DIVERSITY AND TYPOLOGY OF FARMS ACCORDING TO FARMING SYSTEM: A CASE STUDY FOR A DAIRY REGION OF PODLASIE PROVINCE, POLAND

Wiesław Mądry¹, Dariusz Gozdowski¹, Barbara Roszkowska-Mądra², Mariusz Dąbrowski³, Wioletta Lupa^3
1 Department of Experimental Design and Bioinformatics, Warsaw University of Life Sciences (SGGW), Poland
² Department of Entrepreneurship, Faculty of Economics and Management, Białystok University, Białystok, Poland
³ Department of Experimental Design and Bioinformatics, Warsaw University of Life Sciences - SGGW, Poland

ABSTRACT

The survey was based on interviews with owners of 123 family farms was carried out in two communes: Klukowo and Kulesze Koœcielne located in a western part of Podlasie province, Poland. The 32 variables which characterize socio-economic and agricultural condition of the farms were obtained from the questionnaires. The main aims of the survey were characterization of diversity and typology according farming system. The used statistical methods were principal component analysis (PCA) and cluster analysis. On the base of the results 6 different types of farms were distinguished and characterized.

Key words: farm diversity, multivariate methods, diary production.

INTRODUCTION

Implementation of development agricultural policy, public intervention, expert knowledge, extension, decision support systems in regional or local planning, recommendations and support of non-governmental organizations, prototyping approaches, and other external activities do not usually take into account sufficiently the regional level diversity of the farming systems at the farm level to which these actions may be applied. These approaches are often traditionally directed to the 'average farmer' or to a few farming systems, which may be both far from reality [3,7,16,33,37]. Several authors believe also this to be one of the main reasons for the lack of adoption of certain technologies at the farm level. Some political and innovative decisions or extension might be very efficient in some farming systems and completely inadequate in others [3,7,16,28,39], mostly due to specific environmental, economic and technical constraints, which vary widely among farmers [2,16] and which can influence the economic and biophysical performance of innovations. Then, characterizing spatial variability of farming systems within a target region and their typology is a key step in the implementation of flexible (demand-based) external support (public management, support policy, impact professional organizations etc.) and internal activities (farmer's and local authorities' decisions, extension service, integration of all rural actors' enterprises etc.) to farmers which could mobilize agro-ecological and social-economic local knowledge and entrepreneurial human capital.

Farming system at the farm level is a logical consequence of the systems and holistic approaches to natural, agricultural and economic research, as well as extension services and other support activities [1,4,6,7,8,15]. From this perspective farming system refers to complementary interactions between agricultural land use system corresponding to both agricultural production system and non-agricultural (alternative) business within a farm household, and a set of the economic, biophysical, social and technical factors (conditions) that influence the whole economic activity of the farm household [3,5,16,21,22,29,30,41,42]. All aspects of a farming system are described by a set of variables. Idea of farming system is an integrated (multi-attribute) description of very complex agronomic, ecological and social-economic reality in both farm and farm household. Therefore, it is very powerful to implement all public and private actions in order to promote and stimulate sustainable rural and agriculture development as based mainly on the concepts of multifunctional agriculture, farm diversification, endogenous development and rural revitalization [38,44]. Depending on goals of farming systems' typology and reality in a studied rural area, various categories of farming systems can be established [3,7,9,14,21,23,24,25,26,28,30,32,35,40,41,42,43].

Usually, in literature a term 'farming system diversity (variability) and typology at the farm level' is equivalent to a term 'farm diversity and typology according to farming system'. The both have been exchangeable often orientations of many studies in agronomy, ecology and agricultural economics [3,5,9,10,13,14,16,23,24,25,26,29,30,32,42]. Effective assessment and typology of farming systems can be done by a statistical analysis of a set of variables, quantitative (most often) or both quantitative and qualitative (categorical) ones collected for a sample of farms from its population within a region to be studied [3,10,13,14,15,16,19,29,43]. Recorded variables should be chosen so they characterize farms and farm households regarding all mentioned aspects of farming system.

The farm's typology is most often built using Ward's cluster analysis method to identify groups of farms with similar variables that were collected through a survey of a sample of farms. Each such a homogenous farm group shows a given type of farming system. Characterization and synthetic description of farm variability using a set of mostly quantitative variables and distinguished types of farming systems are usually conducted by principal component analysis (PCA) used on this set of variables [3,5,9,13,14,16,23,24,29]. When there are more qualitative variables than quantitative ones, a multiple correspondence analysis (MCA), which is the equivalent for qualitative data of PCA, must be performed instead [3].

These studies are focused on evaluation of variability and typology of farms according to farming systems in a rural area of western part of Podlasie province (Fig. 1) where farming with predominant agricultural function of food production is oriented mainly on conventional (high-input) or integrated agricultural system including mostly dairy production system. In spite of intensive agricultural system used in the assessed area it has kept typical rural landscape including traditional architecture and biodiversity, which had not been changed substantially by multi-year processes of modernization in the agricultural sector. Because of such circumstances western part of Podlasie province is a good example of beauty rurality in Poland. In addition, this region demonstrates required environmental, cultural, economic, social and human capital to progress in the future. It constitutes the most representative intensive dairy farming in family farms around Poland.

Although farms in the studied area are oriented generally to agricultural activity, mainly within dairy farming, their farming systems are substantially differentiated. Unfortunately, up to now no formal studies of farming systems' variability and their typology at the farm level within family dairy farms in Podlasie province and around Poland have been conducted. Then, we would like to undertake this task in the paper assuming it as a case study in Podlasie province, which is also more representative to other similar rural areas in the country. The aim of the paper is 1) to establish a farm's typology according to farming system in the region under consideration on the basis of a survey including interviews with owners of 123 farms and 2) using this farm typology as a tool by support decision makers, experts, local planners, farmer and rural organization and farmers for identifying opportunities and constraints to improve each farming system type determining efficient technical and economic pathways, strategies and ways of these improvements in both agricultural production systems and non-agricultural on-farm and off-farm activities accordingly to present challenges of Common Agricultural Policy based on paradigm of sustainable and multifunctional rural areas and agriculture.

MATERIAL AND METHODS

STUDY SAMPLE

A survey based on interviews with owners of 123 family farms was carried out in two communes: Klukowo and Kulesze Kościelne located in a western part of Podlasie province, Poland including two municipalities Wysokie Mazowieckie and Zambrów (Fig. 1). The studied communes are assumed to be representative to the considered rather homogenous western part of the province belonging to an area having relatively most advanced and intensive agriculture [34]. In Klukowo the share of utilized agricultural areas (UAA) and forests approached 85% and 9% of total area, respectively; while share of arable areas (AA) and permanent grasslands corresponded to 81% and 18% of UAA, respectively. In Kulesze Kościelne the share of UAA and forests were equal to 67% and 27% of total area, respectively while the share of AA and permanent grasslands corresponded to 70% and 29% of UAA, respectively. Soils in Klukowo are more fertile than in Kulesze Kościelne but general farming systems were quite similar. In each gmina a representative sample of 62 farms was drawn using stratified sampling based on randomly chosen villages as strata [36,41,42]. From each village two farms were chosen to be representative to all farms in t hat village. After checking interviews one farm was rejected from the studies because some answers were missing. Then, the sample of 123 family farms was recognized to be representative to both all farms in the two administrative districts and to the rural area identified by the green color in the Fig. 1.

Fig. 1. Location of the study area within Poland and Podlasie province: assessed gminas marked by red color are representative to a western part of the province which is marked by green

Source: the Authors' research.

Interviews carried out between July and November 2008. The survey questionnaire included questions regarding many current characteristics of farming system describing facts and farmer's scores of reality in the year 2008 or within last 5 years. The 32 variables obtained from the questionnaires included 25 quantitative (mainly continuous) and 7 categorical   qualitative (2 binary and 5 score-order) variables. These variables related to: (1) natural conditions, (2) social–economic conditions, (3) infrastructure, (4) structure of agricultural production, (5) inputs on agricultural production and (6) profitability of agricultural production and farm diversity (Table 1).

DATA ANALYSIS

In this research there are substantially more quantitative variables than categorical ones (they are mostly transformed to quantitative discrete variables) and, therefore, we used classical multivariate methods suitable for quantitative variables [3,10]. The used methods are principal component analysis (PCA) and cluster analysis.

Principal component analysis is a form of factor analysis which first looks for a linear combination of variables that extracts maximum variance from variables and, therefore, identifies a second linear combination to explain the remaining variance, leading to orthogonal, or uncorrelated, usually called factors. The purpose of principal component analysis (PCA) is to reduce the number of variables and thus the 'dimensionality' of the problem. Each principal component in PCA is such a dimension, called a factor, interpreted in category of a subset of original variables which are mostly correlated with the principal component [25]. A few first principal components account for majority of variability within units (here farms) as measured by Euclidean distance, thus they are the most important factors separating mostly the units. Variables mostly correlated with these first PCs contribute most to the farm variation for the complex of variables examining farming systems in these farms and, therefore, they are most important in discriminating the farms. PCA was used for the set of all variables after they were standardized by extracting means from a value of some variable for a given farm and dividing such a result by standard deviation [17,19,20].

Cluster analysis is used to make 'best' typology of farms classifying them into groups (types) showing a maximum amount of variability between the groups and obtaining maximum homogeneity within particular groups [17,20]. It was carried out by Ward's method taking Euclidean distance for all standardized variables as a dissimilarity measure. This method of clustering has been most often used in farm typology and also in other taxonomical studies within biology, agronomy, economics, geography and others. The number of groups to build in this paper was determined by looking for an obvious break in the level of dissimilarity in the hierarchical clustering [31]. This cluster analysis was completed with a principal component analysis to get graphical visualization of multivariate separating in an ordination space and characterizing the farm groups (clusters), which are useful for description of their similarity (relationships, distances).

Table 1. Definition of the observed variables used to develop a typology of 123 farms according to farming systems in Podlasie province, Poland

Category	Code	Variables	Units
Natural conditions	X1	Percentage of the best quality soils in UAA	%
	X2	Percentage of moderate quality soils in UAA	%
	X3	Percentage of poor quality soils in UAA	%
Social–economic conditions	X4	Age of farmer	Year
	X5	Number of persons in the farm household working in farm-agriculture	Units
	X6	Number of persons in the farm household working off-agriculture both on- and off-farm	Units
	X7	Number of persons living in the farm household	Units
	X8	Number of professional advices and courses performed by the farmer in year 2008	-
	X9	Education of the farmer [order scale: from 1 (primary) to 4 (higher)]	-
	X10	Future of the farm within next 5 years [order scale: 1 (land rent), 2 (stabilization), 3 (succession), 4 (establishment)]	-
Infrastructure	X11	Number of innovation invests in the farm over last 5 years	–
	X12	Manure pad in the farm [binary scale: 1 (yes), 0 (no)]	–
	X13	Sludge storage tank [binary scale: 1 (yes), 0 (no)]	–
Structure of agri-cultural production	X14	Farm area	ha
	X15	Share of cereals in AA	%
	X16	Share of root crops in AA	%
	X17	Share of fodder crops AA	%
	X18	Dairy cattle density	LSU ha-1 UAA
	X19	Pig density	LSU ha-1 UAA
	X20	Change in livestock density within 5 last years	%
Inputs	X21	Rate of organic fertilizers	ton ha-1 year-1
	X22	Rate of NPK fertilizers	kg ha-1 year-1
	X23	Contribution of commercial feeds	%
Profitability of agricultural production and farm diversification	X24	Average yield of cereals	ton ha-1
	X25	Contribution of farm agricultural production incomes to these from all farm household activities	%
	X26	Contribution of farm non-agricultural activities to these from all farm household activities	%
	X27	Contribution of crop production incomes to these from total agricultural production on a farm	%
	X28	Contribution of livestock production incomes to these from total agricultural production on a farm	%
	X29	Farmer's score of crop production profitability in 2008 [order scale: from 1 (very small) to 4 (very large)]	–
	X30	Farmer's score of livestock production profitability in 2008 [order scale: from 1 (very small) to 4 (very large)]	–
	X31	Farmer's score of agricultural production profitability trend over last 5 years [order scale: 1 (decreasing), 2 (fluctuating), 3 (stable), 4 (increasing)]	–
	X32	Index of agricultural production intensity#	–

# index of agricultural production intensity of a farm, I, was calculated by normalizing the four observed variables, e.g. share of fodder crops in total arable area (X17), cattle density (X18), pig density (X19) and rate of NPK fertilizers (X22) according to the following formula [11,18]:

where Xi is the observed value of the i-th variable in the farm, Xi min is the minimum observed value of the i-th variable within the studied farms, Xi max is the maximum observed value of the i-th variable within the studied farms, n is the number of the considered variables in the formula. Variables attributed to index I should be defined in such way t hat their increasing values exhibit increasing value of the index and, therefore, improvement of agricultural production intensity. Index I shows larger value agricultural production of a farm is more intensive. Source: the Authors' research.

RESULTS AND DISCUSSION

UNIVARIATE EVALUATION OF THE FARMS

Measures of central tendency and variability for all variables are presented in Table 2. Soils of the studied area are mostly moderate quality (their mean share corresponds to 65% of UAA showing also small variability among farms), while mean shares of best and poor quality soils are similar, approaching about 16-18% of UAA and showing large range of variability. Social-economic conditions of the farms demonstrated some favorable characteristics and trends for farming with agricultural production to be predominating function.

The mean age of the heads of farm households approached 40 years and their basic education and training in farming demonstrate accumulation of entrepreneurial human capital in this area. Farm households were generally based on strong families consisting on the avearge of close to five persons of which 2 persons worked on farm in agriculture and 0.4 person worked in non-agricultural business (including both on- and off-farm activities), demonstrating substantial variation (Range 0 to 2). This demonstrates that farm diversity in non-agricultural (non-conventional) business was generally rather weakly developed although this farm characteristic was substantially variable in the area. The mean number of people not employed in the farm household (children or retired people) corresponded to about 2. Perspective of the nearest future for the farms is generally promising; most farms will survive in the present forms or have a chance to be progressed within next 5 years. A minority of farms may make a decision involving abandonment of the conventional agriculture as a major activity (including even renting their land or all farm) escaping in strong farm diversity like part-time farming, hobby farming, retired farming, renting land, buildings or all farm as well as off-farm employment and business.

Table 2. Descriptive statistics for the observed variables used to develop a typology of the 123 studied farms

Variable	Mean	Min	Max	SD	CV %
Natural conditions
X1	18.15	0.00	80.00	20.33	112.0
X2	65.76	10.00	100.00	18.60	28.3
X3	16.18	0.00	90.00	16.49	102.0
Social-economic conditions
X4	40.05	19.00	63.00	10.53	26.3
X5	2.11	1.00	5.00	0.91	42.9
X6	0.39	0.00	2.00	0.62	159.5
X7	4.93	2.00	10.00	1.62	32.8
X8	1.91	0.00	10.00	1.88	98.3
X9	2.39	1.00	4.00	0.86	36.2
X10	2.81	0.00	4.00	1.12	39.8
Infrastructure
X11	1.84	0.00	10.00	1.97	107.4
X12	0.73	0.00	1.00	0.44	60.8
X13	0.73	0.00	1.00	0.44	60.8
Structure of agri-cultural production
X14	22.26	5.00	90.00	14.28	64.2
X15	62.02	10.00	100.00	21.97	35.4
X16	5.00	0.00	70.00	9.51	190.2
X17	32.46	0.00	90.00	23.33	71.9
X18	1.42	0.00	4.50	0.83	58.6
X19	0.12	0.00	3.00	0.35	304.7
X20	-29.59	-200.0	100.00	60.50
Inputs
X21	22.55	0.00	60.00	12.35	54.8
X22	228.82	10.00	600.00	99.92	43.7
X23	17.32	0.00	100.00	19.11	110.4
Profitability of agricultural production and farm diversification
X24	4.03	2.00	6.50	0.67	16.7
X25	86.61	20.00	100.00	23.26	26.9
X26	13.80	0.00	80.00	23.46	170.1
X27	23.20	0.00	100.00	29.97	129.2
X28	76.80	0.00	100.00	29.97	39.0
X29	2.50	1.00	4.00	0.73	29.1
X30	2.68	1.00	4.00	0.85	31.8
X31	2.17	1.00	4.00	1.11	51.0
X32	0.26	0.05	0.57	0.12	45.5

Source: the Authors' research.

Infrastructure of the farms was mostly suitable with respect to agri-environmental CAP requirements. About 75% of farms have a manure pad or sludge storage tank. Mean number of innovation invests in the farm made over last 5 years approached about 1.84, ranging from 0 to 10, belonging to most unstable variables among studied ones.

Among variables describing the structure of agricultural production, the share of root crops of the total arable area (Range 0 – 70%) and pig density (Range 0.1 – 3 LSU ha^-1 UAA) were extremely variable. The average farm size in the sample was 22 ha, while the respective means for Podlasie province and Poland as a whole are currently only about 12 ha and 10 ha, respectively. Crop diversity of the farms was smaller compared to province and country. This conclusion resulted from mean share of cereals, root (only potato) and fodder (only silage maize) crops in total arable area corresponding to about 62%, 5% and 32%, respectively while respective means in the country were about 75%, 5% and 6% (all fodder crops), respectively. In the studied region only three crop groups were grown (cereals, potato and silage maize), while crop diversity in other parts of the country is currently much richer. The farms have been characterized by relatively very large dairy cattle density, its mean corresponded to 1.42 LSU ha^-1 UAA while the respective mean for the country is about only 0.4 LSU ha^-1 UAA and 0.6 LSU ha^-1 UAA for Podlasie province. Mean pig density was relatively very small (0.12 LSU ha^-1 UAA) but showing extraordinary variability among farmers, while the respective mean for the country is about 1 and for Podlasie province, 0.6. Livestock density within 5 last years decreased 30% to average but it was changing very differently among the farmers, from decreasing about 200% to increasing about 100%.

Generally, inputs on agricultural production were large confirming and reflecting high-input agricultural system existing on this area. Mean rate of organic and NPK fertilizers were about 22 t ha^-1 and 228 kg ha^-1, respectively, their variation was rather moderate. A high degree of variation was observed in contribution of commercial feed, which showed maximum and minimum values of 0 and 100%, and their mean corresponded to only 17%.

Characteristics of outcomes and profitability of agricultural production and farm diversity on the studied rural area confirmed and complemented empirical evidence of existing predominant production function in agriculture with frequently occurred high-input cropping (stable average yield of 4 t ha^-1) and especially livestock system, mainly dairy cattle. This conclusion is drawn on the basis of found substantial predominance of contribution of farm household incomes from agricultural activities which corresponded about 87% as measured by mean and showed rather small coefficient of variability. By contrast, the contribution to farm household income from non-agricultural activities was rather small (average of 23%) and very variable. It reflects a rather weakly developed non-agricultural activity and not undertaking it by members of most studied farm households in the area. Generally, high-input agricultural production system of assessed farms was characterized by predominance of contribution of farm incomes from livestock production (mean of 77%) compared to respective incomes from row crop production (mean of 23%). Farmer's opinion of agricultural production profitability in 2008 was moderate to average being also moderately ranged in the set of farms.

Table 3. Correlation coefficients between the first three principal components and the observed variables used to develop a typology of the 123 studied farms

Principal components (PCs) Variables	PC1 (28.9%)#	PC2 (7.9%)	PC3 (7.3%)
X1	0.20	-0.37	0.26
X2	-0.25	0.06	0.01
X3	0.02	0.37	-0.34
X4	-0.25	0.05	0.39
X5	0.59	0.06	0.36
X6	-0.42	0.66	0.08
X7	0.49	0.24	0.33
X8	0.34	0.39	-0.11
X9	0.35	0.01	-0.46
X10	0.66	0.14	-0.25
X11	0.66	0.19	-0.13
X12	0.71	0.15	-0.14
X13	0.79	0.11	-0.03
X14	0.54	0.12	0.03
X15	-0.73	0.10	-0.15
X16	-0.30	0.38	0.04
X17	0.77	-0.18	0.15
X18	0.80	0.04	-0.01
X19	-0.21	0.32	0.12
X20	0.47	0.43	0.27
X21	0.67	-0.16	0.22
X22	0.41	-0.07	0.17
X23	0.52	0.03	-0.19
X24	0.49	-0.05	0.00
X25	0.55	-0.56	-0.08
X26	-0.55	0.59	0.10
X27	-0.66	-0.37	-0.27
X28	0.66	0.37	0.27
X29	0.14	0.03	-0.68
X30	0.48	0.13	-0.54
X31	0.31	0.18	-0.52
X32	0.86	-0.09	0.14

# the percentage of total variation attributable to each principle component Source: the Authors' research.

PRINCIPAL COMPONENT ANALYSIS

Results of principal component analysis for normalized data are reported in Table 3. Taking the information in this table, the first principal component (PC1) was substantially correlated positively mostly with two variables in category of social-economic conditions, i.e. number of persons in the farm household working in farm-agriculture (X₅) and future of the farm within next 5 years (X₁₀), three variables in category of infrastructure, i.e. number of innovation invests in the farm over last 5 years (X₁₁), existing manure pad in the farm (X₁₂) and sludge storage tank (X₁₃), three variables in category of structure of agricultural production, i.e. farm area (X₁₄), share of fodder crops in total arable area (X₁₇) and dairy cattle density (X₁₈), two variables in category of inputs, i.e. rate of organic fertilizers (X₂₁) and contribution of commercial feeds (X₂₃) as well as with four variables in category of profitability of agricultural production and farm diversity, i.e. contribution of farm household incomes from agricultural production (X₂₅), contribution of farm incomes from livestock production (X₂₈) and index of agricultural production intensity (X₃₂). PC1 was also substantially correlated negatively mostly with share of cereals in total arable area (X₁₅), contribution of farm household incomes from non-agricultural activities (X₂₆) and contribution of farm incomes from crop production (X₂₇). Then, PC1 identified observed variables, which were correlated mutually either positively and negatively. It provides the first as the main factor accounting for about 30% of the total variance among farms. This factor accumulates simultaneous information on expression of sixteen variables mutually correlated with PC1. Additionally, these correlated variables belong to those important components of farming systems and, therefore, contributed significantly (mostly) to the total variation observed in a set of the farms. They are relatively (among studied variables) most important in discriminating farms and, therefore, they need to be taken into consideration when distinguishing farms in other similar studies on spatial and temporal variability of farming systems in this area. PC1 (the first factor) described by interrelated variables could be called "social backgrounds, structure, importance and intensity of agricultural production". It should be underlined that soil quality was not incorporated in the first factor from PCA. It exhibits that soil conditions in the area have not been related to characteristics of agricultural production systems being the most important component of farming systems.

The PC2 was related positively mostly to number of persons in the farm household working off-agriculture both on- and off-farm (X₆) and contribution of farm household incomes from non-agricultural activities (X₂₆) but negatively to contribution of farm household incomes from agricultural production (X₂₅). Then, PC2 detected the second factor called "non-agricultural activity of farm household".

The PC3 is correlated substantially and negatively with farmer's score of crop and livestock production profitability (X₂₉ and X₃₀) as well as with farmer's score of agricultural production profitability trend over last 5 years (X₃₁). Being consisted mainly of these three positively interrelated variables, the third principal component has represented the third factor called "farmer's score of agricultural production profitability". One should underline this factor have not attributed substantially to other variables; the only variable stronger (although weakly) correlated with PC3 is education of the farmer (X₉). This indicates that own farmer's score of agricultural production profitability in the studied area was associated slightly with farmer's education and less have resulted from structure, intensity of agricultural production and non-agricultural activity of farm household and also its economic conditions. The obtained results have exhibited that feeling real farmer's economic situation in the studied area is to large extent subjective.

CLUSTER ANALYSIS, DESCRIPTION OF FARMING SYSTEMS TYPES AND THEIR DISSIMILARITIES

The assessed farms were classified into six clusters (groups), each of them using different farming systems to maintain economic viability. These groups were compared by ANOVA in order to confirm significant differences among them for each studied variable (Table 4). Differences between groups of farms proved to be significant for means of all variables excluding only percentage of poor quality soils in UAA (X₃) and age of farmer (X₄).Those two variables were found by PCA to have no relative substantial discriminating power (Table 3).

The characteristics of the six farms' groups, i.e. types of distinguished farming systems are the following, as is based on results shown in Table 4. In the description of the farming systems types focus was made mainly on those variables having relatively large discrimination power.

Group 1 (type 1 of farming system characterized by specialized very intensive dairy production and traced farm diversification)
Farms (n = 25, 20.3%) had the largest in their area and best quality soils of UAA including large share of best quality soils and small share of poor quality soils in UAA. Heads of these farms were young, well educated and experienced farmers (mean age of 37 years). The farm families were very strong in their human capital (about 6 persons in average living in farm household), half of these working on farm in agriculture (0.9 AWU/10haUAA to average) and the others not employed, very few persons working off-agriculture. Crop diversity was very limited; in crop area structure only two kinds of crops predominated, being fitted to very intensive dairy production, including cereals (39% of AA) and silage maize (58% of AA) as sources of own-farm feeds, while contribution of commercial feeds approached to 30% in average. Dairy cattle density was the largest among all farm groups attributing 2.2 LSU ha^-1 UAA in average and it was very stable across last five years; the farms did produced almost no pigs. Technologies used in farm feeds production and conserving were very modern attributed mainly to hay, silage maize and grain cereal mix. In crop production very large rates of organic (30 ton ha^-1 year^-1 in average) and mineral NPK fertilizers (300 kg ha^-1 year^-1 in average) were used. Because of the used small diversified, intensive crop and livestock production system, index of agricultural production intensity was the largest among studied farms. Contribution of incomes from agricultural production in these farm households was almost 100% in average and originated predominantly from dairy production, diversifying farm business activities were only marginal. Farms in this group are exclusively monofunctional from the point of view their farm business activities. Farmer's score of agricultural production profitability was relatively optimistic but not best among studied farms. Demographic structure, farmer's professional knowledge, good farm succession perspective and attitude to innovations in the farms as well as good farm infrastructure for dairy production are a sufficient source of forecasting future stabilization and sustainable development through adoption of innovative technological and organization solutions including also European Union measures for the farms using this farming system.

Group 2 (type 2 of farming system characterized by semi-intensive dairy production and moderately substantial farm diversification)
Farms (21, 17,1%) having about 95% of moderate and poor quality soils focused also on dairy production system. Mean farm area was about 27 ha with number of persons working on farm in agriculture approaching to 1.95 (0.7 AWU/10haUAA) and 0.43 persons working off-agriculture. Crop diversity was also limited, including cereals (67% of AA) and silage maize (29 of AA) as sources of own-farm feeds for dairy cattle which density was 1.7 LSU ha^-1 UAA in average (it was slightly decreasing across last five years) and a little pigs produced on own needs. Organic and mineral fertilizers used were adequate to semi-intensive agricultural production system. Contribution of incomes from agricultural production in these farm households was almost 90% in average and originated predominantly from dairy production (90%), thus, importance of non-agricultural farm business activities was visible. Farmer's professional knowledge, good farm succession perspective and attitude to innovations in the farms, good farm infrastructure for dairy production, stable dairy production across last five years, very optimistic (highest degree among studied farms) viewing small diversified agricultural production to be profitability as well as moderately substantial diversifying farm business activities are strong arguments to predict their future stabilization, innovative sustainable development and well being.

Table 4. The farm numbers the identified clusters quantifying types of farming systems and the their means and standard deviations for the assessed variables

Cluster	1		2		3		4		5		6		F-emp	p-value
Farm numbers (%)	25 (20.3%)		21(17.1%)		36(29.3%)		13(10.6%)		18(14.6%)		10(8.1%)
Parameters	Mean	SD	Mean	SD	Mean	SD	Mean	SD	Mean	SD	Mean	SD
X1	24.80	16.17	5.71	7.95	26.44	24.51	16.92	19.21	13.33	20.29	8.00	15.31	4.61**	0.001
X2	59.72	19.43	73.81	12.64	60.14	20.27	69.23	14.70	66.11	15.39	79.00	20.11	3.37**	0.007
X3	15.08	19.45	20.48	13.12	13.97	18.12	13.85	10.64	20.56	16.62	13.00	14.94	0.81 NS	0.545
X4	37.08	10.07	39.29	8.91	40.14	10.94	41.08	12.24	43.67	10.35	40.90	11.59	0.88 NS	0.498
X5	3.08	1.04	1.95	0.50	2.08	0.55	2.08	0.95	1.67	0.69	1.00	0.00	15.45**	0.000
X6	0.12	0.33	0.43	0.60	0.08	0.28	0.15	0.38	1.39	0.50	0.60	0.70	24.89**	0.000
X7	5.72	1.14	5.29	1.27	5.08	1.30	4.69	2.56	4.50	1.54	2.70	0.67	6.95**	0.000
X8	2.52	1.81	3.76	2.57	1.31	1.01	1.15	1.68	1.33	1.03	0.70	1.06	9.32**	0.000
X9	2.76	0.83	2.57	0.93	2.36	0.76	2.23	0.73	1.89	0.90	2.30	0.95	2.58*	0.030
X10	3.48	0.87	3.57	0.75	2.94	0.86	2.38	1.19	1.78	0.94	1.50	0.53	16.32**	0.000
X11	3.76	2.22	3.05	1.86	1.36	1.13	0.62	1.04	0.61	1.04	0.00	0.00	18.81**	0.000
X12	0.92	0.28	1.00	0.00	0.94	0.23	0.23	0.44	0.50	0.51	0.00	0.00	30.22**	0.000
X13	1.00	0.00	0.95	0.22	0.97	0.17	0.23	0.44	0.39	0.50	0.00	0.00	42.99**	0.000
X14	34.30	20.89	27.13	9.38	19.24	8.05	21.77	12.80	12.61	6.59	10.79	5.14	9.99**	0.000
X15	38.60	17.23	67.14	8.74	55.39	16.73	75.77	20.19	73.33	13.50	95.50	8.32	27.50**	0.000
X16	1.24	4.39	4.29	5.76	3.31	7.53	6.92	8.30	13.33	16.63	4.50	8.32	4.46**	0.001
X17	57.96	20.69	28.57	11.85	39.78	15.85	17.31	17.39	15.94	15.29	0.00	0.00	29.31**	0.000
X18	2.21	0.92	1.74	0.48	1.59	0.43	0.73	0.51	0.87	0.30	0.10	0.32	30.22**	0.000
X19	0.00	0.00	0.02	0.09	0.05	0.10	0.53	0.85	0.28	0.39	0.00	0.00	7.07**	0.000
X20	-0.04	47.40	-20.95	51.64	-28.19	42.75	-40.00	48.30	-11.89	49.31	-145.00	68.52	13.54**	0.000
X21	31.28	11.39	21.52	7.47	28.39	10.77	16.23	9.23	16.50	4.09	1.00	2.11	21.80**	0.000
X22	317.60	119.45	230.71	57.15	221.53	88.72	154.62	42.55	190.28	97.69	195.00	68.35	7.94**	0.000
X23	31.60	20.80	18.81	10.60	20.42	21.72	6.15	10.44	7.22	11.27	0.00	0.00	8.57**	0.000
X24	4.62	0.73	4.06	0.44	4.01	0.69	3.82	0.54	3.61	0.41	3.60	0.41	8.32**	0.000
X25	96.80	7.34	89.05	17.00	97.44	7.31	96.92	8.55	49.17	22.90	71.00	35.42	28.06**	0.000
X26	3.20	7.34	10.95	17.00	2.56	7.31	3.08	8.55	53.61	19.08	29.00	35.42	34.16**	0.000
X27	7.48	13.59	8.81	13.22	17.95	23.92	27.69	19.32	26.39	19.08	100.00	0.00	42.98**	0.000
X28	92.52	13.59	91.19	13.22	82.06	23.92	72.31	19.32	73.61	19.08	0.00	0.00	42.97**	0.000
X29	2.48	0.71	2.81	0.51	2.56	0.84	2.23	0.44	2.17	0.62	2.70	0.95	2.36*	0.053
X30	3.00	0.71	3.33	0.80	2.72	0.85	2.15	0.55	2.17	0.51	2.00	0.82	8.96**	0.000
X31	2.36	1.11	3.33	0.91	1.94	1.01	1.62	0.87	1.67	0.77	1.70	0.82	9.12**	0.000
X32	0.42	0.08	0.27	0.05	0.29	0.07	0.15	0.07	0.16	0.08	0.09	0.03	52.65**	0.000
PC1	3.53	0.90	1.23	1.04	0.92	0.92	-2.19	1.38	-3.20	1.49	-6.12	0.75	170.83**	0.000
PC2	-0.20	0.81	1.11	1.09	-0.81	0.84	-0.63	1.52	1.97	1.53	-1.65	1.66	22.77**	0.000
PC3	0.39	1.44	-1.24	0.95	0.19	1.71	0.54	0.94	0.96	0.98	-1.51	0.94	9.17**	0.000

Source: the Authors' research.

Group 3 (type 3 of farming system characterized by semi-intensive dairy production, producing silage maize to other farmers and traced farm diversification)
This group represents the largest number of the farms (36, 29.3%) having about 74% of moderate and poor quality soils and using semi-intensive dairy production system and excluding broader non-agricultural farm business activities. The farms were the smallest among the first three groups using intensive or semi-intensive dairy system, attributing about 19 ha, with number of persons working on farm in agriculture approaching to 2.08 (1.1 AWU/10haUAA) and only 0.08 persons working off-agriculture. Crop diversity was also limited, including cereals (55% of AA) and silage maize (40% of AA) as sources of own-farm feeds for dairy cattle and for sale to other farmers using more intensive dairy production. Share of silage maize in AA being the largest among studied farms because of good quality soils and moderate dairy cattle density (mean is approached to 1.6 LSU ha^-1 UAA and decreasing substantially -28% – across last five years) denote that silage maize was the major feed for dairy cattle and surplus was sold to other farmers. Pigs density was very small being produced only on own needs. Organic and mineral fertilizers used were adequate to semi-intensive agricultural production system. Contribution of incomes from agricultural production in these farm households was 97% in average and originated predominantly from dairy production (82%). Contribution of incomes from crop production in these farm households was as 18% averaged and was the largest among the comparable first three farms groups, additionally illustrating a new phenomena involving producing feeds to other farmers. Importance of non-agricultural business activities was small. Worse t han in the first two groups farmer’s professional knowledge, worse farm succession perspective and attitude to innovations in the farms, decreasing livestock production (intensity of production), less optimistic viewing agricultural production profitability as well as small attitude to farm diversification are symptoms to predict their sensitive and threatened well being and innovative development in the nearest future. Farms using this type of farming system involving large productivity potential due to relatively quality soils and intensive dairy production in the past but using now semi-intensive dairy production and not diversified farm business activities should be specially supported by public intervention like expert knowledge, extension, decision support systems, prototyping approaches, adoption of EU and local measures, financial services etc. It is a large chance for farms using this farming system in the area and having potential and tradition of agricultural production to become more stable productive, sustainable, efficient and profitable using either present less intensive agricultural production (less dairy cattle density and selling surplus of crop productions not used in own farms as feeds – in such a agricultural production effective cooperation between farmers using intensive and semi-intensive agricultural system in the area is very important and should be still improved) or increase of agricultural production intensity (shifting more in direction of dairy production). Additionally, improvement of this farming system could be made also through developing non-agricultural farm business activities, i.e. increase of farm diversification and number of adopters of alternative farming activities.

The first three farms groups using types 1, 2 and 3 of farming systems are relatively compact (small dispersive intra-groups) as it is showed on the two-dimensional principal component plot (Fig. 2). Also, these groups are relatively more similar one to the other as compared to the other three groups. Then, this PC plot illustrates clearly similarity of the first three farming systems which involve intensive or semi-intensive agricultural production specialized in dairy production as the predominant farm business activity (showed on PC1 axes) and associating it small and very small diversifying farm business activities (showed on PC2 axes).

Group 4 (type 4 of farming system characterized by semi-extensive mixed crop-dairy-pigs production and traced farm diversification)
This rather small group of farms (13, 10.6%) having soils of comparable quality to the intensive and semi-intensive agricultural production farms but the dairy cattle density and index of their agricultural production intensity were twice or three times less t han in the first three farming systems. Livestock production included both dairy cattle and pigs, their density was similar (0.73 and 0.53 LSU ha^-1 UAA, respectively). Livestock density decreased substantially -40% – across last five years. It means t hat farmers using this farming system have recently abandoned substantially from livestock production. The farm area was relatively large attributing about 21 ha as averaged, with number of persons working on farm in agriculture approaching to 2.08 (1.1 AWU/10haUAA) and only 0.15 persons working off-agriculture, similarly as in two semi-intensive farming systems. Crop diversity was larger t han in the first three farming systems, including predominantly cereals (76% of AA), silage maize (17% of AA) and root crops – potatoes (7% of AA). Organic fertilizer used was related to the livestock density and mineral fertilizer was the smallest among assessed farms; contribution of commercial feeds used was only 6% in averaged. Contribution of incomes from agricultural production in these farm households was 97% in average and originated predominantly from livestock production (72%), and contribution of incomes from crop production was 28%. Importance of non-agricultural business activities was small delivering only 3% of farm household incomes.

Fig. 2. Configuration of the farms according to the scores obtained for PC1 (social backgrounds, structure, intensity and importance of agricultural production) and PC2 (non-agricultural activity of farm household)*

* the colours of points denote farms assigned to particular groups shown in table 4; right or left direction of arrows along PC1 and PC2 axis concerning some variables denote direction of positive or negative correlations, respectively between these variables and PCs

Disadvantages of farm infrastructure and social conditions of these farms underline also facts t hat only about 23% of them had manure pad and sludge storage tank as well as seldom adoption of professional advices and innovation invests in the farm. Low agricultural specialization of these farms and low incomes from non-agricultural business affected negatively their economic status, although farmer's score of agriculture profitability was relatively well. Additionally, farmers attitude to agricultural adjustment their farm households to the new agricultural and rural challenges, however the farmers want to continue farming themselves or to pass them by succession. Worse than in the intensive or semi-intensive farming systems farmer's professional knowledge, attitude to innovations in the farms, decreasing livestock production as well as small attitude to farm diversification show their threaten of innovative development in the nearest future. To survive this kind of farms and adopt adjustment strategies in agricultural sustainable production and, especially, in farm diversification and in sustaining entrepreneurship, they should be specially supported by a number of public intervention forms both financial and non-financial ones.

Group 5 (type 5 of farming system characterized by semi-extensive mixed crop-dairy-pigs production and substantial farm diversification)
This rather small group of farms (18, 14.6%) is substantially spread (Fig. 2). Structure of agricultural production, its intensity, contribution of incomes from crop and livestock to agricultural production and farmer's score of agricultural production profitability were similar to these in type 4 of farming system. Rather stable across five years livestock production included both dairy cattle and pigs, their density was 0.87 and 0.28 LSU ha^-1 UAA, respectively. Exclusively, the farm area was relatively small attributing about only 13 ha as averaged. The number of persons working on farm in agriculture approached to 1.67 (1.3 AWU/10haUAA) and 1.39 persons working off-agriculture, showing rationale employment of all farm family members. Crop diversity was similar to t hat in type 4 of farming system, however, including twice more share of root crops – potatoes (13% of AA). Contributions of incomes from agricultural production and non-agricultural business activities in these farm households were the same (both equal to about 50%). Then, farm households of this type were substantial diversified in their activities. Small farm area in this farming system, seldom adoption of professional advices and innovation invests in the farms, utilization of semi-extensive farming and associating them large adoption of alternative farming activities, i.e. farm diversification (it is clearly seen on the Fig. 2 because points for these farms have positive relatively large values) create adequate backgrounds for this kind of farms to survive as multi-product traditional semi-extensive farms, diversified in on- and off-farm business activities farm households. Public intervention could be mainly focused on support temporal sustainable stabilization of the farming system.

Group 6 (type 6 of farming system characterized by extensive cereal production and moderate farm diversification)
The smallest group of farms (10, 8.1%) in spite of quality soils which are comparable to other farms shaped extensive agricultural production system (Fig. 2). The farm area was relatively small attributing about only 11 ha as averaged. Livestock production across five years decreased about 145% and now the farmers abandoned entirely from livestock. Crop diversity was very small, almost cereal monoculture is practiced, share of cereal and potatoes approached to 95% and 5%, respectively. Contributions of incomes from agricultural production and non-agricultural business activities in these farm households were 71% (exclusively from crop production) and 29%, respectively. Therefore, farm households of this type were moderately diversified in their activities. Small, extensive farms in this farming system, doubtable farm succession, and associating it rather large adoption of alternative farming activities being threatened by farming and even land use abandonment and also sensible to globalization processes in rural areas extremely need public intervention both financial and non-financial ones to survive as extensive farms being sustainable in agronomy and social-economic sense but fulfilling basic agri-environmental requirements and extremely diversified in on- and off-farm variety business activities farm households.

FINAL REMARKS AND CONCLUSIONS

Typology of farms according to farming system makes it possible to identify opportunities to improve the farming systems existing on a studied rural area. Distinguished types of farming systems constitute the so called policy, support and recommendation domains, i.e. groups of roughly homogenous farmers with similar circumstances for whom we can make more or less the same external actions. These actions could include among others institutional and extension support, rural labour market enrichment, improvement of adoption and perception of EU measures (founds) by farmers, increasing adoption of new agriculture technology, introduction of simple landscape ecology design principles, economic simulations for each farms type considering different scenarios etc. [2,3,5,12,16,21,23,24,27].

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Accepted for print: 28.03.2010

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Wiesław Mądry
Department of Experimental Design and Bioinformatics, Warsaw University of Life Sciences (SGGW), Poland
Nowoursynowska 159
02-776 Warsaw
Poland

Dariusz Gozdowski
Department of Experimental Design and Bioinformatics, Warsaw University of Life Sciences (SGGW), Poland
Nowoursynowska 159
02-776 Warsaw
Poland
Phone: +48 (22) 59 32 730
email: dariusz_gozdowski@sggw.pl

Barbara Roszkowska-Mądra
Department of Entrepreneurship, Faculty of Economics and Management, Białystok University, Białystok, Poland
Warszawska 63
15-062 Białystok
Poland

Mariusz Dąbrowski
Department of Experimental Design and Bioinformatics,
Warsaw University of Life Sciences - SGGW, Poland
Nowoursynowska 159, 02-776 Warsaw, Poland

Wioletta Lupa
Department of Experimental Design and Bioinformatics,
Warsaw University of Life Sciences - SGGW, Poland
Nowoursynowska 159, 02-776 Warsaw, Poland

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