Available Online: http://www.ejpau.media.pl/volume19/issue1/art-12.html
SOME ASPECTS OF THE CONCEPT, METHODOLOGY AND APPLICATION OF FARMING SYSTEM TYPOLOGY
Wies豉w M康ry1, Barbara Roszkowska-M康ra2, Dariusz Gozdowski1, Ryszard Hryniewski3
1 Department of Experimental Design and Bioinformatics, Warsaw University of Life Sciences (SGGW), Poland
2 Department of Entrepreneurship, Faculty of Economics and Management, Bia造stok University, Bia造stok, Poland
3 Department of Experimental Design and Bioinformatics, Warsaw University of Life Sciences - SGGW, Poland
The paper reviews the farming system definition, methodology
of farming system classification (typology) at farm or an area unit (mostly NUTS
5; NUTS – Nomenclature
of Territorial Units for Statistics) level using different types of data (survey,
census and remote sensing data), and importance of this typology. The diversity
of farming systems is a crucial issue in several studies related to agro-ecosystem
and environmental management, policy implementation and rural development. In
performing the farming system typology within a farmland area, hierarchical classification
of respective units has been adopted in which the three following levels of the
classification are considered: 1) the main type level, 2) the more oriented type
level, and 3) the specific type level. Study of the farming systems typology
at a spatial scale can be performed using expert or statistical methods. The
last are mainly used to distinguishing specific types of farming systems within
a more oriented farming system. The sampling of farms considered for the farming
system typology may rely on geographical or administrative stratification. Data
on the sampled farms are collected through surveys of farmers or national and
EU (European Union) databases (e.g., the FADN database; FADN – Farm Accountancy
Data Network). For farming system diversity assessment at an area unit level,
data should be aggregated at the respective area unit scale. Expert methods are
based on knowledge of guide researchers or agricultural extension experts supported
by land cover maps and recorded databases. Farming system types are inferred
from the statistical characteristics of the sampled farms or the entire set of
the area units, obtained through multivariate methods such principal component
analysis and cluster analysis. Assessment of farming system diversity and typology
have become increasingly important in recent years because of their usefulness
in developing flexible policies for public interventions and for the effective
planning, discussion and support of proper pathways for the development of multifunctional
and sustainable agricultural and rural areas. Comprehensive approaches that take
into consideration various sources of data and track spatio-temporal changes
in farming systems are highly valuable tools for achieving the sustainable development
of rural areas.
The paper reviews the farming system definition, methodology of farming system classification (typology) at farm or an area unit (mostly NUTS 5; NUTS – Nomenclature of Territorial Units for Statistics) level using different types of data (survey, census and remote sensing data), and importance of this typology. The diversity of farming systems is a crucial issue in several studies related to agro-ecosystem and environmental management, policy implementation and rural development. In performing the farming system typology within a farmland area, hierarchical classification of respective units has been adopted in which the three following levels of the classification are considered: 1) the main type level, 2) the more oriented type level, and 3) the specific type level. Study of the farming systems typology at a spatial scale can be performed using expert or statistical methods. The last are mainly used to distinguishing specific types of farming systems within a more oriented farming system. The sampling of farms considered for the farming system typology may rely on geographical or administrative stratification. Data on the sampled farms are collected through surveys of farmers or national and EU (European Union) databases (e.g., the FADN database; FADN – Farm Accountancy Data Network). For farming system diversity assessment at an area unit level, data should be aggregated at the respective area unit scale. Expert methods are based on knowledge of guide researchers or agricultural extension experts supported by land cover maps and recorded databases. Farming system types are inferred from the statistical characteristics of the sampled farms or the entire set of the area units, obtained through multivariate methods such principal component analysis and cluster analysis. Assessment of farming system diversity and typology have become increasingly important in recent years because of their usefulness in developing flexible policies for public interventions and for the effective planning, discussion and support of proper pathways for the development of multifunctional and sustainable agricultural and rural areas. Comprehensive approaches that take into consideration various sources of data and track spatio-temporal changes in farming systems are highly valuable tools for achieving the sustainable development of rural areas.
Key words: farming systems, farm and area unit typology, farm household strategy, multivariate statistical methods, survey data.
Inflexible policies for the development of agricultural and rural areas on a national or regional scale have often been ineffective because the spatial heterogeneity of the natural and socio-economic resources of the target area were not taken into account [18, 28, 49]. A policy that is adequately targeted to the different realities of these areas according to the principle of decentralisation of governance, selection of such pathways and strategies for their development that correspond well to the local demands of the population and to the potential for efficient and sustainable use of resources can be much more effective [2, 15, 23, 65].
Adequate development policies should be discussed, formulated and implemented according to how heterogeneous rural areas are (demand-led rural development policies), together with effective instruments supporting this development [2, 10, 15, 50, 52]. Therefore, one of the most important conditions for high effectiveness of EU, state and local government interventions in agriculture and rural areas is their flexibility. They involve the development and implementation of various options, adequately fitted to the specific types of farming systems in the target area [6, 15, 21, 29, 47]. Such an intervention strategy would require assessment the diversity and identification of the typology (typification, segmentation) of farming systems existing within the target area. Typification of farming systems can also be an important tool as a component of an effective methodology for delimitation and categorisation of less-favoured areas [5, 65]. This approach is an essential step in developing more flexible and decentralised economic interventions, as postulated by economists to promote and stimulate multifunctional and sustainable development of LFAs (Less Favoured Areas) [36, 49, 63, 65]. Typology provides a scheme of farming system diversity, which directs research on techno-economic references based on studying the operational processes of these systems in their complexity. Such an approach also represents a major investment for extension services to organise an effective advice net for farmers as well as for all rural actors and society.
The objective of this paper is to present an overview of recent scientific achievements worldwide in the agricultural economic and agronomic approaches to farming systems. Particularly in terms of the hierarchical typology of these systems and its effective use for improving the efficacy of various forms of public interventions and research studies.
DEFINITION OF A FARMING SYSTEM
A farming system is an economic and agricultural concept holistically describing (as a whole, based on a set of many variables and indicators) a farm household in terms of agricultural land use, i.e., the systems of crop and livestock production, non-agricultural economic activities of farm household members (on-farm and off-farm activities), the income generated and the structure and in terms of the natural, social, economic, infrastructural and institutional resources and environments that determine these all of economic activities. [4, 15, 27, 29, 31, 32, 62]. A farming system is a kind of characterisation of a farm household strategy and the modes of realising the concept of multifunctional agriculture through diversification of farm activities, together with the determinants of these activities [22, 29, 63].
Each farm has its own, unique farming system [15, 18, 31]. There is a wide diversity of farming systems among a group of farms, not only on a large-scale in terms of geographic space but also within restricted rural areas or more oriented types of these systems [6, 10, 11 12, 59, 62].
A HIERARCHICAL TYPOLOGY OF FARMING SYSTEMS
In identifying the typology of farming systems within a given rural area, hierarchical classification of these systems has been adopted [3, 9, 29, 32, 33, 45]. Commonly, the three following levels of classification are considered: 1) the main types of farming systems, 2) the more oriented types of farming systems, and 3) the specific types of farming systems. Depending on the agricultural and economic importance of crop and livestock production associated with the income structure from the agricultural production of a farm, three main types of farming systems are distinguished: crop farming systems, livestock farming systems, and mixed crop-livestock farming systems [15, 21, 55, 57, 64].
Within each of the main types of farming systems, various more oriented types can be distinguished based on the criterion of the crop and livestock species diversity in farms as well as the economic structure of crop and livestock production. Within crop systems, a number of different systems can be distinguished, such as cereal systems (e.g., a maize, wheat wheat-rice and soybean system), cash-crop system (e.g., tobacco, coffee, sugar cane, sugar beet, oilseed rape or horticultural crop production), and mixed crop systems [6, 13, 16, 24, 25, 55]. Within livestock systems, more oriented types of systems, such as dairy cattle, beef cattle, dairy goat, pig or sheep systems can be distinguished, in addition to fishing systems or semi-extensive pastoral systems and extensive, marginal pastoral systems, such as agro-forest pastoral systems [12, 20, 21, 35, 38, 47, 52–54, 61]. Additionally, within mixed system, more oriented system types, such as cereal-forage-dairy systems, cereal-pig, cereal-sheep, agro-forest, and smallholder mixed systems as well as mixed crop-livestock farming systems involving a specialised crop or livestock species exhibiting highly profitable production, such as glasshouse or arable vegetables, tobacco, hops, pigs, or poultry [7, 36, 55, 64]. Recently, high nature value (HNV) farming systems have been considered as a special type of more oriented farming system [1, 8, 19, 26, 39, 44, 45,52]. These high nature value (HNV) farming systems are also related to less-favoured area (LFA) farming systems [1, 4, 19, 26, 44, 65]. Figure 1 shows a hierarchical classification scheme of the three main and most important more oriented types of farming systems.
Fig. 1. Hierarchical classification of the main and more oriented farming systems (only the most important more oriented types of farming systems within each type of main system are presented)
The names of the main or more oriented types of farming systems often contain the name of the agricultural system that defines the general approach to agricultural production in a given farming system in terms of inputs, environmental impact and the degree of environmental and socio-economic sustainability [5, 43, 58]. Three basic agricultural systems are distinguished (Fig. 2): conventional, integrated and organic [40, 41, 58]. Within conventional systems, there are either intensive or extensive (low-input) agricultural systems [5, 42, 58].
Fig. 2. Basic agricultural systems
Given the above-mentioned rules for naming the more oriented types of farming systems, the following types of systems have been distinguished: intensive, semi-intensive or organic dairy systems; intensive, semi-intensive, extensive, organic or pastoral beef cattle or sheep systems; conventional (intensive), integrated or organic cereal, fruit or vegetable systems; semi-intensive, smallholder or extensive (low-input) mixed systems without specialisation or specialising in highly profitable crops or livestock [2, 5–7, 12, 36, 40, 43, 47, 50, 59]. The names of the specific farming systems distinguished within a more oriented type are unique to that type [6, 12, 13, 20, 43, 47, 52–54, 61].
METHODOLOGY FOR ASSESSING THE DIVERSITY OF FARMING SYSTEMS, THEIR TYPOLOGIES AND BEYOND
Assessment of the diversity of farming systems and their typology in rural areas can be performed using expert methods [14, 18, 33] or analytical methods [11, 50, 51, 56]. Expert methods are based on expert knowledge supported by land cover maps, which guide researchers or agricultural extension experts, and all available official synthetic information collected by state and local administrations . These methods were historically the first to be used, but for more than two decades, they have frequently been replaced by more formal and reliable analytical (statistical) methods, referred to as analytically based farming system typology or statistical farming system typology [6, 31, 34]. Statistical methods for identifying the diversity of farming systems and their typology can be applied at the farm level [6, 11, 16, 31, 33, 59, 63], at the level of administrative units such as rural sub-districts (division equivalent to NUTS 5-level) [4, 43, 56] or to other area units covering a certain rural area [3, 4, 18, 25, 28, 62]. Analytically based typology of farming systems at the level of farms, sub-districts or other area units is a tool that facilitates the simplified description and characterisation of the diversity of these systems through statistical classification of the respective entries under investigation across a rural area into homogenous groups based on their many diagnostic attributes (variables) – [3, 4, 18, 29, 30, 34, 56]. Data on such variables can be recorded on farms through surveys or censuses conducted by European Union Agencies, such as FADN net, state statistical offices, including the Polish Agricultural Censuses of 1996, 2002, and 2010, or other state or local administration organisations [14, 29, 34, 49, 50]. These data can be used directly for farm-level farming system typology [11, 31, 59], or they can be aggregated to the NUTS5 scale to be used in the typology of farming systems at an adequate area unit level [4, 56]. Additionally, geographical information systems (GIS) can deliver geographic, natural and ecological data that are appropriate for the typology of landscape complexity and farming systems at a spatial area unit level [4, 28, 46].
Each homogeneous group of respective entries forms an empirical basis for the identification and multivariate representation of a type of farming complexity occurring in a set of these entries [6, 10, 12, 15, 18, 31, 43, 53, 54]. Each entry assigned to a homogenous group is a representative of a set of entries associated with a type of farming system that is practiced in the rural area under consideration [15, 62]. Expert methods of farming system typology have been used both as individual tools [18, 33] and as complementary tools together with analytical methods [10, 14, 50, 62].
Both approaches to farming system typology can be efficiently used to classify the farming systems found in a studied rural area at each of the three classification levels if the heterogeneity of the area includes the respective types of farming systems. However, studies aimed at farming system typology have usually been conducted within the framework of the more oriented types of these systems in a particular rural area, which is characterised by a more oriented type of the system [12, 20, 40, 47, 52]. System typology has also been performed within selective (specialised) farms belonging to a more oriented type of farming system, e.g., cattle, vegetable, goat, banana or tobacco farms [6, 13, 16, 24, 53, 54, 61].
The statistical methodology for performing farming system typology in a given set of farms or in other area units within the framework of a main or more oriented type of farming system in a targeted rural area consists of the following steps: (1) selection of the aim and aspects of farming system typology, followed by adequate diagnostic variables describing and characterising the global conditions (resources) and activity strategy of farms and households, including natural conditions (land cover, biodiversity features), social (family number, family members working at the farm), socio-cultural, technical resources, the agricultural production systems at the farms (the on-farm land-use system, agricultural, cropping system, livestock density, livestock structure and management), agricultural practices (fertilization input, crop management, use of pesticides, technical equipment use), destine of production (selling or own consumption), economic conditions and production profitability as well as farm income sources from non-agricultural activities (on-farm and off-farm family activities); (2) selection of the targeted rural area to be studied and an appropriate level of farming typology (the farm or area unit level); 3) selection of a representative sample of farms or respective area units (if not all area units are to be studied) from the set (population) in the target area; (4) surveying those farms to collect data related to the selected diagnostic variables or obtaining access to census data; (5) transforming data for the examined diagnostic variables (input variables) into a small set of new synthetic variables (principal components or principal coordinates) with little loss of information using principal components analysis or principal coordinates analysis to assess the structure of their variation (correlation of diagnostic variables) and their relative importance (contribution) to the diversity of the farming systems in the respective entries capturing a target area; and (6) classification of the selected farms or respective area units into groups consisting of entries that are relatively similar, i.e., homogeneous in terms of the diagnostic variables, which is performed using cluster analysis [6, 10, 12, 13, 18, 20, 24, 27, 31, 32, 34, 38, 53, 54, 59]. Each homogeneous group of the studied entries establishes an empirical basis for the identification and multivariate characterisation of a type of farming system in the set of these entries in the target area [6, 10, 12, 15, 18, 31, 43, 53, 54]. An established farming system typology represents the diversity of farms in the delimited area from which the entries (mainly farms) have been sampled and is valid only for that area [18, 31, 34].
Because dividing farms or alternative entries into groups with similar farming systems should facilitate the effective identification and characterisation of the existing diverse types of these systems across a target area, the number of groups distinguished cannot be too large. Researchers usually restrict themselves to distinguishing a few (typically 3 to 6) groups (clusters) of the studied entries that are homogeneous in terms of the multi-variable criterion used for discrimination of the types of these systems [12, 13, 20, 31, 38]. Because hierarchical cluster analysis is usually employed to distinguish these groups of entries, it is possible to further analyse the identified groups, such as through further division of the entries within the selected group, or to combine two or more groups of entries into one larger group . As targeted regions show different degrees of farming system diversity among the entries considered, the number of distinguished groups of entries should be specified according to this diversity. Therefore, the number of distinguished groups of entries in one research case would not be optimal in other circumstances [31, 34]. With an increase in the number of separate groups, the homogeneity of the units (entries) within each group increases, although this also makes it difficult to characterise the farming system typology of a given area at the farm level.
To study the diversity and typology of entries in terms of farming system complexity based on the data on quantitative (both continuous and discrete with more values) diagnostic variables, two multivariate statistical methods are usually employed: principal component analysis (PCA) and cluster analysis. The main objective of the first method (PCA) is to reduce a usually fairly large number of diagnostic variables included in an analysis to a considerably more limited number of formal variables, referred to as principal components . Each principal component accounts for the maximum part of total multivariate variation among the studied entries and generally represents a few mutually correlated diagnostic variables in a synthetic form. For these reasons, the first few principal components can be referred to as factors explaining the variation in mutually interrelated input variables. In many cases, it is possible to reduce a large number of farming system diagnostic variables to two or three principal components with relatively little loss of information (accounting for the total variation among the studied entries to a high degree with the first two or three principal components). Therefore, the first few principal components allow a sufficient graphically approximated representation of the multivariate diversity among the entries to be produced with the two- or three-dimensional coordinates defined by the first two or three principal components. In some cases information presented using two or three principal components can be characterized by high uncertainty. It is especially when a set of examined variables consists of many not correlated or very weakly correlated variables. Total variability explained by two or three principal component can not be sufficient for the proper evaluation of multivariate diversity of the entries. Because the first two or three principal components usually contain the most essential information characterising the diversity of entries in the studied set, they are often used as variables in cluster analysis [6, 11, 13, 34]. The PCA procedure facilitates performing so-called data mining (exploratory data analysis) for multivariate assessment of the diversity among the entries in a set within a rural area [12, 27, 31, 34, 61]. To reduce the dimensionality of the variation system when diagnostic variables are categorical (also referred to as qualitative), including nominal, ordinal and interval variables, principal component analysis should be replaced by principal coordinate analysis, which represents a modification of the first multivariate method .
Cluster analysis is a method that allows distinguishing homogenous groups (clusters) of entries with respect to many quantitative and categorical variables. This technique is carried out in two stages: the first stage consists of definition of the multidimensional distance between entries, and the second is agglomeration of the entries, i.e., grouping them based on their distance. For quantitative and some discrete variables, the Euclidean distance or squared Euclidean distance has most commonly been used. In cluster analysis of entries to identify farming system typology, a hierarchical procedure known as Ward’s method has frequently been used. Ward’s method is a clustering method that usually provides a clear division of entries into homogenous groups [11, 13, 31, 34]. The graphical result of grouping entries via hierarchical methods of agglomeration is a dendrogram. It has to be divided at a proper level of similarity to identify groups of entries showing sufficiently discriminated types of farming systems in a studied area. Different criteria have been proposed and used for this purpose, including heuristic approaches and correlation of the variation among groups and within groups belonging to those most frequently used [11, 13, 31, 34].
In addition to the clustering of farms or other entries into homogeneous groups in terms of diagnostic farming system variables, further statistical analyses, such as analyses of contingency tables, are often employed. The aims of these methods include assessing the allocation of farms from individual groups in a priori assumed sub-areas and characterising the identified groups of farms in terms of attributes outside of the set of diagnostic variables [11, 30, 34]. Based on a representative sample of the studied farms in the target area, it can be assumed that the sub-area distribution of farms included in the study among the distinguished groups is close to the distribution of each identified farming type in the whole population of farms in the study area. However, it should be remembered that the assessment of the proportion of farms exhibiting a specific type of farming system is often associated with a considerable error, especially when the number of farms in the study is not particularly large. In the studies performed to date, the most commonly found sizes are in the range of a few dozen (e.g., 30) to a few thousand farms .
The statistical methodology used for distinguishing clusters of farm entries and identifying farming system typology can be described using the scheme presented in Figure 3. In the subsequent steps of the complete statistical analysis, various multivariate methods are employed. The interpretation of the results obtained in this analysis is also discussed. Beside of these methods in some cases other multivariate methods have been used in farming system studies. One of them is confirmatory factor analysis (CFA) which is applied for verification which of hypothesized variables are of great importance in examined data set. Selection of the hypothesized variables can be performed on the basis of previous research or presumption of the researcher or other sources of information. For validation of the importance of variables in CFA factor loadings for each variable are used .
Fig. 3. Scheme of the statistical analysis process involved in farming system typology based on survey data
Another multivariate method for evaluation of cause and effect relationships between various environmental and sociological variables versus farmers’ attitudes towards different socio-economic phenomena is structural equation modelling (SEM). SEM refers to family of the methods used for evaluation of effects of many predictor variables on one or more response variables. One of the SEM methods being often applied in natural and agricultural studies is path analysis. Another method of SEM is linear structural relations (LISREL). These methods allow explaining cause-effect relationships including selection of variables showing significant influence of predictor variables on response variables .
The obtained outcomes allow characterisation of the distinguished groups of entries exhibiting similar farming systems and determination of the spatial locations of the groups [14, 30]. This is particularly important because it allows one to evaluate the spatial distribution of farms with particular type of system. The spatial distribution of similar types of farming systems is often not random. To evaluate the existence of spatial autocorrelation (i.e., whether farms with the same farming system occur in geographic proximity), spatial statistics are necessary, e.g., analysis based on Moran’s I or Geary’s c for univariate data or Mantel correlograms for multivariate data . The combination of classical methods of statistical analysis and spatial statistics allows a comprehensive inference to be obtained and should be recommended for research on farming system typology.
METHODOLOGY FOR ASSESSING THE DIVERSITY OF FARMING SYSTEMS, THEIR TYPOLOGIES AND BEYOND
Performing farming system typology at the level of farms (mostly family farms run on a small geographical scale) or rural districts is of high theoretical and practical importance. Generally speaking, such a typology allows increasing the effectiveness of flexible public interventions in different forms based on the compatibility of the developed, specific options for interventions with the identified types of farming systems, which by nature, present diverse conditions, opportunities and development needs [6, 31, 50]. In particular, the results of a farming system typology across a given area are very useful for the following purposes:
- to identify major constraints on soil productivity and opportunities, priorities, directions and models for the sustainable development of agriculture and rural areas (known as a farming systems approach) and to develop the most effective forms of public management a priori for the current types of farming systems to improve them, mainly in terms of the direction of multifunctionality and diversification of agricultural and non-agricultural activities [10, 18, 20–22, 27, 29, 33, 50, 56, 59];
- for streamlining and improving the effectiveness of agricultural advisory services and development projects [6, 12, 13, 16, 21, 47, 51, 61];
- for effective implementation of the instruments of the Common Agricultural Policy for sustainable development [2, 62];
- for heuristic, computer-simulated and experimental development and to check the effectiveness of alternative, innovative production systems in the studied area as well as to identify barriers to the implementation of these innovations (prototyping) [6, 13, 22, 25, 31, 57, 59];
- to construct and apply models for simulating the responses of different farming systems to EU, national state and regional (local) agricultural and environmental policies and the new instruments supporting programmes for the sustainable and multifunctional development of agricultural and rural areas [2, 47, 50]; and
- to study the relationships between farming systems (agricultural land-use practices) and landscape patterns within a framework of landscape ecology [3, 9, 10, 18, 28, 48].
Various approaches for farming system typology evolve into spatial approaches that not only include distinguishing groups of farms or administrative units (e.g., NUTS) belonging to a particular typology system but are also extended to spatial approaches [10, 14, 30]. An important source of information under these circumstances is remote sensing data (e.g., Landsat satellite images), which complements survey or census data obtained in traditional way. Combining multiple sources of information makes a comprehensive analysis possible (Fig. 4) and allows larger areas to be covered, which is important for regional policies .
Fig. 4. Comprehensive approach to farming system typology based on multiple data sources
The aims of farming system typology are not only related to the current state and future prospects but also include the important objective of analysing the historical development of farming systems . Long-term tracking of changes in farm typology is especially important in ecologically vulnerable areas. Shifts in farming systems from low-intensity to high-intensity farming systems pose risks to high nature value farmland, and such changes should be monitored and regulated using political tools . The spatial and temporal changes that have occurred in farming systems in recent years are very dynamic, and because of this, it is important to analyse datasets obtained at similar times. One of the effects of these changes in farming systems is an orientation toward more specialised production. Because traditional farming systems are being replaced by highly specialised systems, the number of distinguished systems is increasing, and their classification is becoming more detailed [1, 48]. Contemporary approaches to farm typology methods demand two main types of data: data that characterise the type of production direction (e.g., livestock species, crops) and data that characterise the intensity of production (e.g., fertiliser and pesticide use, machinery). Combining these types of data allows farm typology to be distinguished more completely .
SUMMARY AND CONCLUSIONS
Farms in rural areas produce different crops and livestock, apply diverse agricultural practices and are characterised by different types of compensational income from both on-farm and off-farm non-agricultural activities. They do not generate the same income levels, nor do they have the same life expectancy. Therefore, farming systems are less or more diverse in different rural areas. The diversity of farming systems is a crucial factor in several issues related to agriculture and rural development as well as landscape management. This farming diversity has long been identified as a problem with respect to conceiving and implementing development interventions at the EU, state or local government level as well as related to agricultural organisations and extension services.
In identifying the farming system typology within a rural area, hierarchical classification of these systems has been adopted in which the three following levels of classification are considered: 1) the main types of farming systems, 2) the more oriented types of farming systems, and 3) the specific types of farming systems. Specific types of farming systems are predominantly distinguished within a certain geographical area characterised by rather homogenous natural and socio-economic conditions and then based on one of the more oriented farming systems. Assessment of the diversity of farming systems and their typology in rural areas can be performed using expert methods or analytically based (statistical) methods, which are mainly used to distinguishing specific types of farming systems within a more oriented farming system. There are some general methodological principles involved in statistical assessments of diversity and farming system typology. First, the area for which the typology is valid must be delimited. The typology will therefore represent the diversity of farms in the area. Second, the typology can be based on a sample of farms (it can be performed at the farm level) or on area units, such as rural sub-districts, which is equivalent to the NUTS 5-level. The sampling of farms may be statistical, based on geographical or administrative stratification because they are assumed to be representative of the farming diversity of the area considered. Data on the sampled farms are collected through surveys of farmers as well as national or EU economic databases (e.g., the FADN database). For farming diversity assessment at the area unit level, primary (raw) data from the sources mentioned above should be aggregated at the respective area unit scale. Farm types are inferred from the characteristics of the sampled farms, generally via multivariate analysis and clustering techniques. Expert methods are based on expert knowledge supported by land cover maps, which guide researchers or agricultural extension experts, and all of the available official synthetic information collected by state and local administrations. Expert methods of farming system typology have been used both as independent tools and as complementary tools together with analytically based methods
Studies related to the assessment of farming system diversity and typology and investigations involving the application of their results have become increasingly important in recent years because of their usefulness in developing flexible policies for public interventions and for the effective planning, discussion and support of proper pathways for the development of multifunctional and sustainable agricultural and rural areas by promoting the diversification of agricultural and non-agricultural activities of societies of farm families. This is particularly evident in the EU Common Agricultural Policy, which offers many policy instruments designed appropriately with respect to the heterogeneity of agricultural and rural areas to most effectively stimulate, promote and support a spectrum of pathways for their development. Such public interventions and diverse internal promotion tools are particularly needed in high nature and less-favoured areas, as these are the most threatened areas from a natural and socio-economic perspective.
In recent years, farming system typology has become a very important tool for developing appropriate regional policies, especially in areas of high nature value. Comprehensive approaches that take into consideration various sources of data and track spatio-temporal changes in farming systems are highly valuable tools for achieving the sustainable development of rural areas.
- Andersen E. (ed.), 2003. Developing a high nature value farming area indicator. International report. EEA, Copenhagen.
- Andersen E., Elbersen B., Godeschalk F., Verhoog D., 2007. Farm management indicators and farm typologies as a basis for assessments in a changing policy environment. Journal of Environmental Management, 82, 353–362.
- Antrop M., 2000. Background concepts for integrated landscape analysis. Agriculture, Ecosystems and Environment, 77, 17–28.
- Bertaglia M., Joost S., Roosen J., 2007. Econogene consortium identifying European marginal areas in the context of local sheep and goat breeds conservation: A Geographic Information System Approach. Agricultural Systems, 94, 657–670.
- Bignal E.M., McCracken D.I., 1996. Low-intensity farming systems in the conservation of the countryside. Journal of Applied Ecology, 33, 413–424.
- Blazy J.M., Ozier-Lafontaine H., Doré T., Thomas A., Wery J., 2009. A methodological framework that accounts for farm diversity in the prototyping of crop management systems. Application to banana-based systems in Guadeloupe. Agricultural Systems, 101, 30–41.
- Byrne F., Robertson M.J., Bathgate A., Hoque Z., 2010. Factors influencing potential scale of adoption of a perennial pasture in a mixed crop-livestock farming system. Agricultural Systems, 103, 453–462.
- Caballero R., 2007. High nature value (HNV) grazing systems in Europe: a link between biodiversity and farm economics. Open Agric. J., 1, 11–19.
- Caballero R., Riseth J.A., Labba N., Tyran E., Musial W., Molik E., Boltshauser A., Hofstetter P., Gueydon A., Roeder N., Hoffmann H., Belo Moreira M., Brito O., Seita Coelho I., Gil A., 2007. Comparative typology in six European farming systems of grassland management. Advances in Agronomy, 96, 351–420.
- Carmona A., Nahuelhual L., Echeverría C., Báez A., 2010. Linking farming systems to landscape change: An empirical and spatially explicit study in southern Chile. Agriculture, Ecosystems and Environment, 139, 40–50.
- Castel J.M., Mądry W., Gozdowski D., Roszkowska-Mądra B., Dąbrowski M., Lupa W., Mena Y., 2010. Family dairy farms in the Podlasie province, Poland: farm typology according to farming system. Spanish Journal of Agricultural Research, 8, 946–961.
- Castel J.M., Mena Y., Delgado-Pertínez M., Camúnez J., Basulto J., Caravaca F., Guzmán-Guerrero J.L., Alcalde M.J., 2003. Characterization of semi-extensive goat production systems in southern Spain. Small Ruminant Research, 47, 133–143.
- Chavez M.D., Berentsen P.B.M., Oude Lansink A.G.J.M., 2010. Creating a typology of tobacco farms according to determinants of diversification in Valle de Lerma (Salta-Argentina). Spanish Journal of Agricultural Research, 8, 460–471.
- Clavel L., Soudais J., Baudet D., Leenhardt D., 2011. Integrating expert knowledge and quantitative information for mapping cropping systems. Land Use Policy, 28, 57–65.
- Dixon J., Gulliver A., Gibbon D., Hall M., 2001. Farming systems and poverty: Improving farmers’ livelihoods in a changing world. FAO and World Bank, Rome.
- Dogliotti S., Van Ittersum M.K., Rossing W.A.H., 2005. A method for exploring sustainable development options at farm scale: a case study for vegetable farms in South Uruguay. Agricultural Systems, 86, 29–51.
- Dolisca F., McDaniel J. M., Teeter L.D., 2007. Farmers' perceptions towards forests: A case study from Haiti. Forest policy and economics, 9, 704–712.
- Duvernoy I., 2000. Use of a land cover model to identify farm types in the Misiones agrarian frontier (Argentina). Agricultural Systems, 64, 137–149.
- EEA, 2004. High Nature Value Farmland. Characteristics, trends, and policy challenges. European Environment Agency. Report 1. Copenhagen. <http://www.eea.eu.int>.
- Gaspar P., Escribano M., Mesías F.J., Rodriguez de Ledesma A., Pulido F., 2008. Sheep farms in the Spanish rangelands (dehesas): Typologies according to livestock management and economic indicators. Small Ruminant Research, 74, 52–63.
- Gibon A., Sibbald A.R., Flamant J.C., Lhoste P., Revilla R., Rubino R., Sorensen J.T., 1999. Livestock farming systems research in Europe and its potential contribution for managing towards sustainability in livestock farming. Livestock Production Science, 61, 121–137.
- Gomez Sal A., Gonzalez Garcıa A., 2007. A comprehensive assessment of multifunctional agricultural land-use systems in Spain using a multi-dimensional evaluative model. Agriculture, Ecosystems and Environment, 120, 82–91.
- Hodge I., Monk S., 2004. The economic diversity of rural England: stylised fallacies and uncertain evidence. Journal of Rural Studies, 20, 263–272.
- Hryniewski R., Mądry W., Gozdowski D., Roszkowska-Mądra B., 2011. Typology of tobacco-based farming systems at the farm level in South-Eastern Poland. Acta Scientiarum Polonorum-Oeconomia, 10, 21–34.
- Hudson D.P., White J.W., 2007. Use of spatial analyses for global characterization of wheat-based production systems. Journal of Agricultural Science, 145, 115–125.
- IEEP 2007. Final Report for the Study on HNV Indicators for Evaluation. Contract 2006-G4-04. Institute for European Environmental Policy-DG Agriculture, Brussels.
- Iraizoz B., Gorton M., Davidova S., 2007. Segmenting farms for analysis agricultural trajectories: A case study of the Navarra region in Spain. Agricultural Systems, 93, 143–169.
- Jellema A., Stobbelaar D.J., Groot J.C.J., Rossing W.A.H., 2009. Landscape character assessment using region growing techniques in Geographical Information Systems. Journal of Environmental Management, 90, 161–174.
- Keating B.A., McCown R.L., 2001. Advances in farming systems analysis and intervention. Agricultural Systems, 70, 555–579.
- Kempen M., Elbersen B.S., Staritsky I., Andersen E., Heckelei T., 2011. Spatial allocation of farming systems and farming indicators in Europe. Agriculture, Ecosystems and Environment, 142, 51–62.
- Köbrich C., Rehman T., Khan M., 2003. Typification of farming systems for constructing representative farm models: two illustrations of the application of multi-variate analyses in Chile and Pakistan. Agricultural Systems, 76, 141–157.
- Kostrowicki J., 1977. Agricultural typology concept and method. Agricultural Systems, 2, 33–45.
- Landais E., 1998. Modelling farm diversity: new approaches to typology building in France. Agricultural Systems, 58, 505–527.
- Lesschen J.P., Verburg P.H., Staal S.J., 2005. Statistical methods for analysing the spatial dimension of changes in land use and farming systems. LUCC Report Series No. 7.
- Mądry W., Mena Y., Roszkowska-Mądra B., Gozdowski D., Hryniewski R., Castel J.M., 2013. An overview of farming system typology methodologies and its use in the study of pasture-based farming system: a review. Spanish Journal of Agricultural Research, 11, 316–326.
- Meert H., Van Huylenbroeck G., Vernimmen T., Bourgeois M., van Hecke E., 2005. Farm household survival strategies and diversification on marginal farms. Journal of Rural Studies, 21, 81–97.
- Michielsens C.G., Lorenzen K., Phillips M.J., Gauthier R., 2002. Asian carp farming systems: towards a typology and increased resource use efficiency. Aquaculture Research, 33, 403–413.
- Milan M.J., Bartolome J., Quintanilla R., Garcia-Cachan M.D., Espejo M., Herraiz P.L., Sanchez-Recio J.M., Piedrafita J., 2006. Structural characterisation and typology of beef cattle farms of Spanish wooded rangelands (dehesas). Livestock Science, 99, 197–209.
- Moreau P., Ruiz L., Mabon F., Raimbault T., Durand P., Delaby L., Devienne S., Vertès F., 2012. Reconciling technical, economic and environmental efficiency of farming systems in vulnerable areas. Agriculture, Ecosystems and Environment, 147, 89– 99
- Morris C., Winter M., 1999. Integrated farming systems: the third way for European agriculture? Land Use Policy, 16, 193–205.
- Nemecek T., Dubois D., Huguenin-Elie O., Gaillard G., 2011a. Life cycle assessment of Swiss farming systems: I. Integrated and organic farming. Agricultural Systems, 104, 217–232.
- Nemecek T., Huguenin-Elie O., Dubois D., Gaillard G., Schaller B., Chervet A., 2011b. Life cycle assessment of Swiss farming systems: II. Extensive and intensive production. Agricultural Systems, 104, 233–245.
- Oñate J.J., Atance I., Bardají I., Llusia D., 2007. Modelling the effects of alternative CAP policies for the Spanish high-nature value cereal-steppe farming systems. Agricultural Systems, 94, 247–260.
- Ondersteijn C.J.M., Giesen G.W.J., Huirne R.B.M., 2003. Identification of farmer characteristics and farm strategies explaining changes in environmental management and environmental and economic performance of dairy farms. Agricultural Systems, 78, 31–55.
- Ottaviani D., Ji L., Pastore G., 2003. A multidimensional approach to understanding agro-ecosystems. A case study in Hubei Province, China. Agricultural Systems, 76, 207–225.
- Papadimitriou F., 2002. Modelling indicators and indices of landscape complexity: an approach using G.I.S. Ecological Indicators, 2, 17–25.
- Pardos L., Maza M.T., Fantova E., Sepulveda W., 2008. The diversity of sheep production systems in Aragon (Spain): characterisation and typification of meat sheep farms. Spanish Journal of Agricultural Research, 6, 497–507.
- Persson A.S., Olsson O., Rundlöf M., Smith H.G., 2010. Land use intensity and landscape complexity – Analysis of landscape characteristics in an agricultural region in Southern Sweden. Agriculture, Ecosystems and Environment, 136, 169–176.
- Ridley A.M., 2005. The role of farming systems group approaches in achieving sustainability in Australian agriculture. Australian Journal of Experimental Agriculture, 45, 603–615.
- Righi E., Dogliotti S., Stefanini F.M., Pacini G.C., 2011. Capturing farm diversity at regional level to up-scale farm level impact assessment of sustainable development options. Agriculture, Ecosystems and Environment, 144, 63–74.
- Riveiro J.A., Marey M.F., Marco J.L., Alvarez C.J., 2008. Procedure for the classification and characterization of farms for agricultural production planning: Application in the Northwest of Spain. Computers and Electronics in Agriculture, 61, 169–178.
- Ruiz F.A., Mena Y., Castel J.M., Guinamard C., Bossis N., Caramelle-Holtz E., Contu M., Sitzia M., Fois N., 2009. Dairy goat grazing systems in Mediterranean regions: A comparative analysis in Spain, France and Italy. Small Ruminant Research, 85, 42–49.
- Serrano Martínez E., Giráldez García F. J., Lavín González P., Bernués Jal A., Ruiz Mantecón A., 2004b. The identification of homogeneous groups of cattle farms in the mountains of León, Spain. Spanish Journal of Agricultural Research, 2, 512–523.
- Serrano Martínez E., Lavín González P., Giráldez García F.J., Bernués Jal A., Ruiz Mantecón A., 2004a. Classification variables of cattle farms in the mountains of León, Spain. Spanish Journal of Agricultural Research, 2, 504–511.
- Sulc R.M., Tracy B.F., 2007. Integrated crop-livestock systems in the U.S. Corn Belt. Agronomy Journal, 99, 335–345.
- Thapa G.B., Rasul G., 2005. Patterns and determinants of agricultural systems in the Chittagong Hill Tracts of Bangladesh. Agricultural Systems, 84, 255–277.
- Thornton P.K., Herrero M., 2001. Integrated crop-livestock simulation models for scenario analysis and impact assessment. Agricultural Systems, 70, 581–602.
- Tilman D., Cassman K.G., Matson P.A., Naylor R., Polasky S., 2002. Agricultural sustainability and intensive production practices. Nature, 418, 671–677.
- Tittonell P., Muriuki A., Shepherd K.D., Mugendi D., Kaizzi K.C., Okeyo J., Verchot L., Coe R., Vanlauwe B., 2010. The diversity of rural livelihoods and their influence on soil fertility in agricultural systems of East Africa – A typology of smallholder farms. Agricultural Systems, 103, 83–97.
- Trisorio A., Povellato A., Borlizzi A., 2010. High Nature Value Farming Systems in Italy: a Policy Perspective, OECD Workshop on OECD Agri-environmental Indicators: Lessons Learned and Future Directions, 23–26 March, 2010, Leysin, Switzerland.
- Usai M.G., Casu S., Molle G., Decandia M., Ligios S., Carta A., 2006. Using cluster analysis to characterize the goat farming system in Sardinia. Livestock Science, 104, 63–76.
- van de Steeg J., Verburg P., Baltenweck I., Staal S., 2010. Characterization of the spatial distribution of farming systems in the Kenyan Highlands. Applied Geography, 30, 239–253.
- van der Ploeg J.D., Laurent C., Blondeau F., Bonnafous P., 2009. Farm diversity, classification schemes and multifunctionality. Journal of Environmental Management, 90, 124–131.
- Villano R., Fleming E., Fleming P., 2010. Evidence of farm-level synergies in mixed-farming systems in the Australian Wheat-Sheep Zone. Agricultural Systems, 103, 146–152.
- Zabbini E., Grandi S., Dallari F., 2007. Relative Remote Rural Areas (RRRA) in developed regions: An analysis of the Emilia-Romagna Region to support policy decision making, MPRA Paper, No. 4661, November 2007, Online at: (http://mpra.ub.uni-muenchen.de/4661/)
Accepted for print: 09.03.2016
Department of Experimental Design and Bioinformatics, Warsaw University of Life Sciences (SGGW), Poland
Department of Entrepreneurship, Faculty of Economics and Management, Bia造stok University, Bia造stok, Poland
Department of Experimental Design and Bioinformatics, Warsaw University of Life Sciences (SGGW), Poland
Phone: +48 (22) 59 32 730
Department of Experimental Design and Bioinformatics, Warsaw University of Life Sciences - SGGW, Poland
Responses to this article, comments are invited and should be submitted within three months of the publication of the article. If accepted for publication, they will be published in the chapter headed 'Discussions' and hyperlinked to the article.