Hello I did an internship in agricultural department of pakistan last year and got this data on cotton yield, Now as a project report, I've to analyse the data, My supervisor has asked me to apply appropriate analysis, But I could'nt think of one. .
However, I though of applying experimental design techniques, so I arranged the data in form of plots.
Now, the data is of cotton (of a district naming vehari in one of the city multan), The cotton is picked 5 times from the vehari district from 3 randomly selected plots, and under these fields, 2 plots are nested,
the picked cotton contains no of good balls and bad bolls along with their wights,
As their are 5 pick dates, each contains it's good and bad bolls separately.
So I made a design like this and then entered in spss and excel, (file attached)
HBno. Field plot Pickdate1 Pick2 Pick3
No. of No. of wt of wt of
good boll1 bad boll1 good boll1 bad boll1
210 F1 P1 708 15 1820 10
P2 872 36 24 20
F2 P1 638 45 1679 50
P2
F3 P1
P2
26 F1 P1
P2
and so on. . pick dates are 5 but not written here due to short space,
Now my quetion is that , is this correct what I've thought of or not? what else can I do to this data? the choice is open, I mean I am free to apply whatever is appropriate.
Although your research question is not clear, the data looks suitable for ANOVA as the method of analysis.
Millind Pakalkar has made a good answer. You need to develop a hypothesis to test the data by ANOVA.
ANOVA a might tell you if there is any difference between the fields, HBNos or pick dates, depending on which way you aggregate the data subset above, but it's not going to give you information about why or what's going on. There seems to be an awful lot of data about variables in your original data set that you aren't including. I would be figuring out:
1.how do you measure what is your important outcome eg is it total yield from each field or plot by weight ie sum all weights for all pick dates per field or plot OR are you more interested in the proportion of good balls to bad balls ie does this indicate some sort of crop failure OR is time of peak production important ie does it give you a sales advantage if you produce earlier, etc
2. Then I would perform regression analysis with the other variables such as fertiliser, water, seed type, planting date, etc to see which of these variables influences the outcome you are looking for.
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