Cow herders

Cowherders in Burkina Faso. Photo credit: CIFOR via Flickr


CLEANED, the Comprehensive Livestock Environment Assessment for improved Nutrition, secured Environment and sustainable Development, was initiated as a framework to assess environmental impacts from rapidly evolving livestock value chains in the developing world 1,2.

Two different implementations of the framework have been developed: i) the CLEANED X – Version 1.0.1 tool in Microsoft Excel, calculating impacts at the farm scale 3 and ii) the CLEANED R tool in R (R Core Team, 2017), a spatially explicit tool calculating impacts at landscape scale 4.

The original framework and CLEANED R tool were funded by the Bill & Melinda Gates Foundation and the CGIAR Research Program on Livestock .

Taking an innovative step forward in the use of analytical tools for the management of environmental and livelihood change in developing country contexts, the CLEANED R tool (see left panel) provides a rapid assessment of livestock production system changes in a data poor environment undergoing fast change. The assessment is in terms of water, land/soil, biodiversity and Greenhouse gas (GHG) emissions, undertaken at the landscape scale, using modeling to generate maps showing the distribution of environmental change.

The project explores how CLEANED R can be used to secure equity and inclusion in decision making around agricultural intensification through an action research methodology. CLEANED R has been rolled out in case studies locations in three countries (Burkina Faso, Ethiopia and Tanzania), embedded within a participatory ‘social learning’ process, which is designed to engage all stakeholders. This includes policy makers and those who are frequently marginalized from decision making, such as smallholder farmers and women. As an action-research project, learning is developed alongside stakeholders who are engaged in assessing alternative intensification scenarios.

This research project addresses three fundamental characteristics of smallholder livestock production that need to be considered to ensure an inclusive and sustainable intensification process:

Natural resource use and environmental impacts of livestock production at a landscape scale.
Feeds can be cultivated at multiple sites across the landscape and transported considerable distances to where the animals are kept. This is not only true for concentrates including crops like maize or soy, but also for crop residues, hay or other roughage feeds. In addition, often the livestock itself is moving across the landscape to graze and browse. This makes it necessary to address environmental dimensions at a landscape scale.

Smallholder livestock rearing generates multiple benefits.
Classic cost-benefit analyses might not capture all the benefits generated. We apply a participatory approach that allows the smallholders (and other stakeholders) to define relevant economic indicators. In this way, we are able to assess the “real” benefits of intensification scenarios from multiple perspectives, including that of smallholder livestock keepers.

Inclusive decision making.

To achieve sustainable intensification of agricultural production trade-offs are required that consider the balance between production and environmental impact. In a developing country setting there is major risk of exclusion of marginal smallholders in such a process. Applying a participatory and inclusive ‘social learning’ decision process ensures that these marginalised groups have their interests represented.

Livestock production systems in developing countries are undergoing very rapid development. Sustained participation in development decisions regarding equitable, long term production rest upon a continued and inclusive learning and adaptive process.

To support this, we operationalise our project through an environmental and production trade-off ‘learning space’ in each case study country that can be continued beyond the life of the project.

Dr Dawit W. Mulatu, Environment and Climate Research Center (ECRC) at the Policy Studies Institute (PSI) shares his take-home messages from the ResLeSS project in Tigray, Ethiopia.

Salifou Ouédraogo (IRD) and Catherine Pfeifer (FiBL) share their take-home messages from the ResLeSS project in Bama, Burkina Faso. Using a social learning approach, the project held two multi-stakeholder participatory workshops to explore the opportunities and constraints for future livestock development in the area. (Video in French / En Francais)

The Learning Process – the Transformation Game

Transforming livestock production, and agricultural production more broadly, requires tough choices and trade-offs between environmental, economic and social goals. Each stakeholder has a different vision of what ‘sustainable’ livestock or agricultural production should provide, and different preferences for which system or pathway will achieve sustainable production.

Communication between different stakeholders is hampered by different types of knowledge and experience of the agricultural system and connections to other parts of the socio-ecological system.

Different stakeholders playing the Transformation Game. The game enables conditions for open discussion and sharing of knowledge, experience and expectations.

The learning process carried out in this project aims to support communication between stakeholders by provide enabling conditions for open discussion and sharing of knowledge. The learning process was designed according to the principles for creating the enabling conditions for social learning 6,7,8.

The Transformation Game provides a tangible structure to a learning process, providing something stakeholders can engage with in a safe space for learning and open discussion. Through discovering each other’s perspectives and priorities, the Game aims to build a shared understanding of opportunities and constraints for achieving a sustainable livestock sector in a study area that could contribute to national goals of both:
i) improving nutrition and livelihoods through livestock; and
ii) protecting the environment and reducing environmental impact.

The process involves guiding participants in four stages from setting up, through to playing the game. The Transformation Game combines the CLEANED R tool with a board game, combined with a computer-based decision-support tool to provide environmental impact information, and a participatory economic approach to provide information on stakeholders’ goals for improved livelihoods.

Overview of the game flow.

Training package
To support further use of this approach the following training package was developed:

Facilitation Guide – providing detailed guidance for preparing and carrying out the two participatory, multi-stakeholder learning workshops as we undertook them during the ResLeSS project.
Download Facilitation Guide (pdf)

CLEANED R Generic Manual – a general description of the CLEANED R tool, its rationale, the modules it contains and the data and equations used in the modules, and considerations for using the CLEANED R tool outside of the study locations for which it is parameterised.
Download Cleaned R Generic Manual (pdf)

Interface User Guide – annotated image of the CLEANED R online user interface, describing how to use it.
Download Interface User Guide (pdf)

Country-specific CLEANED R Documentation  – supporting information for each tool in each country, describing site-specific choices made when setting up the tool, including study area boundary, livestock categories, land use allocation and land use change rules.

Game Boards and Vignettes (pre-set management options) for each study site

Poster Overview of the learning approach and Transformation
Game and lessons from the case study areas in Burkina Faso, Ethiopia and Tanzania.
Poster Download (pdf)


CLEANED R User Interface

The CLEANED R tool is a spatially explicit ex-ante simulation tool that computes locally specific environmental impacts and productivity of livestock value chains. The tool focuses on four environmental pathways, namely water used for fodder production; greenhouse gas emissions (including enteric fermentation), manure management and fodder production; biodiversity losses resulting from land use change for fodder; and soil fertility which compares nitrogen added from manure with nitrogen extracted by fodder production.

The tool produces indicative estimates of water use, greenhouse gas emissions, biodiversity loss and soil fertility, and identifies relative changes in these parameters compared to a baseline.

The CLEANED R tool is available in two versions:

  1. A simplified version comes with pre-set realistic livestock and feed baskets that correspond to realistic management options
  2. An expert version, where all livestock parameters and feed basket can be manually defined.
Village stakeholders playing the Transformation Game – the CLEANED R tool is an important standalone component.

In the ResLeSS project, the CLEANED R tool is integral to the Transformation Game. A key reason for embedding the tool in the Transformation Game is that it does not, itself, provide answers. It provides information about potential changes in key environmental variables (water use, soil nitrogen balance, greenhouse gas emission, loss of habitat and land use) and meat and milk production.

It does not make the judgement about whether these changes are acceptable or not because there are countless factors related to the socio-economic and biophysical context that influence what impacts these changes might have. Therefore, the Transformation Game defers assessment and judgement of the information to a collection of interested and invested experts (the actors, local or other, who will be affected by or make decisions on the impacts).

Site specific tools

The CLEANED R tool aims at balancing context specificity and speed of implementation. To achieve this, the tool harvests open source geographical data (maps) to get context specific climatic, soil and production information for the study area. It is context sensitive, as the livestock categories in the interface and the land use change dynamics are adjusted to the local reality, as are the potential interventions that can be made within each category, such as changing feed baskets.


For every CLEANED R tool that has already been set up, i.e. for each study area the tool was adjusted for, a site-specific report explains how procedures were implemented concretely.

The tool has been set up for Bama commune in Hauts-Bassins region, Burkina Faso, Lushoto district in Tanga region, Tanzania  and Atsbi woreda in Tigray, Ethiopia.

Accessing the tools

The online CLEANED R tools are available here:

The tool is released under GNU General Public License v3.0 and can be found on Github:

The CLEANED R tools can also be used offline, which requires downloading a standalone version of the R-Shiny tool. Links to this standalone can be found on the GitHub site (this requires some knowledge of R. Instructions are found in Section 4 of the Generic CLEANED R Manual ).


The authors are not responsible for any errors or omissions, or for the results obtained from the use of this tool. This tool makes use of open access global geographical data which can be inaccurate at local level.

The authors therefore suggest to focus on differences between scenarios rather than on the level of the impact and to use the tool as part of a participatory process where potential inaccuracies can be pointed out and taken into account.


The ResLeSS project is funded by the UK Department of International Development, as part of the Sustainable Agricultural Intensification Research and Learning in Africa (SAIRLA) programme. This five year programme (2015 to 2020) seeks to generate new evidence and design tools to enable governments, investors and other key actors to deliver more effective policies and investments in sustainable agricultural intensification (SAI) that strengthen the capacity of poorer farmers, especially women and youths, to access and benefit from SAI. SAIRLA has commissioned research and facilitated multi-scale learning to understand different ways of achieving SAI and its developmental implications. The SAIRLA programme is funded by DFID and managed by WYG International Ltd and the Natural Resources Institute, University of Greenwich.


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