-18.

Witcombe, J.R., Joshi, A., Joshi, K.D. & Sthapit, B.R. 1996. Farmer participatory crop improvement: I. Varietal selection and breeding methods and their impact on biodiversity. Expl. Agric., 22: 443-460.

Wricke, G. 1962. Über eine Methode zur Erfassung der ökologischen Streubreite in Feldversuchen. Z. Pflanzenzüchtg., 47: 92-96.

Wricke, G. & Weber, W.E. 1986. Quantitative genetics and selection in plant breeding. Berlin, W. de Gruyter.

Yan, W., Hunt, L.A., Sheng, Q. & Szlavnics, Z. 2000. Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Sci., 40: 597-605.

Yates, F. & Cochran, W.G. 1938. The analysis of groups of experiments. J. Agric. Sci., 28: 556-580.

Yau, S.K. 1991. Need of scale transformation in cluster analysis of genotypes based on multi-location yield data. J. Genet. & Breed., 45: 71-76.

Yau, S.K. & Hamblin, J. 1994. Relative yield as a measure of entry performance in variable environments. Crop Sci., 34: 813-817.

Zavala-Garcìa, F., Bramel-Cox, P.J., Eastin, J.D., Witt, M.D. & Andrews, D.J. 1992a. Increasing the efficiency of crop selection for unpredictable environments. Crop Sci., 32: 51-57.

Zavala-Garcìa, F., Bramel-Cox, P.J. & Eastin, J.D. 1992b. Potential gain from selection for yield stability in two grain sorghum populations. Theor. Appl. Genet., 85: 112-119.

Zobel, R.W., Wright, M.J. & Gauch, H.G. 1988. Statistical analysis of a yield trial. Agron. J., 80: 388-393.

Previous Page Top of Page Next Page