Multi-environmental evaluation for grain yield and its physiological determinants of quinoa genotypes across Northwest Argentina

Curti, Ramiro N.; Vega, Abelardo J. de la; Andrade, Alberto; Bramardi, Sergio Jorge; Bertero, Héctor Daniel

doi:10.1016/j.fcr.2014.06.011

Cited by 51 publications

(49 citation statements)

References 38 publications

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“…The genetic diversity of quinoa for TKW and consequently for grain size, an important factor regarding marketability of the raw product, was also reported in former literature [47,49]. Contrary to results of Curti et al, our cultivars showed no negative correlation between grain yield or its main determinant grain number and TKW [53]. In general, the TKWs of our cultivars agreed with ranges reported for studies in South Asia (1.4-3.7 g; [47]) or southern Europe (1.8-3.6 g; [51]), but differed in comparison with results from the Andean region (3.0-4.7 g; [54]).…”

Section: Discussionsupporting

confidence: 63%

Yield and Quality Characteristics of Different Quinoa (Chenopodium quinoa Willd.) Cultivars Grown under Field Conditions in Southwestern Germany

et al. 2018

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Due to its highly nutritive compounds, the demand for quinoa, a small grain originating from the Andean region of South America, increased rapidly over the last years. However, the main producing countries Bolivia and Peru cannot cover the growing demand. Therefore, the interest of European farmers in cultivating quinoa as a profitable source of income rose very fast. Thanks to a broad genetic diversity an adaption to various climatic conditions is possible. The objective of this study was to evaluate the stability of agronomic performance in two consecutive growing periods (2015 and 2016) of four European quinoa cultivars (Puno, Titicaca, Jessie, Zeno), originating from different genepools to identify a suitable cultivar to grow in southwestern Germany. Measurements included grain yield, thousand kernel weight (TKW), saponin content, protein content, crude fat content, amino acid profile and fatty acid profile. This study demonstrated the possibility of an economic production of quinoa under the environmental conditions in southwestern Germany, combining competitive yields (1.73-2.43 Mg ha −1 ) with a high grain quality regarding protein content (11.9-16.1%), essential amino acid content (20.35-30.02 g 100 g −1 crude protein), fat content (5.5-7.5%) and fatty acid profile (consists of 60% linoleic acid). Depending on cultivar, the investigated yield (TKW and protein content)-and quality ((semi-)essential amino acids)-traits varied more or less sensitive, which was attributed to lower precipitation and higher temperatures in 2015. Furthermore, best yield-and quality-characteristics were not combined in one cultivar, wherefore the selection of a specific quinoa cultivar has to be aligned with the production aim. Figure 3. Thousand kernel weight (g) for the two years and four quinoa cultivars (Zeno, Jessie, Puno, Titicaca). Within year, bars headed by the same lowercase letter are not significantly different (p < 0.05). Capital letters compare the two years for the same cultivar, bars headed by the same letter are not significantly different (p < 0.05). Error bars indicate the standard error of the mean.Regarding grain protein concentrations, the four cultivars showed significant differences within and between years, however, within year differences occurred only in 2015 (Figure 4). In 2015, grain Agronomy 2018, 8, 197 9 of 19 protein contents varied significantly between 11.9 (cv. Zeno) and 16.1% (cv. Jessie), whereas values in 2016 were nearly at the same level (12.3-13.1%).

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Section: Discussionsupporting

confidence: 63%

Yield and Quality Characteristics of Different Quinoa (Chenopodium quinoa Willd.) Cultivars Grown under Field Conditions in Southwestern Germany

et al. 2018

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“…These limitations motivated the accomplishment of MET analyses in the context of mixed models, which theoretically have greater flexibility to deal with missing data in which is commonly observed in datasets obtained from MET experiments (Curti et al, 2014;Orellana et al, 2014). In this sense, we can highlight the versions of the linearbilinear mixed AMMI and GGE models that were initially proposed by Piepho (1997Piepho ( , 1998, Smith et al (2001), and Piepho and Mohring (2006).…”

Section: Study Of Predictive Capacity In Ammi Modelmentioning

confidence: 99%

Research Article Predictive ability of AMMI and factorial analytical models in the study of unbalanced multi-environment data

Romão¹,

Nuvunga²,

Silva³

et al. 2019

Genet. Mol. Res.

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Efficient analysis of datasets from multi-environment trials (MET) is of paramount importance in plant breeding programs. Several methods have been proposed for this purpose, each of them having advantages and disadvantages, depending on the objectives of the study. We examined the robustness in the predictive power of models that have been widely used in the study of genotype-byenvironment interaction such as AMMI (additive main-effects and multiplicative interaction) models via EM algorithm, Bayesian AMMI models with homogeneity (BAMMI), heterogeneity of variances (BAMMI-H) and the Analytical Factorial model (FA). To check the efficiency of these methods, genotype and genotype-byenvironment interaction effects were simulated and further unbalances were included at levels of 10, 33 and 50% loss of genotypes in the environments. To evaluate the predictive power of the proposed models, the PRESS (prediction error sum square) statistics and the Cor (correlation between predicted and observed ©FUNPEC-RP www.funpecrp.com.br Genetics and Molecular Research 18 (3): gmr18176 R.F. Romão et al. 2value) were used. The genotype-environment interaction models had low sensitivity to missing data since all models showed correlations above 0.5 in all scenarios -even with high unbalance levels (50%). In general, there were differences in predictive accuracy among the models in different scenarios, with a slight advantage for the Bayesian models in the correlation among observed and predicted data ranging from 0.79 to 0.855 compared to 0.591 to 0.853 obtained from the competing models. Similar results were observed for the PRESS (4.988 to 8.027) in Bayesian models compared to competing models (5.411 to 23,361). Overall, there was slight advantage of the Bayesian models in unbalanced scenarios.

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“…Williams (1976) has been used to predict the response to selection across the environments. A higher variance contribution of G x E effect as compared to genotype effect was recorded in quinoa (Curti et al, 2014).…”

mentioning

confidence: 92%

Multi-Environmental Evaluation for Grain Yield and Its Components of Quinoa Genotypes Across the North Western Coast of Egypt

El-Sadek

2017

Egyptian Journal of Desert Research

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multi environmental trial involving five quinoa genotypes i.e., KVL-SRA2, KVL-SRA3, Regalona, Q-37 and Q-52 was conducted along the North Western Coast of Egypt. The eight tested environments included two sites in the first season of 2014/2015 i.e. irrigated Matrouh and rainfed Matrouh, while in the second season of 2015/2016 there were six sites including three rainfed sites of Ras El Hekma, El Neguilla and Matrouh as well as an irrigated site in Matrouh with three planting dates i.e., 1 Nov.,15 Nov., and 1 Dec. Grain yield was significantly influenced by both genotypes and environments; Regalona genotype produced the highest grain yield in most of the studied environments (6 out of 8 environments) with the highest grain yield of 3.08 t/ha in irrigated Matrouh site in the first season. Moreover, it showed more drought tolerance as compared to the other genotypes. The environment effect was the major source of variation as compared to the genotype and genotype × environment interaction effects, and attributed to 88.5% of the total variation in the grain yield. Two-mode pattern analysis of the environment-standardised matrix of grain yield revealed three genotypic groups of different response pattern across environments. In general, the grain yields of the genotypes were lower under rainfed conditions as compared to irrigated conditions, except for Matrouh in the second season for the late irrigated planting dates i.e., 15 Nov. and 1Dec. The high tolerance of quinoa to drought makes it an excellent choice for the diversification of future agriculture in the North Western Coast of Egypt and other regions with similar harsh conditions.

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Multi-environmental evaluation for grain yield and its physiological determinants of quinoa genotypes across Northwest Argentina

Cited by 51 publications

References 38 publications

Yield and Quality Characteristics of Different Quinoa (Chenopodium quinoa Willd.) Cultivars Grown under Field Conditions in Southwestern Germany

Yield and Quality Characteristics of Different Quinoa (Chenopodium quinoa Willd.) Cultivars Grown under Field Conditions in Southwestern Germany

Research Article Predictive ability of AMMI and factorial analytical models in the study of unbalanced multi-environment data

Multi-Environmental Evaluation for Grain Yield and Its Components of Quinoa Genotypes Across the North Western Coast of Egypt

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