The effects of biofuels on food security: A system dynamics approach for the Colombian case

Sustainable Energy Technologies and Assessments 34 (2019) 97–109

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The effects of biofuels on food security: A system dynamics approach for the Colombian case

T

Juan E. Martínez-Jaramilloa, Santiago Arango-Aramburob, , Diana P. Giraldo-Ramírezc ⁎

a

Institución Universitaria Politécnico Grancolombiano, Calle 57 # 3-00 Este, Bogotá, Colombia Universidad Nacional de Colombia, Sede Madellín, Facultad de Minas, Decision Sciences Group, Carrera 80 # 65 – 223 Bloque, M8a-211, Medellin, Colombia c Universidad Pontificia Bolivariana seccional Medellín, Escuela de Ingenierías, Grupo de investigación Gestión de la Tecnología y la Innovación, Circular 1 # 73-76, piso 2, Bloque 22B, Medellín, Colombia b

ARTICLE INFO

ABSTRACT

Keywords: Food security Agro-fuel System dynamics Policy analysis Energy security

There has been a growing interest in biofuel production to reduce oil dependence in the transportation sector. However, this has also sparked a debate on how the introduction of biofuels may jeopardize food security. In this paper we evaluate the effects of biofuel production on food security using system dynamics. First, we developed a system dynamics model to understand the long-term interaction between food production, biofuel production and livestock farming. Then, we calibrated and applied the model to the Colombian case. The base scenario results show that the introduction of biofuels to Colombia reduces the country’s agriculture-allocated land by 2030. This, in turn, leads to a decrease in food supply and an increase in food prices. Alternative scenarios suggest that policies focused on increasing land use efficiency, especially with livestock, could have a larger impact on food security and biofuel production in Colombia. This is due to the fact that said policies foster the co-existence of bioenergy and food production. Our simulation model could be relevant to other countries in order for them to assess their food policies and biofuel strategies. The model could especially be relevant in the developing world, where similar political and environmental conditions may be present.

Introduction

different types of agriculture and biofuel production policies in order to understand the interaction between biofuels and food security. FS is a critical prerequisite for any population’s welfare and can threaten its existence if not managed properly. The FAO (The Food and Agriculture Organization of the United Nations), which publishes data regarding undernourishment, reported that undernourishment increased 37% between 1989 and 2009 [11]. This increase was followed by a decrease of 16.8% between 2009 and 2015 [12]. In order to study FS properly, it is necessary to have a theoretical framework. In this paper, we cover four well-known dimensions of FS: 1) availability, 2) access, 3) use, and 4) stability [13]. This definition is based on the assumption that even though there is enough food available to meet the demand, this does not imply that households can access the food due to price, distribution, limited income, or other factors [12,14,15]. Even if access to food is enough to avoid hunger, the supply may not guarantee the nutrients needed for proper nutrition (food utilization). In this approach, stability is generated by production security [12,13]. Food availability has been jeopardized due to population growth, land degradation, climate change, and a rising demand for raw materials and meat. All these factors encourage certain changes in the use of

Humanity is facing two critical problems simultaneously: food security and climate change. Many have proposed Biofuels as an alternative to reduce pressure on fossil fuel consumption and mitigate GHG emissions. However, biofuel production requires the use of land, which raises a central question: do biofuels (BF) threaten food security (FS)? Evidence and scientific literature have shown different views regarding the relationship of these two variables, such as Statistical time series models [1–4], economic cost-benefit analyses [5], a perfect storm scenario [6], the framework for ecological-economic world food system analysis OFID-IIASA [7], and simulation models [8–10]. The results of these models differ. For instance, Headey & Fan emphasize the difficulty of isolating the effect of biofuel production on food prices—the increase in food prices is a consequence of climate change, higher oil prices and Asian production [6]. A vast amount of literature shows that biofuels have been one of the main factors explaining the rise of food prices in recent years [7–9]. In contrast, Pruyt & Sitter claim that biofuel production does not affect food prices significantly [10]. Given these different views, we aim to develop a holistic model to assess

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Corresponding author. E-mail address: [email protected] (S. Arango-Aramburo).

https://doi.org/10.1016/j.seta.2019.05.009 Received 12 September 2017; Received in revised form 27 April 2019; Accepted 16 May 2019 2213-1388/ © 2019 Elsevier Ltd. All rights reserved.

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land [11,16,17]. The increasing demand for food puts pressure on arable land, water, energy, capital, labor, and natural reserves [18–21]. These pressures, in turn, threaten food production and reduce food availability. Access to food is another critical factor of FS and depends mainly on income and food prices [1,22]—between 2000 and 2014 the food price index increased over 120% [23]. There are different theories to explain the behavior of the food price index. For instance, there is empirical evidence of a cyclical price behavior and a correlation between energy markets and food production [9,24,25]. In fact, between 2000 and 2012, both fossil fuels and food prices increased. The rising demand for fossil fuels and their substitutes are a consequence of economic and population growth. Among these substitutes, biofuel has grown significantly—between 2000 and 2014, bioethanol production grew almost 400%, while biodiesel experienced an exponential growth [26,27]. This growth pattern may continue into the future [28], which would increase the pressure exerted on food production [19,29,30]. In order to quantify the potential effects that introducing biofuels has on food security, this paper explores the long-term effects of strategies focused on incentivizing biofuel production in a developing country. In this vein, we analyzed the Colombian case through a simulation. The strategies include blend policies, tax exemptions, and financial incentives for biofuel producers. We focused on the economic decision of ultimately using a given area of land (its final use) for biofuel crops and/or food production. To understand property owners’ decisions, we developed a behavioral model for food and biofuel production systems. The results provide important insights for policymakers, not only in Colombia, but also in other developing countries with similar backgrounds. We analyzed the social and economic impact of food production policies through a system dynamics (SD) model. SD has been used by government agencies, researchers, utility companies, and consulting firms at a local, regional, and national level to understand a variety of socio-economic problems such as homelessness, economic bubbles, or crime [10,31–36].

international food prices. Since 2005, these variables have shown growth, thus forcing Colombian food prices up in recent years [43]. Supply stability mainly depends on two factors: land and labor. In 2012, land was disproportionately misused for livestock farming. During that same year, the country also experienced a reduction in forestry and agriculture [44]. The Colombian national cattle-breeders and the Ministry of Agriculture, identified the misuse of land and developed a program to shift 10 million hectares from livestock to forestry and agriculture [44–46]. The rural labor force in Colombia has been declining in recent years—the rural population has declined from 28.1% of the overall population in 2000, to 22.6% in 2012 [47]. Biofuels Biofuel production in Colombia was regulated by Law 693 of 2001 [48,49]. Since the promulgation of the biofuel law, the production has been stimulated through incentives for farmers such as tax exemptions on their net income for 10 years (for palm oil farmers) or VAT exemption (for sugar cane farmers). As for producers, policies provide sales tax exemptions on biodiesel and ethanol [48]. Domestic biodiesel production commonly comes from palm oil. Meanwhile, sugar cane is the most common crop used for ethanol in Colombia. Fig. 2 (a) shows the evolution the total area used for palm oil and sugar cane in Colombia. After the regulations were approved, palm oil area (that is, the area of land used for palm oil production) has increased 91%: from 270,000 ha in 2005 to 516,960 ha in 2017. Likewise, sugar cane area has grown from 198,000 to 243,000 ha. Fig. 2 (b) shows the evolution of biofuel production (bioethanol and biodiesel).It is important to note that biodiesel distilleries started operating in late 2008. By 2009, the biodiesel production reached 120 MMBBL [55] and increased following a logarithmic trend up to 530 MMBBL in 2017 [56]. As for Bioethanol, distilleries started operating in 2005. Since then, production has followed a linear trend, rising from 30 MMBL to 320 MMBBL, from 2005 to 2017. [54]. As biofuel policies have only existed for a relatively short period of time in Colombia, their effects on long-term food security are still unknown. In this vein, in the next sections we discuss the methodology that we used and introduce our proposed model to study the relationship between food and bioenergy in Colombia.

The Colombian case Food security Over 40% of Colombian households suffer from FS deficiencies. Of these households: 27.9% are slightly affected, 11.9% are moderately affected and 3% are severely affected [37]. In Colombia, rural areas have a higher prevalence in their lack of FS compared to urban areas, due to economic limitations, internal conflicts, and the lack of transportation infrastructures [37]. Overall, food insecurity increased by 5% in 2011 compared to 2006 [37,38]. An analysis of FS requires an understanding of all its dimensions. For our model, we studied the evolution of food availability, food access and supply stability. Food availability can be measured with two variables: i. Agricultural GDP by sector, and ii. Food self-sufficiency. Fig. 1 shows the annual variation of the agricultural GDP, total GDP and food selfsufficiency (which shows the ratio between a country’s total food production and its total food demand). On one hand, agricultural GDP grew at a slower rate than the overall GDP [39]. On the other hand, Colombia has performed poorly in terms of food self-sufficiency. Between 2011 and 2012 the self-sufficient index decreased by 13%, thus creating the need for an increase in food imports—food imports grew by 70.6% between 2001 and 2012. Food access is associated with income, food prices, and purchasing power [41]. Purchasing power is calculated using the unemployment rate and the percentage of people living in poverty and extreme poverty. Unemployment rates fell from 18% in 2003 to 10.4% in 2012 [42]. Similarly, poverty decreased 24.3% and extreme poverty dropped 30.1% [42] during the same period. Furthermore, the most influential variables on food prices are production costs, oil price volatility and

The system dynamics model We developed a model based on System Dynamics (SD) method, which allows for the modeling of the structure of a system as a stock, flow and information network. This methodology is useful to incorporate causalities, feedbacks, and delays between variables [35,50]. The incorporation of these factors allows for an understanding of the behavior of a system by studying its structure. Model uses two main elements: levels (state variables which accumulate information or material) and flows (which are the inflows and outflows of the levels). SD models are based on non-linear differential equations [51]. The overall methodology follows an iterative process that Sterman defined in five steps: problem articulation (boundary selection), dynamic hypothesis, formulation, testing and policy formulation, and evaluation (for more information see [35]). Problem articulation and model overview As a first step in our modelling process, we started out by defining the model’s purpose, boundaries, and reference mode. The purpose of the model is to evaluate the effects of biofuel production on food security in Colombia and evaluate alternative energy policies. The reference mode was defined in section two in which we explored the key variables of both the agro-food and the biofuel systems. Regarding model boundaries, we developed our model based on an existent agrofood model for Colombia [52]. 98

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Fig. 1. Colombian food availability indicators [40].

Fig. 2. Biomass crops area (a) and Biofuel production (b) [50–52,54].

Formal model formulation

model is based on an agro-food model for Colombia [59]. This model has 9 state variables: population, other income, agricultural goods, capital, food imports, agricultural land, potential arable land, eroded agricultural land, and livestock land. We have enriched this model by adding the elements required for the model’s purpose: i. raw material production for biofuels, ii. biofuel production, iii. biofuel production costs, iv. agricultural land, and v. the decision-making process of the farmers. Appendix (Table A.1) contains detailed information on the definitions of the parameters and the numerical values needed to calibrate the model and obtain numerically meaningful results (for further information, see [62] or contact the authors for details regarding the modelling process).

An overview of our model is given in Fig. 3. This figure shows a block diagram in which we classified the variables into exogenous, endogenous, and those that were excluded. The diagram shows an overview of the main blocks that were considered in our theoretical framework. Given our objective, we considered the following endogenous blocks: biofuel production, land use, and the agro-food system. The main link between these three blocks is the decision-making process by which farmers and producers decide how to use their land and raw materials. Finally, the exogenous drivers block, shows the variables that affect the model.” We developed a SD simulation model made up of a stock and flow diagram and an information network. The relationships between variables are represented by mathematical functions [35,50]. Stock variables are represented by rectangles: they accumulate information or material. The level of a stock is regulated by its in-and out flows. Stocks can only be modified by in-and out flows. The other elements are parameters or auxiliary variables used for calculations and are captured by the information network. The simulation model1 was developed in Vensim DSS V.5.7a. The

Raw material production for biofuels The production of raw material for biofuels depends on the availability of land for biomass crops. Our model integrates these biofuel crops (sugar cane and palm oil). In this subsystem, there are three stocks: sugar cane (SC), mature palm oil (MOP) and palm oil in the developing stage (OPDS). SC has an inflow (sugar cane sowing—SCS) and an outflow (sugar cane harvesting SCH), while the other two stocks form a material delay in which OPDS is the palm oil that needs to mature to be able to produce biofuel. This last stock increases with palm oil sowing (OPS) and decreases with maturing. The auxiliary variables form the information network. In this case,

1 For further information of the equations and considerations see [68], the model can be made available upon request to the authors.

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Fig. 3. SD model block diagram.

Fig. 4. Stock and flow diagram of raw material production for biofuels.

the information network for both crops indicate the amount of new area needed for plantation. Fig. 4 illustrates raw material production for sugar cane. Stock-flow dynamics are represented by differential equations. For example, Eq. (1) shows the relation between SC and its flows: SCS and SCH.

d (SC ) = SCS dt

SCH

biofuel—and biofuel supply. Fig. 5 illustrates the stock and flow diagram of this subsystem. Both biofuels (bioethanol and biodiesel) have the same structure. We separated production capacity, into the capacity under construction and installed biofuel capacity (already built). This structure is a material delay. The production capacity (which is still under construction) increases when production capacity starts and decreases with capacity add-ons. At the same time, capacity add-ons increase installed capacity. Finally, installed capacity is reduced by capacity dismantling. Eqs. (2) and (3) illustrate an example of how the model quantifies biofuel production. These equations describe the dynamics of two stocks: bioethanol inventory (BI) and the percentage of bioethanol blend in gasoline (BB) (seen in Fig. 5). The bioethanol inventory (BI) dynamic depends on bioethanol production (BP) and bioethanol

(1)

Biofuel production Biofuel production has 8 stocks—four for bioethanol and four for biodiesel. The total production depends on the availability of raw material and on production capacity. For this subsystem we considered biofuel production capacity—including the decision to invest in 100

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Fig. 5. Stock and flow diagram of bioethanol production.

dispatch (BDS). BP increases the stock and BDS decreases the stock. BB accumulates the variance in the percentage of the bioethanol blend (CBB).

As an example of one stock, Eq. (5) describes the relation of land use for biofuel crops (LUB), as well as its flows: new land use for biofuel crops (NLUBC) and erosion (BFE).

d (BI ) = BP dt

d (LUB ) = NLUBC dt

BDS

(2)

d (BB ) = CBB dt

(3)

P (BDE ) = P (BE ) =

BPC 0,152 IBPC

+ RMBC

PBD PBD + PEPP

PB PB + PES

(6) (7)

where P(BDE) and P(BE) are the probability to choose biodiesel and bioethanol, respectively, PBD and PB are the profitability of biodiesel and bioethanol, PEPP is the profitability of exporting palm oil products, and PES is the profitability of exporting sugar. We also estimated Alpha and Betha in the calibration process [60]. Both probabilities vary between 0 and 1; where 1 indicates that all of the raw materials will be allocated to biofuel production and 0 is that all will be allocated to food production.

if BPC < IBPC ; IBPC + RMBC otherwise IBPC

(5)

Farmer’s decision-making process The most critical process in this system is the competition for land between food and biofuel production. We assumed that producers compare the economic returns of food and biofuel production, where both returns are functions of price. We modelled the decision process as a Logit model [58,59]. The equations used for these choices are:

Biofuel production costs This subsystem has two stocks which accumulate annual biofuel production. This approach allows for the use of a learning curve cost [53–57]. We assumed a curve of 10% for bioethanol and of 5% for biodiesel. We selected both learning curve values from an analysis of the first 15 years of the ethanol case in Brazil [55]. As an example of this, equation (4) shows how the bioethanol production cost (BPC) is modeled as a learning curve. This is done using the Initial Bioethanol Production Cost (IBPC) and the cost of the raw material (RMBC). To quantify the profitability of the biofuels, we calculated their total production costs, using the production cost (with the learning curve function) and the cost of the raw materials.

BPC =

BFE

(4)

Agricultural land In this subsystem, we kept all the same stocks of the Colombian agrofood system [52], adding only one new stock: Land use for biofuel crops. This subsystem has five stocks for land use—agriculture, livestock, biofuel crops, eroded land, and potential arable land. Fig. 6 shows the main structure of land use in the Colombian agricultural sector.

Validation We performed a validation process to build confidence in both the model itself and the simulation results, following the standards used in 101

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Fig. 6. Stock and flow diagram of the land use.

SD [61,35]. For the structural validation, we performed empirical tests to analyze the consistency of the structure (dimensional consistency and parameter confirmation tests). For the parameter confirmation, we specifically used primary data bases in order to have reliable information to construct the model as shown in Appendix A (Table A.2). We tested the model’s adequate representation of the real world and basic laws of physics, such as mass and energy balance. We checked for support in the existing literature for relationships, as discussed in the presentation of the model, and reviewed the current knowledge regarding the Colombian system. Moreover, we applied structure-oriented behavior tests (extreme-condition tests) in which no structural flaw was seen (see Appendix A). We also tested the model capability to replicate past behaviors (i.e., behavior validation). Thus, we compared historical data with simulation results, and we calibrated the model for unknown parameters by minimizing the error (MSE). The selected parameters were alpha, beta (parameters of the decision rule), elasticity to imports, and the erosion factor. We calibrated the model using Powell’s algorithm embedded in Vensim [62] (Fig. A2). Having achieved a structural and behavioral validation of the model, we then moved on to use the model. The application of the model is described in the following section.

market. Section “Introduction of Biofuels into the Agro-Food System” shows the result of the base case scenario (BF) quantifying the effects of biofuel production on food security. This scenario is the status quo of the system. Section “Proposed scenarios analysis” describes the proposed scenarios and shows the discussion of the results. Introduction of biofuels into the agro-food system The base case scenario (BF) refers to the introduction of biofuels into the Colombian food system. The key variables for the analysis were: i. land use, ii. proportion of vulnerable population regarding basic food needs and iii. food prices. This scenario is considered a business as usual set up; as such, the parameters considered in BF are the current status and were discussed in the previous section. This scenario is directly linked with the current system, i.e. the scenario holds the same set of parameters used for the model’s validation. In this vein, the scenario directly connects to the policies at the beginning of the simulation. Fig. 7 shows the dynamics of land use after the introduction of biofuel production. As land use for biofuel crops increases (by 2030, 365 thousand hectares are to be allocated to biofuel production), land use for food production decreases by around 40% by 2030 (compared to levels in 2006). Thus, there is not enough evidence to conclude that the decrease in land use for food production is a consequence of the increase of land use for biofuel crops. Fig. 8 shows a steady increase in food prices (which moves from 317

Simulation and analysis of the results This section presents a series of simulation experiments with different scenarios of the penetration of biofuel into the Colombian 102

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Fig. 7. Land use for biofuel crops and biofuel production.

Fig. 8. Food price and proportion of the population which is vulnerable to basic, unmet food needs.

projections done by the Ministry of Mines and Energy [63]. Likewise, blends were taken by the sectorial perspective for the upcoming years [64]. The two scenarios oriented towards the performance of the agrofood system (high food and low food) are based on international perspectives regarding the agro-food sector in the global economy [12,15]. Finally, the livestock scenario focuses on land use. This scenario is centered around a goal of a 10 million ha reduction in land use for livestock production by 2020 [45]. This goal is set by the Strategic Plan for Sustainable Stockbreeding in Colombia. The livestock scenario holds that 5 million hectares would be shifted from livestock to food crops between 2015 and 2020, in addition to the conditions given in the high BF scenario for the biofuel sector. Table 1 shows the experimental setup and the definition of each scenario. The aim of the alternative scenarios is to understand the response of the key variables to different environments. We analyzed the long-term dynamics of food prices, PPVBFN, agricultural land use, biodiesel production, bioethanol production, and land use for biofuel crops. We started our analysis with FS indicators which take food prices into account, which are summarized in Fig. 9. The results indicate that the livestock scenario is the scenario which reduces food prices the most—prices decreased $155,000 COP per grain ton equivalent. This represents a 30% reduction compared to the BF scenario. The lower food price of the livestock scenario stems from a higher food production due to a larger proportion of land allotted to food production. As for the low food, high food, and low BF scenarios, the food price reduction compared to the BF amounted to 7.31%, 2.88% and 1.27%, respectively. Finally, the high BF scenario triggered a 1.5% price increase

thousand COP per ton of grain equivalent in 2016, to 507 thousand COP in 2030). This increase is the consequence of a decrease in local food production and an increase in food imports. This figure also shows how the proportion of population vulnerable to basic unmet food needs (PPVBNF) declines over the years, dropping from 69% in 2016 to 62% by 2030. To quantify the effect of the introduction of biofuel production, we compared the BF scenario with the results from the agro-food model proposed by Giraldo [52], which is the foundation of this study. Comparing both models’ results, we found that after the introduction of biofuels, the agricultural land use decreased by 12.4%, food prices increased by 3.3% and the proportion of the population which is vulnerable and has basic, unmet food needs increased by 1.6%. Although the results indicate that agricultural land use, food prices and PPVBFN are influenced by the introduction of biofuels in the long run (by 2030), the impact of introducing biofuels is low for two of the main FS variables. Therefore, we suggest considering that these variations could be produced by the uncertainties of the model. Proposed scenarios analysis We proposed a set of scenarios as follows: two are focused on biofuel demand, two are oriented towards food production performance, and one analyzes a livestock policy. In the biofuel demand scenario (high BF and low BF), the government sets the blends. The high BF scenario assumes that blends increase due to environmental commitments, while the low BF scenario assumes that supply cannot match demand, causing the policy to fall through. Both biofuel scenarios are based on fossil fuel 103

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Table 1 Model setting for the proposed simulation scenarios. Parameters

High BF

Low BF

High Food

Low Food

Livestock

Biodiesel blends

2020: 15% 2025: 20% 2030: 25% 2020: 20% 2025: 25% High scenario projection [65] – – – – – – – –

2020: 10% 2025: 15% 2030: 15% 2020: 12% 2025: 15% Low scenario projection [65] Base – 20% – – – – – – –

– – – – – – – Base + 10% Base + 20% Base + 10% Base + 5% Base + 10% – –

– – – – – – – Base + 60% Base + 70% Base + 20% Base − 5% Base + 50% – –

– – – – – – – – – – – – −2.3% annual (2015–2020) Parameters from the high BF scenario

Ethanol Blends Diesel and gasoline demand Biofuel profitability International grain prices Demand percentage for other uses Harvest losses Other income Oil prices Livestock land use Biofuels

because it requires higher land use for biofuel crops. Fig. 10 shows the results of the simulation for the PVBFN proportion. On one hand, the livestock and the high food scenarios reduce the PPVBFN by 23.13% and 6.70%, respectively (compared to the BF scenario). On the other hand, low food and the high BF deteriorate this indicator by increasing the proportion by 4.9% and 0.8%, respectively. The improvement of this indicator in the livestock and high food scenarios is explained by the additional land allotted to food production. Furthermore, the more land for food production, the higher the local food supply and the lower the food prices. The simulations also show that the livestock scenario increases land use for food production by more than 150%, in the form of 3.19 million hectares that were dedicated to livestock and shifted to food production. Likewise, land use for food production increases for the low BF with an increment of 5.1% by 2030 (compared to the BF). This increment is due to a lower demand for biofuels. Consequently, less land use shifts from food production crops to biofuel crops. The High BF scenario decreases land allotted to food production by 6.76%, due to a higher demand for biofuels by 2030. This increment in the demand for biofuels would put pressure on farmers to change the land use from food production crops to biofuel crops. In this way, farmers can match the demand for the raw materials needed for biofuel. Finally, land use for food production remains unchanged in the high food and low foo scenarios, because the biofuel demand remains constant (compared to BF). As shown in Fig. 11, the livestock and high BF scenarios increase biofuel production. As biodiesel demand is higher in both scenarios, production increases by 56%. The worst-performing scenario is low BF, in which biodiesel production would fall 25% by 2030 (compared to the BF). This result is explained by a decrease in biodiesel demand. Lastly, biodiesel production remains static in the agro-food scenarios compared to BF.

Bioethanol production shows a similar behavior as that of biodiesel. Simulations show an increase in bioethanol production for the livestock and high BF scenarios. Bioethanol production increases by 43% for both scenarios compared to BF by 2030. The increase in bioethanol production is caused by 2 factors: i. a higher demand of gasoline (both scenarios simulate the high scenario developed by UPME [65]); and ii. the mandatory blends regulated by the government. The low BF scenario showed the worst performance—where bioethanol production falls 53% compared to the BF scenario by 2030. As with biodiesel, the agro-food scenarios do not show variations in bioethanol production. Table 2 summarizes the results of all scenarios by 2030. A global analysis suggests that the livestock scenario is the best alternative. This scenario generates an increase for biodiesel and bioethanol production by 56% and 43%, respectively. The scenario also improves food security indicators—food prices drop by 31% and the PPVVBFN decreases 23% by 2030. Biofuel scenarios affect FS indicators. However, the impact is relatively low. On one hand, the PPVBFN grows by less than 1% (High BF), and in the low BF scenario, it falls by 0.8%. On the other hand, food prices rise by 1.5% (high BF), and in the low BF scenario they fall by 1.3%. In terms of biofuel indicators, the high BF scenario shows an increase in biodiesel and bioethanol production of 56% and 43% by 2030, respectively. Even if the results show that biofuels are not a key variable for food production, it is important to mention that our results are only valid according to our framework and assumptions. It is also important to note that uncertainties produced in the long-term simulation runs could change our results. Discussion This paper evaluated the potential consequences of biofuel market penetration on food security using a SD-based model. We specifically

Fig. 9. Comparison of food prices between scenarios. 104

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Fig. 10. Comparison between scenarios. PVBFN proportion (%).

Fig. 11. Comparison between scenarios. Biodiesel production. Table 2 Scenario results by 2030. Variable

High BF

Low BF

High Food

Low Food

Livestock

BF

PVBFN proportion (%) Food Prices (1 × 106 COP/grain ton equivalent) Agricultural land use (1 × 106 ha) Biodiesel Production (MMBBL) Bioethanol Production (MMBBL) Biofuel crops (1 × 106 ha)

0.61 0.52 1.88 11.75 11.09 0.53

0.60 0.50 2.12 5.62 3.61 0.25

0.57 0.49 2.02 7.51 7.75 0.36

0.64 0.47 2.02 7.51 7.75 0.36

0.47 0.35 5.21 11.75 11.09 0.53

0.61 0.51 2.02 7.51 7.75 0.36

assessed the effect of biofuel production on food prices, changes in land use, and food production. We used the case study of Colombia as an example that could be applied to other developing countries that are considering implementing biofuel policies. We considered availability and food access as key dimensions of food security. We compared both of these dimensions to food production and the proportion of the population which is vulnerable to basic, unmet food needs. The base case simulation showed how the introduction of biofuels has a limited impact, since it reduced agricultural land use by 12.4%, increased food prices by 3.3%, and increased the proportion of vulnerable population by 1.6% (by 2030). Although the introduction of biofuels to the agro-food system weakens food security indicators, these indicators do not show a variation of more than 4%. Thus, biofuels are a minor contributor to the reduction of food security in the base case. These results suggest that, despite the fact that biofuels could change food prices, there is little evidence of how this correlation could cause malnutrition, or that said malnutrition is caused by biofuels or other primary causes. We simulated different pathways for the biofuel and livestock sector. For biofuels, we developed 2 scenarios that showed a correlation

between biofuel indicators and food security. Biofuel production requires raw materials that involve common food production factors such as land use. Therefore, competition for land use causes an increase in food prices which, in turn, increases the proportion of the population which is vulnerable to basic, unmet food needs. We analyzed a scenario which included a variation in the land allotted to livestock. It was based on a policy proposal developed by FEDEGAN, which aims to improve stockbreeding productivity. This scenario showed that an improvement in stockbreeding productivity would free up land for both biomass and food. In this scenario, we studied the effect of switching 5 million hectares of land allotted to stockbreeding, to biomass crops and food production crops. The results showed an improvement of 45.15% in biodiesel production and of 43.07% in bioethanol production by 2030. The results also showed an increase in food production of over 150%. Furthermore, food prices and the proportion of the population which is vulnerable to basic, unmet food needs decreased by 2030. In this vein, our results revealed the key land use policy elements, especially regarding (unproductive) stockbreeding land, and its impact on food security and biofuel production. It is important to clarify that we were able to model the land shift from 105

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livestock to food production due to the social and economic structure of the Colombian country side. The results for the livestock scenario could not be directly extrapolated to other cases, which would require a casespecific analysis.

structures. Policy makers could also benefit from this study by using our modeling approach to propose and evaluate the effectiveness of different scenarios to reduce dependence, emissions, food insecurity and to know if their strategies would improve national welfare. For future research papers, we suggest an integration of this study with spatial models using geographic information systems (GIS). These systems allow the identification of the most appropriate land uses and the output could be used to formulate policies focused on improving land productivity. We also suggest exploring a liberalized biofuel market, were it is possible to consider stronger relationships between energy and food.

Conclusions Our analysis highlighted the importance of integrated models (a holistic view) over individual subsystem evaluations. This approach enables us to perceive synergies and interrelations between economic sectors, which are useful to deepen our understanding of counterintuitive behaviors. This research paper validates the extent to which our modeling process could be a useful tool to evaluate food production policies in developing countries. This kind of analytical framework is an important tool for governments to be able to assess the real potential of their biofuel and food security goals. In addition to this, our analytical framework could be expanded in order to understand the socio-economic impacts produced by changes in counties’ food production

Acknowledgments We gratefully acknowledge support from the blind referees for their contribution. We want to acknowledge Jeffrey for his hard work. Finally, we gratefully acknowledge support from the Universidad Nacional de Colombia.

Appendix A. Variables, parameters and validation tests results This section provides further detailed information on the model specifications in order to make the reported results replicable. This appendix shows the assumptions held in terms of the main parameters that we established to set the model in motion. It also shows the results of the validation tests that we ran in our modeling process. The implications of some of these assumptions are tested in the results section of this paper (Fig. A1). Table A3 shows that almost all the errors are focused on the variance and the covariance, except for the biodiesel production variable.

Fig. A1. a). Extreme-condition test for population and domestic consumption. Birth rate = 0. b) Extreme-condition test for biofuel production. Biofuel profitability = 0.

Table A1 Main parameters of the model. Parameter

Explanation

Alpha Beta Biodiesel production initial cost Bioethanol production initial cost Desired stock coverage Food demand elasticity to income Average erosion rate Labor share Non-food goods share from agriculture Slope of the ethanol learning curve Slope of the biodiesel learning curve

Ethanol price elasticity Biodiesel price elasticity The production cost of a barrel of biodiesel in 2009 The prodution cost of a barrel of bioethanol in 2006 The percentage of food demand that is desired to have in stock The effect of changes in income on food demand The average fraction of eroded land per year The total rural population with jobs The total percentage of non-food products from agriculture per year Log of the learning rate/log of 2 (a 10% curve) Log of the learning rate/log of 2 (a 5% curve)

106

Symbol

Constraints

Bpic Beic Dsc Fdei Aer Ls Nfsa a b

0.8 0.7 $23.3/biodiesel barrel $30.2/bioethanol barrel 0.5 0.02 0.0001 0.6 0.07 −0.152 −0.074

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Table A2 Parameter confirmation. Parameter

Notes and source

Initial inhabitants Birth rate Death rate Net migration rate Life expectancy effect over death rate Normal life expectancy Poverty line reference Elasticity of the poverty line to food prices Reference Income Initial food production

Population for 2005 source: DANE World Bank World Bank World Bank Adaptation from T21 model DANE DNP, 2005 methodology Weishuang Qu y Gerald O. Barney (2002) (Qu & Barney, 2002) Agro-food production value in 2005 provided bu Ministerio de Agricultura expressed in grain ton equivalent Agricultural land by 2005 (IGAC) (Leibovich, Nigrinis & Ramos, 2006) DANE (Leibovich, Estrada & Vásquez, 2009) (Leibovich, Estrada, & Vásquez, 2009) (Leibovich, Estrada, & Vásquez, 2009) DANE DANE Quantified

Initial land value Initial labor factor value Initial capital factor value Land share Labor share Capital share Agriculture capital growth rate Rural population Elasticity of the productivity to rural transport infrastructure Elasticity of the labor productivity to food access Elasticity of the productivity to fuel price Percentage of non food products Minimum required nutrient consumption Biofuel capacity construction time Biofuel blends Historical biofuel capacity Harvest time Average internal sugar demand Yield per ha of sugar cane Historical sugar cane crop area Yield per ha of palm oil Palm oil crop area Palm oil lifetime Internal demand for oil Palm oil international price Sugar international price Biodiesel price Bioethanol price

Quantified Quantified Agronet FAO Assumption Ministerio de Minas y Energía Fedebiocombustibles Cenicaña Linear regression using data from asocaña [66] Asocaña [66] Fedepalma Fedepalma Fedepalma Fedepalma London Stock Exchange Ministerio de Minas y Energía Ministerio de Minas y Energía

Table A3 Behavior validation results. Variables

Theil

Biodiesel production Bioethanol production Sugar cane crops (ha.) Total palm oil crops (ha.) Agricultural land Agricultural goods 1

Mean squared error: MSE =

1 n

n i=1

(Ys

R (dmnl)

MSE1 (million unit2)

UM (dmnl)

US (dmnl)

UC

0.994 0.975 0.952 0.998 0.899 0.924

41,315 20,728 47 60 4,236,919 7,142,399

0.658 0.009 0.085 0.368 0.487 0.061

0.127 0.095 0.095 0.006 0.001 0.139

0.276 0.956 0.846 0.651 0.533 0.859

Yh )2 .

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Fig. A2. Historical vs. simulation results for key variables.

Fig. A2 compares the historical data with the simulation results for the area used for sugar cane, ethanol production, palm oil plantation area, biodiesel production, agricultural land use, and food production. The graphic representations below suggest that all six simulated variables maintained their historical trends. Through a visual inspection analysis and an estimation of the Pearson coefficient (R) (Table A.3), we found a strong correlation between historical and simulated data. This shows the model’s capability to replicate historical behavior. As expected, the error in the model was concentrated in the variance and the covariance [67]. In order to improve the confidence of the model, we performed a Theil-test,2 which allowed us to disaggregate the error into the Mean (U M ), variance (U S ) and covariance (U C ) [67]. Appendix B. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.seta.2019.05.009.

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