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Statistics from Space: Next-Generation Agricultural Production Information for Enhanced Monitoring of Food Security in Mozambique. Component 4.

This document discusses using satellite data and crop modeling to forecast crop yields in Mozambique. It summarizes previous studies conducted in the US, Argentina, and Brazil to test a remote sensing crop growth and simulation model (RS-CGSM) for predicting corn and soybean yields. For Mozambique, additional data is needed on crop cultivars, management practices, planting and harvest seasons. It also describes using earth observation data and machine learning models to forecast crop yields and conditions across many countries as part of the GEOGLAM program, though this is currently only implemented in South Africa for Africa. Finally, it mentions a production efficiency model for estimating yield from satellite estimates of gross primary production.

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STATISTICS FROM SPACE
Next-Generation Agricultural Production
Information for Enhanced Monitoring of
Food Security in Mozambique
Component #4. Crop Analytics for Forecasting Yields
Led by Seoul National University
Seasonal crop yield forecast
• Forecast before harvest
System Design
run control simulations
Predict crop yield
Data processing flow
주요 연구 결과
5
1) Case study in USA
▪ The RS-CGSM system based on satellite imagery for 2022 in the U.S. was
used to predict corn and soybean yields using bias correction. (DOY201, 209)
<Comparison between observed and estimated yield in Corn belt>
Maize Soybean
Forecast
observation
Forecast
observation
<Brazil>
S
o
y
M
a
i
z
e
<Argentina>
S
o
y
M
a
i
z
e
2) RS-CGSM tests at Argentina, Brazil
▪ Improvement for crop management data is needed
주요 연구 결과
3) For Mozambique
▪ Cultivar and crop management data are needed
• Cropping season
• Planting : Sep - Oct
• Harvest : Feb - Mar
• No fertilization
• Major crop cultivar?
• Rainfed
Crop Yield and Condition
Forecasting from Field
to Global Scales
• Using EO datasets (NDVI, Temperature,
Precipitation, Evaporative Stress Index,
Soil Moisture, Growing Degree Days),
GEOGLAM crop calendars, GEOGLAM
crop masks to create dashboards of
crop conditions across a range of crops
and countries.
• These EO datasets are used to create
machine learning models of crop yields
that are used for in-season yield and
condition forecasting.
• Currently used in 26 countries globally
(only in South Africa in Africa)
Optimizing a remote sensing
production efficiency model for
macro-scale GPP and yield
estimation in agroecosystems
• Uses Production Efficiency Models
(PEMs) to estimate Gross Primary
Production (GPP) of crops as a function
of the fraction of photosynthetically
active radiation absorbed by the
canopy, which is derived from satellite
remote sensing data.
• GPP can be summed over the growing
season and adjusted by a crop-specific
harvest index to estimate yield.
• Tested using the field-observed data in
the United States.

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Statistics from Space: Next-Generation Agricultural Production Information for Enhanced Monitoring of Food Security in Mozambique. Component 4.

  • 1. STATISTICS FROM SPACE Next-Generation Agricultural Production Information for Enhanced Monitoring of Food Security in Mozambique Component #4. Crop Analytics for Forecasting Yields Led by Seoul National University
  • 2. Seasonal crop yield forecast • Forecast before harvest
  • 3. System Design run control simulations Predict crop yield
  • 5. 주요 연구 결과 5 1) Case study in USA ▪ The RS-CGSM system based on satellite imagery for 2022 in the U.S. was used to predict corn and soybean yields using bias correction. (DOY201, 209) <Comparison between observed and estimated yield in Corn belt> Maize Soybean Forecast observation Forecast observation
  • 6. <Brazil> S o y M a i z e <Argentina> S o y M a i z e 2) RS-CGSM tests at Argentina, Brazil ▪ Improvement for crop management data is needed
  • 7. 주요 연구 결과 3) For Mozambique ▪ Cultivar and crop management data are needed • Cropping season • Planting : Sep - Oct • Harvest : Feb - Mar • No fertilization • Major crop cultivar? • Rainfed
  • 8. Crop Yield and Condition Forecasting from Field to Global Scales • Using EO datasets (NDVI, Temperature, Precipitation, Evaporative Stress Index, Soil Moisture, Growing Degree Days), GEOGLAM crop calendars, GEOGLAM crop masks to create dashboards of crop conditions across a range of crops and countries. • These EO datasets are used to create machine learning models of crop yields that are used for in-season yield and condition forecasting. • Currently used in 26 countries globally (only in South Africa in Africa)
  • 9. Optimizing a remote sensing production efficiency model for macro-scale GPP and yield estimation in agroecosystems • Uses Production Efficiency Models (PEMs) to estimate Gross Primary Production (GPP) of crops as a function of the fraction of photosynthetically active radiation absorbed by the canopy, which is derived from satellite remote sensing data. • GPP can be summed over the growing season and adjusted by a crop-specific harvest index to estimate yield. • Tested using the field-observed data in the United States.