Agriculture

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

Phase 2: B-EPICC (2022-2023)- Achievements

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

In Ethiopia, agricultural sustainability faces challenges in the changing climate, particularly for rain-fed systems. B-EPICC examined the combined impacts of climate change and soil acidity on future crop potential using the statistical crop suitability model EcoCrop. This model demonstrated a direct relationship between the lowering of soil pH and increasing crop losses. Precautionary measures to avoid soil acidification should be a key element in the design of climate change adaptation strategies. Details in:

• Jimma, T. B., Chemura, A., Spillane, C.,Demissie, T., Abera, W., Ture, K., Terefe, T., Solomon, D.,  and Gleixner, S. (2024). Coupled Impacts of Soil Acidification and Climate Change on Future Crop Suitability in Ethiopia" Sustainability 16, no. 4: 1468. https://doi.org/10.3390/su16041468

In this project phase, B-EPICC also went a step beyond the yield information and assessed 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. A climate risk profile for East Africa was published providing an overview of projected climate parameters and related impacts on different sectors including yield projections and Agro-ecological zones in Eastern Africa until 2080 under different climate change scenarios. Details in:

• Binder, L., Gleixner, S., Gornott, C., Lange, S. Šedová B., Tomalka, J. (2023). Climate Risk Profile: Eastern Africa. Potsdam Institute for Climate Impact Research (PIK) and Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH.

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 Sensing, 13, 2379. https://doi.org/10.3390/rs13122379

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
stephanie.gleixner[at]pik-potsdam.de

Scientific advisors

Dr. Frank Wechsung 
Agriculture 
frank.wechsung[at]pik-potsdam.de