Sunflower yield: adjustement of data means by the combination of ANOVA and Regression models.

Sunflower is an important oilseed crop. Besides producing high quality edible oil for human consumption, it also produces meal for animal feeding, and is an alternative for biodiesel production as well. Sunflower is a crop well adapted to several environmental conditions and is tolerant to low temperatures and to relatively short periods of water stress. In Brazil, the sunflower cultivated area reaches 75,000 hectares and its yield averages 1,460 kg/ha (CONAB). Much effort has been spent on research work at management of sunflower and consequently higher yield. Research efforts are specifically directed to the control of diseases and pests, which can cause defoliation, damages to the roots, and yield losses. The need for macro- and micronutrient fertilizations is another research demanding aspect of the crop. Within this context, two extremely important aspects in solving these research demands are: the appropriate agronomical planning and the adequate experimental design. These procedures will allow decisions on selection of size and shape of plots, on experimental unit, on qualitative and quantitative factors, on experimental design, and on the choice of the variables that influence the response and the ways of choosing and distributing the treatments in the plots. The selection of the suitable statistical methods, which allow precise estimates of the treatments and the reduction of the residual variance, uncontrolled in the planning, is also essential. One of these methods is the Analysis of Covariance (ANCOVA). This method combines the Analysis of Variance (ANOVA) and the Regression Analysis, and besides controlling the experimental error, it adjusts the treatment means, thus helping the interpretation of the experimental results as well as the comparison of regressions among several groups of treatments. The model representing this combination is :Yij = ? + ? i + ? j + ? (xij - x.. ) +? ij , where: Yij is the observed value of the response variable; ? is the mean value of the response variable; i ? is the effect of treatment I, with i = 1, 2,?, I; j ? is the effect of the block j, with j = 1,2,?, J; ? is the effect of the combined linear regression Yij as related to x; ij x is the observed value of the co-variable; and ij ? is the experimental error associated toYij, with ?ij ?N (0,?2 ) . The covariate should not be influenced by the treatments initially tested, maintaining the independence among them. Therefore, the treatments were: one control (0), and the P2O5 dosages of 40 kg ha-1, 80 kg ha-1, 120 kg ha-1, and 160 kg ha-1, applied to the sunflower hybrid Aguara 4. The experiment was carried out as a randomized block design, with six replications and the variables studied were: yield (kg ha-1) and the number of achenes per sunflower plant. The descriptive analysis indicated consistency in the tests concerning normality and independence of errors, additivity of the model, and homogeneity of treatments variances. The F statistics presented significant response for the treatments, for the response variable and covariate (5.48 and 4.93), respectively. The highest sunflower yield, obtained with the dosage of 120 kg ha-1 P2O5, statistically differed only from the control (Tukey p? 0, 05). The ANCOVA, adjusted by the number of achenes, reduced the error variance from 49,768.84 to 32,887.40. An interesting fact is that after ANCOVA, the effect of treatments became non-significant (F = 2.62), even with the reduction of the error variance. The mean values adjusted by the Tukey-Kramer test were reduced when compared to the original means. The interaction of treatment with the covariable was not significant, indicating that the angular coefficients for the treatments were similar. We concluded that the analysis of covariance reduces the error variance and indicates the real significance of the treatment effects and of the angular coefficients for the non-homogeneous treatments

Quantum Dissipation in a Neutrino System Propagating in Vacuum and in Matter

Considering the neutrino state like an open quantum system, we analyze its propagation in vacuum or in matter. After defining what can be called decoherence and relaxation effects, we show that in general the probabilities in vacuum and in constant matter can be written in a similar way, which is not an obvious result in this approach. From this result, we analyze the situation where neutrinos evolution satisfies the adiabatic limit and use this formalim to study solar neutrinos. We show that the decoherence effect may not be bounded by the solar neutrino data and review some results in the literature. We discuss the current results where solar neutrinos were used to put bounds on decoherence effects through a model-dependent approach. We conclude explaining how and why this models are not general and we reinterpret these constraints.Comment: new version: title was changend and was added a table. To appear at Nucl. Physic.

Collapse of Primordial Clouds

We present here studies of collapse of purely baryonic Population III objects with masses ranging from $10M_\odot$ to $10^6M_\odot$. A spherical Lagrangian hydrodynamic code has been written to study the formation and evolution of the primordial clouds, from the beginning of the recombination era ($z_{rec} \sim 1500$) until the redshift when the collapse occurs. All the relevant processes are included in the calculations, as well as, the expansion of the Universe. As initial condition we take different values for the Hubble constant and for the baryonic density parameter (considering however a purely baryonic Universe), as well as different density perturbation spectra, in order to see their influence on the behavior of the Population III objects evolution. We find, for example, that the first mass that collapses is $8.5\times10^4M_\odot$ for $h=1$, $\Omega=0.1$ and $\delta_i={\delta\rho / \rho}=(M / M_o)^{-1/3}(1+z_{rec})^{-1}$ with the mass scale $M_o=10^{15}M_\odot$. For $M_o=4\times10^{17}M_\odot$ we obtain $4.4\times10^{4}M_\odot$ for the first mass that collapses. The cooling-heating and photon drag processes have a key role in the collapse of the clouds and in their thermal history. Our results show, for example, that when we disregard the Compton cooling-heating, the collapse of the objects with masses $>8.5\times10^4M_\odot$ occurs earlier. On the other hand, disregarding the photon drag process, the collapse occurs at a higher redshift.Comment: 10 pages, MN plain TeX macros v1.6 file, 9 PS figures. Also available at http://www.iagusp.usp.br/~oswaldo (click "OPTIONS" and then "ARTICLES"). MNRAS in pres

Collapse of Primordial Clouds II. The Role of Dark Matter

In this article we extend the study performed in our previous article on the collapse of primordial objects. We here analyze the behavior of the physical parameters for clouds ranging from $10^7M_\odot$ to $10^{15}M_\odot$. We studied the dynamical evolution of these clouds in two ways: purely baryonic clouds and clouds with non-baryonic dark matter included. We start the calculations at the beginning of the recombination era, following the evolution of the structure until the collapse (that we defined as the time when the density contrast of the baryonic matter is greater than $10^4$). We analyze the behavior of the several physical parameters of the clouds (as, e.g., the density contrast and the velocities of the baryonic matter and the dark matter) as a function of time and radial position in the cloud. In this study all physical processes that are relevant to the dynamical evolution of the primordial clouds, as for example photon-drag (due to the cosmic background radiation), hydrogen molecular production, besides the expansion of the Universe, are included in the calculations. In particular we find that the clouds, with dark matter, collapse at higher redshift when we compare the results with the purely baryonic models. As a general result we find that the distribution of the non-baryonic dark matter is more concentrated than the baryonic one. It is important to stress that we do not take into account the putative virialization of the non-baryonic dark matter, we just follow the time and spatial evolution of the cloud solving its hydrodynamical equations. We studied also the role of the cooling-heating processes in the purely baryonic clouds.Comment: 8 pages, MN plain TeX macros v1.6 file, 13 PS figures. Also available at http://www.iagusp.usp.br/~oswaldo (click "OPTIONS" and then "ARTICLES"). MNRAS in pres