FAO

This course explores the role of forests and trees in climate-smart agriculture (CSA). It takes into consideration the ecosystem services and goods that forests provide, and the importance of forests for the food security of forest-dependent people. It explores the complex relationship between climate change and forests, and how adaptation and mitigation measures can benefit forests, forest-dependent people, and global climate change. The synergies and trade-offs involved in climate-smart forest management are also considered.

Learning Objectives

• Relevant concepts, especially sustainable forest management.
• Areas of intervention for climate smart forestry – livelihoods and food security, vulnerability and resilience and deforestation and forest degradation.
• The steps needed to integrate a climate-smart forestry approach into the plan and practices of any forest management unit.
• The role of forest monitoring and national forest monitoring systems.
• The elements that can create an enabling environment for the adoption of climate-smart forestry management practices and approaches.

Anticipatory actions in agriculture vary greatly depending on the type of anticipated hazard and the context. This job aid provides you with some examples of anticipatory actions that can be implemented to mitigate the effects of drought and flood and cyclone, plus a few examples of cross-cutting actions that can be applied to different hazards.

Learning Objectives

• Anticipatory actions to mitigate the effects of drought on crops
• Anticipatory actions to mitigate the effects of flood and cyclones on crops
• Cross-cutting and Cash-based Anticipatory Actions

This short course provides an overview of how to build a crisis timeline to support the design of anticipatory actions using a phased approach. It focuses on slow-onset hazards and uses drought as an illustrative example. 

Learning Objectives

• What is a crisis timeline and what is its purpose
• How to build a crisis timeline by following a step-by-step approach

Good practice shows that early warning systems should combine forecast indicators with seasonal observation indicators in order to predict the impact of hazards on different agricultural sectors. Ideally, regularly available vulnerability indicators should also be integrated to detect any impacts on livelihoods and food security of potentially affected population.

This interactive job-aid offers a sample of the most well-known early warning indicators and information sources to support anticipatory action in the context of agriculture and food security.

Learning Objectives

• What early warning indicators and information sources can support anticipatory action in the context of agriculture and food security.
• Strengths and weaknesses and example thresholds of:
  • Hydro-meteorological forecast indicators.
  • Seasonal observation indicators.
  • Vulnerability indicators.
• Examples of information sources at global, regional, and country level.

Connect with the videos, lecture notes and ongoing conversations from the massive open online course (MOOC) on National Adaptation Plans: Building Climate Resilience in Agriculture in this comprehensive online toolkit. Materials presented here were originally shared via the One UN Climate Change Learning Partnership Platform (UNCC:Learn) Platform during a six-week MOOC. The course is still available for self-paced learning on UNCC:Learn. The NAP-MOOC learning materials are presented as part of the Integrating Agriculture in National Adaptation Plans (NAP-AG) Programme and are a joint effort between the United Nations Development Programme (UNDP), the Food and Agriculture Organization of the United Nations (FAO), the United Nations Institute for Training and Research (UNITAR), and more than 40 leading experts on climate change, finance, agriculture, communications and more.

The joint UNDP-FAO Integrating Agriculture in National Adaptation Plans (NAP-Ag) Programme supports partner countries to identify and integrate climate adaptation measures for the agricultural sectors into relevant national planning and budgeting processes.

This course provides a definition of vulnerability and illustrates the three critical dimensions used to define it. It also presents the most commonly used methods to assess vulnerability, and provides examples and criteria for selecting the appropriate vulnerability indicators.

You will learn about

• The concept of vulnerability
• Using vulnerability assessments for decision making
• Tools available for conducting vulnerability assessments
• Selecting vulnerability indicators

This course is part of a series which introduces the FAO damage and loss methodology, developed to support countries to generate precise and holistic data for the agricultural sector, in response to climate- and weather-related disasters.

This course introduces the formulas which are used to compute damage and loss in the agricultural sectors (crops, livestock, forestry, aquaculture and fisheries). It then considers the data requirements and possible sources, and how the data can be used both to report on Sendai and Sustainable Development Goal (SDG) targets, and at national level.

You will learn about

• The key concepts of the FAO Damage and Loss methodology – production damage, production loss and asset damage
• How these are calculated in five agricultural sectors – crops, livestock, forestry, aquaculture and fisheries
• Data requirements, sources and collection methods for calculation of damage and loss using the methodology
• The importance of institutional collaboration for data collection and analysis
• How the D&L data can be used at global level in the Sendai Framework Monitor, and to inform national level policy making and planning

Agriculture is a risky sector. Extreme weather conditions, pests and diseases and market uncertainties are just few examples of events that can negatively affect farming activities, posing a major threat to households livelihood and food security. This course will present an overview of concepts linked to agricultural risk management, describing which risks farmers face, within a holistic approach that covers the entire agrifood system.

You will learn about

• Different risks affecting farming activities and their characteristics and main causes
• Implications of agricultural risks to farmers and other stakeholders in the agrifood system
• A holistic approach to risk management in agriculture
• Managing risks to improve livelihood and food security

This course focuses on Sustainable Land Management (SLM) practices, and their place within the global development agenda, specifically in order to achieve target 15.3 of the Sustainable Development Goals (SDGs), which aims "to achieve a land degradation-neutral world". The course assists policy makers, practitioners and land users in the selection, planning, implementation and monitoring of SLM interventions, and related enabling environment.

You will learn about

• Basic definitions and principles of land management, degradation and restoration
• The participatory SLM land use planning approach
• SLM tools and methods (WOCAT global network, the Economics of Land Degradation (ELD) initiative and their 6+1 approach)
• The use of financial and non-financial incentives for an enabling environment, to overcome barriers and encourage SLM adoption and uptake
• SLM in the context of the global policy agenda, specifically SDG goal 15
• The concept of Land Degradation Neutrality (LDN)

This course describes how the information derived from remote sensing is obtained and best used for crop monitoring in a food security context. It outlines what the exact meanings of the products are and shows how their early warning and food availability information contents can be combined efficiently with other sources (e.g. households surveys, market analyses, nutritional surveys, etc.).

This course has been developed as part of the EU-FAO Programme on "Improved Global Governance for Hunger Reduction" and the technical contents have been provided by the Joint Research Centre of the European Commission.

You will learn about

• Data sources and products used for remote sensing analysis of crop monitoring
• Calculation and evaluation of anomalies derived from rainfall and Normalized Difference Vegetation Index (NDVI)
• Production of seasonal graphs for rainfall and NDVI
• Phenological stages in the over-time analysis of crop conditions
• Creating, maintaining and archiving remote sensing images
• Most common software for processing remote sensing data