Evaluation of the Profitability and Competitiveness of Strategic Products with the Policy Analysis Matrix: The Case of Tekirdağ, Türkiye

Abstract

This study evaluates the profitability and competitiveness of wheat and sunflower, strategic crops vital for food security, in Tekirdağ, Türkiye. Utilizing the Policy Analysis Matrix (PAM) and Stochastic Frontier Analysis (SFA), we analyze their profitability, competitiveness, and production efficiency under current agricultural policies. The findings reveal positive private profitability for wheat (354.86 USD/ha) and sunflower (240.82 USD/ha), with higher social profitability (431.78 USD/ha for wheat, 641.39 USD/ha for sunflower) indicating inherent efficiency. Policy analysis shows wheat benefits from a 7% price protection (NPCO 1.07), while sunflower is implicit taxed at 20% (NPCO 0.80). The SFA results indicate average technical efficiencies of 79.9% for wheat and 88.9% for sunflower, highlighting the potential for cost reduction through improved input use. In this study, the profitability rate at private prices was about 16% for both crops, while sunflower had a 35% rate at social prices. These findings highlight the need for holistic agricultural policies, including climate adaptation, efficient input use, and market competitiveness measures, to ensure the long-term sustainability of wheat and sunflower production in Tekirdağ and similar regions.

1. Introduction

Today, agricultural policies play a central role in economic development processes. Until the 1960s, industrialization was considered the main driver of development in developing countries and state resources were primarily allocated to this sector. Agriculture, on the other hand, was considered as a resource to support the development of the industrial sector. However, this industry-oriented approach has led to problems such as domestic market constraints, competitive disadvantages, and low-productivity agricultural practices. In the last two decades, agriculture has been re-evaluated as a priority sector in economic growth strategies [1].

Agricultural policies are a set of strategies and measures implemented by governments to regulate, develop, and support a country’s agricultural sector. These policies have significant impacts in various areas, such as food security, rural development, environmental sustainability, and economic growth [2]. It is a tool often used by governments to influence agricultural markets. Government intervention in the agricultural sector through policies plays an important role in pursuing key objectives, such as the supply of raw materials for industry and labor protection [3]. Globalization and international organizations significantly influence agricultural policies worldwide. As a result, major transformations are occurring in agricultural policies around the world. Initiatives such as the European Union’s Common Agricultural Policies, World Trade Organization agreements, and structural adjustment policies from the IMF and World Bank play important roles in this transformation [4].

With the global expansion of trade negotiations, countries have started to examine their competitiveness in international markets more closely. Especially in the last two decades, the expansion of free trade through international organizations, such as the General Agreement on Tariffs and Trade (GATT) and the subsequent World Trade Organization (WTO), has pushed countries to assess their competitiveness. Assessing the competitiveness of agricultural sectors, traditionally supported by protection policies in developed countries, becomes even more crucial if such measures are reduced or removed [5].

The Organization for Economic Cooperation and Development (OECD) defines competitiveness as the capacity of economic actors to sustainably generate high levels of factor income (e.g., profits, wages, salaries) and employment over the long term while competing in international markets. This definition implies that competitiveness refers not only to short-term commercial success, but also to the long-term performance of economic actors and their ability to maintain their presence in markets [6].

Agricultural support policies aim to increase competitiveness through inter-sectoral resource transfer and thus contribute to producer and social welfare. Therefore, evaluating the policies implemented in terms of both producer efficiency and socially rational distribution of resources is essential for the sustainability of development in the sector [7].

Economic sustainability refers to the capacity of an economic unit to support a given level of production indefinitely. It includes strategies to sustain economic growth while ensuring that such growth does not deplete resources or harm the environment or society [8]. A more comprehensive definition is the study of the dynamic interactions between profitability, resource management, and competitive advantage. This is the essence of developing a resource-based strategy that allows us to put in place appropriate mechanisms to create a sustainable competitive advantage over time [9]. In global industries, like tourism and agriculture, economic sustainability involves not only short-term profits but also establishing systems that ensure ongoing revenue while preserving resources and ecological balance [10,11].

In order to achieve economic sustainability, factors such as maximizing productivity, implementing strategic foreign trade, monitoring value chain flows, giving importance to infrastructure investments, and increasing the competitiveness of SMEs should be the focus of study [9]. These include factor endowments (land and production capacity), self-sufficiency, and environmental impact. Economic sustainability can be achieved by optimizing the strength of each of these dimensions individually, as well as the interactions between them [12].

A special set of economic sustainability indicators was developed for the assessment of the activities of agricultural enterprises. Production results (e.g., amount of revenue, earnings, and production costs) and indices of cost-effectiveness, liquidity, stability, and efficiency are the most popular [5,13].

The competitiveness of agricultural policies can be defined as the ability of a country’s agricultural sector to compete successfully in national and international markets. This ability depends on various factors, such as productivity in agricultural production, product quality, costs, technological development, market access, and government support. A competitive agricultural sector contributes significantly to a country’s economic growth, job creation, food security, and rural development.

Policymakers use agricultural policy analysis as a tool to assess the expected and unexpected consequences of policy changes on agricultural markets. It is also important to conduct an analysis to identify the potential impact of these market-specific responses on the overall viability of the development plan [3]. As cited by Waterman and Wood [14], Susan Hansen (1983) wrote: “Policy analysis refers to a clear, focused and systematic analysis of government outcomes and their impact on society.” Outputs and outcomes are important because they represent the end result of a particular policy process.

There is a strong relationship between economic sustainability and the policies to be implemented. It is especially important to support agricultural products with high resource efficiency and foreign trade potential [15]. At the center of agricultural competitiveness is the production factor endowments of countries. This is because a sustainable, competitive, and resilient agri-food system cannot be established solely based on available production resources and the output capacity [16].

Different methods are used to determine the comparative advantage of countries. David Ricardo’s and Heckscher-Ohlin’s theories of comparative advantage, Revealed Comparative Advantage (RCA) method, Revealed Symmetric Comparative Advantage (RSCA), and Trade Balance Index (TBI) are widely used. In addition to these, the Policy Analysis Matrix (PAM) method is a frequently used method, especially in the agricultural sector in terms of the tools it contains [17].

The Policy Analysis Matrix (PAM) focuses on assessing the impact of agricultural policies on competitiveness, productivity, and technology investments. It analyzes the private profitability of the system with current technology and prices, while comparing the effects of price policies and public investments on social and private profitability. How investments in infrastructure and agricultural innovations improve productivity by increasing output values or reducing costs is measured by the change in social profitability [1]. The PAM approach basically measures the effects of agricultural price policies implemented by governments on producer incomes. It also measures the effects of policies controlled by policymakers on the ability to identify transfers between consumers and producers and to measure the allocation of public budgets [18].

This study aims to analyze policy impacts and competitiveness for citrus production in the Çukurova Region. Using the Policy Analysis Matrix (PAM) approach, net policy impacts and the competitiveness of the citrus system were assessed based on survey data for the 2009–2010 marketing period. The results of the study reveal that citrus production is sustainable in terms of social profitability and competitive in terms of private prices [19]. Similarly, Subaşı (2023) used a combination of PAM and Data Envelope Analysis (DEA) methods to assess the productivity of lemon producers in Mersin and analyze their competitiveness at the international level [20]. This study provides a comprehensive framework of the economic structure of lemon production in the region [20]. Another study applied the PAM method to analyze the competitiveness of seaweed production in Tarakan City [21]. Jumiati et al. (2024) [21] showed that seaweeds provide both competition and comparative advantage with a PCR (Private Cost Ratio) of 0.37 and DRC (comparative advantage ratio) of 0.08. The study extends the policy analysis to seaweed production, noting that government incentives of a protective nature have generally had positive effects, but that this level of protection is vulnerable to policy changes. Another study conducted in the Pacitan Region in Indonesia examined the private and social profitability of maize farming in three different cropping periods (rainy season I, rainy season II, and dry season) using the PAM method. The study found that only in rainy season II, maize farming is both privately and socially profitable and competitive. This is mainly because maize yields are higher in this season compared to other seasons. The analysis of government policies on maize producers revealed that while input incentives had positive outcomes, output price protection measures negatively impacted producers. These findings emphasize the need for more effective agricultural policies [22].

Wheat is considered one of the main food sources in many countries around the world and the basic raw material of feed in animal production. World cereal production is approximately 2.6 billion tons, 30% of which is wheat production. Wheat, which has 220.8 million hectares of cultivation area and 770.1 million tons of production in the world, is considered among the most important food sources. Major wheat-producing countries, such as China, EU, India, Russia, the USA, and Australia, account for 70% of the total world wheat production. Türkiye’s wheat cultivation area will constitute 3.1% of the world wheat cultivation area by 2023 [23]. In Türkiye, 234 million decares of land are available for agriculture. After subtracting the fallow areas from these areas, 64.4%, i.e., 171 million decares of land, is devoted to arable agriculture. Cereal production is realized in 65% of these areas. Wheat is cultivated on 69.2 million decares, which is 62% of the 111 million decares of cereal production in Türkiye [24]. In 2017, the production amount of 20 million tons until 2021 decreased to 17.6 million tons due to yield and cultivation area. As of 2022, Türkiye’s total exports and imports amounted to 7.9 million tons and 9.5 million tons, respectively. While Türkiye has been sufficient in wheat production until recent years, this ratio decreased to 87.3% in 2022 [23].

Another important strategic crop for the world and Türkiye is sunflower, one of the oilseed crops. Sunflower has a cultivation area of 27.8 million decares and a production capacity of approximately 51.6 million tons. Ukraine accounts for 21.7% of world sunflower production, Russia 31.5%, and the EU 18%. Türkiye ranks first in world sunflower imports with a share of 25%. As of 2022, Türkiye paid USD 598.6 million for sunflower seed imports and USD 1.5 billion for sunflower crude oil imports. Türkiye’s sunflower production sufficiency rate is 59.6% as of 2022 [25]. In these two strategic crops, Tekirdağ ranks second after Konya in wheat production and first in sunflower production in the country as of 2021. The fact that the average yield in agriculture under dry conditions in the province is above the average of Türkiye reveals the importance of agriculture in the region for the country. Tekirdağ province has a share of 4.1% in Türkiye’s wheat production and 14.3% in sunflower production [26].

When the percentage distribution of the supports implemented in Turkey is analyzed, it can be seen that 26% is area-based support, 22% is premium-based support, 32% is animal-based support, and 20% is other support. Area-based support is a type of support provided to agricultural producers in Turkey to incentivize certain agricultural activities and is based on the size of land cultivated or planted. The most important of these supports is the diesel and fertilizer support (per decare) depending on the cultivated area. Premium-based support, on the other hand, is a price support mechanism implemented in Turkey to encourage agricultural production and especially the production of strategic products. In this support system, farmers who produce and sell certain products are paid a premium per kilogram or per liter based on the amount of product sold. Between 2002 and 2021, agricultural subsidies distributed to producers in Turkey amounted to USD 1.5 billion in 2002 and USD 2.7 billion by 2021. It was determined that agricultural subsidies increased 1.8 times in dollar terms. When agricultural supports are compared with real prices and dollar-based payments, it is seen that the balance has deteriorated against dollar-based payments since 2016, and the gap has widened [27]. While the amount of agricultural support received by Tekirdağ province was USD 30.9 million in 2002, this figure increased 6.6 times in 2018 and reached USD 62.1 million. The rate of increase in 2002–2020 is 9.5 times. This increase was realized as 12.5 in the whole country and increased more than Tekirdağ province. While the share of the province from the total agricultural supports in the country was 2.4% in 2002, this rate decreased to 1.9% in 2020 [28]. As can be seen from these data, government subsidies, although fluctuating over time, maintain the level of early 2000 in real terms.

The aim of this study is to reveal the profitability and competitiveness of these products and the effects of agricultural policies on these products based on wheat and sunflower production in Tekirdağ, which has an important place in wheat and sunflower production in Türkiye.

Building upon this context and employing the Policy Analysis Matrix (PAM) and Stochastic Frontier Analysis (SFA) frameworks, this study aims to evaluate the profitability and competitiveness of wheat and sunflower production in Tekirdağ. This research is guided by several key hypotheses. We hypothesize that wheat and sunflower production in Tekirdağ is economically sustainable, demonstrating profitability at both private and social price levels. Furthermore, we expect these crops to be competitive in the region, indicated by favorable Private Cost Ratios (PCRs) and Domestic Resource Cost (DRC) when compared to international markets. A central hypothesis is that government agricultural policies significantly influence this profitability and competitiveness by creating price distortions and transfers affecting producers and consumers. Utilizing Stochastic Frontier Analysis (SFA), we also hypothesize that there are significant variations in technical efficiency among producers within the study area and that improving production efficiency, as identified through the SFA, has the potential to enhance the overall profitability and competitiveness of wheat and sunflower production. These hypotheses form the basis of our analysis, allowing us to systematically examine the current economic status, policy effects, and efficiency potential of these strategic crops in the region.

2. Materials and Methods

The main material of this study consists of surveys conducted with enterprises producing wheat and sunflower in the 2021–2022 production year in Tekirdağ. It is aimed to reveal the cost, profitability, and competitiveness of wheat and sunflower production, which are among the main strategic products that Tekirdağ contributes significantly to the production of, by using the Policy Matrix Analysis method based on the data obtained from the producers in the region [28].

The Policy Analysis Matrix (PAM) is an analytical framework, introduced by Monke and Pearson (1989) [1] to measure the efficiency of input use in production, competitiveness, and the impact of government policy interventions. This method is based on profit and loss equations [18,29]. With the PAM method, the impact of distorted market prices as a result of government interventions in agricultural product and input prices can be revealed [30]. In addition, the competitiveness of different production systems and resource utilization efficiency can also be evaluated [7,31]. In addition, the PAM allows the measurement of transfers made as a result of policies in practice [32,33]. By filling in the elements of Policy Matrix Analysis, an analyst can measure both the transfers caused by the set of policies acting on the system and the inherent economic efficiency of the system [1,18,34]. The basic logic of the PAM is that it contributes to the determination of policies and competitiveness as a result of comparing income and costs with private and social prices [7,30].

Table 1. Policy Analysis Matrix.

	Revenue	Costs		Profits
		Tradable Inputs (Fertilizer, Pesticide, Seed)	Domestic Factors (Capital, Land, etc.)
Private Prices (Current Prices)	A	B	C	D
Social Prices (World Prices)	E	F	G	H
Policy Effects (Transfers)	I	J	K	L

Source: [1].

provides information on the concepts used in the PAM and their explanations. In the calculation of private profits (D) (Equation (1)), depending on the data obtained from the producers through the survey, (A) revenue was calculated by multiplying the yield obtained by the producers by the unit price; (B) shows the market value of the inputs (diesel, fertilizer, pesticides, etc.) tradable in cell (B); and cell (C) shows the market price and value of domestic resources used by producers.

The following formulas were used to construct the PAM table [35,36]:

Private Profit; D = A − (B + C)

(1)

Social Profit; H = E − (F + G)

(2)

Output Transfers; I = A − E

(3)

Input Transfers; J = B − F

(4)

Factor Transfers; K = C − G

(5)

Net Transfers; L = D − H or I − (J + K)

(6)

In the calculation of social profitability (H) (Equation (2)), social profitability is used as a measure of international profitability and a measure of efficiency. Social prices (SF) refer to market conditions in which there is no government intervention in the product and input market. In cell I of the Policy Matrix Analysis (A − E) (Equation (3)), the difference between domestic and international prices of the relevant product is determined by monitoring the difference between private prices and social prices. In cell J (B − F) of the PAM (Equation (4)), transfers and interventions in input prices can be calculated. Cell K of the PAM refers to factor transfers (Equation (5)); (C − G) refers to the transfers of non-tradable domestic resources, such as land, capital, labor, etc., which result from the calculation with SC and SF. In the last row of the PAM table (Equation (6)), net transfers or policy impact are calculated by the formula (D − H) or I − (J + K).

This study also makes use of some coefficients developed for a more strategic and broader PAM analysis for related products, which allow for an advanced analysis. We used the Private Cost Ratio (PCR), the Domestic Resource Cost Ratio (DRC), the Nominal Protection Coefficient Ratio of Competitiveness and Outputs (NEPCO), the Nominal Protection Coefficient Ratio of Tradable Inputs (NPCI), and the Effective Protection Coefficient Ratio (EPC) (

Table 2. PAM competitiveness coefficients and calculations.

Coefficients	Calculation Method	Description
Private Cost Coefficient (PCR)	C/(A − B)	PCR < 1 indicates the presence of competitive production.
Effective Protection Coefficient (EPC)	(A − B)/(E − F)	EPC < 1 indicates that the effect of price policies is negative and reduces private profits.
Domestic Resource Cost Coefficient (DRC)	G/(E − F)	DRC > 1 indicates that domestic resources are used inefficiently and production has a negative impact on social welfare.
Nominal Protection Coefficient on Tradable Outputs (NPCO)	A/E	Measures the effects on product prices. NPCO > 1 indicates that production activities are protected by the policies implemented.
Nominal Protection Coefficient on Tradable Inputs (NPCI)	B/F	Measures the impact on input prices used in production. NPCI > 1 indicates that producers purchase domestic inputs at higher prices than world prices.
Profitability Coefficient (PC)	(A – B − C)/(E − F − G)	The Profitability Coefficient (PC) measures the impact of all transfers on private profits.
Subsidy Rate Provided to Producers (SRP)	L/E	The subsidy to producer ratio (SRP) is a single measure of all transfer effects. SRP > 1 indicates a strong subsidy policy.
Profitability with Private Prices	D/A	“A” stands for total income at private prices while “D” stands for net profit at private prices. In other words, this ratio shows the profit margin at private prices.
Profitability with Social Prices	H/E	“E” stands for total income at social prices and “H” stands for net profit at social prices. This ratio also shows the net profit margin at social prices.

Source: [18,29,30,31,32].

).

The distribution of costs and revenue in the PAM is presented in Figure 1. Inputs used for production are divided into tradable inputs (fertilizers, pesticides, seeds, etc.) and domestic resources (labor, capital, rent, transportation, etc.). The sum of these inputs is the “Total Cost”. Revenue sources consist of product sales, by-product (i.e., straw), and agricultural support. After calculating the revenue, we deducted the costs to obtain the net profit.

In this study, Stochastic Frontier Analysis (SFA) was used to evaluate producer behavior at the micro-level and policy effects at the macro-level in a holistic manner. While the PAM analyses the economic efficiency and competitiveness of production systems and the impact of policy interventions by taking into account the differences between private and social prices, SFA quantifies the differences in resource utilization by determining the technical efficiency levels of producers. The integration of these two methods makes it possible to analyze the effects of different production scenarios on the PAM depending on their efficiency levels, thus providing more accurate and realistic assessments for policymakers.

Furthermore, this study also examines the input utilization efficiency (technical efficiency) of wheat and sunflower producers using Stochastic Frontier Analysis (SFA). The SFA developed by Aigner et al. (1977) [37], Meeusen and van den Broeck (1977) [38], and Battese and Coelli (1995) [39] is defined by the function $Y_i=\left(X_i;\beta \right).exp\left({\upsilon }_i-{\mathcal{u}}_i\right)$.

In this formula, $Y_i$ is the observed output, $X_i$ is the input factor, $\beta$ is the estimator vector, (u_i ≥ 0) is the deviation from output technical inefficiency, and (v_i) is the random error [40,41].

The logarithmic Cobb–Douglas (CD) production frontier function is used to estimate the production function (Equation (7)).

$$ln\left(Y_i\right)={\beta }_0+\sum _{j=1}^n{\beta }_j\ln \left(X_{ij}\right)+vi-ui$$

(7)

where $Y_i$ is the wheat or sunflower yield per hectare, X_ij is the input (diesel, labor, fertilizer, field rent, etc.), and β is the elasticity to be estimated. Standard assumptions are made for the error terms. uᵢ: Non-negative (uᵢ ≥ 0) technical inefficiency term. For the distribution of uᵢ, various inefficiency calculation methods, such as half-normal $u_i~iid{N}^{+}\left(0,{\sigma }_u^2\right)$, truncated normal $u_i~iid{N}^{+}\left(u,{\sigma }_u^2\right)$, exponential, or gamma, were used [42]. This makes the SFA more suitable for efficiency analyses, especially in sectors subject to stochastic effects (drought or excessive rainfall), such as agriculture [43]. In parallel with common practices in the literature [44,45], the half-normal distribution assumption was used. The model parameters β $(\beta ,{\sigma }_{\upsilon }^2,{\sigma }_u^2)$ were estimated by the Maximum Likelihood Method (MLE) [46]. The share of technical inefficiency in total variation is calculated by the formula in Equation (8):

$$\gamma ={\displaystyle \frac{{\sigma }_u^2}{{\sigma }_v^2+{\sigma }_u^2}}$$

(8)

Individual Technical Efficiency (TE) scores were estimated by the formula [47] in Equation (9):

$${TE}_i={\displaystyle \frac{Y_i}{f\left(X_i;\beta \right).\mathrm{e}\mathrm{x}\mathrm{p} \left(v_i\right)}}=\exp (-u_i)$$

(9)

3. Results

In this study, income for wheat and sunflower production in Tekirdağ was obtained by multiplying product unit prices by yield as of 2021–2022. In order to measure the impact of government support policy on these crops, the total income was calculated without taking into account the non-support that producers benefit from [28].

In

Table 3. Wheat and sunflower Policy Analysis Matrix (USD/ha).

Crops		Revenue	Costs		Profits
			Tradable Inputs (Fertilizer, Pesticide, Seed)	Domestic Factors (Capital, Land, etc.)
Wheat	Private Prices (Current Prices)	2232.80	633.02	1244.92	354.86
	Social Prices (World Prices)	2091.25	527.76	1131.71	431.78
	Policy Effects (Transfers)	141.55	105.26	113.21	−76.92
Sunflower	Private Prices (Current Prices)	1460.94	274.80	945.32	240.82
	Social Prices (World Prices)	1822.27	239.30	941.58	641.39
	Policy Effects (Transfers)	−361.33	35.50	3.74	−400.57

, the product revenue per hectare of wheat at private prices is calculated as 2232.80 USD/ha. Inputs (fertilizer, pesticides, seed) tradable in the table are 633.02 USD/ha, while domestic resources (land, capital, etc.) are calculated at 1244.92 USD/ha. It can be seen that the net profit obtained by the producers in the region is 354.86 USD/ha.

In the PAM study conducted by Saad et al. (2019) [48], according to the results obtained from various provinces of China in 2017, according to the wheat average results, total income obtained with private prices was calculated as 2269.57 USD/ha and 1748.66 USD/ha with social prices [48]. Traded input costs were 982.05 USD/ha at private prices and 2255.91 USD/ha at social prices.

The total cost of wheat producers in the region, calculated at social prices, was USD 1659.47/ha (

Table 4. Private and social prices in wheat production (USD/ha).

Factors	Private Prices	Social Prices
Fertilizer	412.19	344.87
Pesticide	118.29	79.08
Seed	102.54	103.81
Tradable Inputs	633.02	527.76
Labor	223.71	134.23
Capital Interest	71.77	56.47
General Administrative Cost	45.22	36.78
Rent	325.57	325.57
Harvesting and Transportation	140.44	140.45
Capital	438.21	438.21
Domestic Resources	1244.92	1131.71
Costs	1877.94	1659.47
Product Sales	2169.43	2027.88
By-Product (Straw)	63.37	63.37
Agricultural Supports	113.75	0
Revenue	2232.80 *	2091.25
Net Profit	354.86	431.78

* Agricultural support was not taken into account when calculating the revenue.

). Since the wheat base price practice in Türkiye is above the world wheat price in order to support producers, it can be seen that the producers in the region earn USD 141.55 more from one hectare. As of 2022, according to the results of the Policy Analysis Matrix of wheat production in the region, a profit of USD 354.86 per hectare is obtained with private prices, while a profit of USD 431.78 is obtained with social prices (Table 4). The main reason for this difference is that while world wheat market prices are lower than Türkiye, the cost items incur for production are also lower.

In sunflower production in Tekirdağ, fertilizer was calculated at 125.54 USD/ha, seed at 61.06 USD/ha, and pesticides at 88.20 USD/ha at private prices. Fertilizer has a 46% share among the inputs tradable. As of 2022, the net profit per unit area of sunflower production in Tekirdağ is calculated as 240.83 USD/ha (

Table 5. Private and social prices in sunflower production (USD/ha).

Factors	Private Prices	Social Prices
Fertilizer	125.54	126.42
Pesticide	88.20	43.22
Seed	61.06	69.66
Tradable Inputs	274.80	239.30
Labor	170.31	102.65
Capital Interest	40.18	40.18
General Administrative Cost	25.31	25.31
Rent	351.08	351.08
Harvesting and Transportation	122.60	122.60
Capital	235.84	299.76
Domestic Resources	945.32	941.58
Costs	1220.11	1180.87
Product Sales	1460.94	1822.27
By-Product (Straw)	-	-
Agricultural Supports	133.97	0.00
Revenue	1460.94 *	1822.27
Net Profit	240.83	641.40

* Agricultural support was not taken into account when calculating the revenue.

). Total cost was calculated as 1180.87 USD/ha and total product sales income as 1822.27 USD/ha at social prices, and the net profit was found to be 641.40 USD/ha at social prices.

Cereals and other crops are grown on 196,243,701 decares of the total agricultural area of 237,625,724 decares in Türkiye. Wheat cultivation has a 43% share in Türkiye’s crop production with an area of approximately 67 million decares, while sunflower cultivation has an important share of 6.3% with an area of approximately 9.8 million decares. It is of great importance to analyze the competitiveness and profitability of these two strategic crops in Tekirdağ.

Table 6. Descriptive analysis of variables.

	Wheat		Sunflower
Variables	Mean	Std. Dev.	Mean	Std. Dev.
Yield (kg/ha)	5174	1007.27	2117.24	263.63
Fuel (L/ha)	106.69	15.38	105.95	16.22
Labor (h/ha)	19.98	7.85	14.04	2.04
Seed (kg/ha)	250.29	29.99	4144.83	350.09
Nitrogen (kg/ha)	174.03	30.70	37.65	8.18

and

Table 7. Model results of Stochastic Frontier Analysis.

	Wheat			Sunflower
	Coefficient	Std. Error	z-Value	Coefficient	Std. Error	z-Value
Constant	4.7540	0.7972	5.9635 ***	3.0006	1.2270	2.4454 **
ln(Fuel)	0.2806	0.2080	1.3489	0.7664	0.0533	14.3731 ***
ln(Labor)	−0.0869	0.1309	−0.6639	−0.1344	0.0524	−2.5623 **
ln(Seed)	0.2836	0.0686	4.1287 ***	0.1338	0.1117	1.1972
ln(Nitrogen)	0.2722	0.1429	1.9047 *	0.1244	0.0464	2.6811 ***
SigmaSq.	0.0695	0.0175	3.9695 ***	0.0316	0.0085	3.7225 ***
Gamma	0.99998141	0.0006	1652.4302 ***	0.9990073	0.0012867	776.4241 ***
Log-Likelihood	14.45821			29.6467
Returns to Scale	0.78			0.89
Mean Technical Efficiency	0.7995461			0.888526

***, **, and *: p-values at 1%, 5%, and 10% levels.

present the results of the SFA analysis used in this study. While the average wheat yield in the research area was 5174.56 kg per hectare, the average sunflower yield was calculated as 2117.24 kg. While an average of 250.29 kg of seed is sown per hectare in wheat, an average of 4144.83 kg is sown per hectare in sunflower production. The amount of nitrogen applied per hectare was calculated as 174.03 kg in wheat and 37.65 kg in sunflower (Table 6).

The Stochastic Frontier Analysis (SFA) results presented in Table 6 evaluate the technical efficiency of wheat and sunflower producers and the effects of production factors on production in the research area. The models present significant results for both products. In particular, the fact that the gamma parameter is above 0.99 for both products indicates that inefficiency effects have an important place in the model and that there are significant differences between producers (Table 7).

The coefficient of seed input in wheat production (0.2836) is high and highly statistically significant (p < 0.01), indicating that increases in seed quality or quantity strongly affect wheat production. Similarly, in sunflower, the coefficient of fuel input (0.7664) is quite high and statistically significant (p < 0.01). Modern tractors widely used in the study area may explain this high fuel input effect. However, since producers with low technical efficiency generally use small and old tractors, it can be observed that labor use becomes inefficient and time losses increase, especially in wheat production. As a matter of fact, the coefficient of labor input in wheat was found to be negative (−0.0869), which indicates that an inefficient use of labor may have a negative effect on production. Nitrogen content was found to be statistically significant in both crops.

Average technical efficiency values were calculated as 79.9% for wheat and 88.9% for sunflower. This shows that sunflower producers are more technically efficient than wheat producers. In addition, returns to scale were found to be 0.78 for wheat and 0.89 for sunflower. The fact that both values are below 1 indicates a situation of diminishing returns and shows that producers cannot increase yields after a certain input level (Table 7).

According to the efficiency values found in the SFA model, wheat producers will be able to produce at the current output level even if they reduce the number of inputs they use by 20.05% and sunflower producers by 11.15%. This situation will have a positive effect on the profitability of the enterprises since the output level does not change, but the costs are reduced. These values are used as an efficiency-based scenario in the PAM analysis (Scenario 1 in

Table 8. Competitiveness indicators of wheat and sunflower.

			Scenario 1 *		Scenario 2 **		Scenario 3 ***
Coefficients	Wheat	Sunflower	Wheat	Sunflower	Wheat	Sunflower	Wheat	Sunflower
Private Cost Ratio (PCR)	0.78	0.80	0.65	0.77	0.72	0.76	0.65	0.66
Effective Protection Coefficient (EPC)	1.02	0.75	1.09	0.76	1.15	0.78	1.21	0.91
Domestic Resource Cost Coefficient (DRC)	0.72	0.59	0.72	0.59	0.75	0.59	0.75	0.59
Nominal Protection Coefficient on Tradable Outputs (NPCO)	1.07	0.80	1.07	0.80	1.10	0.80	1.32	0.96
Nominal Protection Coefficient on Tradable Inputs (NPCI)	1.20	1.15	1.00	1.09	0.96	0.92	1.43	1.31
Profitability Coefficient (PC)	0.82	0.38	1.37	0.44	1.31	0.46	1.81	0.77
Subsidy Rate Provided to Producers (SRP)	−0.04	−0.22	0.08	−0.20	0.06	−0.19	0.15	−0.08
Profitability with Private Prices	0.16	0.16	0.26	0.19	0.22	0.20	0.25	0.28
Profitability with Social Prices	0.21	0.35	0.21	0.35	0.18	0.35	0.18	0.35

* Calculated according to the results of the Stochastic Frontier Analysis model. Accordingly, there is a 20.05% decrease in the use of inputs in the model for wheat; 11.15% decrease in the use of inputs in the model for sunflower; ** 20% decrease in the cost of traded inputs for wheat and sunflower (20% increase in government support or 20% decrease in indirect taxes); *** 20% increase in crop yield with a 30% increase in fertilizer use.

).

Scenario 2, which envisages a 20% reduction in the cost of traded inputs (20% increase in government support or 20% reduction in indirect taxes), and Scenario 3, which envisages a 20% increase in crop yields with a 30% increase in fertilizer use only, were also constructed in this study (Table 8).

In the current situation, the Private Cost Ratio (PCR) was calculated as 0.78 for wheat and 0.80 for sunflower. A ratio less than 1 indicates that wheat is a profitable and competitive product (Figure 2). In Scenario 1, the PCR for wheat decreases to 0.65, indicating an increase in cost-effectiveness, while for sunflower there is only a slight improvement with 0.77. In Scenario 3, the PCR for wheat decreases again to 0.65, while for sunflower there is a significant decrease (0.66), indicating that wheat production in particular has become more advantageous in terms of private costs (Table 8).

The Effective Protection Coefficient (EPC) for wheat is 1.02, indicating that the producer is supported. However, this ratio is calculated as 0.75 for sunflower, indicating that sunflower producers are taxed. In similar studies, this ratio was calculated as 1.09 for wheat in Türkiye [31], 1.68 for Chinese wheat [48], 1.01 in Zambia [49], and 1.96 in Iraq [50]. Analyzing the scenarios, the wheat EPC increases steadily, reaching 1.21 in Scenario 3, indicating that increasing protection is provided by government intervention. The EPC for sunflower improves from 0.75 to 0.91, but is still unfavorable to the producer.

The Domestic Resource Cost Coefficient (DRC) was found to be 0.72 for wheat and 0.59 for sunflower. With the scenarios, this value increases very slightly to 0.75, but it is still below 1, so the social efficiency of wheat production continues. For sunflower, the DRC remains constant at 0.59, indicating a high level of resource efficiency and strong social rationality.

The Nominal Coefficient of Protection on Tradable Outputs (NPCO) is calculated as 1.07 for wheat production in this study. This ratio shows how far the interventions in the price of the product have moved away from world prices. According to some researchers, a ratio between 0.85 and 1.15 indicates that price interventions do not create a distortion [23]. Ratios below 0.85 mean that producers are taxed through prices and ratios above 1.15 mean that producers are protected. This means that the wheat price in Türkiye is 10% higher than the world wheat market and that producers are protected. For sunflower, this ratio is calculated as 0.80. This implies that sunflower producers are implicit taxed at a rate of 20% and a price below world sunflower prices is accepted. According to this ratio, sunflower is seen as a more competitive and advantageous product for regional agriculture. The coefficient for wheat increases with the scenarios and reaches 1.32 in Scenario 3, indicating that government support has increased and producer prices have risen well above international levels. For sunflower, the NPCO increases to 0.96, but the level of support is still insufficient.

While the PAM framework primarily quantifies the aggregate impact of policies through transfers, a degree of disaggregation regarding the effects on specific input categories is provided in Table 4 and Table 5. The differences between private and social prices for individual tradable inputs (fertilizer, pesticide, seed) and domestic factors (labor, capital, rent, etc.) reflect the combined influence of all relevant policy interventions on the cost structure of production for each specific category. For instance, the difference between the private and social price of fertilizer for wheat (Table 4) illustrates the net effect of policies, like subsidies or tariffs, on fertilizer cost per hectare for wheat producers in the region.

The Nominal Protection Coefficient on Tradable Inputs (NPCI) is another policy indicator. In this study, this ratio was calculated as 1.20 for wheat and 1.15 for sunflower. The highest input protection reached a ratio of 1.43 in Scenario 3 due to a 30 percent increase in input utilization. The lowest input utilization is in 2 Scenario (0.96 for wheat and 0.92 for sunflower).

The Profitability Coefficient (PC) is a comparison of private profits and social profits, and a value less than 1 means that private profits are always lower than social profits. In this study, this ratio was calculated as 0.82 for wheat and 0.38 for sunflower. With the scenarios, the PC value of wheat increases to 1.81 in Scenario 3, showing a significant profitability. For sunflower, profitability also increases, but only approaches 1 in Scenario 3 with 0.77, i.e., a potential improvement, but not yet sufficient. In the Policy Analysis Matrix study of wheat in Zambia, this ratio was calculated as 0.32 [49]. The Subsidy to Producers Ratio (SRP) was calculated as −0.07 for wheat and −0.21 for sunflower. Although the financial sustainability of production is still competitive, there is a reduction in gross revenues of 0.007 for wheat and 0.21 for sunflower. In the study conducted by Alves (2017) in Brazil, this ratio is similar to the result of this study (−0.21) [36].

Although sunflower production demonstrates high profitability (641.40/ha), reflecting its inherent efficiency based on world market conditions and lower input requirements compared to wheat, its profitability at private prices (240.83/ha) is significantly lower. This substantial gap, and the resulting negative net transfer of −400.57 USD/ha (Table 3), clearly indicate that current government policies effectively implicit tax sunflower production. This taxation, primarily through suppressed output prices relative to world markets (NPCO of 0.80), creates a disincentive for producers despite the crop’s strong social efficiency and resource rationality (DRC of 0.59). Therefore, while socially sustainable and efficient, the real-world viability of sunflower production for farmers is constrained by the current policy regime.

Profitability ratios with private and social prices, one of the most important economic analysis criteria of the enterprise, is the net profit margin percentage calculated by the Net Profit/Sales formula. As of 2021–2022 in Tekirdağ, this ratio was calculated as 0.16 with private prices and 0.21 with social prices in enterprises engaged in wheat agriculture. In the same region, the results in sunflower agriculture are calculated as 0.16 with private prices and 0.35 with social prices. The profitability of wheat at private prices reaches 0.26 in Scenario 1 and 0.25 in Scenario 3, while at social prices it falls from 0.21 to 0.18. This shows that government interventions increase profitability in favor of private prices, but that efficiency in terms of social cost is slightly reduced. For sunflower, profitability at private prices increases to 0.28 in Scenario 3, while profitability at social prices remains constant (0.35). This suggests that productivity is socially sustainable, especially for sunflower, but profitability at private prices should continue to be supported. In order to compare this with the data obtained in this study, economic analyses of wheat production in different regions of Turkey were examined and net profit margins at private prices (the profitability ratio with private prices) were calculated. In a study conducted in Çukurova region, which is one of the important production regions of Turkey [51], the net profit margin was found to be the same as in Tekirdağ (0.16), while in Konya (known as the granary of Turkey), this ratio was calculated as 0.11 [52]. In the studies conducted in Yozgat [53,54], 0.08 and 0.11, respectively, in Erzurum [55] −0.22 and in Ağrı 0.14 [56], it was observed that the results were considerably lower than Tekirdağ. The regions with negative profit margins are generally where animal production is concentrated. It was observed that the values in the studies on Tokat (0.22) [57] and Ankara (0.31) [58] were higher than the net profit margin in the research area. The soil structure and climatic factors of the region played an important role in the differentiation of these values.

4. Discussion

We conducted this study in Tekirdağ, a key agricultural region in Türkiye. This research aimed to assess the economic sustainability of wheat and sunflower production, crops vital for food security and strategic importance. Using the Policy Analysis Matrix (PAM), we analyzed the profitability and competitiveness of production in this region. Data from surveys conducted in Tekirdağ show that wheat production yields a profit of 354.86 USD/ha at private prices and 431.78 USD/ha at social prices. This is based on the fact that wheat prices in Türkiye are in line with or above international markets. The social price advantage, especially in fertilizer, pesticide, and labor costs, stands out as an important factor in the economic evaluation of wheat production. In a study on wheat production in Turkey, profitability for 2010–2011 was found to be 298.73 USD/ha at private prices and 326.87 USD/ha at social prices in the PAM analysis [30]. Similarly, according to the results of the Policy Analysis Matrix for Iraqi wheat production in 2017, it was reported that the net profit was 684 USD/ha at private prices and a loss of −57.32 USD/ha at social prices [49].

Sunflower production exhibits a higher profitability and competitive advantage in Tekirdağ compared to wheat. Private prices yield a profit of USD 240.83 per hectare, while social prices raise this figure to USD 641.40. This is supported by higher sunflower prices in global markets and lower fertilizer, pesticide, and energy requirements. These results reflect how Turkish agricultural policies attempt to balance support for different crops.

Positive profitability at both private and social prices indicates that wheat and sunflower production in Tekirdağ is economically sustainable. Negative social profitability would mean that the production of these crops is only possible with government subsidies. However, while a 13% yield loss in wheat production causes profitability to break even, this rate is calculated as 16% for sunflower. This highlights the critical impact of climate change on yields, especially in the region where irrigated agriculture is widespread. In Tekirdağ, wheat yields decreased from 534 kg/da in 2021 to 351 kg/da in 2023, and sunflower yields decreased from 240 kg/da to 115 kg/da. These data clearly show that wheat and sunflower production in the region has a low margin of safety and climate sensitivity.

A Private Cost Ratio (PCR) greater than 1 means that the system uses more domestic factors than value added and is not profitable (0.78 for wheat and 0.80 for sunflower). In similar studies, this ratio is 0.77 for Zambian wheat agriculture [49], 0.36 for Iraq [48], 0.90 for Chinese wheat agriculture [50], and 0.84 for Turkish wheat agriculture [31]. For other crops in Türkiye, this ratio was 0.90 for corn in Hatay and 0.80 for corn production in Türkiye [29]. In shelled peanut production in Türkiye, this ratio was calculated as 0.90 [5]. The fact that this ratio is below 1 in wheat, sunflower, and other crops shows the sustainability and competitiveness of production.

The Domestic Resource Cost Coefficient (DRC) provides a measure of the level of comparative advantage by the selected system. If the result is below 1, it means that the system has a comparative advantage. According to this result, USD 0.72 shows that we earn and save approximately USD 1 by using our domestic resources in wheat production. It shows that Tekirdağ has a comparative advantage in wheat and sunflower production and that savings are achieved through domestic production. However, it can be seen that sunflower production provides higher earnings and savings than wheat production. In similar studies, this ratio was calculated as 0.22 by [49], 0.71 for Türkiye by [31], and 1.69 for Chinese wheat production by [50]. Nguyen and Heidhues (2004) calculated this ratio as 0.59 for Zambian wheat production [59]; Candemir (2018) calculated this ratio as 0.63 for corn production in the TR63 region [29].

A Nominal Protection Coefficient on Tradable Inputs (NPCI) value of less than 1 indicates that producers are supported through inputs, while an NPCI of more than 1 indicates that producers are taxed through inputs. According to this result, it is understood that producers incur a 20% higher cost for wheat and 15% higher cost for sunflower production. In a similar study conducted for wheat in Zambia, this ratio was calculated as 0.92 [49]. In a study conducted by Macic (2015) for wheat production in Türkiye, this ratio was calculated as 1.14 [31]. In a study conducted by Alves (2017) for sunflower production in Brazil, this ratio was found to be 1.05 [36].

Profitability ratios at private and social prices are an indicator of the profitability of an investment. In a similar study on sunflower farming in Brazil, the profit margin was calculated as 0.38 with private prices and 0.54 with social prices [36]. In the results of the research conducted by Macic (2015) in Türkiye, this ratio was calculated as 0.25 with private prices and 0.32 with social prices [31]. In the results of the Policy Analysis Matrix for lemon production in Türkiye by Subaşı (2023), the profit margin was calculated as 0.29 with private prices and 0.33 with social prices [20].

The findings of this study offer several potential avenues for future research. In particular, empirically examining the development and feasibility of climate change adaptation strategies in wheat and sunflower production could be an important focus of future research. The economic and environmental impacts of alternative production techniques and circular economy principles to reduce input dependency can be analyzed quantitatively. In addition, regional comparative analyses and studies from a value chain perspective can provide a deeper understanding of the structural dynamics of the sector. Finally, policy impact analysis to identify the causal effects of different policy instruments (e.g., subsidies, price supports) on production decisions and welfare, and socio-economic research focused on farmer welfare can make important contributions to the field.

5. Conclusions

This study employed the Policy Analysis Matrix (PAM) and Stochastic Frontier Analysis (SFA) frameworks to evaluate the economic sustainability and competitiveness of wheat and sunflower production in Tekirdağ, Türkiye, a region vital for national food security. This analysis aimed to reveal the profitability of these strategic crops under current market conditions and policy interventions, assess production efficiency, and identify the effects of agricultural policies.

In Türkiye as a whole, the average yields of wheat and sunflower were 296 kg/ha and 261 kg/ha, respectively. In 2022, while Tekirdağ’s relative superiority in wheat yields demonstrates the region’s potential, it also points to the need for more effective agricultural policies for competitiveness, profitability, and sustainability of wheat in Türkiye as a whole. Product pricing and government support mechanisms are of vital importance at this point. Establishing a price policy at or above world prices for wheat and at world prices for sunflower will pave the way for sufficient profitability and sustainability in both crops, along with productivity increases.

The PAM analysis revealed that both wheat and sunflower production in Tekirdağ are economically sustainable, exhibiting positive profitability at both private and social prices. Wheat production yielded a profit of USD 354.86 per hectare at private prices and USD 431.78 at social prices. For sunflower, profitability per hectare was USD 240.82 at private prices and USD 641.39 at social prices. The significant difference between private and social profitability, particularly for sunflower, highlighted the profound impact of government policies. This analysis demonstrated that wheat prices are implicitly protected (NPCO 1.07), while sunflower prices are implicitly taxed (NPCO 0.80), resulting in a substantial negative net transfer for sunflower producers (−400.57/ha). Despite this policy-induced distortion at the private level, the low Domestic Resource Cost coefficients (DRCs: 0.72 for wheat, 0.59 for sunflower) indicate that both crops maintain a comparative advantage and strong social rationality in resource use.

Complementary SFA results indicate average technical efficiency levels of 79.9% for wheat and 88.9% for sunflower, suggesting room for efficiency improvements that can further reduce costs without necessarily increasing inputs, as explored in the efficiency-based scenario analysis. Returns to scale were below one for both crops, indicating diminishing returns to scale at the current input levels.

The findings show that there is 6% effective protection in wheat production, 20% nominal protection coefficient in tradable inputs (NPCIs), and 7% nominal protection coefficient in tradable outputs (NPCOs). Increased nitrogen fertilizer prices due to the Russian–Ukrainian War are an important reason for this situation. In the case of sunflower, there is a 15% state protection value on tradable inputs (fertilizer, pesticides, seed).

The analysis also highlights a critical discrepancy for sunflower: despite its high social profitability and efficiency, the current support policies affect producers, resulting in significantly lower private profitability. This implicit taxation undermines the real-world viability of a crop that is socially efficient and competitive. Therefore, future policies should specifically consider measures to reduce this implicit taxation on sunflower, perhaps through targeted support mechanisms or price adjustments that better align domestic prices with world market levels, thereby enhancing both social and private profitability and incentivizing expanded production of this strategic crop.

Based on these findings, several key policy implications emerge for ensuring the long-term sustainability of wheat and sunflower production in Tekirdağ and similar regions.

Firstly, establishing price policies at or above world market levels is crucial for both crops to ensure sufficient profitability, especially at the private level. The current implicit taxation on sunflower producers identified by the negative net transfer and NPCO needs specific policy intervention to better align private incentives with the crop’s high social efficiency. This can involve targeted subsidies or price support mechanisms that mitigate the effects of domestic price distortions relative to global markets, such as direct payments per unit of output or area, or minimum purchase price guarantees for sunflower.

Secondly, increasing productivity through the adoption of more efficient farming practices and improved input use remains vital. The SFA results suggest the potential for cost reduction by enhancing technical efficiency. Policies should focus on disseminating best practices, supporting access to modern technology and inputs, and providing training to farmers to close efficiency gaps. Specific examples include subsidized access to certified seed, training on optimal fertilizer application techniques, incentives for investment in fuel-efficient machinery, and support for adopting water-saving irrigation technologies.

Finally, government support mechanisms should be holistic, encompassing not just price support but also initiatives promoting sustainable resource management, climate resilience, and market competitiveness to ensure the economic, social, and environmental sustainability of these strategic crops. This holistic approach should integrate measures like subsidies for climate-resilient varieties, crop insurance schemes to manage climate risks, and financial incentives for sustainable farming practices that reduce environmental impact.

This study, while providing valuable insights through the integrated use of the PAM and SFA, has certain limitations. This analysis is based on cross-sectional survey data from a specific production year (2021–2022) and region (Tekirdağ), which may limit the generalizability of the findings to other years or regions with different climatic, economic, or policy conditions. Future research should address these limitations by using panel data covering multiple years and regions to capture temporal dynamics and spatial heterogeneity

This study highlights the economic dynamics of agricultural production in Türkiye and its global relevance. Profitability and competitiveness in key crops, like wheat and sunflower, are vital for food security, rural development, and trade. Climate change, especially in water-scarce regions, is causing yield losses and reducing production safety margins. This situation underscores the need for global agricultural policies to incorporate climate change adaptation and risk management. Rising input costs—especially for energy and fertilizers—and global supply chain vulnerabilities threaten agricultural profitability. This study emphasizes reducing Türkiye’s reliance on inputs in wheat production and boosting domestic capacity. In sunflower production, aligning price policies with global markets enhances competitiveness in liberal conditions, while controlling input costs remains essential.

In conclusion, this analysis of the Tekirdağ case clearly shows that for the sustainability of wheat and sunflower production in Türkiye and other countries with similar agricultural conditions, policymakers need to implement measures to increase productivity, reduce input costs, and counter the negative impacts of climate change. Designing government support policies in line with market conditions and protecting producers’ incomes is a critical factor for the long-term sustainability of agricultural production. For the future of food systems, the development and dissemination of sustainable and competitive agricultural practices that are appropriate to local conditions are of great importance.