With his dissertation, Ponraj has supported climate risk management applications such as climate risk transfer and adaptation measures. In the first section of his cumulative thesis he provides near real-time, high-resolution, and in-season crop monitoring using bio-physical crop modeling for application in crop insurance decisions, particularly the Indian governmental PMFBY scheme. The second sections relies on machine-learning models for remote sensing data with Gradient Boosting Algorithm to provide further higher high-resolution crop yield outcomes. His analysis shows that the GBR is feasible across India and may complement current approaches for timely rice yield estimation required by insurance companies and government agencies. In the final part of his thesis he simulated sorghum yields using bio-physical modeling in Burkina Faso under current and projected climatic conditions and evaluated four adaptation strategies.
The thesis encompasses three publications:
- Arumugam P., Chemura, A., Schauberger, B., Gornott, C., 2020, Near Real-Time Biophysical Rice (Oryza sativa L.) Yield Estimation to Support Crop Insurance Implementation in India, Agronomy, Volume 10
- 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, Volume 13
- Arumugam P., Chemura, A., Aschenbrenner, P., Schauberger, B., Gornott, C, 2023, Climate change impacts and adaptation strategies: An assessment on sorghum for Burkina Faso, European Journal of Agronomy, Volume 143
Ponraj is currently working as a climate change adaptation specialist at Wageningen Environmental Research, Wageningen University and Research, The Netherlands. He is contributing to various projects by applying process-based models, machine learning, and logistic models to evaluate climate change impacts, adaptation measures, and nature-based solutions.
