Feed the Future
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Farm Level Economic and Nutritional Analysis in Ethiopia: A Case Study in Reducing Hunger

This post was written by Jean-Claude Bizimana and James W. Richardson.

Introduction
Global food security remains an important topic in the political and development agendas of many governments especially those in the developing world where the vast majority of the world’s undernourished people are located (FAO, 2010). The 2016 Global Hunger Index (GHI) report shows that, since 2000 the level of hunger was cut by 29 percent in developing countries, but its levels are still alarming (von Grebmer et al., 2016). Most of the countries with “alarming” GHI scores are in sub-Saharan Africa as reported in the 2014 Global Hunger Index report. In addition to hunger and undernourishment, which are mainly characterized by a lack of minimum required caloric intake (1800 calories/day/person), the other aspects of hunger that are often overlooked and ignored relate to micronutrient deficiency, known as “hidden hunger” (von Grebmer et al., 2014).

Although the issue of malnutrition and hunger is multifaceted and needs a multidimensional approach, part of the solution to combat malnutrition and hunger is to increase food production and promote consumption of balanced diets specifically in regions or zones of food deficits. For instance, a study in Ethiopia by Diao and Pratt (2007) identified that more than 50 percent of the poor people live in food-deficit areas where the staple food availability per household is half the national average. Given that the majority of the Ethiopian population depends on agriculture, broad-base agriculture growth, especially in staple food and livestock production, is key to reducing poverty and increasing food security. However, to achieve these goals, there is a need to reduce the productivity gap between old and modern agricultural technologies that still exist in the farming community of Ethiopia (Diao and Pratt, 2007; Bogale and Shimelis, 2009).

In general, adoption and proper use of agricultural technologies contribute to an increase in the quantity and variety of crops produced. More specifically adopting irrigation technologies allows households to grow crops during the dry season in addition to the wet season, which expands the variety of crops produced and consumed by the household (e.g. vegetables). The implications for family nutrition vary according to the types of crops grown and consumed. Moreover, surplus crops can be sold and resulting revenues can be used to buy food items needed to complement nutrition requirements. Several studies have shown that a better socio-economic status is key to increasing food diversity and security at the household level (Pinstrup-Andersen, 2002; Diao and Pratt, 2007; Barrett, 2010). 

In addition to producing enough food for consumption, which ensures availability, the food security concept also requires accessibility and good utilization of produced food (Barrett, 2010). While accessibility relates to the well-being of the family, utilization reflects more on the good use of the accessible food by individuals at the household level, which emphasizes the knowledge and practices of good nutrition (von Grebmer et al., 2014). In brief, combatting hunger and malnutrition, especially “hidden hunger,” requires specific, community-based approaches. It is in this line that a farm level economic and nutrition analysis model such as FARMSIM would be a good approach in evaluating the level of food security and accessibility at the household level. The model takes into account increased food production and income generated from adopting improved agricultural technologies and the related implications on nutrition through food produced and purchased. In the FARMSIM model, households are allowed to purchase supplemental foodstuffs only if there is a profit and excess cash from their agricultural production (crops and livestock).

Methods
The farm simulation model FARMSIM is a Monte Carlo simulation model designed to simultaneously evaluate baseline and alternative technologies for a farm. The model is programmed in Microsoft® Excel and utilizes the Simetar© add-in (Richardson et al., 2006) to estimate parameters for price and yield distributions, simulate random variables, estimate probability distributions for key output variables (KOVs) and rank technologies. FARMSIM is programmed to recursively simulate a five year planning horizon for a diversified crop and livestock farm and repeats the five-year planning horizon for 500 iterations. FARMSIM is programmed to simulate 1-15 crops as well as cattle, dairy, sheep, goats, chickens and swine annually for five years. The farm family is modeled as the first claimant for crop and livestock production with deficit food production met through food purchases using net cash income from selling surplus crops and livestock production. Standard accounting procedures are used to calculate: receipts, expenses, net cash income and annual cash flows. The KOVs for the model can include all endogenous variables in the model but most attention is focused on the following KOVs: annual net cash income, annual ending cash reserves, net present value and annual family nutrient consumption of protein, calories, fat, calcium, iron and vitamin A. The baseline and alternative technology scenarios are simulated by FARMSIM using the same equations so the only difference in the economic and family nutrition outcomes are due to the technology differences.  

The nutrition component of the FARMSIM model is evaluated as follows: the total kilograms of each raised crop consumed by the family plus the kilograms of purchased foodstuffs are multiplied by their respective nutrient scores to calculate total calories, protein, fat, calcium, iron and vitamin A from the food stocks. Similar calculations are made to simulate the nutrients derived from consuming cattle, oxen, milk, butter, chickens, eggs, mutton, lamb, nannies, kids, and pig meat. Total nutrients consumed by the family from all sources, including donated food, are summed across plant and animal food stocks and compared with minimum daily recommended amounts for adults based on the FAO minimum requirements standards (FAO, 2001 and FAO, 2008).
 
Summary Results
A case study of Robit village (kebele), in the Amhara region of Ethiopia was carried out using the FARMSIM model. Several crops that include three grains (millet, teff and maize), two vegetables (cabbage and tomato), one legume/pulse (chickpeas), one tuber (potato) and two animal feeds (fodder from oats and vetch and napier grass) were considered for simulation. A baseline and several alternative scenarios based on small scale irrigation technologies were studied. Increasing milk and meat production from livestock feeding and the implications on nutrition were also analyzed. The summary results on nutrition in Robit village are as follows (see details in Richardson and Bizimana, 2017, p. 23): The quantities of crops and livestock products consumed by families in both the baseline and alternative scenarios meet minimum daily requirements for calories, proteins, iron and vitamin A but are insufficient to meet minimum daily requirements for calcium and fat (see more detailed information on minimum requirements in FAO, 2001 and FAO, 2008). Moreover, the household survey used in this study shows that individual households did not purchase large quantities of food or receive any food aid to supplement the food that they produce (baseline) which leaves some room for nutrition improvement. To close the gap in calcium and fat intake, an increase in consumption of milk, eggs, cheese, and butter and meat are recommended. Livestock production technologies can be one of many options to achieve this goal at the household/farm level.

The table below summarizes simulation results based on the last year of the five-year planning horizon. Specifically, it lists the nutritional variables measured, the probability that the quantity of each nutrient consumed will exceed the minimum daily requirement, and whether the amounts consumed in the alternative scenarios show an improvement as compared to the baseline scenario. 

Summary results for nutritional and scenarios performance in Robit village


References
1. Barrett, C. 2010. Measuring Food Insecurity. Science 327 (5967), 825-828.
doi: 10.1126/science.1182768.
 
2. Bogale, A. and A. Shimelis. 2009. Household level determinants of food insecurity in rural   areas of Dire Dawa, Eastern Ethiopia. African Journal of Food, Agriculture, Nutrition and Development, 9 (9): 1914-1926.
Accessed: https://www.ajol.info/index.php/ajfand/article/view/50072 on May 2017.
 
3. Diao, X. and N. A. Pratt. 2007. Growth options and poverty reduction in Ethiopia –
An economy-wide model analysis. Food Policy, 32: 205-228. doi:10.1016/j.foodpol.2006.05.005
 
4. Food and Agriculture Organization of the United Nations (FAO). (2001). Human Vitamin and Mineral Requirements. Report of a Joint FAO/WHO Expert Consultation. Nutrition Division, Food and Agricultural Organization of the United Nations, Rome, Italy.
 
5. Food and Agriculture Organization of the United Nations (FAO). (2008). Fats and Fatty Acids in Human Nutrition.  Report of an Expert Consultation. Food and Agricultural Organization of the United Nations, Rome, Italy.
 
6. Pinstrup-Andersen, P. 2002. Food and Agricultural Policy for a Globalizing World: Preparing for the Future. American Journal of Agricultural Economics, 84 (5), Proceedings Issue: 1201-1214.
 
7. Richardson, J. W., K. Schumann, and Feldman, P. (2006). Simetar: Simulation for Excel to analyze risk. Unnumbered staff report, Department of Agricultural Economics, Texas A&M University, College Station, Texas.
 
8. Richardson, James W. and Bizimana, Jean-Claude. "Agricultural Technology Assessment for Smallholder Farms in Developing Countries: An Analysis using a Farm Simulation Model (FARMSIM)." Agricultural and Food Policy Center, Department of Agricultural Economics, Texas A&M University, Research Report 17-1, January 2017: https://www.afpc.tamu.edu/pubs/2/683/FARMSIM.pdf
 
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http://dx.doi.org/10.2499/9780896299580
 
10. von Grebmer, Klaus; Bernstein, Jill; Nabarro, David; Prasai, Nilam; Amin, Shazia; Yohannes, Yisehac; Sonntag, Andrea; Patterson, Fraser; Towey, Olive; and Thompson, Jennifer. 2016. 2016 Global hunger index: Getting to zero hunger. Bonn Washington, DC and Dublin: Welthungerhilfe, International Food Policy Research Institute, and Concern Worldwide. http://dx.doi.org/10.2499/9780896292260