Financial Energy as a Determinant of Financial Security: The Case of European Union Farms

Abstract

This research focuses on an interdisciplinary approach to the financial energy of farms, allowing it to be linked to the overall financial health of the entity, which is influenced by various factors, including the use of equity and debt capital, and the treatment of money as a source of energy. The data source for the analysis consisted of statistics from the Farm Accountancy Data Network (FADN). This study encompassed the average agricultural holdings in the European Union, as well as average farms classified by individual regions and economic size classes. This research covered both the European Union as a whole and individual regions within EU member states. The objective of this study is to assess the factors that determine the financial security of EU farms using factor analysis. The results of this research indicate that financial energy may be one of the main factors shaping the financial security of farms. This is particularly true for small farms, which, in line with the goals of sustainable development, should increase their income (financial energy).

1. Introduction

The interdisciplinary approach to financial energy (cf. [1]) allows it to be linked to the overall financial condition of an entity, which is influenced by various factors, including the utilization of equity and external capital, as well as the treatment of money as a source of energy [2,3]. Using the energy of finance, it is possible to increase the capacity of organizations and their ability to carry out their functions [4]. Building on our previous research [3,5] and drawing upon Korol’s conceptualizations [6,7], this paper adopts the notion of “financial energy of a farm”, defined as the entity’s overall financial situation. Financial energy allows organizations to make investments and serves as the basis for their development. The financial energy of agricultural holdings is therefore closely linked to their level of financial liquidity, indebtedness, and profitability. Agricultural holdings are characterized by a high or optimal level of financial liquidity [8], which is related both to the specific nature of agricultural production and to the financial decision-making conditions of these entities. These farms maintain high inventory levels, which is associated with the continuity of agricultural production and with livestock farming. Moreover, the indebtedness of agricultural holdings remains relatively low [3], which is determined by both micro- and macroeconomic factors. The profitability of agricultural holdings, in the context of financial energy, generally ensures their capacity for self-financing operations. It is also important in the process of obtaining external capital. Thus, sources of financial energy can be understood as the ability to self-finance operations, access to external capital, and its effective use—in other words, the broadly understood flow of money. The flow of financial energy in agricultural holdings is crucial for the development of individual farms as well as from a broader, global perspective. Consequently, it was considered that, financial energy is pivotal in shaping farms’ financial security. Drawing on the approach that considers money as a source of energy [2], this paper explores the significance of financial energy for ensuring farm financial security. Financial security is understood as the ability of an agricultural holding to meet its current liabilities [9], while also being capable of increasing the return on equity through the optimal use of its available resources.

Furthermore, financial security provides a sense of certainty in running a business and creates the conditions for its growth [10]. Davydenko et al. [11] point out the complexity of the concept of financial security and the need to take into account in this aspect both the external and internal risks of the business, the justification of the company’s development strategy, taking into account the changing external environment, as well as the company’s available financial resources, i.e., access to financial energy. In a broad sense, financial security is derived from the various categories of risks that directly or indirectly affect an entity’s financial position [12]. The literature also emphasizes that financial security can be ensured not only by maintaining a stable financial position of an entity, but also through its strategic development and the creation of conditions that will enable the implementation of a financial mechanism capable of adapting to future changes in the environment, both internal and external [13]. Financial energy may constitute the potential to create such a financial mechanism.

An interesting issue is the link between financial energy and financial security in the context of sustainable agricultural development. The idea of creating and implementing a development model that takes into account the necessity to meet the needs of future generations to the same extent as the current generation [14] is extremely difficult and complex. This is due to both the importance of the problem and the global dimension of this issue, as well as the multidimensionality of the sustainable development goals of the 2030 Agenda [15]. One important issue is the implementation of economic aspects. In this context, eliminating hunger and poverty is especially significant [16]. Agriculture is therefore the sector around which many of the defined goals are focused. This is due to the fact that farmers manage the majority of natural resources. Therefore, they focus on activities aimed at eliminating hunger or poverty, as well as those related to environmental protection and adaptation to climate change.

In the context of sustainable development in agriculture, attention should be paid to the determinants for the functioning of farms [17]. On the one hand, agriculture can lead to the degradation of the natural environment, but on the other hand, it creates natural assets. The positive, negative, or environmentally neutral effect of agricultural work depends on the practices used as well as the sources of energy used. The full sustainability of agriculture occurs only when significant changes occur simultaneously in three spheres: economic, ecological, and social [17]. The sustainable development goals pay particular attention to small (small-scale) farms. The assumption is that the income of these entities should be doubled by 2030. Research conducted in the field of financial management of farms, in the sphere of both investment and operational activities, shows that there are many factors determining farm income [18,19,20,21,22,23]. Financial security takes into account many of the aspects discussed in this area; therefore, the identification of the determinants of the financial security of economically small farms is part of the trend of research on the sustainable development of agriculture. Research conducted using factor analysis proves that there is a need to take into account the specificity of agricultural activity when examining the financial security of farms [24], as well as their economic power. The level of financial security of farms classified according to their economic size varies greatly, which is also linked to access to financial energy. Previous studies have shown that economic size determines the financial performance of farms [8]. The strongest units make greater use of external financing sources and, at the same time, are exposed to a higher risk of doing business. Farms with a small economic size achieve higher profitability than medium-sized entities, which may result from a more cautious management strategy. Medium-sized entities, on the other hand, demonstrate high liquidity as well as financial independence. Therefore, there is a noticeable variation in financial management among farms of different economic sizes [25], which confirms the need to analyze financial security separately for each economic size class.

Our previous study [24] demonstrated that when conducting research on the financial security of farms, the direction of their production should be taken into account. In the course of the study, we proved that it is necessary to consider these factors separately for farms that focus on crop production and those that focus on livestock production [24]. This study analyzes the importance of financial energy for farm financial security. The objective of the study is to assess the factors that determine the financial security of EU farms using factor analysis. The remainder of the paper is structured as follows: Section 2 delineates the research methodology and data sources. Section 3 presents the empirical research findings. The last section of the paper summarizes the results and propose potential areas that could be explored in future research.

2. Materials and Methods

The data source used in this study is FADN (Farm Accountancy Data Network) statistics. These data are collected in a uniform way for farms conducting agricultural activity in European Union (EU) countries that have a significant share in creating added value in agriculture [26]. The FADN system includes a representative selection of farms in the sample based on the criteria of economic size, agricultural type, and geographical location, both at the level of the European Union as well as individual countries or FADN regions). The published data refer to an average farm in a given category. The data used were extracted taking into account geographical location and economic size. This research concerns the European Union and individual regions of European Union countries (spatial scope of research). Data for Great Britain were not included in the analyses. The subjective scope is an average European Union farm and average farms distinguished in individual regions and according to individual economic size classes. The economic size of a farm means the sum of the values of Standard Output (SO) of all agricultural activities occurring on the farm. Standard Output (SO) is the 5-year average value of production of a specific agricultural activity (plant or animal) obtained from 1 ha or 1 animal in 1 year, under average production conditions for a given region. In order to eliminate fluctuations in production values, averages from 5 years of the relevant period are used for calculations [26]. On this basis, six classes of the economic size of farms can be distinguished: very small farms (1) where the sum of the values of Standard Output (SO) falls within the range of EUR 2000 to less than EUR 8000; small farms (2): 8000 ≤ EUR < 25,000; medium–small (3): 25,000 ≤ EUR <50,000; medium–large (4): 50,000 ≤ EUR < 100,000; large (5): 100,000 ≤ EUR < 500,000; and very large (6): ≥EUR 500,000. All analyses were conducted based on data provided by the EUFADN system [27].

The temporal scope of the study for assessing the level of financial security of farms covers the years 2014–2021. For the factor analysis, data for these regions for 2014, 2017, and 2020 were adopted. The choice of three periods results from the fact that agriculture is characterized by high price variability and production value, which is related, among others, to natural risk [28].

This research was carried out in two stages. The first included an assessment of the level of financial security of an average European Union farm, with particular emphasis on small farms. For this purpose, the indicator proposed by Ryś-Jurek [29] was used:

FS = GB + QR − DR + NWCTA + PEC

(1)

where:

FS—Financial security ratio

GB—golden balance sheet rule (equity/fixed assets)

QR—quick ratio (current assets – inventory – non-breeding livestock)/current liabilities)

DR—total liabilities to total assets (total liabilities/total assets)

NWCTA—coverage of assets by net working capital ((current assets-current liabilities)/total assets))

PEC—profitability of equity capital (farm net income/equity)

A score of <0 means no financial security, (0.0–2.0) means a risk of losing financial security, (2.00–5.0) means financial security, (5.0–7.0) means high financial security, and a result >7.0 means above-threshold financial security. The research was carried out taking into account the breakdown into six classes of economic size.

In the second stage of the research, the links between the determinants of farm financial security were identified. Six classes of economic values distinguished within the FADN system have been divided into three groups. The small farm group included the very small (1) and small (2) classes. The medium group included medium–small (3) and medium–large (4) farms. The large group included large (5) and very large (6) entities. For this purpose, data on average farms separated by economic size in individual EU regions were obtained from the FADN System. In total, 1679 cases were taken into account, including 349 small farms, 690 medium-sized farms, and 640 large farms. Factor analysis was used in the research conducted in stage two.

The financial security of farms depends on a large number of correlated variables. Including all of them, e.g., in regression models, may lead to multicollinearity. Factor analysis was used instead of removing some variables, which would result in losing some of the information contained in them.

Factor analysis postulates that there are a small number of latent variables (factors) and that each observed variable contains part of one or more factors. This method reduces the number of variables in a different way than principal component analysis, which replaces the observed variables with several orthogonal linear combinations of those variables [30,31,32,33]. The main reason for choosing factor analysis was the possibility of identifying and naming these latent variables. PCA, in the presence of highly positively correlated variables, often produces orthogonal components that are difficult to interpret.

The validity of using factor analysis is checked using Bartlett’s test of sphericity and the Kaiser–Meyer–Olkin (KMO) measure, and both criteria are based on the analysis of the correlation matrix of observed variables [34].

In matrix form, the model can be written using the formula [32]:

$$\mathrm{X}=\mathrm{A}·\mathrm{F}+\mathrm{B}\mathrm{U}$$

(2)

where:

X—vector of observed variables

A = (a_ij)—matrix of coefficients of linear combinations called factor loadings

F—vector of common factors

U—vector of specific factors

B—diagonal matrix of factor loadings of specific components

Based on research conducted in earlier periods [9,10,11,12,13,29,35,36,37,38,39,40], our previous studies [24], and the methodological assumptions of the FADN system, a set of variables shaping the financial security of European Union farms was identified (

Table 1. Catalogue of variables accepted for factor analysis with their description from the FADN system.

Variable Name	Description According to the FADN System	Category Explanation
Area	(SE025) Total utilized agricultural area (ha)	Total utilized agricultural area of holding.
Inputs	(SE270) Total inputs (EUR)	Specific costs + overheads + depreciation + external factors.
Taxes	(SE390) Taxes (EUR)	Farm taxes and other dues (not including VAT and the personal taxes of the holder) and taxes and other charges on land and buildings.
NetIncome	(SE420) Farm net income (EUR)	Remuneration to fixed factors of production of the family (work, land, and capital) and remuneration to the entrepreneur’s risks (loss/profit) in the accounting year.
FixedAssets	(SE441) Total fixed assets (EUR)	Agricultural land and farm buildings and forest capital + buildings + machinery and equipment + breeding livestock; intangible assets and other non-current assets. Closing valuation.
Livestock	(SE470) Non-breeding livestock (EUR)	Value at closing valuation of all livestock except breeding livestock (SE460). Closing valuation.
Stock	(SE475) Stock of agricultural products (EUR)	Value at closing valuation of all crop and livestock products (except young plantations).
CircCapital	(SE480) Other circulating capital (EUR)	Cash and other assets that can be easily converted to cash, short-term assets, amounts owed to the holding, normally arising from business activities, and any other assets that are easily sold or expected to be paid within a year. Closing valuation.
LoMeLoans	(SE490) Long- and medium-term loans (EUR)	Loans contracted for a period of more than one year.
ShLoans	(SE495) Short-term loans (EUR)	Loans contracted for less than one year and outstanding cash payments.
GrossInv	(SE516) Gross investment on fixed assets (EUR)	Purchases—sales of fixed assets + breeding livestock change of valuation.
NetInv	(SE521) Net investment on fixed assets (EUR)	Gross investment on fixed assets—depreciation.
CashFlow1	(SE526) Cash flow 1 (EUR)	The holding’s capacity for saving and self-financing.
CashFlow2	(SE530) Cash flow 2 (EUR)	The holding’s capacity for saving and self-financing.
Subsidies	(SE605) Total subsidies—excluding on investments (€)	Subsidies on current operations linked to production (not investments). Payments for cessation of farming activities are therefore not included.Entry in the accounts is generally on the basis of entitlement and not receipt of payment, with a view to obtain coherent results (production/costs/subsidies) for a given accounting year.

Source: own study based on EU FADN data [26,27].

). The first variable is Area, which reflects the size of the farm in terms of its area. Land is one of the basic production factors in agriculture. The next variables are Inputs and Taxes. Both influence the income level achieved by the surveyed entities. FixedAssets, Livestock, and Stock reflect the assets held, which are used in the agricultural production process. It was assumed that the level of investments made (GrossInv, NetInv) is also important for the financial security of farms. Moreover, taking into account the adopted definition of the financial energy of a farm, it can be assumed that its source is the following variables: NetIncome, CircCapital, LoMeLoans, ShLoans, CashFlow1, CashFlow2, and Subsidies. NetIncome is a variable that reflects the economic surpluses achieved by the farm. The increase in Farm Net Income meets the assumptions of implementing the economic aspects of the sustainable development goals. It also means the source of financial energy of the farm generated as a result of agricultural activity. The ability to self-finance the activity is also reflected by the variables CashFlow1 and CashFlow2. Financial energy obtained from internal sources is the most frequently used capital in agricultural production. Farm income is also supplied by subsidies for operating activity. In line with the FADN methodology, CircCapital represents the amount of cash held by a farm. Moreover, farms also obtain external capital, which, in the context of maintaining financial liquidity, impacts their overall financial security (LoMeLoans, ShLoans). The variables were tested in the second stage of the research.

Factor analysis was carried out in R, ver. 4.3.0 [41] using packages psych [42], GPArotation [43], and graphics package ggplot2 when creating diagrams.

3. Results

The assessment of the financial security of small farms in the European Union began with presenting the indicator level for farms classified as small (very small farms (1) and small farms (2)), compared to other classes (Figure 1). Detailed data on the individual economic size classes and the components of the financial security indicator are presented in

Table A1. Characteristics of average European Union farms by economic size in 2014–2021.

Year	Economic Size	(SE441) Total Fixed Assets (EUR)	(SE465) Total Current Assets (EUR)	(SE470) Non-Breeding Livestock (EUR)	(SE475) Stock of Agricultural Products (EUR)	(SE485) Total Liabilities (EUR)	(SE495) Short-Term Loans (EUR)	(SE501) Net Worth (EUR)	(SE420) Farm Net Income (EUR)	(SE436) Total Assets (EUR)
2014	(1)	38,348	5925	805	906	646	324	43,627	2756	44,273
	(2)	131,721	21,688	2064	2259	3737	703	149,672	8579	153,409
	(3)	243,097	42,235	5481	5168	19,520	4265	265,812	15,355	285,332
	(4)	352,923	73,461	9849	9280	45,305	10,482	381,079	26,651	426,384
	(5)	664,812	171,238	25,435	30,508	184,409	43,304	651,641	55,669	836,051
	(6)	2,055,234	677,194	105,795	124,522	1,091,885	219,268	1,640,543	150,912	2,732,428
2015	(1)	35,682	3922	715	717	267	92	39,337	1673	39,604
	(2)	124,231	20,618	1893	1853	3722	677	141,127	8276	144,849
	(3)	232,167	39,447	4807	4079	15,923	3161	255,691	15,366	271,614
	(4)	343,526	74,911	8735	8061	44,223	10,271	374,215	26,035	418,437
	(5)	629,996	169,081	23,528	30,040	170,776	41,312	628,302	52,349	799,077
	(6)	2,030,007	658,978	104,034	119,675	1,010,458	202,175	1,678,527	146,585	2,688,985
2016	(1)	37,250	4351	806	668	366	103	41,234	1963	41,600
	(2)	124,949	21,803	1978	1812	3564	659	143,188	8273	146,752
	(3),	236,706	43,044	4794	4139	16,958	3716	262,792	15,334	279,750
	(4)	345,834	78,053	8857	7755	43,986	10,071	379,902	26,321	423,888
	(5)	630,599	169,697	23,781	27,942	173,093	42,419	627,203	50,231	800,296
	(6)	2,055,890	670,431	110,783	114,278	1,004,581	214,633	1,721,740	177,760	2,726,321
2017	(1)	38,183	4460	807	730	404	155	42,239	2424	42,643
	(2)	123,974	22,295	1967	2003	3748	738	142,522	9251	146,270
	(3)	249,600	45,223	4991	4419	17,902	4025	276,921	16,744	294,823
	(4)	343,510	79,503	8974	8306	42,284	10,133	380,729	28,616	423,013
	(5)	637,077	175,307	23,864	28,025	171,452	41,626	640,932	61,016	812,384
	(6)	2,079,054	681,576	104,640	113,599	1,010,055	216,105	1,750,574	205,332	2,760,630
2018	(1)	56,690	5895	883	1019	404	159	62,180	2139	62,585
	(2)	126,662	24,928	1921	1875	3935	855	147,654	8361	151,589
	(3)	233,348	45,398	4646	4507	17,374	4314	261,372	16,017	278,746
	(4)	328,889	77,681	7896	7922	38,053	9799	368,517	28,819	406,570
	(5)	637,857	180,873	22,654	27,315	163,561	41,395	655,170	60,981	818,730
	(6)	2,126,861	708,752	95,381	127,076	974,422	219,442	1,861,191	173,888	2,835,613
2019	(1)	55,898	6112	996	1226	579	159	61,431	2602	62,010
	(2)	127,616	26,714	1943	2034	4053	986	150,277	8696	154,330
	(3)	236,440	49,523	4580	4424	17,293	4126	268,669	16,151	285,963
	(4)	333,576	83,304	7815	8049	40,031	9833	376,849	28,620	416,880
	(5)	648,293	193,026	22,646	27,926	166,093	41,155	675,225	61,977	841,318
	(6)	2,113,312	782,866	109,934	121,151	967,915	209,542	1,928,264	232,463	2,896,178
2020	(1)	56,388	6675	872	1400	633	155	62,429	2561	63,062
	(2)	124,191	27,853	1921	1893	2542	678	149,501	9063	152,043
	(3)	236,801	53,631	4456	4367	16,329	4228	274,103	17,133	290,432
	(4)	336,609	93,556	7858	8058	39,534	9815	390,632	30,178	430,166
	(5)	657,091	208,766	22,423	27,621	171,840	42,693	694,018	61,470	865,858
	(6)	2,149,613	852,778	106,500	124,860	983,279	212,876	2,019,112	208,892	3,002,392
2021	(1)	58,401	7109	843	1479	443	144	65,067	2957	65,510
	(2)	123,677	32,123	2011	2252	2560	750	153,239	10,612	155,799
	(3)	240,029	59,948	4767	4947	17,301	4011	282,676	19,909	299,977
	(4)	340,604	103,265	8150	9408	41,076	9860	402,793	36,091	443,868
	(5)	667,851	240,208	23,010	29,681	173,315	43,654	734,744	78,849	908,060
	(6)	2,185,805	941,628	102,830	132,810	1,006,914	227,844	2,120,519	238,673	3,127,434

Legend: (1) 2000 ≤ EUR < 8000; (2) 8000 ≤ EUR < 25,000; (3) 25,000 ≤ EUR < 50,000; (4) 50,000 ≤ EUR < 100,000; (5) 100,000 ≤ EUR < 500,000; (6) ≥EUR 500,000. Source: own study.

.

There are very large differences in the level of financial security of farms in different economic size classes. Small farms (groups 1 and 2) had an average index level of over 26, where from a level above 7, above-threshold financial security is demonstrated. It is worth emphasizing the high increase in the indicator level of this group in 2020–2021. The period of economic uncertainty contributed to an even more conservative strategy regarding the level of financial security. This was primarily due to the increase in financial liquidity (Table A1). Medium-sized farms (groups 3 and 4) also showed above-threshold financial security; however, the average values in these groups were much lower. They did not exceed the level of 13.9. Those entities that achieved the highest economic size (groups 5 and 6) maintained an optimal or high level of financial security. In the context of the sustainable development of farms, the following question arises: What factors determine such a high level of financial security, which at the same time means low economic size? An increase in the income of these entities can also be achieved by optimizing the interaction between investment, financial, and operational decisions, including financial energy supply.

In the next stage of the research, the links between variables potentially determining the financial security of small farms were assessed, comparing them with all farms, as well as with the medium and large groups. These groups were differentiated from each other. Descriptive statistics of the variables are presented in

Table A2. Descriptive statistics—all sizes of farms.

	Min	Median	Max	Mean	SD
Area	0.79	44.36	1700.3	112.9	234
Inputs	1997	72,066	3,448,688	234,910.7	450,378.4
Taxes	0	888	76,746	2758.1	6701.4
NetIncome	−33,502	26,755	1,168,253	59,287.4	98,822.8
FixedAssets	5863	261,661	7,073,145	526,994.9	774,828.5
Livestock	0	5511	773,328	21,909.5	45,623.7
Stock	0	4203	1,211,339	30,481	87,810.9
LoMeLoans	0	14,213	4,183,575	113,017.9	300,374.9
ShLoans	0	4087	1,076,329	48,908.2	124,461.1
GrossInv	−57,941	7902	646,053	33,397.7	71,672.6
NetInv	−111,280	−640	411,998	6350.4	32,980.9
CashFlow1	−8269	36,224	1,207,431	82,348.4	121,902.7
CashFlow2	−865,123	24,525	1,481,363	50,551.4	114,010.4
Subsidies	385	17,708	645,200	38,459.2	70,772.6
CircCapital	544	54,616	2,365,315	138,155.2	241,285.8

Source: own study.

,

Table A3. Descriptive statistics—small farms.

	Min	Median	Max	Mean	SD
Area	0.79	9.85	51.13	12.69	9.029
Inputs	1997	12,120	88,996	13,927.2	10,238.7
Taxes	0	172	3685	280.7	362.9
NetIncome	−14,943	7444	29,146	7658.2	5135.6
FixedAssets	5863	79,984	775,693	105,127	101,251.3
Livestock	0	962	16,339	1451.3	1831.4
Stock	0	988	25,530	1652.7	2323.6
LoMeLoans	0	361	297,300	3914.5	19,968.2
ShLoans	0	101	40,169	937.8	2965.5
GrossInv	−10,095	898	18,030	1656.5	2828.1
NetInv	−16,491	−1094	9831	−1109.6	2299.6
CashFlow1	−8269	9494	31,226	9870	5438.1
CashFlow2	−41,555	7521	29,313	7828.4	6243.7
Subsidies	385	3949	20,339	4801	3531.7
CircCapital	544	8934	107,214	17,452.5	20,023.4

Source: own study.

,

Table A4. Descriptive statistics—medium farms.

	Min	Median	Max	Mean	SD
Area	2.04	38.05	153.2	44.87	27.82
Inputs	13,650	51,848.5	165,711	56,648.5	27,623.2
Taxes	0	708.5	9927	921.4	962.4
NetIncome	−7777	20,835.5	112,480	22,953.9	12,803.7
FixedAssets	29,217	193,518	1,591,743	255,720.5	210,965.8
Livestock	0	4153.5	54,260	6344.8	6827.1
Stock	0	3627	125,408	6718.9	9871.4
LoMeLoans	0	10,430	624,364	25,763.7	53,268.4
ShLoans	0	3655	84,420	9699.5	13,290.5
GrossInv	−57,941	6017	161,293	8744.5	11,870.8
NetInv	−64,748	−1232	146,323	−394.2	9677.5
CashFlow1	90	29,265	107,948	31,139.6	12,223.2
CashFlow2	−147,892	20,709.5	109,257	22,154.8	15,448.9
Subsidies	1276	14,880.5	93,569	17,310.9	11,027.9
CircCapital	942	41,172.5	310,837	50,803.4	41,243.3

Source: own study.

and

Table A5. Descriptive statistics—large farms.

	Min	Median	Max	Mean	SD
Area	2.11	118.2	1700.3	240.8	340.6
Inputs	61,150	267,721.5	3,448,688	547,604.8	610,622.5
Taxes	0	2753.5	76,746	6089.1	9937.4
NetIncome	−33,502	76,101	1,168,253	126,613.4	134,286.3
FixedAssets	107,312	641,396	7,073,145	1,049,512.3	1,035,780.1
Livestock	0	25,524.5	773,328	49,846.3	64,358.9
Stock	0	23,599	1,211,339	71,820	131,776.5
LoMeLoans	0	132,182.5	4,183,575	266,584.2	441,939.6
ShLoans	0	50,900.5	1,076,329	117,338.9	181,316.6
GrossInv	−49,751	42,350.5	646,053	77,285.8	100,982.7
NetInv	−111,280	4607.5	411,998	17,689.9	50,440.3
CashFlow1	13,198	106,834	1,207,431	177,081.3	155,423.5
CashFlow2	−865,123	65,666.5	1,481,363	104,463.9	170,517.2
Subsidies	859	42,451.5	645,200	79,613.9	101,078.9
CircCapital	8734	163,411.5	2,365,315	298,152.2	330,141.4

Source: own study.

. Small farms stand out from the other groups. They have a smaller area (median is 9.85 ha) and the value of fixed assets (79,984 EUR). This also affects the amount of economic surpluses generated. The medium farm income is PLN 7658.2 EUR, and for large entities 126,613.4 EUR.

The variables selected for the study on the basis of merit are highly correlated with each other (Figure 2).

The following variables achieved coefficients exceeding the level of 0.80: Area and Subsidies (0.89); Inputs together with CircCapital (0.81); Subsides (0.89); GrossInv (0.87); ShLoans (0.86); FixedAssets (0.81), as well as NetIncome with CashFlow1 (0.95); ShLoans from GrossInv (0.86); GrossInv with NetInv (0.84) and CashFlow1 and CircCapital (0.82). All but one variable (Area) are expressed in Euros. These variables have not been scaled so as not to change their actual weight. Area has been scaled to equate its mean with the average mean of the monetary variables.

The KMO measure of the adequacy of the selection of input variables for factor analysis was 0.86 for farms of all sizes; 0.61, 0.66 and 0.82 for small, medium, and large farms, respectively (all above the admissibility threshold of 0.6). The adequacy of the selection of individual variables (SMA) is given in

Table 2. Adequacy of the selection of variables (SMA) for different farm sizes.

	Group of Farms
	All	Small	Medium	Big
Area	0.91	0.79	0.83	0.88
Inputs	0.89	0.76	0.76	0.87
Taxes	0.98	0.72	0.77	0.97
NetIncome	0.69	0.50	0.53	0.59
FixedAssets	0.94	0.79	0.63	0.91
Livestock	0.92	0.60	0.78	0.87
Stock	0.86	0.81	0.80	0.79
LoMeLoans	0.89	0.66	0.79	0.85
ShLoans	0.93	0.57	0.78	0.91
GrossInv	0.76	0.37	0.52	0.73
NetInv	0.62	0.26	0.39	0.58
CashFlow1	0.78	0.51	0.58	0.72
CashFlow2	0.93	0.76	0.85	0.88
Subsidies	0.91	0.82	0.83	0.88
CircCapital	0.96	0.74	0.75	0.94

Source: own study.

. Bartlett’s test of sphericity also confirmed the validity of using factor analysis ($p<0.001$ in all four cases).

Most of the variables considered are, to one extent or another, related to the scale of farms in a given region; therefore, the assumption of the orthogonality of factors is unjustified. For this reason, a non-orthogonal oblimin rotation was used [44].

First, the variables were reduced for all the surveyed farms in the European Union. Horn’s parallel analysis [45] showed the optimal number of 4 factors. Loadings of variables with values above 0.1 in individual factors are presented in

Table 3. Factor loadings of variables, all farm sizes.

	Factor 1	Factor 2	Factor 3	Factor 4
Area	0.915	0.107	−0.176
Inputs	0.595	0.131	0.407
Taxes	0.464	0.166	0.303	−0.128
NetIncome		1.014
FixedAssets	0.150	0.266	0.582
Livestock	0.214	0.336	0.433
Stock	0.346		0.125	0.196
LoMeLoans			0.776	0.22
ShLoans	0.668	−0.132	0.290	0.157
GrossInv	0.391		0.269	0.534
NetInv				0.952
CashFlow1	0.150	0.814	0.106
CashFlow2	−0.172	0.793		−0.103
Subsidies	0.908
CircCapital	0.361	0.483	0.271	−0.101

Source: own study.

.

The variables most loaded by factor 1 are Area, Subsidies (financial energy), ShLoans (financial energy), Inputs, Taxes, and Stock; factor 2—NetIncome (financial energy), CashFlow1 (financial energy), CashFlow2 (financial energy), and CircCapital (financial energy); factor 3—LoMeLoans (financial energy), FixedAssets, and Livestock; factor 4—NetInv and GrossInv. The allocation of variables to factors and factor loadings are presented in Figure 3.

Thus, through factor analysis, out of fifteen variables shaping the security of farms from the regions of the EU countries, four synthetic factors emerged: factor 1: operational activities; factor 2: income and the ability to self-finance a business; factor 3: working stock and the effects of long-term financial and investment decisions; factor 4: investment activity. The names of the factors were assigned based on the author’s concept, taking into account both previous research presented in the literature [9,10,11,12,13,29,35,36,37,38,39,40] and the author’s own studies in this field [24]. The cumulative variance index is 0.61, which is an acceptable level in social sciences [46]. Then, the output variables were reduced for small farms, which are particularly important in the context of achieving the sustainable development goals. In this case, the optimal number of five factors was selected based on Horn’s parallel analysis. Loadings of variables with values above 0.1 in individual factors are presented in the

Table 4. Factor loadings of variables, small farms.

	Factor 1	Factor 2	Factor 3	Factor 4	Factor 5
Area		0.756			−0.224
Inputs		0.535	0.393		0.278
Taxes	0.327	−0.226	0.129		0.419
NetIncome	0.993				−0.112
FixedAssets	0.125		0.261		0.526
Livestock		0.323
Stock	−0.140	0.447			0.236
LoMeLoans			0.854		0.160
ShLoans		0.112	0.854		−0.176
GrossInv		0.255		0.817	0.349
NetInv		−0.149		0.957	−0.197
CashFlow1	0.918	0.147			0.147
CashFlow2	0.810		−0.151	−0.290
Subsidies		0.858
CircCapital	0.489		0.481	−0.123

.

The variables most loaded by factor 1 are NetIncome (financial energy), CashFlow1 (financial energy), CashFlow2 (financial energy), and CircCapital (financial energy); factor 2—Subsidies (financial energy), Area, Inputs, Stock, and Livestock; factor 3—LoMeLoans (financial energy) and ShLoans (financial energy); factor 4: NetInv i GrossInv; factor 5—included: FixedAssets and Taxes. The allocation of variables to factors and factor loadings are presented in Figure 4.

Reducing the variables to five factors allowed for the generalization of their names. Factor 1 was defined as income and the ability to self-finance a business, and factor 2 as operational activities. Factor 3 reflects debt, and the fourth one is investment activity. Factor 5 is fixed assets and taxes. The cumulative variance index is 0.64.

Similarly to all farms, for entities of medium economic size, Horn’s parallel analysis showed the optimal number of factors as 4. Loadings of variables with values above 0.1 in individual factors are presented in

Table 5. Factor loadings of variables, medium farms.

	Factor 1	Factor 2	Factor 3	Factor 4
Area	0.681			−0.255
Inputs	0.777			0.302
Taxes		0.28		0.351
NetIncome	−0.117	0.924
FixedAssets	−0.121			0.83
Livestock	0.448			0.13
Stock	0.314
LoMeLoans	0.275	−0.184		0.661
ShLoans	0.625	−0.2
GrossInv	0.246		0.885
NetInv	−0.136		1.001
CashFlow1	0.202	0.914	0.104	0.127
CashFlow2		0.728	−0.43
Subsidies	0.804	0.131		−0.131
CircCapital		0.216		0.526

Source: own study.

.

The variables most loaded by factor 1 are Subsidies (financial energy), Inputs, Area, ShLoans (financial energy), Livestock and Stock; factor 2: NetIncome (financial energy), CashFlow1 (financial energy) and CashFlow2 (financial energy); factor 3: NetInv and GrossInv; factor 4: FixedAssets, LoMeLoans (financial energy), CircCapital (financial energy) and Taxes. The allocation of variables to factors and their factor loadings are presented in Figure 5.

In the case of farms of medium economic size, the first factor isolated in the factor analysis is operational activities, factor 2 is income and the ability to self-finance a business, and factor 3 includes investment activity. The last of the extracted factors is Taxes, other current assets, and the effects of long-term financial and investment decisions. The cumulative variance index is 0.59.

In order to better assess the financial security of farms, the output variables for economically large farms were also reduced. In this case, 5 factors were identified. Loadings of variables with values above 0.1 in individual factors are presented in

Table 6. Factor loadings of variables, large farms.

	Factor 1	Factor 2	Factor 3	Factor 4	Factor 5
Area	0.133	0.725	−0.131	0.113	0.18
Inputs	0.106	0.416	0.419		0.284
Taxes	0.139	0.345	0.358	−0.134
NetIncome	1.023
FixedAssets	0.174	0.15	0.731
Livestock	0.284	0.134	0.453
Stock			−0.101		0.747
LoMeLoans	−0.1	−0.125	0.791	0.176	0.1
ShLoans	−0.11	0.29	0.195		0.57
GrossInv		0.22	0.253	0.534	0.263
NetInv				0.96
CashFlow1	0.855		0.118		0.105
CashFlow2	0.695	−0.142		−0.134
Subsidies		0.978
CircCapital	0.472	0.25	0.273	−0.141	0.161

Source: own study.

.

The variables most loaded by factor 1 are NetIncome (financial energy), CashFlow1 (financial energy), CashFlow2 (financial energy), and CircCapital (financial energy); factor 2: Subsidies (financial energy) and Area; factor 3: LoMeLoans (financial energy), FixedAssets, Livestock, Inputs, and Taxes; factor 4: NetInv and GrossInv; factor 5: Stock and ShLoans (financial energy). The allocation of variables to factors and factor loadings are presented in Figure 6.

In the case of economically strong farms, out of fifteen variables shaping the security of farms from the regions of the EU countries, extracted according to the FADN methodology, five synthetic factors were identified thanks to factor analysis. The factors were given the following names: 1—income and the ability to self-finance a business, 2—land and subsidies for operating activities, 3—taxes, costs, trading stock, and the effects of long-term financial and investment decisions, 4—investment activity, and 5—inventories and short-term liabilities. The cumulative variance index is 0.61.

4. Discussion and Conclusions

The aim of this paper was to assess the factors that determine the financial security of EU farms using factor analysis. This article discusses four issues that are important for the functioning of farms and have been the subject of extensive scientific research: financial energy [1,2,3,4,5,6,7], financial security [9,10,11,12,13,24,29,35,36,37,38,39,40], the economic size of farms [8], and the goals of sustainable development [11,15,16,17]. The results obtained contribute to the ongoing discussion on the interrelation of these four fundamental issues. Financial energy, understood as the treatment of money—namely, the ability to increase equity and access debt capital—is a key factor in shaping the financial security of farms. An interdisciplinary approach to the issue of financial energy makes it possible to emphasize its importance in achieving sustainable development goals. The acquisition of financial energy provides farms with the ability to initiate processes through which this energy is multiplied. This is an important issue both in terms of optimizing the level of financial security and increasing farm income (in line with the implementation of sustainable development goals), as well as shaping the economic size of a given entity.

This study used data on farms participating in the FADN. The economically weakest entities show an index more than 8 times higher than economically strong units. However, it is economically strong units that generate higher income. These results confirm the view presented in the literature regarding the high level of financial security among farms, particularly small entities [29]. The reasons for this may include, among others, the relationship between the level of financial liquidity and farm profitability. While a risky liability management strategy would typically be expected to support higher profitability, in the case of farms, these trends may be reversed [47].

Factor analysis allowed for the reduction of 15 variables potentially determining financial security to several factors, without a significant loss of the information contained within them. This study thus confirmed the applicability of factor analysis to the variables shaping the financial security of European Union farms. Based on factor analysis, it was shown that the output variables reduce in a different way for all farms: small, medium, and large. In the case of all farms, four factors were identified. In this case, the factor that explains the largest part of the phenomenon turned out to be operational activities. The situation is similar in the case of medium-sized entities. This factor includes, among others: variables reflecting the farm area, subsidies for operating activities (financial energy), but also costs incurred. Operational activity is a key factor influencing financial security and largely depends on the inflow of capital (financial energy). In the context of sustainable agriculture, there is a discussion about, among other things, the need to incur high costs of fertilizing the land and sources of energy used, which contributes to lower profitability of agricultural production [48,49,50,51,52]. This also has implications for the financial security of these entities. In the case of small and large economic entities, the first factor is income and the ability to self-finance a business. It includes income from the family farm, as well as cash flow, which means the ability to self-finance the activity. This factor thus provides an inflow of financial energy. It should be noted here that farms show a high tendency to use equity capital to finance their activities [53]. If a farm generates financial energy in the form of own capital, then it is financially secure. In the case of very large farms, the share of debt in the structure of financing sources was, on average, 16%, while in the case of very small farms it was 0.38% (Table A1). In the factor analysis, only for small farms did a separate factor of Debt appear, which contains variables expressing short-term and long-term liabilities. This demonstrates how important it is for small farms to have access to financial energy from external sources. There is a need for continued research in this area. These farms demonstrate above-threshold financial security, which may affect the amount of economic surpluses achieved. Moreover, debt capital is considered a source of financial energy that determines investment activity and, therefore, the amount of economic surpluses [3]. The realization of the sustainable development goals in relation to the increase in the income of small (small-scale) farms requires research into the financial and investment decisions made by these entities. It is worth emphasizing that, despite significant differences in the level of financial security between farms with the lowest and highest economic size, these entities exhibit smaller disparities in terms of profitability indicators. The ROE ratio (measured as farm net income to net worth) is, on average, 2.2 times higher for the economically strongest farms compared to the weakest ones. The ROA ratio (measured as farm net income to total assets) is 1.4 times higher (Table A1). These findings confirm the need for further research in this area, particularly in the context of the excessive accumulation of assets by these entities relative to the economic surpluses they generate [54,55].

The conducted research therefore contributes to both the literature and practice. It is part of the trend of research on the financial aspects of achieving the sustainable development goals. Small farms constitute a large part of all European Union farms and are therefore responsible for food security. Moreover, in their case, increasing income is a complex process that should involve both changes in production techniques and the process of understanding financial, investment, and operational decisions, which is also linked to access to financial energy. Therefore, the conducted research constitutes a source of information for agricultural advisory institutions. There is a need to educate farms on the determinants of the use of external sources of financing in the context of a possible reduction in the level of financial security, while increasing the income of small farms.

The obtained results constitute a continuation of research in the field of financial security of agricultural farms [24] and the use of financial energy [3,4,5]. At the same time, they indicate directions for further research. There is a need to conduct analysis in the field of financial determinants of achieving sustainable development goals. An important issue in this regard is the connection between the achievement of small farm income growth and the above-threshold financial security that small (small) farms maintain. It is typical for farms to have a high correlation of variables. Through factor analysis, the present study reduced the correlated variables to several factors without significant loss of information contained within them. In the next stage of the research, the strength and direction of the impact of the selected variables on the financial security of European Union farms will be tested. This is particularly important in the context of achieving the sustainable development goals relating to agriculture.