M & E, Data Management & Analysis for Agriculture Sector Programmes

The demand for data on impact of policies, programmes and interventions on agriculture and rural development is rapidly growing. Both governments and civil society have a need for information on food and nutrition conditions and trends and the impact of policies and interventions. Monitoring, evaluation and impact assessment are undertaken by governments, NGOs, international agencies and donors. The course emphasizes the need for careful selection of indicators in designing information support systems at various administrative levels and the skills required for analysis and interpretation of data collected for policy making. The course is job-oriented and the training approach is interactive. It provides the participants with the possibility to learn from expert facilitators as well as from each other.

WHO SHOULD ATTEND

• Government Ministries, Departments & NGOs
• Food security staff & experts in various sectors like Education & Health
• Rural Development staff in community development tasked with implementation and developing food security policy.
• Agriculture, Food security, rural development and Nutrition experts.

LEARNING OBJECTIVES

• To learn how to design an M&E-system including assessments for food and nutrition security programmes.
• To Understand the principles of participatory and learning orientated planning, monitoring and evaluation in food and nutrition security
• Understand the role of food and nutrition security in attaining the SDGs
• Have a clear idea for improving M&E systems and impact assessments for food and nutrition security

IntroductionData demand and use for Agriculture and rural development

• Introduction
• Data use frameworks and key concepts
• Data demand
• Information availability

Introduction to Data analysis and interpretation for Agriculture and rural development

• Introduction
• Data analysis key concepts
• Types of variables
• Basic analysis

Summarizing data

• Numerical summaries for discrete variables
• Tables for dichotomous variables
• Tables for categorical variables
• Tables for ordinal variables
• Graphs and charts for dichotomous and categorical variables
• Graphs and charts for ordinal variables
• Tabulations for summary statistics for continuous variables
• Graphs and charts for continuous variables

Introduction to qualitative data Analysis

• Planning for qualitative data analysis
• Reviewing the data
• Organizing your data
• Coding the data
• Introduction to using qualitative data analysis software (NVivo)

Quantitative data Analysis

• Planning for qualitative data analysis
• Comparison of Data analysis packages
• Basics for statistical analysis
• Testing for normality of data
• Choosing the correct statistical test
• Hypothesis testing
• Confidence intervals
• Tests of statistical significance (Parametric and non-parametric tests)
• Hypothesis testing versus confidence intervals
• Interpreting the data

Assessing Programme Impact on Agriculture and rural development

• Introduction to Impact Assessment
• Programme Design Implications
• Impact Assessment in Programme Design

Methods and Approaches for Assessing Impact

• Overview of Methods and Approaches
• Comparison overtime (Time series)
• Comparison over space (Sub-national comparisons)
• Counterfactual comparison (With/without project or programme)
• Quantitative Methods: Household Surveys
• Quantitative Methods: Secondary Data
• Qualitative Methods
• Selecting Methods and Approaches

M& E Fundamentals

• What is M&E?
• M&E Plans
• M&E Frameworks
• Indicators
• A core set of priority indicators for agriculture and rural development programmes
• Data sources collection and use
• Defining a good M&E system
• Relating Monitoring and evaluation to your project cycle
• Including M&E in agriculture and rural development programme design
• Identifying the challenges facing Monitoring and evaluation professionals in the Agriculture and rural development sector
• Participatory M&E systems -Setting up an M&E strategy in Agriculture and Rural development programmes
• International framework (MDG and SDGs)

M&E Frameworks

• M&E Frameworks basics for and rural development programme
• M&E in and rural development programme context
• M&E Frameworks
• Developing M&E frameworks
• Linking M&E frameworks to indicators
• Using data
• Logical framework approach to the monitoring and evaluation of agricultural and rural development projects

Gender M&E in Agriculture and rural development

• Introduction to M&E in Gender and Food Security
• Prioritizing gender in M&E plans
• Exploring gender in M&E plans
• Selecting indicators to measure gender related outputs and outcomes
• Gender considerations for data collection, interpretation and use

Step by Step approaches to M&E

• Agree on the purpose and principles of the project M&E system
• Agree on and design core documents to setup an M&E system
• Establish project M&E system
• Agree on field monitoring data collection and management process
• Agree on Monitoring data analysis process
• Agree on process for monitoring data utilization and reporting
• Review and revise M&E plans based on progress
• Agree on process of evaluation management

Performance monitoring

• What it is
• Tracking inputs and outputs
• Tools for monitoring inputs and outputs

Measuring results in Agriculture and RD programmes

• Early results and outcomes (Access, use and satisfaction)
• Sector and national level outcomes

Interpreting and Communicating results for M&E

• Communication and reporting for M&E
• Contemporarily methods of disseminating

Data collection, management and data quality

• Data collection methods: How to undertake data collection (quantitative and qualitative)
• M&E field trips
• Increasing questionnaires response rates
• Data collection versus data analysis
• Data quality and data management
• Data quality dimensions
• Functional areas of data management systems
• Double counting
• •data from Social media

ICT tools for data collection, monitoring and evaluation in Agriculture and rural development

• ICT innovations
• Data collection implementation models
• Key choice of application to collect data in rural areas
• ICT tool for Data processing
• Using Mobile phones for data collection
• Dashboards; analytics, data management and stakeholders access
• Case study