4 research outputs found
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Feeding efficiency gains can increase the greenhouse gas mitigation potential of the Tanzanian dairy sector.
We use an attributional life cycle assessment (LCA) and simulation modelling to assess the effect of improved feeding practices and increased yields of feed crops on milk productivity and GHG emissions from the dairy sector of Tanzania’s southern highlands region. We calculated direct non-CO2 emissions from dairy production and the CO2 emissions resulting from the demand for croplands and grasslands using a land footprint indicator. Baseline GHG emissions intensities ranged between 19.8 and 27.8 and 5.8–5.9 kg CO2eq kg−1 fat and protein corrected milk for the Traditional (local cattle) and Modern (improved cattle) sectors. Land use change contributed 45.8–65.8% of the total carbon footprint of dairy. Better feeding increased milk yields by up to 60.1% and reduced emissions intensities by up to 52.4 and 38.0% for the Traditional and Modern sectors, respectively. Avoided land use change was the predominant cause of reductions in GHG emissions under all the scenarios. Reducing yield gaps of concentrate feed crops lowered emissions further by 11.4–34.9% despite increasing N2O and CO2 emissions from soils management and input use. This study demonstrates that feed intensification has potential to increase LUC emissions from dairy production, but that fertilizer-dependent yield gains can offset this increase in emissions through avoided emissions from land use change
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Embedding stakeholders’ priorities into the low-emission development of the East African dairy sector
A growing body of evidence shows that more intensive dairy systems can be good for both nature and people. Little research considers whether such systems correspond with local priorities and preferences. Using a mixed methods approach, this study examined the effects of three intensification scenarios on milk yield and emission intensities in Kenya and Tanzania. Scenarios included (a) an incremental change to feed management; (b) adaptive change by replacing poor quality grass with nutrient-rich fodder crops; and (c) multiple change involving concurrent improvements to breeds, feeds and concentrate supplementation. These scenarios were co-constructed with diverse stakeholder groups to ensure these resonate with local preferences and priorities. Modelling these scenarios showed that milk yield could increase by 2%–15% with incremental changes to over 200% with multiple changes. Greenhouse gas emission intensities are lowest under the multiple change scenario, reducing by an estimated 44%. While raising yields, incremental change conversely raises emission intensities by 9%. Our results suggest that while future interventions that account for local priorities and preferences can enhance productivity and increase the uptake of practices, far-reaching shifts in practices are needed to reduce the climatic footprint of the dairy sector. Since top-down interventions does not align with local priorities and preferences in many situations, future low-emission development initiatives should place more emphasis on geographic and stakeholder heterogeneity when designing targeting and implementation strategies. This suggests that in low-income countries, bottom-up approaches may be more likely to improve dairy productivity and align with mitigation targets than one-size-fits-all approaches
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Methods for prioritizing protected areas using individual and aggregate rankings
Despite their legal protection status, protected areas (PAs) can benefit from priority ranks when ongoing threats to their biodiversity and habitats outpace the financial resources available for their conservation. It is essential to develop methods to prioritize PAs that are not computationally demanding in order to suit stakeholders in developing countries where technical and financial resources are limited. We used expert knowledge-derived biodiversity measures to generate individual and aggregate priority ranks of 98 mostly terrestrial PAs on Madagascar. The five variables used were state of knowledge (SoK), forest loss, forest loss acceleration, PA size and relative species diversity, estimated by using standardized residuals from negative binomial models of SoK regressed onto species diversity. We compared our aggregate ranks generated using unweighted averages and principal component analysis (PCA) applied to each individual variable with those generated via Markov chain (MC) and PageRank algorithms. SoK significantly affected the measure of species diversity and highlighted areas where more research effort was needed. The unweighted- and PCA-derived ranks were strongly correlated, as were the MC and PageRank ranks. However, the former two were weakly correlated with the latter two. We recommend using these methods simultaneously in order to provide decision-makers with the flexibility to prioritize those PAs in need of additional research and conservation efforts
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High-yield dairy cattle breeds improve farmer incomes, curtail greenhouse gas emissions and reduce dairy import dependency in Tanzania
Tanzania’s dairy sector is poorly developed, creating reliance on imports for processed, value-added dairy products and threatening food security, particularly when supply chains are disrupted due to market volatility or armed conflicts. The Tanzanian Dairy Development Roadmap is a domestic development initiative that aims to achieve dairy self-sufficiency by 2030. Here, we model different outcomes of the roadmap, finding that adoption of high-yield cattle breeds is essential for reducing dairy import dependency. Avoided land use change resulting from fewer, higher yielding dairy cattle would lead to lower greenhouse gas emissions. Dairy producers’ average incomes could increase despite capital expenditure and land allocation required for the adoption of high-yield breeds. Our findings demonstrate
the importance of bottom-up development policies for sustainable food system transformations, which also support food sovereignty, increase incomes for smallholder farmers and contribute towards Tanzania’s commitments to reduce greenhouse gas emissions