2b:null 2c:["$","title",null,{"children":"Prioritizing Livestock Investments | Livestock Prioritization | Observable"}] 31:T5a6,

Overview

No conceivable Global Future can neglect livestock production. Livestock production systems are the main source of income and livelihood for 300 million people in sub-Saharan Africa. Animals generate revenue through milk and meat production, provide draught power for on-farm activities, and act as insurance when everything else fails. Livestock are at risk from a plethora of climate hazards, and they also generate greenhouse gas emissions that warm the planet. Given livestock are both at risk from and a source of climate change, where should investment be directed for impact?

This spotlight is inspired by the Nature Sustainability Publication Priority areas for investment in more sustainable and climate-resilient livestock systems.

32:T771,

Quick Insights for the identified priority countries

The selected weighting of 50% adaptation, 50% mitigation, and number of geographies selected, result in investment priorities that span 5 countries and encompass [xx billion USD] in livestock production. Climate actions in these countries can benefit [xx million] people exposed to hazards, and contribute to reducing some of the [xx MT CO2e] in greenhouse gas emissions.

The most critical lever of change in the priority list is [top country in list or selected country in country selector if selected], which constitutes [xx%] of exposed value of production and [xx%] of emissions in relation to the overall sub-Saharan Africa total.

In this country, the systems that have the greatest exposure and emissions, and should therefore be the highest priorities are [LPS#1], followed by [LPS#2], and [LPS#3].

The most significant hazard(s) in [top country in list] is [hazard/hazard with most VOP exposed] potentially impacting [USD million VoP].

Livestock production systems within priority countries

Explore the overall country ranking and their livestock production systems using the table below.

33:T21a9,

Methods & Sources

Datasets

The data and methods used here are all documented by Bonilla-Cedrez et al. (2023). Here we provide a summarized version, and encourage the readers to read the original study. The data and code for the analysis of the original study is available in GitHub. The original study and this Spotlight were conducted in collaboration with the CGIAR Livestock & Climate Initiative.

Livestock production systems

We used the livestock production system classification from the Food and Agriculture Organization of the United Nations (FAO) (Robinson et al., 2011). This classification divides livestock production systems into landless, rangeland, mixed irrigated, and mixed rainfed systems. Rangelands, and the mixed systems are each divided by agroecology into arid, hyperarid, temperate, and humid. This gives rise to twelve categories (the category landless is not considered for the analysis) for analysis.

Exposure data

Exposure was represented by the total value of production, total rural population, pasture area, and Total Livestock Units (TLUs). TLU values were computed using data from the Gridded Livestock of the World database (GLW version 3).

Hazard data

This notebook uses six climate hazard data layers, namely, rainfall variability, heat stress, flooding risk, and risk of drought. Rainfall variability is represented by the coefficient of variation of annual mean rainfall (15–30% = highly variable; >30% = extremely variable) computed using rainfall data from the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) dataset. Areas of heat stress were defined as having thermal stress with a Thermal Humidity Index (THI) of ≥79, with THI data derived from Thornton et al. (2021). To define areas at risk of flooding, we used the UN Environment Programme–Division of Early Warning and Assessment–GRID-Europe dataset, in which flooding risk is ranked from 0 (no risk) to 5 (extreme). Areas at risk of drought were defined as having more than 25 days without rain per month on average based on historical rainfall records from CHIRPS.

Poverty data

National-level poverty headcount ratios at US$1.90 per day at 2021 international prices were gathered from The World Bank indicators database (World Bank Maps).

Greenhouse gas emissions data

Greenhouse gas emissions, totals of livestock, and value of production per year for the period 2010-2021 were all gathered from FAOSTAT, and used to produce the stacked line plot in the first section of this notebook. For the remaining sections, total livestock emissions by geography were computed as the sum of direct and estimated emissions from deforestation. Direct emissions data come from Herrero et al. (2013) and are for the year 2010, updated to 2019 using FAOSTAT. Emissions linked to deforestation combined above- and below-ground carbon biomass data from Soto-Navarro et al. (2020), tree cover data (Hansen et al., 2013), and commodity-linked deforestation data (for soybean and pasture) from Goldman et al. (2020). We included deforestation due to soybeans given that a substantial portion of soybean production is used for livestock feed.

Boundaries

Administrative areas used in this notebook come from geoBoundaries 6.0.0. The gbHumanitarian boundaries are used and if not available then the gbOpen boundaries are substituted.

Adaptation and mitigation options

Adaptation and mitigation options were compiled through literature review and expert input.

Methods

Prioritization analysis

All indicators were normalized by dividing the value of each indicator per country by the maximum value across all considered countries to rank countries and visualize adaptation and mitigation indicators. Each normalized exposure indicator was then weighted and summed, creating a unique adaptation index. Adaptation indicators were four: (i) TLUs exposed to climate hazards, (ii) rural population exposed to climate hazards, (iii) value of production exposed to climate hazards, and (iv) pasture area exposed to climate hazards. For the case of mitigation, each indicator corresponding to emissions was normalized by dividing by the maximum value across geographies. A total of six individual indicators were considered, namely, emissions from bovine, pigs, poultry, shoats, forest loss to pasture, and forest loss to soybean. In the prioritization analysis, a weight factor is considered that shifts the relative importance of adaptation and mitigation.

References

Bonilla-Cedrez, C., Steward, P., Rosenstock, T.S., Thornton, P.K., Arango, J., Kropff, M., and Ramirez-Villegas, J. 2023. Priority areas for investment in more sustainable and climate-resilient livestock systems. Nature Sustainability, 6, 1279–1286. https://doi.org/10.1038/s41893-023-01161-1
Goldman, E., Weisse, M. J., Harris, N. & Schneider, M. (2020) Estimating the Role of Seven Commodities in Agriculture-Linked Deforestation: Oil Palm, Soy, Cattle, Wood Fiber, Cocoa, Coffee, and Rubber. World Resources Institute.
Hansen, M. C. et al. (2013) High-resolution global maps of 21st-century forest cover change. Science 342, 850–853. https://doi.org/10.1126/science.1244693
Herrero, M. et al. (2013) Biomass use, production, feed efficiencies, and greenhouse gas emissions from global livestock systems. Proc. Natl Acad. Sci. USA 110, 20888–20893. https://doi.org/10.1073/pnas.1308149110
Robinson TP, Thornton PK, Franceschini G, Kruska RL, Chiozza F, et al. (2011) Global livestock production systems. Rome: Food and Agriculture Organization of the United Nations (FAO) and International Livestock Research Institute (ILRI). pp. 152.
Soto-Navarro, C. et al. (2020) Mapping co-benefits for carbon storage and biodiversity to inform conservation policy and action. Phil. Trans. R. Soc. B Biol. Sci. 375, 20190128. https://doi.org/10.1098/rstb.2019.0128
Thornton, P., Nelson, G., Mayberry, D. & Herrero, M. (2021) Increases in extreme heat stress in domesticated livestock species during the twenty‐first century. Glob. Change Biol. 27, 5762–5772. https://doi.org/10.1111/gcb.15825

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Quick Insights for Sub-Saharan Africa

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Summary

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Countries as levers of change

\n Livestock’s exposure to climate hazards and GHG emissions totals imply that climate actions need to address both adaptation and mitigation outcomes. Investing in actions begs the question of where those investments should be targeted. This section explores geographic priorities for investment in climate action for livestock systems. Hover over the selected priorities to get an overview of emissions totals and hazard exposure.

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Scalable solutions

Many actions exist that would be no-regret investments, delivering benefits for both adaptation and mitigation. However, why are they not yet reaching scale? This section helps explore some key adaptation and mitigation actions and their typical adoption constraints.

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'@tashapiro/dropdown-input'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":744,"value":"dropdownCSS","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":746,"value":"debounce = (delay, input) => {\n class DelayedEvent extends Event {};\n let id;\n input.addEventListener(\"input\", (e) => {\n if(e instanceof DelayedEvent) return;\n e.stopImmediatePropagation();\n clearTimeout(id);\n id = setTimeout(() => input.dispatchEvent(new DelayedEvent(\"input\", {bubbles: true})), delay);\n });\n return input;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":748,"value":"// admin multi-select: countries\nviewof select_species = {\n const data = getSpecies().map((d) => ({\n value: d,\n label: d\n }));\n const input = dropdownInput({\n inputLabel: \"Species\",\n inputId: \"positionsTest\",\n placeholderText: \"Select Species...\",\n options: data,\n selected: [data[0].value]\n });\n return debounce(500, input);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":759,"value":"// admin multi-select: countries\nviewof select_system = {\n const data = getLivestockSystem().map((d) => ({\n value: d,\n label: d\n }));\n const input = dropdownInput({\n inputLabel: \"Livestock System\",\n inputId: \"positionsTest\",\n placeholderText: \"Select Livestock System...\",\n options: data,\n selected: [data[0].value]\n });\n return debounce(500, input);\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":601,"value":"dataStock = FileAttachment(\"1_FAO_stocks_years.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":612,"value":"dataVOP = FileAttachment(\"1_FAO_vop_year.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":618,"value":"dataGHG = FileAttachment(\"1_FAO_ghg_years.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":657,"value":"dataSummary = FileAttachment(\"country_emissions_hazards.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W39","stdlib":"1","titling":"auto"}} 30:"$2f:body"