Central African Republic agriculture data

Central African Republic Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2022

Central African Republic Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2022

Central African Republic Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2020

Central African Republic Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2021

Central African Republic Near Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2020

Central African Republic Medium Term Projection FEWS NET Acute Food Insecurity Classifications Geographic data for 2021

This geospatial dataset represents the area of intervention of the Projet d’Appui à la Résilience et à la Sécurité Alimentaire et Nutritionnelle (PARSANKO) in the Central African Republic, funded by the African Development Bank (AfDB). The project aims to improve food and nutrition security and strengthen the resilience of vulnerable populations through sustainable agriculture, climate adaptation, and livelihood support. The dataset is designed for integration with other geospatial layers under the FAO Hand-in-Hand Initiative (HiHI), supporting evidence-based investment planning and targeted interventions in the agricultural sector. **Data creation:** 2025-07-30 **Data publication:** 2025-08-04 **Citation:** FAO. [YYYY (year of last update)]. [Name of database: Name of dataset OR Name of database]. [Accessed on [DD Month YYYY]]. [URL] Licence: CC-BY-4.0 **Contact points:** Metadata Contact: [Nelson Ribeiro](mailto:nelson.rosasribeirofilho@fao.org) **Data lineage:** The project area of intervention was created with information from the FAO TCP/CAF/3904/LERSA, RAPPORT FINAL, ETUDE D’IDENTIFICATION ET DE CARTOGRAPHIE DES CHAINES DE VALEURS DES FILIERES PRIORITAIRES ET DES PARTENAIRES POTENTIELS ET DE LEURS DOMAINES D’INTERET, from the 31st March 2025. **Resource constraints:** This work is made available under the Creative Commons Attribution 4.0 International license (CC-BY 4.0) **Online resources:**

Vector polygon geometry features representing zones with high potential for intensive tilapia farming using cages in water bodies. Produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multi-criteria Decision Analysis (GIS-MCDA) suitability assessment. Intensive systems are characterized by high densities and artificial feed input. The score is achieved by processing sub-model outputs characterizing biophysical, infrastructure, and demand factors Criteria: a. Market accessibility (large urban areas). b. Crop input (CropAggGAEZ) availability of agricultural by-products. c. Livestock input (weighted animal density aggregation) availability of livestock byproducts. d. Accessibility to ports - considers that high quality/performance tilapia feed is still imported. e. Tilapia potential yield. The location score is a simple arithmetic weighted sum of normalized/scaled grids (0 to 100): (“Accessibility MajorUrbanAreas” X 0.40) + (“CropsInput” X 0.15) + (“LivestockInput” X 0.15) + ("Accessibility Ports" X 0.15) + (Tilapia Yield x 0.15) Constraints: a. Protected areas. The Global Surface Water seasonality (seasonality = 12 months) to identify permanent water. Based on the literature the minimum area considered for tilapia cage farming is 5 acres. The final zoning follows the approach: 1. Clipping - Location score mapping is clipped using a 1km buffer from permanent waters with >5ac area polygon layer. 2. Zonal statistics extracted with the maximum (max) score value from the grid layer to the buffer layer polygons. 3. The polygons are selected and displayed for the top 95th percentile.