Factors affecting the farm sustainability in Lithuania

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Agriculture plays an important role in many countries economy in terms of its potential to influence a wide range of issues that are related to sustainable development, including the economy, employment, food security, trade flows, poverty, human health, climate change, the use of natural resources (especially land and water), and biodiversity. As noted, current situation in agriculture is characterized by declining rates of growth in productivity, a decreasing share of global agricultural exports from developing countries, an increase in the use of agrochemicals, resulting in negative impacts on human health, ecosystems, and biodiversity, increasing levels of greenhouse gas emissions and, the inequitable distribution of benefits among countries and among different segments of societies within countries [17]. According to FAO [5] the family and the farm are linked, coevolve and combine economic, environmental, social and cultural functions. The focus of international organisations on agricultural sustainability has prompted emergence of studies in this area and, as noted by C. Schader et al. [15], sustainable development has become one of the most frequently used frameworks for analysing the agricultural and food sector in a comprehensive and holistic way. However, as argued by M. Astier et al. [1], most formal sustainability analyses are only applied to regional, national, or global scales. Moreover, indicators used for assessment are not applicable enough to initiate changes in farms that would lead to mitigation of negative impact on natural resources by farms, improve social responsibility, etc. The most commonly-used data source for evaluation of farms economic, social and environment sustainability is farmers questionnaire, employing structured questionnaire or/and in-depth interview (e.g. X. Sauvenier et al. [14]; V. Urutyan and C. Thalman [18]; J. Jalilian [10]; etc.). Studies of this kind, however, are difficult to repeat and compare, as they are time and money intensive, involving only a small number of farms studied simultaneously. In the recent years, researchers (e.g. D. Longhitano et al. [11]; H.A.B. Van der Meulen et al. [19]; A.P. Barnes and S.G. Thomson [2]; M. Ryan et al. [13]; etc.) suggested employing the available databases as information sources, such as the EU Farm Accountancy Data Network (FADN). Analysis of literature on application of FADN data to farms sustainability assessment showed that D.B. Westbury et al. [22] emphasized the importance of this database on formation of the environmental indicators; to the contrary, M. Ryan et al. [13] narrowed the approach towards environmental assessment; S. Van Passel and M. Meul [20] did not develop the social indicators; H.A.B. Van der Meulen et al. [19], A.P. Barnes and S.G. Thompson [2] used FADN data only for a certain farming type of farms sustainability assessment; D. Longhitano et al. [11] emphasized regional context in the study.

Empirical studies on farm sustainability have been conducted in Belgium [14], Spain [8], Iran [10], the Netherlands [19], Armenia [18], Greece [4], Tuscany region in Italy [11], Scotland [2], Ireland [13] and etc. In Lithuania, farm sustainability has been analysed from the perspective of ecological farming only [3; 16].

The objectives of the article. The aim of the article is to perform factors affecting the farm sustainability in Lithuania. Objectives of the study are to frame an assessment methodology based on FADN data; to assess family farm sustainability and to reveal the relationship between calculated farm relative sustainability index and factors influencing it.

Methodology. The farm relative sustainability index (FRSI) has been developed for this study following the OECD handbook for constructing composite indicators [12]. The set of guidelines, the succession of stages and methods used are universal and applicable for analysis at micro-level. The principles of Bellagio, SMART and other indicator selection principles have been considered during formation of the set of indicators. Based on the sets of indicators and the rationale behind their selection in earlier studies of farm sustainability and the analysis of FADN variables, the final sets of indicators were identified (Table 1).

Table 1. Farm sustainability indicators and their weights based on factor analysis

The min-max approach was employed to normalise the selected indicators expressed in variety dimensions for their need to be put on a common basis. In this research, FRSI has been scaled into three intervals, assuming that the closer to 1 were the values of the index and sub-indices the higher was relative sustainability of the farm:

- low sustainability score [0; 0.33], meaning that the farm is unsustainable or of low sustainability;

- medium sustainability score [0.34; 0.66] as considered to be the medium level of farm sustainability,

- high sustainability score [0.67; 1], meaning that the farm is either fairly sustainable or sustainable.

The factor analysis was used to estimate weights for selected indicators constructing sub-indices (see Table 1) and then sub-indices (economic, environmental and social) were aggregated into farm relative sustainability index, i.e. FRSI. The weights to the sub-indices are based on the triple bottom line approach of sustainability, i.e. sub-indices are weighted equally for the calculation of FRSI. (The detailed methodology for constructing family farm relative sustainability index is presented in V. Vi- tunskiene and V. Dabkiene [20]).

The research of family farm sustainability in Lithuania is based on survey sample composed of randomly selected 450 family farms in Lithuania in the year 2003, 2008 and 2012 and based on multivariate regression analysis, oneway ANOVA and t- test. A p value of less than 0.05 (p<0.05) is considered to indicate a statistically significant difference between the groups. The statistical package for social science (SPSS 21) was employed for processing and analysis of collected data. sustainable agricultural factor

Statement of the main results of the study.

The spider diagrams presented in Figure 1 facilitates examination of the family farms relative sustainability by comparing the average values of economic, environmental and social indicators in the year 2003, 2008 and 2012. The results of the research showed that economic sustainability was low. The average values of economic sub-indices were 0.21, 0.26 and 0.30, in 2003, 2008 and in 2012, respectively. It was determined by very low average values of normalized indicators like farm diversification and farm risk management in the considered years of analysis. It can be concluded that in 2012 the economic sustainability has increased in comparison to 2003 due to better productivity, solvency and family farm income results. Established family farms relative social sustainability was medium, the average values did not differ significantly, varied from 0.50 in 2003 to 0.52 in 2008 and 2012. Analysis showed that social normalized indicators jobs on farm and wage ratio on farm average values were the lowest in the years of study. The observed values of environmental sustainability sub-indices were 0.69, 0.71 and 0.67, in 2003, 2008 and in 2012, respectively. Indicated high environmental sustainability was accompanied by lower use of chemical fertilizers, pesticides, lower GHG emission and lower livestock density on farms. It should be noted that GHG emission assessment has been based on the breed animals on the farms. GHG emissions from chemical fertilizers have not been estimated, as fertilizer quantities are currently not reported in the Lithuanian FADN. Average values of the FRSI for the years fell within the medium sustainability interval and concentrate just below its middle (i.e. 0.46 and 0.49, in 2003 and 2008, 2012, respectively).

Figure 1. Family farm sustainability results in 2003, 2008 and 2012

The multivariate regression analysis was chosen to analyse the relationship between calculated FRSI and relevant variables as it is provided in OECD [12] guidelines. Family farm FADN database in 2012 was employed for the multivariate regression model. Here the dependent variable was FRSI, while as independent variables were chosen related to family farm: (i) social aspects (farmer's age, family annual work units per ha UAA, hired labour annual work units per ha UAA); (ii) environmental aspects (the amount (kg) of chemical fertilizers per ha UAA, costs of pesticide, thousand EUR/ha UAA); (iii) economic aspects (production-linked payments, thousand EUR/ha UAA), agro-environmental payments, thousand EUR/ha UAA, income from sales of agricultural products, thousand EUR/ha UAA); (iv) farm structural characteristic (farm size ha UAA). Farm size as independent variable was included due to ambiguous results in farm sustainability studies. D.B. Westbury et al. [22] estimated the significant effect of farm size on the environmental performance of lowland livestock holdings in United Kingdom. This supports H.A.B. Van der Meulen et al. [19] who also found significant effect of farm size on dairy farms in Netherlands. The results revealed that large-scale dairy farms had a higher labour productivity and net farm income, lower solvency ratio and higher pesticide use. While a farm size had no effect on nitrogen use, energy use and GHG emission in dairy farms. Though G. Herzog et al. [9] concluded that no relationship exists between farm intensity and farm size (cited in D.B. Westbury et al. [22, p. 908). As well, T. Dantsis et al. [4] indicated economic sustainability of farms was not determined by bigger holding size. J.A. Gomez-Limon and L. Riesgo [7] explored farm sustainability in Spain and concluded that small to medium-sized holdings and sowing higher value-added crops ran most sustainable farms. J.A. Gomez-Limon and G. Sanchez-Fernandez [8] employed double censored Tobit regression analysis to investigate the factors determining farm sustainability and stated that farm sustainability increases as the area of the farm increases. The authors the greater sustainability of large farms explained by: (i) the existence of economies of scale in agricultural production, which makes for more efficient production and thus, greater economic sustainability; (ii) the generation of sufficient income to permit the continuity of agricultural activity among farm owners (greater social sustainability); and (iii) higher generation of environmental benefits (large farms are better able to implement techniques that allow them to cut costs and that are ecologically compatible, they can develop a more diversified and extensive range of agricultural products, in view of the need to spread the work-load over the year and they can participate to a greater extent in agro-environmental programmes) [8].

Pearson's correlation coefficients of independent and dependent variables revealed that interaction between FRSI and family farm size (ha UAA) was not significant (p=0.590, p>0.05), therefore the size of farm was excluded from regression model. The strong relationship (correlation coefficient equal to 0.85) between use of chemical fertilizers kg per ha UAA and costs of pesticide thousand EUR per ha UAA was estimated. To avoid the multicol- linearity these two variables were transformed into costs of chemical fertilizers and pesticides thousand EUR per ha UAA. Evaluated regression model is statistically important and significant (R2 > 0.20=0.669). Regression analysis results are presented in Table 2.

Table 2. Multivariate regression analysis results