2. Overview of the Dairy Sector in Senegal

The livestock sector comprising cattle, goats, sheep, and poultry plays a significant role in improving household income and food security for subsistence farmers and pastoralists in Senegal. Although livestock accounts for only 3.6% of the national GDP, it is an integral part of many other agricultural enterprises providing draught power, organic fertilizer, and despite accounting for only 3.6% of national GDP, livestock is an integral part of many other agricultural enterprises, providing draught power, organic fertilizer, and transportation (ANSD Senegal, 2020). Senegal’s cattle population is 3.7 million head, accounting for 1% of Africa’s cattle population (FAOSTAT, 2019). This comprises indigenous and exotic cattle breeds and their cross-breeds. Cattle rearing is classified under three major dairy production systems in Senegal: pastoral, agro-pastoral, and, most recently, the intensive peri-urban (confined silage) system (Figure 1).

Figure 1.

Location of dairy production systems in Senegal.

Source: Own presentation based on Dieye (2006).

The pastoral system is extensive farming practiced mainly in the north and the north-central regions of the country (Ferlo and the Senegal River areas). The Ferlo covers one-third of Senegal’s landmass and is home to two-thirds of the country’s domestic ruminants, including 15% of the cattle population. This system accounts for approximately 38% of national milk production, which is primarily exploited by the Gobra and Gouzerat cattle breeds (Seck et al., Reference Seck, Marshall and Fadiga2016). The average herd size and annual milk yield are 15 dairy cows and 179 l per cow, respectively. Despite this system’s contribution, there are production constraints such as irregular water supply, which worsens during the dry season, and insufficient veterinary coverage for farm animals. Apart from these constraints, Nestlé Senegal built a milk collection network in this zone from 1992 to 2003 because it is the only one that produces the most milk during the rainy season.

The agro-pastoral system is found in the groundnut basin/production zone (administrative regions of Diourbel, Louga, Kaolack, Thiès, and Fatick) and the south administrative regions of Kolda, Ziguinchor, and Tambacounda. Around 25% and 20% of the national cattle herd are located in the groundnut zone and the southern administrative regions, respectively (Duteurtre, Reference Duteurtre2006). In this production system, cattle are typically kept for beef production and animal traction by traditional Fulani pastoralists. Moreover, the average herd size and annual milk yield are 15 dairy cows and 600 l per cow, respectively (Gunarathne et al., Reference Gunarathne, Almadani, Behrendt, Chibanda and Deblitz2022). Artificial Insemination first appeared in the groundnut zone in 1994 with the Livestock Support Project (PAPEL)Footnote ² , which was intended to improve the level of milk production of local cattle breeds. This project enabled the exploitation of cross-bred cows and enhanced the level of milk production (about 6 l/cow/day) and the income of the producers (Dia, Reference Dia2004). Despite the performance recorded in this system, constraints to the improvement of production persist. In this production system, breeding is achieved through Artificial Insemination or natural service depending on the farmer’s production goal, whether dairy or beef products. This decision on the production goal is mainly dependent on the availability of food (forage) in the dry season.

The intensive peri-urban system (confined silage) is usually practiced mainly in the Niayes area of Dakar-Thiès. It represents less than 1% of the cattle herd in Senegal and is primarily based on the use of exotic cows (Montbeliard, Jersey, Holstein, and Gir) in permanent stabling for milk production. Milk production is of the highest interest in this production system and because of that Artificial Insemination is widely applied to increase the production of milk. The average daily milk yield per cow is considerably high compared to the other two systems with the production of 30.0 l in the rainy season (June–October) and 15.0 l in the dry season (November–May). The average dairy farm has about 90 cows, and the annual milk yield is 3,150 l per cow (Gunarathne et al., Reference Gunarathne, Almadani, Behrendt, Chibanda and Deblitz2022).

Due to the low quantities of milk produced in the dominant systems (pastoral and agro-pastoral), the national supply is unable to meet the growing demand for milk and dairy products. In addition to other dairy products, the country imports 100,000 metric tons of powdered milk annually, representing more than USD 400 million (Zamani et al., Reference Zamani, Pelikan and Schott2021). Moreover, in 2018, the total dairy imports amounted to about 595 million tons of milk equivalent, accounting for about 85% of the milk powder and full-fat milk by value (UN Comtrade, 2018). According to Zamani et al. (Reference Zamani, Pelikan and Schott2021), the self-sufficiency rate of the Senegalese dairy sector steadily declined from 41% to 20% between 2000 and 2018 (Figure 2). This indicates that the dependency on imported milk and milk products will continue to increase in the future.

Figure 2.

Development of the Dairy Sector in Senegal from 1996 to 2018 (in 1000 tons, milk equivalent).

Note: Domestic consumption is calculated using imports plus production minus exports. Storage was not considered. Artificial Insemination projects are shown in black, while other livestock policies are shown in red. The policies are discussed in detail in the following section.

Source: Exports and imports are based on UN Comtrade (2018). the production data is retrieved from FAOSTAT (2019).

3. Dairy Policies and Programs in Senegal

The public policies in Senegal are generally formulated to make the agricultural sector a driver for economic growth and farmers' livelihood improvement (Demont and Rizzotto, Reference Demont and Rizzotto2012). After an expensive period of state intervention between the 1960s and 1980s, Senegal adopted the Structural Adjustment Programs in Agriculture in the 1980s, intended to remove too much state control in the agricultural sector. In this program, privatization and market liberalization were the main components (Resnick and Birner, Reference Resnick and Birner2010; Weissman, Reference Weissman1990).

In the dairy sector, the reduction of import dependency through increasing domestic production has been a central objective for public interventions that are jointly implemented by the private sector, NGOs, and public projects (Dieye et al., Reference Dieye, Duteurtre, Sissokho, Sall and Dia2005). The dairy sector’s policies cover five thematic areas including institutional policies (e.g., organization of dairy industries, farmers' associations), access to natural resources (e.g., water and land), livestock development (e.g., genetic improvement), economic and trade policies such as tariffs and non-tariff barriers, subsidies, and macroeconomic policies (Dieye et al., Reference Dieye, Duteurtre, Sissokho, Sall and Dia2005; Seck et al., Reference Seck, Marshall and Fadiga2016).

Adopted in 2004, the Agriculture, Forestry, and Livestock Act (LOASP)Footnote ³ represents an important institutional framework for reviving the agricultural sector of Senegal. Aimed at achieving food security and increasing the income sources of farmers, this law constitutes a legal framework for implementing the agricultural development plan in Senegal for the next 20 years (FAO, 2015). This law led to the implementation of several operational plans and projects, including the National Agricultural Development Program, the National Program for Livestock Development (PNDE)Footnote ⁴ , and the Grand Agricultural Offensive for Food and Abundance (GOANA)Footnote ⁵ . These programs are common in identifying livestock among the priority sectors that significantly impact the achievement of the Millennium Development Goals (Diouf et al., Reference Diouf, Marshall and Fadiga2016).

As part of the LOASP, the Ministry of Livestock launched the PNDE as a framework for the implementation of interventions in the livestock sector. This plan specifically addresses animal husbandry. More specifically, it seeks to increase the productivity and competitiveness of animal value chains and to reach self-sufficiency in this market by 2026 (Seck et al., Reference Seck, Marshall and Fadiga2016; World Bank, 2020; WTO, 2017). The program became operational in 2013, and it covers five specific pillars namely; improving productivity, developing breeding systems, improving product marketing, and strengthening institutional structure (Seck et al., Reference Seck, Marshall and Fadiga2016; World Bank, 2020).

From 2000 to 2005, Senegalese dairy imports grew substantially from 23 to 42 billion CFA (35–64 million Euro) (Duteurtre, Reference Duteurtre2009). However, the 2007–2008 food price spike highlighted the high vulnerability of Senegal’s food security to international food price variations (Seck et al., Reference Seck, Marshall and Fadiga2016). As a result, several contingency policies were implemented to control milk prices, such as tax exemptions for powdered milk imports. As already mentioned, GOANA, which combines technical components like animal feed, cross-breeding, and Artificial Insemination with trade-related policies like tax exemptions for production inputs and the processing of local milk, was also implemented by the government in 2008 to lessen Senegal’s dependence on imported food (Demont and Rizzotto, Reference Demont and Rizzotto2012; Magnani et al., Reference Magnani, Ancey and Hubert2015). Nevertheless, due to a lack of finances, only Artificial Insemination effectively became operational under the GOANA project which finances breeding and genetic improvement (Magnani et al., Reference Magnani, Zucchella and Strange2019). Further, the GOANA got replaced by the New Alliance for Food Security and Nutrition in 2012 (FAO, 2015). The Artificial Insemination projects are discussed in the following section. Recently, in June 2018, the “my milk is local” campaign was launched in several countries in West Africa by a coalition of organizations of professionals in the dairy sector, NGOs, and research institutes. The goal of this advocacy was to encourage domestic milk consumption in milk-producing nations including Burkina Faso, Mali, Mauritania, Niger, Ghana, and Senegal (GRET, 2019). There is currently no information available to assess the effectiveness of this campaign.

3.1. Genetic Improvement Policy and Programs

In Senegal, Artificial Insemination has been widely supported by successive national programs. Subsidized by the public sector, all dairy genetic improvement programs in SenegalFootnote ⁶ have been implemented at no cost to cattle keepers (Marshall et al., Reference Marshall, Tebug, Juga, Tapio and Missohou2016). The main stakeholders of the genetic improvement policies include the state, livestock professionals, public services, and private companies, including veterinarians, livestock technicians, and the beneficiary dairy farmers (Diouf et al., Reference Diouf, Marshall and Fadiga2016).

As laid out above, in 1992, the Livestock Support Project (PAPEL) was launched to improve the production of milk and meat in the Groundnut and Sylvopastoral zones. This project was funded by the government of Senegal with the support of the African Development Bank. In this project, around 5,000 cows located in these production zones were inseminated between 1995 and 2005. The results showed an overall 43.4% pregnancy rate per Artificial Insemination recorded for the years 1995–1998. A higher pregnancy rate (73.6%) was obtained in 1996, and the lowest rate of 38.8% was recorded in 1997. According to Seck et al. (Reference Seck, Marshall and Fadiga2016), the decrease in the pregnancy rate in 1997 was most likely due to a lack of forage in that year. The PAPEL project was followed by the Agricultural Development Project in Matam (PRODAM)Footnote ⁷ implemented in northern Senegal. In this project, 768 cows were inseminated in two phases (1996/1997 and 1998/1999) with an average success rate of 31% and 42% recorded for the first and second campaigns, respectively (Diouf et al., Reference Diouf, Marshall and Fadiga2016).

As part of the national milk production development policy, three breeding campaigns were conducted under the National Artificial Insemination Program (PNIA)Footnote ⁸ in 1999, 2001, and 2004. This was done predominantly by private companies using protocols based on the specifications of agroecological zones. As a result, the overall insemination success rate increased from 31% to 42% between 1999 and 2001 (Gueye, Reference Gueye2003; Magnani et al., Reference Magnani, Ancey and Hubert2015). However, challenges with feeding, technicians' lack of experience, and the geographical dispersion of activities were noted as some of the major obstacles that adversely affected Artificial Insemination programs. This can be observed in Figure 1, where the earlier insemination programs (including PAPEL and PNIA) resulted in little changes in domestic production from 1996 to 2004.

Later, in 2008, the GOANA program was implemented to increase livestock production through the implementation of various genetic improvement initiatives (Cabral, Reference Cabral2016). From 2008 to 2014, the livestock component of GOANA, known as the Special Artificial Insemination Program (PSIA)Footnote ⁹ , operated as an autonomous genetic improvement program. The production objective of PSIA was to inseminate 500,000 cows by 2012 with the expectation of obtaining 100,000 cross-breeds and additional milk production of up to 400 million liters (Seck et al., Reference Seck, Marshall and Fadiga2016). From 2008 to 2014, 116,024 cows were artificially inseminated under this program, with a 42.5% success rate (Ministry of Livestock and Animal Production, 2012, 2014). Because of this insight, the Senegalese government proposed Artificial Insemination as the greatest technical solution for significantly raising domestic milk production and lowering imports. Due to some good progress by the government, the insemination programs were used to showcase the presidential commitment to modernity testifying to a growing “technicist” attitude in dairy development (Magnani et al., Reference Magnani, Zucchella and Strange2019, pp. 143–58). Out of the 20,000 cows that were intended to be inseminated as part of the PSIA (from 2010 to 2011), 19,209 were actually inseminated, representing 96% of the initial target. However, the evaluation of PSIA highlights a reduction in the pregnancy rate from 47.4% to 44.2% over the implementation period (Seck et al., Reference Seck, Marshall and Fadiga2016). Additionally, critics have expressed concerns over the ineffective monitoring of project outcomes, which is required for the project’s evaluation.

In spite of the challenges with PSIA, the government made the decision to continue the genetic improvement plan through the Dairy Industry Development Support Project (PRADELAIT)Footnote ¹⁰ . This project was carried out within the framework of the 2014–2018 Emerging Senegalese Plan (PSE)Footnote ¹¹ with a budget of 30 million euros (Diouf et al., Reference Diouf, Marshall and Fadiga2016). The PRADELAIT project, like PSIA, sought to improve milk production through production systems intensification and modernization. The project’s goal was to help create jobs and generate income, as well as to alleviate extreme poverty, and improve food security, especially in rural areas. Figure 3 shows the timelines of Artificial Insemination and livestock improvement projects implemented in Senegal.

Figure 3.

Timeline of different Artificial Insemination programs and livestock policies in Senegal (1995–2021).

Source: own representation.

4. Data and Method

4.1. Synthetic Control Method

Due to limited data availability, the empirical analysis of policy effects in developing countries is a difficult task. To overcome this challenge, some scholars have proposed the SCM (Luo and Kostandini, 2021; Olper et al., Reference Olper, Curzi and Swinnen2018). This technique has been widely used in recent years to estimate the effects of policy interventions in various contexts (see e.g., Cole et al., Reference Cole, Elliott and Liu2020; Gibson, Reference Gibson2020; Luo and Kostandini, 2021; Mohan, Reference Mohan2017). The SCM is “arguably the most important innovation in the policy evaluation literature in the last 15 years.” (Athey and Imbens, Reference Athey and Imbens2017, p. 9). This method provides several advantages over other similar methods, e.g., propensity score matching and difference-in-difference (DID). First, it can control endogenous problems due to selection bias and other factors associated with control group selection and relaxes the parallel trend assumption of the DID method (Li et al., Reference Li, Wong, Chen and Duvenaud2020; Olper et al., Reference Olper, Curzi and Swinnen2018). Second, it does not calculate weights without using the post-intervention data (Cole et al., Reference Cole, Elliott and Liu2020).

Following Abadie et al. (Reference Abadie, Diamond and Hainmueller2010), we split our sample into two periods, a pre-intervention period, $ T_{0} $ , and the post-intervention period, $ T_{1} $ , where $ T=T_{0}+T_{1} $ . We assumed there are K + 1 countries, among which the first country (i.e., treated unit) was affected by the Artificial Insemination projects over the pre-intervention period $ T_{0}+1,\ldots,T $ , and the other K countries (so-called “donor pool”) is considered as the control samples. The idea of the SCM is to estimate the preintervention characteristics of the treated unit using a weighted average of control units in the donor pool, known as the synthetic control, that approximates the pretreatment outcomes for the treated unit (Abadie et al., Reference Abadie, Diamond and Hainmueller2015; Ben-Michael et al., Reference Ben-Michael, Feller and Rothstein2021).

For each country j and time t, let $ Y_{j,t}^{I} $ be the production of milk observed for the countries that did not experience Artificial Insemination projects, and $ Y_{j,t}^{N} $ be the milk production for the treated unit (i.e., Senegal) after it had adopted the projects. Accordingly, the net effect of the initiative ( $ \rho _{j,t} $ ) for the treated unit is defined by the gap between $ Y_{j,t}^{N} $ and $ Y_{j,t}^{I} $ , as follows:

(1) $$\rho _{j,t}=Y_{1,t}^{I}-Y_{1,t}^{N}$$

It is assumed that the Artificial Insemination projects have no effects on production in the preintervention period, i.e., $ Y_{j,t}^{N}=Y_{j,t}^{I} $ so for $ t\lt T_{0} $ and all units. We define $ D_{j,t} $ as an indicator that takes the value 1 if country j is exposed to the Artificial Insemination projects at time t, and zero otherwise. Accordingly, the observed outcome for country j at time t is

(2) $$Y_{j,t}=Y_{j,t}^{N}+\rho _{j,t}D_{j,t}$$

According to Abadie et al. (Reference Abadie, Diamond and Hainmueller2010), the potential effect of the intervention for the affected country on our study (Senegal) in period $ t\gt T_{0} $ is measured by

(3) $$\rho _{j,t}=Y_{1,t}^{I}-Y_{1,t}^{N}=Y_{j,t}-Y_{1,t}^{N}$$

Since $ Y_{1,t}^{I} $ is known, one can estimate the post-intervention trend of milk production by estimating $ Y_{1,t}^{N} $ which is the milk production of Senegal where no intervention occurred. Abadie et al. (Reference Abadie, Diamond and Hainmueller2010) apply the following linear factor model to estimate $ Y_{j,t}^{N} $ .

(4) $$Y_{j,t}^{N}=\beta _{t}+\theta _{t}X_{j}+\delta _{t}Z_{j}+\varepsilon _{j,t}$$

where $ \beta _{t} $ denote the time-variant fixed effect, $ X_{j} $ are the observed variables, and $ Z_{j} $ is the unobserved variable affecting milk production, and $ \varepsilon _{j,t} $ is the random error term with zero means. According to Abadie (Reference Abadie2021), a weighted average of units in the donor pool may approximate the characteristics of the treated unit much better than any untreated unit alone. Given a set of weights for each untreated unit $ W=(w_{2},\ldots, w_{J+1}){\rm '} $ , a synthetic control estimate of $ Y_{1,t}^{N} $ is

(5) $$\hat{Y}_{1,t}^{N}=\sum _{j=2}^{J+1}w_{j}Y_{j,t}$$

where $ \hat{Y}_{1,t}^{N} $ stands for counterfactual domestic production. In Equation (5), the weights are assumed to be nonnegative and sum up to one, i.e., $ \sum _{j=2}^{J+1}w_{j}=1 $ . An optimization algorithm is applied to determine the optimal weights ( $ w_{j} $ ) by minimizing the deviation of the outcome variable path of the synthetic treatment country for the preintervention period (Abadie and Gardeazabal, Reference Abadie and Gardeazabal2003).

6. Summary and Concluding Remarks

This paper investigates the effects of public interventions on production in Senegal’s dairy market. This is accomplished by reviewing literature and milk production trends in order to assess the potential effects of Artificial Insemination projects on domestic milk production via a comparative case study developed by Abadie and Gardeazabal (Reference Abadie and Gardeazabal2003) and Abadie et al (Reference Abadie, Diamond and Hainmueller2010). The SCM calculates a weighted average of potential comparative countries that were not affected by Artificial Insemination projects to form a “synthetic” control group with characteristics similar to Senegal prior to intervention. Furthermore, to investigate the short and long-run causal effects, we project the spillover effects of increasing milk production on water resources as an important constraint in the agriculture sector of Senegal and discuss the possible synergies and trade-offs between food security, water resources, and milk production.

This study complements previous descriptive analyzes by providing empirical evidence on the impact of interventions regarding livestock genetic improvements in Senegal. Our findings show that the Artificial Insemination projects caused milk production to increase by 59 thousand tons on average from 2008 to 2018 (equal to 651 thousand tons in total). From our estimates, production changes as a percentage of annual milk production stood at 37% in 2009 and 47% in 2018. According to Marshall et al. (Reference Marshall, Tebug, Juga, Tapio and Missohou2016), an increase in domestic production with higher productivity (e.g., more productive breeds) may benefit food security by increasing milk consumption. Nonetheless, we lack sufficient information to accurately estimate the potential effects on food security. This could be a venue for future research.

While Artificial Insemination projects have increased milk production to some extent, Senegal’s market trend shows a significant gap between imports and domestic milk production. This shows that the primary goal of these projects was not achieved. Different barriers hinder the real impacts of Artificial Insemination projects. Water scarcity in Senegal can be considered a negative externality to milk production. Our results highlight the significant effects of dairy sector development on water resources in Senegal. Apart from the direct effect of water scarcity on livestock watering in dairy production, livestock feed production is highly dependent on the constant availability of water throughout the year. For instance, the water required for implementing Artificial Insemination projects in 2018 was estimated at 5% of total agricultural water withdrawals in Senegal, which can be used in other sectors with higher water productivity. The dry season which spans from November to May is a period during which rainfall ceases entirely in Senegal. Access to good quality feed and water is a great challenge and results in low milk yields. Meanwhile, milk production in terms of quantity and quality begins with what animals feed on. Thus, the factors and challenges impeding the actual outcomes of Artificial Insemination should be considered in order to improve the efficiency of genetic improvement policies. Furthermore, Senegalese policymakers should ensure that the negative externalities of production changes do not outweigh the positive effects.

Overall, our analysis suggests that dairy policies should be based on a better understanding of the interdependence and coherence of various policy objectives. This helps policymakers identify the synergies and trade-offs between policy objectives and improves policy efficiencies. It should be noted that better animal health and nutrition are required for the successful implementation of Artificial Insemination projects; otherwise, genetic improvement will have little effect on production. For instance, the more confined dairy systems, particularly those around Dakar, address nutrition, health, and genetics. Therefore, feed and herd health management are critical for the development and continued success of an Artificial Insemination project. As previously stated, it is especially important when the feed market faces a shortage due to the drought. Promoting drought-tolerant breeds may improve the synergy between milk production and water resources. Additionally, feed imports during drought seasons may prevent feed price increases and thereby prevent the increase in production costs when the market faces shortages.

Additionally, the lack of experience of technicians is identified as a significant challenge in implementing Artificial Insemination projects. Therefore, the government should arrange more training programs for technicians to improve their skills and enhance the cattle artificial insemination success rate. Also, dairy farmers should be trained on how to recognize estrous signs in dairy cows to improve reproductive efficiency. Last but not least, the effects of using Artificial Insemination are broader than genetic improvement, and it has a range of benefits for dairy farmers, such as the elimination of venereal diseases, more accurate dry-off dates, reduced incidence of dystocia, and increased safety for farm workers (Vishwanath, Reference Vishwanath2003). In light of the fact that our approach assesses the genitive improvement policies from an economical perspective, future research on the economic implications of technical requirements in implementing policies may be worthwhile.