faostat-trade

name

faostat-trade

description

Use when the user asks about food import dependence, self-sufficiency ratio, supply chain risk, trade partners, trade concentration, food security vulnerability, import reliance, export dependence, or whether a country can feed itself for a specific commodity. Keywords — import, export, trade, self-sufficiency, dependency, supply chain, food security, trade partners, concentration risk, import reliance, vulnerability. Do NOT use for a comprehensive country food security profile → `faostat-country-profile`. Do NOT use for a global commodity briefing → `faostat-commodity`. Do NOT use for side-by-side country comparison → `faostat-compare`.

Trade Dependency Analyzer

Assess a country's import dependence for critical food commodities, calculate self-sufficiency ratios, identify supply chain concentration risks, and track dependency trends over time.

Prerequisites

Before starting, verify that the FAOSTAT MCP tools are available: faostat_search_codes, faostat_get_data. If they are not available, inform the user they need the FAOSTAT MCP server configured and stop.

Domain Reference (CRITICAL -- use exactly these codes)

Domain	Code	Content	When to use
Crops & Livestock Products	QCL	Domestic production (area, yield, production volume)	Always for production
Crops & Livestock Trade (aggregate)	TCL	Country-level totals for import/export quantity and value	Use for total imports / total exports of a country
Detailed Trade Matrix	TM	Bilateral trade by partner country	Use ONLY for "who are the top partners" breakdown
Food Balance Sheets	FBS	Supply utilization including feed, seed, stock change	Use for food-specific SSR (element 645 Food, 5131 Feed, etc.)

CRITICAL — TM vs TCL. TM returns one row per (reporter × partner × element × year). Summing TM rows to get national imports/exports is fragile (mirror-data gaps, re-exports). Pull aggregate import/export volumes from TCL. Only drop into TM when you need the partner breakdown.

Element Code Reference (CRITICAL)

Element codes below are verified hints. Resolve at runtime via faostat_search_codes before use.

• Production quantity (QCL): filter code 2510, display code 5510
• Import quantity (TCL): filter code 2610
• Export quantity (TCL): filter code 2910
• Import value (TCL, USD 1000): filter code 2612
• Export value (TCL, USD 1000): filter code 2912
• For faostat_get_data queries, use FILTER codes in the element parameter (e.g., element='2510')
• For faostat_get_rankings queries, use DISPLAY codes in the element_code parameter (e.g., element_code='5510')

Area-Code Pitfall: China

FAOSTAT has both China (area code 351, aggregate including Hong Kong SAR, Macao SAR, and Taiwan Province of China) and China, mainland (area code 41, mainland only). Default to composite China (351) for any single-country analysis (user preference, Apr 2026). Do NOT substitute China, mainland (41) unless the user asks for 41 explicitly. In trade data this means the "China" total will include trade through HK / Macao / Taiwan — flag this to the user and note that FAOSTAT's own publications default to 41, so the numbers here are larger than the FAO data-portal default.

Workflow

Step 1: Accept inputs

Ask the user for:

• Country (required) -- e.g., "Egypt", "Japan", "Nigeria"
• Commodity (required) -- e.g., "wheat", "rice", "soybeans", "palm oil"

If the user provides both in their initial message, proceed directly. If either is missing, ask.

Step 2: Resolve codes

1. Resolve the country to an area code: faostat_search_codes(domain_code='QCL', dimension_id='area', query='<country>')

   • If requires_confirmation is true, present the matching options and ask the user to choose. Do NOT proceed until confirmed.
   • If the user asked about "China" generically, default to composite China (351) and offer China, mainland (41) as an opt-in. Do not proceed with 41 unless the user asks for 41 explicitly.

2. Resolve the commodity to an item code in the production domain: faostat_search_codes(domain_code='QCL', dimension_id='item', query='<commodity>')

   • Handle requires_confirmation the same way.

3. Resolve the commodity in the aggregate-trade domain (item codes may differ): faostat_search_codes(domain_code='TCL', dimension_id='item', query='<commodity>')

   • Handle requires_confirmation.

4. If you will also do a partner breakdown in Step 6, resolve the item in TM: faostat_search_codes(domain_code='TM', dimension_id='item', query='<commodity>')

Step 3: Pull domestic production

Query the QCL domain for the country's domestic production of this commodity:

# Resolve element at runtime: faostat_search_codes(domain_code='QCL', dimension_id='element', query='production') → e.g. 2510
faostat_get_data(
  domain_code='QCL',
  area='<area_code>',
  item='<item_code_qcl>',
  element='<resolved_production_code>',
  year='2014,2015,2016,2017,2018,2019,2020,2021,2022,2023',
  response_format='compact'
)

Extract production quantities for the most recent 10-15 years to establish a trend.

Year-range syntax. Use an explicit comma-separated year list. Colon ranges like '2014:2023' have returned empty results in practice — avoid them.

Step 4: Pull aggregate trade volumes (TCL, not TM)

Pull import and export quantities for this country-commodity pair from TCL (country-level aggregates):

# Resolve elements at runtime:
# faostat_search_codes(domain_code='TCL', dimension_id='element', query='import quantity') → e.g. 2610
# faostat_search_codes(domain_code='TCL', dimension_id='element', query='export quantity') → e.g. 2910
faostat_get_data(
  domain_code='TCL',
  area='<area_code>',
  item='<item_code_tcl>',
  element='<resolved_import_qty_code>,<resolved_export_qty_code>',
  year='2014,2015,...,2023',
  response_format='compact'
)

For USD trade values, also resolve import value and export value elements via faostat_search_codes (hints: 2612 and 2912).

Extract, per year:

• Total import quantity (tonnes)
• Total export quantity (tonnes)
• Import value and Export value (USD 1000, if needed)

Do NOT pull these from TM. TM gives partner-level rows that must be aggregated, and mirror-data gaps / re-exports distort the total. Use TM only in Step 6.

Step 5: Calculate self-sufficiency ratio

For each year where data is available, calculate:

Self-Sufficiency Ratio (SSR) = Production / (Production + Imports - Exports)

This is a "net availability" proxy. For a stricter food-focused SSR that accounts for feed, seed, and stock change, pull from FBS instead (element 5511 Production vs 5301 Domestic Supply). If FBS is available for the country-commodity, prefer it and note the switch in the methodology section.

Interpretation:

• SSR > 1.0 -- the country produces more than it consumes; net exporter
• SSR = 1.0 -- perfectly self-sufficient (rare)
• SSR 0.7-1.0 -- mostly self-sufficient, moderate import reliance
• SSR 0.5-0.7 -- significant import dependence
• SSR < 0.5 -- heavily import-dependent; vulnerable

Calculate SSR for the latest year and for 5 and 10 years ago (if data available) to show the trend.

Step 6: Identify trading partners and concentration risk (TM)

Now — and only now — pull the partner breakdown from TM:

# Resolve elements at runtime: faostat_search_codes(domain_code='TM', dimension_id='element', query='import') → e.g. 5622
# and faostat_search_codes(domain_code='TM', dimension_id='element', query='export') → e.g. 5922
faostat_get_data(
  domain_code='TM',
  area='<area_code>',
  item='<item_code_tm>',
  element='<resolved_tm_import_code>,<resolved_tm_export_code>',
  year='<latest available year>',
  response_format='compact',
  limit=500
)

Note: TM uses different element codes than TCL. Always resolve via faostat_search_codes(domain_code='TM', dimension_id='element', query='import') — do not assume TM codes match TCL codes.

From the partner data, rank import partners by volume:

1. List the top 5 import source countries with their share of total imports (use the TCL total from Step 4 as the denominator — TM partner totals sometimes don't perfectly reconcile).
2. Calculate a concentration metric: what percentage of imports comes from the top 1, top 2, and top 3 suppliers?
3. Assess concentration risk:
   • Top 1 supplier > 50% -- HIGH concentration risk (single point of failure)
   • Top 3 suppliers > 80% -- MODERATE concentration risk
   • No single supplier > 30% -- LOW concentration risk (diversified)

If TM data is sparse for the latest year (common — TM reporting often lags TCL by 1 year), step back to the most recent year with partner data and note the year mismatch.

Step 7: Trend analysis

Analyze the trajectory:

• Is the SSR improving (moving toward 1.0), stable, or deteriorating (moving away from 1.0)?
• Is import volume growing faster than domestic production?
• Are trading partners becoming more or less concentrated over time?

Step 8: Present the dependency assessment

Structure the output as follows:

1. Self-Sufficiency Summary

• Current SSR with interpretation
• SSR 5 years ago and 10 years ago
• Trend direction (improving / stable / deteriorating)

2. Domestic Production

• Latest production volume with units
• Production trend (growing / shrinking / stagnant)

3. Trade Position

• Total imports (volume and value)
• Total exports (volume and value)
• Net trade position (net importer or net exporter)

4. Supply Chain Risk

• Top 5 import partners with share percentages
• Concentration risk rating (HIGH / MODERATE / LOW)
• Any single-supplier vulnerability

5. Trend & Outlook

• Direction of dependency over time
• Key risk factors (e.g., "Production is flat while imports grow 5% annually")
• One-line vulnerability narrative (e.g., "Egypt imports 55% of its wheat, with 40% coming from a single supplier -- Russia")

6. Source Attribution "Source: FAOSTAT (FAO), accessed [current date]"

Important Rules

Element and item code resolution. Never use a hardcoded numeric element or item code as the primary value in a faostat_get_data call. Always resolve at runtime: faostat_search_codes(domain_code='<dom>', dimension_id='element', query='<metric name>') for elements; faostat_search_codes(domain_code='<dom>', dimension_id='item', query='<item name>') for items. Numeric codes shown in reference tables and code examples are verified hints — use them to validate the search result, not as the authoritative source. Domain letter-codes (QCL, TCL, GT, EM, FBS, FS…) are stable and may be used directly.

• Always use faostat_search_codes before faostat_get_data to resolve codes. Never guess or hardcode domain-specific codes.
• When requires_confirmation is true in a search result, always present options to the user and wait for their choice.
• Use response_format='compact' for multi-year or multi-partner queries to keep response sizes manageable.
• For faostat_get_data, use FILTER element codes (e.g., '2510'). For faostat_get_rankings, use DISPLAY element codes (e.g., '5510').
• If production data returns zero or null for a country-commodity pair, note this explicitly -- it may mean the country does not produce this commodity at all (SSR = 0, fully import-dependent).
• Always include the source attribution line at the end of any output.

Error Handling and Reliability Notes

• faostat_get_rankings sometimes returns HTTP 500. If the tool fails, reconstruct rankings by calling faostat_get_data for the relevant element/year across all reporting countries and sorting client-side. Document the fallback in the methodology section of the output.
• China composite rule (user preference, Apr 2026). Default to composite China (351) for single-country analysis. Offer China, mainland (41) as an opt-in. Flag the choice in output.
• TM reporting lag. TM partner data typically lags TCL aggregate data by ~1 year. If TM has no data for the latest year, drop to the most recent year available and state the mismatch.
• Year range syntax. Prefer explicit comma-separated year lists ('2014,2015,...,2023'). Colon ranges ('2014:2023') have returned empty results in some MCP configurations.

Related Skills

If you need…	Use
Full country food security profile	/faostat-country-profile
Global commodity supply overview	/faostat-commodity
Trend ranking over time	/faostat-trends
Side-by-side country comparison	/faostat-compare