Cultivation of maize in different environments and their effects on agronomic traits

Carvalho, Ivan Ricardo; Eickhoff, Francisco Goi; Silva, Tiago Silveira da; Schulz, Adriano Dietterle; Ourique, Rafael Soares; Malheiros, Tainá Froncek; Foguesatto, Felipe da Rosa; Sarturi, Marlon Vinicius Rosa; Loro, Murilo Vieira; Hutra, Danieli Jacoboski

doi:10.33158/asb.r125.v7.2021

Cited by 3 publications

(3 citation statements)

References 23 publications

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“…AMMI analysis is one of the most used methodologies in the area of genotype selection due to the ease of interpretation of results. Its application includes several crops such as soybean (Glycine max (L.) Merrill) (Carvalho et al, 2021), wheat (Triticum aestivum L.) (Schneider et al, 2021, however these cited studies showed explanations by the principal components inferior to the present study. The first main component was responsible for representing 68.1% of the general effects attributed to the G x E interaction and the second 26.3%.…”

Section: Resultscontrasting

confidence: 74%

“…The interaction genotype x environment in white oat exerts a great variation in the characters that express grain yield, which is one of the greatest difficulties faced in the positioning of cultivars. In corn, Carvalho et al (2021) concluded that the characters plant height, cob insertion height, thousand grain weight and grain yield are significantly influenced by the genotype x environment interaction.…”

Section: Introductionmentioning

confidence: 99%

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Positioning of White Oat Cultivars in Different Environments for High Grain Productivity in Organic System

Savicki

Carvalho

Loro

et al. 2023

Trop Subtrop Agroecosyst

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Background. White oat is a multifunctional species with significant benefits to human health, so the positioning of genotypes in the organic system is substantial to promote the expression of maximum productive potential. Objective. To select and identify the genotypes with greater stability and productive adaptability. Methodology. The study was carried out in 11 environments located in the countries of Brazil (states of Rio Grande do Sul and Paraná) and Paraguay (Itapúa) in 2019 and 2020, evaluating in each of them four genotypes of white oats (Avena sativa) (URS Corona, URS Brava, IPR Artemis and IPR Afrodite) each considered as treatments. The experimental design was randomized blocks with four replications per treatment. The variables analyzed were grain yield (GY, kg ha-1) and the cycle in days from emergence to physiological maturity (PM). With the presence of G x E interaction, AMMI and GGE biometric methods were used to study adaptability and stability. Results. With the data obtained, it was possible to form three mega-environments with the identification of specifically adapted genotypes. The URS Brava genotype was characterized as the ideal genotype, with high stability and wide adaptability for grain yield, which can be positioned in all environments. High altitudes promoted a longer crop cycle and lower grain yield, while low altitudes induced a shorter cycle and grain yield maximization of white oat genotypes. Implications. The current results indicate that it is possible to position a single genotype within a region formed by similar environments, as well as it was identified that the crops should preferably be carried out in regions of lower altitudes. Conclusion. The URS Brava genotype is considered the ideal genotype with high potential for productivity at low altitudes.

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Section: Resultscontrasting

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Positioning of White Oat Cultivars in Different Environments for High Grain Productivity in Organic System

Savicki

Carvalho

Loro

et al. 2023

Trop Subtrop Agroecosyst

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“…With the data exposed (Figure 2), it is evident that there were periods with lower rainfall, October and March, but in the other periods, the rainfall and temperatures were consistent for the corn crop. Pearson correlation analysis has been used in studies with corn (Carvalho et al, 2021;Barbosa et al, 2019) to show linear relationships between agronomic and meteorological characteristics. According to the Pearson correlation (Figure 3), in the association of 16 variables, it was possible to verify that for higher grain yields, there is a strong correlation (0.61) of altitude (AL) with grain yield (GY); similarly, the population (PO) obtained a medium magnitude correlation (0.43) with grain yield (GY).…”

Section: Resultsmentioning

confidence: 99%

Prediction of Corn Grain Productivity as a Function of Altitude and Plant Population

Sarturi

Teixeira

Carvalho

et al. 2022

Rev. Agric. Neotrop.

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This study aimed to evidence the best arrangement of plants according to altitudes to potentialize corn grain yield. An information survey was performed in the 2019/20 harvest, using 144 producers responsible for corn grain production fields totaling 2,301.3 hectares in 144 environments of Rio Grande do Sul. Within 144 environments, 14 corn hybrids were arranged: DKB 240 PRO, DOW 2A620, AG 9045 PRO3, AS1555 PRO3, AS1572 PRO2, AS1656, BIOGENE 7046, DKB 250 PRO2, DKB 290 PRO3, DKB 330RR, NS56, P1630HX, P30F53HX, and SH7910. Yield, morphological, and phenological variables were measured, and geographic and meteorological information was collected. There is a positive influence on grain yield according to altitude and plant population characteristics. At lower altitudes, increasing plant population is essential to enhance grain yield.

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Factor analysis and environmental stratification in the assessment of grain sorghum adaptability and stability

Ribeiro¹,

Salvador²,

Oliveira³

et al. 2021

ASB J.

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The environmental stratification studies are crucial when releasing hybrids for different growing regions. An outstanding performance of a genotype in one environment does not qualify it for indication to all environments, due the occurrence of GxE interaction. Environmental stratification aim the breeders to form groups of environments that minimize GxE interaction. The purpose of this work was to evaluate the use of factor analysis in preliminary environmental stratification assisting at the recommendation of grain sorghum cultivars. Twenty-five hybrids were evaluated, using a randomized block design, in 12 locations during the 2015/16 season. Initially, the individual analysis of the experiments was carried out and later the joint analysis, aiming to examine the existence of G×E interaction. The means of the hybrids in the individual analyses were used to obtain the correlation matrix between pairs of environments. The factorization of this matrix was also carried out via factor analysis in order to group together the environments that most correlated with respect to the hybrids performance. Thus, differential performance between hybrids was observed through individual analyses for all the environments, with the exception of Sete Lagoas and Teresina. The joint analysis revealed the existence of a significant G×E interaction, that is, a differential behavior of the hybrids in relation to the evaluated environments. Based on the criterion of the analysis of the proportion of explained variance, it was found that six factors captured an accumulated variation of 86.29%, and the average communality observed was of 0.86. Considering the geographic and edaphoclimatic variables in the cultivation period, a pattern was not observed among the grouped places, but it is noteworthy that the grouping of places is a function of the performance of the evaluated genotypes, which can be similar even under different conditions. Given the results presented, factor analysis proved to be a tool with potential to perform environmental stratification and assist in the recommendation of grain sorghum cultivars for different regions.

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Cultivation of maize in different environments and their effects on agronomic traits

Cited by 3 publications

References 23 publications

Positioning of White Oat Cultivars in Different Environments for High Grain Productivity in Organic System

Positioning of White Oat Cultivars in Different Environments for High Grain Productivity in Organic System

Prediction of Corn Grain Productivity as a Function of Altitude and Plant Population

Factor analysis and environmental stratification in the assessment of grain sorghum adaptability and stability

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