The Effect of Eco-Scheme Support on Romanian Farms—A Gini Index Decomposition by Income Source at Farm Level

Abstract

The Common Agricultural Policy 2021–2027 includes stronger environmental and climate targets to contribute to Green Deal objectives. By using direct payment funds for sustainable agricultural practices, the CAP aims to strengthen incomes, reduce climate impact, protect biodiversity, etc. However, there are many conditions farmers must meet to access funds under eco-schemes, and there are many concerns about their impact on income and profitability. It is, therefore, important to understand the impact of subsidies on Romanian farms. This study analyses income inequality on three Romanian farms (with a cultivated area between 2400 and 2600 ha, 550 and 610 ha, and 40 and 66 ha during the 2019–2021 period), focusing on the impact of different income sources (production and subsidies). The study is based on data collected during the 2019–2021 period and uses Gini coefficients and concentration indicators. The results show the following: the inequality-reducing effect of subsidies depends on crop rotation and changes in income from agricultural production; the influence of subsidies on inequality at the farm level is very low; the dependence on direct payments can be overcome by good crop selection and management; farmers cannot survive without subsidies, especially in years with difficult conditions; the impact of subsidies was higher for the lowest-profit variants. These findings are important because eco-schemes are voluntary, and stakeholders are not expected to spend the money allocated to eco-schemes.

1. Introduction

The post-2020 Common Agricultural Policy (CAP) aims to adapt European agriculture to environmental challenges, with 40% of the measures supported aimed at supporting climate objectives. Even before 2020, the CAP’s measures were in line with these objectives by “linking CAP first pillar payments per hectare to compliance with (baseline) environmental requirements”. Farmers had to “respect cross-compliance, which includes SMRs and Good Agricultural and Environmental Condition (GAEC) standards” [1].

In the current CAP, we have a new “Green Architecture”, which includes mandatory elements (GAEC), voluntary measures from GAEC, and eco-schemes. The voluntary nature of some measures, the eligibility of eco-schemes limited to farmers, and the way the CAP addresses chemical use and water consumption are seen as obstacles to achieving climate objectives [2]. The current “European Green Architecture” is not the first initiative of the CAP to respond again to environmental objectives, and, over time, various instruments have been used in the European Union, such as the following: payments for less-favored areas, which have a compensatory function [3] and were intended to limit the abandonment of agricultural land [4]; cross-compliance and direct payments under the first pillar, as well as payments under the second pillar for rural development projects “in line with wider EU environmental and climate objectives” [5]. Throughout this period, there has been much work analyzing the relationship between the CAP and environmental objectives. These analyses covered various issues, such as the following: inequality in the distribution of direct payments [6]; bioenergy production from agriculture and climate impacts [7]; pre-2020 CAP environmental effectiveness [4]; uptake of greenhouse gas reduction subsidies [8]; inconsistency and poor functioning of agri-environment measures [9], etc. As far as the impact and consequences of post-2020 CAP and the introduction of new requirements are concerned, there are numerous studies with different approaches. Many of them mainly revolve around the design of the reform [10] or the degree of compliance with the SDG targets [11].

The eco-scheme measures can relate to permanent crops (Spain), livestock (Bulgaria or the Netherlands), grassland (Spain), afforestation (Ireland), etc. The design of eco-schemes varies widely depending on their main objectives: conservation of unproductive arable land, maintenance of land covered with vegetation, reduced or no tillage, water-saving irrigation, grazing, etc. Member states can choose ecosystem measures, and in the new programming period 2023–2027, the budget for environmental and climate benefits is higher. The number of eco-schemes varies from 3 to 21 per country, and the implementation system varies widely, from a combination of several eco-schemes or mutually exclusive eco-schemes to a single measure with multiple requirements [12].

As far as the eco-scheme measures are concerned, some authors consider that they are not environmentally ambitious enough [7,13,14,15], they lead to spatial disruptions in the distribution of funds [16], or they create new challenges for farmers if access to them is too complex [17]. Another important point is that eco-schemes are funded under Pillar 1 (direct payments and market measures) and that any reduction in BISS (basic income support for sustainability) would be “in favor of eco-schemes and may decrease farmers’ level of enrolment in voluntary schemes and the overall adoption of environmentally friendly practices when the compensation for such schemes does not fully compensate for forgone income and cost incurred” [18].

Therefore, the authors consider that it is very important to study the impact of the changes in subsidies on farm incomes, especially environmental incomes. This type of study is very important for Romania, considering the following: there are large farms with higher incomes that receive a large share of direct payments and small- and medium-sized farms that rely on subsidies; Romania is one of “the largest CO₂ emitters in the EU from drained organic soils” [19]; only 2.9% of total agricultural land is managed organically [20,21]; the number of organic producers has decreased by 37% in the last ten years [22]; direct payments per hectare have led to a real phenomenon of land grabbing [23].

In Romania, stakeholders have not carried out an ex ante impact assessment of farmers adopting eco-schemes, and concerns have been raised during negotiations that eco-schemes funds will not be used due to their voluntary nature. The Romanian Ministry of Agriculture and Rural Development did not submit a research plan for the 2023–2026 period until April 2023, and this plan included objectives such as analyzing the impact of different agricultural policy instruments to support farmers (including the impact of environmental policies on current farming practices). There are also very few studies addressing this issue. Most of the identified studies regarding Romania (about 100 on Google Academics) present eco-schemes in conjunction with CAP, agri-environmental policy, etc., but do not estimate the financial impact at the farm level.

The paper will demonstrate that the introduction of eco-scheme subsidies affects the income of Romanian farms by analyzing three farms (with a cultivated area between 2400 and 2600 ha, 550 and 610 ha, and 40 and 66 ha during the 2019–2021 period) with different crop structures. The paper will estimate the financial gaps created by adopting the requirements of the eco-scheme “PD-04—Environmentally friendly practices in arable farming” and the influence of changing subsidies on income concentration between crops. The main objective of this work is to assess the contribution of subsidies to income generation and the impact on income equity. Our main research questions focused on the following: (a) How much of the income inequality is explained by subsidies? (b) How and to what extent would eco-schemes support affect the income of farmers of different sizes?

The paper is structured as follows: the Introduction is followed by the Materials and Methodology section, which presents the farm data used for data analysis and the income decomposition methodology. Then, in the Results section, six income change scenarios were estimated for each farm, based on the eco-scheme requirements and starting from changing cropping patterns. Finally, we discuss the results, draw conclusions, highlight the limitations of the research, and outline some future research directions.

2. Materials and Methods

CAP subsidies aim to ensure a sufficient income for farmers, so the way income is distributed between farmers and between crops influences agricultural policy making. As far as the agricultural sector is concerned, subsidies have been shown to help stabilize incomes in the short and medium term (especially Pillar 1) [24] and especially at the small-sized farm level [25].

It is, therefore, very important to assess and quantify the links between subsidies and income, especially if new instruments such as eco-schemes are to be introduced.

At the European level, the study of Petsakos et al. [26] should be mentioned, in which the authors created six scenarios to evaluate eco-schemes and demonstrated that there could be an improvement in “environmental performance, but at a cost to farms”. In addition, Barreiro-Hurle et al. [27] created three scenarios based on the reform proposals and quantitative targets of the “Farm to Fork” and “Biodiversity” strategies and demonstrated that “the current implementation of the CAP brings significant environmental benefits in the form of reductions in greenhouse gases and ammonia emissions as well as in gross nutrient surplus, although the magnitude in terms of positive environmental and economic benefits is not fully quantified”.

There are also numerous studies that attempted to measure the impact of eco-schemes in different member states, such as the following: in Greece, a simulated introduction of the eco-scheme adoption resulted in a 7–8% decrease in farm income [28]; in a study on Spanish farms (using the individual farm model for the analysis of the Common Agricultural Policy), different scenarios were applied to show that the new CAP environmental measures are beneficial for farmers but impose additional costs [29]; in Italy, a “rebalancing in the allocation of financial resources to the benefit of small and medium-sized farms (between 3 and 50 hectares)” is expected for the 2023–2027 period and losses in income support (BISS) should be compensated by coupled subsidies, eco-schemes, etc. [30]; Pilvere et al. [31] estimate in their paper that CAP reform payments for the 2023–2027 period in Latvia will be higher in the cattle and dairy sectors, which will lead to an increase in greenhouse gas emissions and therefore a reduction in the achievement of the European Green Deal policy objectives; in Poland it has been shown that in the regions with disadvantaged agricultural structures new organic regulations and good agricultural practices cannot be implemented and that the implementation of eco-schemes can only be compensated by increasing redistribution payments [32]. In Romania, Alexandri [33] has shown that the redistribution of direct payments for farm support (if 30% of direct payment funds are used for eco-schemes) will reduce income inequalities and stabilize the incomes of small- and medium-sized farms.

This research complements studies in this area and aims to understand how inequality changes when eco-scheme measures are introduced using an income decomposition technique. There are authors who, using this method of decomposing the Gini index according to income source, demonstrated the inequality-reducing effect of farm income and decoupled payments while “market support has an inequality-increasing effect” [34,35,36]. There are also some authors who claim that subsidies such as direct payments do not reduce farm income inequality [37].

In this research, the decomposition method described by Dona et al. [34] was used:

$$\mathrm{G}={\displaystyle \sum }_{\mathrm{k}=1}^{\mathrm{K}}{\mathrm{R}}_{\mathrm{k}}{\mathrm{G}}_{\mathrm{k}}{\mathrm{S}}_{\mathrm{k}}$$

(1)

where (according to Lerman and Yitzhaki [35]) as follows: Sk—the share of income k in total farm income; Gk—the Gini coefficient for income k (measures the inequality of the distribution); Rk—the Gini correlation of income k with total income (“Measures the extent to which the relationship between Yk and the cumulative rank distribution of Y coincides with the relationship between Yk and its own cumulative rank distribution:

Rk = cov(yk, F)/cov(yk, Fk). Rk lies in the interval [−1, 1]” [36]).

Using this equation, one can determine how much of the total income inequality is attributable to income source k (equally distributed income has no effect on inequality; unequally distributed income increases or decreases inequality). In order to determine whether this source k increases or decreases inequality, the following equations can be applied:

$${\mathrm{g}}_{\mathrm{k}}={\mathrm{R}}_{\mathrm{k}}\frac{{\mathrm{G}}_{\mathrm{k}}}{\mathrm{G}}$$

(2)

where (Adams [37]) gk—“the coefficient of relative concentration of income source k in overall inequality”, “income source k increases or decreases inequality depending on whether gk is greater than or less than unity”.

$$\mathrm{Absolute}\_\mathrm{Change}={\mathrm{S}}_{\mathrm{k}}\left({\mathrm{G}}_{\mathrm{k}}{\mathrm{R}}_{\mathrm{k}}-\mathrm{G}\right)$$

(3)

where (according to Adams [37]) absolute change—“the relative effect of a marginal percentage change in income source k on overall inequality equals the relative contribution of source k to overall inequality minus the relative contribution to total income”.

$$\mathrm{Percentage}\_\mathrm{Change}=\frac{{\mathrm{S}}_{\mathrm{k}}\left({\mathrm{G}}_{\mathrm{k}}{\mathrm{R}}_{\mathrm{k}}-\mathrm{G}\right)}{\mathrm{G}}\times 100$$

(4)

where (according to Kaditi and Nitsi [38]) “The percentage change in inequality resulting from a small percentage change in income from source k is therefore equal to its initial share in inequality minus its share in total income”.

In order to apply the decomposition method and the above formulas, the research followed the following steps: extracting subsidies and income data from the accounting data of the selected farms and determining their value per hectare; collecting data from the operational departments on crop rotation for the analyzed years; estimating new crop rotations based on eco-scheme requirements for 2023 and 2024; calculating the value of eco-scheme payments based on the minimum and maximum value per hectare known at the time of the research; estimating direct payments based on the new land structure.

The research was based on data provided by three different sizes farms (with a cultivated area between 2400 and 2600 ha, 550 and 610 ha, and 40 and 66 ha during the 2019–2021 period) for the years 2019, 2020, and 2021. At the time of paper elaboration, figures for 2022 were not available. Three variants of crop structures for each farm were thus created as a baseline for applying the requirements of the eco-scheme “PD-04—Environmentally friendly practices in arable farming”. The following indicators were selected for each crop: area, income, subsidies (direct payments and other subsidies, such as diesel subsidies), costs, and profit (

Table 1. Baseline data.

Farm	Year	Structure	Area (ha)	Agricultural Income (RON)	Total Subsidies (RON)	Direct Payments (RON)	Costs (RON)	Profit (RON)
Farm_A	2019	Wheat	811.24	3,772,266.0	1,037,961.9	942,745.6	4,340,117.8	470,110.1
		Barley	240.11	1,022,868.6	307,214.9	279,032.9	1,280,021.6	50,061.9
		Maize	486.61	4,334,721.9	622,605.7	565,491.6	2,864,979.6	2,092,348.0
		Soybean	677.21	4,875,912.0	866,473.8	786,988.7	3,643,274.7	2,099,111.1
		Sunflower	267.07	1,442,178.0	341,709.6	310,363.2	1,349,539.4	434,348.2
		Total	2482.24	15,447,946.5	3,175,965.9	2,884,622.1	13,477,933.1	5,145,979.3
	2020	Wheat	720.60	1,815,912.0	1,047,361.9	874,042.1	4,002,493.4	−1,139,219.5
		Barley	208.16	763,114.6	302,551.8	252,484.9	1,154,120.2	−88,453.8
		Maize	564.04	5,372,481.0	819,808.5	684,144.8	3,347,814.3	2,844,475.2
		Soybean	470.47	3,808,520.3	683,808.4	570,650.3	2,801,078.3	1,691,250.4
		Sunflower	528.30	2,308,671.0	767,861.9	640,794.4	2,881,860.7	194,672.2
		Total	2491.57	14,068,698.9	3,621,392.5	3,022,116.4	14,187,366.9	3,502,724.5
	2021	Wheat	709.62	5,337,052.0	933,404.2	833,797.0	3,941,506.2	2,328,950.0
		Barley	214.79	1,440,811.3	282,525.7	252,376.3	1,445,678.5	277,658.5
		Maize	759.09	10,476,960.2	1,783,847.5	891,923.8	6,506,965.0	5,753,842.7
		Soybean	536.66	5,822,761.0	705,899.9	630,570.6	3,328,842.9	3,199,818.0
		Sunflower	423.76	3,159,554.6	557,396.0	497,914.1	2,495,654.0	1,221,296.6
		Total	2643.9	26,237,139.1	4,263,073.4	3,106,581.7	17,718,646.7	12,781,565.8
Farm_B	2019	Wheat	203	854,756.5	258,115.4	226,248.6	815,726.3	297,145.6
		Maize	215	796,833.0	273,373.5	239,622.8	1,410,140.4	−339,933.9
		Rapeseed	154	663,807.0	195,811.7	171,636.8	658,955.6	200,663.1
		Soybean	15.76	35,075.5	20,038.9	17,564.9	95,968.5	−40,854.1
		Alfalfa	20.37	23,795.2	25,900.5	22,702.9	49,469.8	225.9
		Total	608.13	2,374,267.1	773,240.0	677,776.0	3,030,260.7	117,246.4
	2020	Wheat	157	26,533.3	187,050.0	159,817.4	557,243.7	−343,660.4
		Maize	250	1,510,915.0	297,850.3	254,486.2	1,363,034.9	445,730.40
		Rapeseed	125	535,937.5	148,925.1	127,243.1	510,200.0	174,662.6
		Barley	45	35,887.7	53,613.0	45,807.5	177,677.9	−88,177.2
		Alfalfa	20.37	35,993.0	24,268.8	20,735.5	49,375.7	10,886.1
		Oat	6.31	11,714.0	7517.7	6423,2	20,679.3	−1447.6
		Total	603.68	2,156,980.5	719,225.0	614,513.0	2,678,211.5	197,994.0
	2021	Wheat	86	498,294.3	112,872.4	101,053.9	503,955.6	107,211.1
		Maize	232	2,818,415.1	304,492.9	272,610.6	2,696,483.8	426,424.2
		Soybean	156.35	1,467,820.1	205,204.6	183,718.4	1,301,378.4	371,646.3
		Barley	45	263,970.9	59,061.1	52,877.1	278,968.5	44,063.5
		Alfalfa	22.3	38,006.0	29,268.1	26,203.5	72,533.1	−5259.0
		Oat	7.71	13,302.0	10,119.1	9059.6	37,208.3	−13,787.2
		Total	549.36	5,099,808.4	721,018.1	645,523.1	4,890,527.7	930,298.8
Farm_C	2019	Wheat	10	34,000.0	29,881.8	10,058.2	47,413.9	16,467.9
		Maize	16	59,520.0	47,810.9	16,093.1	82,624.6	24,706.3
		Alfalfa	5	22,500.0	14,940.9	5029.1	26,290.2	11,150.7
		Total	31	116,020.0	92,633.6	31,180.5	156,328.7	52,324.9
	2020	Wheat	40.5	62,370.0	50,081.5	16,857.4	148,495.7	−36,044.2
		Maize	15.5	22,630.0	19,167.0	6451.6	62,660.2	−20,863.2
		Alfalfa	5	15,000.0	6182.9	2081.2	12,839.4	8343.5
		Peppers	2	30,000.0	2473.2	832.5	71,817.1	−39,343.9
		Melons	2	15,000.0	2473.2	832.5	71,817.1	−54,343.9
		Total	65	145,000.0	80,377.7	27,055.1	367,629.5	−142,251.8
	2021	Wheat	35.5	174,660.0	105,944.8	35,661.0	175,818.4	104,786.4
		Maize	20.5	129,150.0	61,179.4	20,593.0	139,072.6	51,256.8
		Alfalfa	5	25,200.0	14,921.8	5022.7	15,186.1	24,935.7
		Peppers	3	102,000.0	8953.1	3013.6	108,337.9	2615.2
		Melons	2	10,000.0	5968.7	2009.1	73,885.3	−57,916.6
		Total	66	441,010.0	196,967.8	66,299.3	512,300.3	125,677.5

Source: data received from the owners.

).

The research started from these data, after which the area was modified accordingly with the following conditions of the eco-scheme “PD-04—Environmentally friendly practices in arable farming” for the years 2023 and 2024 (

Table 2. Eco-scheme PD-04 conditions.

			2023	2024
General conditions	1	Nonproductive elements (incl. fallow land) from arable land	0%	5%
	2	Cultivation of leguminous crops rich in vegetable protein (soybean, peas, clover, beans, lentil, alfalfa, etc.).	10%	5%
	3	Soil covering from June 15 to October 15 with agricultural crops or minimal cover with stubble left after harvest, catch crops, green cover crops, or newly established autumn crops	85%	85%
Specific conditions (one choice)		Crop diversification (a) a crop of any of the various genera defined in the botanical classification of crops; (b) a crop of any of the species in the case of Brassicaceae, Solanaceae, and Cucurbitaceae; (c) fallow land; (d) grasses or other herbaceous fodder. Period: May–September	Farm area	Diversification conditions:
			10.01–30 ha	minimum 2 crops
				1 main crop ≤ 75%
			≥30 ha	minimum 3 crops
				1 main crop ≤ 70%
				2 main crops ≤ 85%

Notes (legend): one specific condition according to the farmer’s option; two levels of potential payment, respectively, a minimum of 56.28 euro/ha (the planned unit amount in the 2023–2027 period) and a maximum of 73 euro/ha (maximum depending on the reallocations that will be made). Source: National Strategic Plan 2023–2027 (PNS) of Romania, https://apia.org.ro/planul-national-strategic-2023–2027-pns-al-romaniei/, Retrieved on 28 February 2023.

):

Farm_A had a 5-crop structure in 2019–2021 and the following cultivated area: 2482.2 ha in 2019, 2491.6 ha in 2020, and 2643.9 ha in 2021. The main feature of this farm is that it already has a structure that meets the conditions for the first year of eco-scheme implementation: the first condition will not change the structure; the second condition is already met because the area cultivated with soybean represents more than 10%; the third condition is met because wheat is harvested in autumn and the rest of the area is covered with stubble in summer; the main crop (wheat) represents less than 70% (about 28–30%) and, together with soybean it reaches a share of 48–60% (below 85%). Farm_A_V1, Farm_A_V3, and Farm_A_V5 variants are thus identical to the baseline scenario (

Table 3. Estimation of crop structures for Farm_A.

Farm		Structure	Baseline	Year I	Year II
Farm_A	Crop structure 1	Wheat	811.24	811.24	770.68
		Barley	240.11	240.11	228.10
		Maize	486.61	486.61	462.28
		Soybean	677.21	677.21	643.35
		Sunflower	267.07	267.07	253.72
		Total	2482.24	V1 = 2482.24	V2 = 2358.13
	Crop structure 2	Wheat	720.60	720.60	684.57
		Barley	208.16	208.16	197.75
		Maize	564.04	564.04	535.84
		Soybean	470.47	470.47	446.95
		Sunflower	528.30	528.30	501.89
		Total	2491.57	V3 = 2491.57	V4 = 2366.99
	Crop structure 3	Wheat	709.62	709.62	674,14
		Barley	214.79	214.79	204,05
		Maize	759.09	759.09	721,14
		Soybean	536.66	536.66	509,83
		Sunflower	423.76	423.76	402,57
		Total	2643.92	V5 = 2643.9	V6 = 2511.72

Source: own estimation.

).

After applying the conditions for the second year of implementation, there are the following changes: Condition 1—5% of each crop will be fallow; Condition 2 is met because soybean is grown on more than 5% area; Condition 3—same crops as in the first year, but 5% will be fallow (95% > 85%); Special condition—main crop (wheat) is below 70% (about 29%) and the first two main crops (wheat and soybean) are below 85% (about 47–60%). Thus, Farm_A_V2, Farm_A_V4, and Farm_A_V6 variants are 5% lower than the basic variant.

In the case of the medium-sized farm, the cropping structure was adapted to the eco-scheme conditions as follows (

Table 4. Estimation of crop structures for Farm_B.

Farm		Structure	Baseline	Year I	Year II
Farm_B	Crop structure 1	Wheat	203	194.77	192.85
		Maize	215	206.77	204.25
		Rapeseed	154	145.77	146.30
		Soybean	15.76	28.10	14.97
		Alfalfa	20.37	32.71	19.35
		Total	608.13	V1 = 608.13	V2 = 577.72
	Crop structure 2	Wheat	157	147.00	146.82
		Maize	250	240.00	235.17
		Rapeseed	125	115.00	116.42
		Barley	45	35.00	40.42
		Alfalfa	20.37	60.37	28.67
		Oat	6.31	6.31	5.99
		Total	603.68	V3 = 603.68	V4 = 573.50
	Crop structure 3	Wheat	86	86.00	81.70
		Maize	232	232.00	220.40
		Soybean	156.35	156.35	148.53
		Barley	45	45.00	42.75
		Alfalfa	22.3	22.30	21.19
		Oat	7.71	7.71	7.32
		Total	549.36	V5 = 549.36	V6 = 521.89

Source: own estimation.

):

• Crop structure 1 for year I: Condition 1—0%; Condition 2—the area under soybean and alfalfa was only 6%, so hectares of wheat, maize, and rapeseed were converted to these crops until the proportion reached 10% (24.6 ha in total); the other conditions are fully met—Farm_B_V1 variant has the same total area of 608.1 ha, but with a larger area for protein crops; Crop structure 1 for year II: Condition 1—5% of each crop area remains fallow; the other conditions are fully met—Farm_B_V2 area under cultivation will decrease by 5%.
• Crop structure 2 for year I: Condition 1—0%; Condition 2—the alfalfa area represents only 3.3%, so the area has been increased by 40 ha to reach 10% (of wheat, maize, rapeseed, and barley); the other conditions are fully met—Farm_B_V3 has a total area of 603.7 ha, but with a larger area of alfalfa; Crop structure 2 for year II: Condition 1—reduction in each cultivated area by 5% and addition of fallow land; Condition 2—in order to reach 5%, the area of alfalfa has been increased by 9.3 ha; the other conditions are fully met—in Farm_B_V4 variant, the cultivated area is reduced by 5%, and the area of alfalfa is increased.
• Crop structure 3 for year I: no change is needed here, as all conditions apply to the whole field—Farm_B_V5 variant is the same as the baseline; Crop structure 3 for year II: the structure is modified in line with the first condition (a 5% reduction), while the other conditions are fully met—for Farm_B_V6 the cultivated area is reduced by 5%.

For the third case study, the following modifications were made to meet the conditions of the eco-scheme (

Table 5. Estimation of crop structures for Farm_C.

Farm		Structure	Baseline	Year I	Year II
Farm_C	Crop structure 1	Wheat	10	10	9.5
		Maize	16	16	15.2
		Alfalfa	5	5	4.8
		Total	31	V1 = 31	V2 = 29.5
	Crop structure 2	Wheat	40.5	39.8	37.7
		Maize	15.5	14.8	14.0
		Alfalfa	5	6.5	5.5
		Peppers	2	2.0	1.9
		Melons	2	2.0	1.9
		Total	65	V3 = 65.0	V4 = 61.0
	Crop structure 3	Wheat	35.5	34.7	33.7
		Maize	20.5	19.7	19.5
		Alfalfa	5	6.6	4.8
		Peppers	3	3.0	2.9
		Melons	2	2.0	1.9
		Total	66	V5 = 66.0	V6 = 62.7

Source: own estimation.

):

• Crop structure 1 for year I: all conditions were met (e.g., protein crop account for 16% and the two main crops account for 83.9%)—Farm_C_V1 variant corresponds to the baseline situation; Crop structure 1 for year II: only one change was made, the reduction by 5%—for Farm_C_V2 the cultivated area is reduced by 5%.
• For Crop structure 2, Condition 2 and the Specific condition were not met, so the area of alfalfa was increased by 1.5 ha in year I and by 0.72 ha in year II by reducing the area under wheat and maize; reduction in area under cultivation by 5% in year II—after modifications, Farm_C_V3 has a total area of 65 ha, but with a larger area of alfalfa and in variant Farm_C_V4, the area under cultivation is reduced by 5%, and the area of alfalfa is increased.
• Crop structure 3 for year I: Condition 1—0%; Condition 2—alfalfa area represents only 7.5%, so the area was increased by 1.6 ha to reach 10% (from wheat and maize); the other conditions are fully met—Farm_C_V5 has a total area of 66 ha, but with a larger alfalfa area; Crop structure 3 for year II: Condition 1—keeping 5% of the area as fallow land; the other conditions are fully met—for Farm_C_V6, the cultivated area is reduced by 5%.

With the modifications described above, 18 variants of crop structures that meet the requirements of the eco-scheme were created. For each farm, there are three crop structures with two types of crop structure modifications (corresponding to year I and year II of the eco-scheme). When estimating the impact of subsidies on income, two payment levels (minimum and maximum) were applied for each case study (resulting in 36 estimates).

The impact was measured by the change in inequality resulting from a small percentage change in income or subsidies.

3. Results and Discussions

During the period analyzed, the profit of Farm A was more than 5 million RON in 2019, 3.5 million RON in 2020, and 12.8 million RON in 2021 (Table S1). The large-sized farms with almost 3000 ha have the potential to survive in the market without direct payments.

In terms of the implementation of the eco-scheme, the document points out that the farm used as a case study has a specific crop rotation that meets the required conditions. Six financial development scenarios were estimated, i.e., V1, V3, and V5 with the conditions for the first year and V2, V4, and V6 with the second-year conditions (

Table 6. Decomposition of subsidy inequality and income effects on income inequality—Farm_A.

	Variant	Income Source	Share in Total Income (Sk)	Gini Coefficient for Income Source (Gk)	Correlation Coefficient (Rk)	Contribution of Income Source to Income Inequality (Sk Gk Rk)	Relative Concentration Coefficient of Income Source (gk)	Percentage Contribution to Income Inequality (Gk/G × 100)	Effects of a 1% Increase in a Source Income on Income Inequality
									Absolute Change	Percent Change in Gini from a 1% Change in Income
Crop Structure 1	Initial	Agricultural income	0.829	0.274	0.954	0.217	1.019	84.499	0.0040	1.55
		Subsidies	0.171	0.250	0.934	0.040	0.909	15.501	−0.0040	−1.55
V1 and V2	Min	Agricultural income	0.799	0.274	0.946	0.208	1.020	81.548	0.0041	1.60
		Subsidies	0.201	0.250	0.936	0.047	0.920	18.452	−0.0041	−1.60
V1 and V2	Max	Agricultural income	0.791	0.274	0.944	0.205	1.020	80.701	0.0041	1.60
		Subsidies	0.209	0.250	0.937	0.049	0.923	19.299	−0.0041	−1.60
Crop Structure 2	Initial	Agricultural income	0.795	0.319	0.840	0.213	1.120	89.110	0.0229	9.58
		Subsidies	0.205	0.180	0.708	0.026	0.532	10.890	−0.0229	−9.58
V3 and V4	Min	Agricultural income	0.765	0.319	0.816	0.199	1.134	86.769	0.0236	10.27
		Subsidies	0.235	0.180	0.719	0.030	0.563	13.231	−0.0236	−10.27
V3 and V4	Max	Agricultural income	0.756	0.319	0.809	0.195	1.138	86.069	0.0236	10.43
		Subsidies	0.244	0.180	0.722	0.032	0.572	13.931	−0.0236	−10.43
Crop Structure 3	Initial	Agricultural income	0.860	0.316	1.000	0.272	1.007	86.599	0.0018	0.58
		Subsidies	0.140	0.317	0.950	0.042	0.959	13.401	−0.0018	−0.58
V5 and V6	Min	Agricultural income	0.840	0.316	0.990	0.263	1.005	84.367	0.0012	0.39
		Subsidies	0.160	0.301	1.010	0.049	0.975	15.633	−0.0012	−0.39
V5 and V6	Max	Agricultural income	0.834	0.316	0.987	0.260	1.004	83.730	0.0011	0.35
		Subsidies	0.166	0.297	1.025	0.051	0.979	16.270	−0.0011	−0.35

Source: own estimation.

). Our main observations are as follows:

• When the crop structure is not changed, subsidies increase profits by 6–20% in the minimum variant and by 7–26% in the maximum variant in the first year of implementation.
• In the second year, when 5% of the land is not cultivated, the percentages are lower, 0.5–14% in the minimum variant and 2–20% in the maximum variant; if the farm maintains good agricultural and environmental conditions, it also receives direct payments for the fallow land, so the increase in profits is several percent higher (but below the level in the first year).
• When direct payments are not paid, the profit losses are very high; the profit decreases by 56% to 85%. However, eco-scheme subsidies have the potential to partially compensate for these losses, especially under first-year conditions.

The Gini coefficients of subsidies and income are almost equal, except for crop rotation 2 (2020), where income is more unevenly distributed between crops due to price fluctuations and land is more evenly distributed between crops (due to a lower Gini for subsidies). The correlation coefficient (Rk) was higher for income in all three rotations, implying that this source was more important for large-sized farms (Table 6). The same conclusion can be drawn when the percentage contribution to income inequality is analyzed.

In rotations 1 and 3, the income contributes 85–86% to inequality, and 89% in rotation 2. When the farm income is higher and there are no major differences between income concentration and subsidy concentration, the impact of subsidies on inequality is very small, ranging from 0.001 to 0.004, which means that even if the farm receives additional subsidies again (through eco-schemes), a 1% increase in subsidies leads to a decrease in inequality of only 0.35–1.55%. In the case of crop rotation 2 (2020), a larger inequality-reducing effect through subsidies of more than 10% was observed in both V3 and V4.

The medium-sized farm (about 600 ha) obtained a profit of 117.3 thousand RON in 2019, 198 thousand RON in 2020, and 284.8 thousand RON in 2021 (Table S2). Without direct payments, Farm_B with crop rotation variants 1 and 2 would have registered losses of 400–560 thousand RON. In the case of Crop structure 3, the larger area of maize and soybean provided a higher profit of 930.3 thousand RON.

For 2019 and 2020, the cropping structure was adapted to the eco-scheme conditions by increasing the area under protein crops in the first year (variants V1 and V3). The incomes and costs decreased, but the farm also obtained a double profit after receiving the new subsidies. However, the amount is not enough to cover losses when farms do not receive direct payments. Two situations were identified after applying the conditions in the second year. In the case of the V2 variant (only a 5% reduction), a higher return was registered, ranging from 134% to 175%. For variant V4 (5% reduction plus increased protein cultivation), there was a slightly lower profit (from 80% to 104.4%) due to cost reduction.

If the crop structure does not change (V5), the subsidies increase the profit in the first year of implementation by 17% in the minimum variant and by 22% in the maximum variant. In the V6 variant, if 5% of the land is not cultivated, the profit increases by only 11–16%. If the farm also receives direct payments for the fallow land, the profit increases by 14–19%.

In terms of the income distribution, the medium-sized farm has a rather unbalanced crop rotation with a polarization towards a few crops that bring most of the income to the farm. Subsidies, whether direct payments or from eco-schemes, are disproportionately directed towards crops at the higher end of the income distribution (Rk is positive and large means that subsidies have the same distribution as total income), so subsidies, in this case, increase inequality. However, the impact of changing subsidies on inequality is very small. A 1% increase in subsidies leads to an increase in inequality of only 0.24% to 1.41% (

Table 7. Decomposition of subsidy inequality and income effects on income inequality—Farm_B.

	Variant	Income Source	Share in Total Income (Sk)	Gini Coefficient for Income Source (Gk)	Correlation Coefficient (Rk)	Contribution of Income Source to Income Inequality (Sk Gk Rk)	Relative Concentration Coefficient of Income Source (gk)	Percentage Contribution to Income Inequality (Gk/G × 100)	Effects of a 1% Increase in a Source Income on Income Inequality
									Absolute Change	Percent Change in Gini from a 1% Change in Income
Crop Structure 1	Initial	Agricultural income	0.754	0.408	0.979	0.302	0.997	75.199	−0.0009	−0.23
		Subsidies	0.246	0.382	1.059	0.099	1.010	24.801	0.0009	0.23
V1	Min	Agricultural income	0.710	0.392	0.956	0.266	0.997	70.786	−0.0009	−0.24
		Subsidies	0.290	0.342	1.110	0.110	1.008	29.214	0.0009	0.24
V1	Max	Agricultural income	0.699	0.392	0.955	0.262	0.996	69.686	−0.0010	−0.25
		Subsidies	0.301	0.342	1.108	0.114	1.008	30.314	0.0010	0.25
V2	Min	Agricultural income	0.715	0.408	0.976	0.285	0.996	71.275	−0.0011	−0.27
		Subsidies	0.285	0.382	1.056	0.115	1.009	28.725	0.0011	0.27
V2	Max	Agricultural income	0.705	0.408	0.975	0.280	0.996	70.186	−0.0011	−0.28
		Subsidies	0.295	0.382	1.055	0.119	1.009	29.814	0.0011	0.28
Crop Structure 2	Initial	Agricultural income	0.750	0.697	0.866	0.453	0.986	73.928	−0.0065	−1.07
		Subsidies	0.250	0.472	1.355	0.160	1.043	26.072	0.0065	1.07
V3	Min	Agricultural income	0.704	0.684	0.834	0.402	0.981	69.079	−0.0077	−1.32
		Subsidies	0.296	0.430	1.412	0.180	1.045	30.921	0.0077	1.32
V3	Max	Agricultural income	0.692	0.684	0.828	0.392	0.980	67.822	−0.0082	−1.42
		Subsidies	0.308	0.430	1.405	0.186	1.046	32.178	0.0082	1.42
V4	Min	Agricultural income	0.707	0.694	0.841	0.413	0.981	69.397	−0.0078	−1.32
		Subsidies	0.293	0.458	1.357	0.182	1.045	30.603	0.0078	1.32
V4	Max	Agricultural income	0.696	0.694	0.835	0.403	0.980	68.143	−0.0084	−1.41
		Subsidies	0.304	0.458	1.352	0.188	1.046	31.857	0.0084	1.41
Crop Structure 3	Initial	Agricultural income	0.876	0.606	0.965	0.513	0.993	87.043	−0.0034	−0.57
		Subsidies	0.124	0.475	1.298	0.076	1.046	12.957	0.0034	0.57
V5 and V6	Min	Agricultural income	0.853	0.606	0.959	0.496	0.992	84.683	−0.0039	−0.66
		Subsidies	0.147	0.475	1.290	0.090	1.045	15.317	0.0039	0.66
V5 and V6	Max	Agricultural income	0.847	0.606	0.957	0.491	0.992	84.005	−0.0040	−0.69
		Subsidies	0.153	0.475	1.288	0.094	1.045	15.995	0.0040	0.69

Source: own estimation.

). The biggest effect was in the second crop structure where, in V3 and V4 variants, the structure was modified to meet the eco-scheme conditions (an increase in protein crops).

The farm (between 31 and 66 ha) also had a 5-crop structure during the 2019–2021 period, with 31 ha in 2019, 65 ha in 2020, and 66 ha in 2021 (Table S3). By applying the first-year conditions, variant V1 is the same as the baseline, but variants V3 and V5 have been adapted with a small increase in protein crops. As for the second-year conditions, in variants V2 and V6, a 5% reduction was applied, but in variant V4, changes in crop structure were also made.

In the first cropping patterns, Farm_C registered a profit of 52.3 thousand RON in 2019, a loss of 16.9 thousand RON in 2020, and a profit of 125.7 thousand RON in 2021 (Table S3). After the application of the eco-scheme in the first year, there was an increase in profit between 17% and 22% (variants V1 and V5) and a decrease in losses in variant V3. After 5% of the cultivated area is left uncultivated according to the condition for the second year, the calculation shows an increase in profit from 17% to 20% when considering the minimum subsidy and from 21% to 24% when considering the maximum subsidy in the eco-scheme. If the farm complies with the set-aside rules for fallow land and receives direct payments for it, the profit increase is 40–70%.

The Gini coefficients of subsidies and income are almost equal, except for crop rotation 1 (2019), where income is more unevenly distributed between crops due to price fluctuations and land is more evenly distributed between crops (due to a lower Gini for subsidies). The correlation coefficient (Rk) was higher for income in all three rotations, implying that this source was more important for large-sized farms (Table 6). The same conclusion can be drawn when looking at the percentage contribution to income inequality.

In rotations 1 and 3, the income contributes 85–86% to inequality, and 89% in rotation 2. When farm income is higher, and there are no major differences between income concentration and subsidy concentration, the impact of subsidies on inequality is very small, ranging from 0.001 to 0.004, which means that even if the farm receives additional subsidies (through eco-schemes), a 1% increase in subsidies leads to a decrease in inequality of only 0.35–1.55%. In the case of crop rotation 2 (2020), a greater inequality-reducing effect of subsidies of over 10% can be observed in both V3 and V4.

The Gini coefficients of subsidies and income are almost equal in the case of rotation 1 (2019). In rotation 1, subsidies contribute 37% to inequality and increase to over 39% in variants V1 and V2. In this case, subsidies reduce total income inequality (have an inequality-reducing effect) by 7.4%. In the other two structures, subsidies are more unequally distributed between crops (a Gini for subsidies of over 0.5). However, even if the effect is very small (0.4–2.12%), we can observe that subsidies reduce inequality in Crop structure 3 and increase inequality in Crop structure 2 (when the crop structure changes in both V3 and V4). (

Table 8. Decomposition of subsidy inequality and income effects on income inequality—Farm_C.

	Variant	Income Source	Share in Total Income (Sk)	Gini Coefficient for Income Source (Gk)	Correlation Coefficient (Rk)	Contribution of Income Source to Income Inequality (Sk Gk Rk)	Relative Concentration Coefficient of Income Source (gk)	Percentage Contribution to Income Inequality (Gk/G × 100)	Effects of a 1% Increase in a Source Income on Income Inequality
									Absolute Change	Percent Change in Gini from a 1% Change in Income
Crop Structure 1	Initial	Agricultural income	0.556	0.213	1.180	0.140	1.133	63.010	0.0164	7.41
		Subsidies	0.444	0.237	0.780	0.082	0.833	36.990	−0.0164	−7.41
V1 and V2	Min	Agricultural income	0.534	0.213	1.189	0.135	1.139	60.795	0.0165	7.42
		Subsidies	0.466	0.237	0.789	0.087	0.841	39.205	−0.0165	−7.42
V1 and V2	Max	Agricultural income	0.527	0.213	1.191	0.134	1.141	60.165	0.0165	7.42
		Subsidies	0.473	0.237	0.792	0.089	0.843	39.835	−0.0165	−7.42
Crop Structure 2	Initial	Agricultural income	0.643	0.303	1.238	0.241	1.002	64.480	0.0005	0.14
		Subsidies	0.357	0.557	0.669	0.133	0.996	35.520	−0.0005	0.14
V3 and V4	Min	Agricultural income	0.599	0.280	1.310	0.219	0.994	59.490	−0.0014	−0.39
		Subsidies	0.401	0.554	0.672	0.149	1.010	40.510	0.0014	0.39
V3 and V4	Max	Agricultural income	0.586	0.280	1.319	0.216	0.992	58.116	−0.0017	−0.47
		Subsidies	0.414	0.554	0.679	0.156	1.011	41.884	0.0017	0.47
Crop Structure 3	Initial	Agricultural income	0.691	0.393	1.061	0.288	1.031	71.302	0.0088	2.18
		Subsidies	0.309	0.512	0.733	0.116	0.930	28.698	−0.0088	−2.18
V5 and V6	Min	Agricultural income	0.671	0.375	1.083	0.272	1.032	69.244	0.0083	2.12
		Subsidies	0.329	0.511	0.720	0.121	0.936	30.756	−0.0083	−2.12
V5 and V6	Max	Agricultural income	0.666	0.375	1.084	0.270	1.031	68.657	0.0082	2.09
		Subsidies	0.334	0.511	0.723	0.123	0.937	31.343	−0.0082	−2.09

Source: own estimation.

).

4. Conclusions

The present study, which aimed to investigate the impact of subsidies and, in particular, subsidies from the eco-scheme “PD-04—Ecological practices in arable agriculture”, achieved its objective by answering farmers’ questions about income development after adapting cropping structures to the requirements of the aforementioned eco-scheme. The innovative nature of the study stems from the examination of the two sources of income (income from the sale of agricultural products and income from subsidies), which revealed that a 1% increase in subsidies leads to a reduction in crop inequality in more than half of the models of the cropping models studied. In this situation, the following results were obtained: In Farm A, this situation was encountered in variants V1–V6, and in Farm C, in variants V1–V2, V5–V6. For the remaining variants, estimates were made, and it was found that farm income has a decreasing effect on inequality (a relevant example is Farm B, where the cropping structure is unbalanced and income is difficult to estimate). The results that affect farm profit levels are as follows: Additional subsidies for large farms (Farm A) can increase profit by 6–7% to 20–26% in the first year of implementation and by 0.5–2% to 14–20% in the second year of implementation. On medium farms (Farm B), profit increases vary between 16 and 21% if no annual structural changes are made to the cropping plan, but can reach increases of up to 154% with the introduction of protein crops. And if 5% of the land is left uncultivated, the profit of this farm can increase by up to 175%. If the cropping structure is changed in favor of protein crops, the increase is somewhat smaller (about 80–100%). For the small farms (Farm C), the increase in profit ranged from 17% to 24% in the two years studied and can increase by several percentages if the farm meets the condition for uncultivated land. The main recommendation for farmers is to implement an adequate crop rotation according to the requirements of the eco-scheme. This research is exhaustive, and the inclusion of more farms in this analysis will provide a more comprehensive assessment framework of the impact of eco-scheme payments on income. The methodology is also adaptable to other farms with different cropping plans [39] and rotations, and expanding the study to a larger number of farms will add to the research, which will be able to anticipate their financial evolution after the implementation of the eco-scheme conditions.