The Global Burden of Animal Diseases Program (GBADs) has developed a framework to estimate economic burden of animal disease in a population. In this framework, overall disease burden is enveloped as a difference in income from a livestock population between the current and an ideal health situations, and this is called animal health loss envelope (AHLE). This framework was used to estimate the economic burden of diseases in small ruminants in Ethiopia. The AHLE was estimated at USD 3.22 billion for the year 2021 (3% of national GDP). Of this burden, 68% was due to morbidity (production losses) and 32% to mortality. Expenditure on animal health accounted for less than 1% of the overall burden. The estimated high economic burden and small expenditure on animal health relative to the losses indicate potential for substantial improvement in animal health and reduction of the burden of ill health through appropriate animal health interventions.



Small ruminant production is an important economic activity in Ethiopia. It supports livelihood for millions of households in the country. The small ruminant population was 95.4 million head in 2020, constituting 13% of total livestock biomass in the country and contributing 2% to national GDP (Jemberu et al., 2022). Most small ruminants in the country are kept into two major production systems: the crop livestock mixed (CLM) system and the pastoral systems. Small ruminants are kept by up to 60% of households in the crop-livestock mixed production system of the highlands and 95% of households in the pastoral production system of the lowlands across the country (ILRI, 2020; Gebremedhin et al., 2017). The CLM systems contain 58% of the national sheep population and 42% of the national goat population while the pastoral system contains 42% of the national sheep population and 58%of the  national goat population (Jemberu et al., 2022)

Despite the importance of small holder livestock production for livelihoods and economic welfare, a substantial yield gap has been reported for small holder livestock production in Ethiopia (Herrero et al., 2016). Inadequate animal health is a major contributor to yield gaps and improving animal health through increased veterinary expenditure has been shown to improve profitability of small holder goat production (Mayberry et al., 2018). Animal health problems affect livestock production in different ways, mortality takes away the production asset, morbidity reduced production and productivity, and disease prevention and control expenditure increase production cost. Understanding and quantifying the burden of animal disease and health loss is thus essential for prioritising disease control interventions that will most efficiently reduce yield gaps. Estimates for the economic burden of animal diseases are scarce, particularly in Ethiopia and economic impact estimates are often only available for individual diseases. One may be tempted to sum up individual disease impacts to calculate overall disease burden within a sector, however this approach has several limitations. One of the main problems is that independent individual disease impact studies do not consider occurrence of comorbidity and a linear addition of them would lead to overestimation (Torgerson and Shaw, 2021).  This approach also tends to ignore health problems which are lesser studied such as predation, injuries, and malnutrition. The Global Burden of Animal Diseases program (GBADs) has developed a framework to overcome these limitations. In this framework, overall disease burden is enveloped as the difference in farm income comparing the current situation to an ideal situation where there are no health or nutritional problems (Torgerson and Shaw 2021). This animal health loss envelope (AHLE) encapsulates the overall burden due to poor health and nutrition, and includes losses due to mortality, morbidity (production loss) and animal health expenditure. Having quantified the AHLE, losses are then attributed to individual diseases, disease groups or other causes of health loss. In this study we estimated the overall economic burden of the small ruminant health problems in CLM and pastoral production system in Ethiopia using the GBADs framework.

Materials and Methods:

Estimating the animal health loss envelope (AHLE)

The animal health loss envelope is the difference in the the income from livestock kept under the current state of health and livestock kept under an idealised perfect health situation where there are no health or nutritional problems. This envelope represents the overall economic burden of diseases in that livestock population. The income from a livestock farm or population can be calculated using enterprise budgeting. In this study enterprise gross margin (enterprise profit before fixed costs) was used instead of enterprise budget (net profit). The main reason for this was that disease burden has little effect on fixed costs and decision on improvement of animal health are often operational management measures that take place within the scope of the fixed cost. The other reason was that the fixed costs under traditional extensive small ruminant production are not significant. Hence the AHLE was estimated as the difference in gross margin under the current health conditions (with current disease and nutritional problems, and veterinary expenditure) and ideal health condition (with no disease or nutritional problems, and no veterinary expenditure) in small ruminant production systems.

Calculating gross margin using herd model

Livestock herd models are useful to asses production and herd growth (Upton, 1989). These type of models have been also extensively used for simulation of the effect of disease on production (Shaw et al., 2014). In this study a dynamic herd growth model was developed in R (R Core Team ,  2021). The model simulated the herd growth based on demographic (birth and death rates) and offtake rates. While demographic and offtake rates were entered on an annual basis, a monthly time interval is used for removing the competing risks problem between the different demographic events. The simulation of the model produces production offtakes (live animal, milk, skin, and manure) and their respective monetary values (revenue) based on price inputs for these production outputs. It also estimates cost of production inputs; feed, labor and animal health care based on the price of these inputs. These revenue and cost outputs of the model enables calculation of gross margin for the simulated population. The model was parametrized with stochastic values for the main inputs of the model and simulated for 10,000 iterations. The R herd model used to calculate gross margins can be obtained on request to the authors. 

Current condition:

All data used for calculating the gross margin for the current conditions are derived from secondary data including statistical databases, reports, and the literature. The data that were needed for parametrizing the herd model for calculating gross margin included 1) the year’s initial population structure in terms of sex and age category, 2) reproduction parameters such as proportion of adult females giving birth and prolificacy rate, 3) mortality for each sex category, 4) production outputs such as net live animal offtake, milk production (daily yield and lactation length), manure and skin ouputs, 5)  production inputs; labor, purchased feed, and animal health care, and 6 ) price of live animals, production outputs and inputs. A systematic literature search was done for parameters that were expected to have published information such as mortality, live animal weight and reproduction parameters. A random effect metanalysis was used for pooling the estimates collected through systematic literature search (Harrer et al., 2022). For parameters where there was no sufficient published literature for meta-analysis, the best available single estimates were used. The list of parameters (distribution) values used by the model for the crop livestock mixed and pastoral production systems are documented in Annex (Table A1 and Table A2). 

Ideal condition:

The parameter values of the herd model that change for calculating of gross margin under ideal condition were those related to mortality (zero in idealised scenario) and improvements in live weight gain, milk production and reproduction parameters. The ideal values for these parameters were derived from structured expert elicitation.

The structured expert elicitation was done using the classical (Cooke’s) method. Briefly, the classical model is an approach for eliciting and mathematically aggregating expert judgments, with validation incorporated as a core feature (Colson and Cooke, 2018). In this method, experts quantify their uncertainty for two types of questions: target questions and calibration questions. The target questions are the ones used to elicit the needed parameter values that are unknown by other means. The calibration questions are those whose true value are known or can be known to the researchers but not to the expert and are used to validate/calibrate the experts’ judgments. The experts are asked to quantify their uncertainty about quantities asked both in the target and calibration questions, by providing subjective assessments for percentiles of the distributions; usually the 5th, 50th and the 90th percentiles. The best estimate is denoted by 50th percentile and a credible interval provided by the 5th and 95th percentiles. The experts’ judgments are then linearly pooled by giving weights to their estimates for the target questions based on their performance (statistical and information score) for the calibration questions (Colson and Cooke, 2018). This combined estimate is called decision maker and once calculated the decision maker acts as virtual expert representing the combined judgments of real experts.

In the expert elicitation for this study, first some experts were identified based on their research/publication in the subject matter i.e., production and reproduction of small ruminants in Ethiopia. Additional experts were included by snowballing from the identified experts.  In total 10 experts participated in a half day elicitation workshop at ILRI in Addis Ababa. In the structured expert elicitation questionnaire, the current production and reproduction parameter values were used as calibration questions and the required ideal parameters values were the target questions. There were 12 questions about the current liveweights of sheep and goats, in the different age-sex groups that were used as calibrating questions and the corresponding liveweights under ideal condition were the target question for elicitation of ideal growth (liveweight) parameter values. Similarly, six questions on current age at first parturition, parturition proportion and litter size for sheep and goats were used as calibrating questions and the corresponding questions for ideal condition were the target questions for elicitation of the ideal reproduction parameters. This elicitation was done separately for CLM and pastoral system.

The elicited data was analyzed by the EXCALIBUR software (EXALIBURE v.1.6.1 pro, 1989-2018 T.U., Delft, In development by Lighttwist Software). The experts’ judgments were combined using global weight performace measures in which the experts are weighted based on their average score across all calibration questions as described in Aspinall (2008). The ideal parameter values derived from expert elucidations were used as beta pert distribution in the herd gross margin model. The 50th percentiles estimate of the decision maker (combined experts’ judgment) was used as the most likely values, and the minimum and maximum values of the ranges for the target variables were used as minimum and maximum values of beta pert distribution.

The parameters values used by the ideal models for the CLM and pastoral production systems are documented in the Annex (Table A3 and Table A4). 


The AHLE (ideal gross margin minus the current gross margin) which represents the national burden of poor animal health and nutrition for small ruminants in Ethiopia was estimated at 129.1 billion Ethiopian birr (95% CI 128.9 – 129.2) or 3.227 billion USD (95% CI 3.222 – 3.230) for 2021. This is equivalent to 1,340 birr (33.5 USD) per head/year. This loss is 86% of the potential gross margin which could be gained under ideal condition of no disease and nutrition problems. The ideal and the current gross margins, and the entries used for calculating them are indicated in Figure 1.

Figure 1: Gross margin and different entries of gross margin under current and ideal condition. Note that the values of some entries such as skin value, health care cost and herd increase value under current condition are so small their bars are barely or not visible at the scale of the graph.

The AHLE has three major components: loss due to mortality, loss due to morbidity (production loss) and animal health expenditure (AH expenditure). Morbidity causes the major loss contributing about 68% of the overall burden. The AH expenditure not only contributes the least to the overall burden (less than 1%) but also is insignificant relative to the morbidity and mortality losses (Figure 2)

Figure 2: Relative share of the different components of the AHLE

The distribution of AHLE by production system and species is presented in Table 1. Larger AHLE per head of animal was estimated for goats and the largest was for goats in the CLM system.  The least AHLE was for sheep in the pastoral system.

Table 1: The distribution of AHLE across production systems and species


In this study we used GBADs framework to estimate animal health loss in the small ruminant population of Ethiopia. This is a first worked example of estimating the burden of diseases (sub optimal health) in a specific livestock sector. This estimate gives an insight on the scale of economic loss due to suboptimal health in the sector and the level of loss that can potentially be averted by implementation of interventions that improve animal health. By using the AHLE approach (overall disease burden estimate) all causes of health loss can be included in the burden estimate, including nutritional problems and external forces such as predation, accidents and injuries which are not usually represented in the animal health economics literature. This approach also addresses the potential overestimation problem which is encountered through linear summation of individual disease losses which do not account for co-morbidities (Honeycutt et al., 2011; Torgerson and Shaw, 2021).
In the AHLE framework, the ideal situation represents a utopian scenario where animals are free from any disease or nutritional insufficiencies. We recognise that in reality these problems exist, but through interventions these problems can be minimized or avoided. Irrespective of their efficiencies these interventions would incur a cost i.e., the animal health expenditure, which is component of the AHLE. This means the AHLE as estimated in this analysis cannot be made zero. What can realistically be achieved is employment of efficient animal health interventions that reduce the burden of ill health on livestock production and resource use to its lowest possible level. This is what has been achieved by some sectors and countries where livestock keepers have access to improved animal health technologies and access to good animal health care resources.

The small ruminant disease burden estimated in this study (3.22 billion USD per year, which is about 3% of the national GDP), appears very high. Given high disease prevalence and nutritional problems in the country with reported crude mortality reaching up to 18% in the small ruminant population (CSA, 2021) and up to 40% in lambs and kids (Fentie et al. 2016), this estimated burden is not surprising. Nevertheless, this high estimated burden may also be partly accounted for by some assumptions made in the analysis. The estimate burden is a loss in a gross margin which is the revenue derived from the sector minus the variable cost. The main variable cost in livestock operations (specially in intensive systems) is feed. The systems involved in this analysis are traditional extensive systems where the main source of feed is communal grazing, with little or no supplementary feeding. In this analysis feed cost was considered only for the purchased supplement feed which was zero in the pastoral system and about 12.5% in the CLM system. This would exaggerate the gross margin derived from increasing herd size in the ideal scenario and without much or no increase in feed cost. This exaggerated ideal gross margin results in exaggerated AHLE. The exclusion of non-purchased feed was done for two main reasons. One reason was the difficulty to estimate the cost of grazing feed and the other reason was from the individual farmers perspective the feed derived from communal grazing has no opportunity cost. But at community level the grazing feed resources do have opportunity costs and if this cost was considered the estimated burden could have been lower.

Looking into distribution of the burden across different production system and species, both overall and per head disease burden was higher in goats. This can be partly explained by higher mortality reported in the literature and used in the models of the goat production systems. The lowest burden was observed in sheep in the pastoral system. Looking closer into the input data for the model of this system, there is low estimates for some ideal parameter values. The experts estimated low ideal liveweight for sheep in this system and for some sex-age classes they estimated lower body weight under ideal conditions compared to under current conditions. This could happen due to various factors. One could be the experts were not able to estimate the ideal values correctly and another could be the current parameter values used from existing data sources were overestimated. There were more data gaps in the pastoral system and for some parameter inputs, including liveweight, either single estimates from literature were used or the same values were used from the CLM system. Eitherway, this analysis shows that better data are needed at production system specific levels, potentially for multiple breeds kept within the different production systems, if more specific and sensitive results are required at production system level.

An import insight shown in this AHLE burden estimate is the relative contribution of each of the three components; mortality, morbidity and health expenditure. The proportion of burden due to animal health expenditure relative to the direct disease losses (morbidity (production loss) and mortality) was miniscule, highlighting the existing low level of investment in the animal health sector. If the relationship between disease loss and investment/expenditure on disease control is represented in a loss-expenditure frontiers (McInerney et al., 1992), the small ruminant sector is operating on the left end of the frontier where the expenditure is very low relative to loss. This is far from the optimum point in the frontiers curve which is usually around the inflection point of the curve. This implies that there is a great potential to reduce the high burden of disease loss by investing in animal health, gleaning significant returns, until the optimum point in the frontiers is achieved (i.e., until the return from a unit of additional investment is no more greater than the cost of that additional unit of investment).

There are also some limiations included in this analysis which will need to be smoothed out in future versions of the model. Estimation of production and reproduction parameters for the dynamic herd and flock models under ideal health conditions was a difficult task. An ideal situation of no mortality is straight forward, it is simply setting the premature (unplanned) mortality to zero. The impact of zero morbidity and nutritional problem is however reflected in many production and reproduction parameters and it is not straightforward to estimate these parameters in the ideal situation. For this study, production and reproduction parameters under the ideal scenario were derived from expert opinion but this aspect of the estimation might lack sufficient rigor. More robust data generation is needed in this aspect to support or complement estimates from expert elicitation.

In conclusion, we estimate a high economic burden of small ruminant diseases and nutritional problems. While this high economic burden seems plausible due the high levels of infectious and non-infectious diseases and nutrition problems facing small ruminants in the country, some further model fine tuning is required to improve some assumptions that may exaggerate results. Ultimately however, the estimated AHLE shows very little investment in animal health relative to the morbidity and mortality losses which indicates a potential to reduce the burden of livestock ill health by employing appropriate disease control interventions.


This study was supported by Global Burden of Animals Diseases Program (GBADs) funded by the Bill & Melinda Gates Foundation, Seattle, WA, and Foreign, Commonwealth and Development Office of UK, and International Livestock Research Institute funded by CRP livestock of Consultative Group on International Agricultural Research. We highly acknowledge the support of indviudals within the GBADs consortium who helped us in various ways for this study.


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1ML – most likely, 2SD = standard divation, 3Min = minimum, 4Max = maximum, 5Pt.est = point estimate

N= neonate (0 to 6 months of age), J =  juvinle (>6 months – 1 years of age,  A = adult (>1 years of age), NF= neonatal female, NM= neonatal male, JF = juvinle female, JM = juvenile male, AF = adult female, AM = adult male.

l – Abegaz et al., 2002; Berhe et al., 2019; Talore, 2009; Gemeda, 2009; Debele and Habtie, 2015; Tera et al, 2021; Mekuriaw et al., 2013; Tesfaye, 2008; Abebe, 2020, 2 – Dadi et al., 2008; Assefa, 2007; Gemeda , 2009; Talore, 2009; Debele and Habte, 2015, 3 – Deribe et al., 2014; Urgessa et al., 2012; Debele et al., 2015; Tifashe et al., 2017, Tibbo et al., 2010; Berhan and Van Arendonk 2006; Belay and Haile, 2011; Mukasa-Mugerwa et al., 2000; Getachew et al., 2015; Hadgu et al., 2021; Woldemariam et al., 2014; Fentie et al., 2016; Gebremedhin et al., 2015; ILRI, 2020; Zewdu et al., 2009,4 – Tifashe et al., 2017; Tibbo et al., 2010; Belay and Haile, 2011; Mukasa-Mugerwa et al., 2000; Zesdu et al., 2009, 5 – Urgessa et al., 2012; Debele et al., 2015; Tifashe et al., 2017; Gebremedhin et al., 2015; ILRI, 2020; Zewdu et al., 2009 6 – Urgessa et al., 2012; Debele et al., 2015; Tifashe et al., 2017; Gebremedhin et al., 2015; ILRI, 2020; Zewdu et al., 2009, 7 – Alemayehu et al., 2021; Urgessa et al., 2012; Debele et al., 2015; Tifashe et al., 2017; Petros et al., 2014; Alemnew et al., 2020; Hailu et al., 2006, Fentie et al., 2016; Gebremedhin et al., 2015; ILRI, 2020, 8 – Tifashe et al., 2017, 9 – Urgessa et al., 2012; Debele et al., 2015; Tifashe et al., 2017; Gebremedhin et al., 2015; ILRI, 2020, 10 – Urgessa et al., 2012; Debele et al., 2015; Tifashe et al., 2017; Gebremedhin et al., 2015; ILRI, 2020, 11 – Haile et al., 2020; Adimasu et al., 2018; Talore, 2009; Kefelegn etal., 2019; Hassen et al., 2004; Abegazet al., 2011; Haile et al, 2014, 12– Atsbha et al., 2021; Ayele et al., 2017; Abegaz et al., 2011; Haile et al., 2014, 13 – Zewdu et al., 2009; Gizaw et al., 2008; Tolera, 1998; Abegaz et al., 2011, 14 – Zewdu et al., 2009,  Abegaz et al., 2011, 15 – Solomon et al., 2014; Gatew et al., 2009; Zergaw et al., 2016, Talore, 2009, Dadi et al., 2008, 16 – Solomon et al., 2014; Dadi et al., 2008, 17– Tolera, 1998; Zergaw et al., 2016; Dadi et al., 2008, 18 – Tolera, 1998, Dadi et al., 2008, 19 – Derived from the national drug and vaccine utilization data and unpublished animal health expindature survery

1ML – most likely, 2SD = standard divation, 3Min = minimum, 4Max = maximum, 5Pt.est = point estimate

N= neonate (0 to 6 months of age), J =  juvinle (>6 months – 1 years of age,  A = adult (>1 years of age), NF= neonatal female, NM= neonatal male, JF = juvinle female, JM = juvenile male, AF = adult female, AM = adult male.

1 – Hassen and Tesfaye, 2014; Getachew, 2008, 2 – the same value as CLM, 3 – Fentie et al., 2016, 4 – Alemayehu et al., 2021; Mekuriaw, 2007; Fentie et al., 2016, 5 -11 –   the same value as CLM, 12 – 6 – Yusuf et al., 2019; Ayele et al., 2017, 13 – Gizaw et  al., 2008, 14 – the same value as CLM,  15 – Solomon et al., 2014; Gatew et al., 2009; Zergaw et al., 2016, Talore et al., 2009, Dadi et al., 2008, 16 – Solomon et al., 2014, 17 – Zergaw et al., 2016, 18 –  same value as CLM, 19 – Derived from the national drug and vaccine utilization data and unpublished animal health expindature survery.

N= neonate (0 to 6 months of age), J =  juvinle (>6 months – 1 years of age,  A = adult (>1 years of age), NF= neonatal female, NM= neonatal male, JF = juvinle female, JM = juvenile male, AF = adult female, AM = adult male.

1ML – most likely, 2SD = standard divation, 3Min = minimum, 4Max = maximum, 5Pt.est = point estimate

6The experts’ elicitation estimate for milk yield was less than the current, so the current is maintained

N= neonate (0 to 6 months of age), J =  juvinle (>6 months – 1 years of age,  A = adult (>1 years of age), NF= neonatal female, NM= neonatal male, JF = juvinle female, JM = juvenile male, AF = adult female, AM = adult male.

1ML – most likely, 2Min = minimum, 3Max = maximum, 4Pt.est

6The experts’ elicitation estimate for milk yield was less than the current, so the current is maintained