The impact of farm size on sustainability of beef cattle farms: A case study of the Samsun province, Turkey

ABSTRACT

Many global agricultural problems, such as the decline of agricultural land, soil pollution, climate change, water scarcity, and rural poverty threaten the sustainability of agricultural production systems. This study aimed to (i) develop a methodology to measure the sustainability of beef cattle farms, (ii) measure economic, social and environmental sustainability, and (iii) determine the impact of farm size on the sustainability of beef cattle farming. The study data were collected from 155 beef cattle sample farms via face-to-face surveys in Samsun Province, Turkey. A multi-stage process was employed to measure sustainability. The most important factors contributing to sustainability were determined as the debt-asset ratio, beef yield, and production cost for economic sustainability; the adequacy of educational institutions in the village or district and misunderstanding with neighbours for social sustainability; paying attention to meteorological conditions for pesticide use and nature conservation for environmental sustainability. The composite sustainability index was determined as 0.43 in small farms, 0.51 in medium farms and 0.61 in large farms. It has been found that an increase in the farm size had a positive impact on economic, social, and environmental sustainability of beef cattle farming. The methodology and indicators developed can be used to ensure the sustainability of different agricultural sectors.

KEYWORDS:

• Sustainability
• farm size
• factor analysis
• index
• Turkey

1. Introduction

Population growth brings pressure on agricultural production and increased the use of intensive inputs in production systems. In recent years, food safety and security have become more critical because of excessive pollution of agricultural land, crises and wars between countries, climate change and epidemics such as Covid-19. This situation has increased the importance of sustainable farming systems. The Brundtland Commission first defined sustainability in 1987 as development that meets the needs of current generations without compromising future generations’ ability to meet their own needs (Frater & Franks, 2013). The concept of sustainability was first addressed in the form of purification of pollutants from the environment and ecosystems, adopting environmentally and nature-friendly production techniques, and transferring existing resources to future generations. Today, besides farm production systems protecting nature, the concept of sustainability has expanded to the continuation of economic existence of farms.

The concept of sustainability is one of the forefront issues in discussions of the challenges facing global agriculture, given the mounting pressure to increase food production in both socially responsible and environmentally friendly ways (Jane Dillon et al., 2016). Studies on sustainability have been generally considered in 3 dimensions: economic, social and environmental. Pandian et al. (2013) defined economic sustainability as follows: a balance of responsibility and benefit achieved in the long term by using the existing workforce and other resources at the optimum level by using various strategies. Economic sustainability is one of the most critical indicators to be taken into account in ensuring farms’ continuity. The benefits of economic sustainability in agricultural sector are follows: ensuring food security and generating income for investment and keeping the population in the agricultural sector (Wrzaszcz & Zegar, 2014). Social sustainability is defined as the continuation of expanding social values, identities, network and institutions in the future (Black, 2004). Environmental sustainability is defined as maintaining the factors and practices that contribute to environmental quality in the long run (Pandian et al., 2013). Sustainability is the continuation of production, diversity and productivity required for human needs and natural resources to sustain the existence of the ecosystem and all individuals within the ecosystem. Therefore, sustainability should be assessed in the aforementioned three dimensions. There is no exact judgement about which sustainability dimension is more important. Thus, economic sustainability cannot be assessed independently from social sustainability and environmental sustainability. Otherwise, an important sustainability factor would be ignored and it would be difficult for farms to survive in the future (Zahm et al., 2008). Therefore, this study discussed the sustainability of the beef cattle farms in terms of their economic, social and environmental dimensions.

Despite efforts by many governments, the adoption rate of sustainable practices amongst farmers is still low (Benitez-Altuna et al., 2023). To establish a sustainable livestock sector, it is important to determine the level of sustainability of the farms and effective factors on these levels. The benefits of sustainable agriculture systems are as follows: (i) Increasing the welfare of farms (ii) ensuring the continuity of farms, (iii) balancing the production quantity and prices (iv) providing market-oriented production, (v) applying environmentally friendly production techniques and (vi) providing individual and development-oriented approach in production.

In Turkey, 1,118,695 tons of red meat was produced in 2018, and 89.7% (1.003.859 tons) of it was obtained from beef cattle (TurkStat, 2020). Beef is one of the primary sources of animal protein in humans. Beef consumption in the world was 6.4 kg per person in 2019. Turkey ranked above the world average beef consumption per capita with 8.5 kg in 2019, but far behind the OECD countries (14.5 kg) (OECD, 2020). Despite the support provided to the livestock sector in Turkey, it proved insufficient to meet the country's need through domestic production (Alhas Eroğlu et al., 2019). The livestock sector provided a significant contribution to Turkey's economy (Alhas Eroğlu et al., 2020) by employing agricultural labourers, meeting the nutritional needs of the population, providing inputs of raw materials to different production branches and contributing to the development of logistics and retail sectors (MAF, 2015). Moreover, the livestock sector supports regional rural development, ensures more balanced development and prevents migration from rural areas (Bayraç & Çemrek, 2011).

In Turkey, the average land size (6.5 hectares) and the average number of cattle (14.28) per farm are far behind European farms (TurkStat, 2020). The government has supported the farms through young farmer support, area-based support, and rural development supports to increase their size and sustainability. Farm size is one of the main factors affecting the sustainability of the livestock sector. Because farm size may have a significant impact on many economic aspects of a farm's operation, including its profitability (Kryszak et al., 2021). However, larger farms tend to exhibit both higher productivity and profitability (Mugera et al., 2016) and a reduction in per unit costs (Langemeier & Jones, 2000). Small-scale farms mostly face problems surviving economically, while larger ones face social and environmental sustainability problems. Therefore, it is important to reveal the effects of the farm size on economic, social and environmental sustainability levels. Because, the impact of size on farms structure differs, depending on the adopted farm size (Asche et al., 2018)

Today, there is a considerable number of studies on sustainability. Some studies measured and monitored the effects of production on the environment and ecosystem using life cycle analysis and carbon footprint methods (Cecchini et al., 2016; Devakumar et al., 2018; Dubey & Lal, 2009; Hu, Zhang, et al., 2019; Rivera et al., 2017). Other studies measured sustainability using different indicators such as IDEA, SAFA and RISE (Biret et al., 2019; Bonisoli et al., 2019; De Olde et al., 2016; Fadul-Pacheco et al., 2013; Gayatri et al., 2016; Salas-Reyes et al., 2015; Sotamenou & Pogha, 2018). However, many studies measured sustainability through the index of specific indicators and the computed sustainability index is bounded between 0 and 1, with the extent of farm practices sustainability increasing from 0 to 1 (Batalla et al., 2014; Gunduz et al., 2011; Marandure et al., 2017; Mohamed et al., 2016; Prasad et al., 2016; Ren et al., 2019; Singh et al., 2016; Sulewski et al., 2018; Terano et al., 2015; ul Haq & Boz, 2020). Broom (2021) determined that semi-intensive silvopastoral systems are the most sustainable beef-production systems. Pérez-Lombardini et al. (2021) compared the sustainable performance of silvopastoral (native and intensive) and monoculture cattle farms in the Mexican Tropics using the SAFA framework. They found that native and intensive farming systems had better sustainable performances than monoculture farming. Sulewski et al. (2018) found a positive relationship between economic, social, and agri-environmental indicators. Marandure et al. (2017) determined that the agricultural farms in Ncorha and Gxwalibomvu were partially socially and environmentally sustainable, while economically unsustainable. Rivero and Daim (2017) emphasized that methane emissions represent one of the riskiest threats from livestock cultivation, and capturing methane from animals has not been fully engaged by sustainable approaches. Gayatri et al. (2016) found that the main drivers that explain a lower sustainability performance related to limitations in access to information and knowledge, networks, and economic resources in smallholder beef cattle farming. Singh et al. (2016) studied the economic, social and environmental sustainability of cattle farms in India and found that 0.68 of the farms were moderately sustainable. M’hamdi et al. (2017) determined that environmental and economic sustainability have quite status (fairly sustainable) with 65.4 and 51.35 in beef cattle farms. According to Prasad et al. (2016), economic sustainability was observed to be higher than social and environmental sustainability in dairy cattle farms. Escribano et al. (2015) determined that the most sustainable system is the organic production system in beef cattle farming. Batalla et al. (2014) found that intensive farms had higher indicator values in economic, social and environmental sustainability in Spanish sheep farms.

The aim of this study was to; (i) develop a method for measuring sustainability, (ii) measure economic, social, and environmental sustainability levels in beef cattle farms and (iii) determine the effect of farm size on sustainability. This study could contribute to the current literature and policy development on cattle farm sustainability in Turkey by determining the magnitude of farm size effects and their causes.

2. Data and methods

2.1. Data and sampling

The research data were obtained from face-to-face surveys with beef cattle farms. The survey included questions about socio-demographic characteristics (age, experience, household size, education of householders, social security), structural characteristics (number of cattle, total land size of the farm, number of parcel), technical and economic characteristics (labour use, capital use, yield, production, production cost, sale price, sources of income) and sustainability indicators (economic, social, and environmental indicators) for beef cattle farming. Secondary data were used from the current literature, databases of the Ministry of Agriculture and Forestry (MAF, 2018), the Turkish Statistical Institute (TurkStat, 2020), and the Food and Agricultural Organization (FAO, 2015). In 2017, the total cattle population was 422,672, while the number of farms was 39.688 in the Samsun Province (MAF, 2018). The Districts of Alaçam, Bafra, Çarşamba, Havza, Lâdik, Tekkeköy, Terme and Vezirköprü of the Samsun Province were selected as the research area, as these districts provided 75.76% of the total beef cattle population in the province (Figure 1). It is geographically located 40° 50′–41° 51′ north latitudes and 37° 08′ and 34° 25′ east longitudes (MAF, 2023). The total number of beef cattle in the farms in 2017 was taken as sampling criteria to determine the sample size. The sample size was calculated to be 155 farms using Neyman's method in Equation (1) (Yamane, 1967). Then, sample farms were randomly chosen from the beef cattle farms. (1) $n={\displaystyle \frac{{\left(\sum N_h{S}_h\right)}^2}{N^2{D}^2+\sum N_h{S}_h^2}}$(1) where n is the sample size; N is the total beef cattle farms; N_h is the number of farms in the h strata; and S_h is the standard deviation of the number of farms in the h strata. In order to increase the representativeness of the population, the cattle farms were divided into 5 strata based on the distribution of the number of cattle. Stratas were determined as the 1st layer with 1–2 head of cattle, the 2nd layer with 3–5 head of cattle, the 3rd layer with 6–30 head of cattle, the 4th layer with 31–70 head of cattle, and the 5th layer with +71 head of cattle. D2 equals d2/z2 and indicates the standard error of estimate at the 10% significance level (d), whereas z is the value of z in the table of standard normal distribution. Besides, 95% confidence interval was used in the sampling. The sample size were calculated as 55 farms for the 1st strata, 24 farms for the 2nd strata, 16 farms for the 3rd strata, 20 farms for the 4th strata and 40 farms for the 5th strata.

Figure 1. Study area in the Province of Samsun, Turkey.

2.2. Methods for sustainability assessment

The most critical point in measuring sustainability is to select the related indicators with a high representation ability of the relevant dimension. This study employed a multi-stage process for measuring sustainability. By examining the literature, the sustainability indicators of beef cattle farming were developed using both sustainability indicators from SAFA (2013), IDEA (2006), FAO (2015), and special characteristics from study region and livestock farms. The sustainability index development process was shown in Figure 2.

Figure 2. Procedure of indicator development.

The indicator selection criteria, measurement levels, descriptive statistics, factor analysis results and the indicators’ references were given in Table 1. Based on the stakeholder and expert opinion, 9 economic sustainability indicators (income, net profit, relative profit, yield, cost, economic rentability, financial rentability, debt-asset ratio and economic efficiency) were selected for the beef cattle farms. Farm income refers to the money generated by farm or agribusiness operations. Therefore, farm income was included in the economic sustainability indicator. Net profit was chosen for long-term profit because it includes total cost. Relative profit indicates how well a farm can generate a profit relative to its asset base and it was included in the economic sustainability indicator.

Table 1. Descriptions of selected sustainability indicators.

Indicators	Measure	Mean or %	Factor loadings	Explained variance (%)	References
Economic sustainability indicators*
Income	₺	106,155.7	–	–	Sajadian et al. (2017)
Net profit	₺	69,784.37	–	–	SAFA (2013)
Relative profit	%	1.23	–	–	FAO (2015)
Yield	Kg/head	273.58	–	–	FAO (2015)
Cost	₺/kg	21.89	–	–	SAFA (2013)
Economic rentability	%	8.67	–	–	Chand et al. (2015)
Financial rentability	%	9.62	–	–	Chand et al. (2015)
Debt-asset ratio	%	0.13	–	–	FAO (2015)
Economic efficiency	%	0.25	–	–	Gunduz et al. (2011)
Social sustainability indicators**				49.30
Indicators of the future and investment				15.41
Do you think to invest in your farm in the future?	0.No 1.Yes	72.26	0.770		FAO (2015)
Do you want to increase the number of animals in your farm?	0.No 1.Yes	68.97	0.677		FAO (2015)
Have you invested in your business in the last 2 years?	0.No 1.Yes	54.55	0.613		FAO (2015)
Do your children want you to be a farmer?	0.No 1.Yes	67.88	0.564		FAO (2015)
Education, agricultural extension, and collaboration indicators				13.63
How many years of formal education did you take?	Year	7.25	0.741		Sajadian et al. (2017)
Have you taken agricultural extension and education training?	0.No 1.Yes	15.13	0.632		Latruffe et al. (2016)
Do you cooperate with local organizations or institutions?	0.No 1.Yes	17.65	0.593		SAFA (2013)
Are there local rural development services in your village or area?	0.No 1.Yes	7.10	0.460		FAO (2015)
Institutional and social behaviour indicators				11.50
Are educational institutions in the village or district sufficient?	0.No 1.Yes	96.48	0.796		Sulewski and Kłoczko-Gajewska (2018)
Do you have any problems with the people in your village?	0.No 1.Yes	87.66	0.636		FAO (2015)
Can you spend money for social activities?	0.No 1.Yes	75.16	0.375		FAO (2015)
Indicators on migration and improvements activities				8.76
How many people have migrated from your farm in the last 10 years?	Person	0.63	–0.662		FAO (2015)
Do you have activities to improve the quality and yield of beef?	0.No 1.Yes	61.84	0.645		FAO (2015)
Environmental sustainability indicators**				54.19
Indicators on soil and environmental hygiene				11.59
Do you have soil analysis?	0.No 1.Yes	41.20	0.847		Sulewski and Kłoczko-Gajewska (2018)
Do you apply fertilizers based on soil analysis?	0.No 1.Yes	24.20	0.694		SAFA (2013)
Do you know the soil organic matter content?	0.No 1.Yes	60.70	0.818		IDEA (2006)
Do you know the pH of the land?	0.No 1.Yes	11.30	0.617		Terano et al. (2015)
How far is the manure from your house?	Metric	67.60	0.382		FAO (2015)
Indicators on environmental protection awareness				10.39
Do you take care to protect nature?	0.No 1.Yes	96.80	0.854		Sulewski and Kłoczko-Gajewska (2018)
Do you take care to protect biodiversity?	0.No 1.Yes	93.40	0.809		Sulewski and Kłoczko-Gajewska (2018)
Do you take care to protect places such as streams and lakes?	0.No 1.Yes	94.80	0.638		Sulewski and Kłoczko-Gajewska (2018)
Do you take precautions for animal disease?	0.No 1.Yes	97.40	0.595		Sulewski and Kłoczko-Gajewska (2018)
Indicators on the usage of pesticides and water				10.03
Do you follow the instructions for the use of pesticides?	0.No 1.Yes	95.20	0.809		FAO (2015)
Do you follow meteorological conditions for the use of pesticides?	0.No 1.Yes	99.30	0.642		FAO (2015)
Do you clean the equipment after the use of pesticides?	0.No 1.Yes	96.60	0.607		FAO (2015)
Do you take precautions (mask, glove, etc.) for use of pesticides?	0.No 1.Yes	83.60	0.574		FAO (2015)
Do you take precautions to protect and conserve water?	0.No 1.Yes	83.90	0.395		SAFA (2013)
Indicators on awareness of input usage				8.61
Do you use environmentally friendly pesticides?	0.No 1.Yes	28.70	0.816		SAFA (2013)
Do you do organic farming?	0.No 1.Yes	11.30	0.595		Sajadian et al. (2017)
Have you been trained for pesticide use?	0.No 1.Yes	26.40	0.518		FAO (2015)
Have you been trained for fertilizer use?	0.No 1.Yes	8.60	0.465		FAO (2015)
Do you know the active substances of the drugs you use?	0.No 1.Yes	32.00	0.355		SAFA (2013)
Indicators for erosion				6.97
Have you been trained to prevent erosion?	0.No 1.Yes	3.20	0.800		FAO (2015)
Do you take precautions against soil erosion on your land?	0.No 1.Yes	9.04	0.755		SAFA (2013)
Indicators for manure management				6.60
Is there a cesspool in your farm?	0.No 1.Yes	37.00	0.824		Sulewski and Kłoczko-Gajewska (2018)
How do you keep animal manure?	0.Out 1.Semi 2.Full	57.10	0.804		Sulewski and Kłoczko-Gajewska (2018)

*Expert decision.

**Factor Analysis results.

Efficiency measurement in enterprises consists of technical efficiency (TE), allocative efficiency (AE) and economic efficiency (EE). While technical efficiency shows the ability of obtain maximum output with a certain input, allocative efficiency shows the optimal use and distribution of inputs considering production technology and prices as constant. Economic efficiency is sum of technical efficiency and allocative efficiency (Coelli et al., 2005). Therefore, economic efficiency score was selected as the economic sustainability indicator. Rentability is the ability of a company to get more earnings during a certain period. In other words, the rentability of a firm indicates the comparison between earnings and assets or capital that can obtain the related earnings (Nalurita, 2018). Financial rentability indicates whether equity was used effectively or not, whereas economic rentability indicates whether total capital was used effectively or not. For this reason, financial and economic rentability were added to the list of economic sustainability. However, debt/asset ratio added the economic sustainability list, because it shows the debt status of the farms.

The income indicator refers to the income from agricultural activity on the farm. Net profit was calculated by subtracting total production costs from gross production value (GPV). The relative profit shows the ratio of GPV to production costs. Beef yield is the average beef yield per beef cattle. Carcass weight was taken into account in the beef yield. Meat production cost was calculated by first subtracting the by-product revenues from the total meat production costs and then dividing the remaining amount by the total meat production. Economic rentability was calculated by dividing pure profit by active capital. Financial rentability was found by dividing equity income by equity (Erkuş et al., 1995). The debt-asset ratio indicates how much of the active capital of the farms consists of debt (foreign capital). The economic efficiency indicator was calculated by using data envelopment analysis (Coelli, 1996). The DEA model was constructed by assuming that each cattle farm produces a quantity of beef (yi) using multiple inputs (xi*) and that each farm (i) is allowed to set its own set of weights for both inputs and output. One output (the amount of production) and six inputs (concentrate feed, roughage, labour, capital, veterinary, and other) were used in the DEA models. The DEA model for efficiency was calculated by Equation (2): (2) $\begin{array}{rl}& Mi{n}_{\theta ,\lambda }\theta ,\\ & \mathrm{S}\mathrm{u}\mathrm{b}\mathrm{j}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{t}\mathrm{o}-y_i+Y\lambda \ge 0\\ & \theta x_i-X\lambda \ge 0\\ & \lambda \ge 0\end{array}$(2) where θ is a scalar, λ is an N × 1 vector of weight. DEA model was solved once for each farm, looking for the largest radial contraction of input vector x_i within the technology set (Speelman et al., 2008). The TE score (θ) ranges between zero and one. By solving the following linear programming problem (LP), the input-based cost efficiency for the ith farm is calculated: (3) $\begin{array}{rl}& Mi{n}_{\lambda ,x{i}^{\ast }}{w}_i^T{x}_i^{\ast }\\ & \mathrm{S}\mathrm{u}\mathrm{b}\mathrm{j}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{t}\mathrm{o}-y_i+Y\lambda \ge 0\\ & x_i^{\ast }-X\lambda \ge 0\\ & \lambda \ge 0\end{array}$(3) where wi is a vector of input prices for the ith cattle farm; xi* is the cost-minimizing vector of input quantities for the ith cattle farm, superscript T is the transpose function, given the input prices wi and output level yi; and λ is a N × 1 vector of constant. Equation (3) represents the cost minimization under constant returns to scale (CRS) technology. CRS states that when output increases by the same proportional change as all inputs change. The economic efficiency (EEi, CRS) of the ith cattle farm is calculated as, (4) $E{E}_{i,CRS}=w_i^T{x}_i^{\ast }/w_i^T{x}_i$(4) where $E{E}_{i,CRS}$ is the ratio of the minimum cost to the observed cost at the given input prices and CRS technology. Efficiency is measured on a scale of 0 to 1, where a value of 1 indicates the unit is efficient, and a value less than 1 indicates the unit is inefficient.

The data of 35 indicators related to social sustainability and 45 indicators related to environmental sustainability was collected through the surveys conducted with the beef cattle farms. Factor analysis was applied to reduce too many social and environmental sustainability indicators, and indicators that did not contribute to the explained variance were not included in the scale. Factor analysis is a multivariate statistical technique, and it is used to obtain a small number of unrelated variables from a large number of variables (Çelik, 2012). Factor loadings obtained from factor analysis were used in the selection of indicators. There are different opinions in determining the threshold limits of factor loadings. It is stated in the literature that factor loads ranging from 0.30 to 0.40 can be taken as the lower cut-off point in forming factor groups (Büyüköztürk, 2002). Tabachnick and Fidell (2014) suggested that factor loads with an absolute value of less than 0.32 should be ignored. However, Field (2013) stated that the threshold value is 0.30, while Samuels (2016) expressed that at least 3 of the factor loads should be greater than 0.40. To expand and elaborate on the scope of sustainability, factors with a factor load of 30 or more were accepted in the study.

This study used the Kaiser Meyer Olkin (KMO) test to determine the sampling adequacy of data to be used for factor analysis. Bartlett's Test of Sphericity checks whether there is a certain redundancy between the sustainability variables that we can summarize with a few numbers of factors (Shrestha & Kazama, 2007; Varol & Şen, 2009; Zhang et al., 2021). According to KMO and Bartlett test results, the data in this study was determined to be suitable for factor analysis (for social sustainability KMO: 0.624, Barlett's Tests Chi-Square: 207.881, Sig: <0.01; for environmental sustainability KMO: 0.592, Barlett's Tests Chi-Square: 933.784, Sig: <0.01). Rotated Component Matrix results were used in the classification and evaluation of factor groups.

Reliability analysis was performed for the indicators, and as a result of the reliability analysis, the Cronbach's Alpha coefficient was found to be 0.801 for economic sustainability, 0.610 for social sustainability, and 0.730 for environmental sustainability. These coefficients showed that the selected economic, social and environmental indicators were reliable.

In economic sustainability indicators, the average income of the farms was 106,155.79 TL, the net profit was 69,784.37 TL, while the relative profit was 1.23. In beef cattle farms, the economic efficiency score was also very low (0.25). According to factor analysis results, 13 indicators were determined for social sustainability, and these indicators explained 49.30% of the total variance. Social sustainability indicators were grouped by using factors analysis as future and investment; education, agricultural extension and collaboration; institutional and social behaviour; and migration and improvements activities. In the factor analysis, 23 indicators related to environmental sustainability were determined, and these indicators explained 54.19% of the total variance. Environmental sustainability indicators were also grouped under six subgroups as soil and environmental hygiene; environmental protection awareness; use of pesticides and water; awareness of input usage; erosion; and manure management. Survey questions with categorical answers were converted into percentages (Table 1).

The sustainability indicators of the beef cattle farm were determined using the expert opinion and factor analysis results, and the economic, social, and environmental sustainability indexes were calculated from these indicators. The development of the agricultural sustainability index generally consists of 10 stages (Gomez-Limon & Riesgo, 2008). These stages are as follows; (i) development of the theoretical framework, (ii) selection of key indicators, (iii) multivariate analysis, (iv) completion of missing data, (v) normalization, (vi) weighting and aggregation, (vii) robustness and sensitivity analysis, (viii) the connection of composite indicators with other variables (ix) back to real data, and (x) presentation and spreading.

The development of the theoretical framework, which is the first step in creating the index, should focus on the subject to be measured. In creating the index, the missing data was completed, and then normalization was conducted for the data. The purpose of normalization is to transform indicators with different units into one standard unit. The min-max method in Equations (5 and 6) was used for the normalization of the data (OECD, 2008). (5) $I_{\dot{I}J}={\displaystyle \frac{X_{ij-}Min{X}_{ij}}{Max{X}_{ij-}Min{X}_{ij}}}$(5) (6) $I_{\dot{I}J}={\displaystyle \frac{Max{X}_{ij-}{X}_{ij}}{Max{X}_{ij-}Min{X}_{ij}}}$(6) In the formulas, i denotes the number of indicators up to 1, 2, 3 … n, j describes sustainability indicators, and X_ij indicates the indicator's values. Equation 5 is selected if the indicator has a positive effect on sustainability, and Equation 6 is selected if it has a negative effect. Equal weights were given to the indicators in the weighting stages, and the index was calculated with the linear aggregation method. Equations for aggregation were shown in Equations 7, 8 and 9: (7) $\mathrm{E}\mathrm{S}\mathrm{I}={\displaystyle \frac{{\sum }_i^{n}Iij}{n}}$(7) (8) $\mathrm{S}\mathrm{S}\mathrm{I}={\displaystyle \frac{{\sum }_i^{n}Iij}{n}}$(8) (9) $\mathrm{E}\mathrm{n}\mathrm{S}\mathrm{I}={\displaystyle \frac{{\sum }_i^{n}Iij}{n}}$(9)

In the above formulas, ESI refers to the economic sustainability index, SSI to the social sustainability index, EnSI to the environmental sustainability index, and I_ij to the indicator values. After calculating each sustainability dimension, the composite sustainability index was calculated using Equation (10). (10) $\mathrm{C}\mathrm{S}\mathrm{I}={\displaystyle \frac{{\mathrm{W}}_1\ast \mathrm{E}\mathrm{S}\mathrm{I}+{\mathrm{W}}_2\ast \mathrm{S}\mathrm{S}\mathrm{I}+{\mathrm{W}}_3\ast \mathrm{E}\mathrm{n}\mathrm{S}\mathrm{I}}{3}}$(10) where CSI refers to the total sustainability index, W stands for the assigned weights. There is no exact judgment about which sustainability dimension is more important. Therefore, equal weights were given to the sustainability pillars. The composite sustainability index was calculated by aggregating all the sustainability dimensions after assigning equal weights to each dimension. It was assumed that all the pillars of sustainability are equally important, hence the equal weights. In the robustness and sensitivity analysis stage, the reliability test was applied to the prepared scales. In the stage of linking composite indicators with other variables, index results were compared with those in the literature. In the stage of back to real data, the current situation of the beef industry was analysed and discussed, and the index results were presented as radar indicators. The sustainability score in the study varies from 0 to 1. If the sustainability score of farms is close to 1, the sustainability increases; if the sustainability score is close to 0, the sustainability of farms decreases. To determine the effect of scale on sustainability, they were divided into three strata by taking the distribution into account as (i) 1–20 heads, (ii) 21–100 heads, and (iii) 101 and above heads. The survey data were analysed using the Statistical Package for Social Sciences (SPSS v.25) and descriptive statistics were summarized as means, standard deviations, and percentages. The F and chi-square tests were used to evaluate differences between the sustainability groups regarding the socio-demographic and farm characteristics. Duncan's post hoc multiple comparisons test was used to determine differences among the farm groups.

3. Results

3.1. Characteristics of beef cattle farms

The general characteristics of the beef cattle farms were given in Table 2. The average age of the farm owners was 46.35, while the average household size in the beef cattle farm was 5 persons. In beef cattle farms, 58% of the farm owners graduated from primary school, 16.70% from secondary school and 15.30% from high school. The average education level of owners of large farms was higher. In the study, 96.80% of the farmers had social security, while the main profession of 67.1% of the farm owners was beef cattle farming. The average beef cattle number in all farms was 28.56 heads, while it was 8.62, 26.40, and 113.14 heads in small, medium, and large farms, respectively. There was a statistically significant difference among the groups in terms of the number of cattle (p < 0.01) (Table 2).

Table 2. General characteristics of the beef cattle farms.

Characteristics	Small farms (69)		Medium farms (68)		Large farms (18)		All farms (155)
	Mean	S.D.	Mean	S.D.	Mean	S.D.	Mean	S.D.
Age (year)	47.31	11.08	46.29	10.31	42.94	11.21	46.35	10.79
Experience (year)**	17.35^a	6.32	20.88^b	9.09	19.67^b	11.25	19.17	8.39
Household size (person)	5.11	2.76	5.49	2.70	6.00	2.89	5.38	2.75
Education (%)
Elementary school	59.10	–	63.60	–	33.30	–	58.00	–
Secondary school	18.20	–	15.20	–	16.70	–	16.70	–
High school	12.10	–	15.20	–	27.80	–	15.30	–
Vocational school	7.60	–	1.50	–	16.70	–	6.00	–
University	3.00	–	4.50	–	5.60	–	4.00	–
Social security (%)	95.70	–	97.10	–	100.0	–	96.80	–
Main profession as farmer (%)	65.20	–	66.10	–	77.80	–	67.10	–
Number of cattle***	8.62^a	5.58	26.40^b	11.62	113.14^c	69.94	28.56	40.35

Notes: *, **, *** significance at the 10%, 5% and 1% level, respectively. a, b and c show statistically significant differences among the farm groups.

3.2. Measuring sustainability in beef cattle farm

3.2.1. Economic sustainability

Economic sustainability index results were given in Table 3 and Figure 3. The economic sustainability index of beef cattle farms varied between 0.14 and 0.73, while its average was calculated as 0.37. The most important indicators contributing to economic sustainability in beef cattle farms were found as the debt-asset ratio (0.86), beef yield (0.54), and production cost (0.46). The indicators with the lowest index value were determined as income (0.10), net profit (0.10), economic efficiency (0.26) and financial rentability (0.26), respectively. According to the index results by scale in the study, the average economic sustainability index was found as 0.31 for small farms, 0.40 for medium farms and 0.54 for large farms. It is observed that an increase in the farm size had a positive impact on economic sustainability in beef cattle farms and on almost all indicators except for debt-asset ratio. Although the debt-asset ratio was 0.87 in small farms, it decreased to 0.85 in medium-sized farms and increased to 0.89 in large farms. This difference was not statistically significant (p > 0.10) (Table 3). The selected indicators, except for the debt-asset ratio, were found to be statistically different among the farm groups (p < 0.05). Also, the change in economic sustainability caused by the increase in farm size was given through a radar chart in Figure 3. The sustainability increases if the farm is away from the origin. However, the sustainability decreases if it is close to its origin.

Figure 3. Economic sustainability index results with a radar chart (1 is more sustainable, 0 is less sustainable).

Table 3. Economic sustainability index (ESI) results.

Indicators	Small farms (69)		Medium farms (68)		Large farms (18)		All farms (155)
	Mean	S.D.	Mean	S.D.	Mean	S.D.	Mean	S.D.
ESI	0.31	0.07	0.40	0.07	0.54	0.10	0.37	0.10
Income***	0.03^a	0.03	0.09^b	0.06	0.39^c	0.25	0.10	0.07
Net profit***	0.02^a	0.01	0.09^b	0.04	0.48^c	0.25	0.10	0.08
Relative profit***	0.31^a	0.14	0.45^b	0.16	0.50^c	0.08	0.40	0.12
Yield***	0.50^a	0.15	0.56^a	0.11	0.60^c	0.08	0.54	0.10
Cost***	0.40^a	0.15	0.51^b	0.13	0.53^c	0.08	0.46	0.11
Economic rentability***	0.23^a	0.11	0.46^b	0.16	0.65^c	0.15	0.38	0.15
Financial rentability***	0.16^a	0.09	0.32^b	0.14	0.45^c	0.15	0.26	0.12
Debt/asset ratio	0.87	0.17	0.85	0.16	0.89	0.14	0.86	0.09
Economic efficiency**	0.23^a	0.22	0.26^a	0.19	0.39^b	0.30	0.26	0.07

Notes: **, *** significance at the 5% and 1% level. a, b and c show statistically significant differences among the farm groups.

3.2.2. Social sustainability

The social sustainability index was found to be 0.58. The lowest social sustainability index was 0.15, while the highest was 0.99. The average social sustainability index was determined as 0.52 in small-scale farms, 0.61 in medium-sized farms and 0.71 in large farms. Furthermore, the change in social sustainability caused by the increase in farm size was given through a radar chart in Figure 4. The most important indicators contributing to social sustainability in beef cattle farms were found as the sufficiency of educational institutions (0.96), misunderstanding with other people (0.88), migration from farms (0.87) and spending money for social activities (0.75). Indicators with the lowest contribution were determined as local rural development services (0.07), agricultural extension and education services (0.15), collaboration with local organizations or institutions (0.18) and education time (0.45). There was a statistically significant difference among the farm sizes in terms of social sustainability indicators (Table 4).

Figure 4. Social sustainability index results with a radar chart.

Table 4. Social sustainability index (SSI) results.

Indicators	Small farms (69)		Medium farms (68)		Large farms (18)		All farms (155)
	Mean	S.D.	Mean	S.D.	Mean	S.D.	Mean	S.D.
SSI***	0.52^a	0.12	0.61^b	0.10	0.71^c	0.13	0.58	16.30
Indicators of the future and investment***	0.54^a	0.22	0.73^b	0.20	0.85^c	0.20	0.66	32.52
Education, agricultural extension and collaboration**	0.20^a	0.10	0.18^a	0.17	0.34^b	0.23	0.21	19.81
Institutional and social behaviour**	0.82^a	0.16	0.89^b	0.20	0.94^c	0.13	0.86	22.58
Indicators on migration and improvements activities**	0.69^a	0.17	0.78^b	0.24	0.85^c	0.21	0.75	25.87

Notes: *, **, *** significance at the 10%, 5% and 1% level, respectively. a, b and c show statistically significant differences among the farm groups.

3.2.3. Environmental sustainability

The environmental sustainability index results were given in Table 5 and Figure 5. The environmental sustainability index of beef cattle farms was calculated between the range of 0.14 and 0.73, while its average was found as 0.51. The environmental sustainability index was 0.48 in small farms, 0.52 in medium farms, and 0.57 in large farms. Figure 5 shows that the large farms differed from the small and medium-sized farms especially on manure management, pesticide, and water usage. There was a statistically significant difference among the farm sizes in terms of pesticide and water usage and manure management. The most important indicators contributing to environmental sustainability were determined as monitoring meteorological conditions for the use of pesticides (0.99), taking precautions to protect nature (0.97), taking precautions for animal diseases (0.97), cleaning the equipment after the use of pesticides (0.97), taking precautions to protect places such as streams and lakes (0.95) and following the instructions for the use of pesticides (0.95). Indicators with the lowest contribution were found as taking training for erosion (0.03), training for fertilizer use (0.09), precautions against soil erosion (0.09), the distance of the manure to the house (0.10), doing organic farming (0.11) and knowing the pH level of the land (0.11).

Figure 5. Environmental sustainability index results with a radar chart.

Table 5. Environmental sustainability index (EnSI) results.

Indicators	Small farms (69)		Medium farms (68)		Large farms (18)		All farms (155)
	Mean	Std. Dev.	Mean	Std. Dev.	Mean	Std.Dev.	Mean	Std. Dev.
EnSI	0.48	0.13	0.52	0.10	0.57	0.12	0.51	12.98
Soil and environmental hygiene	0.21	0.17	0.27	0.21	0.26	0.24	0.24	29.23
Environmental protection awareness	0.93	0.10	0.98	0.10	0.96	0.10	0.95	15.11
The usage of pesticides and water***	0.85^a	0.20	0.96^a	0.09	0.98^b	0.07	0.92	19.04
Awareness of input usage	0.21	0.22	0.20	0.14	0.31	0.25	0.21	24.13
Erosion	0.08	0.20	0.05	0.18	0.03	0.12	0.06	20.25
Manure management***	0.24^a	0.25	0.34^a	0.27	0.53^b	0.33	0.32	35.50

Notes: *** significance at the 1% level. a, b and c show statistically significant differences among the farm groups.

3.2.4. Composite sustainability index

The composite sustainability index results were given in Table 6 and Figure 6. The composite sustainability index was found as 0.49. The lowest composite sustainability index was 0.27, while the highest was 0.76. The average composite sustainability index was determined as 0.43 in the small-scale farms, 0.51 in the medium-sized farms, and 0.61 in the large farms. Figure 6 shows that the large farms were more sustainable than the small and medium-sized farms, and all sustainability dimension results proved that.

Figure 6. Composite sustainability results with a radar chart.

Table 6. Composite sustainability index (CSI) results.

	Small farms (69)		Medium farms (68)		Large farms (18)		All farms (155)
	Mean	Std. Dev.	Mean	Std. Dev.	Mean	Std. Dev.	Mean	Std. Dev.
CSI***	0.43^a	0.08	0.51^b	0.07	0.61^c	0.08	0.49	0.10
ESI***	0.31^a	0.07	0.40^b	0.07	0.54^c	0.10	0.37	0.10
SSI***	0.52^a	0.12	0.61^a	0.10	0.71^b	0.13	0.58	0.11
EnSI**	0.48^a	0.13	0.52^a	0.10	0.57^b	0.12	0.51	0.11

Notes: **, ***significance at the 5% and 1% level. a, b and c show statistically significant differences among the farm groups.

4. Discussion

The income and net profit of small beef cattle farms were found to be quite lower than the other farms. Because, the small farms have low yield and economic and financial rentability. However, economic efficiency in the small farms was found to be lower than the other farms. Woodhouse (2010) emphasizes that while there is evidence that small-scale farms are more efficient in terms of energy use, it generally involves lower labour productivity and earnings than large-scale farms. Van der Meulen et al. (2014) found that large-scale dairy farms had higher labour productivity and net farm income than other dairy farms.

The factor with the highest index value was institutional and social behaviour, while the factor with the lowest index value was education, agricultural extension, and collaboration for social sustainability. This study found that an increase in farm size had a positive impact on both social and environmental sustainability in the beef cattle farms. The most contributing factor to environmental sustainability was the indicator of environmental protection awareness in the small and medium-sized farms, while it was the usage of pesticides and water in the large farms. It is clear that small and medium-sized farms have higher nature protection awareness. However, large farms have higher levels of education, and it raises awareness of pesticide and water use. However, there are some literatures with opposite findings. Hu, Zheng, et al. (2019) found that larger farmers were better than smaller farmers on environmental sustainability. The study also found that an increase in the farm size had a positive impact on the total sustainability of beef cattle farms. The most important factor contributing to sustainability was determined as social sustainability, while the lowest contribution was economic sustainability. Compared with our research results, M’hamdi et al. (2017) found similar composite sustainability level (0.49), higher economic sustainability (0.51) and environmental sustainability (0.65) levels, while lower social sustainability level (0.30) in the cattle farms. However, Isyanto and Dehen (2015) found a lower sustainability level (0.37) for cattle breeding in Indonesia. Fallahinejad et al. (2022) stressed that an increase in agricultural land increased the yield and income of the farms. Zhang et al. (2021) also determined that increasing farm size could contribute sustainability of maize production. Ren et al. (2019) found similar results with this study, as the farm size plays an important role in the performance of agriculture and increasing farm size could increase environmental sustainability.

As a consequence, larger farmers tend to use more capital and land, and smaller farmers use more labour in agricultural production. Large farms are economically sustainable because they use resources effectively. Also, large farms have more advantageous in terms of economic sustainability due to decreased fixed costs per animal, higher yield and credit opportunities. Ellis (1988) and Griffin et al. (2002) stressed that labour cost was relatively lower in smaller farmers, whereas capital and land costs were relatively lower in larger farmers. On the other hand, intensive use of inputs in large farms reduces environmental sustainability. However, large farms have obstacles in rural areas on accessing some social facilities such as schools, hospitals, etc.

Livestock farm sizes in Turkey are smaller compared to those in Europe and the USA. Large livestock farms in USA and Europe have high negative impacts on environmental sustainability regarding carbon emissions and carbon footprint. Because agricultural activities such as animal feeding and care, herds and waste management, etc., in large livestock farms are significantly larger scale than other farms, these agricultural activities prove that large herds negatively affect environmental sustainability. The smaller herd sizes in Turkey compared to farms in USA and Europe have provided an advantage regarding environmental practices. The increase in farm sizes in Turkey has brought about more modern, technological, and environmentally friendly techniques.

5. Conclusions

Beef is one of the essential foods in human nutrition. Providing beef to society at affordable prices depends on ensuring the sustainability of farms. This study measured the economic, social and environmental sustainability levels of beef cattle farms and determined the effect of the farm size on sustainability. The lowest sustainability level for beef cattle farming was the economic sustainability dimension, while the highest was the social sustainability dimension. The indicators of the debt-asset ratio, yield and production cost were the most important indicators in increasing the economic sustainability of the farms. However, the most important indicators contributing to social sustainability were found as the sufficiency of educational institutions, misunderstanding with other people, migration from farms and spending money for social activities. Besides, monitoring meteorological conditions for the use of pesticides, taking precautions to protect nature and animal diseases and cleaning the equipment after using pesticides were found to be the most important indicators for environmental sustainability. The environmental sustainability of the beef cattle farms was moderate, while their social sustainability level was above moderate. Nevertheless, the economic sustainability level in beef cattle farms was low. Therefore, economic sustainability should be increased in order to increase the sustainability of beef cattle farms.

5.1. Policy implications

The first way to increase sustainability in the beef cattle farms is to extend the size of the farms. The study shows that a greater farm size had a positive impact on economic, social and environmental sustainability levels. Therefore, the government should apply policies such as credit subsidies for infrastructure investments, incentives for animal purchase, allocation for land and pasture and incentives for individual and livestock insurance to extend farm sizes. Moreover, increasing the diversity of production activities and training farmers for livestock and other production activities may also have an important effect. Subsidizing the social security insurance premiums of the farmers and supporting the enlargement of the farm lands by the government can contribute to the increase of the social sustainability of the beef cattle farms. Farmers should be trained on irrigation, erosion, input use and environment protection to increase the environmental sustainability of beef cattle farming. Furthermore, to increase environmental sustainability, investment incentives should be given to control and conserve animal fertilizers and wastes. Fertilizers and wastes should be recycled. It is vital for beef cattle farms to establish waste collection centres in provincial and district agricultural directorates for agricultural wastes. This could in turn increase environmental sustainability.

This cross-sectional study was limited to the Province of Samsun. The same study should be conducted with panel data from beef cattle farms. Future studies should focus on value chain sustainability at the regional and/or national level in the beef sector.

Acknowledgements

This study is quoted from a part of the Ph.D. thesis of Uğur Başer. This work was supported by TUBITAK with a scholarship under the 2211-C Priority Areas Related to Doctoral Scholarship Program. The authors are very grateful to Sinem Ceylan (University of Leicester School) for the help given in the English editing of this article.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the TÜBİTAK under 2211-C Priority Areas Related to Doctoral Scholarship Programme.