Humans, Machines and Ecosystems: can big data disrupt livestock development?

Report back from the 2019 CGIAR Big Data in Agriculture Convention

By Vanessa Meadu

How can Big Data approaches like artificial intelligence, machine learning and text mining help researchers generate insights that conventional data collection and analysis cannot? Members of the LD4D Community of Practice attended the recent CGIAR Big Data in Agriculture Convention to gain new insights, share experience and connect with researchers and industry experts working on livestock, fish and crops.

This year’s Convention on the theme Trust: Humans, Machines & Ecosystems was hosted by The International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) in Hyderabad, India. The booming city of Hyderabad – also known as Cyberabad – is a hotbed for the tech sector, and home to countless startups, innovators and disruptors. And India, which is home to nearly 18% of the world’s population (and growing), faces very real food security challenges. It was an appropriate setting to explore potential solutions to feed the future – “byte by byte”.  

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LiveGAPS team release nutrient production app

by Jeda Palmer, CSIRO

The LiveGAPS team have released a new app to compare nutrient production of livestock by farm size for Tanzania, Nigeria and Ethiopia.

With this app, you can visualise the quantity of different nutrients, for example protein, that is produced by livestock from farms of different sizes.

The data shows that small farms (<20 ha) produce the majority of protein for Tanzania, Nigeria and Ethiopia, with very small farms (<2 ha) contributing a considerable amount of protein.

Check out the nutrient production app on the LiveGAPS website.  

LiveGAPS is led by the Global Food and Nutrition Security team at Australia’s Commonwealth Scientific and Industrial Research Organisation (CSIRO). The project aims to identify ways to maximise yields in livestock systems for poverty alleviation, global food security and sustainability, by estimating the gap between actual and potential productivity of livestock. This will be achieved by using new information from surveys and livestock monitoring systems to develop livestock and household simulation models


Jeda Palmer is a research technician in the CSIRO Global Food and Nutrition group, based in Brisbane, Australia.

Photo credit: Jeda Palmer (CSIRO)

Big Data approaches offer hope for improved livestock analysis

By Louise Donnison, Karen Smyth and Vanessa Meadu

LD4D members report back from the CGIAR Platform for Big Data in Agriculture Convention, which took place 3-5 October in Nairobi, Kenya.

How can data-driven approaches improve the sustainability and resilience of food systems? And can these approaches be successfully applied in the livestock sector? In October, members of the LD4D community met in Nairobi to participate in the CGIAR Platform for Big Data in Agriculture Convention, which brought together agriculture and data scientists from around the world to share their innovative tools and approaches. Due to its unique role in bringing together livestock data producers and users, LD4D also acts as the CGIAR’s Big Data in Agriculture livestock data community of practice. The convention presented plenty of ideas which could offer inspiration to the LD4D community. Read more

Large increases in goat meat production are possible in Ethiopia and India, says new study

by Jeda Palmer and Vanessa Meadu

Small ruminants such as goats are an important source of income for smallholder farmers in South Asia and Sub Saharan Africa. The LiveGAPS project, led by CSIRO (the Commonwealth Scientific and Industrial Research Organisation) work investigated potential for different intervention packages to increase yields and profitability of goat meat production in Ethiopia and India. The results are published in a new paper, “Closing yield gaps in smallholder goat production systems in Ethiopia and India“.  Read more

What livestock and where? Q&A with Timothy Robinson on Gridded Livestock of the World 3.0

by Vanessa Meadu

New data on global livestock distribution to help sector along sustainable pathways

Today, the United Nations Food and Agriculture Organization (FAO), in collaboration with the Université Libre de Bruxelles (ULB) and other partners, releases a new, 10 km global dataset of livestock distributions: the Gridded Livestock of the World, version 3.0 (GLW3), published in Nature Scientific Data.

GLW3 has a reference year of 2010 and includes global distributions of cattle, buffaloes, sheep, goats, horses, pigs, chickens and ducks at a spatial resolution of 5 minutes of arc.

The digital maps in geotiff format are freely available for download via FAO’s Livestock Systems website, which also provides data on production systems and links to resources related to sustainable livestock sector development.

Timothy Robinson, Livestock Policy Officer at the FAO, who co-led GLW3, answers a few questions about this new data.

Q: Why map livestock? Read more

Livestock fact check: climate change, yield gaps and more

The LD4D community has released four new factsheets that dig into the data and evidence behind commonly held livestock facts. The latest factsheets investigate claims relating to livestock and climate change, the multiple functions and uses of livestock in Low and Middle Income Countries, the impacts of disease on livestock productivity and the gaps between current and potential yield from livestock.  Read more

Livestock fact check: watch the webinar

Unearthing the data behind the most prevalent livestock facts

On June 8 2018 LD4D hosted an online discussion to launch a set of factsheets and discuss the provenance of popular livestock facts, recognise gaps in knowledge, and get up to speed on the latest figures. Watch the recorded webinar and read a summary of the discussion below.

Watch the recorded webinar

We welcome further questions and comments, please leave them at the end of this page.

Why check the facts?

Livestock is often at the centre of economic, social and environmental discussions, but popular facts are not always rooted in reliable data. Read more