The recent changes in the Portuguese farms land uses: an application of an approach based on HJ-Biplot ANTÓNIO XAVIER Faculdade de Ciências e Tecnologias and CEFAGE-UE (Center For Advanced Studies in Management and Economics) Universidade do Algarve Gambelas Campus, Edf. 8, 8005-139 Faro, Portugal PORTUGAL amxav@sapo.pt MARIA DO SOCORRO ROSÁRIO Gabinete de Planeamento e Políticas -Direção de Serviços de Estatística, Metodologia e Estudos PORTUGAL socorro@gpp.pt MARIA DE BELÉM MARTINS Faculdade de Ciências e Tecnologias and CEFAGE-UE (Center For Advanced Studies in Management and Economics) Universidade do Algarve Gambelas Campus, Edf. 8, 8005-139 Faro, Portugal PORTUGAL mbmartins@ualg.pt Abstract: - The Portuguese 2009 Agricultural Census (RGA 2009) is still not completely analyzed by the researchers and a more precise analysis may provide new information about the tendencies and the actual situation of Portuguese agriculture, namely regarding land uses changes and farms orientation. Therefore, the objective of this work is to present an approach based on HJ-Biplot analysis that is able to identify the main tendencies regarding the farms land uses for Continental Portugal at the municipality level and to create groups with different orientations and tendencies regarding land uses. Results show that the HJ-Biplot methodology allows extrapolating information on the different trends in terms of land use specialization at municipality level. The groups of municipalities created according to trends and specialization may be an added value for analysis and policy evaluation. Key-Words: - common agricultural policy, Agricultural Census, policy evaluation, HJ Biplot, Portuguese agriculture, land uses. 1. Introduction The agricultural activity is still an activity of reasonable importance in some Portuguese regions such as the Alentejo, where it has a weight in the regional Gross Internal Product higher than the national value. However, the agricultural activity has not evolved at the same rhythm of national economy, leading to a decrease of the weight of agriculture in national Gross Added Value (GAV), different through the years [11]. Common Agricultural Policy (CAP) reforms had several impacts in the different European Union territories, leading to different behaviors of farmers and inducing several changes in the different agricultural systems [5,22]. In Portugal this led to considerable consequences in what concerns territorial occupation in the last decade. For designing specific development measures and to better intervening in the territory, the GPPAA [7] created a typology which allowed defining homogenous territories. Moreover, since May 2011, there is data available regarding the Agricultural Census of 2009 (RGA 2009), which may provide new information on the tendencies and actual situation of Portuguese agriculture, namely regarding land uses changes and farms orientation. Therefore, a methodology is needed that is able to analyze the main farms land uses types and their tendencies allowing the creation of similar units groups and showing specific relations among the variables. Dividing an area in different groups allows a better allocation of resources and the establishment of more individualized measures according to those groups enabling a more informed decision. The Biplot methodology provides an added value for analyzing these kind of data. This methodology ISBN: 978-1-61804-088-6 185
was created by Gabriel [8] and is an advance in data analysis in recent decades which evolved into a powerful, generic tool for research, allowing simultaneous representation of individuals and variables in a very simplified way, and the creation of similar groups [22]. The main objective of this work is to present an approach based on Biplot analysis that is able to identify the main tendencies regarding the farms land uses for Continental Portugal at municipality level and to create groups with different specializations and tendencies regarding land uses. The remainder of this paper is presented as follows: in chapter 2 the main issues regarding the different Biplot methodologies are revised; in chapter 3 the methodological approach is explained; in chapter 4 the results and discussion are presented. Finally, chapter 5 presents the main conclusions of this work. 2. The Biplot methods The Biplot analysis is a multivariate analytical technique proposed by Gabriel [8] which allows the simultaneous graphical representation of individuals and variables [13,21]. It approximates the distribution of a multivariate population in a reduced space and the prefix bi relates to the simultaneous representation of individuals and variables. According to Gabriel [8], Biplot is a graphical representation of a data matrix X (n p) using markers 1., n a 1 a n for rows and markers b 1 ; b j for columns, chosen in such a way that the internal represents the element x ij of the matrix XX, which is i T obtained as follows x = a b [1, 21, 13]. j The initial matrix can be written according the singular value decomposition: i j X=UDV (1) where U is the matrix of eigenvectors of the matrix XX; D is the matrix of eigenvalues of the previous matrix ordered from the largest to the smallest, and V is the matrix of eigenvectors of the matrix X X. According to the initial studies in this area possible factorings are: G= H = U k V k Λ Λ a k 1 a k (2) (3) where U (n by p) and V (p by p) are matrices of singular vectors and Λ (p by p) is a diagonal matrix of singular values. U is the matrix with columns corresponding to the p orthogonal eigenvectors of YY' and V is the orthogonal matrix corresponding to the eigenvectors of Y'Y. When the value 1 is selected, the result is called a JK or RMP (row metric preserving) biplot. In this display the distances between pairs of rows is preserved and the display is useful for studying objects. When the value 0 is selected, the result is a GH or CMP (column metric preserving) biplot. This display preserves distances between the columns and is useful for interpreting variance and relationships between variables. Gabriel in his studies described essentially these two types of biplots: CMP-bi-plot Column Metric Preserving and RMP-biplot -Rows Metric Preserving; but also presented the SQRT (symmetric biplot), which is a compromise situation. However, improvements were needed. Therefore, Galindo (1986) cited by Vicente-Villardón [21] updated these kind of methodologies and created what she called the HJ-biplot. This is a symmetric, simultaneous representation technique similar in some way to correspondence analysis, but not restricted to frequency data. This method achieves an optimum quality of representation for both rows and columns, as rows and columns are represented on the same reference system, over passing some problems of previous studies. 3. The methodological approach The HJ-Biplot analysis is proposed to solve the investigation problem. The selected method is based on the decomposition of singular values as follows: X=UDV (4) Using as markers: J(s)=U(s)D(s) (5) H(s)=V(s)D(s) (6) It achieves an optimum quality of representation for both rows and columns, since both are represented on the same reference system. The distance between row points is interpreted as similarity, and the angle formed by the vectors (variables) is interpreted as correlation. Finally, if a row point is close to a column point (variable), this is interpreted as preponderance [9]. Several measures are essential for a correct HJ- Biplot interpretation: the Relative Contribution of the Factor to the Element relates to the part of the variability of the element explained by the axis; and the Quality of Representation is the sum of the Relative Contribution of the Factor to the Element of the factors considered and only the points with good ISBN: 978-1-61804-088-6 186
quality of representation can be interpreted correctly [9]. 3.1.The group formation methodology For defining the different typologies of farms regarding the different land uses, we used the Biplot coordinates to develop a hierarchal cluster analysis, accounting both the distance and the linkage method. For distance we may consider the Euclidean distances as a dissimilarity index. Regarding the linkage method we considered the ward s method (used before by authors such as Castela and Purificacion Villardón [2]), which means that it uses an analysis of variance approach to evaluate the distances between clusters. In short, this method attempts to minimize the sum of squares (SS) of any two (hypothetical) clusters that can be formed at each step [17]. 3.2. Empirical application To correctly apply the proposed methodology, adaptations had to be made regarding data used and the technical application of the methodology. The data used is the information of the last two Agricultural Census (1999 and 2009), which needed to be aggregated in a more consistent form. So, regarding the data treatment, one first important issue was the data collection and simplification. The data of Continental Portugal municipalities was collected from the 2009 Agricultural Census database available on the National Statistics Institute (INE) website since June, 2011. This data was filtered, according to data needs, and the main land use classes were simplified in 7 classes, as follows: Wooded area (WLAN)- without pastures or cultures-, Un-utilized agricultural land (UNUT), Temporary crops (TEMP), Fallow land (FO), Permanent crops (PERM), Permanent grasslands (PP) and Other Surfaces (OSF). Then a 278 rows and 14 columns table was built including data from the two last agricultural census and representing the main farms land uses and several variants of the HJ-Biplot were designed: 1) Annual results for 2009; 2) Joint tendency results (1999 and 2009). For the methodology technical application of this we used a Multibiplot Alpha version of 2007 developed by Vicente-Villardón [19]. 4. Results and discussion Using as basis the data presented in the empirical application, the HJ-Biplot methodology was applied and the accumulated variance for the first line of approach is presented in the following table. The retaining of only two axis would lead to a low general accumulated variance in 2009. Therefore, regarding the main land uses, we considered the retaining of 3 axis with 70.214% of the information (table 1). Table 1-The absorption of the inertia-2009 Eigenvalue Inertia Ac. Inertia Axis 1 22.531 26.086 26.086 Axis 2 22.440 25.877 51.963 Axis 3 18.846 18.251 70.214 The analysis of the contributions relating the element factor (table 2) shows that the axis 1 is highly correlated with the permanent pastures and woodland area, which means that this axis represents the use of the land in a non intensive way, together with the forest (forest and cattle farming system) without agricultural lands. The axis 2 is highly correlated with several agricultural uses (temporary crops, permanent crops, but also non utilized agricultural land), representing the agricultural uses but also the reserve area (agricultural and reserve system). Finally, the axis 3 is correlated with other activities such as fallow, temporary crops or woodland area, representing the general agricultural system, where there is the existence of several activities. Relating to axis 1 (fig. annex 1), we were able to conclude that there is an inverse correlation between the permanent pastures and the woodland areas which is explained by the fact that when the livestock is bred in an extensive way, there is the use of permanent pastures and woodland areas that may be used as pastures; on the other hand there was the conversion of areas into pastures which were before woodland areas due to minor clarifications of the statistical concepts. For other land uses better represented in factorial axis 2, it seems to be a strong correlation between the permanent crops and the non used agricultural land. This fact is related with a situation of methodological clarification of concepts regarding permanent crops, because there is only the existence of permanent crops when there is actually production that is harvested by the farmer; if the farmer doesn t harvest them, they are considered to be non used agricultural land. Finally, it also seems to be a correlation between the other uses and the woodland area (agricultural and reserve system). Regarding the axis 3, the fallow seems to be correlated with the non used agricultural land and the permanent crops, because today the fallow incorporates the areas left in good agricultural and environmental conditions, according to conditionality criteria, being able to select or not areas of crops in the fallow area. There is also a correlation between the other surfaces and the temporary crops (agricultural system). ISBN: 978-1-61804-088-6 187
Table 2- The contributions relating the element factor-2009 Land uses Axis 1 Axis 2 Axis 3 WLAN09 384 29 374 UNUT09 104 438 83 TEMP09 1 566 286 FO09 160 97 294 PERM09 256 417 43 PP09 733 17 105 OSF09 188 248 94 Considering a group formation methodology the following groups were created (fig. 1): Group 1- Municipalities with an orientation and/or specialization in permanent pastures, which will have the livestock breeding in an extensive way. There are here included many of the Alentejo s municipalities and there is an extensive production in a forest and cattle farming system. Group 2- Municipalities with intensive and extensive land uses. Group 3- Municipalities with extensive uses, with fallow and other temporary crops and possible rotation systems. Note that we were analysing the relative preponderance and there may be cases in which there is a considerable area of temporary crops with different characteristics related to production, being specially this applied in the Ribatejo area. Therefore, several municipalities of the Ribatejo area are included here, in spite of having specific characteristics. Group 4- Municipalities with an orientation and/or specialization in permanent crops and non used agricultural land. Group 5- Municipalities with an orientation and/or specialization in temporary crops. Group 6- Municipalities with an orientation and/or specialization in forest uses. Fig. 1- The groups of municipalities in 2009 The second variant of application of the methodology considered the two years (1999 and 2009). We used the double centring and two axis were retained with 66.482% of the accumulated inertia (table 3). Table 3- The absorption of the inertia Eigenvalue Inertia Ac. Inertia Axis 1 561.282 38.162 38.162 Axis 2 483.516 28.32 66.482 Axis 3 424.178 21.795 88.277 The analysis of the contributions relating the element factor are presented as follows (table 4). The first axis is highly correlated with the permanent pastures and forest uses, but also with the fallow areas (where as we know there was a decrease in 2009). This axis represents the extensive uses without agricultural production. On the other hand the axis 2 is highly correlated with land uses related to the production activities uses or that may be used to it, such as permanent crops or temporary crops. This axis represents therefore the agricultural intensive production. Table 4-The contribution relating the element factor Land use Axis 1 Axis 2 WLAN09 497 15 UNUT09 31 115 TEMP09 6 898 FO09 54 0 PERM09 134 395 PP09 836 39 OSF09 34 35 WLAN99 497 1 UNUT99 23 76 TEMP99 4 878 FO99 276 1 OSF99 12 1 PERM99 138 363 PP99 744 44 The bidimensional representation of the HJ Biplot is presented in fig. 2. It could be inferred that there are some correlations and tendencies regarding land uses. In fact, it seems to be a correlation between the temporary crops and non used agricultural land as well as an inverse one between permanent pastures and woodland. Regarding tendencies, for instance, the permanent pastures reveal a tendency of gains which may be connected with a process of extensification and a clarification of concepts. There is an increase of permanent crops (there are included now here the Pinus Pinea fruit production) and temporary crops. The non agricultural land use shows a tendency of increase explained by the concept of non production and improvement of farming conditions. ISBN: 978-1-61804-088-6 188
agricultural lands. This fact is related with the clarification of concepts in statistics. Group 4- Municipalities with an orientation or specialization in woodland. Group 5-Municipalities with an orientation or specialization in permanent pastures and other related extensive activities. Group 6- Municipalities with an orientation or specialization in temporary crops. Fig. 2- Bidimensional representation of HJ-Biplot Therefore it was made a detailed analysis regarding the relevant groups of municipalities considering the tendencies and they were cartographed as follows (fig. 3): 5. Conclusions The HJ-Biplot methodology used allowed deriving information on the different trends in terms of land use orientation of the several municipalities which constitute Continental Portugal. The groups of municipalities created according to trends and specific specialization may be an added value for analysis of different situations and to define specific measures and developing scenarios for policy analysis. However, further studies are needed, namely by accounting the situations of land use dominating typologies, therefore contributing to improve the typology created by GPPAA [7]. For further improvements several lines of investigation have been defined. This methodology may also be applied to other specific situations namely agricultural products markets, which is an important issue today, imports and exports of agricultural products and to analyze temporary and permanent crops data, including the production issues. Acknowledgements The authors gratefully acknowledge partial financial support from FCT, program POCTI. The authors gratefully acknowledge the valuable advices provided by professors Eugénia Castela and Guilherme Castela. Fig.3- The geographical distribution of the municipalities groups Group 1- Represents the municipalities with a great diversity in the land uses. It s group that needs further analysis in other axis. Group 2-Municipalities with tendency of orientation or specialization in mixed extensive activities relating to the fallow areas, but also to permanent pastures. In these there is a tendency towards extensification of land uses. Group 3- Municipalities with a specialization tendency in permanent crops. Attending to the specific movement of the land uses we conclude that, in spite the general evolution of permanent crops there is a slightly tendency of decreasing the permanent crops and increasing the non used References: [1] Bradu, D. and Gabriel, K.R., The Biplot as a Diagnostic Tool for Models of Two Way Tables, Technometrics, nº 20, 1978, pp. 47-68. [2] Castela, E. and Purificacion Villardón, M., Ecological inference for the characterization of electoral Turnout: the portuguese case, Discussion papers Nº 3, Spatial and organizational dynamics. Quantitative Methods Applied to Social Sciences, 2010, pp. 6-25. [3] Cabrera, J., Martínez, M., Mateos, E., Tavera, S., Study of the evolution of air pollution in Salamanca (Spain) along a five-year period (1994 1998) using HJ-Biplot simultaneous representation analysis, Environmental Modelling & Software, nº21, 2006, pp. 61 68. [4] Dorado, A., Vicente, S., Blazquez, A., Martin, J., Analisis hj-biplot de la evolucion de la ISBN: 978-1-61804-088-6 189
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