Álvaro De Oliveira Santos

Quantitative characterization of corn plant components according to planting time and grain maturity stage

The objective of this experiment was to characterize the dry matter production of vegetative components (stalk, leaf, husk, and cob) and reproductive components (grain) of the corn plant according to planting time and the grain maturity stage. Eight corn hybrids with different agronomic characteristics were used to carry out the experiments. Two experiments were performed with planting undertaken on 11/11/2007 and 12/12/2007 under a conventional tillage system. The eight corn cultivars were harvested at three grain maturity stages, half milk line, three quarters milk line and black layer. An 8 × 3 factorial (eight hybrids and the three cutting times) randomized blocks design was used, with three replications. Individual analysis of variance was performed for each experiment. Then joint analysis of variance was performed involving the two planting times. The means were grouped and the hybrids used as replications to characterize the components. Delay in planting significantly reduced the dry matter productivity of the whole plant; however, it did not reduce its degradability. The greatest dry matter productivity obtained was that of grain, followed by stalk, leaf, husk and cob. The vegetative components, leaves and husk, lose quality with delay in planting. The leaf is the vegetative component of highest quality, while the cob is the worst. Delay in planting increases the neutral detergent fiber content of the stalk, husk and cob.

Reação de híbridos de milho e comparação de métodos para avaliação da Cercosporiose e Mancha Branca

With the objective of evaluating the resistance level of commercial corn hybrids and comparing the efficiency of the estimates of the area under the disease progress curve (AUDPC) and that of the phenotypical stability parameters in the evaluation of resistance maize white spot and gray leaf spot, three experiments were carried out in the agricultural year of 2007/2008 in three environments. Twelve commercial maize hybrids were used, in a randomized block experimental design with three replications. Five evaluations of disease severity (maize white spot and gray leaf spot) based on visual symptoms were performed at seven-day intervals from the 80th day after maize emergence, ranging from 1 (highly resistant) to 9 (highly susceptible). The area under the disease progress curve (AUDPC) was estimated, as were the phenotypical stability parameters, i.e., the linear regression coefficient (bi) between the independent variable evaluation time (x), and the dependent variable, disease severity (y) and the determination coefficient (R2). It was found that in the case of maize white spot and maize gray leaf spot, both methodologies used proved to be effective in the discrimination of the resistance level of the hybrids, enabling them to be ranked in a similar way. The most resistant hybrids to maize white spot and maize gray leaf spot were AG7088, AG 7010 and 2B707, and the most susceptible were 30F44, 30F53 and AG8021. The 30K64, DKB177, DKB390 and Impacto hybrids showed variable levels of resistance with the environment.

Maize hybrid stability in environments under water restriction using mixed models and factor analysis

In several crops, the water deficit is perhaps the main limiting factor in the search for high yields. The objective of this study was to evaluate the phenotypic stability of maize hybrids in environments with and without water restriction using the analytical factor (AF) approach. We evaluated 171 maize hybrids in 14 environments, divided into environments with (A1, A2, A3, A4, A5, A6, and A7) and without (A8, A9, A10, A11, A12, A13, and A14) water restriction, over a period of 7 years. Each year, 36 hybrids were evaluated. A square lattice design (6 x 6) was used, with common treatments between years. The characteristics of grain yield (GY), male flowering (MF) and female flowering (FF), plant height (PH), and ear height (EH) were evaluated. Phenotypic adaptability and stability of the hybrids were also verified. Hybrids G66, G99, G86, and G26 were the most stable and showed potential for use in environments with and without water restriction. The AF models showed to be useful for evaluating hybrids over many years, allowing selection of better hybrids with adaptability, specific and general stability, and correlation of hybrids with their production components, in addition to allowing identification of mega-environments that permit stability in the response of the adapted hybrids.