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Feed the Future MEL Webinar Series: Feed the Future’s Agrometeorological Context Indicators. Interpreting Agriculture-Related Outcomes through the Lens of Rainfall, Vegetation Greenness, and Temperature. 30 May 2019. 2018-19 Feed the Future MEL Webinar Series.
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Feed the Future MEL Webinar Series:Feed the Future’sAgrometeorologicalContext Indicators Interpreting Agriculture-Related Outcomes through the Lens of Rainfall, Vegetation Greenness, and Temperature 30 May 2019
2018-19 Feed the Future MEL Webinar Series • Introduction to the MEL System (link) • Standard Indicator Overview (link) • New Indicators: Application of improved practices & technologies (link) • New Indicators: Sales, finance & investment (link) • Annual FTFMS user’s webinar (link) • Nutrition, sanitation & hygiene indicators (link) • New Indicators: Gender (link) • Reporting on policy change (link) • New Indicators: Yield Revealed! (link) • New Indicators: Agrometeorological context indicators
Webinar objective #1 • Introduce new agrometeorological context indicators that will allow us to interpret agriculture-related outcomes through the lens of: Rainfall Greenness Temperature
Webinar objective #2 and #3 • Discuss how the new indicators will be calculated • Discuss how these indicators can be used to interpret agricultural measures • yield • other measures (e.g., levels of input use)
New Context Indicators Rainfall: FTF Context-12: Average Standard Precipitation Index score during the main growing seasons (p203-4 of the Feed the Future Indicator Handbook) Greenness: FTF Context-13: Average deviation from 10-year average NDVI during the main growing season (p205-7 of the Feed the Future Indicator Handbook) Temperature: FTF Context-14: Total number of heat stress days above 30°C during the main growing season (p208-09 of the Feed the Future Indicator Handbook)
Rationale for adding these indicators • In places where rainfed agriculture is predominant, the weather is a key factor in determining yields (as well as some practices, like how much area to plant) • Without having a sense of weather-related variables from year to year, it is not possible to interpret trends and performance in yields • Did el Niño negatively impact farmers’ production in the Zone of Influence this year? Or were our programs slow to get started? • Was there a bumper crop for everyone this year because of perfect weather, or were our programs especially effective in the ZOI?
What each indicator tells us individually… Rainfall: Extremely Wet Very Wet Moderately Wet Near Normal Moderately Dry Severely Dry Extremely Dry Data Source: Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) datasets. CHIRPS is a 30+ year quasi-global rainfall dataset spanning 50°S-50°N (and all longitudes), starting in 1981 to near-present. Maize from the Upper West region of Ghana
What each indicator tells us individually… Greenness: Above Normal Normal Bellow Normal Data Source: NASAMODIS Maize from the Upper West region of Ghana
What each indicator tells us individually… Temperature: Data Source: Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), NASA’s atmospheric reanalysis for the satellite era using the Goddard Earth Observing System Model, Version 5 (GEOS-5) with its Atmospheric Data Assimilation System (ADAS), version 5.12.4 Maize from the Upper West region of Ghana
Maize …and taken together. • A wet start to the season followed by a moderately dry period during the main vegetative period. • Above average NDVI values during the season showing healthy crops. • Average number of heat stress days during the season. • Despite the reduced rainfall during the main vegetative period, extremely wet to moderately wet conditions earlier in the season combined with normal temperatures allowed for soil moisture to carry a healthy crop until harvest. • Overall the season was above average with final yields of the season above the 10-yr average.
How these indicators will be calculated • USAID/BFS and Feed the Future are building the capacity to use the remotely-sensed data that inform these indicators. • We are currently being supported in these efforts by the NASA Harvest Consortium as well as FFP/FEWS NET colleagues. • Implementing partners will not need to calculate these context indicators. • All of us need to learn how to interpret our performance indicators in the context of these indicators.
How to interpret trends in production in light of rainfall, greenness & temperature: Ghana Average Year Below Average Year Above Average Year Maize
How to interpret trends in production in light of rainfall, greenness & temperature: Ghana Below Average Year On the ground example of below average conditions:
How to interpret trends in production in light of rainfall, greenness & temperature: Ghana Average Year On the ground example of average conditions:
How to interpret trends in production in light of rainfall, greenness & temperature: Ghana Above Average Year On the ground example of above average conditions:
How to interpret trends in production in light of rainfall, greenness & temperature: Senegal Average Year Below Average Year Above Average Year Rice
How to interpret trends in production in light of rainfall, greenness & temperature: Senegal Below Average Year On the ground example of below average conditions:
How to interpret trends in production in light of rainfall, greenness & temperature: Senegal Average Year On the ground example of average conditions:
How to interpret trends in production in light of rainfall, greenness & temperature: Senegal Above Average Year On the ground example of above average conditions:
Maize An examination over time Maize in northern Ghana: • A comparison of the indicators over time along with the final yields from implementing partners.
What other trends might we better understand in light of rain, temp & NDVI? • If there is no rain, • farmers might not purchase seeds, fertilizer • planted area might be reduced • Currently in the process of assessing whether data are available that allow us to look at this • implementing partners may be able to assess their own data on input use and area under cultivation through the lens of rainfall, temperature and NDVI • if available data allow, we may organize a future webinar to go into more depth on this topic