Which is the best statistical tool for agronomic and soil science research? As SAS and R packages are quite popular for social sciences and breeding; whether these two overtly complicated command-line driven tools are really needed for agronomy and soil science experiments? does having expertise on these tools adds any extra value to the research or not?
Honestly, Subhradip, statistical packages are not really 'designed' for particular disciplines, although they do tend to better support certainly analytical approaches favoured by one discipline more than another (think SAS for public health and Stata for economics). But at the end of the day, you need to choose the package that offers the best tools to achieve a given form of analysis (that you selected to deliver in relation to a specific set of questions). In other words, focus on what you're trying to achieve, not what other people typically use. R may in fact offer you something nifty for a social science question, but it may not. Learn the stats you'll actually use. And good luck!
Honestly, Subhradip, statistical packages are not really 'designed' for particular disciplines, although they do tend to better support certainly analytical approaches favoured by one discipline more than another (think SAS for public health and Stata for economics). But at the end of the day, you need to choose the package that offers the best tools to achieve a given form of analysis (that you selected to deliver in relation to a specific set of questions). In other words, focus on what you're trying to achieve, not what other people typically use. R may in fact offer you something nifty for a social science question, but it may not. Learn the stats you'll actually use. And good luck!
R is good because you can do almost any analysis you can invent, but you have to know the language. A standard program is good because you don't need to invent software but can't always do exactly what you want
SAS and R packages can surely add much value to research in the agronomy and soil science fields. However, it depends a lot on the problem you are trying to solve. For example, a standard software can be used for simple analysis, such as ANOVA and t-test, which are common in these areas. However, SAS and R packages can be useful in studies that require complex analysis of data.
As a matter of fact, R and SAS are good analytic tools in Agronomy and soil science. In fact, R will give you more access to visualize your data perfectly but you need to understand the programming aspect of it.
Finally, your research objective (s) is quite paramount during the analysis. best of luck!
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