Model for decision-making in agricultural production planning

Computers and Electronics in Agriculture 82 (2012) 87–95

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Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag

Model for decision-making in agricultural production planning Marta Cardín-Pedrosa ⇑, Carlos José Alvarez-López Research Group 1716 – Projects and Planning, Agroforestry Engineering Department, Santiago de Compostela University, Spain

a r t i c l e

i n f o

Article history: Received 18 April 2011 Received in revised form 7 November 2011 Accepted 8 December 2011

Keywords: Agricultural production planning Model Indicators Galicia (Spain)

a b s t r a c t This paper explains the process followed to generate the model used as a decision support tool for agricultural production planning in the most agrarian areas in Galicia (NW Spain). The model comprises three procedures that use 44 social, environmental and economic indicators developed using both monographic information and field data. The indicators allow for the selection of the most suitable crops and land uses for each agrarian area, and allow decision makers to define key factors for performing a diagnostic analysis and proposing relevant actions regarding agricultural production planning. The potential of the tool for defining a hierarchy of potential crops and land uses according to their degree of suitability has been illustrated by applying the model to one of the 88 Ecological and Economic Units studied (EEUs). The proposed model can be a useful tool for production managers, agricultural associations, governmental agencies and even non-governmental organizations in underdeveloped countries. The most innovative aspects of the model are the feasibility of grouping indicators to perform a diagnostic analysis of different scenarios, and that it can be adapted to any other region in the world, by adjusting the objectives of agricultural production planning and the corresponding indicators. Ó 2011 Elsevier B.V. All rights reserved.

1. Introduction Galicia is a region with a wide variety of landforms, a diversity of climatic conditions and many land uses that are distributed according to different production systems and levels of intensification. This explains the importance that for Galician Administration the issue of agricultural production planning has. In addition, Galician agriculture is structured around family farms, and dairy production is the most important activity (Riveiro et al., 2008). At the end of 2001, the University of Santiago de Compostela, successfully tendered for a contract to provide technical assistance on the project ‘Development of Agricultural Production Planning Studies in 22 comarcas of the Autonomous Community of Galicia, 20012002’, funded by the Galician Administration, in North–West Spain. Galicia is a region with a wide variety of landforms, a diversity of climatic conditions and many land uses that are distributed according to different production systems and levels of intensification. In addition, Galician agriculture is structured around family farms, and dairy production is the most important activity (Riveiro et al., 2008). The aim of the project was to generate a Spatial Model of Agricultural Production Planning (comprising agriculture, farming and ⇑ Corresponding author. Address: Escola Politécnica Superior, Campus Universitario, s/n. 27002 – Lugo, Spain. Tel.: +34 982 823 323; fax: +34 982 285 926. E-mail address: [email protected] (M. Cardín-Pedrosa). 0168-1699/$ - see front matter Ó 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.compag.2011.12.004

forestry) for implementation at the comarca1 level, for use as a decision-making tool for Galician Administration to implement policies, schemes and plans aimed at rural comarcas. Subsequently, the model was used as an instrument to allocate agricultural land uses, to rationalize and optimize the sustainable use of rural land and to promote rural development (Rodríguez-Couso et al., 2006). The model was developed under the supervision of experts from the Galician Administration, and provided a common basis for the generation of a collection of documents that formed the corpus of the Agricultural Production Planning Studies (Xunta de Galícia, 2004), aimed to obtain Comarca level-Objective Agricultural Planning Models, which should include, at least, the following information: (1) identification of former and current agricultural products, as well as of new products that could be introduced at the comarca level; (2) market expectations for the identified products; (3) technical and economic assessment of the profitability of 1 Comarca: A division that is usually understood in Spain as a spatial reference rather than as an administrative division. The size of comarcas varies depending on the conditions of their geographical location; the map of Galician Comarcas – approved by Decree 65/1997 of the Galician Government Xunta de Galicia – comprises 53 comarcas. Municipalities (termed ‘concellos’ in Galicia): A municipality is an administrative division composed of a clearly defined territory and its population. The term municipality usually refers to a town, village or hamlet, or a group of villages or hamlets that is ruled by a Board of Governors, generally termed a corporation, municipality, mayor’s office or council. Parish: The Statute of Autonomy of Galicia defines a parish as a territorial division smaller than a municipality and larger than a hamlet.

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M. Cardín-Pedrosa, C.J. Alvarez-López / Computers and Electronics in Agriculture 82 (2012) 87–95

Fig. 1. Model Scheme.

Fig. 2. Indicator construction method.

every crop or land use, according to the methodology by Riveiro et al. (2005); and (4) planning and development of the productive sectors in each comarca. Since the 1930s (Storie, 1933), mathematical methods have been developed to allocate agricultural uses to land areas. According to Rossiter (1996) the two main lines of research in land evaluation (the process of predicting the use potential of land on the basis of its attributes) differ in the unit of analysis. In the first line, the land is analyzed and the results of the analysis are extrapolated to farms. Within this approach, the first methods were multi-criteria evaluations based on mathematical programming (Voogd, 1983). Later, cellular automata (Parker et al., 2003) and heuristic

models were used because of the lower computational cost and the versatility of the solutions of such models (Nalle et al., 2002; Boyland et al., 2004). Other authors have combined the mathematical basis with social participation (De Wit and Van Keulen, 1988; Leitner et al., 2002; Snyder, 2003). In the second line of research, the farm is considered as the unit of analysis (Loftsgard and Heady, 1959) and the results are used to choose the most suitable species to allocate to each crop area (Duloy and Norton, 1983; Hwang et al., 1994). In this approach, the results can be applied to the whole territory (Glen and Tipper, 2001) or to strategic land use planning (Carsjens and Van Der Knaap, 2002).

M. Cardín-Pedrosa, C.J. Alvarez-López / Computers and Electronics in Agriculture 82 (2012) 87–95 Table 1 List of the indicators used in the model. Indicators 1 2 3 4 5

Physical environment Type of farming (TF) Suitability for agriculture Landscape quality Landscape fragility Climate units

6 7 8

Farm structure Size Structural Limitations Adequacy for the main TFs

9 10 11 12 13 14 15 16 17 18 19

Structure of the production unit Training level Amount of labour Hired labour Difficulty in finding hired labour Owner dynamism Interest in Farm Management Associations Interest in Agricultural Buying Groups Interest in Service Provision Groups Interest in Animal Health Groups – Integral Treatment Groups Interest in Farm Machinery Cooperatives Interest in Collective farming

20 21 22 23 24 25 26 27 28 29 30 31

Production support % Irrigated area to UAA % Hydrological network to total area Accessibility Problems for input supply % Cooperative members to population engaged in agriculture % SAT to population engaged in agriculture Availability of Processing Industries % Area under LC projects to total area Land Availability Financial capacity /TGM Financial capacity /FU income Management innovation capacity

32 33 34 35 36 37 38 39

Marketing Availability of and level of satisfaction with marketing channel Proximity to urban areas Availability of PDO or PGI Proximity to seasonal use areas Internal market potential Foreign market potential Marketing innovation capacity Commercial constraints

40 41 42 43 44

Potential of the land use/crop Current weight of the crop New crop/land use Technical/Crop production problems Potential of the crop Production innovation capacity

All these methods are mainly theoretical and case-specific, so, in pursuing a practical application in a real context, we developed a model consisting of systematic processes for preparing action plans based on methods that allow for the use and exploitation of the available information in the most practical way. This paper briefly describes the model and focuses on its most innovative aspect, i.e. the grouping of indicators for performing a diagnostic analysis of different situations. The Agricultural Planning Model for Galician Comarcas synthesizes information based on the analysis of a number of elements that characterize the agricultural subsystem of an area (natural environment, socioeconomic conditions, infrastructure and legal framework). By using this model, the Agricultural Galician Administration can check the potential situation of agricultural production in each considered area, and use this information for the decision-making process for defining general guidelines or specific measures for enhancing particular productive sectors.

89

To build the model, a number of aspects were considered: (1) a number of variables obtained from monographs or field surveys that could be used as elements for characterizing a sector and/or making within-comarca comparisons (characteristic variables of the model); (2) the design and construction of a set of numerical values or indicators that were assigned to the characteristic variables of the model, such that the variables could be incorporated into the model in a quantitative manner; (3) the grouping of indicators, which allowed for the retrieval of information for all the indicators considered as a whole.

2. Material and methods From among the 53 comarcas of Galicia, the 22 more agrarian were analyzed, accounting for 43.7% of the total surface area (12.924 ha). The most significant steps in building the model are described below (see Fig. 1) 2.1. Characterization of comarcas The characterization phase involved the systematic and comprehensive collection of data pertaining to aspects that could be used to describe the current situation of the structures and productive sectors that form the agricultural context of the comarca. The collected data were structured into three levels: (1) ‘Objective information’, obtained by reviewing all the reliable documentary sources available (literature, maps, statistics, monographics, internet information. . .). (2) ‘information from local experts’, obtained from a survey of the agricultural sector that was conducted through direct and personal interviews with experts who were well acquainted with the situation of the comarca (more than 350 experts over the 22 comarcas studied). (3) ‘farm information’, obtained from a survey among 4384 farm owners of all the parishes and productive sectors in the different comarcas (Escariz et al., 2005). The analysis of this corpus of information was the first phase of the model construction. It consisted in a systematic, objective and realistic description of the current situation of the productive sectors in each comarca. The three steps followed to characterize comarcas are detailed below. 2.1.1. Review of information A geographic information system was developed over the 1:10.000 Official Topographic Galician Digital Map, and implemented using Digital Terrain Maps, hydrologic, land-use and transportation infrastructure maps, climate units according to the Papadakis classification (Elías and Ruiz, 1973), network maps, Forest Maps (Ruiz de la Torre, 1991), Population Censuses, 1999 Agricultural Census (Mapa, 2002), Agribusiness Directories and data of agricultural supply cooperatives and agricultural processing companies that were obtained from the individual information survey. Also the review comprised all the information that was prepared and supplied directly by the Administration, i.e.: Agricultural Statistics Yearbooks, Commercial yearbooks of La Caixa, (Fundación La Caixa, 2002), Censuses of Agriculture (Xunta de Galicia, 1998) and Farm Censuses (Xunta de Galicia, 2002), Forest and land-use maps (Mapa, 1988), agricultural registries (Xunta de Galicia, 2001), and statistical data from statistical agencies such as Instituto Nacional de Estadística and Instituto Gallego de Estadística (IGE 2002, 2003).

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M. Cardín-Pedrosa, C.J. Alvarez-López / Computers and Electronics in Agriculture 82 (2012) 87–95

Fig. 3. Graphic of the computational operations in the model.

2.1.2. Interviews with comarca experts and marketing agents Face-to-face interviews were conducted. On average, interviews were one hour and thirty minutes long. In order to elicit the opinion of interviewees about the current situation and future prospects of agricultural production in the comarca, an open-ended questionnaire was used. Experts were chosen according to their professional activity, mainly: Area Managers of Comarca Agriculture Offices. Heads of Comarca Agriculture Offices. General Managers of Comarca Foundations. General Managers of Cooperatives. Technicians of Trade Unions. Owners of significant farms. A total of 349 agents distributed proportionally over the comarcas, were interviewed. Often, one agent was interviewed two or more times to check differing opinions, aspects that had not been considered, or new aspects. A final meeting was held in each comarca with some representative and significant agents interviewed to report on partial results and clarify a number of issues.

2.1.3. Survey of farmers A survey targeted at farmers of the 22 comarcas was carried out in order to elicit their opinions. A sample questionnaire was designed to help characterize the agricultural sector in each comarca and to find out the main characteristics of farmers in terms of their attitudes and competences. The farmer survey checked a variety of aspects related to the attitudes and competence of producers: this is particularly relevant in the assessment of the likely response of affected farmers to the implementation of specific measures and actions (Alvarez, 2006). The questionnaire included 62 complex questions and 130 items, grouped into the following categories: identification and verification data; ownership; farm characteristics; marketing channels; farm evolution at the parish level; limiting factors; participation in groups or associations; quality and Designations of Origin; attitude towards structural changes; attitude towards change; labour and quality of life; attitude and competence.

The sample space was defined based on the register of workers under the Special Agricultural Social Security Scheme of the Spanish State (SAS) for 2002. This source guaranteed that the survey sample was composed of professional farmers. The sample size was determined by stratified random sampling (Scheaffer, 1990), according to the distribution of farmers over the 22 comarcas considered. At a confidence level of 0.95 (K = 1.6), and for p and q equal 0.5, 4384 interviews were conducted among a population of 31 285 registered farmers in the 22 comarcas studied, which yielded a sampling error of 0.0115. The first phase of the field campaign was carried out between May and June 2002, the second phase between June and August 2003, and the last between January and March 2004. The quality of the survey process was verified by sampling 0.10 of the total interviews for performance control and data coherence. 2.2. Zoning of comarcas in Ecological and Economic Units Although the study was regional in scope, the research team searched for homogeneous units within the comarcas in order to account for their internal heterogeneity and to increase the definition of the Agricultural Planning Model. These units were composed of groups of parishes with similar characteristics; and to delineate them, the environmental, structural and socioeconomic characteristics of the different parishes were analyzed. The 88 units resulting from the subdivision of comarcas were termed in the project Ecological and Economic Units (EEUs) and became the basic units of analysis and production of results. The method used to perform such a subdivision was based on multivariate cluster analysis (Romero, 1998). This analysis defined parish groups (EEUs) or clusters, so that the variance between variables of the same group was minimized and the variance between variables of different groups or EEUs was maximized (Jobson, 1992). 2.3. Selection of potential crops and land uses First, a list of 130 potential land uses for the analyzed area was compiled. Then, literature information and field data about the

0.55

0.5

0.55

0.5

0.73

Semi-intensive ecological dairy cattle farming

Forage maize

Low-yield conifer plantations

Low-yield hardwood plantations

0.5

Alfalfa

0.55

Semi-intensive pig farming

0.55

Nuts

Forage peas

0.55

Short-rotation grasslands

0.64

0.55

Oats

0.55

0.55

Roots and tubers

Long-rotation grasslands

0.55

Barley

High-yield hardwood plantations

0.5

Extensive farming

0.55

Animal breeding for canned hunting

0.55

0.86

High-yield conifer plantations

0.5

0.55

Beans

Chestnuts

0.55

Pears

Semi-intensive poultry farming

0.5

Mushrooms

0.5

0.89

Eucalypt

0.5

0.5

Intensive pig farming

Semi-intensive ecological cattle farming (meat)

0.5

Intensive rabbit farming

Semi-intensive cattle farming (meat)

0.5

Intensive poultry farming

0.55

0.55

Small fruits

0.55

0.55

Turnip greens

Wheat

0.55

Potatoes

Rye

0.5

Snail farming

0.5

0.55

Ecological horticultural crops

Semi-intensive dairy cattle farming

0.55

Kiwi

0.55

0.55

Ornamental plants

Maize kernels

0.55

Cut flowers (grown in the field)

0.55

0.5

Intensive cattle farming

Apples

0.55

Mirabelle plum

0.5

0.55

Vineyard

0.5

0.55

Cut flowers (grown in greenhouses)

Semi-intensive sheep and goat farming

0.55

Horticultural crops (grown in the field) 1

Honey

0.55

Horticultural crops (grown in the field) 2

0.12

0.27

0.37

0.12

0.12

0.37

0.37

0.37

0.12

0.5

0.37

0.5

0.37

0.5

0.23

0.5

0.37

0.12

0.37

0.37

0.5

0.37

0.37

0.37

0.37

0.5

0.37

0.13

0.37

0.37

0.5

0.27

0.5

0.5

0.5

0.62

0.37

0.37

0.5

0.12

0.37

0.5

0.62

0.5

0.37

0.87

0.62

0.62

0.62

1

0.75

0.75

1

1

1

0.75

1

1

1

1

1

0.75

1

1

1

1

1

1

1.00

1

1

0.75

0.75

1

1

0.75

0.75

1

0.75

0.75

0.5

0.75

0.75

0.75

0.5

1

1

0.75

1

0.75

0.75

1

0.75

0.75

1

1

1

1

0.75

3

4

1

0.75

1

1

1

1

1

1

1

0.75

1

1

1

1

1

0.75

1

1

1

1.00

1

1

1

0.75

1

1

0.75

0.5

1

0.75

0.75

0.5

0.75

0.75

0.75

0.75

1

1

0.75

1

0.75

1

1

0.75

0.75

1

0.75

1

1

0.75

5

0.92

0.25

0.09

0.55

0.7

0.55

0.7

0.68

0.92

0.81

0.68

0.7

0.61

0.7

0.68

0.81

0.55

0.55

0.55

0.70

0.7

0.55

0.07

0.83

0.55

0.64

1

0.79

0.54

0.5

1

0.56

0.4

0.4

0.3

0.83

0.7

0.55

1

0.63

0.48

0.66

0.61

0.66

0.48

0.11

0.81

0.68

0.61

0.81

0.03

0

0

0

0

0

0

0

0.03

0

0

0

0

0

0

0

0

0

0

0.00

0

0

0

0

0

0.5

0

0

0

0

0.5

0

0.5

0.5

0.5

0.06

0.5

0.12

0.5

0.5

0

0

0.5

0.5

0

0.03

0.5

0.5

0.5

0.5

0.5

0.57

0.66

0.57

0.66

0.66

0.66

0.66

0.5

0.66

0.66

0.66

0.66

0.66

0.57

0.66

0.66

0.57

0.66

0.66

0.66

0.66

0.66

0.66

0.66

0.5

0.66

0.47

0.66

0.66

0.5

0.47

0.5

0.5

0.5

0.66

0.91

0.66

0.5

0.91

0.66

0.66

0.91

0.5

0.66

0.66

0.91

0.91

0.91

0.91

7

6

0.62

2

1

0.55

Farm STR

Indicators

pH Environment

Horticultural crops (grown in greenhouses)

Crops/land uses

Table 2 Suitability matrix for EEU Baixo Miño-1.

8

0.52

0.46

0.65

0.6

0.61

0.64

0.62

0.52

0.52

0.62

0.54

0.59

0.63

0.59

0.53

0.62

0.64

0.59

0.59

0.63

0.63

0.6

0.63

0.7

0.59

0.6

0.63

0.62

0.64

0.7

0.61

0.6

0.58

0.59

0.63

0.68

0.64

0.67

0.59

0.67

0.72

0.62

0.62

0.66

0.72

0.74

0.64

0.67

0.67

0.7

0.5

0.5

0.45

0

0.45

0.2

0.45

0.45

0.25

0.2

0.45

0.45

0.45

0.45

0.45

0.2

0.2

0

0.2

0.45

0.45

0.2

0.45

0.2

0.2

0

0.2

0.25

0.45

0.2

0.2

0.75

0.45

0.45

0.45

0.45

0.45

0.45

0.45

0

0

0

0.2

0.45

0.2

0

0.2

0.2

0.2

0.2

9

0.5

0.5

0.5

0.27

0.5

0.27

0.5

0.5

0.5

0.27

0.5

0.5

0.5

0.5

0.5

0.27

0.27

0.27

0.27

0.50

0.5

0.27

0.5

0.27

0.27

0.5

0.5

0.5

0.5

0.27

0.52

0.5

0.5

0.02

0.02

0.5

0.5

0.5

0.5

0.27

0.27

0.27

0.27

0.5

0.27

0.27

0.27

0.5

0.27

0.27

10

0.5

0.5

0.42

0.5

0.42

0.5

0.42

0.42

0.5

0.17

0.42

0.42

0.42

0.42

0.5

0.17

0.5

0.5

0.5

0.42

0.42

0.5

0.42

0.17

0.5

0.5

0.42

0.5

0.42

0.17

0.42

0.5

0.5

0

0

0

0.42

0.33

0.5

0

0.17

0

0

0.5

0.17

0.17

0

0

0

0

11

0.5

0.5

0.5

0.64

0.5

0.64

0.5

0.5

0.64

0.89

0.5

0.5

0.5

0.5

0.64

0.89

0.64

0.64

0.64

0.50

0.5

0.64

0.5

0.89

0.64

0.64

0.89

0.64

0.5

0.89

0.75

0.5

0.64

0.64

0.64

0.77

0.5

0.75

0.64

0.77

0.89

0.77

0.77

0.64

0.89

0.89

0.77

0.77

0.77

0.77

12

0

0.25

0.4

0

0.15

0.15

0.15

0.5

0.15

0.15

0.5

0.15

0.5

0.15

0.4

0.5

0.15

0

0.15

0.50

0.5

0.15

0.6

0.15

0.15

0

0

0.5

0.5

0.15

0.15

0.5

0.4

0.4

0.4

0.15

0.4

0.5

0.4

0

0.5

0

0.15

0.4

0.15

0.5

0.15

0.5

0.5

0.25

13

Structure of the production unit 14

0.25

0.25

0.5

0.34

0.5

0.34

0.5

0.5

0.25

0.5

0.5

0.5

0.5

0.5

0.34

0.5

0.34

0.34

0.34

0.50

0.5

0.34

0.5

0.5

0.34

0.5

0.34

0.25

0.5

0.5

0.34

0.25

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.34

0.5

0.5

0.34

0.5

0.5

0.5

0.34

0.34

0.34

0.34

15

0.25

0.25

0.29

0.5

0.29

0.5

0.29

0.29

0.25

0.5

0.29

0.29

0.29

0.29

0.29

0.5

0.5

0.5

0.5

0.29

0.29

0.5

0.29

0.5

0.5

0.5

0.5

0.25

0.29

0.5

0.5

0.25

0.5

0.5

0.5

0.5

0.29

0.29

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.29

0.5

0.29

0.29

0.29

16

0.25

0.25

0.31

0.5

0.31

0.5

0.31

0.31

0.25

0.5

0.31

0.31

0.31

0.31

0.31

0.5

0.5

0.5

0.5

0.31

0.31

0.5

0.31

0.5

0.5

0.5

0.5

0.25

0.31

0.5

0.5

0.25

0.5

0.5

0.5

0.5

0.31

0.31

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.31

0.5

0.31

0.31

0.31

17

0.38

0.25

0.37

0.5

0.37

0.37

0.37

0.37

0.37

0.5

0.37

0.37

0.37

0.37

0.37

0.5

0.37

0.5

0.37

0.37

0.37

0.37

0.37

0.37

0.37

0.5

0.5

0.37

0.37

0.37

0.5

0.37

0.5

0.5

0.5

0.37

0.37

0.37

0.5

0.5

0.37

0.5

0.5

0.5

0.37

0.37

0.5

0.37

0.37

0.37

18

0.25

0.25

0.33

0.5

0.33

0.5

0.33

0.33

0.25

0.5

0.33

0.33

0.33

0.33

0.5

0.5

0.5

0.5

0.5

0.25

0.25

0.5

0.25

0.5

0.5

0.5

0.5

0.25

0.33

0.5

0.5

0.25

0.5

0.5

0.5

0.5

0.5

0.33

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

19

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.50

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.50

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.25

0.25

0.5

0.25

0.5

0.25

0.25

0.5

0.5

0.5

0.25

0.25

0.25

0.25

20

0.52

0.5

0.52

0.52

0.52

0.52

0.52

0.52

0.52

0.5

0.52

0.52

0.52

0.52

0.5

0.5

0.52

0.52

0.52

0.52

0.52

0.52

0.52

0.5

0.52

0.5

0.5

0.5

0.52

0.5

0.5

0.52

0.5

0.5

0.5

0.5

0.52

0.52

0.5

0.52

0.52

0.52

0.52

0.5

0.52

0.5

0.52

0.52

0.52

0.52

21

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.39

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.50

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.39

0.5

0.5

0.5

0.39

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

22

Production support 23

0.75

0.75

0.78

0.78

0.78

0.78

0.78

0.78

0.78

0.72

0.78

0.78

0.78

0.78

0.75

0.72

0.78

0.78

0.78

0.78

0.78

0.78

0.78

0.75

0.78

0.78

0.78

0.78

0.78

0.75

0.78

0.78

0.78

0.78

0.78

0.75

0.78

0.78

0.78

0.75

0.75

0.78

0.78

0.78

0.75

0.75

0.78

0.78

0.78

0.78

24

0.5

0.5

0.5

0.62

0.5

0.62

0.5

0.5

0.5

0.62

0.5

0.5

0.5

0.5

0.62

0.62

0.62

0.62

0.62

0.50

0.5

0.62

0.5

0.62

0.62

0.5

0.5

0.5

0.5

0.62

0.5

0.5

0.62

0.62

0.62

0.5

0.62

0.62

0.62

0.62

0.5

0.5

0.62

0.62

0.5

0.5

0.62

0.62

0.62

0.62

0.5

0.5

0.5

1

0.5

1

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

1

0.5

1

1

1

0.50

0.5

1

0.5

0.5

1

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

25

26

0.75

0.75

0.5

0.5

0.5

1

0.5

0.5

0.75

0.5

0.5

0.5

0.5

0.5

0.75

0.5

0.75

0.5

1

1.00

1

1

1

0.5

1

0.5

0.5

0.75

0.5

0.5

0.5

0.75

1

0.5

1

0.5

1

0.75

0.5

0.5

0.75

0.5

0.5

1

1

1

0.5

1

1

1

27

0.25

0.25

0.18

0.18

0.18

0.25

0.18

0.18

0.18

0.18

0.18

0.18

0.18

0.18

0.18

0.18

0.25

0.18

0.25

0.18

0.18

0.25

0.18

0.18

0.25

0.5

0.5

0.18

0.5

0.18

0.5

0.18

0.5

0.5

0.5

0.5

0.18

0.18

0.5

0.5

0.43

0.5

0.5

0.5

0.18

0.5

0.5

0.5

0.5

0.5

28

0.17

0.33

0.42

0.42

0.42

0.42

0.42

0.42

0.42

0.42

0.42

0.42

0.42

0.42

0.42

0.42

0.42

0.42

0.42

0.42

0.42

0.42

0.42

0.42

0.42

0.5

0.5

0.42

0.5

0.42

0.5

0.42

0.5

0.5

0.5

0.5

0.42

0.42

0.5

0.5

0.5

0.5

0.5

0.5

0.42

0.5

0.5

0.5

0.5

0.5

29

0.07

0.07

0.5

0.07

0.5

0.07

0.5

0.5

0.32

0.25

0.5

0.5

0.5

0.5

0.32

0.25

0.07

0.07

0.07

0.50

0.5

0.07

0.5

0.25

0.07

0.32

0.32

0.32

0.5

0.25

0.32

0.32

0.32

0.32

0.32

0.5

0.5

0.5

0.32

0.32

0.25

0.07

0.32

0.32

0.25

0.25

0.32

0.5

0.5

0.32

30

0.02

0.02

0.5

0.02

0.5

0.02

0.5

0.5

0.27

0.25

0.5

0.5

0.5

0.5

0.27

0.25

0.02

0.02

0.02

0.50

0.5

0.02

0.5

0.25

0.02

0.27

0.27

0.27

0.5

0.25

0.27

0.27

0.27

0.27

0.27

0.5

0.5

0.5

0.27

0.27

0.25

0.02

0.27

0.27

0.25

0.25

0.27

0.5

0.5

0.27

31

0

0

0.5

0.47

0.5

0.47

0.5

0.5

0.22

0.5

0.5

0.5

0.5

0.5

0.47

0.5

0.47

0.47

0.47

0.50

0.5

0.47

0.5

0.5

0.47

0.5

0.5

0.47

0.5

0.5

0.5

0.47

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.33

0.5

0

0.4

0

0.1

0

0

0.33

0.2

0

0.1

0

0.1

0.3

0.2

0.5

0.7

0.3

0.10

0.1

0.8

0.1

0.2

0.6

0.3

0.7

0.5

0.1

0.2

0.2

0.5

0.5

0.4

0.5

0.3

0.2

0.3

0.5

0.6

0.6

0.8

0.6

0.6

0.9

1

0.6

0.7

0.7

0.7

32

33

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.50

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.49

0.5

0.5

0.49

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.49

0.49

0.49

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0.5

0.00

0

0.15

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0.75

0

1

0

0

0

0

34

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.50

0.5

0.5

0.5

0.75

0.5

0.5

0.5

0.5

0.5

0.75

0.5

0.5

0.5

0.5

0.5

0.5

1

0.5

0.5

1

0.75

0.5

1

0.5

0.75

0.5

1

1

1

1

35

Marketing 36

0

0.25

0.2

0.3

0.3

0.2

0.2

0.1

0.25

0.2

0.1

0.2

0.3

0.2

0.2

0.2

0.2

0.4

0.4

0.20

0.1

0.1

0.3

0.3

0.2

0.2

0.4

0.5

0.3

0.2

0.3

0.7

0.15

0.15

0.15

0.5

0.1

0.5

0.1

0.7

0.4

0.8

0.4

0.3

0.7

0.5

0.4

0.4

0.6

0.6

37

0

0

0

0.3

0

0.1

0

0

0

0.1

0

0

0

0

0.2

0.2

0.2

0.5

0.4

0.00

0

0.2

0

0.2

0

0.1

0.2

0.5

0.3

0.1

0.2

0.5

0

0.1

0.1

0.6

0.2

0.4

0.7

0.4

0.1

0.5

0

0.4

0.3

0

0

0.2

0

0

38

0.5

0.5

0.25

0

0.12

0

0.12

0.5

0.5

0.37

0.5

0.12

0.12

0.25

0.5

0.37

0

0

0.5

0.37

0.37

0.5

0.37

0.37

0.5

0

0

0.5

0.37

0.37

0.37

0.5

0.5

0.5

0.5

0.12

0.5

0.5

0.12

0

0.12

0.12

0

0.5

0.12

0.5

0

0.25

0.25

0.12

39

0.43

0.43

0.36

0.68

0.43

0.5

0.43

0.5

0.5

0.5

0.5

0.43

0.5

0.43

0.5

0.43

0.5

0.5

0.5

0.43

0.43

0.5

0.43

0.43

0.5

0.43

0.43

0.5

0.43

0.43

0.43

0.5

0.68

0.68

0.68

0.43

0.43

0.43

0.43

0.36

0.43

0.5

0.5

0.68

0.43

0.43

0.5

0.5

0.5

0.61

0.01

0.01

0.01

0

0

0

0

0

0.04

0.04

0.02

0.02

0.06

0.05

0

0.05

0

0

0.05

0.01

0

0.09

0.57

0.55

0.61

0.55

0

0.04

0.51

0.99

0

0.04

0.28

0.68

0.56

0

0.27

0.51

0

0

0.68

0.85

0.75

0

0.78

0.6

1

0.46

0.52

0.78

40

0

0

0

1

0

1

0

0

0

0

0

0

0

0

0

0

1

1

0

0.00

0

0

0

0

0

0

1

0

0

0

1

0

0

0

0

1

0

0

1

1

0

0

0

1

0

0

0

0

0

0

41

0.24

0.5

0.49

0.49

0.49

0.5

0.49

0.49

0.49

0.5

0.49

0.49

0.49

0.49

0.51

0.5

0.5

0.5

0.5

0.49

0.49

0.49

0.49

0.49

0.5

0.5

0.5

0.49

0.49

0.49

0.5

0.5

0.5

0.5

0.5

0.49

0.49

0.25

0.5

0.49

0.49

0.5

0.49

0.5

0.49

0.49

0.5

0.49

0.49

0.5

42

Crop potential

0.16

0.16

0.05

0

0.05

0

0.05

0.05

0.14

0

0.05

0.05

0.05

0.05

0

0

0

0

0

0.10

0.1

0

0.1

0.1

0

0

0

0.33

0.1

0.1

0.1

0.33

0

0

0

0.2

0.1

0.3

0.3

0.75

0.9

0.7

0.5

0.3

0.9

0.5

0.7

0.7

0.9

0.9

43

44

0.2

0.2

0.35

0

0.35

0.2

0.35

0.5

0.35

0.5

0.5

0.35

0.35

0.35

0.5

0.5

0.2

0.2

0.5

0.50

0.5

0.5

0.5

0.5

0.6

0.1

0.1

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.5

0.25

0.5

0.5

0.35

0.1

0.35

0.35

0.35

0.5

0.25

0.5

0.35

0.35

0.35

0.35

266.8

279.0

292.5

295.4

297.1

298.2

303.1

311.7

312.7

313.3

314.6

315.6

317.7

320.9

323.3

323.6

325.8

328.9

333.5

337.4

339.2

345.9

367.7

368.6

369.5

371.5

373.1

378.7

384.0

387.1

393.0

393.9

399.2

401.8

406.5

408.3

421.7

427.3

439.2

444.3

445.7

460.4

474.0

478.8

487.6

491.6

512.5

518.4

527.4

534.1

Score

50

49

48

47

46

45

44

43

42

41

40

39

38

37

36

35

34

33

32

31

30

29

28

27

26

25

24

23

22

21

20

19

18

17

16

15

14

13

12

11

10

9

8

7

6

5

4

3

2

1

Rank

M. Cardín-Pedrosa, C.J. Alvarez-López / Computers and Electronics in Agriculture 82 (2012) 87–95 91

92

M. Cardín-Pedrosa, C.J. Alvarez-López / Computers and Electronics in Agriculture 82 (2012) 87–95

different production processes and their implementation were compiled for each land use. Three main sources of information were used to assess potential crops and land uses. First, to assess former crops and land uses, a monographic review (Bouhier, 2001) was conducted, and specific questions regarding this issue were included in the interviews with agents and farmers. Second, available information on current crops and land uses was analyzed (Alvarez et al., 2008) and specific methodologies for defining a typology, classification and geographical distribution of crops and land uses were developed (Riveiro et al., 2008). Third, to assess future crops and land uses, information provided by Research Centres funded by the Administration and by Galician Universities was used, and regional agents were surveyed. As a result, 50 crops and land uses were selected and the suitability of the selected crops and land uses was analyzed. 2.4. Analysis of comarcas By using the information obtained during the initial phases of the project, 22 reports summarized the most significant data from the 1999 Agricultural Census, the survey results, and the geographic information system developed. The reports were submitted for evaluation to regional panels of experts, composed of the aforementioned comarca experts and marketing agents. 2.5. Development of a data management system Because the amount of data available after the analysis performed during the first phases of the project was enormous and performing a diagnostic assessment using the entire data set was extremely difficult, we developed a data management system from which the final Agricultural Planning Model for Comarcas was generated. The aim was to compare the suitability of every crop or land use for a given Ecological and Economic Unit (EEU). 2.5.1. Indicator design A set of Indicators was defined to provide information about the social, environmental or economic constraints that determine the feasibility of farms for a given agricultural or forest land use in every EEU. By constructing these indicators, the total score obtained (number of positive responses or specific value), was transformed into a relative value that was used to compare different EEUs and Comarcas. By using relative values, the deviations of the analyzed values (e.g. responses to survey questions or census data) from the mean of the entire set of comarcas can be known. The general construction of the indicators can be best described by way of an example. Let us consider the percentage of positive responses to a question in the field survey, named question K. The construction of the corresponding indicator, EK, consists of the following steps: Calculation of the percentage of positive responses to question K in a EEU named ‘‘Unit X’’, denoted by PK,X. Calculation of the percentage of positive responses to question K for the whole set of comarcas, denoted by PK,T. This value, which coincided with the mean value of positive responses, was assigned an Indicator EK value of 0.5 (this will be useful later for the SWOT analysis). Search for the Unit with the lowest value of positive responses to question K from among the whole set of comarcas. Such a value was denoted as MINK, and assigned an Indicator EK value of 0. Search for the Unit with the highest value of positive responses to question K from among the whole set of comarcas. Such a value was denoted as MAXK, and was assigned an Indicator EK value of 1.

The value of the Indicator EK for Unit X was obtained by interpolation: if the value of PK,X was lower than the mean for the Comarcas, the EK value for Unit X was obtained by interpolation between MINK and PK,T. If the value of PK,X was higher than the mean, the EK value for Unit X was obtained by interpolation between PK,T and MAXK. Fig. 2 shows the construction method. A key element must be considered in the calculation of indicators: the sensitivity of every crop or livestock product to each of the issues included in the numerical values of the indicators. For example, one can obtain a numerical value for the average slope of a given geographical area, and this value will be included numerically in the Slope Indicator. However, the effect of slope on the suitability of a crop is dependent not only on the average slope value, but also on the suitability of the slope value obtained for crop productivity. Thus, slope values exceeding 0.10 may impede the introduction of a given crop in an area, while the same slope range may be suitable for other crops. Accordingly, the value of each indicator must be adjusted to the sensitivity of each crop or livestock product. For that purpose, various transformations were applied. The transformations were different for each indicator and for each crop or livestock product considered within each indicator. By applying the specific transformation for a crop to an indicator, a new value was obtained: the Transformed Indicator. Table 1 shows the 44 indicators used in the model. Because of the complexity of some indicators, detailed analyses were required to handle the available information properly: Suitability for agriculture (Santé and Crecente, 2007); Landscape quality and fragility (Calvo-Iglesias et al., 2006a,b); Farm Size (González et al., 2004, 2007); proportion of irrigated area to Utilised Agricultural Area (UAA), and proportion of hydrological network to total area (Alvarez et al., 2005; Neira et al., 2005); proportion of cooperative members to population engaged in agriculture (Fandiño et al., 2006); proportion of area under Land Consolidation Projects to total area (Crecente et al., 2002); sustainability (Marín et al., 2009). 2.5.2. Construction of ‘‘suitability matrices’’ Based on multicriteria analysis techniques (Kenney and Raiffa, 1976; Roy, 1990; Belton and Stewart, 2002), 88 ‘‘suitability matrices’’ (one for each EEU) were constructed, aimed at comparing the degree of suitability of a crop or land use being introduced in a given Ecological and Economic Unit. These ‘‘suitability matrices’’ regarded all the factors that might have a positive or negative effect on carrying capacity (physical environment, territorial structure, human environment, production structure of farms, commercial factors and crop production factors). Each factor was assessed by using the indicators defined in the above subsection, which were transformed into numerical values that varied for every EEU and crop or land use. Each ‘‘suitability matrix’’ included 50 different crops or livestock productions in rows and 44 indicators in columns. The suitability, adequacy or carrying capacity of a crop/land use for a given EEU was represented by a single numerical value (in the range 0–1000) that allowed for comparisons between different crops in the same EEU, such that the crops or land uses considered could be organized into a hierarchy. The suitability of the crop or land use ‘n’ was measured by the following value:

PSn ¼

44 X

WFi xTEi;n

ð1Þ

i¼1

where: PSn: Total value of the suitability of crop or livestock product ‘n’ for an EEU. The matrix construction method allows for the comparison of the PSn value obtained with the values obtained for other

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EEUs, such that the suitability of the different comarcas for producing a crop or land use can be compared. WFi: Weighting factor for indicator i. Weighting factors are used to adjust the relative importance of the 44 indicators, by distributing 1000 points among them Weighting factors are constant for all the EEU and crops, but can be modified by the user. In our particular case the WFi for each indicator was assessed by an expert panel. TEi,n: Transformed Indicator i for crop or land use n (values between 0 and 1). 2.5.3. Construction of an ‘allocation matrix’ An ‘allocation matrix’ enables the user to allocate a crop or land use as a function of its suitability for every Ecological and Economic unit, i.e., EEUs can be organized into a hierarchy according to the carrying capacity for a specific crop or land use. There are 50 ‘‘allocation matrices’’, one for each crop or land use. Each ‘‘allocation Matrix’’ includes the 88 UEEs in rows, and the 44 indicators in columns. The ‘allocation matrix’ is useful in determining the areas where the application of development measures for a given crop or product would be most effective. 2.5.4. Construction of a SWOT Matrix SWOT analysis (analysis of Strengths, Weaknesses, Opportunities and Threats) (Hill and Westbrook, 1997) was used to perform an objective assessment of the situation of the different Ecological and Economic Units. Based on the analysis, different strategies can be defined: Defensive Strategies (Strengths vs. Threats, resources of a Unit to avoid threats), Offensive Strategies (Strengths vs. Opportunities, which represent the growth potential of the unit or crop considered), Survival Strategies (Weaknesses vs. Threats, which suggest the capacity of the Unit to face external threats with its own resources), and Adjustment Strategies (Weaknesses vs. Opportunities, which suggest the capacity of the Unit to seize opportunities that may arise without having strong points). All the elements considered in the SWOT analysis were objective data obtained from the sources used to characterize the Units (work with experts, census analysis, literature review and/or field survey) and showed numerical values that were obtained from the construction of the indicators described above. The method used to obtain the SWOT matrix was based on the 44 indicators considered. The mean value of each indicator for all the comarcas was set at 0.5. Based on this value, a score above 0.5 for an indicator of a given EEU suggested that the suitability of the EEU considered was higher than the average for all the comarcas, while a score below 0.5 suggested a poorer suitability than the average. The operating procedure of the SWOT matrix automatically selected for each EEU all the values of the indicators that were equivalent to or higher than 0.8 and equivalent to or lower than 0.2. Such indicators were classified into strengths, weaknesses, opportunities and threats, which allowed for a straightforward design of strategies. Fig. 3 synthesizes the computational operations used to build the model.

3. Results and discussion To illustrate the potential of the model, an example is provided below, using the results obtained for Baixo Miño, a comarca located in Southwest Galicia; more specifically, for its Ecological and Economic Unit number 1 (EEU-1).

Table 2 includes the suitability matrix for EEU-1, and Table 3 shows the final results of the allocation matrix for mirabelle plum. As shown in Table 2, the most suitable crops in the EEU-1 area are horticultural crops, followed by a group of crops composed of cut flowers, vineyard, mirabelle plum (native variety of plum, Prunus domestica L. var. syriaca) and ornamental plants. Table 3 summarizes the overall classification of a crop or land use (in this case, mirabelle plum) for all the 88 EEUs. The Baixo Miño comarca ranks first because of the current importance of the crop in this area. The SWOT matrix for EEU-1 in Baixo Miño is partly reported in Tables 4, 5 and 6 , which identify the characteristic elements of

Table 3 Allocation matrix results for mirabelle plum: ranking of the 88 EEUs according to their suitability for this particular crop. Crop Mirabelle plum Rank

EEU

Score

Rank

EEU

Score

1 2 3 4 5

BAIXO MIÑO-1 BERGANTIÑOS-3 BERGANTIÑOS-4 BAIXO MIÑO-3 BAIXO MIÑO-4

487.6 483.7 483.1 472.1 463.8

45 46 47 48 49

374.1 374.0 373.2 373.1 371.7

6 7 8 9 10 11

BERGANTIÑOS-5 SALNÉS-3 CALDAS-2 BERGANTIÑOS-1 TERRA CHÁ-3 NOIA-5

453.9 451.5 448.1 445.9 445.3 440.0

50 51 52 53 54 55

12 13 14

SALNÉS-5 BERGANTIÑOS-2 NOIA-4

437.9 437.4 433.9

56 57 58

15 16 17 18 19 20 21 22 23

431.7 429.1 425.1 421.8 419.7 417.9 416.8 416.6 415.1

59 60 61 62 63 64 65 66 67

414.9 413.7 412.4 407.9 407.7 405.1

68 69 70 71 72 73

LIMIA-3 PARADANTA-1 PARADANTA-3 FONSAGRADA-2 NOIA-2 TERRA DE TRIVES-3

343.7 340.9 340.3 340.3 339.6 339.5

397.7 396.8 394.4

74 75 76

389.9

77

34 35 36

PARADANTA-2 TERRA DE LEMOS-5 TERRA DE LEMOS-3

388.7 388.6 387.2

78 79 80

37 38

387.1 385.8

81 82

39 40 41 42

LIMIA-2 TABEIRÓS-TERRA DE MONTES-5 ARZÚA-2 CARBALLIÑO-3 TERRA DE MELIDE-5 TERRA CHÁ-1

ORTEGAL-4 PARADANTA-4 TERRA DE CELANOVA-3 TERRA DE CELANOVA-4 TERRA DE TRIVES-1 CARBALLIÑO-2 TERRA DE CELANOVA-1 FONSAGRADA-4 ORTEGAL-2

337.5 337.1 335.5

33

TERRA DE LEMOS-6 OS ANCARES-3 NOIA-1 ORDES-1 ORDES-2 TERRA DE LEMOS-2 CALDAS-5 ULLOA-1 MARIÑA OCCIDENTAL-5 ARZÚA-5 TERRA DE LEMOS-1 TERRA CHÁ-4 ULLOA-2 BAIXO MIÑO-5 TABEIRÓS-TERRA DE MONTES-1 CALDAS-3 ORDES-4 MARIÑA OCCIDENTAL-1 OS ANCARES-2

TERRA CHÁ-2 ULLOA-4 OS ANCARES-7 TERRA CHÁ-7 MARIÑA OCCIDENTAL-2 TERRA DE MELIDE-3 SALNÉS-2 CALDAS-1 NOIA-3 FONSAGRADA-3 MARIÑA OCCIDENTAL-4 OS ANCARES-6 ORTEGAL-5 TABEIRÓS-TERRA DE MONTES-3 ULLOA-5 CARBALLIÑO-1 TERRA DE MELIDE-1 CARBALLIÑO-4 TERRA DE MELIDE-4 BAIXO MIÑO-2 FONSAGRADA-1 LIMIA-1 ORTEGAL-1

382.0 381.1 380.6 378.4

83 84 85 86

315.5 313.7 313.6 313.2

43 44

ORDES-3 TERRA DE TRIVES-2

377.6 377.1

87 88

RIBEIRO-3 LIMIA-4 RIBEIRO-4 TERRA DE CELANOVA-2 RIBEIRO-2 RIBEIRO-1

24 25 26 27 28 29 30 31 32

369.4 369.2 368.8 367.2 366.8 364.3 361.7 361.2 360.7 360.1 359.6 354.2 351.5 349.5 349.3 349.1 345.2 344.0

334.5 333.8 327.6 325.6 323.8 318.7

307.7 293.3

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Table 4 Strengths of EEU Baixo Miño-1. Suitable climate for the main products Good infrastructure characteristics in the EEU Product marketing Availability of commercial channels for the main products Proximity to large consumption areas Training Proximity to training centres and availability of agricultural training in Compulsory Secondary Education Availability of hired labour High level of common use or exploitation of communal forests (MVMC) Membership of associations or groups Great interest in marketing groups or associations Vineyard PDO certificate Success of the processed product in the internal and foreign markets Strong presence of processing industries in the region High product price Strong product demand Product linked to tourism: wine trail Possibility of enlarging the PDO Area Forest owner communities are interested in renting land Horticultural crops High membership of associations or groups High seasonal use Presence of a processing industry in the region Good weather conditions for growing the crops in the field Earliness of production Mirabelle plum Presence of five processing industries in the region Production of this crop is almost exclusive of the region High price of the processed product Ornamental plants Competitive advantage due to the earliness of some species (Rhododendron, Azalea) Strong demand in the internal and foreign markets

Table 5 Opportunities of EEU Baixo Miño-1. Presence of an Administration with autonomous rulemaking and capacity Access to European, National and Regional grants and subsidies for production and marketing Presence of a regional network of Agricultural Advice Presence of a regional network of Agricultural Research Potential demand for products from Integrated Production Peripherality of Galicia within Europe, which increases transportation costs for foreign products Reputation of Galician products, particularly in the internal market. Galician products are seen as natural products Weak knowledge of products in foreign markets Vineyard Demand for PDO wine grapes is not met by the production in the region Possibility of satisfying the demand for red wine in the internal market Horticultural Products Potential for implementation of integrated production Fidelity of consumers to Galician products Mirabelle plum Lack of competition with products from different origins Possibility of new forms of presentation and marketing Ornamental plants Availability of grants for production Non-perishable products

EEU-1 (with values above 0.8 and below 0.2 in the suitability matrix). Tables 4 and 5 contain the strengths and opportunities for EEU1 and for the main crops and land uses proposed by the suitability matrix for EEU-1.

Table 6 Offensive strategies for EEU Baixo Miño-1. Consolidation of horticultural cooperatives Enhancement to transform communal forests into vineyards Accumulation of rights to vineyards not assigned to PDO Enhancement of Integrated Production Systems for horticultural production Enhancement of the development of test fields for native horticultural varieties suited to the area Diversification of horticultural products in the field by recovering traditional varieties Analysis of horticultural varieties suitable for processing Aid measures for the establishment of new ornamental plant nurseries

Table 6 summarizes the offensive strategies derived from contrasting the strengths and opportunities defined in EEU-1. The strategies from SWOT matrices were used to define a set of operational proposals that were implemented by the Administration. Such proposals were structured into three levels: (a) proposals for specific geographical areas (EEUs), (b) proposals for all the productive sectors in the comarca and (c) specific proposals for the most significant crops and agricultural and forest land uses. The planning analysis can be performed either for the EEU or for the principal crops and land uses, i.e. the model allows for two types of diagnostic analysis, both using indicators for data management: (1) spatial diagnostic analysis, focused on determining the most outstanding elements from the perspective of production planning in the different EEUs, and (2), a diagnostic analysis of the most suitable products.

4. Conclusions Our first consideration for the drawing of conclusions is based on the premise that no model, regardless of its quality, can replace the work of a technician. However, our aim was to develop an efficient and useful tool that provided technicians with elaborate information. The model presented in this paper has proved useful in agricultural production planning and has enabled the Galician Administration, Xunta de Galicia, to define operational measures and action policies based on the results of the model. However, the achievements of the model should be re-evaluated after some years of implementation. The results suggest the usefulness of the model for the analysis and diagnostic assessment of the situation. The model shows the following features: adaptability to decision making advice at different levels, from Public Administration to farmers or farmers associations (by changing the scale of analysis – farm, group of farms, EEU, comarca – the indicators, and/or its weight); capability of performing risk analysis, modelling situations and hypotheses; and capability of performing sensitivity analysis to detect key factors. The limitations of the model derive from two main aspects: (1) the need to analyze the weighting of the indicators, i.e. the relative weight of each indicator in the final result and (2) the use of transformations that represent the suitability of environmental factors for the characteristics of each crop or land use. Because the information used was obtained from a variety of official sources, it corresponds to different scales or levels. Thus, agricultural census data, which were obtained from the national Administration, correspond to municipalities, while data obtained from the Galician Administration correspond to parishes or comarcas. Such differences caused problems in EEU zoning and in the definition of the final proposals. This model can be further developed by introducing more (comprising more aspects) and better (more detailed) information. The

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