SAIRLA Dashboard - Tanzania

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In 2015, 608 households were interviewed in Kilolo and Mbarali districts in Tanzania using a structured questionnaire, as part of a project funded by the International Fund for Agricultural Development (IFAD), led by the International Center for Tropical Agricultural (CIAT) in collaboration with CCAFS.

Find the data in brief here:

Mwungu, Chris M.; Mwongera, Caroline; Shikuku, Kelvin M.; Nyakundi, Fridah; Twyman, Jennifer; Winowiecki, Leigh; Ampaire, Edidah; Acosta, Mariola; Laderach, Peter. 2017. Survey data of intra-household decision making and smallholder agricultural production in northern Uganda and southern Tanzania.

Find paper here

Average household size in Kilolo and Mbarali districts by gender of the household head
Level of education Kilolo and Mbarali districts disaggregated by gender of the household head
Average farm size by age category of the household head
Proportion of households experiencing food shortage in a year
Main source of inome in Kilolo and Mbarali by household head occupation
Average crop income by the gender of the household head
Proportion of agricultural practices adopted in Kilolo and Mbarali Districts
Proportion of households in different wealth groups in Kilolo and Mbarali districts
Proportion of households that have access to credit information and membership to a group

The root cause analysis aims to understand the root causes to barriers of adoption, moving beyond the identification of symptoms. During each of the SHARED district-level and national-level workshops, participants identified key barriers to adoption of sustainable agriculture intensification (SAI) (see guide to root cause analysis here ). To complete the root cause analysis each group wrote their barrier in the centre of a flipchart paper and then illustrated causes for the barrier on the paper. For each of these causes, participants were asked again, “What causes that?” ( similar to a 2 year old asking “but why?” to every answer). The exercise was continued until the causes were exhausted!

Below are the barriers to adoption of SAI identified for Mbarali district and also at national level in Tanzania and their associated root causes!

Select constraint to visualize root cause

NOTE: The constraint is labelled in red. Incase the nodes overlap, click and drag them to your preffered location.

Stakeholder mapping and engagement is an important component of the project.

Stakeholders are those that have a stake in an activity or programme. These can be organisations, groups or individuals that come from government, public and private sectors. Stakeholders were identified through consultations with in-country experts as well as previous and ongoing projects. Participatory workshops with stakeholders were held to assess and identify the connections between stakeholders, which were also captured and analysed using social network analysis (SNA). Power and decision-making authority of different stakeholders were identified through participatory exercises. This information will guide future engagement with stakeholders and gather/share/communicate evidence on SAI. The Stakeholder Mapping Guide is available online here.

Objectives of stakeholder mapping activity:

  • Identify key stakeholders engaged in the various aspects of SAI.
  • Introduce the project to targeted stakeholders at each action site.
  • Capture information on the key stakeholders, their roles and connectivity in relation to SAI and value chains where appropriate.
  • Introduce the Stakeholder Approach to Risk Informed and Evidence Based Decision Making (SHARED) approach.
  • Initiate discussion on the SAI interventions at each site.
  • Capture baseline information for the project.
  • Conduct Social Network Analysis (SNA).

Explore the Stakeholder Mapping for Tanzania below!

Note: Zoom the plots in and out for clarity.

Prioritisation of Indicators of Success and of SAI Practices, by Gender and by Agro-Ecological Zone

During the district-level workshops, farmers, extension agents and community members gathered to discuss and share their indicators of successful agricultural practices, as well as to develop an initial list of prioritised sustainable agricultural practices (SAI) they would like to trial. During the workshop, participants were asked to divide themselves by the agro-ecological zones that exist within the district as well as by gender. The guide to the workshop methodology is available here:

The results of the exercises are below.

In November 2017, these SAI practices, among others, including composting of farmyard manure, agro-forestry, etc. will be trialled on farmers fields in collaboration with extension officers and monitored for performance.

Choose either of the options below for visualisation of the prioritised SAI practice:
Note: The size of the bubbles represent the ranking of the SAI practice. The larger the bubble the higher the ranking.



The SAI trials in Mbarali District were set up to generate evidence on the effectiveness of the prioritized SAI practices to inform decision making. The SHARED approach was used as a platform to identify and prioritize context-specific interventions through participatory workshops with local stakeholders. This prioritization process combined assessments of soil and land health, with qualitative assessments and discussions to develop truly context- specific options.

Farmers prioritized early land preparation, agroforestry options and use of inputs in both maize-based and rice-based systems across two agroecological zones: upland and lowland. The lowland agroecological zone focuses on rice, both rainfed and irrigated while the upland agroecological zone focuses on upland crops such as maize, sunflower, cassava and beans among others.

The prioritized SAI practices were designed and piloted as follows:

  • Lowland agroecological Zone: early land preparation (ELP), use of improved seeds, use of fertilizers, irrigation water management
  • Upland agroecological zone with agroforestry: early land preparation (ELP), use of improved seeds, Use of fertilizers
  • Upland Agroecological zone without agroforestry: early land preparation (ELP), use of improved seeds, use of fertilizers


The trials to test the effectiveness of prioritized SAI practices in Mbarali district were ran from February to April 2018.

Site selection and set up

Sites were selected based on the following criteria:

  • Accessibility
  • Closeness to a road or path where other farmers can see and learn
  • High level of farmer commitment to participate in the trial

The trials in the upland agroecological zone were set up in Manienga village, while the trials in the lowland agroecological zone were set up in Mtamba village. The extension staff in each village were involved in monitoring the trials in their respective villages. Details on the trials conducted in each agroecological zone can be found here.

Trial pictures

Trial results

Crop: Maize, variety: SEEDCO 403
Agroforestry variety: Mipogolo (Faitherbia albida), a common tree left in the farms in Mbarali was chosen for agroforestry testing
Note: Elp stands for Early land preparation
Crop: Paddy rice
Note: Early land preparation + farmer normal practices trial used local variety while the other trials used improved variety SARO 5 (TXD 306)
Trade-off analysis

The primary research question this project aims to address is, 'How can the trade-offs between increased production and environmental impact be analysed and managed across different scales?' Several frameworks used to assess tradeoffs have been reviewed and assessed in order to determine which framework the project will use. Widely applied approaches include participatory methods, empirical analyses, optimization models and simulation models (Klapwijk et al., 2014). Of these approaches we use the participatory, empirical analysis and simulation modelling.
Therefore, a participatory tradeoff activity was designed to facilitate a discussion around the various tradeoffs and synergies around the prioritized practices as well as identify key investments that need to be made to overcome constraints. This activity was modified from: Musumba, M., Grabowski, P., Palm, C., & Snapp, S. (2017). Guide for the Sustainable Intensification Assessment Framework. Specific objectives of the activity were:

  • To gather perspectives on the tradeoffs and synergies of the Sustainable Agricultural Intensification (SAI) practices from various stakeholders.
  • To identify key areas for action to minimize tradeoffs.
  • To identify key data needs to assess the tradeoffs and synergies.

The facilitation guide for the activity is available here and the form used by the groups is as shown below:

The output of the workshop included comparative analyses, such as the radar graphs displayed below.
Choose either of the Sustainable Agricultural Intensification (SAI) practices from the list below :

An assessment of land and soil health was carried out in Mahongole, in Mbarali Tanzania (see map below) for a sister project funded by the Australian Centre for International Agriculture Research (ACIAR). This SAIRLA project is building on these data to share the land and soil health indicators with stakeholders.

A field survey was conducted using the Land Degradation Surveillance Framework(LDSF).

The LDSF is a spatially stratified, randomised sampling design, developed to provide a biophysical baseline at landscape level and a monitoring and evaluation framework for assessing processes of land degradation and effectiveness of rehabilitation measures, over time. The LDSF was developed by ICRAF scientists and is implemented in projects, globally.

The LDSF employs a hierarchical sampling design. Each site is 100 km2 and contains 16-1 km2 clusters. Each cluster contains 10-1000 m2 sampling plots and each plot contains 4-100 m2 subplots. The map below shows the layout of the Mahongole LDSF site, green points are the 160 sampled plots. This replication of measurements at various spatial scales (subplot, plot, cluster and site) allows for a systematic assessment of variability and spatial dynamics for each metric.

Measurements and observations were made at the plot and subplot level. Variables measured at the plot level include: land cover, landform designation, position on topographic sequence, vegetation structure, primary current use, along with an assessment of impact to habitat and occurrence of soil conservation structures.

Subplot measurements include: tree and shrub densities, erosion prevalence, root depth restrictions, as well as herbaceous and woody cover ratings.

Composite soil samples were collected at each plot at 0-20 cm and 20-50 cm (n~160 topsoil and 160 subsoil samples). Soil samples were air-dried and sieved to 2 mm and analysed at the ICRAF Plant and Soil Spectroscopy Laboratory using mid infrared spectroscopy in Nairobi as well as Crop Nutrition Laboratory in Nairobi for soil pH and exchangeable bases and also IsoAnalytics Lab for the carbon analysis using dry combustion.

Soil health variables measured are as listed below. Click on either to visualise;
  • An interactive map showing individual values for the variable selected.
  • A density plot showing distribution of the variable for the top soil.

Click on the map to the left for spatial predictions of land health indicators in Mbarali district (outlined in orange). Use this map to navigate in order to explore new areas. Spatial predictions of land health indicators will be updated automatically.


Climate is the statistics of weather over long periods of time.It is measured by assessing the patterns of variation in temperature, humidity, atmospheric presssure, wind and precipitation among other meteorogical variables given over long periods of time.

In this section, rainfall will be the focus with its detailed analysis over the years.The source of the data is a satellite mission known as Tropical Rainfall Measuring Mission(TRMM) whose data ranges from 1998 to 2017.

Click on any point within the Tanzania map to output rainfall trends
Use the slider to the right to select the years you would like to compare in the below graphics, using the the location you already selected on the above map.
The dotted line shows the cumulative rainfall for year1(i.e 1998), while the solid line shows the cumulative rainfall for the selected year2 (i.e 2017).
The boxplot shows the monthly variation in rainfall for the selected years for the location you selected on the map.
Type any two district names on the dropdown box to compare their rainfall trends. Use the slider to select the specific year you wish to visualise.
satelite image for the selected district1
satelite image for the selected district2
The boxplot shows the monthly variation in rainfall in the course of the selected years for district 1. Note that the wider the box, the greater the variation.
The boxplot shows the monthly variation in rainfall in the course of the selected years for district 2. Note that the wider the box, the greater the variation.
Note that the report downloaded will be on the district selected in District 1 dropdown above.