Forecasting of Cereal Yields in a Semi-arid Area Using the Simple Algorithm for Yield Estimation (SAFY)...

Area of Science:

• Agricultural Science
• Remote Sensing
• Agrometeorology

Background:

• Semi-arid regions face significant challenges with frequent droughts, necessitating reliable grain yield forecasting for import planning.
• Accurate monitoring of crop canopy and production capacity, particularly for cereals, remains a complex task in these environments.

Purpose of the Study:

• To develop and validate an operational grain yield forecasting system for semi-arid areas.
• To enhance crop monitoring by combining an agro-meteorological model with satellite data.

Main Methods:

• A novel approach integrating the Simple Algorithm for Yield estimation (SAFY) model with optical SPOT/High Visible Resolution (HRV) satellite data was employed.
• Grain yield was statistically estimated as a function of Leaf Area Index (LAI) during peak growth, with LAI retrieved from SAFY and calibrated using SPOT/HRV data.
• The methodology was validated and calibrated using a two-year dataset from central Tunisia, encompassing over 60 fields and 20 satellite images.

Main Results:

• The study successfully calibrated and validated a new yield estimation technique.
• An inversion technique was applied to estimate the overall yield for the entire study site.

Conclusions:

• The combined SAFY model and SPOT/HRV satellite data approach offers a robust method for operational grain yield forecasting in semi-arid regions.
• This integrated system can aid decision-makers in managing agricultural resources and planning imports effectively, especially under drought conditions.