A farm household model for agri-food policy analysis in developing countries: Application to smallholder farmers in Sierra Leone
Introduction
Food security has become one of the most important items on today’s international political agenda and a serious issue for governments around the world. Guaranteeing sustainable and equitable food in the context of climate change, price volatility and global financial crisis is, in fact, a challenging task. Even if food availability has grown significantly and consistently over time, both globally and in developing countries, access to food is still limited particularly in many low income economies. According to the 2008 World Development Report (World Bank, 2008), three-quarters of the world’s poor live in rural areas and most of them are farming. Although there are food security challenges across the world, major progress is yet to be made in Africa and South Asia’s rural areas where most of the population is extremely poor (i.e. with less than 1 USD-equivalent per person per day at their disposal) and dependent on small holdings. To reduce rural poverty and improve food security both national governments and the international community have developed several policies and programs. These support policies have taken different forms such as: (i) increasing agricultural productivity through the support of agricultural inputs (mainly improved seeds and fertilizers), training and mechanization; (ii) facilitating the use of agricultural knowledge and technologies; (iii) improving infrastructure (rural roads, storage facilities, processing, etc.); (iv) facilitating access to credit markets; etc. Impact assessments of such supports upon the food security of farm households are however scarce and not always founded on solid science-based methods. Most studies have focused on the food security issue at the national level which may mask food insecurity at the household level. For a better understanding of farm household food security status, it is preferable to use methods and tools working at micro-level, capable of providing detailed results on a farm household scale and of capturing heterogeneity across households. Within this context, the main aim of this paper is to present FSSIM-Dev (Farm System Simulator for Developing Countries), a decision support tool to be used in the context of low income developing countries to improve knowledge on food security and rural poverty alleviation under different policy options. FSSIM-Dev is a generic farm household model that enables to (i) capture five key features of developing countries and/or rural areas; (ii) to provide detailed disaggregation regarding commodities and technology choices; and (iii) to smoothly integrate results from bio-physical models needed to improve knowledge on land degradation, land resources tenure and use.
Model use is illustrated in this paper with an analysis of the impacts of rice seed policy on the livelihood of 400 smallholder farmers in Sierra Leone. The aim is to improve knowledge on farmers’ livelihood strategies and to assess the microeconomic effects of the seed policy, using a set of FSSIM-Dev indicators1 such as land use, production and consumption of basic food commodities, farm household income and poverty gap.
The paper is structured as follows: in Section ‘Literature review on farm household models’, an overview of farm household models is provided. In Section ‘The farm household model: FSSIM-Dev’, the proposed FSSIM-Dev model is described. In Section ‘Empirical application’, the model is applied to a representative sample of farm households in Sierra Leone. In Section ‘Results and discussion of the application’, the results of the application of FSSIM-Dev are described. In Section ‘Conclusions’, we conclude on the added value of our results compared to other studies and discuss the relevance and the limitation of the proposed model and study.
Section snippets
Literature review on farm household models
Farm household models are a sample of micro-research on rural economies. They are often applied to family-run or peasant agriculture where production, labour allocation and consumption decisions are linked due to market imperfection (De Janvry et al., 1991, Taylor and Adelman, 2003). As long as markets are perfect, households are indifferent to consuming own-produced and market-purchased goods. The household model is then said to be separable and the optimization program can be solved
General features
FSSIM-Dev is a farm household model for use in the specific context of low income developing countries where farm household production, consumption and labour allocation decisions are non-separable due to market imperfections. Contrary to most well-known household models which are econometric based, FSSIM-Dev is a non-linear optimization model which relies on both the general household’s utility framework and the farm’s production technical constraints, in a non-separable regime. This model is
The case study area
For this study, FSSIM-Dev is applied to a representative sample of smallholder farmers belonging to the Northern Province of Sierra Leone. One of the West African countries, Sierra Leone has a total area of 71,740 km2 and an estimated population of 6.7 million in 2008 (World Bank, 2009). In economic terms, Sierra Leone is one of the poorest countries in the world (WFP, 2008). Its gross domestic product (GDP) per capita was estimated to be only slightly more than 300 US dollars (USD) in 2010. The
Results and discussion of the application
The impacts of simulated scenarios are represented by the following set of structural and economic indicators computed at individual (i.e. farm household) and regional levels: land use, cropping pattern, production and consumption of basic food commodities, farm household income and poverty gap. In order to ease interpretation of the results and their comparison across scenarios, most impacts were measured as percentage changes to the baseline.
Conclusions
In this paper, a farm household model has been presented as a generic tool designed to simulate farm households’ response to food security policies in the context of low income economies. The model’s capabilities are illustrated with an analysis of the combined effects of rice seed policy and reduction of the fallow period on the livelihood of farm households in Sierra Leone. The main findings of this application in terms of policy impacts are that: (i) the rice seed policy will improve the
Acknowledgements
The authors would like to thank Dr. Guillermo Flichman, Dr. Allen Thomas and Dr. Jacques Delincé for valuable discussions on this topic.
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 (50)
Modeling the impact of HIV/AIDS upon food security of diverse rural households in Western Kenya
Agric. Syst.
(2010)- et al.
Non-farm income, household welfare and sustainable land management in a less-favored area in the Ethiopian highlands
Food Policy
(2004) - et al.
Assessing farm innovations and responses to policies: a review of bioeconomic farm models
Agric. Syst.
(2007) - et al.
Farmers’ welfare, food production, and the environment: a model-based assessment of the effects of new technologies in the northern Philippines
Wagening. J. Life Sci.
(2009) - et al.
FSSIM, a bio-economic farm model for simulating the response of EU farming systems to agricultural and environmental policies
Agric. Syst.
(2010) - et al.
Micro and macro-level approaches to modelling decision making
Agric. Syst.
(2001) - et al.
Calibrating spatial models of trade
Econ. Model.
(2011) - et al.
Technical coefficients for bio-economic farm household models: a meta-modeling approach with applications for Southern Mali
Ecol. Econ.
(2001) - et al.
Public policies for rural poverty alleviation: the case of agricultural households in the Plateau Central area of Burkina Faso
Agric. Syst.
(2012) - et al.
The impact of increasing farm size and mechanization on rural income and rice production in Zhejiang province, China
Agric. Syst.
(2007)
Integrated assessment of agricultural systems – a component-based framework for the European Union (SEAMLESS)
Agric. Syst.
Income diversification and entry barriers: evidence from the Tigray region of northern Ethiopia
Food Policy
Economics of the impact of alternative rice cropping systems on subsistence farming: whole-farm analysis in northern Ghana
Agric. Syst.
The Development Policy Evaluation Model (DEVPEM): Technical Documentation. OECD Food, Agriculture and Fisheries, Working Papers 51
Farm-based modeling of the EU sugar reform: impact on Belgian sugar beet suppliers
Eur. Rev. Agric. Econ.
The Theory of Peasant Economy
Peasant household behavior with missing markets: some paradoxes explained
Econ. J.
General Equilibrium Models for Development Policy
Modeling farm households for estimating the efficiency of policy instruments on sustainable land use in Haiti
Land Use Policy
Maximum Entropy Econometrics: Robust Estimation with Limited Data
Rural Poverty Reduction and Food Security: The Case of Smallholders in Sierra Leone. Final Report
Model based on positive mathematical programming: state of the art and further extension
Positive mathematical programming approaches – recent developments in literature and applied modeling
Bio-based Appl. Econ.
Modeling farm households’ price responses in the presence of transaction costs and heterogeneity in labor markets
Am. J. Agric. Econ.
Positive mathematical programming
Am. J. Agric. Econ.
Cited by (33)
What determine livestock feed and marketing? Insights from rural Ethiopia
2024, Scientific AfricanImpact of seed system interventions on food and nutrition security in low- and middle-income countries: A scoping review
2022, Global Food SecurityCitation Excerpt :The impact on crop productivity was positive in nine studies, neutral in two and not reported in four studies. Improving seed access also impacted seed diversity, with an increase (Galie, 2013; Levy, 2003), a decrease (Meles et al., 2009), a shift in diversity or replacement of varieties by target seed (Chenoune et al., 2017; Louhichi and Paloma, 2014; Tiwari et al., 2010), and no report (8 studies). Sixteen studies assessed the impact of improved seed previously released or promoted on nutrition and food security (13 studies) or evaluated aspects of agriculture that included seed and nutrition outcomes (3 studies) (Table 5).
Rainfall shocks and household welfare: Evidence from northern Ghana
2021, Agricultural SystemsFood security outcomes in agricultural systems models: Current status and recommended improvements
2021, Agricultural SystemsThe potential of green ammonia for agricultural and economic development in Sierra Leone
2021, One EarthCitation Excerpt :Several studies have previously examined the possibility of green ammonia displacing existing supplies of brown ammonia,26,27 including the building of a pilot plant for producing green ammonia from a wind turbine in Morris, Minnesota.28 Conversely, this work analyses the economic benefit of green ammonia in an underdeveloped market without previous fertilizer use, considering its effect on the national economy rather than the economics for individual farmers.29–31 As such, the economic benefit to rice cultivation through increased yields when importing fertilizer or synthesizing fertilizer from local hydroelectric energy is examined with respect to the cost of importing rice as a baseline.
Agricultural Activity concept for simulating strategic agricultural production decisions: Case study of weed resistance to herbicide treatments in South-West France
2018, Computers and Electronics in AgricultureCitation Excerpt :The behaviour towards risk is modelled using the mean-standard deviation method in which the expected utility is defined as expected income and risk (Norton and Hazell, 1986). This approach has been used in recent similar studies (Chenoune et al., 2017; Komarek et al., 2017; Louhichi and Gomez y Paloma, 2014). The risk term in the model is then calculated as a product of the Arrow-Pratt relative risk aversion coefficient (∅) (considered as a constant in the model) and the standard deviation of farm income (δ) calculated by considering market price and yield variability in the past 4 years (Komarek et al., 2017).