Embedding stakeholders' priorities into the low-emission development of the East African dairy sector

Dairy production is a source of smallholder revenue, nutrition, and can function as a safety net particularly for women (Herrero et al 2013). Some studies suggest that increasing dairy productivity can positively influence smallholder dairy farm (SHDF) income and help overcome market access constraints (Westermann et al 2018). In many developing countries, smallholders fail to realize yield potentials, and are often confronted by adoption barriers arising from lack of capacity, resources, and incentives (Orr et al 2018). In particular, the agricultural sector in Sub-Saharan Africa (SSA) continues to be disproportionally affected by climate change, which discourages investment in better dairy practices and technologies (Vermeulen et al 2012, Taylor et al 2017). Compared to more industrialized regions, the dairy sector in SSA accounts for a particularly high proportion of total greenhouse gas (GHG) production (Valin et al 2013, FAO and GDP 2018). Recognizing this, many donors and development agencies in SSA now actively promote low emission development (LED), also because of the dairy sector's significance to food and nutritional security and rural incomes.

It is widely claimed that delivering on dairy sector mitigation targets demands a reduction of GHG emissions intensities. This is generally achievable through milk yield-oriented interventions (Gerber et al 2011, Forabosco et al 2017). However, this often necessitates significant capital and labour investments into more sustainable management practices (Odhong' et al 2019). With enteric methane accounting for approximately 60% of sectoral emissions in East Africa (Mottet et al 2017), much mitigation can be achieved through improvements in feed quality (Caro et al 2016). Doing so, could also increase milk yield (by approximately 51%–96%) and smallholder dairy income (Ortiz-Gonzalo et al 2017, Brandt et al 2020). To achieve this, a large-scale food system transition is needed (Rufino et al 2013), but is hindered by smallholder capacity, resource constraints, and pervasive feed availability issues. Interventions more responsive to what are often highly variegated, addressing feed adoption barriers are therefore sorely needed.

The success of LED interventions in dairy is contingent on local buy-in and responsiveness. This demands more participatory and bottom-up strategies that depart from technologist one-size-fits-all approaches (Schoneveld et al 2019). Multi-stakeholder planning is increasingly regarded as an essential first step to designing locally appropriate solutions sensitive to context and group-specific adoption challenges (Dunnett et al 2018). Smallholders prioritizing milk outputs in land scarce areas, for example, may prefer adopting nutrient-rich feedstuffs (Cameron et al 2018) or introducing higher yielding grasses (e.g. Pennisetum purpureum) (Maleko et al 2018), while more subsistence-oriented farmers are likely to be more receptive to practices and technologies that help manage seasonal water and feed shortages (Campbell et al 2014, Gebremeskel-Haile et al 2019). Complex land management decisions typically underpin smallholder adoption dilemmas, which may range from preserving native grasslands under increasing cattle densities to food-feed crop production trade-offs within small production units (Herrero et al 2014). In some cases, adopting improved feeding practices may be achievable through incremental changes that seek to minimize disruptions on dairy farms (Garnett et al 2013). Other dairy farms may require highly integrated and more radical approaches involving multiple concurrent changes to several aspects of farm management (e.g. introducing exotic cattle breeds alongside nutrient-rich diets and improved health care) (Notenbaert et al 2018).

Given the complexity of designing LED interventions that account for complex SHDF decision-making patterns, this study explored how future LED strategies can be better grounded in stakeholder priorities whilst not losing sight of mitigation targets. We did this by positioning LED within diverse decision-making spaces. Specifically, through multi-stakeholder workshops held in Kenya and Tanzania, we co-constructed LED scenarios that resonate with diverse stakeholder priorities and preferences. Three scenarios were constructed, which included an incremental change scenario (ICS), an adaptive change scenario (ACS) and a multiple change scenario (MCS). By means of a mixed-methods analytical strategy that draws on large-scale survey data, we subsequently analysed how these three scenarios were likely to impact milk output, land use and GHG emissions. In doing so, we showed that stakeholders' priorities varied across the study area and demonstrated how more locally appropriate and evidence-based LED interventions could be designed.

2.1. Study area

The study covered important dairy production areas in the highlands of Kenya and Tanzania, covering approximately 70 000 km² (dark grey, figure 1). Dairy households were surveyed in three dairy counties in Kenya (Bomet, Nandi and Murang'a) and four dairy districts in Tanzania (Njombe, Rungwe, Mvomero and Mufindi). Drawing on survey results, stakeholders' workshops were held in Bomet, Nandi, Njombe and Rungwe. These areas were selected because of the significance of dairy in local livelihoods and economies. Scenario models were then implemented at the administrative unit and the regional level.

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2.2. The baseline and household surveys

2.3. Mapping stakeholders' priorities

To understand the adoption potential for improved management practices, we organized and facilitated four multi-stakeholder workshops in 2019. These workshops involved a total of 55 participants in Tanzania and 38 in Kenya. Participants were selected based on their direct engagement in the dairy sector (i.e. dairy farmers, farmer organisations, developmental organisations, milk buyers and processors) or their regulatory functions (see table 1 for an overview).

Table 1. Representation of stakeholders categorized by sector who participated in the assessment workshops in the counties and districts of Kenya and Tanzania.

	Kenya		Tanzania
	Bomet	Nandi	Njombe	Rungwe
Dairy farmers (%)	29	29.4	33.3	22.6
Farmer organisations (%)	5	5.9	—	16.1
Buyers (%)	9.5	5.9	4.2	6.5
Processors (%)	14	11.7	25	9.7
Local government officials (%)	33	35.3	25	38.7
NGOs (%)	9.5	11.7	12.5	6.4

Prior to the workshops, a literature review of global and SSA case studies was conducted to identify best dairy practices and their impact on farm output (figure 2, appendix 2). This review guided the framing of questions on management practices and promising LED strategies. Stakeholders' priorities and preferences were identified through a questionnaire on various feed and dairy management practices. Participants were specifically asked about the importance of selected practices to SHDF milk output on a five-point Likert scale (i.e. priorities), as well as preferred intervention strategies (i.e. preferences) on a seven-point Likert scale (see appendix 3 for details).

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Analysis of the farm management practices revealed three priority areas: adoption of improved cows, better diets, and improved health (figure 2). Three priority areas related to feed management were also identified: zero-grazing (cattle confined all the time), grazing on native grasslands and supplementation with concentrates (figure 2). To analyse the data on stakeholders' preferences for future interventions, we employed the 'top-two box' and 'Z-score to percentile rank' approaches (Nielsen and Levy 1994). The analysis revealed a distinct preference for interventions that increase feeds purchased, the cultivation of nutrient-rich pasture on-farm, the allocation of land for grazing and the replacement of commercial feeds with on-farm feeds, and also involve market-oriented actions that raise milk prices (figure 2). Because this analysis revealed no significant differences between the different stakeholder groups, we then pooled these results on priorities and preferences at counties and districts level.

2.4. Modelling future dairy production under different scenarios

2.5. Scenario modelling

3.1. Stakeholders' priorities and preferences

3.2. Milk production under scenarios

3.3. Dairy carbon footprints at sectoral level

There were no significant differences between emission intensities at both regional and administrative level scales (tables S9 and S10). Nevertheless, the adoption of the proposed management practices by SHDFs would have a varied effect on total GHG emissions in Kenya. Specifically, emission intensities numerically increased from 3.43 kg CO_2eq kg FPCM⁻¹ in the baseline to 3.75 kg CO_2eq kg FPCM⁻¹ (9% increase) under ICS (figure 3(A)). However, GHG emission intensities decreased by 28% and 44% in Kenya under ACS and MCS, respectively. Under the baseline and ICS low-genetic merit crossbreed cows continued to be kept, while under ACS and MCS genetic improvements were introduced. LUC was the largest source of emissions, increasing by at least 500% in all change scenarios (figure 3(C)). Emissions associated with fertiliser application on croplands decreased by more than 40% in all scenarios. Similarly, emission intensities due to enteric fermentation, methane from manure management and manure on grassland decreased in all scenarios.

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For the dairy production region of Tanzania, GHG emission intensities numerically increased from 3.22 kg CO_2eq kg FPCM⁻¹ in the baseline scenario to 3.51 kg CO_2eq kg FPCM⁻¹ (9%) and numerically decreased to 2.08 kg CO_2eq kg FPCM⁻¹ (35%) under ICS and ACS, respectively (figure 3(B)). The total emission intensity decreased by 30% under MCS. In all scenarios, LUC was the largest source for increased emission intensities (figure 3(D)). Emission intensity decreased by more than 70% due to fertiliser application on croplands. Emissions from enteric fermentation and methane from manure management decreased for all scenarios (figure 3(D)). The emissions from forest losses were negligible, accounting for less than 0.01% of total emissions in all locations.

3.4. County/district-level carbon footprint

Kenya's county level analysis of GHG emission intensities revealed differing patterns across change scenarios compared with the baseline (figure 4). For instance, the total emission intensity numerically decreased from 3.2 kg CO_2eq kg FPCM⁻¹ in the baseline scenario to 2.73 and 2.16 kg CO_2eq kg FPCM⁻¹ under ICS and ACS, respectively, in Bomet (figure 4(A)). In Nandi, the GHG emission intensity decreased for all scenarios (figure 4(B)). The largest source of increase in emissions was from LUC (>500%) in all scenarios (figures 4(C) and (D)). Emissions intensities from enteric fermentation, manure management and manure on grassland reduced under all scenarios (figures 4(C) and (D)). In both counties, emissions intensities from methane from manure management decreased by a minimum of 90% across scenarios.

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In Njombe, the total emission intensity numerically decreased from 3.22 kg CO_2eq kg FPCM⁻¹ under the baseline to 3.15 kg CO_2eq kg FPCM⁻¹ under ICS (figure 5(A)), representing a 2% decrease. However, the total emission intensity decreased by 16% and 28% under ACS and MCS, respectively (figure 5(A)). In Rungwe, emission intensities also decreased for all scenarios compared with the baseline (figure 5(B)), ranging from 12% under ICS to 40% under MSC. In Njombe and Rungwe, emissions intensities from enteric fermentation, fertiliser application and manure management decreased by at least 90% under all scenarios (figures 5(C) and (D)). Emissions from LUC increased by at least 500% for each scenario. Across the study area, interventions in feed management would reduce emission intensities from manure on grasslands (figures 5(C) and (D)).

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3.5. Emissions from the sourcing of concentrates

Our study shows that stakeholders' priorities and preferences are more heterogeneous across space than across social groups. This points to a miss-match between a hegemonic vision of LED and many sub-national development interests. However, what is preferred by stakeholders is not necessarily in line with LED objectives. For instance, we show that incremental changes to feed management, which are more relevant to some areas, increases emission intensities. Adaptive changes to native grasslands and cow genetics in contrast reduced total emission intensities by 28%, while multiple concurrent changes reduce these by an estimated 44%. However, notable intra-regional variations were observed. For instance, emission intensities did reduce under ICS in several administrative areas, suggesting that incremental change was not always incompatible with emission reduction goals. Therefore, in certain areas (in this case Rungwe), resource-constrained interventions may still be impactful. In other areas, LED interventions were only likely to raise yields and lower emission intensities simultaneously with a more encompassing approach that targets multiple practices concurrently. However, in some areas (notably Bomet and Rungwe in this case), some stakeholders were likely to resist such interventions, which could affect efficacy as a result of uptake problems and/or reduced local buy-in and legitimacy. This highlights that LED strategies should be tailored to local interests and practice preferences, or, alternatively, invest more in sensitization. While calling into question one-size-fits-all LED, top-down approaches may still have a place alongside more bottom-up approaches, albeit if the geographic focus of such interventions is restricted to amenable geographies. This, however, is unlikely to be truly transformational without more deliberate large-scale efforts to institutionalize more progressive and reflexive alternatives.

Accounting for stakeholders' priorities and preferences at scale in uncertain contexts clearly requires national-level LED design strategies that are responsive to changing and variegated geographic conditions. This can be achieved by embedding multi-stakeholder processes within all phase of the project lifecycle; from design to implementation and back to re-design. Institutionalizing such processes requires sectoral governance structures that facilitate inter-organizational coordination and helps overcome the fragmented policy space and development programming pervasive in many developing country contexts.

While results had important implications, they also offered some empirical evidence that could help delineate the technical direction of national LED strategies. This study demonstrated that replacing low-quality grasslands and reducing dependence on commercial feeds (e.g., under ACS), were likely to satisfy the interests of the many stakeholders that oppose the allocation of cropland to fodder production. Doing so increased milk yield, reduced emission intensities and aligns with 'food-first' priorities of most districts/counties. However, emissions from LUC increased significantly—as it does under the other scenarios. This study found that feed cultivation raised LUC emissions in particular (in line with Herrero et al 2014, Brandt et al 2018b). Upscaled results suggest that LUC account for between 25% and 42% of absolute emissions, peaking at 63% in sampled counties/districts. This suggested that LED viability assessments should place more emphasis on LUC-related emissions to increase adoption and effectiveness (e.g. FAO and NZAGGRC 2017, 2019, Michael et al 2018).

While LUC emissions could be largely offset by the inclusion of other practices in the scenarios, the results of this study do give reason to caution against land-centric and -intensive intervention strategies, especially in areas experiencing land constraints or comprising ecologically- and socially-significant ecosystems. This is similar to common practices adopted by dairy farmers in high-income countries to reduce GHG emission intensities (van Meijl et al 2006). Greater use of concentrates (e.g. under MCS) could also increase milk yield, while reducing the risk of forest disturbance (Brandt et al 2018a). However, concentrate use could be associated with increase off-farm and overseas emissions (Styles et al 2018), as well as, increasing pressure on existing land capacity (Brandt et al 2020). This study also showed that concentrate use under MCS could increase total emission intensity by 17%–29%. While on-farm fodder production is generally more desirable from an emission-reduction perspective (Dawson et al 2014), this is considered socially detrimental and therefore, politically contentious by most sampled stakeholders. This further highlights that an acute understanding of contextual factors that condition socio-environmental trade-offs is needed in LED design.

Overall, the emission intensities calculated here were consistent with recent studies on East African smallholder production systems of East Africa (e.g. Mottet et al 2017, Wilkes et al 2020). However, total emission intensities were not substantially different between scenarios, which could be attributed to lack of drastic changes in proposed feeding interventions (e.g. supplying only Napier grass under ICS). Nevertheless, by calibrating feeding strategies using stakeholders' priorities and preferences, this study added an important social dimension. However, there is a risk that the scenarios co-developed here are biased in favour of the priorities of better represented groups such as dairy farmers and government officials. However, since there was no statistically significant difference in priorities and preferences between groups, therefore, the risk was considered low. In future studies, particularly in areas where priorities and preferences diverge, attention to balanced representation is needed; possibly through larger-scale surveying.

This article demonstrates the importance of accounting for spatially differentiated stakeholder priorities and preferences in future LED programming and policy-making. It further shows that there is need for caution when implementing mainstream top-down LED particularly when dealing with smallholder dairy farmers who exhibited different preferences for management strategies in this study. Rather dairy policies should include more bottom-up approaches that can help integrate local priorities and preferences that condition uptake and, by extension, intervention efficacy into national LED strategies. This can be achieved by improving multi-stakeholder participation structures and nesting different scales and aligning different thematic domains of governance. This is also more aligned with ongoing processes of devolution. While this article does show that socio-environmental trade-offs are likely inherent to any feed-oriented LED strategy, these can be managed and optimized through participatory processes. In this regard, we show that land use change dynamics deserved particular emphasis and both social and environmental safeguards should be expressly considered.

This study was supported by the CGIAR program on Climate Change, Agriculture and Food Security (CCAFS), the CGIAR program on Forests, Trees and Agroforestry (FTA) and the International Fund for Agricultural Development (IFAD) project 'Greening livestock: Incentive-based interventions for reducing the climate impact of livestock production in East Africa' (Grant No. 2000000994). We wish to acknowledge the field assistants who helped with data collection during the assessment workshops.

The data that support the findings of this study are available upon reasonable request from the authors.

The authors declare that they have no known competing interests.

This study includes stakeholders' assessment workshops undertaken in accordance with the Data Protection Act (UK) 2018 and the General Data Protection Regulation (GDPR). For this reason, all stakeholders were aware that their responses were collected anonymously and gave their consent to partake in the survey.