SOME POSSIBILITIES OF IMPROVING THE QUALITY OF GRAPE PRODUCTION IN HUNGARY

Transcription

1 SOME POSSIBILITIES OF IMPROVING THE QUALITY OF GRAPE PRODUCTION IN HUNGARY András Ittzés 1 Károly Szenteleki 2 Szent István University, Faculty of Horticulture Department of Mathematics and Informatics Budapest One of the non-negligible factors blocking Hungarian wines from getting on market is the extreme division in domestic grape production. As a consequence, the grape varieties show quite different quality. The procedure described in this paper attempts to analyze the variety structure of grape production and to gradually change the plantation structure. The cluster analysis described in the first part of this paper groups the wine communities and regions into different categories, taking into account average production and production quality (must-degree) together. The second part applies the model of operational research taking into account the grape marketing problems, and especially looking at the quality indicator, the must-degree of the grape and gives quantitative suggestions for developing a new, more effective variety structure. 1. Introduction On the basis of methodical studies done with the help of the Hungarian Scientific Research Fund (OTKA), a branch informational database could be formed which is new in its quality. In addition to directly processing this informational database statistically, possible theoretical models were examined and new procedures elaborated [1]. Within the framework of the study, we tried these methods on the test data of the primary and secondary database, which was expanded on the basis of the theoretical concepts, and the results can be used on regional and national levels after the launch of the informational system [2]. These methodical examinations also meant both the application of already existing procedures and the elaboration of completely new procedures. 2. Classification of wine regions and wine communities The test results came from 169 Hungarian wine communities. The 3,002 records used for the calculations contained the size of the agricultural lands and the average production, as well as the must-degree of many tens of thousands of plantation data, according to wine communities (together with the connected communities) and varieties. The task aimed to classify the wine regions and communities, and thereby rank them on the basis of two characteristics (average production and average must-degree) according to variety, that is: - for all varieties; - for white grapes (999 < variety code < 2000 or 2999 < variety code < 4000); - for blue grapes (1999 < variety code < 3000); - for white grapes of the 5 largest agricultural lands, separately (variety code = 1020, 1200, 1270, 1320, 1080); - for blue grapes of the 5 largest agricultural lands, separately (variety code = 2060, 2130, 2100, 2030, 2090). 1 andras@kee.hu 2 karesz@kee.hu 844

2 The examination meant completing altogether 26 partial tasks according to two methods, i.e. 52 classification tasks. We completed the calculations and analyses with the SPSS for Windows 10.0 software. We based the classification on the standardized variables, with the help of two types of algorithms: - k-means clustering using running means (forming 4 clusters with the initial centres chosen by the software); - hierarchical classification (forming 4, 3 and 2 clusters calculating the distance between the points with Euclidean metric and using the between-groups linkage cluster method) Average production Figure 1. Clustering 169 wine communities based on all varieties (k-means method) Average production Figure 2. Clustering 169 wine communities based on all varieties (hierarchical method) 845

3 In the majority of cases, there was relatively little difference between the results of the two types of clustering. The minimal differences arrived in this way did not legitimate initiating further examinations in the interest of revealing their causes. The two evaluations gave characteristically different results when we based our examination on the data of the 169 test wine communities for all wine varieties. These two results can be seen in Figure 1 and Figure 2. Compared to the analysis of the wine regions, we arrived at greater differences with the grouping of the wine communities, which gave grounds for further examinations. 3. Applying operational research to modifying the plantation structure The analysis of economic processes is realised most commonly through the use of a statistical model. In spite of their importance, those applications belonging to the topic of operational research are unreasonably ignored, which not only serve the analysis of the data of the past together with showing their inner relations, but which also offer an alternative for changing the existing economical processes and growing-potentials [3]. The process set forth in the following attempts to provide an analysis of the wineproducing variety-structure and to gradually change its plantation structure. The clustering procedure put forth in the earlier part of the paper placed the wine communities and regions into different categories by taking into consideration the production-average and the production quality (must-degree) together. On the basis of these data, we can grade the individual wine communities and regions, but we did not arrive at quantitative suggestions for how or in what proportion it is worthwhile to grow the individual grape varieties on each wine region. We took today s market relations as a starting point for our optimizing model. It is well known that the grape-wine branch struggles with a significant crisis of overproduction at present, especially for cheap table wines [6]. Taking this into consideration we made the following restrictions: - in our optimizing model, we did not allow increase in size of already existing vineyards in the wine regions; - for reasons of employment policy and social considerations, we did not restrict use of the present grape regions (this is also in harmony with EU norms); - we undertook assessments by starting out from the present variety-structure, by gradually modifying it, and also by completely changing it. The calculations were started by processing the vintage data of Each wine community s data was processed on each grape variety, and was summarized according to wine communities and regions. Thus we arrived at data on the basis of which we could rank the individual varieties and asses the production potential. Naturally, an examination becomes the more objective, the more years of data it relies on, but it was only the year 2001 when we could first truly lean on the effective functioning of the information system which gave usable basis for the decision supporting. Figure 3 gives the must-degree data by wine region, calculated using the vintage data of the Chardonnay grape variety [4]. The same diagrams were at our disposal for all grape varieties in Hungary, so there was no obstacle in undertaking the desired analyses in whatever depths necessary. 846

4 MUST-DEGREE 19,00 18,50 18,00 17,50 17,00 16,50 16,00 15,50 15, WINE-REGIONS Figure 3. Sugar contents of Chardonnay variety according to wine regions (Source: National Council of Wine Communities, 2001) Our first examination involved the analysis of Hungarian white grapes. The aim of the examination was to create and evaluate an optimizing model which could compare the vineyards by quality and change them in structure. An increase in quantity was not regarded as a stressed task, thus we decided not to enclose it in the examination. The results of our examination are contained in Table 1. Specification 1. Original plantation structure Optimized plantation structure % land-increase per variety % land-increase per variety % land-increase per variety % land-increase per variety % land-increase per variety Without variety restriction Table 1. Effect of modifying the plantation structure of white grape 847

5 To prepare the evaluations, we made an independent model-constructing program built on the database available, with the use of Clipper program language. The program could be parameterised in the field of the grape varieties enclosed in the examination of the wine regions and of the regional proportions, so it was possible to run through and try a relatively large number of alternatives extremely fast. The automatically prepared models were built of many hundreds of conditions, and of a variable-system concerning the wine regions and grape varieties. We evaluated the table with the Solver and the QSB LP program packages. Specification 1. Original plantation structure Optimized plantation structure % land-increase per variety % land-increase per variety % land-increase per variety % land-increase per variety % land-increase per variety Without variety restriction Table 2. Effect of modifying the plantation structure of blue grape Basically, with both the white and the blue grape varieties, we started out from the current variety-structure, and took the same variety-structure and redistributed it among the wine regions in an optimal way. Such a structure can serve as the starting point for providing for constant demanding market structure. Following this, by gradually liberating the regional barriers subscribed to the individual varieties, we re-optimized the white, blue and whole grape region. We put the results of these calculations in the tables. The results arrived at can be considered as starting points for further examinations. We consider the results reassuring from the point of view that we got results relatively fast which show qualitative improvement. The results arrived at can spare the use of many thousands of hundreds of kilos of sugar to reach higher quality. The parameters of the task can be related to variety, wine region, market demand estimations: after adjusting the desired parameters, the results of the optimization are almost instantly at our disposal. 848

6 Specification 1. Original plantation structure Optimized plantation structure % land-increase per variety % land-increase per variety % land-increase per variety % land-increase per variety % land-increase per variety % white grape blue grape Without variety restriction Table 3. Effect of modifying the complete plantation structure 4. Conclusions We can conclude even without putting forth the other tables of results that the newly planned and realised branch informatics system is suitable for creating decision supporting databases, which can be the basis of scientific, professional and economic questions [5]. However, the examinations done on the test data raise the importance of further duties in the interest of the relevant establishment of the branch decision tasks, such as: - the range of the established informatics system must be extended to the vineyards of all of the wine communities; - the established theoretical informatics model must be expanded to reach thos regions not belonging to the wine regions; - in order to eliminate the anomalies of collected data due to the weather conditions and other circumstances, we have to switch from the statical examination of a given year to the dynamical overall examination of several years; - consistency examinations of the possible alternative methods have to be done, in order to choose the methods that can be best applied. 849

7 LITERATURE [1] Botos E. P., Harnos Zs., Horváth Cs., Szenteleki K., Urbán A., (1999): A szolo-bor ágazat szakirányú kérdései. (The specialized questions of the grape-wine sector.) Budapest, Amulett Kiadó [2] Botos E. P., Szabó A., Horváth Cs., Urbán A., Harnos Zs., Kollányi L., Szenteleki K. (2001): A évi szolokataszteri felmérés stratégiai terve. (The strategy plan of the grape cadastral survey of the year 2001.) Budapest, FVM -SZBKI-HNT-SZIE tanulmány [3] Herdon M. (1995): Mezogazdasági döntéstámogató rendszerek. (Agricultural decision support systems.) Tessedik Sámuel Tiszántúli Mezogazdasági Tudományos Napok, Hódmezovásárhely [4] Kállay M., Szenteleki K. (2001): A bor cukortartalom növelésének számítási módszerei. (The calculation methods for increasing the sugar content of wine.) Borászati Füzetek, 2001/5, Budapest [5] Pitlik L. (1996) A döntéstámogatás jövoje az agrárinformatikában (The future of decision support in agricultural informatics) OMÉK Konferencia, Gödöllo [6] Szenteleki, K., Lakner, Z. (2001): The Hungarian Wine Sector on Threshold of European Union. ENOMETRICS VIII. Conference, St. Helena, California, USA 850