Many studies have examined the role of mean climate change in agriculture, but an understanding of the influence of inter-annual climate variations on crop yields in different regions remains elusive. We use detailed crop statistics time series for ~13,500 political units to examine how recent climate variability led to variations in maize, rice, wheat and soybean crop yields worldwide. While some areas show no significant influence of climate variability, in substantial areas of the global breadbaskets, >60% of the yield variability can be explained by climate variability. Globally, climate variability accounts for roughly a third (~32-39%) of the observed yield variability. Our study uniquely illustrates spatial patterns in the relationship between climate variability and crop yield variability, highlighting where variations in temperature, precipitation or their interaction explain yield variability. We discuss key drivers for the observed variations to target further research and policy interventions geared towards buffering future crop production from climate variability.

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Climate variation explains a third of global crop yield variability

Livestock contributes directly to the livelihoods and food security of almost a billion people and affects the diet and health of many more. With estimated standing populations of 1.43 billion cattle, 1.87 billion sheep and goats, 0.98 billion pigs, and 19.60 billion chickens, reliable and accessible information on the distribution and abundance of livestock is needed for a many reasons. These include analyses of the social and economic aspects of the livestock sector; the environmental impacts of livestock such as the production and management of waste, greenhouse gas emissions and livestock-related land-use change; and large-scale public health and epidemiological investigations. The Gridded Livestock of the World (GLW) database, produced in 2007, provided modelled livestock densities of the world, adjusted to match official (FAOSTAT) national estimates for the reference year 2005, at a spatial resolution of 3 minutes of arc (about 5×5 km at the equator). Recent methodological improvements have significantly enhanced these distributions: more up-to date and detailed sub-national livestock statistics have been collected; a new, higher resolution set of predictor variables is used; and the analytical procedure has been revised and extended to include a more systematic assessment of model accuracy and the representation of uncertainties associated with the predictions. This paper describes the current approach in detail and presents new global distribution maps at 1 km resolution for cattle, pigs and chickens, and a partial distribution map for ducks. These digital layers are made publically available via the Livestock Geo-Wiki (http://www.livestock.geo-wiki.org), as will be the maps of other livestock types as they are produced.

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Mapping the global distribution of livestock.

Human African trypanosomiasis

Animal production is a significant source of greenhouse gas (GHG) emissions worldwide. Depending on the accounting approaches and scope of emissions covered, estimates by various sources (IPCC, FAO, EPA or others) place livestock contribution to global anthropogenic GHG emissions at between 7 and 18 percent. The current analysis was conducted to evaluate the potential of nutritional, manure and animal husbandry practices for mitigating methane (CH4) and nitrous oxide (N2O) – i.e. non-carbon dioxide (non-CO2) – GHG emissions from livestock production. These practices were categorized into enteric CH4, manure management and animal husbandry mitigation practices. Emphasis was placed on enteric CH4 mitigation practices for ruminant animals (only in vivo studies were considered) and manure mitigation practices for both ruminant and monogastric species. Over 900 references were reviewed; and simulation and life cycle assessment analyses were generally excluded. In evaluating mitigation practices, the use of proper units is critical. Expressing enteric CH4 energy production on gross energy intake basis, for example, does not accurately reflect the potential impact of diet quality and composition. Therefore, it is noted that GHG emissions should be expressed on a digestible energy intake basis or per unit of animal product (i.e. GHG emission intensity), because this reflects most accurately the effect of a given mitigation practice on feed intake and the efficiency of animal production. EntErIC CH4 MItIgAtIon prACtICEs Increasing forage digestibility and digestible forage intake will generally reduce GHG emissions from rumen fermentation (and stored manure), when scaled per unit of animal product, and are highly-recommended mitigation practices. For example, enteric CH4 emissions may be reduced when corn silage replaces grass silage in the diet. Legume silages may also have an advantage over grass silage due to their lower fibre content and the additional benefit of replacing inorganic nitrogen fertilizer. Effective silage preservation will improve forage quality on the farm and reduce GHG emission intensity. Introduction of legumes into grass pastures in warm climate regions may offer a mitigation opportunity, although more research is needed to address the associated agronomic challenges and comparative N2O emissions with equivalent production levels from nitrogen fertilizer. Dietary lipids are effective in reducing enteric CH4 emissions, but the applicability of this practice will depend on its cost and its effects on feed intake, production and milk composition. High-oil by-product feeds, such as distiller’s grains, may offer an economically feasible alternative to oil supplementation as a mitigation practice, although their higher fibre content may have an opposite effect on enteric CH4, depending on basal diet composition. Inclusion of concentrate feeds in the diet of ruminants will likely decrease enteric CH4 emissions per unit of animal product, particularly when above 40 percent of dry matter intake. The effect may depend on type of ‘concentrate’ inclusion rate, production response, impact on fibre digestibility, level of nutrition, composition of the basal diet and feed processing.

Mitigation of greenhouse gas emissions in livestock production - A review of technical options for non-CO2 emissions

Global data sets on the geographic distribution of livestock are essential for diverse applications in agricultural socio-economics, food security, environmental impact assessment and epidemiology. We present a new version of the Gridded Livestock of the World (GLW 3) database, reflecting the most recently compiled and harmonized subnational livestock distribution data for 2010. GLW 3 provides global population densities of cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in each land pixel at a spatial resolution of 0.083333 decimal degrees (approximately 10 km at the equator). They are accompanied by detailed metadata on the year, spatial resolution and source of the input census data. Two versions of each species distribution are produced. In the first version, livestock numbers are disaggregated within census polygons according to weights established by statistical models using high resolution spatial covariates (dasymetric weighting). In the second version, animal numbers are distributed homogeneously with equal densities within their census polygons (areal weighting) to provide spatial data layers free of any assumptions linking them to other spatial variables. Machine-accessible metadata file describing the reported data (ISA-Tab format)

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Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010.

Estimating the costs of tsetse control options: an example for Uganda.

Geographic distribution and environmental characterization of livestock production systems in Eastern Africa.

Mapping the benefits: a new decision tool for tsetse and trypanosomiasis interventions.

Technological trends and their potential impact on economy and society are well documented in many publications. Hence, we chose not to rehash what are in Six Exponential Technologies, Fifth Kondratiev Wave of Innovation, Industry 4.0, Third Industrial Revolution and Second Machine Age.

Drivers of change

Increasing the production of meat and milk within sub-Saharan Africa should provide significant food security benefits. However, greenhouse gas (GHG) emissions represent a challenge, as cattle production in the region typically has high emissions intensity (EI), i.e., high rates of GHG emissions per unit of output. The high EI is caused by the relatively low production efficiencies in the region, which are in turn partly due to endemic cattle diseases. In theory, improved disease control should increase the efficiency and decrease the emissions intensity of livestock production; however quantitative analysis of the potential GHG mitigation effects of improved disease control in Africa is lacking. This paper seeks to respond to this by using a hybrid modelling approach to quantify the production and emissions effects of removing trypanosomiasis from East African cattle production systems. The emissions are quantified for each cattle production system using an excel version of GLEAM, the Food and Agriculture Organization’s Global Livestock Environmental Assessment Model. The results indicate that removing trypanosomiasis leads to a reduction in the emissions intensity per unit of protein produced of between 0% and 8%, driven mainly by the increases in milk yields and cow fertility rates. Despite the limitations, it is argued that the approach provides considerable scope for modelling the GHG impacts of disease interventions.

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Alexandra Shaw

Papers

Estimating the costs of tsetse control options: an example for Uganda.

Geographic distribution and environmental characterization of livestock production systems in Eastern Africa.

Mapping the benefits: a new decision tool for tsetse and trypanosomiasis interventions.

Drivers of change

Assessing the Greenhouse Gas Mitigation Effect of Removing Bovine Trypanosomiasis in Eastern Africa