Modeling conversion to organic agriculture with an EU-wide farm model

From Firenze University Press Journal: Bio-based and Applied Economics (BAE)

Dimitrios Kremmydas, European Commission, Joint Research Center (JRC)

Pavel Ciaian, European Commission, Joint Research Center (JRC)

Edoardo Baldoni, European Commission, Joint Research Center (JRC)

The Farm to Fork (F2F) strategy of the EU Green Deal (European Com-mission, 2019, 2021) aims to stimulate the transition to a sustainable food system that is fair, healthy, and environmentally friendly. Among other pro-posed solutions, such as nutrient surplus reduction, pesticide risk reduction, antimicrobial use reduction, or increase of biodiversity, one of the key tools to achieve the transition is to promote the expansion of organic farming. The F2F strategy sets the target of 25% of the EU’s agricultural area under organ-ic farming by 2030 (European Commission, 2020). Currently, only 9% of the utilized agricultural area is under organic farming in the EU. Therefore, to achieve the F2F goal, a sizable agricultural area (17%) would need to convert from conventional to organic agriculture. Organic farming is significantly different from conventional farming, particularly regarding management practices and productivity (Alvarez, 2021; Baker et al., 2020; Bonfiglio et al., 2022; Reganold & Wachter, 2016; Watson C.A. et al., 2002). For this reason, the conversion of a large share of the agricultural area to organic farming may have a significant effect on the EU agri-food system. More specifically, while organic farming is generally perceived to have positive environmental impacts, concerns exist about potential decreases in food production when shifting from conventional to organic farming methods (Meem-ken & Qaim, 2018; Reganold & Wachter, 2016; Seufert & Ramankutty, 2017; Timsina, 2018). The potential pro-duction decrease associated with reaching the F2F target raises the issue of food security both in the EU and glob-ally, given that EU is a major food producer and exporter. The main contribution of this paper is to shed light on these issues by developing (individual) farm level mod-eling of EU-wide organic conversion in order to bring quantitative insights into the potential production effects of reaching the 25% organic target in the EU.Four main modeling approaches have been applied in the literature to simulate the impacts of conversion to organic farming: (i) spatially explicit agronomic/bio-physical models, (ii) partial equilibrium agro-economic models, (iii) individual or representative agro-economic farm models1, and (iv) non-conventional models. In the first approach, the interplay between nutrient inputs, spatially explicit biophysical characteristics and outputs are explored to analyze the impacts of the conversion to organic production on the whole food system. The geo-graphic scope of this approach spans from the regional level to world coverage by applying different spatial reso-lution depending on the study objectives (Barbieri et al., 2019; Jones & Richard Crane, 2014; Lee et al., 2020; Mul-ler et al., 2017). The second approach relies on partial equilibrium models, which depict the behavioral inter-actions of economic agents within the agriculture sector at the regional, country or global level (Barreiro Hurle et al., 2021; Bremmer et al., 2021). In the third approach, the study scale is either the individual (Acs et al., 2007, 2009; Kerselaers et al., 2007) or representative farms (Smith et al., 2018), where the allocation of activities is usually modeled as a constrained optimization problem. This approach captures more disaggregated behavioral choices. Finally, the last approach relies on non-conven-tional modeling methods like agent-based modeling and system dynamics (Rozman et al., 2013; Xu et al., 2018).Each of these modeling approaches has several limitations in modelling organic conversion. The main limitation of the agronomic/biophysical models is that they do not consider the economic dimension of con-version, neither at the farm level nor at the aggregate regional or country level. Hence, they cannot capture the organic conversion of specific farms. They usually assume full conversion of the modeled food system and then compare it with the situation before the conver-sion (Barbieri et al., 2019; Muller et al., 2017). Although partial equilibrium agro-economic models consider the economic dimension of organic conversion by construc-tion they do not capture micro behavior at the farm level. Instead, they attempt to model organic production and input relationships by adjusting general productiv-ity parameters (e.g., yields, input use) and/or introduc-ing organic-related aggregate production constraints. Representative farm models suffer from similar limita-tions as the food system and partial equilibrium agro-economic models. However, they can capture in greater detail some organic farm practices and their differ-ences across farm types. They also usually assume full conversion to organic production of all modeled farm types (Smith et al., 2018). Finally, regarding the non-conventional models, agent-based models can capture the organic conversion and specific aspects of organic farm practices in more detail. However, they are not applied at a larger geographical scale due to their high data requirements (Kremmydas et al., 2018). In contrast, system dynamic models may represent well the interac-tions between the elements of the system and provide answers to strategic decisions, but they cannot model details of organic conversion and organic farm practices (R ic h a rd s on , 2 011).Applying an individual farm-level model for mod-eling organic conversion has several advantages. First, since organic conversion choice and organic produc-tion practices are farm-specific, applying an indi-vidual farm-level approach can offer a more accurate representation of organic farming without imposing strong assumptions on farmers’ behavior. For example, detailed agronomic and behavioral constraints repre-senting the technological differences between the two systems (conventional and organic) can be introduced. Second, individual farm models incorporate individual farms and technology representation, enabling the selec-tion of specific farms that are more likely to convert. A third advantage is their effectiveness in modeling policy incentives, especially those targeting environmental and organic production. Indeed, the Common Agricultural Policy (CAP), among others, includes farm-specific envi-ronmental measures (including support for organic pro-duction) which aim to improve the environmental and climate performance of the EU farming sector. Finally, an individual farm-level model can provide distribution-al effects across the farm population, allowing for more nuanced impact analyses for policy making (Buysse et al., 2007; Ciaian et al., 2013).However, the individual farm models applied in the literature to simulate conversion to organic produc-tion exhibit several limitations. First, they rely solely on expert knowledge, which restricts their applicability to a broader geographical scale, such as the entire EU. Indeed, they are either applied to a single farm (Acs et al., 2007) or a single country (Kerselaers et al., 2007). Moreover, these models do not develop a methodol-ogy for selecting specific farms to undergo conversion; instead, they assume the conversion of all farms.This paper aims to fill the gap in the existing lit-erature on individual farm modelling of organic con-version. Specifically, it focuses on the challenges of adjusting an EU-wide model — IFM-CAP (Individual Farm Model for Common Agricultural Policy Analy-sis) — to account for changes in farm performance and management practices associated with organic produc-tion. Achieving these model adjustments requires con-ducting several econometric estimations to identify the difference in performance between organic and con-ventional production across individual farms in all EU countries. This is due to the scarcity of readily avail-able expert knowledge for such a wide geographic area encompassing a heterogeneous range of production sys-tems. To fully leverage the farm-level model, we con-sider behavioral constraints that are relevant to organic farming such as crop rotation, nitrogen management, maximum stocking density, feed self-sufficiency and minimum share of fodder in the diet, respecting the heterogeneity across the EU farms. Additionally, to simulate the effects of the F2F organic target on farm income, production (quantities and value) and produc-tion costs, we consider two alternative approaches to select specific farms for conversion to organic produc-tion. This differs from the modeling approaches applied in the existing literature, which typically assume 100% conversion. The first approach, referred to as ‘endoge-nous’ approach, is based on profitability (utility maxi-mization) differences between organic and convention-al production systems. Under this approach, the subset of the most profitable farms are assumed to convert to organic farming. The second approach, referred to as ‘exogenous’ approach, employs a probabilistic frame-work to econometrically estimate the likelihood of farms converting to organic production. The underlying idea is that conventional farms sharing characteristics similar to organic farms are more likely to convert to organic farming. In econometric estimation, we take into account both monetary (e.g. subsidies, intensity of input use) and non-monetary factors (e.g. farm struc-tural characteristics) that are often found in the litera-ture to affect the likelihood of farmers adopting organic agriculture (Canavari et al., 2022; Sapbamrer, 2021; Serebrennikov et al., 2020; Willock et al., 1999). Using Farm Accountancy Data Network (FADN), we conduct a comparative assessment of multiple probability models to identify the best-performing approach, which is then utilized for the selection of a subset of farms con-verting to organic production. The paper is structured as follows. The next section describes the methodology of modelling organic pro-duction in the IFM-CAP. Section 3 presents the meth-odology applied for the selection of converting farms to organic production. Section 4 describes the simulated results, while Section 6 concludes.

DOI: https://doi.org/10.36253/bae-13925

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