Considerable improvements are necessary for many national agricultural statistics. Subjective guesses should be progressively replaced by more objective methods. In countries where administrative data are very reliable, they could be used for producing agricultural statistics, with some caveats and methodology development for detecting and removing sources of bias. Combining administrative data with sample surveys provides a means to adjust for such biases. Different groups collect these data using different methods and with different reliability; benefits would be accrued from increased standardization of data collection and increased data sharing. In general these data are not made accessible outside of the project for which they are collected. In some cases high impact, low cost improvements such as access to the internet would greatly improve data access. In other cases increased coordination and capacity building for data collection and dissemination are needed. Another alternative to a subjective guess is list sample surveys, where a complete list can be created and updated. However, the effect on agricultural statistics of incomplete or out of date lists should be taken into account. This list approach should not be adopted where creating a complete and updated list of farms is difficult, for example where a high percentage of farms are small and census tends to have a low coverage, or where the list changes rapidly such as in transition countries, where information such as taxation, social insurance and subsidies data, traditionally used for updating a list created by a census are unreliable. In all these cases, one of the various kinds of area frame sample survey, e.g. segments with physical boundaries, square segments, transects, points, clusters of points should be adopted since it guaranties an unbiased, objective and repeatable estimate procedure. The choice of the kind of an area frame should be made on the basis of agricultural and socio-economic characteristics of the country. Considerable benefit could be gained from combining traditional agricultural statistics with remotely sensed data
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