Table 1.
Classification of commodities.
Fig 1.
Interstate trade of agriculture and non-agriculture commodities in India.
(a) and (b) show the chord diagram representing the time-averaged agricultural exports and non-agricultural commodities, respectively. The link indicates the flow between different states, the link width showcases the trade volume in ₹, and the links’ color corresponds to the exporting regions. (c) and (e) shows time-averaged exports and imports for agriculture, whereas (d) and (f) depict the same for non-agriculture commodities. The state is represented by two letters. All averages are calculated for the period 2010–2018.
Table 2.
Top five states with highest exports and imports for overall agriculture trade and non-agriculture trade.
The full name of the states is mentioned in the abbreviation section.
Fig 2.
Temporal changes in total trade value and topological characteristics of the network.
(a)Total traded value (in ₹100 billion), (b) Average network degree, (c) Network density, (d) Average clustering coefficient, (e) Average betweenness centrality, and (f) modularity for 2010–2018. here, β indicates the slope of a linear trend line fitted to scattered points (values with * represent a significant trend at p − value < 0.05). The results are presented for the Agriculture (green) and Non-Agriculture (purple) interstate trade networks.
Fig 3.
Spatial variation in the trends of exports and imports.
(a and c) shows the spatial variations of export and import for agricultural products through trade trends, whereas (b) and (d) present the same for Non-agriculture commodities. Here the states with * represent states with a significant trend (at p-value < 0.05) of commodities over the period of 2010–2018. All values are in billion ₹.
Fig 4.
Relative influence of states on interstate trade network.
(a) and (b) shows the time-averaged strength centrality of each state for agriculture and non-agriculture DITN, respectively, (c) and (d) depict the time-averaged betweenness centrality for the same. In comparison, (e) and (f) indicate the time-averaged PageRank centrality of each state.
Fig 5.
The temporal changes in the trade value of leading exporters.
The temporal variations of exports and imports of leading exporters of agriculture (Punjab and Haryana) and non-agriculture (Odisha and Chhattisgarh) commodities over the period 2010–2018. Also shown are the respective linear regression slope (β).
Fig 6.
Spatial variations of in-degree, out-degree and community.
Study area showing average out-degree and average in-degree, respectively, for agriculture (A—(a and c)) and non-agriculture (A—(b and d)) commodities. All averages are calculated from 2010 to 2018. (B—(a and c)) show the community formation of agriculture, while (B—(b and d)) depict the communities in non-agriculture trade for 2010 and 2018, respectively.
Fig 7.
Spatio-temporal statistical relationship between import and export commodities.
(A) Scatter plots of time-averaged in-degree and out-degree over nine years (2010–2018) for Indian states. β indicates the slope of the fitted linear regression line to scattered points, and r indicates the Pearson correlation coefficient. (B) Scatter plots of the year-wise slope of in-degree and out-degree. The size and numerical above the marker represent the respective correlation value. The fitted regression line (dash line) and corresponding 95% confidence band are shown in all panels. The results in sub-panels (a) and (b) are for agriculture trade and non-agriculture trade, respectively.