faostat-climate

name: faostat-climate description: Use when the user asks about agricultural emissions, climate impact of farming, agrifood carbon footprint, greenhouse gases from agriculture, deforestation, forest carbon sinks, temperature change, fertilizer emissions, N2O, land use change, emissions intensity, or the climate-agriculture nexus. Keywords — emissions, climate, carbon, greenhouse gas, GHG, deforestation, forest, temperature, warming, N2O, fertilizer emissions, land use, agrifood, emissions intensity, carbon sink. Do NOT use for a full country food security profile → faostat-country-profile. Do NOT use for trend ranking across commodities or countries → faostat-trends.

Agrifood Climate Analyzer

Analyze the climate-agriculture nexus using FAOSTAT emissions, temperature, land use, and agricultural inputs data. This skill has five sub-workflows. Detect the appropriate one from the user's question, or present the options and ask.

Prerequisites

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

Domain Reference (CRITICAL -- use exactly these codes)

Element Filter Reference

Every faostat_get_data call below specifies an element filter. Without one, emissions domains return dozens of sub-elements per year and the payload explodes.

Rule: For GT, GF, ET, RL, RFN, RP — the codes below are stable and may be used directly. For EM domain elements, always resolve at runtime via faostat_search_codes(domain_code='EM', dimension_id='element', query='<metric>') — never hardcode them. The verified codes shown are reference hints only.

EM domain — resolve at runtime (hints only):

EM domain item codes (for filtering the agrifood breakdown):

Year-range syntax: use explicit comma-separated lists (year='2014,2015,...,2023'). Colon ranges ('2014:2023') have returned empty in practice.

Sub-workflow Detection

Read the user's question and select the most appropriate sub-workflow:

• Country emissions profile -- questions about a single country's agrifood emissions footprint, climate impact, or warming trend. Use Sub-workflow A.
• Emissions comparison -- questions comparing emissions across countries or regions, "who emits the most," rankings. Use Sub-workflow B.
• Crop emissions / efficiency -- questions about emissions from specific crops, which countries produce most efficiently, emissions intensity. Use Sub-workflow C.
• Deforestation / forest / land use -- questions about forest loss, carbon sinks, agricultural expansion, land use change. Use Sub-workflow D.
• Fertilizer-emissions link -- questions about fertilizer use and emissions, N2O, pesticide trends, input intensity. Use Sub-workflow E.

If the question does not clearly match one sub-workflow, present the five options with a one-line description of each and ask the user to choose.

Sub-workflow A: Country Emissions Profile

1. Ask for the country if not already specified.
2. Resolve the country name to an area code using faostat_search_codes(domain_code='GT', dimension_id='area', query='<country>').
   • If requires_confirmation is true in the response, present the matching options and ask the user to choose. Do NOT proceed until the user confirms.
   • China rule. Default to composite China (area code 351) — the roll-up of mainland + HK SAR + Macao SAR + Taiwan — not China, mainland (41), unless the user specifies 41 explicitly. Note in the output that FAOSTAT's own publications default to 41, so this profile's China total is marginally larger than the FAO data-portal default.
3. Pull agrifood systems emissions totals from the GT domain (with element and year filters so the payload is manageable):

   faostat_get_data(
     domain_code='GT',
     area='<area_code>',
     element='724313',                    # Emissions (CO2eq) in CO2 equivalents
     year='2014,2015,2016,2017,2018,2019,2020,2021,2022,2023',
     response_format='compact'
   )
   

   Identify total agrifood emissions trend and the breakdown by source category (farm gate, land use, pre/post production — these come through as different item codes within GT).
4. Resolve EM element codes at runtime (do not hardcode):

   faostat_search_codes(domain_code='EM', dimension_id='element', query='per capita')
   faostat_search_codes(domain_code='EM', dimension_id='element', query='share CO2eq')
   

   Use the returned codes (e.g. 7279 for per capita, 726313 for share) in the data call:

   faostat_get_data(
     domain_code='EM',
     area='<area_code>',
     item='6518',                          # Agrifood systems total
     element='<per_capita_code>,<share_code>',
     year='2019,2020,2021,2022,2023',
     response_format='compact'
   )
   

   Extract per capita emissions and share of national total.
5. Pull temperature change data from the ET domain:

   faostat_get_data(
     domain_code='ET',
     area='<area_code>',
     element='7271',                       # Temperature change (°C)
     year='2014,2015,2016,2017,2018,2019,2020,2021,2022,2023',
     response_format='compact'
   )
   

   Identify the warming trend over the available period. ET data is monthly — filter to annual items if needed or aggregate to annual averages.
6. Synthesize into a structured profile:
   • Agrifood Emissions Overview -- total emissions, trend (rising/falling/stable), breakdown by source
   • Normalized Metrics -- per capita, share of national emissions, per dollar of agricultural output
   • Temperature Trend -- warming trajectory for this country
   • Key Findings -- 2-3 headline insights connecting the data
7. Attribute: "Source: FAOSTAT (FAO), accessed [current date]"

Sub-workflow B: Emissions Comparison

1. Ask for the countries or region to compare (minimum 2, maximum 10). Accept country names, region names, or a mix.
2. Resolve each entity to area codes using faostat_search_codes(domain_code='GT', dimension_id='area', query='<name>').
   • Handle requires_confirmation for each entity separately. Do NOT proceed until all codes are confirmed.
   • Default China → composite China (351), not China, mainland (41).
3. Pull emissions totals from the GT domain for all entities — always with element and year filters:

   faostat_get_data(
     domain_code='GT',
     area='<code1>,<code2>,...',
     element='724313',
     year='2014,2015,...,2023',
     response_format='compact'
   )
   

4. Resolve EM element codes at runtime, then pull indicators for all entities:

   faostat_search_codes(domain_code='EM', dimension_id='element', query='per capita')
   faostat_search_codes(domain_code='EM', dimension_id='element', query='share CO2eq')
   

   faostat_get_data(
     domain_code='EM',
     area='<code1>,<code2>,...',
     item='6518',                          # Agrifood systems total
     element='<per_capita_code>,<share_code>',
     year='2019,2020,2021,2022,2023',
     response_format='compact'
   )
   

   Extract per capita and share metrics for normalization.
5. Present comparison:
   • Absolute emissions -- rank by total agrifood emissions (latest year)
   • Per capita emissions -- re-rank by per capita to show a different picture
   • Per value of output -- emissions efficiency ranking
   • Trend comparison -- who is reducing, who is increasing?
   • Key insights -- highlight surprising reversals or contrasts between absolute and normalized rankings
6. Attribute: "Source: FAOSTAT (FAO), accessed [current date]"

Sub-workflow C: Crop Emissions Analysis

1. Ask for the crop and optionally a set of countries. If no countries specified, use global top producers.
2. Resolve the crop to an item code using faostat_search_codes(domain_code='GCE', dimension_id='item', query='<crop>').
   • Handle requires_confirmation as always.
3. If countries were specified, resolve their area codes. If not, use faostat_get_rankings to identify top 10 producers for context.
4. Pull crop-specific emissions from the GCE domain: faostat_get_data(domain_code='GCE', item='<item_code>', area='<codes>', response_format='compact')
5. Pull emissions intensities from the EI domain: faostat_get_data(domain_code='EI', item='<item_code>', area='<codes>', response_format='compact')
6. Rank countries by emissions intensity (lowest = most efficient).
7. Present analysis:
   • Total crop emissions -- who emits the most growing this crop?
   • Emissions intensity ranking -- who produces most efficiently per unit of output?
   • Efficiency trends -- is intensity improving or worsening over time?
   • Key insight -- highlight the gap between the most and least efficient producers
8. Attribute: "Source: FAOSTAT (FAO), accessed [current date]"

Sub-workflow D: Forest & Land Use

1. Ask for the country or region if not specified.
2. Resolve area codes using faostat_search_codes(domain_code='GF', dimension_id='area', query='<name>').
   • Handle requires_confirmation.
3. Pull forest emissions and removals from the GF domain: faostat_get_data(domain_code='GF', area='<area_code>', response_format='compact') Look for: net forest conversion (deforestation proxy), forest land emissions, forest land removals (carbon sinks).
4. Pull land use data from the RL domain: faostat_get_data(domain_code='RL', area='<area_code>', response_format='compact') Look for: agricultural land area, forest area, changes over time.
5. Analyze the relationship:
   • Forest carbon balance -- are removals (sinks) offsetting emissions? What is the net?
   • Net forest conversion -- proxy for deforestation rate and trend
   • Land use shift -- is agricultural land expanding at the expense of forest?
   • Timeline -- when did the biggest changes occur?
6. Present findings:
   • Forest Emissions & Sinks -- net balance, trend
   • Deforestation Proxy -- net forest conversion figures and direction
   • Land Use Dynamics -- agricultural vs. forest area over time
   • Key Finding -- is agriculture driving deforestation here? How do sinks compare to emissions?
7. Attribute: "Source: FAOSTAT (FAO), accessed [current date]"

Sub-workflow E: Inputs-Emissions Link

1. Ask for the country or set of countries if not specified.
2. Resolve area codes using faostat_search_codes(domain_code='GT', dimension_id='area', query='<name>').
   • Handle requires_confirmation.
3. Pull fertilizer use data from the RFN domain (nitrogen fertilizers are the primary N2O driver): faostat_get_data(domain_code='RFN', area='<area_code>', response_format='compact')
4. Optionally pull from RFM (manufactured) and RFB (bulk) for a fuller picture.
5. Pull pesticide use from the RP domain: faostat_get_data(domain_code='RP', area='<area_code>', response_format='compact')
6. Pull agrifood emissions from the GT domain: faostat_get_data(domain_code='GT', area='<area_code>', response_format='compact') Focus on N2O emissions components if available.
7. Analyze the correlation:
   • Fertilizer use trend -- is nitrogen fertilizer use increasing or decreasing?
   • Emissions trend -- do N2O / total agrifood emissions track fertilizer use?
   • Pesticide trend -- increasing or decreasing? Any policy-driven shifts?
   • Correlation assessment -- does higher fertilizer use correspond to higher emissions in this country/set?
8. Present findings:
   • Fertilizer Use -- volumes, trends, nitrogen share
   • Pesticide Use -- volumes, trends
   • Emissions Correlation -- how closely do inputs track emissions?
   • Policy Implications -- what does this suggest for emissions reduction strategies?
9. Attribute: "Source: FAOSTAT (FAO), accessed [current date]"

Important Rules

• Always use faostat_search_codes before faostat_get_data to resolve codes. Never guess 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' when querying multiple entities or large time ranges.
• For rankings, use DISPLAY element codes (e.g., '5510' for production). For data filtering, use FILTER codes (e.g., '2510' for production).
• Always pass an element filter to faostat_get_data. Emissions domains return dozens of sub-elements per year and the payload explodes without one.
• Always pass an explicit comma-separated year list. Colon ranges like '2014:2023' have returned empty in practice.
• Always include the source attribution line at the end of any output.
• 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.

Error Handling and Reliability Notes

• faostat_get_rankings sometimes returns HTTP 500. If you need rankings (e.g., "top 10 emitters" before the comparison in Sub-workflow B), fall back to faostat_get_data across all reporting countries and sort client-side. Document the fallback in the output.
• China aggregate rule (user preference, Apr 2026). Default to composite China (351) — the roll-up of mainland + HK SAR + Macao SAR + Taiwan. Do NOT substitute China, mainland (41) unless the user specifies 41 explicitly. State the choice in the output and note that FAOSTAT's own publications default to 41, so the numbers here are marginally larger than the FAO data-portal default.
• Empty return with colon year range. Retry with a comma-list.
• ET domain is monthly. Filter to a single month (e.g., Annual item='6078') or compute an annual mean client-side.

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