11. Data can be gathered in various ways. The principal means of collecting data are:
1. Census - involves complete enumeration of the population (code 0);2. Probability and purposive sampling - a subset of the population is covered in accordance with some probability weights or in terms of accessibility of the respondents (code 1);
3. Subjective and recorded estimates of government personnel involved in implementing a fiscal function and extension services, parastatal activities, and other law enforcement duties (code 2);
4. Subjective estimates of commodity specialists (code 3);
5. Data derived from international documents published by other countries or by an international agency (code 4).
The codes corresponding to items (1) to (5) are not reflective of the accuracy of the data but rather yield a profile of the objectivity of the information and an implicit measure of the efforts of the government to build a sound livestock data base
12. The data collection schemes mentioned previously are applied to the information collated through the Nigerian Livestock Information Service 1984 annual report. The results are:
|
Date Required |
Code |
|
Domestic slaughter cattle, goats, sheep and pigs |
3, 2 |
|
Live cattle imports |
3, 4, 2 |
|
Immigrant slaughter cattle, sheep, goats |
3, 2 |
|
Importation of animal feedstuffs |
3, 4 |
|
Importation of milk and dairy products |
3, 4 |
|
Transportation of cattle by road, rail, hoof |
3, 2 |
|
Transportation of sheep, goats, camels, etc. |
3, 2 |
|
Average prepared feed prices |
1 |
|
Average slaughter cattle, sheep, goat, etc. price |
1 |
|
Average price for beef, mutton, pork eggs, etc. |
1, 2 |
|
Average red Sokoto goat skin prices (heavy type) |
1 |
13. Several remarks can be made regarding the preceding classification exercise. Firstly, a large portion of the livestock data base is created through the subjective opinions of livestock specialists. The basis of their estimates is seldom documented. Since different specialists will tend to report different levels within and across time periods, it will be difficult to separate systematic variations arising from a given set of factors of interest to policy makers from variations arising from educated guesses. Such a situation is illustrated by the data on cattle movements for the period 1970-84.
(a) Animal numbers moved by hoof rose from 171897 (1970) to 344883 (1971), an increase of 100%;
(b) Animals transported by road went up from 49461 (1970) to 842461 head (1984);
(c) Animals moved by rail declined from 96308 (1970) to 46412 (1984);
(d) Cattle numbers moved on hoof plummeted from 171897 (1970) to 24406 head (1984).
14. Sharp changes are noted on the said data set. A valid inquiry is: are such patterns of live cattle transport reflective of changing transport costs or marketing institutions or simply data aberrations arising from the temperament and innate livestock knowledge of the person in charge of the collection of the animal movement statistics?
15. A second major point to be made is the absence of any standard sampling scheme at the field level. For example, while only retail livestock prices are reported, the policy adopted by the livestock officer at the state level with respect to market coverage and selection of respondents is highly arbitrary. As a result, the average livestock prices reported may not be truly representative of market conditions if respondents are limited to specific market areas. In the current reporting form, livestock prices are already difficult to use for policy analysis purposes since they are not correlated with specific livestock qualities due to the absence of carcass grading schemes and other modes of market standardization.
16. A possible offshoot of the price measurement errors is a distorted price margin between supply and final consumption points. For example, market reports indicate at times higher retail beef prices in areas like Ibadan relative to Kaduna resulting from the use of the averaging process.
17. Thirdly, the present livestock data collection scheme is oriented more towards moderate monitoring of disease outbreaks and tax collection rather than generating information needed to evaluate overall policy impacts within the livestock subsector and on other related sectors. As a result, data quality checks are seldom undertaken since the utilization of the data inputs for quantitative policy modelling is very low.
