Climate Smart Agriculture Sourcebook

Climate-smart livestock production

Production and Resources

Assessing the role of livestock in building resilience to climate change in Zambia

Background and objective

In Southern Africa, demand for animal products will double between 2006 and 2050. Africa as a whole is expected to be one of the most vulnerable regions to climate change with recurring droughts, significant changes in temperature and rainfall patterns affecting agricultural yields. In some of the world harshest environments and climate conditions, livestock is the main asset to produce food. However, there is a lack of large-scale and long-term assessments of livestock productivity under climate constraints. The objective of this case study was to understand the role of livestock in building resilience in Zambia. Using modelling and the results from a workshop in the country where assumptions and parameters were tested, the contribution of interventions to enhance productivity, mitigation and adaptation were quantified. 


A modelling framework was developed where feed requirements from livestock were compared with feed availability from agricultural and natural vegetation, in order to estimate feed baskets (i.e. the proportion of grass, crop residues and crop by-products in the animal diet and total intake) in kg, and livestock production, and predict potential deficits. This modelling framework was applied to raster data (10 x 10 km resolution) at the country level in Zambia. Data on livestock were available from the Global Livestock Environmental Assessment Model (GLEAM1). Data on vegetation productivity were available from the DevCoCast project2 (dry matter productivity of natural vegetation) and through GAEZ3 (agricultural yields).

The modelling framework was applied to a past time series covering years 1999-2011 and to two scenarios (2012-2030), a baseline scenario and a drought scenario. Within each year, the wet and dry season were modelled separately. Scenarios reflected the growth of the sector by estimating the number of animals. The baseline scenario had the same overall vegetation biomass productivity than in the 1999-2011 time series. The drought scenario included 3 years of severe drought and 7 years of mild drought calibrated using the 1999-2011 time series. A drought year was defined as a year with low vegetation productivity (i.e. a direct link was assumed between climate and vegetation productivity).

Several improvement options targeted at the livestock sector were also included in the modelling framework. These options, as well as modelling parameters and assumptions, were discussed with stakeholders in Zambia. These options related to improved animal husbandry and health, as well as animal feeding. Two levels of improvement - low and high - were considered for these options.


Productivity - Table B2.5  indicates an important development trend for the livestock sector: production would increase by 30 percent in the baseline scenario without improvement options, while their adoption could roughly double the production gains. The difference between drought and baseline scenarios was limited, although drought led to lower production increases.

Table B2.5
Relative change (%) in animal production compared to the 1999-2011 time series.


Without improvement options

With improvement options (low-high)

Baseline scenario


+ 57 to 80%

Drought scenario


+ 49 to 71%



Mitigation - In the baseline scenario and without improvement options, emissions would increase by 36 percent compared to the past time series due to higher demand and production. With improved practices, emissions would increase by an additional 7 to 20 percent due to extra production gains and increases in animal numbers. However, by improving livestock productivity, improvement options would lead to a strong decrease in emission intensity (emissions per unit of product), from 21 to 36 percent. An option to offset the emissions associated with the sector’s growth would be the management of pastures for carbon sequestration. This could offset emissions by 25 to 31 percent and decrease emissions intensity as well, depending on the effect of improved carbon sequestration practices in grassland on productivity.

Adaptation - In both the past time series and the two scenarios, the interannual variability of production was higher for vegetation biomass than for livestock products. It suggests that livestock can play an important role in building resilience to climate change, by buffering climate variability and the corresponding variability in vegetation production. Livestock's buffering effect was most important in the drought scenario. In this scenario, the variability of vegetation production was highest and so was its difference in relation to the variability of livestock production. 

The spatial integration of the modelling framework made it possible to map feed balances and to reveal areas with an excess of feed and areas with a feed deficit (Figure B2.7). The feed deficit was more important for crop residues than for grass. The average distance between a location with a feed deficit and the nearest location with sufficient excess to fill it was 8 km for grass and 31 km for crop residues. A feed deficit is also observed at the field level where crop residues are not always used, or used for purposes (e.g. conservation agriculture) that compete with livestock feed.

Figure B2.7.  Spatial distribution of feed balances for two feed components: (a) crop residues and (b) grass. Deficit indicates that livestock requirements exceeds feed availability and excess indicates the contrary.


Results suggest that livestock can enhance climate change adaptation by leading to more stable production than vegetation biomass. Moreover, improvement options exist to increase productivity and mitigate greenhouse gas emissions. Results presented in this case study are preliminary as the modelling framework will be further developed and refined. This work will be done within a cross-divisional FAO project on climate-smart agriculture. The contribution of livestock to economic resilience at household level will be addressed by the Agricultural Development Economics division and scenarios linking natural climate change to natural vegetation productivity will be built by the Climate and Environment division. Field trials will be conducted to complement the modelling approach. These trials will aim at building an evidence base of practices leading to a better integration between crop and livestock. National and regional workshops will be organized in Zambia and Southern Africa to discuss policy implications and scale up the results. Results indicate that the goals of making progress in both climate change adaptation and mitigation and improving productivity cannot be separated, but achieving these connected objectives will require extension services, capacity development and an adequate policy framework.