---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- tabular-classification
- tabular-regression
- other
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- agriculture-livestock
- complex-emergency-conflict-security
- food-security
- livelihoods
- afg
- bgd
- bfa
- khm
- cmr
pretty_name: "FAO Data in Emergencies Monitoring System (DIEM)"
dataset_info:
  splits:
    - name: train
      num_examples: 2008
    - name: test
      num_examples: 502
---

# FAO Data in Emergencies Monitoring System (DIEM)

**Publisher:** Food and Agriculture Organization (FAO) of the United Nations · **Source:** [HDX](https://data.humdata.org/dataset/fao-diem-monitoring-system-household-surveys-aggregated-data) · **License:** `hdx-other` · **Updated:** 2026-05-05

---

## Abstract

The Food and Agriculture Organization of the United Nations (FAO) has developed a monitoring system in 26 food crisis countries to better understand the impacts of various shocks on agricultural livelihoods, food security and local value chains.  
The Monitoring System consists of primary data collected from households on a periodic basis (more or less every four months, depending on seasonality). Data are collected through Computer-Assisted Telephone Interviews (CATI) and in-person surveys where the circumstances allow for field access.  
As the system is developed, the information collected and analyzed is being used to guide strategic decisions, to design programmes and to inform analytical processes such as the Integrated Phase Classification (IPC) and the Humanitarian Needs Overview (HNO).  
At the core of the system is a standardized household questionnaire administered to around 150,000 households per year across the 26 countries. Standardization permits comparisons across time and space, considerably enhancing the utility of the data for decision makers. At minimum the household data are representative at Admin 1 level (e.g. province, or region) and in frequent cases at Admin 2 level (e.g. district). 
In each aggregated field, the values indicate the frequencies of the different responses, expressed as a weighted percentage of the total sample. 
The present datasets represents aggregated data referring to household interviews performed after December 2022. At every new survey data release, after cleaning and validation phases, aggregated data is appended to the present dataset. 

For real-time updates, for accessing archived data or microdata and for additional survey-specific information, please visit the DIEM Hub: https://data-in-emergencies.fao.org/ or contact [DIEM](https://data-in-emergencies.fao.org/pages/contactus "DIEM")

View the column descriptions [here](https://hqfao.maps.arcgis.com/sharing/rest/content/items/04287fcadb994341b0b70d19c8a02035/data "here"). 
Metadata available [here](https://hqfao.maps.arcgis.com/sharing/rest/content/items/01595314154948719aca7325d88c782a/data "here"). 

Reference administrative boundaries (levels 0, 1 and 2) available [here](https://data-in-emergencies.fao.org/maps/3596c3ad318849068eda21517ade30be/about"here") in GIS format.

Each row in this dataset represents country-level aggregates. Temporal coverage is indicated by the `coll_start_date`, `coll_end_date` column(s). Geographic scope: **AFG, BGD, BFA, KHM, CMR, CAF, TCD, COL, and 25 others**.

*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Food security and nutrition |
| **Unit of observation** | Country-level aggregates |
| **Rows (total)** | 2,511 |
| **Columns** | 230 (217 numeric, 10 categorical, 3 datetime) |
| **Train split** | 2,008 rows |
| **Test split** | 502 rows |
| **Geographic scope** | AFG, BGD, BFA, KHM, CMR, CAF, TCD, COL, and 25 others |
| **Publisher** | Food and Agriculture Organization (FAO) of the United Nations |
| **HDX last updated** | 2026-05-05 |

---

## Variables

**Geographic** — `adm0_iso3` (YEM, BGD, AFG), `surveys` (range 1.0–2682.0), `hh_agricactivity_1` (range 0.0204–99.994), `hh_agricactivity_2` (range 0.0573–81.8609), `hh_agricactivity_3` (range 0.1326–100.0098) and 9 others.

**Temporal** — `coll_start_date`, `coll_end_date`, `coll_mid_date`.

**Demographic** — `hh_gender_1` (range 19.0595–100.0124), `hh_gender_2` (range 0.0692–80.9522), `hh_education_1` (range 0.0364–99.9996), `hh_education_2` (range 0.3676–69.8138), `hh_education_3` (range 0.067–67.2747) and 49 others.

**Outcome / Measurement** — `income_main_1`, `income_main_2`, `income_main_3`, `income_main_4`, `income_main_6` and 84 others.

**Identifier / Metadata** — `objectid` (range 736.0–7697.0), `adm0_name` (Yemen, Bangladesh, Afghanistan), `adm1_name` (Dhaka, Chattogram, Balochistan), `adm1_pcode` (BD30, BD20, PK22022), `adm2_pcode` (BD6091, BD6090, BD6058) and 6 others.

**Other** — `adm0_m49` (range 4.0–887.0), `adm_level` (range 1.0–2.0), `round` (range 1.0–30.0), `tot_crop_producers` (range 0.0–2523.0), `tot_ls_producers` (range 0.0–1542.0) and 54 others.

---

## Quick Start

```python
from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-food-security-all")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()
```

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `objectid` | int64 | 0.0% | 736.0 – 7697.0 (mean 4051.5094) |
| `adm0_name` | object | 0.0% | Yemen, Bangladesh, Afghanistan |
| `adm0_iso3` | object | 0.0% | YEM, BGD, AFG |
| `adm0_m49` | int64 | 0.0% | 4.0 – 887.0 (mean 439.0315) |
| `adm1_name` | object | 0.0% | Dhaka, Chattogram, Balochistan |
| `adm1_pcode` | object | 0.0% | BD30, BD20, PK22022 |
| `adm2_pcode` | object | 59.8% | BD6091, BD6090, BD6058 |
| `adm2_name` | object | 59.8% | Sunamganj, Falaba, Maulvibazar |
| `adm_name` | object | 0.0% | Abyan, Al Maharah, Al Mahwit |
| `adm_pcode` | object | 0.0% | YE23, YE22, YE15 |
| `adm_level` | int64 | 0.0% | 1.0 – 2.0 (mean 1.4022) |
| `round` | int64 | 0.0% | 1.0 – 30.0 (mean 9.6623) |
| `coll_start_date` | datetime64[ns] | 0.0% |  |
| `coll_end_date` | datetime64[ns] | 0.0% |  |
| `coll_mid_date` | datetime64[ns] | 0.0% |  |
| `surveys` | int64 | 0.0% | 1.0 – 2682.0 (mean 187.2736) |
| `tot_crop_producers` | float64 | 18.4% | 0.0 – 2523.0 (mean 124.7988) |
| `tot_ls_producers` | float64 | 18.4% | 0.0 – 1542.0 (mean 85.5669) |
| `tot_fish_producers` | float64 | 17.5% | 0.0 – 286.0 (mean 10.3866) |
| `hh_agricactivity_1` | float64 | 4.7% | 0.0204 – 99.994 (mean 27.1802) |
| `hh_agricactivity_2` | float64 | 6.6% | 0.0573 – 81.8609 (mean 13.5993) |
| `hh_agricactivity_3` | float64 | 4.1% | 0.1326 – 100.0098 (mean 30.8245) |
| `hh_agricactivity_4` | float64 | 11.5% | 0.0217 – 97.6869 (mean 31.9715) |
| `hh_gender_1` | float64 | 4.9% | 19.0595 – 100.0124 (mean 83.0519) |
| `hh_gender_2` | float64 | 6.5% | 0.0692 – 80.9522 (mean 17.2139) |
| `hh_education_1` | float64 | 15.4% | 0.0364 – 99.9996 (mean 37.3058) |
| `hh_education_2` | float64 | 15.8% | 0.3676 – 69.8138 (mean 26.6322) |
| `hh_education_3` | float64 | 16.8% | 0.067 – 67.2747 (mean 18.7961) |
| `hh_education_4` | float64 | 21.4% | 0.02 – 64.7391 (mean 12.0769) |
| `hh_education_5` | float64 | 42.5% | 0.0287 – 76.8284 (mean 8.8851) |
| `hh_education_888` | float64 | 76.8% | 0.0209 – 30.5547 (mean 1.4988) |
| `hh_maritalstat_1` | float64 | 63.8% |  |
| `hh_maritalstat_2` | float64 | 60.7% |  |
| `hh_maritalstat_3` | float64 | 76.6% |  |
| `hh_maritalstat_4` | float64 | 77.5% |  |
| `hh_maritalstat_5` | float64 | 61.8% |  |
| `hh_residencetype_1` | float64 | 37.9% |  |
| `hh_residencetype_2` | float64 | 60.0% |  |
| `hh_residencetype_3` | float64 | 74.2% |  |
| `hh_residencetype_4` | float64 | 57.9% |  |
| `resp_isfishproducer_1` | float64 | 47.7% |  |
| `resp_iscropproducer_1` | float64 | 18.4% |  |
| `resp_islsproducer_1` | float64 | 18.5% |  |
| `income_main_1` | float64 | 18.0% |  |
| `income_main_2` | float64 | 32.9% |  |
| `income_main_3` | float64 | 43.9% |  |
| `income_main_4` | float64 | 20.7% |  |
| `income_main_6` | float64 | 62.5% |  |
| `income_main_7` | float64 | 66.9% |  |
| `income_main_8` | float64 | 35.8% |  |
| `income_main_9` | float64 | 25.2% |  |
| `income_main_10` | float64 | 55.3% |  |
| `income_main_11` | float64 | 25.3% |  |
| `income_main_12` | float64 | 20.0% |  |
| `income_main_13` | float64 | 26.4% |  |
| `income_main_14` | float64 | 25.9% |  |
| `income_main_15` | float64 | 56.7% |  |
| `income_main_16` | float64 | 42.3% |  |
| `income_main_17` | float64 | 45.9% |  |
| `income_main_18` | float64 | 62.2% |  |
| `income_main_19` | float64 | 34.0% |  |
| `income_main_gender_1` | float64 | 39.1% |  |
| `income_main_gender_2` | float64 | 41.8% |  |
| `income_main_gender_3` | float64 | 40.9% |  |
| `income_main_control_1` | float64 | 48.9% |  |
| `income_main_control_2` | float64 | 50.1% |  |
| `income_main_control_3` | float64 | 50.3% |  |
| `income_main_comp_1` | float64 | 36.3% |  |
| `income_main_comp_2` | float64 | 3.2% |  |
| `income_main_comp_3` | float64 | 1.1% |  |
| `income_main_comp_4` | float64 | 1.0% |  |
| `income_main_comp_5` | float64 | 7.3% |  |
| `income_main_comp_888` | float64 | 67.7% |  |
| `income_sec_1` | float64 | 29.3% |  |
| `income_sec_2` | float64 | 40.6% |  |
| `income_sec_3` | float64 | 50.7% |  |
| `income_sec_4` | float64 | 24.5% |  |
| `income_sec_6` | float64 | 71.6% |  |
| `income_sec_7` | float64 | 75.7% |  |
| `income_sec_8` | float64 | 64.0% |  |
| `income_sec_9` | float64 | 37.5% |  |
| `income_sec_10` | float64 | 74.2% |  |
| `income_sec_11` | float64 | 31.7% |  |
| `income_sec_12` | float64 | 24.1% |  |
| `income_sec_13` | float64 | 33.9% |  |
| `income_sec_14` | float64 | 45.1% |  |
| `income_sec_15` | float64 | 69.7% |  |
| `income_sec_16` | float64 | 48.9% |  |
| `income_sec_17` | float64 | 56.2% |  |
| `income_sec_18` | float64 | 62.4% |  |
| `income_sec_19` | float64 | 21.1% |  |
| `income_sec_gender_1` | float64 | 66.1% |  |
| `income_sec_gender_2` | float64 | 68.7% |  |
| `income_sec_gender_3` | float64 | 68.3% |  |
| `income_sec_control_1` | float64 | 79.4% |  |
| `income_sec_control_2` | float64 | 79.7% |  |
| `income_sec_comp_1` | float64 | 59.2% |  |
| `income_sec_comp_2` | float64 | 25.5% |  |
| `income_sec_comp_3` | float64 | 21.3% |  |
| `income_sec_comp_4` | float64 | 20.9% |  |
| `income_sec_comp_5` | float64 | 37.6% |  |
| `income_sec_comp_888` | float64 | 76.1% |  |
| `income_third_1` | float64 | 64.3% |  |
| `income_third_2` | float64 | 71.8% |  |
| `income_third_3` | float64 | 78.4% |  |
| `income_third_4` | float64 | 48.3% |  |
| `income_third_9` | float64 | 75.1% |  |
| `income_third_11` | float64 | 67.3% |  |
| `income_third_12` | float64 | 58.1% |  |
| `income_third_13` | float64 | 70.8% |  |
| `income_third_16` | float64 | 71.6% |  |
| `income_third_19` | float64 | 21.1% |  |
| `income_third_gender_1` | float64 | 79.8% |  |
| `income_third_comp_2` | float64 | 46.2% |  |
| `income_third_comp_3` | float64 | 38.4% |  |
| `income_third_comp_4` | float64 | 44.7% |  |
| `income_third_comp_5` | float64 | 68.5% |  |
| `hh_wealth_light_1` | float64 | 60.3% |  |
| `hh_wealth_light_2` | float64 | 78.5% |  |
| `hh_wealth_light_3` | float64 | 73.8% |  |
| `hh_wealth_light_4` | float64 | 61.7% |  |
| `hh_wealth_light_5` | float64 | 57.4% |  |
| `hh_wealth_light_666` | float64 | 72.7% |  |
| `hh_wealth_toilet_1` | float64 | 46.9% |  |
| `hh_wealth_toilet_2` | float64 | 40.9% |  |
| `hh_wealth_toilet_3` | float64 | 58.7% |  |
| `hh_wealth_toilet_4` | float64 | 64.5% |  |
| `hh_wealth_toilet_777` | float64 | 74.1% |  |
| `hh_wealth_water_1` | float64 | 55.3% |  |
| `hh_wealth_water_2` | float64 | 49.1% |  |
| `hh_wealth_water_3` | float64 | 49.8% |  |
| `hh_wealth_water_4` | float64 | 70.9% |  |
| `hh_wealth_water_5` | float64 | 56.8% |  |
| `hh_wealth_water_6` | float64 | 64.6% |  |
| `hh_wealth_water_7` | float64 | 55.7% |  |
| `hh_wealth_water_8` | float64 | 59.2% |  |
| `hh_wealth_water_9` | float64 | 61.8% |  |
| `hh_wealth_water_10` | float64 | 74.2% |  |
| `shock_animaldisease_1` | float64 | 13.7% |  |
| `shock_coldtemporhail_1` | float64 | 60.6% |  |
| `shock_drought_1` | float64 | 23.6% |  |
| `shock_firemanmade_1` | float64 | 78.1% |  |
| `shock_firenatural_1` | float64 | 79.2% |  |
| `shock_flood_1` | float64 | 33.0% |  |
| `shock_higherfoodprices_1` | float64 | 1.3% |  |
| `shock_higherfuelprices_1` | float64 | 8.4% |  |
| `shock_hurricane_1` | float64 | 63.5% |  |
| `shock_landslides_1` | float64 | 77.2% |  |
| `shock_lostemplorwork_1` | float64 | 4.1% |  |
| `shock_mvtrestrict_1` | float64 | 55.1% |  |
| `shock_napasture_1` | float64 | 54.0% |  |
| `shock_noshock_1` | float64 | 4.0% |  |
| `shock_othercropandlivests_1` | float64 | 29.0% |  |
| `shock_othereconomicshock_1` | float64 | 8.5% |  |
| `shock_otherintrahhshock_1` | float64 | 9.4% |  |
| `shock_othermanmadehazard_1` | float64 | 49.3% |  |
| `shock_othernathazard_1` | float64 | 46.2% |  |
| `shock_pestoutbreak_1` | float64 | 30.3% |  |
| `shock_plantdisease_1` | float64 | 25.4% |  |
| `shock_sicknessordeathofhh_1` | float64 | 4.9% |  |
| `shock_theftofprodassets_1` | float64 | 63.0% |  |
| `shock_violenceinsecconf_1` | float64 | 41.9% |  |
| `need_0` | float64 | 25.7% |  |
| `need_1` | float64 | 18.4% |  |
| `need_cash_1` | float64 | 18.2% |  |
| `need_cold_storage_1` | float64 | 73.2% |  |
| `need_crop_infrastructure_1` | float64 | 22.1% |  |
| `need_crop_inputs_1` | float64 | 19.9% |  |
| `need_crop_knowledge_1` | float64 | 26.2% |  |
| `need_env_infra_rehab_1` | float64 | 50.7% |  |
| `need_fish_infrastructure_1` | float64 | 56.8% |  |
| `need_fish_inputs_1` | float64 | 52.3% |  |
| `need_fish_knowledge_1` | float64 | 60.4% |  |
| `need_food_1` | float64 | 21.1% |  |
| `need_ls_feed_1` | float64 | 21.2% |  |
| `need_ls_infrastructure_1` | float64 | 25.7% |  |
| `need_ls_knowledge_1` | float64 | 33.3% |  |
| `need_ls_vet_service_1` | float64 | 23.1% |  |
| `need_marketing_supp_1` | float64 | 48.1% |  |
| `need_other_1` | float64 | 50.5% |  |
| `need_received_cash_1` | float64 | 39.9% |  |
| `need_received_crop_assist_1` | float64 | 54.6% |  |
| `need_received_food_1` | float64 | 36.6% |  |
| `need_received_ls_assist_1` | float64 | 76.6% |  |
| `need_received_none_1` | float64 | 18.8% |  |
| `need_received_other_1` | float64 | 71.0% |  |
| `need_received_vouchers_fair_1` | float64 | 77.4% |  |
| `need_vouchers_fair_1` | float64 | 38.4% |  |
| `assistance_quality_1` | float64 | 31.9% |  |
| `assistance_quality_2` | float64 | 76.9% |  |
| `assistance_quality_3` | float64 | 68.9% |  |
| `assistance_quality_4` | float64 | 63.0% |  |
| `assistance_dk_1` | float64 | 79.7% |  |
| `assistance_fao_1` | float64 | 78.8% |  |
| `assistance_gov_1` | float64 | 58.3% |  |
| `assistance_ngo_1` | float64 | 59.3% |  |
| `assistance_otherun_1` | float64 | 73.0% |  |
| `assistance_wfp_1` | float64 | 70.8% |  |
| `hh_age_median` | float64 | 38.9% |  |
| `hh_age_wmean` | float64 | 38.9% |  |
| `hh_age_stddev` | float64 | 0.0% |  |
| `hh_age_ci_low` | float64 | 38.9% |  |
| `hh_age_ci_high` | float64 | 38.9% |  |
| `hh_size_median` | float64 | 23.3% |  |
| `hh_size_wmean` | float64 | 23.3% |  |
| `hh_size_stddev` | float64 | 0.0% |  |
| `hh_size_ci_low` | float64 | 23.3% |  |
| `hh_size_ci_high` | float64 | 23.3% |  |
| `tot_income_median` | float64 | 62.0% |  |
| `tot_income_wmean` | float64 | 62.0% |  |
| `tot_income_stddev` | float64 | 0.0% |  |
| `tot_income_ci_low` | float64 | 62.0% |  |
| `tot_income_ci_high` | float64 | 62.0% |  |
| `income_main_amount_median` | float64 | 61.8% |  |
| `income_main_amount_wmean` | float64 | 61.8% |  |
| `income_main_amount_stddev` | float64 | 0.0% |  |
| `income_main_amount_ci_low` | float64 | 61.8% |  |
| `income_main_amount_ci_high` | float64 | 61.8% |  |
| `income_sec_amount_median` | float64 | 68.1% |  |
| `income_sec_amount_wmean` | float64 | 68.1% |  |
| `income_sec_amount_stddev` | float64 | 0.0% |  |
| `income_sec_amount_ci_low` | float64 | 68.1% |  |
| `income_sec_amount_ci_high` | float64 | 68.1% |  |
| `income_third_amount_median` | float64 | 74.0% |  |
| `income_third_amount_wmean` | float64 | 74.0% |  |
| `income_third_amount_stddev` | float64 | 0.0% |  |
| `income_third_amount_ci_low` | float64 | 74.0% |  |
| `income_third_amount_ci_high` | float64 | 74.0% |  |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-05-05 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `objectid` | 736.0 | 7697.0 | 4051.5094 | 3871.0 |
| `adm0_m49` | 4.0 | 887.0 | 439.0315 | 422.0 |
| `adm_level` | 1.0 | 2.0 | 1.4022 | 1.0 |
| `round` | 1.0 | 30.0 | 9.6623 | 8.0 |
| `surveys` | 1.0 | 2682.0 | 187.2736 | 150.0 |
| `tot_crop_producers` | 0.0 | 2523.0 | 124.7988 | 106.5 |
| `tot_ls_producers` | 0.0 | 1542.0 | 85.5669 | 67.0 |
| `tot_fish_producers` | 0.0 | 286.0 | 10.3866 | 3.0 |
| `hh_agricactivity_1` | 0.0204 | 99.994 | 27.1802 | 18.2415 |
| `hh_agricactivity_2` | 0.0573 | 81.8609 | 13.5993 | 11.433 |
| `hh_agricactivity_3` | 0.1326 | 100.0098 | 30.8245 | 28.9347 |
| `hh_agricactivity_4` | 0.0217 | 97.6869 | 31.9715 | 29.5684 |
| `hh_gender_1` | 19.0595 | 100.0124 | 83.0519 | 87.1382 |
| `hh_gender_2` | 0.0692 | 80.9522 | 17.2139 | 13.0639 |
| `hh_education_1` | 0.0364 | 99.9996 | 37.3058 | 33.4 |

---

## Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (`N/A`, `null`, `none`, `-`, `unknown`, `no data`, `#N/A`) were unified to `NaN`. 84 column(s) with >80% missing values were removed: `hh_agricactivity_888`, `hh_agricactivity_999`, `hh_gender_888`, `hh_gender_999`, `hh_education_999`, `hh_maritalstat_888`.... 3 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

---

## Limitations

- Data originates from Food and Agriculture Organization (FAO) of the United Nations and has not been independently validated by ESA.
- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
- The following columns have >20% missing values and should be treated with caution in modelling: `adm2_pcode`, `adm2_name`, `hh_education_4`, `hh_education_5`, `hh_education_888`, `hh_maritalstat_1`, `hh_maritalstat_2`, `hh_maritalstat_3`....
- This dataset spans 33 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/fao-diem-monitoring-system-household-surveys-aggregated-data) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_asia_food_security_all,
  title     = {FAO Data in Emergencies Monitoring System (DIEM)},
  author    = {Food and Agriculture Organization (FAO) of the United Nations},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/fao-diem-monitoring-system-household-surveys-aggregated-data},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}
```

---

*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*