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
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
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:
Explore the Stakeholder Mapping for Tanzania below!
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.
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:
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:
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.
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:
The facilitation guide for the activity is available here and the form used by the groups is as shown 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.
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.