Abstract
Agriculture monitoring, and in particular food security, requires near real-time information on crop growing conditions for early detection of possible production deficits. In this work, we propose the use of Gaussian processes (GPs). together with in-situ, EO and ERA-Interim climate reanalysis data for crop yield forecasting. Country-level agricultural survey data from FAOSTAT are used for quantitative assessment. The study is conducted in the framework of the ASAP (Anomaly hot Spots of Agricultural Production) early warning decision support system of the European Commission, which aims at providing timely information about possible crop production anomalies worldwide. After grouping countries with similar growing season periods, we We show that GP models allow predicting the yield of the different planted crops within such groups with coefficient of determination R2 ranging from 0.5 to 0.95. For each country and crop, a better fit that the mean of the data is obtained in all cases, and the errors obtained are comparable to the ones obtained for the group. The proposed modelling framework can be potentially adopted in ASAP operations to forecast national level crop yield and production.
Abstract from the paper’s OpenAlex record, shown for indexing and research. Copyright remains with the authors or the publisher. Read at the source ↗
The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.
Claims 4
Claim · model-written summary
Gaussian process models predicted crop yield with R2 from 0.5 to 0.95.
Verbatim
e0“GP models allow predicting the yield of the different planted crops within such groups with coefficient of determination R2 ranging from 0.5 to 0.95.”
Claim · model-written summary
Each country and crop model fit exceeded the data mean.
Verbatim
e0“For each country and crop, a better fit that the mean of the data is obtained in all cases”
Claim · model-written summary
Country and crop forecasting errors were comparable to group-level errors.
Not reportedTaxon
Verbatim
e0“the errors obtained are comparable to the ones obtained for the group.”
Claim · model-written summary
The authors propose adopting the framework for national crop yield and production forecasts.
Verbatim
e0“The proposed modelling framework can be potentially adopted in ASAP operations to forecast national level crop yield and production.”