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Revista mexicana de ciencias agrícolas

Print version ISSN 2007-0934

Rev. Mex. Cienc. Agríc vol.7 n.8 Texcoco Nov./Dec. 2016



Yield stability of 36 cultivars had collected in the Estado de Mexico

Laura Stephanie Flores Carrera1 

Delfina de Jesús Pérez López2  § 

Andrés González Huerta2 

Martín Rubí Arriaga2 

Artemio Balbuena Melgarejo2 

Francisco Gutiérrez Rodríguez2 

1Posgrado en Ciencias Agropecuarias y Recursos Naturales-Facultad de Ciencias Agrícolas- Universidad Autónoma del Estado de México- Campus Universitario “El Cerrillo”. El Cerrillo Piedras Blancas, Municipio de Toluca, Estado de México, México. A P. 435 (CPB-T). Tel: +52 (722) 2965531 Ext. 125 y 128.

2Centro de Investigación y Estudios Avanzados en Fitomejoramiento. FCAgr. UAEMex. CPB-T. Tel. y Fax: 01(722)2965518. Ext.148. (;;;;


This work was done in the spring-summer 2013 in five locations in the Estado de Mexico to assess the stability of the performance of 36 bean cultivars. A series of experiments was chosen in a randomized complete block design with three replications per location. The highly significant differences were observed among cultivars, between localities and their interaction (IGA) suggest that there is enough genetic variability to start a new program of plant breeding, the center environments of the Estado de Mexico are heterogeneous and significant IGA difficult to identify outstanding cultivars. These results were confirmed by applying the AMMI model and Eberhart and Russell changing the classification of stability with the proposal Carballo and Márquez. The best environments were San Nicolás Guadalupe and Barrio de Guadalupe, located in the State of Mexico municipalities of San Felipe del Progreso and San Mateo Atenco. The grain yield in five locations ranged from 0.24 to 6 t ha-1. Although there was no stable cultivars, the most outstanding were identified as T2, T4, T5, T6, T7, T26 and T30 (2.05 to 2.61 t ha-1), collected in the municipalities of Acambay, Jocotitlán, Zinacantepec and Metepec; except T4, which showed better response in good environments and was consistent, T12, T23, T28 and T36 responded better in good environments but were inconsistent.

Keywords: Vicia faba L.; AMMI model; Eberhart and Russell method; stability


The harnessing the variability and genetic diversity of native materials in bean (Vicia faba L.) is important for higheryielding varieties and stability, with larger seeds, resistant and/or tolerant to pests and diseases, among others, among others. The significant IGA may limit the identification of superior genotypes (Crossa et al., 1990; Yan and Kang, 2003; Annicchiarico and Iannucci, 2008; Gauch et al., 2008; Yahia et al., 2012; Pérez et al., 2015), mega-environments favorable or optimal technological packages that contribute to increasing the income of farmers (Annicchiarico, 1997; Flores et al., 2013; Temesgen et al., 2015).

The models of Eberhart and Russell (1966) and main additive and multiplicative interaction (AMMI Model) effects have been widely used in maize (Zea mays L.; González et al., 2010), bean (Mulusew et al., 2008; Karadavut et al., 2010; Al- Aysh, 2013; Abebe et al., 2015) and potato (Solanum tuberosum L.; Pérez et al., 2007; Pérez et al., 2009), among others. The AMMI model is superior to other multivariate techniques to enable the graphical interpretation of the responses of varieties of environments and the IGA and is still efficient with few repetitions (Zobel et al., 1988; Rodríguez et al., 2005; Gauch, 2006).

The bean is well suited to the Center of Mexico, tolerates low temperatures, fixes atmospheric nitrogen, has a high protein content and is advantageously associated with other species such as bean (Phaseoulus vulgaris L.) or maize (Zea mays L.). In the central region of Mexico there is little information on their agronomic performance, yield potential and stability. The main objective of this study was to identify collections of higher production and lower grain genotype*environment interaction using two methodologies.

Materials and methods

Description of the study area

This work was established in the spring-summer cycle of 2013 in five localities in the State of Mexico: San Diego (SD; L1), Rancho San Lorenzo (SL; l2), Barrio de Guadalupe (BG, L3), San Nicolás Guadalupe (SNG; L4) and Los Berros (LB; L5). Its characteristics are described in Table 1.

Table 1 Description of the study area. 

Localidad Municipio Tipo de suelo Altitud Ubicación geográfica Precipitación Temperatura media
(m) LN LO (mm) (°C)
Ll Almoloya de Juárez Vertisol 2 531 19°40'921" 99°69'023" 788 12.5
L2 Metepec Andosol 2 606 19°14'86" 99° 35' 24" 785 13
L3 San Mateo Atenco Feozen 2 570 19° 14' 55.03" 99° 32' 7.4" 800 12
L4 San Felipe del Progreso Andosol 2 740 19°36'30" 100°01'44" 892 15
L5 Villa de Allende Andosol 2 571 19° 23' 44.69" 100° 03' 11.23" 1 000 17

Fuentes: García (1988); Orozco et al. (2013); SEMARNAT (2006).

Genetic material

The 33 cultivars were considered from the State of Mexico and three varieties of the Institute of Agricultural Research and Training, Aquaculture and Forestry of the State of Mexico (ICAMEX) (Table 2).

Table 2 Cultivars evaluated in 2013. 

Código Productores Identificación Municipios
1 Ángel Cisneros Hernández Pathé Acambay
2 Félix Peralta Rivera Boshindo Acambay
3 Porfirio Alcántara Becerril Hodongu Acambay
4 Palemón Becerril Landeros Agua Limpia Acambay
5 Jorge Mateo Estrada San Pedro de los Metates Acambay
6 Porfirio Garfias Frías Pueblo Nuevo Acambay
7 Carlos Barreno González Los Reyes Jocotitlán
8 Benjamín Álvarez Peña Tixmadeje Acambay
9 Pedro Plata García Chanteje Acambay
10 Héctor Muciño Muciño Santa María Nativitas Calimaya
11 Encarnación Estrada González Santa María Nativitas Calimaya
12 Sebastián Matías González San Lorenzo Cuahutenco Calimaya
13 Roberto Hernández Torres Calimaya Calimaya
14 Tiburcio Sánchez Ortega Calimaya Calimaya
15 Carlos Zarza Torres Zaragoza de Guadalupe Calimaya
16 Encarnación Robles Trujillo Zaragoza de Guadalupe Calimaya
17 Edgar Colín Flores Zaragoza de Guadalupe Calimaya
18 Manuel Gutiérrez Navarrete San Marcos de la Cruz Calimaya
19 Moisés Cortéz Gomora San Marcos de la Cruz Calimaya
20 Roberto Muñoz Arriaga Mexicaltzingo Mexicaltzingo
21 Mateo Torres Gutiérrez San Pedro Tlaltizapán Santiago Tianguistenco
22 Teófilo Carrasco Onofre San Pedro Tlaltizapán Santiago Tianguistenco
23 Ramón Martínez Cejudo Santa Cruz Atizapán Santa Cruz Atizapán
24 Jesús Martínez Antúnez Santiago Tianguistenco Santiago Tianguistenco
25 Teodolfo Hernández Cipriano Cacalomacán Toluca
26 Eudoxia Ramírez Rincón Santa Cruz Cuahutenco Zinacantepec
27 Carlos Estrada Velasco Almoloya del Río Almoloya del Río
28 ICAMEX Monarca Metepec
29 ICAMEX Diamante Metepec
30 ICAMEX San Pedro Tlaltizapán Metepec
31 Sara L. González Romero Cacalomacán Toluca
32 Pedro Reyes Carmona San Marcos de la Cruz Calimaya
33 Sara L. González Romero Cacalomacán Toluca
34 Omar Franco Mora Santa María Tlalmimilolpan Lerma
35 Adriana Meza Sosa Santiago Tianguistenco Santiago Tianguistenco
36 Ernesto Eduarte Garay San Nicolás Guadalupe San Felipe del Progreso

Experimental design and size of the plot

An experimental design of randomized complete block with three replicates per location in a series of experiments was used. The plot consisted of three rows of 4 m long and 0.80 m wide; the central sulcus was the useful plot (3.20 m2).

Agronomic management of the trials

The fallow and dredge step was performed. The ridged was 0.8 m. Manual seeding in April made 13 (L1), 17 (L2), 20 (L3) and 27 (L4) and 01 May (L5) 2013. In L1, L2, L4 and L5 fertilized with 60N- 60P- 30K (urea, 46%; calcium triple superphosphate, 46%; potassium chloride, 60%, and 46N-18P-00K). In L2 a foliar organic fertilization 5 ml L-1 it was added and vermicompost to the mixture. The L3 is just 2.5 t ha-1 of cattle manure was applied. The two spuds were performed in L1 and L3 and L2, L4 and L5. In two irrigations L3, L1 and L2 in one and L4 and L5 were handled temporarily applied. The weed control was manual L2, L3, L4, and L5. In L1 was applied basagran 480 (1.5 L ha-1). In L1 and L4 were made three applications of Manzate (Mancozeb) and Cupravit mix (Copper oxychloride + mancozeb; 1 kg ha-1) and one of Velcron 60 (1.5 L ha-1). In L2 was applied Prosal 50 pH (1g L-1 water), Mancozeb (2.5 L-1 water), 40-80 Carioca (2ml L-1 water) and adhesive (Insect soap). L3 and L5 was not applied agrichemical. The harvest took place after physiological maturity.

Statistical analysis

The analysis were performed of individual and combined variance and mean comparison between locations and between bean cultivars for grain yield (Tukey, p= 0.01). To determine yield stability was used model Eberhart and Russell (1966) with the modification proposed by Carballo and Márquez (1970); according to them, a genotype is stable if the average above the overall average and coefficient and deviations regression equal to 1 and zero, respectively. The calculation of indicators stability of Eberhart and Russell took the system for statistical analysis (Statistical Analysis System, SAS, version 6.03 for Windows) using the algorithm built by Mastache and Martínez (1998). With the AMMI model were analyzed environments, cultivars and their interaction in one or two major components (Zobel et al., 1988; Rodríguez et al., 2005; Pérez et al., 2009) using programs for SAS made by Vargas and Crossa (2000).

Results and discussion

The highly significant differences (p= 0.01) than were detected in Table 3, 4 and 5 indicate that at least two locations or between two cultivars there are real differences in grain yield and the behavior of beans varied depending on the environments; They also suggest that the five towns in the Central Estado de Mexico are very heterogeneous, that there is sufficient genetic variability among 36 cultivars of bean and the IGA difficult to identify cultivars greater performance and stability, making it possible that some of the cultivars have better adaptation to a specific environment (Annicchiarico and Iannucci, 2008; Yahia et al., 2012; Flores et al., 2013; Temesgen et al., 2015), very common situation for the Valles Altos the Mexico, where there is great environmental heterogeneity (González et al., 2010; Pérez et al., 2014).

Table 3 Mean squares and statistical significance of the values of F of analysis of individual variance, combined (AC) and AMMI model (AMMI). 

FV GL L1 L2 L3 L4 L5 AC y AMMI
San Diego Rancho San Lorenzo Barrio de Guadalupe San Nicolás Guadalupe Los Berros
Repeticiones 2 0.03 ns 0.06 ns 0.1 ns 1.2 ** 0.02 ns
Cultivares 35 0.16 ** 0.73 ** 3.83 ** 4.54 ** 0.49 **
L 4 68.11 **
R (L) 10 0.28 **
C 35 2.52**
L * C 140 1.81 **
CP1 38 3.24 **
CP2 36 2.86 **
CP3 34 0.55 ns
CP4 32 0.25 ns
CP5 30 0 ns
Error 70 350 0.015 0.052 0.111 0.18 0.063 0.08
Total 107 539
x- 1.05 1.34 2 2.85 0.939 1.63
CV (%) 11.6 17 16.6 14.9 26.8 17.7

* ó ** = significativo al 5 ó 1%. F.V.= fuente de variación; G.L.= grados de libertad, L= localidades; R (L)= repeticiones dentro de L; C= cultivares; L x C= interacción cultivares x localidades; C.V.= coeficiente de variación.

Table 4 Comparison of means between bean cultivars (Tukey, p= 0.01). 

Cultivares L1(SD) L2(SL) L3(BG) L4(SNG) L5(LB) Combinado
1 1.38 gbc 1.11 c-i 3.1 a-f 2.52 e-l 1.24 a-e 1.87 c-f
2 0.88 e-j 0.64 ghi 3.33 a-e 4.74 abc 0.94 a-f 2.11 bcd
3 0.79 g-j 1.12 c-i 2.16 e-k 2.36 f-l 1.53 ab 1.59 e-i
4 1.34 gcd 1.24 b-h 2.96 a-f 3.94 cde 1.17 a-f 2.13 bcd
5 0.94 c-j 0.75 ghi 3.9 a 4.3 bcd 1.27 a-e 2.23 abc
6 0.71 ij 1.76 a-d 0.82 l-p 6 a 1.81 a 2.22 abc
7 1.21 b-g 1.19 b-h 3.56 abc 4.73 abc 1.11 a-f 2.36 ab
8 0.89 d-j 1.21 b-h 4.16 a 1.67 h-l 0.93 a-f 1.77 c-gh
9 1.4 ab 0.97 d-i 3.23 a-f 1.45 i-l 1.34 a-d 1.68 d-h
10 1.19 b-h 2.3 a 0.37 op 2.71 e-k 0.95 a-f 1.5 f-k
11 1.19 b-h 1.18 b-h 1.1 k-p 2.82 d-k 0.69 b-f 1.4 g-k
12 0.99 b-j 1.21 b-h 1.33 i-p 3.61 c-f 0.49 c-f 1.52 f-j
13 1.01 b-j 0.96 d-i 1.76 g-n 3.48 c-g 0.44 def 1.53 f-j
14 1.82 a 1.93 abc 1.46 h-p 3.17 c-h 0.79 b-f 1.83 c-g
15 0.75 hij 1.61 a-e 1.6 h-o 1.7 h-l 0.44 def 1.22 ijk
16 1.05 b-j 1.46 a-f 0.86 l-p 2.83 d-k 0.56 c-f 1.35 h-k
17 1.01 b-j 2.23 a 2 f-l 1.99 g-l 0.58 c-f 1.56 f-j
18 0.98 b-j 1.92 abc 0.99 k-p 2.76 d-k 0.36 ef 1.4 g-k
19 1.19 b-h 1.98 ab 0.65 m-p 1.48 i-l 0.6 c-f 1.18 ijk
20 1.07 b-j 1.61 a-e 2.53 c-i 2.74 d-k 1.18 a-e 1.83 c-g
21 1.25 b-f 1.91 abc 0.92 l-p 2.75 d-k 0.9 a-f 1.54 f-j
22 1.27 b-e 1.92 abc 1.8 g-m 2.95 d-j 0.81 b-f 1.75 d-h
23 1.02 b-j 1.02 d-i 1 k-p 3.58 c-f 1.19 a-e 1.56 f-j
24 0.69 j 1.57 a-f 2.36 c-j 3 d-i 1.03 a-f 1.73 d-h
25 1.14 b-j 0.92 d-i 2 f-l 1.04 l 1.82 a 1.38 g-k
26 1.17 b-i 0.92 d-i 3.8 ab 5.85 ab 1.33 a-d 2.61 a
27 0.91 d-j 1.71 a-d 2.6 b-h 2.68 e-k 0.61 b-f 1.7 d-h
28 1.19 b-h 0.72 ghi 3.43 a-d 3.38 c-g 0.93 a-f 1.93 b-f
29 0.93 d-j 1.26 b-h 2.2 d-k 1.62 h-l 1.41 abc 1.48 f-k
30 1.18 b-h 1.94 abc 3.23 a-f 3.46 c-g 0.43 def 2.05 b-e
31 0.9 d-j 0.33 i 0.72 m-p 0.98 l 0.24 f 0.63 l
32 0.81 f-j 1.71 a-d 0.28 p 1.41 jkl 1.39 abc 1.12 jk
33 0.93 c-j 1.14 b-i 1.23 j-p 1.96 g-l 0.53 c-f 1.16 ijk
34 0.77 g-j 1.31 b-h 2.93 a-g 2.35 f-l 1.11 a-f 1.69 d-h
35 0.96 b-j 0.84 e-i 1.2 j-p 1.34 kl 0.96 a-f 1.06 kl
36 0.86 e-j 0.57 hi 0.56 nop 3.14 d-h 0.52 c-f 1.13 jk
DMSH 0.45 0.84 1.23 1.57 0.93 0.12
Media 1.05 d 1.34c 2 b 2.85 a 0.93 d 1.63
Ia - 0.58 - 0.29 0.36 1.21 - 0.69

Medias con las misma letra dentro de cada columna son iguales estadísticamente; Ia = índice ambiental. Los códigos para cultivares y localidades fueron definidos en las Cuadro 2 y 1, respectivamente.

Table 5 Analysis of variance, arithmetic and stability parameters of Eberhart and Russell (1966)

Fuentes de variación GL Suma de cuadrados Cuadrados medios F calculada
Total (trat) 179 204.804
Genotipos (G) 35 29.452 0.841 10.5 **
Residual 144 175.352
Ambientes (lineal) 1 90.832 90.832 50.18 **
Genotipo x ambiente (lineal) 35 37.314 1.066 2.439 ns
Desviación ponderada 108 47.205 0.437
Cultivar X- Bi s2di
T1 1.87 0.86 ns 0.381**
T2 2.11 2.22 ** 0.297**
T3 1.59 0.71 ns 0.143**
T4 2.13 1.54 ** 0.067*
T5 2.23 1.97 ns 0.634**
T6 2.22 2.12 ns 2.459**
T7 2.36 2.06 ** 0.161**
T8 1.77 0.8 ns 1.933**
T9 1.68 0.35 ns 0.914**
T10 1.5 0.54 ns 0.98**
T11 1.4 0.9 ns 0.178**
T12 1.52 1.42 ns 0.22**
T13 1.53 1.46 * 0.058*
T14 1.83 0.86 ns 0.345**
T15 1.22 0.56 ns 0.153**
T16 1.35 0.91 ns 0.32**
T17 1.56 0.59 ns 0.371**
T18 1.4 0.91 ns 0.443**
T19 1.18 0.12 ns 0.407**
T20 1.83 0.92 ns 0.046*
T21 1.54 0.69 ns 0.396**
T22 1.75 0.93 ns 0.097**
T23 1.56 1.18 ns 0.507**
T24 1.73 1.15 ns 0.05*
T25 1.38 -0.08 ns 0.285**
T26 2.61 2.61 ** 0.371**
T27 1.7 1.08 ns 0.174**
T28 1.93 1.5 ns 0.52**
T29 1.48 0.34 ns 0.169**
T30 2.05 1.5 ns 0.303**
T31 0.63 0.26 ns 0.062*
T32 1.12 0.05 ns 0.406**
T33 1.16 0.62 ns 0.008ns
T34 1.69 0.91 ns 0.364**
T35 1.06 0.22 ns -0.02ns
T36 1.13 1.17 ns 0.506**

In the Valle de Toluca-Atlacomulco, Estado de Mexico and other locations in Central Mexico has been concluded that the environmental variability that prevails in this region is mainly attributed to the differences in altitude, soil types, temperatures and rainfall (Rodríguez et al., 2005; González et al., 2008; Reynoso et al., 2014; Rodríguez et al., 2015; Franco et al., 2015), as those that can be seen in Table 1.

The heterogeneity between localities (Figure 1) is explained by the significant differences were mainly observed among the best environments (SNG and BG) with the other three sites (SL, SD and LB). The average grain yields for LB, SD, SL, BG and SNG were 0.93, 1.05, 1.34, 2 and 2.85 t ha-1, with an arithmetic mean of 1.63 t ha-1 (Table 4). This value is similar to the state of Mexico (1.48 t ha-1; Orozco et al., 2013) and higher than the national (0.66 t ha-1; Pérez et al., 2014). In considering the phenotypic variability within localities it noted that SNG and the BG highest grain yields were recorded (6 and 4.16 t ha-1, respectively). Both values are higher than those obtained commercially in other Mexican states like Morelos, Sonora, Durango, Guanajuato and Veracruz (between 1.48 and 3.42 t ha-1; Pérez et al., 2014). Karadavut et al. (2010) recorded grain yields between 2.52 and 3.21 t ha-1. The above results are similar to those observed by Orozco et al. (2013) and Pérez et al. (2014).

Figure 1 Grain yield for cultivars and environments and their interaction with the principal component 1 of AMMI model (the codes were defined in Table 1, 3 and 4). 

Rodríguez et al. (2015) and Franco et al. (2015) commented that the places that make the Valle de Toluca, in the Estado de Mexico, are very heterogeneous and that an average of 20 km distance between two of them is sufficient to cause statistically significant environmental variability. In this study the greatest distance corresponded to the towns of SNG and SL, and is greater than 100 km.

By applying the method of Eberhart and Russell (1966), with the modification Carballo and Márquez (1970) found that only T27, collected in Almoloya del Río, had good response in all environments but this was inconsistent; production of grain (1.70 t ha-1) was slightly higher than the average for the state of Mexico (Pérez et al., 2014) and ranged in five locations from 0.63 to 2.68 t ha-1 (Table 4, 5 and 6). This method has been widely used but has also been widely criticized because of the dependence of the environmental effects of the varieties chosen and the nonlinearity of these responses to the environment (Shukla, 1972). However, Márquez (1991) noted that this methodology is useful to determine adaptability and, therefore, association genotype x environment, allowing identification and recommendation of genotypes with the best response to favorable environments and unfavorable (González et al., 2010).

Table 6 Classification of stability with the model of Eberhart and Russell (1966) with the modification proposed by Carballo and Márquez (1970)

Categoría bi s2di Definición de estabilidad Cultivares de haba
A 1 0 Estable Sin integrantes
B 1 >0 Buena respuesta en todos los ambientes pero inconsistente T27
C <1 0 Mejor en ambientes desfavorables y consistente T20, T31, T33, T35
D <1 >0 Mejor en ambientes desfavorables pero inconsistente T1, T3, T8, T9, T10, T11, T14, T15, T16, T17, T18, T19, T21, T22, T25, T29, T32, T34
E >1 0 Mejor en buenos ambientes y consistente T4, T13, T24
F >1 >0 Mejor en buenos ambientes pero inconsistente T2, T5, T6, T7, T12, T23, T26, T28, T30, T36

Cultivars identified as T20, T31, T33 and T35 showed better response and were consistent in unfavorable environments (bi <1; S2di= 0); sites with negative environmental indices were LB, SD and SL. The average of this group was 1.17 t ha-1 but T31 only produced 0.63 t ha-1; this group value is higher than the national average (0.66 t ha-1) and lower than the averages of the state of Mexico and global (1.48 and 2.06 t ha-1, respectively; Pérez et al., 2014).

The cultivars T1, T3, T8, T9, T10, T11, T14, T15, T16, T17, T18, T19, T21, T22, T25, T29, T32, and T34 had better response in unfavorable environments but were inconsistent; their grain yields ranged from 1.12 to 1.87 t ha-1. According to Pérez et al. (2014) the arithmetic mean is higher than the national (0.66 t ha-1), slightly larger than the state of Mexico (1.46 t ha-1) and lower than the world (2.06 t ha-1). Of these, the most outstanding were T1, T14, T8 and T22 (1.87, 1.83, 1.77 and 1.75 t ha-1, respectively; Table 4, 5 and 6).

The group formed by T4, T13, and T24 had a better response in good environments and were consistent (bi >1; S2di = 0). T4 (2.13 t ha-1) was the best but the group average was 1.79 t ha-1, higher than the national and the state of Mexico and lower than the world (Table 4, 5 and 6; Pérez et al., 2014).

The above results suggest that the exploitation of specific adaptation should receive more attention as a proposal to increase diversity and bean grain yields because the instability of the genetic material reliable identification difficult in the Central region of the Estado de Mexico. Rodríguez et al. (2005), Crossa et al. (1990); Pérez et al. (2009); Annicchiarico and Iannucci (2008); Pérez et al. (2014) have highlighted the previous situation.

In Figure 2 it was found that the main components 1 (48.68%) and 2 (40.61%) accounted 89.29% of the variation related environments (E), cultivars (C) and their interaction (E x C). In this context the graphical interpretation of the interrelationships in the biplot are reliable (Annicchiarico and Iannucci, 2008, Tadesse and Abay, 2011; Abebe et al., 2015). In the combined variance analysis it can be seen that the contribution of A, C and A x C was 44.37, 14.36 and 41.27%, respectively. These results confirm the fact that genetic variability between beans that are grown in the central region of the state of Mexico is masked by the effects originating locations and interaction A x C. As other researchers have noted, genotype x environment interaction causes confusion in the estimation of genetic parameters, reduces the response to selection, and difficult to identify superior genotypes; however, analysis and proper interpretation identifies mega-environments, detect genotypes with specific or broad adaptation, propose strategies appropriate or generate, validate, apply and/or transfer technology (Márquez, 1992; Crossa, 1990; Rodríguez et al., 2002; González et al., 2010; Pérez et al., 2014).

Figure 2 Biplot for 1 and 2 main components of AMMI model. 

In other multivariate techniques have been recommended values greater than 50% for the interrelationships that can be detected in the CP1 and CP2 are reliable (Sánchez, 1995; González et al., 2010; Reynoso et al., 2014). González et al. (2010) concluded that the contribution of AMMI model variability caused by environments, genotypes and their interaction was 45.11, 48.40 and 6.48%, respectively, when they evaluated the stability of the performance of corns recommended for commercial planting in Valle de TolucaAtlacomulco, Mexico.


The highly significant differences were observed among cultivars, between localities and their interaction (IGA) suggest that there is enough genetic variability to start a new program of plant breeding, the center environments state of Mexico are heterogeneous and significant IGA difficult to identify stable cultivars. These results were confirmed by applying the AMMI model and Eberhart and Russell with the modifications proposed by Carballo and Marquez. The best environment were San Nicolas Guadalupe and Barrio de Guadalupe, located in the State of Mexico municipalities of San Felipe del Progreso and Metepec. Grain yield in five locations ranged from 0.24 to 6 t ha-1. Although there was no stable cultivars, the most outstanding were identified as T2, T4, T5, T6, T7, T26 and T30 (between 2.05 and 2.61 t ha-1), collected in the municipalities of Acambay, Jocotitlan, Zinacantepec and Metepec; except T4, which showed better response in good environments and was consistent, T12, T23, T28 and T36 responded better to good environments but were inconsistent.

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Received: June 2016; Accepted: September 2016

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