B. Ordination bi-plot diagram of farming system characterization based on PCA of variables.
Multivariate statistical techniques have been widely used for farm typology and characterization study (Kobrich et al. [2003]; Andersen et al. [2009]; Guto et al. [2010]).
Identification and characterization of farming systems simplify huge diversity of farm types in complex agro-ecosystems, which is of critical importance for precise technological intervention and informed policy support.
Thus, a total of 72 farmers were interviewed in details for characterization of farm types.
The present study conducted in coastal saline India demonstratively established the usefulness of such methodology in identification of predominant farm types and their characterization.
Table 1 Selected variables used in farm type identification and characterization Variable Description Education Index Weighted average of formal education received by the household members (illiterate = 0, primary = 1, middle = 2, secondary = 3, higher secondary = 4, graduate = 5 and post-graduate = 6).
An innovative method of quantitative identification and characterization of farm typologies was developed, whose advantages are the straightforward communication of results to stakeholders and rapid graphical selection of representative farms.
Instead of conducting a conventional way of identifying and characterizing farming system based on the source of the highest gross income received (from crops, vegetables, fruits and orchard, livestock, dairy, fishery, and poultry), the study employed multivariate statistics for identification of farm types and their characterization.
The May 28th article "Issues of Purity and Pollution Leave Farmed Salmon Looking Less Rosy" yielded an inaccurate characterization of farmed salmon and the salmon farming industry in general.
With economic characterization of farms as the objective at hand, the present study assumes that classification of farms based on economic returns from farm enterprises, along with other related non-economic factors, will give more effective insights into the farm type identification.
