AMPLIFY: Agricultural Model for Production Loss Identification and Failures of Yields

AMPLIFY is a suite of crop models used to estimate or forecast yields and to derive adaptation options.

What does the model do?

The tool makes use of statistical and process based models using historical weather and yield data as well as remote sensing information to remotely assess crop yields and yield losses. The tool crucially differentiates between climate-related and non-weather-related (agronomic management, socio-economic) yield perils. By combining re-analysed weather and remote sensing data, AMPLIFY innovatively assesses yield losses on scales ranging from fields up to national level. The model scheme is shown below: while the core model uses weather data in a regression model to estimate crop yields, extensions in input data, model types and outputs are continuously added.

AMPLIFY is frequently used and maintained by the Working Group Adaptation in Agricultural Systems at PIK, contributing yield forecasts to several scientific and policy-relevant projects.

Click here for a short video explaining AMPLIFY

What are the next steps in the development of the model architecture?

We will expand the combined process-based and statistical crop modeling approach with the help of remote sensing data to assess yield losses at field and local level. Our modeling approach capture influences on yield variability directly attributable to weather as well as controls for yield influences of agronomic management, socio-economic and indirect weather-triggered impacts (e.g. plant health). As further information, the approach should use vegetation indexes of remote sensing data (e.g. Leaf Area Index – LAI, Normalized Difference Vegetation Index – NDVI). For instance, the LAI data will be used to supplement statistical model assessments to quantify influences of weather triggered plant health effects and control for further agronomic management effects. Remote sensing data has a worldwide and constant coverage and is already used for precision farming and crop management in the agricultural sector. In the recent years, the quality, resolution and availability of remote sensing images and processing tools has increased substantially (in particular with the launched Sentinel data of the EU Copernicus program). New satellite sensors provide high spatial, temporal and spectral resolution of optical data. Despite the promising developments in such technologies, this data are so far not used in crop model approaches although the improved data quality would allow integrating this information in the crop models. The incorporation of remote sensing data should increase the crop models’ accuracy, spatial resolution, and availability of field level yield assessments immediately after the occurrence of a crop failure. As far as these assessments meet these specific requirements, these yield assessments are of high interest for insurances companies.

In what way is the model different from other models in the community?

In comparison to other models, AMPLIFY uses weather and agronomic management information as well as earth observation remote sensing satellite data and is based on statistical and process-based approaches. This makes the model unique because it combines the advantages of the different approaches which are used for crop yield assessments.

Fig. 1: AMPLIFY Model

Key Publications

  • Conradt, T. Gornott, C. Wechsung, F. 2016, Extending and improving regionalized winter wheat and silage maize yield regression models for Germany: Enhancing the predictive skill by panel denition through cluster analysis, Agric. For. Meteorol. (216) 68 – 81. [Link]

  • Gornott, C., Wechsung, F. 2015: Level normalized modeling approach of yield volatility for winter wheat and silage maize on different scales within Germany, Journal für Kulturpflanzen (67) 6, 205–223. [Link]

  • Gornott, C., Wechsung, F. 2016: Statistical regression models for assessing climate impacts on crop yields: A validation study for winter wheat and silage maize in Germany, Agricultural and Forest Meteorology (217) 89–100. [Link]

  • Schauberger, B., Gornott, C., Wechsung, F. 2017: Global evaluation of a semiempirical model for yield anomalies and application to within-season yield forecasting, Global Change Biology (23)11, 4750–4764. [Link]