Assessment of carbon footprint of milk production and identification of its major determinants in smallholder dairy farms in Karnataka, India
Supplementary file contains questionnaire (Table S1), and additional tables ( Table S2-S6) and figures ( Figure S1-S2).
Steps to reproduce
A preliminary version of the questionnaire was prepared initially. The questionnaire was tested by collecting data from minimum five randomly selected farms. Subsequently, the questionnaire was checked, reformed and finalized based on the initial study (Table S1). The general information on household, farm characteristics (Table S4) and other specific details were determined (Table S5 and Table S6) from primary information. According to ISO 14040 (2006) and ISO 14044 (2006) guidelines under situations, where allocation cannot be avoided, greenhouse gas (GHG) emissions can be allocated on the basis of casual and physical relationships. In the latter case, the most commonly used approach is economic allocation. Economic allocation allocates emissions to each product according to its share of the products' combined economic value (such as products' weight or protein content). The mass, economic and digestibility allocations were applied to the feed (crop by-products and residues) inputs (Supplementary Table S1). Mass allocation can be calculated for the proportion of each feed after considering the harvesting loss. Economic allocation can be computed based on the proportion of crop by-products and residues for the total economic value of the respective crop. The proportion of digestible dry matter (DM) of each feed ingredient can be calculated for the computation of biological allocation. The digestibility value of each ingredient can be recorded from Feedipedia (2018) or other reliable sources. Total GHG emissions can be allocated among the sale of milk, sold animal and other functions (finance and insurance) based on their economic value. The correlation analysis and principal component analysis (PCA) were performed using R version 4.0.3 (R Core Team, 2020) and RStudio version 1.3.1093. The Pearson’s correlation analysis was performed using R package ‘corrplot’ version 0.90 (Wei and Simko, 2021) and R package ‘Hmisc’ version 4.5-0 (Harrell Jr, 2021). The PCA was performed using R package ‘FactoMineR’ (Le et al., 2008) and visualized using R package ‘factoextra’ version 1.0.7 (Figure S1). The identified major variables from PCA were subjected to multiple linear regression analysis using SPSS version 16.0 (SPSS Inc., Chicago, IL, USA), and a regression equation was developed. The stepwise linear regression method (combination of forward and backward procedures) was used that regressed multiple variables and simultaneously removed those that were not important (Figure S2).