What methodology should be used for measuring the impact of climate change on the agriculture productivity and what variables should be included, particularly with reference to crop and vegetables.
Multiple regression or logistic regression will be sufficient to measure it or otherwise any other models will be used?
Dear Herrero
thanks for your informative answer, Actually i want to study the overall effect of climate change on the production, sowing, harvesting and farmers income with special reference to the food and cash crops and vegetables.
I sense you have continuous dependent variables, in which case multiple regression would be more appropriate. May be good if you say a bit more about the measurement of your variables.
Yes, Herrero your idea is nice. But, usually large data set is suggested for SEM, and as far as MR works well, I might opt for MR just for simplicity.
If you are analyzing climate change I suppose you have data from various periods. Just comparing results will give you misleading results because you may not be taking into account all the other factors influencing agricultural productivity besides climate change. These can be both positive and negative influences.
In order to adequately disentangle the various attributing factors you will need lots of data over a long time period. And here I agree with Francesco Herrero a SEM. Using a SEM does not guarantee that you will correctly disentangle the various contributions to productivity change but it is a first step in the right direction.
Series meteorological weather data and soil fertility levels including the OM should be considered.
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