Evaluation of the Symmetry of Statistical Methods Applied for the Identification of Agricultural Areas

Abstract

The main priorities of the common agricultural policies of the European Union (EU) are improvement of the quality of life in rural areas for their inhabitants as well as the optimum utilisation of rural resources. The most efficient tools to improve the management conditions and utilise the potential of land are land consolidation works aimed at creating more favourable management conditions in agriculture and forestry through improving the territorial structure of farms, forests and forestland; the reasonable configuration of land, aligning the limits of real properties with the system of irrigation; and drainage facilities, roads and terrain. The development of agriculture in Poland and its production capacity are considerably differentiated in terms of space. At present, Poland has agricultural areas which, in many respects, have a chance of competing with agriculture in the other member states of the European Union. However, in some areas, agricultural production run by private farms owned by individuals is on the verge of falling below the limit of profitability or falls below the limit of profitability. Currently, Poland lacks tools (strategies) allowing identification of land for intensive agricultural production as well as information about agricultural land that should be developed for non-agricultural purposes. Therefore, it is necessary to develop a methodology for identifying similar areas using available tools that can facilitate reliable identification of the areas relating to the indicated factors. Taxonomic methods can be used for clustering purposes. The study materials are data derived from real property register databases referring to one of the districts (poviats) situated in east-central Poland. As a final result, a method of clustering villages according to similar land-use categories was developed. It was created using two independent statistical methods: Ward’s method and the complete-linkage method. The highest consistency was observed in two groups of identified types of areas sharing very similar characteristics. A high index of similarity of both methods—the so-called Rand index—testified to the reliability of the results of calculations. The results of clustering corresponded to a large extent to actual features defining the use of land in the analysed villages as well as the terrain relief.

Czekanowski’s Diagram and Spatial Data Cluster Analysis for Planning Sustainable Development of Rural Areas

“…The S matrix, which is the subject of the final analysis, may include additional matrices containing numerical values of other defined indicators characterizing the studied areas in terms of the analyzed phenomenon. In particular, the final S matrix may include values reflecting the indicators proposed in [26] or the characteristics of rural areas considered in [40]. The use of the proposed solution allows for the identification of mutually similar areas, where consolidation works should take place at the same time.…”

An Algorithm for Delimiting Rural Areas According to Soil Classes

“…The study area is a complex of 44 villages forming an administrative part of six communes (gminas) in the district (poviat) of Brzozów, situated in southeastern Poland, in the Subcarpathian voivodeship (Figure 1). Studies carried out in this area [31,32] allowed the authors to collect study material in the form of data from a real property register referring to types and classes of three major uses of land in terms of their share, i.e., arable land, meadows, and pastures. The overall value of the area of land (Wcag) was determined using three factors, i.e., total area of the village (ha), share of the respective type of land (separately for arable land, meadows, and pastures) in the overall area of the village (%), and the mean score for the respective type of land (pts.).…”

“…Those studies resulted in determining the production value of arable land and grassland according to soil-quality classes using a 100-point scale (Table 1). The methods of research comprised calculations for three factors, i.e., overall area of the village in hectares; percentage share (in relation to the overall area of the village) of arable land, meadows, and pastures and the mean score for the respective types of land; Studies carried out in this area [31,32] allowed the authors to collect study material in the form of data from a real property register referring to types and classes of three major uses of land in terms of their share, i.e., arable land, meadows, and pastures. The overall value of the area of land (Wcag) was determined using three factors, i.e., total area of the village (ha), share of the respective type of land (separately for arable land, meadows, and pastures) in the overall area of the village (%), and the mean score for the respective type of land (pts.).…”

“…The whole computational algorithm pro vided for clustering separately for arable land, meadows, pastures, and collectively for a agricultural land (arable land, meadows, pastures). Village clustering used the previousl tested Ward's method [31,32] and the distance matrix was squared as Euclidean distances It should be mentioned that Ward's method provided very good clustering results that i previous studies [31] were controlled using a different clustering method-the complete linkage method-leading to 80% concurrence of the results. The studies were carried ou using the STATISTICA PLUS programme from StatSoft Polska.…”

“…Such a measure allowed a village to be placed in a hierarchy according to an adopted criterion. Next, using two tested statistical methods [32], i.e., Ward's method and the complete-linkage method, rural areas were clustered according to the values of arable land, meadows, and pastures. Studies carried out provided a response to the question of whether land with poor class soils formed a part of the tested object.…”