ABSTRACT An intrinsic part of our lives as scientists, academic writing strongly affects our careers. Even great researchers are much more likely to be successful if they are also proficient writers. Unfortunately, many researchers prefer long hours spent in the field or laboratory to writing. Often, this is a result of their reluctance towards writing, something that characterizes not only scientists, but many people in general. But many researchers do not like writing because they do not know how to do it well—academic writing is not that simple. In this paper, I show how to improve your academic writing skills. I do not teach writing; instead, I share my experience about learning how to write.

ABSTRACT: This paper shows how to apply the lattice package of R to create effective scientific graphs. The readers will learn basic notions of the package and ways to work with it in an easy way. The R code the paper provides will help them create various graphs, including a scatter plot, a box plot, a density plot, and a bar plot; with a little work, the code can be changed to make other graphs. The paper emphasizes the trellis display, a useful but still undervalued technique in scientific visualization.

ABSTRACT Even though data visualization is a common analytical tool in numerous disciplines, it has rarely been used in agricultural sciences, particularly in agronomy. In this paper, we discuss a study on employing data visualization to analyze a multiplicative model. This model is often used by agronomists, for example in the so-called yield component analysis. The multiplicative model in agronomy is normally analyzed by statistical or related methods. In practice, unfortunately, usefulness of these methods is limited since they help to answer only a few questions, not allowing for a complex view of the phenomena studied. We believe that data visualization could be used for such complex analysis and presentation of the multiplicative model. To that end, we conducted an expert survey. It showed that visualization methods could indeed be useful for analysis and presentation of the multiplicative model.

ABSTRACT Modern agricultural research requires a lot of statistics. Unfortunately, most up-todate statistical methods need advanced knowledge, often out of reach for agricultural researchers. Thus, efficient communication between researchers and statisticians is important for the development of agricultural knowledge. Many agricultural researchers claim that communication with statisticians is difficult. On the other hand, many statisticians claim that communication with agricultural researchers is not easy either. This being true, such poor communication can be a barrier to efficient agricultural research. The aim of this research is to study this phenomenon. Do agricultural researchers and statisticians see problems in their communication? What kinds of problems are they? I will try to answer these questions based on a study among scientists representing both groups.

Studying relationships among plant and crop traits is crucial for crop scientists to understand complex biological systems that occur in plants and the field. Such knowledge constitutes the basis for more practical information on how to manage breeding and production to provide better or more suitable cultivars, higher yields, lower yield gaps, and resistance to pests etc. To acquire such knowledge, however, representative models of associations between plant and crop traits must be constructed. In path analysis - one of the major methods for analyzing multivariate relationships between quantitative traits - it is important to decide on an appropriate model for these associations, a model that is representative of the corresponding biological phenomena that are of interest to crop researchers. Adopting this "point of view", we asked various questions relating to such model building: (i) how should sequentiality in sequential path analysis be understood? (ii) how should it be interpreted? (iii) how should such sequential models be formulated? We discussed these issues in the context of crop science. Differences in simple and complex (sequential) models of path analysis are presented. Based on crop science examples, we show how important it is to correctly represent the biological relationships for a path analysis model.

Este artigo discute uma série de aspectos relacionados a análise, interpretação e forma de relatar correlações em ciências Agrárias. São identificados vários problemas que podem ser encontrados, bem como feitas sugestões de como superá-los. Alguns dos exemplos apresentados mostram quão erradas e mesmo enganosas podem ser as interpretações de correlação quando regras simples de análise são ignoradas.

This paper discusses a number of aspects concerning the analysis, interpretation and reporting of correlations in agricultural sciences. Various problems that one might encounter with these aspects are identified, and suggestions of how to overcome these problems are proposed. Some of the examples presented show how mistaken and even misleading the interpretation of correlation can be when one ignores simple rules of analysis.

Adaptation can be studied through various methods, some of which are based on adaptation or stability measures while others on complex models and graphs. In this adaptation measure based on quantiles is presented; the measure is three-dimensional and can be pictured with a ternary (trilinear) plot. A simple algorithm for constructing such a plot is presented and the interpretation of the plot is discussed. By means of this graphical tool one can picture and interpret the specific and wide adaptation of genotypes to environments, offering complementary information to that of other methods of analyzing genotype-by-environment data.

La adaptación puede ser estudiada a través de varios métodos, algunos de los cuales están basados en mediciones de adaptación o estabilidad, mientras otros en modelos complejos y en gráficos. En este artículo se presenta una medición simple de adaptación basada en cuantiles, la medición es tridimensional y puede ser visualizada como un gráfico ternario (trilineal). Se presenta un algoritmo simple para construir tal gráfico y se discute su interpretación. Por medio de esta herramienta gráfica se puede visualizar e interpretar la adaptación específica y amplia de genotipos a los ambientes, ofreciendo información complementaria a aquella de otros métodos de análisis de datos genotipos x ambiente.

A visualização de dados é um aspecto importante da análise de dados e de sua apresentação. Nesse ponto de vista enfocam-se vários elementos da construção de gráficos para publicações. Gráficos ruins e bons são comparados, e uma lista de checagem de elementos de composição gráfica é proposta para ser utilizada durante a confecção de gráficos.

Data visualization is a very important aspect of data analysis and of presentation. Focusing on the latter, this paper discusses various elements of constructing graphs for publications. Bad and good graphs are compared, and a checklist with graphical elements to be used while creating graphs is proposed.

Realizar análises estatísticas pode ser complicado. Em situações práticas muitos pesquisadores utilizam os procedimentos de análise mais comuns, sem investigar se os mesmos são apropriados para os seus resultados, ou mesmo se há outros métodos que poderiam ser mais adequados. Nesse artigo buscarei mostrar que mesmo na análise de experimentos de classificação simples (com um único fator) vários aspectos precisam ser considerados. A forma clássica de análise desse tipo de dados é a análise de variância, que é provavelmente o método estatístico mais usado na agricultura, biologia, ecologia e estudos de meio ambiente. Suspeito que essa é a razão pela qual tal método é frequentemente usado de forma inapropriada: uma vez que ele é muito usado, não suscita maiores considerações. Imagino que seja esse raciocínio que muitos pesquisadores devam empregar. Análises incorretas podem fornecer falsas interpretações e conclusões, e dessa forma é importante prestar atenção na escolha do procedimento a ser usado na análise. Não estou sugerindo que algum método difícil ou complexo deva ser usado, mas sim que um método correto seja adotado, de forma a fornecer os resultados adequados. Dessa forma, vários problemas relacionados à análise de variância e outras abordagens para analisar esse tipo de dados são discutidas nesse artigo, incluindo verificações de normalidade e homogeneidade de variâncias, análise de experimentos com violação dessas pressuposições, presença de dados discrepantes, testes de comparações múltiplas, além de alguns outros problemas.

Statistics may be intricate. In practical data analysis many researchers stick to the most common methods, not even trying to find out whether these methods are appropriate for their data and whether other methods might be more useful. In this paper I attempt to show that when analyzing even simple one-way factorial experiments, a lot of issues need to be considered. A classical method to analyze such data is the analysis of variance, quite likely the most often used statistical method in agricultural, biological, ecological and environmental studies. I suspect this is why this method is quite often applied inappropriately: since the method is that common, it does not require too much consideration-this is how some may think. An incorrect analysis may provide false interpretation and conclusions, so one should pay careful attention to which approach to use in the analysis. I do not mean that one should apply difficult or complex statistics; I rather mean that one should apply a correct method that offers what one needs. So, various problems concerned with the analysis of variance and other approaches to analyze such data are discussed in the paper, including checking within-group normality and homocedasticity, analyzing experiments when any of these assumptions is violated, outliers presence, multiple comparison procedures, and other issues.