Survey ID Number
COL_2014_CNA_v01_EN_M_v01_A_OCS
Title
National Agricultural Census, 2014
Series Information
The 2014 National Agricultural Census (CNA) is the 3rd CNA conducted in Colombia. This first census carried out in 1960 covered 16 departments (Antioquia, Atlántico, Bolívar, Boyacá, Caldas, Cauca, Córdoba, Cundinamarca, Huila, Magdalena, Meta, Nariño, Norte de Santander, Santander, Tolima, Valle del Cauca), for a total of 27,337. 827 hectares that included land dedicated to livestock production located in urban areas and 1,209,672 units of agricultural exploitation. The second census conducted in 1970 covered 21 departments and 815 municipalities (Antioquia, Atlántico, Bolívar, Boyacá, Caldas, Cauca, Chocó, Arauca, Casanare, Quindío, Risaralda, Córdoba, Cundinamarca, Huila, Magdalena, Meta, Nariño, Norte de Santander , Santander, Tolima, Valle del Cauca), for a total of 30,993,190 hectares, which included land dedicated to livestock production located in urban areas and 1,176,811 units of agricultural exploitation.
In general, these national agricultural censuses are a very important reference for the 3rd CNA. The three censuses have some similar technical elements that make them comparable, such as the definitions of the Agricultural Production Unit (UPA) and the Agricultural Exploitation Unit (UEA), the structural issues and the general objectives.
Some of their differences are:
1. The first two censuses did not include: fishing, aquaculture, planted forests, natural forests and non-agricultural activities within the agricultural activity.
2. In the 1960 census size limits were established in area and number of animals to define the UEA or UPA, as follows: crops from 200 square meters; raise a minimum of three pigs; a minimum of 1 head of cattle or raising a minimum of 100 hens, and having sold $100 in agricultural products in 1959.
3. The first two censuses collected livestock information in the urban area, while the 3rd CNA is only a census of the dispersed rural area and as such does not cover the urban area or the populated centers defined in the Administrative Political Division (DIVIPOLA). The dispersed rural area corresponds, according to DIVIPOLA, to the so-called Class 3 of each municipality.
4. In relation to the size limits of the exploitation unit, the 1970 censuses and the current 3rd CNA have no size limits in area or number of heads to be defined as UPA or UEA.
The 3rd CNA, unlike the two previous censuses conducted in the country, covered all the agricultural and non-agricultural productive activity carried out in the dispersed rural area of the country. They included: all units of independent analysis of productive activity; size; land tenure and location, and the Non-Agricultural Productive Unit (UPNA) was created to collect data where the productive activity is only non-agricultural.
Likewise, adaptations were made to the definitions of UPA and producer to better reflect the agricultural and non-agricultural activity in ethnic territory and new topics related to production, the fisheries subsector, environment and some social aspects that reflect the reality of the area were included.
Since a statistical investigation of this type had not been carried out since the last census conducted in the country, the country did not have updated agricultural statistical information of a technical and exhaustive nature at the level of agricultural and non-agricultural units and of national coverage.
Since 1971, the information used in the agricultural sector came from various statistical exercises such as: annual estimates by consensus; annual estimates through probabilistic sampling surveys, and annual estimates by administrative records.
All these estimates had the following limitations: low geographic and thematic coverage; lack of statistical representativeness; non-standardized or harmonized data, and different variables, terms and classifications that do not allow comparability between them. On the other hand, during this period, subsector information systems were strengthened, with some of them having good statistical quality.