Analysis of variance (ANOVA) is a usual way for analysing experiments. However, depending on the design and/or the analysis scheme, it can be a hard task. ExpDes, acronym for Experimental Designs, is a package that intends to turn such task easier. Devoted to fixed models and balanced experiments (no missing data), ExpDes allows user to deal with additional treatments in a single run, several experiment designs and exhibits standard and easy-to-interpret outputs. It was developed at the Exact Sciences Institute of the Federal University of Alfenas, Brazil. Stable versions of package ExpDes are available on CRAN (Comprehensive R Archive Network) since 2012. Based on users' feedback, the package was used to illustrate graduation and post-graduation classes and to carry out data analysis, in Brazil and many other countries. Package ExpDes differs from the other R tools in its easiness in use and cleanliness of output.
This study aimed to characterize Brachiaria brizantha cv. Marandu seasonal production (seasonality) and its variation (climate risk) yearlong throughout Brazil. Data from weather stations in Brazil (1963Brazil ( -2009, were associated with an empirical herbage accumulation rate (HAR; kg DM ha -1 day -1 ) model which considers growing degree-days adjusted by a drought attenuation index. Simulations were performed under 20, 40, 60 and 100 mm of soil water holding capacities (SWHCs). HAR's means and standard deviations were calculated for the seasons of the year. Thereafter, cluster analysis and calculations were performed to gather similar weather stations and characterize seasonality and climate risk indexes. Cluster analysis resulted in four Groups per SWHC. The north of Brazil (Group 1) presented the lowest seasonality and climate risk indexes and low need for precautions. In the middle west (Group 2), the seasonality index ranged from medium-high to high. Winter and Summer presented the lowest and highest production, respectively. In the south of Brazil, some regions in the southeast and northeast (Group 3), Winter presented the lowest production and highest climate risk index, probably due to low temperatures. The northeast (Group 4) presented a seasonality index that ranged from medium-high to very high and low productions.
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