Hi Bhupinder,

You can try to express results in relative terms (GHG flux in one treatment as a proportion relative to the GHG flux in the ‘control’ treatment within one experiment).  In other words: relative difference between ‘treatment’ vs. ‘control’.

‘Relative treatment differences’ from separate experiments can be statistically compared.

This is done in ‘meta-analysis’ of results from large number of different experiments within one ‘subject area’.  Researchers first convert the results into ‘relative differences’ before using statistics.

-SJ

Hi

It seems that you don't have any basic experimental design for analyzing (RCBD and etc.). so, one way to compare the objectives is AHP method. I think this method could more help you to identifying the best crop. please see the AHP primary examples on the web.

Hi Susantha,

Thanks for the input. How would I calculate the relative differences, would it calculated per replication? Could you please give an quick example dataset to show the calculations, it would help me to answer most of my follow-up questions.

Best,

Bhupinder

Hi Behzad,

Thanks for the input, can you please direct me to any particular link that explains AHP primary example in context of my question.

Best,

Bhupinder

Hi Bhupinder,  Relative differences per replication can be calculated. However, it will involve substantial amount of re-processing of data, especially depending on the frequency of measurements per season. How many sampling dates per season?

You mentioned that the fluxes were converted to ‘seasonal cumulative value’.  I would incline to perform the calculation for the ‘seasonal fluxes’.  In this way, you can calculate the relative differences for the ‘seasonal cumulative flux’ between a given treatment vs. the control. Once you have these relative differences, you may be able to compare the ‘size of the difference’ (effect size) and arrive at a meaningful comparison.

By the way what type crops are you working with? grains, grain legumes or forages? If your crops belongs to one type (say for example they are all grain legumes), another way to make a meaningful comparison is to express emissions as GHG intensity (yield-scaled emissions: CO2 eq/unit of yield).

-Susantha

Hi Susantha, thank you again for your input. The crops in my study are Miscanthus, Switchgrass, Maize, Sorghum, and native prairies. As you have mentioned, I have calculated Yield scaled emission based on total harvest biomass and comparing those indices. My challenege is that all crops were grown as separate studies with different type of treatments. For example, I have P-K treatments in Miscanthus and Switchgrass apart from nitrogen treatment, though I have only two levels of each of them: fertilized and unfertilized. Maize and sorghum have only nitrogen treatment. The overall goal is to find out which one is best bio energy crop by making relative comparison of their biomass production and GHG emissions (raw and yield-scaled). At this stage, my focus was to do only side-by-side comparison. In Miscanthus and Switchgrass i have on two reps while in other crops four. This is another issue. I want to also mention if P-K addition increased or decreased GHG emissions in Miscanthus and Switchgrass. Bhupinder