Assessing long-term cultivation risks and short-medium term yield prospects

Phase 2: B-EPICC (2022-2023)

In the second phase of the project, the agricultural assessments of EPICC will be extended to the newly added target country Ethiopia. In collaboration with national actors and experts, B-EPICC will adapt to meet the local needs and provide quantitative information and weather and climate related risks to crop yields.

In this project phase, B-EPICC will also go a step beyond the yield information and assess farmer’s vulnerability to climate change and weather extremes. By identifying particularly vulnerable groups or regions B-EPICC supports more tailored and prioritized adaptation planning.

Phase 1: EPICC (2018-2021) - Achievements

In the project’s first phase existing agricultural information systems were supplemented by crop risk assessments and crop yield forecasts. In close collaboration with local, national and international experts and other stakeholders, the needs within the three target countries Tanzania, India and Peru were identified and addressed.

In Tanzania, the year-to-year variability of crop yield is strong and dominated by weather impacts. EPICC quantified this impact of weather on yields in Tanzania on sub-national level using the statistical crop-model AMPLIFY. This newly developed model can provide a forecast of maize yield about 6 weeks before harvest based on publicly available, global climate data only. Details in:

  • Laudien, R.Schauberger, B., Makowski, D. and Gornott, C. (2020). Robustly forecasting maize yields in Tanzania based on climatic predictors. Scientific Reports, 10(1). DOI: 10.1038/s41598-020-76315-8
  • Volk, J., Gornott, C., Sieber, S., Lana, M. (2021): Can Tanzania’s adaptation measures prevent future maize yield decline? A simulation study from Singida Region, Regional Environmental Change. DOI: 10.1007/s10113-021-01812-z

In India, the current government-backed PMFBY (Pradhan Mantri Fasal Bima Yojana) insurance scheme requires real-time information on yield losses in high spatial resolution and with high accuracy in order to trigger crop insurance payouts. In order to support these efforts, EPICC employed the process-based crop model DSSAT to provide rice yield and soil real-time information at 5km resolution based on weather and management information. A different approach employed machine learning techniques to provide real-time rice yield information at 500m resolution based on remote sensing data. Both efforts complement each other and can be used for yield loss assessments in India’s crop insurance scheme. Details in:

  • Arumugam, P., Chemura, A., Schauberger, B. and Gornott, C. (2020). Near Real-Time Biophysical Rice (Oryza sativa L.) Yield Estimation to Support Crop Insurance Implementation in India. Agronomy, 10(11), p.1674. DOI: 10.3390/agronomy10111674
  • Arumugam, P.Chemura, A.Schauberger, B.Gornott, C. (2021) Remote Sensing Based Yield Estimation of Rice (Oryza Sativa L.) Using Gradient Boosted Regression in India. Remote Sensing13, 2379.

In Peru, EPICC provides information on weather-related risk for maize yield production in Peru based on statistical crop-models and machine learning approaches to support the development of Peru’s Nationally Determined Contributions (NDCs) and the implementation of suggested adaptation measures. While much of yield variability can be explained by weather indicators on a regional scale, this work highlights the spatial inhomogeneity of weather impact on yield. This in turn has implications on adaptation options like increasing the water availability, which has regionally different effects under future climate conditions. Details in:        

  • Laudien, R.Schauberger, B.Gleixner, S., Gornott, C. (2020). Assessment of weather-yield relations of starchy maize at different scales in Peru to support the NDC implementation. Agricultural and Forest Meteorology, 295, 108154. DOI: 10.1016/j.agrformet.2020.108154

Contact person

Dr Stephanie Gleixner
Agricultural systems

Scientific advisors

Prof. Dr. Christoph Gornott 
Crop Insurances, Agricultural Modelling 

Dr. Bernhard Schauberger
Agricultural Modelling using a Statistical Model

Dr. Abel Chemura
Agricultural Modelling

Prof. Dr. Hermann Lotze-Campen
Agriculture, Land and Water Use

Dr. Frank Wechsung