Perceptions of Fishermen Households on the Long-Term Impact of Coastal Resources Management in Panguil Bay

Coastal resources management (CRM) has flourished as a management approach for attaining a more sustainable form of economic development in the coastal areas of the Philippines. Its proliferation, coupled with the reasonably long time it has been in implementation, now calls for an evaluation of its long-term impact as a management and development approach. In this study, the long-term impact of CRM is evaluated not from the perspectives of technical people but based on the perception of its intended primary beneficiaries--the fishermen households. It does so not by looking into a specific CRM program or project but by observing the succession of CRM activities conducted in a single coastal area--Panguil Bay, Mindanao--over many years. The objectives were to ascertain if CRM works, identify its major constraints if it does not, and recommend future courses of actions to address the constraints.coastal resources management, long-term impact indicators, ladder diagram, Panguil Bay

The formation of planetary disks and winds: an ultraviolet view

Planetary systems are angular momentum reservoirs generated during star formation. This accretion process produces very powerful engines able to drive the optical jets and the molecular outflows. A fraction of the engine energy is released into heating thus the temperature of the engine ranges from the 3000K of the inner disk material to the 10MK in the areas where magnetic reconnection occurs. There are important unsolved problems concerning the nature of the engine, its evolution and the impact of the engine in the chemical evolution of the inner disk. Of special relevance is the understanding of the shear layer between the stellar photosphere and the disk; this layer controls a significant fraction of the magnetic field building up and the subsequent dissipative processes ougth to be studied in the UV. This contribution focus on describing the connections between 1 Myr old suns and the Sun and the requirements for new UV instrumentation to address their evolution during this period. Two types of observations are shown to be needed: monitoring programmes and high resolution imaging down to, at least, milliarsecond scales.Comment: Accepted for publication in Astrophysics and Space Science 9 figure

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