Bio -based and A ppl ied Economics BAE Bio-based and Applied Economics 11(1): 55-73, 2022 | e-ISSN 2280-6e172 | DOI: 10.36253/bae-10981 Copyright: © 2022 M. Aghabeygi, K. Louhichi, S. Gomez y Paloma. Open access, article published by Firenze University Press under CC-BY-4.0 License. Firenze University Press | www.fupress.com/bae Citation: M. Aghabeygi, K. Louhichi, S. Gomez y Paloma (2022). Impacts of ferti- lizer subsidy reform options in Iran: an assessment using a Regional Crop Programming model. Bio-based and Applied Economics 11(1): 55-73. doi: 10.36253/bae-10981 Received: May 25, 2021 Accepted: January 21, 2022 Published: July 22, 2022 Data Availability Statement: All rel- evant data are within the paper and its Supporting Information files. Competing Interests: The Author(s) declare(s) no conflict of interest. Editor: Donato Romano. ORCID MA: 0000-0003-4953-2799 KL: 0000-0001-8449-5693 SGyP: 0000-0001-9289-7633 Impacts of fertilizer subsidy reform options in Iran: an assessment using a Regional Crop Programming model Mona Aghabeygi1,2,*, Kamel Louhichi3, Sergio Gomez y Paloma2 1 Mona Aghabeygi, Università degli Studi di Parma, Parma, Italy 2 European Commission, Joint Research Centre (JRC), Seville, Spain, 3 Université Paris-Saclay, INRAE, UMR Economie publique, Thiverval-Grignon, France E-mail: monaaghabeygi17@gmail.com; Kamel.elouhichi@inrae.fr; Sergio.GOMEZ-Y- PALOMA@ec.europa.eu *Corresponding author. E-mail: monaaghabeygi17@gmail.com Abstract. The aim of this paper is to assess the potential impacts of different fertiliz- er subsidy reform options on the performance of the Iranian crops production sector. This is achieved using a Regional Crop Programming (RCP) model, based on Positive Mathematical Programming, which includes in total 14 crop activities and encompass- es 31 administrative regions. The RCP model is a collection of micro-economic mod- els, working with exogenous prices, each representing the optimal crop allocation at the regional level. The model is calibrated against observed data on crop acreage, yield responses to nitrogen application, and exogenous supply elasticities. Simulation results show that a total removal of nitrogen fertilizer subsidies would affect the competitive- ness of crops with the highest nitrogen application rates and lead to a slight reduction of national agricultural income, at approximately 1%. This effect, which is more pro- nounced at the regional level, is driven by area reallocation rather than land produc- tivity. The reallocation of nitrogen fertilizer subsidy to only strategic crops boost their production and income but increase disparity among regions and affects negatively welfare compared to the current universal fertilizer program. The transfer efficiency analysis shows that both target and universal simulated options are inefficient with an efficiency score below one. Keywords: agricultural policy, fertilizer subsidy, land use effect, Regional Crop Model, Positive Mathematical Programing (PMP), Iran. JEL codes: Q18, C13, C61. 1. INTRODUCTION Iran is a country in Western Asia with 82 million inhabitants, standing at the world’s 18th most populous country. Its territory spans 1,648,195 km2, making it the second largest country in the Middle east. In 2016, the Gross Domestic Product (GDP) was 1797 billion IRR, while the per capita GDP was about 219 million IRR, and the country is ranked as an upper-middle 56 Bio-based and Applied Economics 11(1): 55-73, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-10981 Mona Aghabeygi, Kamel Louhichi, Sergio Gomez y Paloma income economy by the World Bank (ICB, 2016). The agricultural sector plays an important role in the Irani- an economy. In 2016, agriculture contributed up to 9.64 % of the GDP, provided up to 87% of the food supply, occupied around 10% of the land, and employed 19% of the labor force (IRICA, 2016; ICB, 2016; SCI, 2016). Smallholder farms with less than 25 acres (10 hectares) largely dominate the Iranian agricultural sector. They represent more than 70% of the country’s agricultural producers and occupy more than 55 % of cropland (CSI, 2014). They are basically family-based and family-man- aged farms with an average size of 3 hectares, with 2 hectares under cultivation (IRNAGRIC, 2015). The crop sector is the most significant agricultural subsector in the country with 65.7% of agricultural’ value added and 2.5 million agriculture production units. Field crops, mainly cereals, constitute the bulk of Iranian’s crop pro- duction. In 2016, wheat makes up 50.39% of total culti- vated land, followed by barley 14.95%, rice 5.07%, and corn 1.35%. However, in spite of input and output sup- port policies, the yields of these crops remain below the world average (WB, 2016; IMAJ, 2016). To boost productivity and foster national food secu- rity and agricultural self-reliance, Iran has deployed a multi-pronged program of subsidies. This includes guaranteed price f loors for more than twenty crops, and which often results in producer prices that are well above world prices. In addition to this price floor, the Iranian Government provides support to farmers in the form of subsidized prices for fertilizer, pesticides, and improved seeds, as well as for equipment and basic inputs like water and energy (Hosseini and Shahnabati, 2015; Pakravan et al., 2016; Hosseini et al., 2017). Ferti- lizer subsidy is the most important of these subsidy pro- grams. It started in the 1970s, but it focuses mostly on export crops and on training farmers in the proper use of fertilizers. However, as food security became a top priority with the explosion of population, fertilizer sub- sidy it was extended to staple crops (IC, 2016). In 2016, mineral fertilizer subsidy represented around 10% of the public expenditure in agriculture. The subsidy was paid to the Iranian Petro-chemical industry, to permit it to sell fertilizers at reduced prices. Subsi- dized fertilizers were universally available to all farmers, regardless their specialization, size, geographical loca- tion, etc. (i.e., universal fertilizer program). However, due to the government’s limited budget, not all farmers have access to subsidized fertilizer. In addition, given that subsidized fertilizers are often traded by intermediate dealers, they were sometimes sold to farmers at inflated prices or even smuggled out of the country. To address this issue, the “Agricultural Support Services Company (ASSC)”, responsible for providing and distributing min- eral fertilizers, has recently implemented a Smart Agri- cultural Input Distribution System (SAIDS) (SITO, 2016) that records detailed farmer information and monitors the transportation of fertilizer from petrochemical com- panies to the different regions (ASSC, 2016). Although the introduction of fertilizer subsidy may contribute to enhancing food availability and food security, it has been subject to increasing criticism in recent years from both national and international play- ers. In fact, several local experts argued that the use of input subsidies in Iran dates to the early 1970’s, however agricultural productivity is still low, self-sufficiency is not achieved yet, and food safety and food security are still major concerns. As such, this instrument is seen as inefficient, given its high budget costs, and source of market distortions since it benefits only specific groups of farmers (e.g., farmers with ease access to input mar- ket). To this, one can add the new pressure coming from the World Trade Organization (WTO). In fact, the Ira- nian government is expecting to become member of WTO and such kind of subsidies are not allowed by this organization (Najafi and Dehghan, 2010; Alijani et al., 2012; Barikani and Shahbazi, 2016). The debate on the ‘efficiency’ of fertilizer subsidy program is not new and not specific to Iran. According to the literature there are two types of subsidy programs depending on whether these are universally applied or targeted to a specific crop, category of farmers or region. Targeted subsidy programs include, for example, the five recent programs implemented in East and Southern Africa: Kenya, Malawi, Rwanda, Tanzania, and Zam- bia. These programs have in common their large scale in terms of number of beneficiaries (e.g., 2.5 million in Kenya), time frame (e.g., 10 years in Zambia), cover- age (nation-wide), and implementation arrangements (voucher-based system). On the opposite, other countries such as Iran, India, and west African countries (Burkina Faso, Ghana, Mali, Nigeria, and Senegal) have adopt- ed fertilizer subsidy programs, which seem to revert to universal (untargeted) price subsidies (Dorward, 2009; Praveen et al., 2017). Both targeted and universal subsidies are highly dis- cussed in the literature and two opposing views are gen- erally identified. Those who sustain their effectiveness in bringing about green revolution (Gardner, 1992; Wright, 1995; Denning et al., 2009; Javdani, 2012) and those who considers them expensive, mainly benefit the wrong peo- ple, and distort agricultural markets (Holden and Tos- tensen, 2011; Chibwana et al., 2014). The main objective of this paper is to contribute to this debate by assessing the economic effects of the ferti- 57Impacts of fertilizer subsidy reform options in Iran: an assessment using a Regional Crop Programming model Bio-based and Applied Economics 11(1): 55-73, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-10981 lizer subsidy program currently implemented in Iran and to compare its performance with an alternative program based on targeting strategic crops. This is achieved using a Regional Crop Programming (RCP) model designed to simulate farms’ responses to policy and market changes. The paper is structured as follows: Section 2 describes the Regional Crop Programming (RCP) model and its major features. Section 3 presents and discusses the results of model simulation. Finally, section 4 draws the main conclusions and policy implications. 2. THE REGIONAL CROP PROGRAMMING (RCP) MODEL 2.1 Model features RCP is a comparative static, regional, positive math- ematical programming model, which includes in total 14 crop activities and encompasses 31 administrative regions. Positive means that the model aim to repro- duces the real conditions as accurately as possible and to simulate “what is likely” to happen to this situation when changing external conditions (Howitt, 1995; Jans- sen and Van Ittersum, 2007). Regional signifies that the model operates at regional level and considers each region as one farm, as is often done in regional program- ming models (CAPRI (Britz and Witzke, 2014); REAP (Johansson, 2007); TASM (Eruygur and Cakman, 2008)). This implies that all farms within the region are assumed to be homogenous, have the same behavior and can per- fectly exchange production factors. The use of a regional approach is motivated by the relative homogeneity1 of arable farms in Iran as well as by the limited access to micro-data (i.e., farm data) for confidentiality reason. Builds on regional data from the Iranian Agricul- ture Ministry-Jihad (IMAJ, 2016), the RCP model is a collection of 31 non-linear regional programming mod- els, working with exogenous prices, each representing the optimal crop allocation at regional level. After being solved, the regional results of the regional models are aggregated to national level. RCP is calibrated using positive mathematical pro- gramming (PMP) (Howitt, 1995) 2. PMP is a methodol- 1 If we exclude large-scale farms which represent less than 0.2% of agricultural holding (SCI, 2014), arable farms within the same region tend to be relatively homogeneous because the majority have small farm size and most of them are sharing the same technology and equip- ment (Ansari et al., 2020). 2 Other methods have been developed to calibrate optimization models to observed allocations, although not perfectly. The well-known ones are the risk (Hazell and Norton, 1986) and the multi-attribute utility theory (Keeney and Raiffa, 1993) based methods. ogy developed to exact calibrate programming models against observed economic behavior without the use of artificial flexibility constraints, while requiring mini- mal data. The PMP method is often preferred to linear mathematical programming as it avoids over specializa- tion in crop production and yields smooth responses to policy changes. Because of these desirable characteris- tics, models calibrated using the PMP approach and its variants are popular in agricultural and environmental policy analysis. Existing agricultural supply models that rely on PMP principles include, among others, the Euro- pean Common Agricultural Policy Regionalized Impact (CAPRI) modelling system (Britz and Witzke, 2014), the US Regional Environment and Agriculture Programming (REAP) model (Johansson et al., 2007), the Canadian Regionalized Agricultural Model (CRAM) (Horner et al., 1992), the Turkish Agricultural Sector Model (TASM) (Eruygur and Cakman, 2008), and the Dutch Regional- ized Agricultural Model (DRAM) (Helming, 2005). Over time, the literature on PMP has evolved and several variants have been developed to accurately cali- brate programming models3. The more recent literature has focused on using supply elasticities and/or shadow prices for resources to reduce the under-determinacy of the model and increase the robustness of the param- eter specification (Heckelei and Wolff, 2003; Mérel and Bucaram, 2010; Jansson and Heckelei, 2011; Mérel et al., 2011, Mérel et al., 2013; Britz and Witzke, 2014; Louhi- chi and Gomez y Paloma, 2014; Garnache et al., 2017; Louhichi et al., 2018; Henry de Frahan et al., 2019). The PMP method used in this study builds upon this strand. It follows the variant proposed by Louhichi et al., (2018), which use cross-sectional data and prior information on supply elasticities and on dual values of land constraints, to calibrate the model to the base year condition. Supply elasticities are taken from the litera- ture (Sabohi and Azadegan, 2014; Garshasbi et al., 2014; Jafari Lisar et al., 2017), while prior information on dual values of land constraints is derived from the Iranian Agriculture Ministry- Jihad (IMAJ) database. RCP model relies on profit maximizing behavior and search for the optimal land allocation among pro- duction activities in each region taking into account land constraints. The regional profit (i.e., agricultural income) is defined as the sum of gross margin minus a nonlinear quadratic cost function for specific activity. The gross margin is equal to the total revenue from the sales of agricultural products plus fertilizer subsidies minus the accounting variable cost of production activi- ties. The accounting costs include cost of seed, fertilizer, 3 For a review of PMP models, see Heckelei et al., (2012) and Henry de Frahan (2019). 58 Bio-based and Applied Economics 11(1): 55-73, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-10981 Mona Aghabeygi, Kamel Louhichi, Sergio Gomez y Paloma pesticides, hired labor, and water. The quadratic activi- ty-specific function is a behavioral function introduced to calibrate the model to the observed land allocation of the base year, as is usually done in positive program- ming models. This function allows capturing the effects of factors that are not explicitly included in the model, such as capital and labor constraints, price expecta- tions, risk-adverse behavior, and other unobserved costs (Heckelei and Wolff, 2003). The crop yields and the nitrogen application rate are endogenously defined in our model to allow their adjust- ments under market and policy changes. This achieved thanks to a crop-specific quadratic4 yield response func- tion to nitrogen fertilizer (considered to be the most important nutrient), econometrically estimated and embedded in the model, under the assumption that yield is independent of the acreage planted. The other fertilizer elements (P and K) are assumed to be applied in fixed proportion to nitrogen fertilizer and the remaining intermediate inputs such as seeds and pes- ticides are supposed to be independent to fertilizer and employed in fixed rate by hectare of each specific crop5. Intermediate inputs are also assumed to be independent on the (unknown) marginal costs that are captured by the quadratic behavioral function (Heckelei and Wolff, 2003). The general mathematical formulation of profit maximization problem of region r = (1, 2, …, R) is as follows: (pr,iyr,ixr,i-wr,inr,ixr,i+sr,inixr,i) Cr,i,kxr,i dr,ixr,i–0.5 xr,iQr,i,i’xr,i’ (1) Subject to: Ar,i,mxr,i ≤ br,m [φr,m] (2) y=αn+βn2+γ (3) 4 We opted for a quadratic functional form because it keeps the mod- el quadratic and simplifies the resolution of the optimization prob- lem. More sophisticated specifications may consider exponential form (Godard et al., 2008; Mérel et al., 2011) or quadratic-plus-plateau form (similar to the conventional quadratic, but a plateau is imposed). 5 This assumption lacks of rationalization given the strong relation- ship between fertilizer and other inputs. In fact, one could expect that an increase in fertilizer use would increase the risk of pest infestation (Rossing et al., 1997) and, as a consequence, the amount of pesticides applied (similar effects could be observed in other inputs). However, due to the lack of data to make a reliable estimate of this relationship and in order to avoid additional bias we have adopted this assumption following previous studies by Mérel et al., 2011; Mérel et al., 2013; Graveline and Mérel, 2014, Britz and Witzke, 2014, etc. x≥0; y≥0; n≥0 (4) Where indices i,i’=1,2,…,I denote the crop activity; k=1,2,…,K the intermediate inputs (i.e., seed, pesticides, hired labor, water, etc.) and m=1,2,…,M the resource constraints (only land is considered here). π is the objective function value of region r, xr,i is the unknown level (hectares) of crop activity i, pr,i is the crop price (i.e. market price), yr,i is the crop yield, wr,i is the fertilizer price, nr,i (per hectares) is the ferti- lizer quantity, sr,i is the fertilizer subsidy (per hectares), and Cr,i,k are accounting variable costs (per hectares) for each intermediate input k and crop i. dr,i is the linear term of the behavioral activity function and Qr,i,i’ is the quadratic term of the behavioral activity function. Ar,i,m are the coefficients of resource (i.e., land) con- straints, br,m is the level of available resources and φr,m are their corresponding shadow prices. α,β and γ are the coefficients of the yield response function to nitrogen. The coefficients α and β are crop, seed variety, season, and agro-ecological zone specifics to take into account technological, soil and climate het- erogeneity. γ is the intercept parameter whose position (value) can be shifted up or down in the calibration step to capture region specification. By setting α and β at agro-ecological level we assumed that regions within the same agro-ecological zone have a common technology and, therefore, they have the same yield curve shapes but with different starting points (i.e., intercept γ is region specific). Five agro-ecological zones are defined for Iran, based on cli- matic conditions, soil characteristics and type of crops grown: Mountain Climate, Moist Climate, Hot and Dry Climate, Temperate Climate and Hot and Moist. 2.2 Model calibration The aim of the calibration process is to ensure that, in each region, the observed crop allocation during the base year period is exactly reproduced by the opti- mal solution of the programming model, which relies on profit maximization. This implies that two key vari- ables need to be calibrated: the regional crop yield and area. This is performed in two successive steps: first, we calibrate yield response to the applied nitrogen rate and then, the land allocation. 2.2.1 Calibrating yield response to nitrogen fertilizer Calibrating yield response to nitrogen fertilizer con- sists of recovering the unknown crop specific nitrogen 59Impacts of fertilizer subsidy reform options in Iran: an assessment using a Regional Crop Programming model Bio-based and Applied Economics 11(1): 55-73, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-10981 fertilizer prices w, the nitrogen response’s intercept γ and the nitrogen fertilization rate n that allows repro- ducing exactly the observed yield y0 assumed to be at the optimum level. Mathematically, the above consists of solving the following model where the objective is assumed to be the maximization of the profit by unit of area with respect to nitrogen fertilization use (Godard et al., 2008; Louhi- chi et al, 2020): maxπr,i=pr,iy(n)-wr,inr,i+sr,inr,i (5) Subject to: y(n)=αinr,i+βin2 r,i+γr,i (6) y(n)=y0 [ηr,i] (7) ηr,i≥0 [μr,i] (8) Where π is profit by unit of area, r is the region, i is the crop activity, y is the crop yield (kg ha-1) and y0 is its observed level in the base year (assumed to be optimal), p is the crop prices assumed to be known with exacti- tude, α,β and γ are the coefficients of the regression model, n is the nitrogen fertilizer quantity (kg ha-1), w is the nitrogen fertilizer prices, sr,i is the fertilizer subsidy, η is the Lagrange multiplier related to the constrained yield level and μ is the Lagrange multiplier related to the non-negativity constraints for n,α and β are estimated by agro-ecological zone (more details are available in Louhichi et al., 2020). 2.2.2 Calibrating production activity levels The calibration of activity levels consists of recover- ing the set of unknown parameters (d, Q and φ), so that the optimization model as described in equations (1) and (4) replicates exactly the observed activity levels (x0) of the base year. This is performed using the results of the yield calibration step and a new variant of Positive Mathematical Programming (PMP) approach proposed by Louhichi et al., (2018). This variant relies on prior information on (i) supply elasticities ( r,i,i), and on (ii) dual values of (irrigated and rainfed) land constraints (φr,m). To perform the estimation, we derive the FOCs of the optimization model, equation (1) and (4) and then we apply the HPD method to estimate the unknown parameters dr,i,Qr,i,i’ and φr,m. The HPD model minimizes, in each region, the weighted sum of normalized square deviations of estimated national and agro-ecological zone own price(diagonal) supply elasticities and dual values of con- straints from their prior subject to set of data consisten- cy (FOC) constraints. Following Louhichi et al., 2018, the general formula- tion of the corresponding HPD problem is the following: (9) Subject to: (10) (11) (12) (13) (14) Bz,i,i’=∑jLbz,i,jLbz,i’,j; Lbz,i,i’=0 for i’>i (15) (16) (17) Where indices j,j’=1,2,…,I (similar to i,i’) denote the crop activities; gmr,i is the gross margin for activity i (IRR/ha) with gmr,i=pr,iy0 r,i-wr,in0 h,i+sr,i-∑kCr,i,k. y0 is the observed yield and w and n0 are, respectively, the nitro- gen fertilization price and quantity estimated in the yield calibration step. and are, respectively, mean and standard deviation of the regional rental prices for irrigated and non-irrigated lands and , , and are mean 60 Bio-based and Applied Economics 11(1): 55-73, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-10981 Mona Aghabeygi, Kamel Louhichi, Sergio Gomez y Paloma and standard deviation of own price elasticities of supply at country and agro-ecologic zone levels used as prior (Jansson and Heckelei, 2011) and δr,i is a scaling factor with δr,i= . The normalized squared deviations of dual val- ues and of agro-ecological zone supply elasticities are weighted (ω) with the inverse number of administrative regions (i.e., 1/31) and the inverse number of agro-eco- logical zones (i.e., 1/5), respectively, to obtain a compa- rable weight with the first component of the HPD objec- tive function. The prior for the own price supply elasticity at agro- ecological zone ( ) is defined as the own price supply elasticity at national level time the ratio of average pro- duction between agro-ecological zone and national level. This allow agro-ecological zone with low (high) average production to be more (less) elastic to price change com- pared to the national average. The endogenous variables of HPD problem defined in equations (9)-(17) are: (i) the dual values of land con- straints, φr,m, (ii) the own and cross price elasticities of supply at regional (εr,i,i’), agro-ecological zone (εz,i,i’) and national (εi,i’) levels, (iii) the elements of the lower trian- gular Cholesky decomposition related to Bz,i,i’ parameters, Lbz,i,i’, and (iv) the regional-specific behavioral parameters dr,i and Qr,i,i’ (including the inverse matrix ). Equations (10) and (11) represent the FOC of the optimization model for crop activities and for land con- straints, respectively. Equations (12), (13) and (14) com- pute supply elasticities at regional, agro-ecological zone and national levels, respectively. Equation (15) is the Cholesky decomposition which ensures appropriate cur- vature properties of the estimated quadratic cost func- tion (i.e., convex in activity levels), Equation (16) calcu- lates the region-specific quadratic parameters Qr,i,i’ and Equation (17) calculates its inverse . 2.3 Data The primary data source used to parametrize RCP model are regional data from the Iranian Agriculture Ministry- Jihad (IMAJ, 2016) for the three-years aver- age around 2015 (2014, 2015 and 2016). IMAJ publish annual report on crop area and production in each region obtained from the aggregation of individual farm data collected through face-to-face survey. The Information and Communication Technology Centre of Iranian Agri- culture Ministry (ICTC- IMAJ) also use these individual farm data to derive input and output prices and quantities of crops in different regions as Cost Bank System (CBS). The CBS and IMAJ database provide detailed regional information for the five groups of crops, namely cereals (wheat, barley, corn, and rice), legumes (pea and lentil), vegetables (onion, potato and tomato), fruit (mel- on and cucumber), and industrial crops (cotton, canola and sugar beet). Table 1 reports the statistical charac- teristics of the key variables for the 14 selected crops in RCP. These variables include total cultivated areas and total production for each crop as well as their yield, rev- enue, estimated fertilizer application rates, fertilizer sub- sidy, production costs (e.g., seed, pesticides, fertilizer, hired labor and water), gross income, estimated implicit costs/revenues and net income per unit of land, average across 31 regions and for the three-year average around 2015. 2.4 Scenarios: layout and implementation As mentioned previously, apart from the pressure coming from the WTO, there is an intensive ongo- ing debate about the effectiveness of the input subsidies in Iran given their high, possibly unsustainable costs and the absence of credible empirical evidence on their impacts on agricultural productivity. Therefore, a reduc- tion or a total removal of these subsidies or their real- location to only specific farm groups or to specific crop sectors are among the reform options that are currently under discussion in the country. In this regard, the aim of this paper is to simulate the impacts of two policy options: (i) a total removal of nitro- gen fertilizer subsidy for all crops and all regions (ABOL scenario) and (ii) a reallocation of nitrogen fertilizer sub- sidy to only strategic crops (wheat, maize, and rice) while keeping the same subsidy budget (TARG scenario). We are aware that this drastic scenario of a total removal of fertilizer subsidy is currently to a great extent unrealistic and cannot represent a prospective or even likely development; however, it might contribute to the on-going debate on their relevance and their legitimacy. Keeping the subsidies (or reallocating all of them) for only strategic crops seems to be more realistic due to the high attention given by the government to these crops for political, economic and food security reasons, par- ticularly under the various international sanctions. Both scenarios are implemented and compared to a baseline scenario representing the business as usual (i.e., the baseline scenario is used for the counterfactual com- parison of the simulated scenarios). 3. RESULTS AND DISCUSSION In this section we examine whether and how the simulated fertilizer subsidy reform options affect land 61Impacts of fertilizer subsidy reform options in Iran: an assessment using a Regional Crop Programming model Bio-based and Applied Economics 11(1): 55-73, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-10981 Ta bl e 1. S ta tis tic al c ha ra ct er ist ic s o f t he k ey v ar ia bl es . C ro ps A re a un de r cu lti va tio n (1 00 0h a) Pr od uc tio n (1 00 0 to n) Yi el d (to n) Re ve nu e (1 00 0 IR R/ ha ) Fe rt ili ze r u se (k g/ ha ) Fe rt ili ze r Su bs id y (I RR /k g) N itr og en u se (k g/ ha ) Pr od uc tio n C os t (I RR /h a) G ro ss In co m e (1 00 0I RR /h a) Im pl ic it co st s/ re ve nu es (1 00 0I RR /h a) N et In co m e (1 00 0I RR /h a) C er ea ls W he at 58 94 .0 7 12 11 7. 70 2. 06 11 81 5. 04 30 2. 02 27 7. 76 19 4. 46 20 90 6. 21 -8 .8 1 42 .7 3 33 .9 2 Ba rle y 17 39 .8 4 32 81 .6 6 1. 89 92 46 .7 8 17 1. 97 13 5. 86 11 0. 06 19 98 2. 51 -1 0. 60 44 .4 3 33 .8 3 M ai ze 17 3. 53 12 49 .7 0 7. 20 73 58 4. 91 67 7. 63 50 6. 43 44 1. 84 38 67 8. 80 35 .4 1 36 .1 3 71 .5 4 Ri ce 33 2. 58 17 37 .1 7 5. 22 92 20 1. 17 11 27 .2 9 10 76 .5 3 69 5. 03 49 73 2. 81 43 .5 4 -2 6. 64 16 .9 0 Le gu m es Le nt il 13 4. 57 74 .7 1 0. 56 27 66 3. 63 5. 13 2. 91 3. 36 79 68 .6 9 19 .7 0 14 .0 7 33 .7 7 Pe as 49 2. 56 23 6. 67 0. 48 70 45 .3 3 18 .4 9 20 .6 9 11 .4 5 79 24 .7 1 -0 .8 6 55 .0 1 54 .1 5 Ve ge ta bl es To m at o 11 8. 76 48 78 .0 6 41 .0 8 13 90 39 .9 7 67 6. 35 96 1. 94 42 7. 57 88 65 6. 44 51 .3 5 5. 88 57 .2 3 Po ta to 14 8. 45 47 26 .7 5 31 .8 4 17 54 94 .4 4 62 0. 01 79 4. 18 37 1. 17 11 00 89 .8 0 66 .2 0 16 .6 1 82 .8 1 O ni on 50 .1 6 18 61 .4 6 37 .1 1 12 61 22 .0 8 10 33 .7 7 18 53 .9 8 62 9. 38 98 89 1. 62 29 .0 8 19 .0 9 48 .1 7 Fr ui t Cu cu m be r 46 .2 5 10 07 .5 2 21 .7 8 23 36 10 .8 4 30 7. 87 27 1. 12 20 9. 55 73 46 2. 27 16 0. 42 -5 3. 94 10 6. 48 M el on 11 2. 39 30 99 .8 0 27 .5 8 10 04 23 .7 48 7. 34 84 1. 26 28 8. 78 55 58 1. 61 45 .6 8 -0 .9 5 44 .7 3 In du st ria l c ro ps C an ol a 60 .0 1 93 .6 3 1. 56 21 86 4. 07 42 5. 24 30 5. 52 25 9. 02 12 63 8. 04 9. 53 24 .9 3 34 .4 7 Su ga r b ee t 10 0. 66 52 78 .9 0 52 .4 4 13 30 50 .2 58 7. 20 46 7. 32 35 0. 48 71 68 6. 40 61 .8 3 9. 54 71 .3 7 C ot to n 72 .0 9 16 6. 30 2. 31 65 60 0. 89 13 2. 61 86 .3 2 81 .2 0 45 81 0. 45 19 .8 8 22 .1 3 42 .0 1 M IN 46 .2 5 74 .7 1 0. 48 23 36 10 .8 4 5. 13 2. 91 3. 36 79 24 .7 1 -1 0. 60 -5 3. 94 16 .9 0 M A X 58 94 .0 7 12 11 7. 70 52 .4 4 70 45 .3 3 11 27 .2 9 18 53 .9 8 69 5. 03 11 00 89 .8 0 16 0. 42 55 .0 1 10 6. 48 ST D EV 15 64 .6 8 32 48 .3 8 18 .1 4 69 27 3. 66 34 5. 43 51 7. 20 21 1. 90 34 38 8. 40 43 .1 8 28 .7 0 23 .8 6 So ur ce : I C TC - I M A J. Th re e- ye ar s a ve ra ge a ro un d 20 15 (2 01 4, 2 01 5 an d 20 16 ) *N et in co m e eq ua ls to th e to ta l r ev en ue fr om th e sa le s of a gr ic ul tu ra l p ro du ct s pl us fe rt ili ze r su bs id ie s m in us th e ac co un tin g va ri ab le c os t o f p ro du ct io n ac tiv iti es p lu s im pl ic it re v- en ue s/ co st s ( i.e ., PM P te rm s) . 62 Bio-based and Applied Economics 11(1): 55-73, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-10981 Mona Aghabeygi, Kamel Louhichi, Sergio Gomez y Paloma allocation, nitrogen fertilizer application rate, produc- tion, agricultural income, and government budget in Iran at both regional and national levels and compare their cost-effectiveness using the transfer efficiency index. Before presenting simulation results it is important to notice that farmers may respond in three ways to a reduction or a removal of fertilizer subsidy: (i) extensive margin, that is, reallocation of acreage among crops by, for example, substituting more fertilizer-intensive crops with less fertilizer-intensive crops (i.e. acreage effects), (ii) intensive margin, that is, reducing fertilizer intensity per hectare for a given crop (i.e. yield effects), and (iii) land abandonment, that is, putting out of production land (i.e. land abandonment effects). With our models we tried to capture only the first two adjustments which represent the main opportunities for farmers to respond to shocks. Land abandonment adjustment is excluded because agricultural utilized area is assumed to be fix in our model. 3.1 Acreage, fertilizer intensity and yield effects 3.1.1 ABOL scenario The implementation of the ABOL scenario, based on a total removal of nitrogen fertilizer subsidy, would lead, as expected, to the reallocation of land from crop groups strongly dependent on fertilizer such as industrial crops, vegetables, and fruit to crop groups less depend- ent on fertilizer like legumes and cereals. As shown in Table 2, the acreage of industrial crops, vegetables and fruit decreased by -1.72%, -1.47%, and -1.16%, while the acreage of legumes and cereals increases by +0.93 and +0.06%, respectively. These results are also confirmed while looking to individual crops. The acreage of crops strongly dependent on fertilizer such as rice, tomato and maize decreased by -5.75%, -2.54% and -1.46%, respectively, whereas the acreage of crops less depend- ent on fertilizer such as barley, peas and lentil increase by +9.56%, +1.02% and +0.63%, respectively. This find- ing is explained by the fact that fertilizer-intensive crops become less competitive with the removal of subsidy and, therefore, lose some of their areas in favor of less fertilizer-intensive crops. Pishbahar and Khodabakhshi (2015) in a study focusing on farmers of Varamin area have found similar results showing a decrease in the acreage of maize and tomato with the removal of input subsidy. Kohansal and Ghorbani (2013) and Shirmahi et al., (2014) have revealed, using estimated price elasticity for nitrate fertilizer, that removing nitrate subsidy would cause a remarkable land reallocation among crops. From this Table it also appears that all crops experi- ence a reduction in their fertilizer application rates when the nitrogen price increase with the removal of subsidy. The average reduction of fertiliser application rate across all crops is around -9.16%, ranging between -0.7 % and -18%. Legumes are the most responsive to nitrogen price increase, in terms of fertiliser application rate. However, this response is small in absolute terms because legumes have relatively low fertiliser application rate in baseline. In the opposite, the response of fertiliser-intensive crop groups (e.g., industrial crops, vegetables, and fruit) is relatively low (less than 20%) but quite large in absolute terms. While comparing individual fertiliser-intensive crops (e.g., rice, onion, tomato, and maize), we found that their responses to nitrogen price increase are quite similar and close to -2.5%. The exception is maize where the percentage change in application rate seems to be very small (less than 1%), explained by the fact that the observed application rate for maize lies on the flatter proportion of the yield response curve. Table II also shows that the reduction of nitro- gen application causes relatively drastic yield losses in nitrogen-intensive crop groups than in less nitrogen- intensive crop groups. This clearly appears for legumes where a -48% decrease of fertiliser application rate caus- es a reduction of only -1.92% for yield, while a –1.84% decrease of fertiliser application rate for vegetable causes a reduction of its yield by -0.37%. However, given the relatively high yield of nitrogen-intensive crop groups their yield losses could be significant in absolute terms. Appendix Table A1 reports the reallocated area in each region as a result of the ABOL scenario. From this Table it clearly appears that all regions seem to be affected by this scenario with different degree depend- ing on their specialization. The largest reallocated area is observed in regions specialised in rice such as Mazandaran, those specialised in industrial crops (mainly canola) like Golestan and those specialised in vegetables (mainly tomato) like Fars. Golestan tend to be more affected because it is the first producer of can- ola with 14200 thousand hectares (around 25% of total canola area) and the second after Mazandaran in culti- vating rice with 59060 thousand hectares (around 10% of total rice land). This result is expected, as these three crops have the highest fertilisation rates and, therefore, a reduction of fertilisation application cause drastic losses in their yields and, thus, in their performances. Regions specialised in maize such as Kurdistan seem to be able to maintain such specialisation although its dependency on fertiliser. This means that maize remains competitive in these regions even with an increase of nitrogen ferti- liser price. 63Impacts of fertilizer subsidy reform options in Iran: an assessment using a Regional Crop Programming model Bio-based and Applied Economics 11(1): 55-73, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-10981 3.1.2 TARG scenario Paying nitrogen fertilizer subsidy to only strate- gic crops (“TARG scenario”), namely wheat, maize and rice boosts their areas at the expense of non-target groups. As shown in Table II, the area devoted to cereals group increase by 0.13%, whereas the area dedicated to all other groups’ decline, reaching -1.69% for industrial crops. The percentage increase of strategic crops area is relatively small; however, measured in absolute terms, it is quite significant (about 57 thousand hectares) due to their large initial shares in the total area (Table 1). Looking at fertilization application rate change under TARG scenario reported in Table II, we can see the same trend as for acreage change: an increase of fer- tiliser intensity for target crops and a decrease for the other crops. However, the magnitude of changes is quite different: the percentage change of fertiliser application is bigger than the percentage change of acreage, which is not surprising given that a large increase of crop area will be costlier due to rising marginal costs. The yield effect of the TARG scenario seems to be limited which means that reducing fertiliser price for target crops, which are nitrogen-intensive crops, boost only margin- ally their yield. As predicted, the reallocation of fertiliser subsi- dies to only strategic crops stimulate their acreages in all regions (see Table A1). Regions specialised in target crops (i.e., with largest share of target crops) react rela- tively more rapidly to a nitrogen price decrease triggered by the TARG scenario, in comparison to the other ones. For example, in the East Azerbaijan, Fars and Kurdistan regions, where target crops area in baseline exceeds 70% of total cropland, the percentage increases are larger, in comparison to regions with small initial share (less than 30%). In these regions the land adjustment occurs, mainly at the expense of barley. For instance, in Fars, the acreage of barley declines by -20% under the TARG scenario and its share in total land drop down from 20% (121693 hectares) to 16% (97411 hectares). 3.2 Production effects Table II shows the production effects of ABOL and TARG scenarios. As can be seen from this Table, the Table 2. Fertilizer application rate, acreage, production, yield, and income changes under ABOL and TARG scenarios (% change relative to baseline). Crop/ group/ Scenario Fertilization Application Rate Acreage Production Yield Average Net Income ABOL TARG ABOL TARG ABOL TARG ABOL TARG ABOL TARG Wheat -8.32 1.31 -2.38 0.90 -4.18 0.90 -1.94 0.00 -1.67 0.22 Barley -18.15 3.65 9.56 -2.63 1.37 -1.06 -7.41 1.59 -0.51 -0.01 Maize -0.72 0.15 -1.46 0.60 -1.73 0.67 -0.28 0.14 -0.39 0.10 Rice -2.68 0.64 -5.75 0.76 -6.24 0.90 -0.38 0.19 10.51 -1.01 Cereals -4.02 0.81 0.06 0.13 -3.22 0.53 -1.34 0.31 0.49 -0.02 Lentil -11.61 -12.20 0.63 -0.38 0.26 -0.38 -1.79 0.00 1.27 -0.10 Peas -58.60 -60.61 1.02 -0.16 -1.37 -2.40 -2.08 -2.08 -0.31 0.05 Legumes -47.94 -49.63 0.93 -0.20 -0.97 -1.92 -1.92 -0.96 0.30 -0.01 Tomato -1.86 -1.63 -2.54 -1.42 -2.79 -1.75 -0.27 -0.34 -0.47 -0.25 Potato -0.97 -0.98 -0.71 -0.68 -0.79 -0.73 -0.06 -0.06 0.12 -1.80 Onion -2.34 -2.33 -1.17 -1.39 -1.93 -2.03 -0.75 -0.65 -2.84 -1.41 Vegetables -1.84 -1.77 -1.47 -1.07 -1.83 -1.37 -0.37 -0.36 -0.90 -1.02 Cucumber -8.90 -8.72 -0.41 -0.38 -0.58 -0.51 -0.14 -0.09 -0.07 -0.16 Melon -0.94 -0.95 -1.46 -1.72 -0.96 -1.03 0.51 0.69 8.05 -1.26 Fruit -4.29 -4.22 -1.16 -1.33 -0.87 -0.90 0.22 0.34 2.33 -0.49 Canola -1.78 -0.66 -3.44 -2.02 -3.69 -1.38 0.00 0.64 0.13 -0.39 Sugar beet -2.25 -2.27 -0.87 -0.42 -0.85 -0.50 0.02 -0.08 0.39 -0.94 Cotton -16.26 -14.10 -1.49 -3.19 -1.36 -3.40 0.00 -0.43 0.07 1.85 Industrial crops -3.72 -3.06 -1.72 -1.69 -0.91 -0.60 0.02 -0.07 0.18 0.12 National -9.16 1.05 0.00 0.00 -2.61 -0.12 -0.23 -0.10 -076 -0.01 Source: Model results. 64 Bio-based and Applied Economics 11(1): 55-73, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-10981 Mona Aghabeygi, Kamel Louhichi, Sergio Gomez y Paloma average production effects of ABOL and TARG scenarios are estimated to be around -2.61% and -0.12%, respec- tively. The main production effects under the ABOL scenario are (i) a decrease of production for nitrogen intensive crop groups (e.g., vegetable decrease by -1.83%, industrial by -0.91%, and fruit by -0.87%) and (ii) an increase of production for less nitrogen intensive crop groups (e.g., legumes). These trends are also confirmed while looking to individual crops. Production of crops less dependent on fertilizer such as barley and lentil increase whereas, whereas production of crops strong- ly dependent on fertilizer like rice, tomato and maize decrease ranging between -1% and -7%. These results are consistent with Rahmani et al., (2011) who also found that increasing fertilizer price led to a decrease in the production of maize by -1.28% and cotton by -1.62%. Under the TARG scenario, the large positive effects in production are observed for the targeted crops, name- ly wheat, maize, and rice, ranging between +0.6% and +0.9%, while negative effects are experienced by less competitive crops such as cotton, tomato, and barley, with a production retraction of -3.4%, -2.4% and -1.06% respectively. At regional level, Mazandaran by -31 %, Golestan by -26% and Kohkiloyeh by -20% show the highest decrease in production under the ABOL scenario. However, in the TARG scenario production increased in Mazandaran by +4% and Bushehr by +2% (see Table A2). As mentioned before, Mazandaran and Golestan are the most impor- tant regions in cultivation rice. These production effects are driven either by land reallocation (i.e., land substitution between crop groups), land productivity (i.e., yield effect) or both. To better understand the contribution of each driver, we decom- posed the production effects into two effects using the Logarithmic Mean DIVISIA Index (LMDI) approach (Ang, 2005): acreage effect (i.e., area) and yield effect (i.e., productivity): production= ×Area=Productivity×Area (18) Where area stands for cultivated area, therefore, production impacts are decomposed into productivity and area effects in an additive form as follows: ∆production=∆productivity+∆Area (19) Where ∆x=x(scenario)+x(baseline). The LMDI approach is used to calculate the above individual con- tributions. For example, the area effect is calculated as follows: (20) Where prod-s and prod-B refer to production (in tons) under ABOL and TARG scenarios and baseline respec- tively, in stand natural logarithm and Area-s and Area-B denote the cultivated area under ABOL and TARG sce- nario and baseline, respectively. Figure 1 reports the decomposition of production effects under ABOL and TARG scenarios. From Fig- ure 1 it clearly appears that the acreage effect explains around 80% of production effect for vegetable, fruit, and industrial groups under both ABOL and TARG sce- narios. As an example, the -1.83% decrease of vegetable production under ABOL scenario is assumed to be a combined effect of yield (-0.37%) and area (-1.47%). The acreage effect accounts for 80.33% of the total change in vegetable production, while the remaining 19.67% is attributed to yield effect. Given that the acreage effect explains most of the changes in production under ABOL and TARG scenario for these three crop groups, it is not surprising to observe that their production and acreage effects are strongly correlated. On the other hand, for cereal and legumes groups, production changes under both ABOL and TARG scenarios seem to be mainly driven by yield effect. For example, the 0.53% production increase of cereals is a result of a 75% yield change and 24 % of acreage change. 3.3 Agricultural income effects The land and production effects presented previously dictate changes in agricultural income reported in Table II. Before interpreting these changes, it is important to notice that agricultural income is equal to the maxi- mized value of the objective function presented in equa- tion (1) and, therefore, it is inclusive of all shadow costs. The impact of the removal of fertilizer subsidy (ABOL scenario) on agricultural income is rather small when aggregated at national level (less than 1% compared with the baseline), and the reallocation of fertilizer subsi- dy to only target crops has very limited effect on national agricultural income, compared to baseline. This is to say that due to the relatively low shares of subsided fertilizers in total fertilizer consumption and of fertilizer costs in total production costs, the removal or reallocation of fer- tilizer subsidy will not engender a large impact on agri- cultural income at national level. However, while looking deeper at the regional and crop levels the impact could be more pronounced and sometime with opposite sign. As shown in Appendix Table A3, income change under ABOL scenario is negative for all regions, which 65Impacts of fertilizer subsidy reform options in Iran: an assessment using a Regional Crop Programming model Bio-based and Applied Economics 11(1): 55-73, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-10981 is not surprising, ranging between -0.35% and 5%. As expected, the most aff ected ones are those specialized in nitrogen-intensive crops such rice, tomato, and onion. This heterogeneous income effect is probably more noticeable when we go at lower levels such as sub-region- al and farm levels. Under TARG scenario (Table 6), the econom- ic impact remains also small for the majority of the regions, ranging between -3% and 0.64%; nevertheless, there is an opposite eff ect: some regions loose and some regions gain from the reallocation of subsidy. Regions specialized in target crops (wheat, maize, and rice) such as Golestan gain from the reallocation, while other regions either they lose or almost no change compared to the current situation (i.e., Khorasan and, South Kho- rasan). 3.4 Policy effi ciency In this section we use the results of the RCP model to compare welfare implications of the two simulated policies: current (i.e., baseline) vs. target (i.e., TARG) fer- tiliser policies. For doing that, we use the ABOL scenar- io as counterfactual. In fact, the diff erence between the baseline and the ABOL scenario provides an estimation of the eff ect of the current policy (universal subsidies), while the diff erence between the TARG and the ABOL scenarios gives an estimation of the alternative policy (target subsidies). From a cost/benefi t perspective, the most effi cient policy instrument is the one best at achieving the target benefi t at lowest cost. Following Brooks et al., (2011), we use the transfer effi ciency (TE) index to compare the rel- ative effi ciency of both policies. Th is index is calculated as follow: (21) Th e implementation of the target fertiliser policy (TARG scenario) came at a total cost to taxpayers and consumers of about IRR 2.83 billion and generates an increase of agricultural income of IRR 2.64 billion, which means a TE of 0.93. Whereas the application of the universal fertiliser policy (baseline scenario) came at the total cost to taxpayers and consumers of IRR 2.83 billion and generates an increase of agricultural income of IRR 2.69 billion, which implies a TE of 0.94. Th e main conclusion coming out from this com- parison is that, fi rst, the two policies are quite similar in terms of welfare implications and, second, both policies seem to be ineffi cient because their TE are lower than one, knowing that all the administrative costs related to the implementation of this policy are not considered in our analysis. Th ese results are in line with the fi nd- ing of Karimzadeh et al., (2006), Mosavi et al., (2009), Bakhshi et al., (2010) and Rahmani et al., (2011) who also reported that fertiliser subsidy in Iran has led to an ineffi cient use of nitrate fertilizer and, therefore, needs to be reviewed. -40% -20% 0% 20% 40% 60% 80% 100% ABOL TARG ABOL TARG ABOL TARG ABOL TARG ABOL TARG Cereals Legumes Vegetable Fruit Industrial La nd a nd y ie ld ch an ge s( % ) Group/Scenario % YIELD % LAND Figure 1. Production change decomposition under ABOL and TARG scenarios. 66 Bio-based and Applied Economics 11(1): 55-73, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-10981 Mona Aghabeygi, Kamel Louhichi, Sergio Gomez y Paloma 4. CONCLUSION This paper presents the results of a comprehensive analysis aiming to assess the economic effects of the fer- tilizer subsidy programs currently implemented in Iran and to compare its performance with an alternative pro- gram based on targeting strategic crops. Two policy sce- narios are simulated, and their results are compared to a baseline scenario representing the business as usual: a total removal of fertilizer subsidy “ABOL”, and a reallo- cation of fertilizer subsidy to only strategic crops (wheat, corn, and rice) “TARG”. This analysis is done using a regional economic model which includes in total 14 crop activities and encompasses 31 administrative regions. This model is a collection of micro-economic models, working with exogenous prices, and calibrated against observed data on crop acreage, yields and exogenous supply elasticities. From a methodological perspective, the novelty of this paper lies in the employ of detailed regional model- ling approach that allow for an adjustment of both crop acreage and input intensities and, therefore, to infer the effects of policies that are likely to have effects at the extensive and intensive margins. From a policy perspective, findings from this study reveal several exciting patterns. First, the effects of fer- tilizer subsidy removal are rather small at national level (less than 1%), although more pronounced at regional level, implying that a large share of farms do not use or use small quantity of fertilizer and, therefore, addi- tional government efforts are needed to facilitate them access. Second, the reallocation of fertilizer subsidy to only strategic crops under TARG scenario boost their production and income, however, it increases disparity among regions and affects negatively national agricul- tural income and welfare compared to the current uni- versal fertilizer policy. This imply that targeting strategic crops could not be the best solution and higher efficiency could be achieved by taking into consideration regional and farm heterogeneities. Policymakers may gain from be cognizant of heterogeneity among regions/farms and that one policy may not fit all regions/farms. Third, based on the result of the Transfer Efficiency (TE) analy- sis, both target and universal simulated options seem to be inefficient, as their TE indexes are lower than one, meaning that one IRR injected in the Iranian’s agricul- ture sector generate less than one IRR. Such results tend to confirm previous studies in the literature showing low productivity of Iranian agriculture (Bakhshi et al., 2010; and Rahmani et al., 2011). Our findings, however, need to be considered with some caution, on account of the model’s assumptions. First, output market prices are assumed to be exogenous. This implies that market feedback (output price chang- es) is not taken into account in the model. This could be an issue mainly when production change is quite high such as for cereals under ABOL scenario. Accounting for price effects requires extending the supply model into a partial or a general equilibrium model which is clearly beyond the scope of the present paper. A relaxation of this assumption would dampen supply effects and partially offset the negative impacts of subsidy remov- al (ABOL scenario) given that a production decrease induced by higher fertilizer prices raises output prices which in turn enhances production. Similar trend would be observed for non-target crops under TARG scenario. Second, due to data limitations the administrative costs related to the implementation of fertilizer policies are not considered. This may lead to an overestimation of the welfare impacts. A third potential caveat to our analysis is that we assume a fixed regional structure, implying that agricultural land extension/retraction (abandonment) in response to the simulated policies is not captured by the model. This may lead to an underes- timation of the simulated impacts, mainly under ABOL scenario. A careful analysis of each of these limitations is, therefore, needed when examining simulation results. Despite these limitations, our paper gives some insights on the potential role of fertilizer subsidy and provides useful recommendations to the policy making process aiming to enhance productivity and sustainabil- ity of the farming sector in Iran. Acknowledgements The authors are grateful to Statistics and Informa- tion Technology Office of Iranian Agriculture Ministry- Jihad for granting access to regional data for crops. The authors would like to warmly thank Prof. Filippo Arfini for his valuable and constructive suggestions during the planning and development of this research work. They also thank the two anonymous referees for their com- ments and suggestions that substantially improved the manuscript. The authors are solely responsible for the content of the paper. The views expressed are purely those of the authors and may not in any circumstances be regarded as stating an official position of the European Commis- sion. ACKNOWLEDGEMENTS The authors are grateful to Statistics and Informa- tion Technology Office of Iranian Agriculture Minis- try- Jihad for granting access to regional data for crops. 67Impacts of fertilizer subsidy reform options in Iran: an assessment using a Regional Crop Programming model Bio-based and Applied Economics 11(1): 55-73, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-10981 The authors would also like to thank Prof. Filippo Arfini for his valuable and constructive suggestions during the planning and development of this research work. The views expressed in this paper are the sole responsibility of the authors and do not reflect those of the European Commission which has not reviewed, let alone approved, the content of the paper. The paper does not reflect the views of the institutions of affiliation of the authors either. REFERENCES Agricultural Support Services Company (ASSC) (2016). Annual Report for 2016. Tehran, Iran. Alijani, F., Salarpour, M., and Sabohi, M., (2012). 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Region/Crop Cereals Legumes Vegetables Fruit Industrial crops Total ABOL TARG ABOL TARG ABOL TARG ABOL TARG ABOL TARG ABOL TARG Alborz 0.05 0.01 - 0.00 -0.55 -0.38 - 0.00 -1.60 0.19 0.00 0.00 Ardabil 0.06 0.03 -0.02 0.00 -0.60 -0.42 -0.20 -0.19 -1.40 -0.11 0.00 0.00 Boshehr 0.01 0.01 - 0.00 -0.68 -0.71 -0.37 -0.39 - 0.00 0.00 0.00 Chaharmahal 0.05 0.03 0.32 -0.10 -0.80 -0.36 -2.97 -0.84 -0.70 -0.06 0.00 0.00 East Azarbaijan -0.43 0.10 4.86 -0.77 -1.28 -0.56 -14.15 2.13 -2.19 -0.54 0.00 0.00 Elam 0.03 0.01 0.48 -0.06 0.00 0.00 -0.75 -0.06 -1.46 -0.01 0.00 0.00 Esfahan 0.58 0.16 -8.97 -2.13 -2.51 -0.98 -4.42 -0.24 0.70 0.75 0.00 0.00 Fars -0.70 0.31 29.08 -8.62 0.02 -0.80 1.56 -1.26 2.62 -1.40 0.00 0.00 Gilan 0.01 0.03 0.08 -0.13 -0.20 -0.55 -0.14 -0.26 - 0.00 0.00 0.00 Golestan 0.56 -0.02 -26.25 -4.72 -9.16 1.49 7.58 -2.82 -8.80 0.62 0.00 0.00 Hamedan 0.03 0.05 0.34 -0.04 -0.62 -0.63 -0.59 -0.52 -0.43 -0.16 0.00 0.00 Hormozgan 0.62 1.22 - 0.00 -0.53 -0.72 -0.05 -0.05 11.22 -23.83 0.00 0.00 Kohkiloyeh 0.33 0.02 -5.24 -0.39 -18.87 -0.99 -9.15 -0.60 - 0.00 0.00 0.00 Kerman 0.09 0.01 -1.91 -0.33 -0.60 -0.26 -0.18 -0.40 -1.57 1.02 0.00 0.00 Kordestan 0.09 0.03 -0.45 0.06 -1.21 -1.21 -0.34 -0.31 1.25 -5.49 0.00 0.00 Kermanshah -0.04 0.08 0.09 0.12 1.57 -5.53 - 0.00 -0.66 0.30 0.00 0.00 Khouzestan 0.11 0.22 1.12 0.97 -0.87 -0.68 -0.51 -0.24 -2.61 -7.64 0.00 0.00 Lorestan -0.02 0.03 0.14 -0.04 -0.39 -0.33 -0.16 -0.15 -0.41 -0.15 0.00 0.00 Markazi - 0.01 0.59 -0.20 -0.10 -0.14 - 0.00 -2.81 0.33 0.00 0.00 Mazandaran 0.19 0.00 - 0.00 -1.49 -2.61 -31.24 3.77 -1.83 0.11 0.00 0.00 North Khorasan 0.14 0.12 1.53 0.02 -1.74 -1.75 7.11 -1.14 -4.33 -1.33 0.00 0.00 Qom -0.01 0.00 - 0.00 - 0.00 - 0.00 0.08 -0.07 0.00 0.00 Qazvin 0.74 0.13 5.69 0.38 -9.34 -1.31 -19.03 -0.93 -12.41 -2.45 0.00 0.00 Razavi Khorasan 0.39 0.94 0.22 -0.42 -1.03 -3.81 -6.58 -9.25 -1.46 -4.72 0.00 0.00 Sistan 0.05 0.12 -0.45 -0.99 -0.40 -0.58 -0.13 -0.32 0.02 -1.07 0.00 0.00 South Khorasan 0.03 0.10 1.76 -8.69 -0.16 0.39 -0.65 -2.08 0.13 0.47 0.00 0.00 Semnan 0.06 0.05 -0.69 -0.32 -0.41 -0.54 -0.12 0.22 -0.04 -0.03 0.00 0.00 Tehran 0.03 0.01 - 0.00 -0.35 -0.16 -0.20 0.00 - 0.00 0.00 0.00 West Azarbaijan -0.01 0.04 0.30 -0.04 -0.58 -0.51 -0.55 -0.30 -0.36 -0.22 0.00 0.00 Yazd 0.05 0.06 - 0.00 -1.17 -1.27 -0.01 -0.17 - 0.00 0.00 0.00 Zanjan 0.07 0.07 0.78 0.12 -2.10 -1.32 -1.79 -1.11 -3.93 -1.57 0.00 0.00 National 0.06 0.13 0.93 -0.20 -1.47 -1.07 -1.16 -1.33 -1.72 -1.69 0.00 0.00 Source: Model results. 71The Role of Energy on the Price Volatility of Fruits and Vegetables: Evidence from Turkey Bio-based and Applied Economics 11(1): 55-73, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-10981 Appendix Table A2. Regional production changes under ABOL and TARG scenarios (% change relative to baseline). Region/Crop Cereals Legumes Vegetables Fruit Industrial crops Total ABOL TARG ABOL TARG ABOL TARG ABOL TARG ABOL TARG ABOL TARG Alborz -1.39 0.26 0.00 - -0.54 -0.40 0.00 - -2.15 -0.26 -1.23 0.12 Ardabil -3.38 0.60 0.01 0.01 -0.69 -0.51 -0.29 -0.27 -1.58 -0.21 -1.95 0.04 Boshehr -9.84 1.94 0.00 - -0.88 -0.92 -0.41 -0.42 0.00 - -4.88 0.66 Chaharmahal -3.21 0.47 -0.06 -0.34 -0.85 -0.40 -3.83 -1.72 -0.98 -0.35 -1.79 -0.06 East Azarbaijan -6.04 0.79 3.08 -1.88 -1.37 -0.64 -8.76 1.06 -3.45 -1.82 -3.82 0.09 Elam -3.32 0.55 0.48 -0.06 0.00 - -0.88 -0.08 -8.94 0.17 -2.56 0.33 Esfahan -3.76 0.03 -7.95 -2.65 -2.96 -1.22 -5.03 -0.30 -0.05 0.37 -3.16 -0.64 Fars -2.41 0.25 14.92 -6.21 -0.39 -1.30 1.34 -1.46 1.68 -1.02 -0.56 -0.70 Gilan -1.09 0.24 0.06 -0.15 -0.20 -0.55 -0.16 -0.26 0.00 - -0.49 -0.09 Golestan -3.47 0.75 -26.26 -4.73 -11.63 1.90 7.45 -2.94 -9.91 0.37 -4.91 0.87 Hamedan -2.55 0.43 -2.51 -2.87 -0.69 -0.70 -0.91 -0.84 -0.57 -0.29 -1.35 -0.27 Hormozgan -0.54 1.44 0.00 - -1.15 -1.35 -0.82 -0.82 2.85 -29.57 -1.02 -1.01 Kohkiloyeh -5.89 0.37 -5.21 -0.44 -19.72 -1.93 -11.91 -0.96 0.00 - -7.10 0.10 Kerman -1.79 0.32 -4.01 -1.72 -0.62 -0.28 -0.24 -0.44 -3.00 -0.07 -1.25 0.03 Kordestan -1.77 0.35 -1.66 -1.20 -1.44 -1.44 -0.43 -0.40 0.36 -0.81 -1.54 -0.26 Kermanshah -2.57 1.06 -2.14 -1.05 1.36 -6.03 0.00 - -0.77 0.07 -1.22 -0.91 Khouzestan -1.85 0.37 1.12 0.97 -1.25 -1.07 -0.63 -0.34 -1.51 -0.48 -1.49 -0.12 Lorestan -2.73 0.50 -2.50 -2.68 -0.41 -0.35 -0.30 -0.27 -0.50 -0.25 -1.52 -0.05 Markazi -2.22 0.39 -3.36 -3.87 -0.19 -0.17 0.00 -1.96 -0.17 -1.90 0.26 Mazandaran -1.83 0.30 0.00 0.00 -5.79 0.13 -31.25 3.76 -1.90 0.04 -2.67 0.38 North Khorasan -5.40 0.44 0.77 -0.73 -2.00 -2.06 6.79 -1.44 -5.03 -1.38 -4.13 -0.66 Qom -0.84 0.16 0.00 - 0.00 - 0.00 - 0.09 -0.10 -0.79 0.15 Qazvin -11.04 -0.73 4.07 -1.15 -8.76 -1.33 -19.09 -1.00 -16.72 -2.02 -10.41 -1.15 Razavi Khorasan -4.60 1.45 -4.89 -5.55 -1.18 -4.06 -1.98 -4.77 -1.16 -1.46 -2.40 -1.46 Sistan -5.66 1.19 -1.00 -1.69 -0.50 -0.70 -0.16 -0.36 -1.23 -2.31 -1.54 -0.05 South Khorasan -3.98 1.23 1.76 -8.69 -6.08 -5.57 -1.06 -1.04 -0.51 -0.18 -2.53 0.16 Semnan -2.28 0.46 -0.70 -0.32 -0.41 -0.49 -0.13 0.22 -0.31 -0.39 -0.97 -0.06 Tehran -1.34 0.20 0.00 - -0.40 -0.21 -0.25 -0.06 0.00 - -0.91 0.03 West Azarbaijan -3.47 0.62 -2.74 -3.08 -0.68 -0.62 -0.61 -0.30 -0.43 -0.29 -1.23 -0.13 Yazd -1.27 0.33 0.00 - -2.50 -2.57 -0.10 -0.26 0.00 - -1.37 -0.59 Zanjan -3.21 0.19 -0.56 -1.29 -2.24 -1.46 -1.85 -1.16 -4.09 -1.74 -2.53 -0.84 National -3.22 0.53 -0.97 -1.92 -1.83 -1.37 -0.87 -0.90 -0.91 -0.60 -2.61 -0.12 Source: Model results. 72 Bio-based and Applied Economics 11(1): 55-73, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-10981 Mona Aghabeygi, Kamel Louhichi, Sergio Gomez y Paloma Appendix Table A3. Regional agricultural income changes under ABOL and TARG scenarios (% change relative to baseline). Region/Crop Cereals Legumes Vegetables Fruit Industrial crops Total ABOL TARG ABOL TARG ABOL TARG ABOL TARG ABOL TARG ABOL TARG Alborz -0.61 0.10 - - -12.95 -11.00 - - 0.14 0.07 -0.60 0.06 Ardabil -0.27 0.05 -0.03 - -0.42 -0.40 -0.22 -0.20 -0.27 -0.24 -0.44 -0.02 Boshehr -1.23 0.33 - - -1.01 -0.95 -0.42 -0.34 - - -0.29 -0.01 Chaharmahal -0.30 0.10 -0.27 -0.06 -0.46 -0.38 0.05 0.04 -0.34 -0.25 -0.56 -0.02 East Azarbaijan -0.12 -0.06 -0.06 -0.02 -0.63 -0.60 0.11 0.11 0.55 0.50 -0.40 -0.01 Elam -0.34 0.08 -0.08 0.02 - 0.00 -0.22 -0.18 0.07 0.06 -0.49 0.06 Esfahan 5.19 -0.48 -29.76 -2.77 -3.28 -2.00 -1.45 -0.69 -0.68 -0.24 -1.43 -0.09 Fars 14.64 -23.26 -9.76 2.40 -1.69 -1.14 -0.78 -0.47 -0.69 -0.29 -1.57 -0.10 Gilan -0.59 0.09 -0.55 0.05 -0.13 0.01 -0.75 -0.51 - - -0.66 -0.02 Golestan -30.67 5.72 0.05 - -0.48 -0.37 0.64 0.54 -2.97 0.64 -4.83 0.64 Hamedan -0.56 0.07 -0.26 0.02 -0.84 -0.73 -0.40 -0.32 -0.35 -0.26 -0.52 -0.08 Hormozgan -3.16 -1.78 - - -3.73 -3.43 -3.08 -2.67 -1.42 -1.17 -4.15 -3.65 Kohkiloyeh -3.14 0.32 1.46 0.10 0.42 0.38 10.43 8.14 - - -1.58 0.22 Kerman -0.86 0.16 - -14.27 -0.53 -0.41 -0.15 -0.11 2.24 0.89 -0.69 0.04 Kordestan -1.70 0.42 -0.13 0.03 -4.62 -4.03 -0.23 -0.15 -0.41 -0.28 -0.44 0.01 Kermanshah -0.27 0.08 -0.14 -0.25 -0.46 -0.43 - 0.00 -0.34 -0.28 -0.35 0.02 Khouzestan -0.53 0.04 -0.30 0.05 -0.75 -0.67 -0.26 -0.21 -0.61 -7.13 -0.67 0.01 Lorestan -0.46 0.09 -0.11 0.02 -0.39 -0.32 -0.20 -0.14 -0.40 -0.31 -0.29 0.01 Markazi -0.47 0.09 -0.52 0.05 -117.64 -89.64 - - 0.11 0.07 -0.62 0.09 Mazandaran 0.37 -0.27 - - 0.22 0.08 0.16 0.05 -4.00 -0.11 -2.03 0.38 North Khorasan -0.41 0.18 -0.32 0.06 -2.53 -2.09 -0.40 -0.23 -1.14 -0.62 -0.80 0.02 Qom -0.49 0.09 - - - - - - 2.58 0.89 -0.47 0.07 Qazvin 0.36 0.20 -0.33 - -1.02 -0.88 -0.32 -0.18 -0.45 -0.30 -1.08 -0.02 Razavi Khorasan -0.69 -0.04 -0.38 -0.19 -15.75 -14.54 2.26 2.33 -0.93 -0.73 -0.70 -0.08 Sistan 0.70 -0.05 -1.54 -0.52 -1.26 -0.84 -0.45 -0.23 -0.79 -0.28 -1.53 -0.28 South Khorasan -1.01 -0.05 -22.51 -20.38 -0.31 -0.29 -3.56 -2.27 -0.55 -0.52 -0.90 -0.08 Semnan -0.83 0.15 -0.28 0.03 -0.52 -0.32 -0.30 -0.12 -0.39 -0.15 -0.85 -0.01 Tehran -0.71 0.10 - - -0.60 -0.41 -0.24 -0.12 - - -0.72 0.04 West Azarbaijan -0.85 0.13 0.51 -0.07 -0.55 -0.52 -0.26 -0.23 -0.23 -0.20 -0.38 -0.03 Yazd -1.12 0.03 - - -3.34 -2.78 -1.19 -0.82 - - -1.49 -0.20 Zanjan -1.94 -0.02 -0.08 0.01 -0.65 -0.60 -0.27 -0.22 -0.34 -0.28 -0.50 -0.03 National 0.49 -0.02 0.30 -0.01 -0.90 -1.02 2.33 - 0.18 0.12 -076 -0.01 Source: Model results. 73The Role of Energy on the Price Volatility of Fruits and Vegetables: Evidence from Turkey Bio-based and Applied Economics 11(1): 55-73, 2022 | e-ISSN 2280-6172 | DOI: 10.36253/bae-10981 Appendix Table A4. Regional agricultural cultivated area (1000 ha) and production (1000 T). Region/Crop Cereals Legumes Vegetables Fruit Industrial crops Total Area Prod Area Prod Area Prod Area Prod Area Prod Area Prod Alborz 18.96 85.65 - - 0.64 21.30 - - 0.39 1.06 20.01 108.02 Ardabil 469.41 863.32 33.16 19.42 25.61 840.94 2.48 77.50 7.43 15.70 538.11 1816.90 Boshehr 121.69 125.82 - - 0.79 18.31 3.14 123.35 - - 125.64 267.49 Chaharmahal 94.8 173.49 2.78 2.44 6.15 219.08 0.03 1.29 1.2 47.69 104.98 444.02 East Azarbaijan 510.51 828.19 58.51 36.87 19.26 741.71 2.45 47.21 1.97 2.98 592.72 1656.98 Elam 195.78 376.31 9.86 6.48 0 0.00 7.12 190.12 3.55 8.28 216.32 581.20 Esfahan 138.94 466.83 3.18 1.90 18.76 696.11 1.8 52.41 3.9 80.13 166.6 1297.41 Fars 536.92 1706.22 9.51 8.20 29.87 1327.49 18.48 733.72 27.5 620.26 622.31 4395.91 Gilan 16.77 21.08 0.82 0.57 0.05 1.16 1.64 35.66 - - 19.3 58.48 Golestan 529.17 1583.54 0.78 0.66 12.62 393.84 6.62 49.78 24.1 44.58 573.31 2072.42 Hamedan 490.24 841.32 20.84 10.64 29.25 1110.40 4.41 143.18 8.59 305.26 553.35 2410.82 Hormozgan 18.54 81.69 - - 24.66 719.85 12.11 250.88 0.18 0.10 55.51 1052.53 Kohkiloyeh 155.67 221.24 6.4 5.68 0.24 4.98 1.44 44.77 - - 163.78 276.68 Kerman 79.96 325.08 0.88 1.15 3.43 101.87 4.28 122.91 1.74 3.47 90.31 554.50 Kordestan 603.16 831.53 98.28 30.30 11.87 374.73 2.4 45.87 1.57 53.26 717.29 1335.71 Kermanshah 603.46 1264.86 135.54 60.41 11.91 592.79 0 0.00 14.27 549.71 765.19 2467.78 Khouzestan 628.27 1975.09 1.1 0.77 23.52 776.63 21.4 599.42 15.56 263.93 689.87 3615.86 Lorestan 363.75 619.61 110.34 64.58 7.58 227.68 11.33 264.32 6.51 242.27 499.52 1418.47 Markazi 258.48 539.49 8.04 3.61 3.71 104.73 - - 1.47 26.48 271.71 674.32 Mazandaran 309.01 1418.05 0 0.00 1.92 43.55 1.52 37.15 4.74 6.31 317.21 1505.07 North Khorasan 211.6 354.23 13.98 6.76 6.37 207.13 0.57 8.19 10.04 125.75 242.58 702.09 Qom 31.76 104.67 - - - - - - 2.19 5.11 33.95 109.79 Qazvin 200.95 493.04 7.55 3.56 12.56 669.46 1.16 25.01 4.27 99.98 226.51 1291.06 Razavi Khorasan 477.59 1069.87 9.81 3.44 22.8 823.85 15.28 290.13 45.59 994.63 571.1 3181.95 Sistan 105.9 236.45 0.43 0.47 6.73 172.76 22.98 573.91 1.27 1.83 137.32 985.43 South Khorasan 43.17 103.39 0.08 0.02 0.33 5.64 3.71 52.41 9.1 46.74 56.41 208.22 Semnan 56.08 149.93 1.47 0.65 5.1 120.91 2.33 59.94 4.75 119.35 69.75 450.81 Tehran 77.35 310.64 - - 5.28 200.21 2.04 50.08 0 0.00 84.68 560.94 West Azarbaijan 426.04 739.55 68.44 34.19 9.52 317.49 2.37 65.32 30.4 1873.51 536.79 3030.09 Yazd 20.95 68.39 - - 0.93 36.57 0.99 27.51 - - 22.88 132.48 Zanjan 344.96 407.51 25.24 8.50 15.77 594.95 4.4 135.16 0.35 0.33 390.74 1146.48 National 8139.84 18386.08 627.02 311.27 317.23 11466.12 158.48 4107.2 232.63 5539 9475.75 39809.91 Source: ICTC- IMAJ. Three-years average around 2015 (2014, 2015 and 2016).