Periodic Reporting for period 1 - CLIM4CROP (Climate monitoring and seasonal forecast for global crop production)
Reporting period: 2018-09-09 to 2020-09-08
The overall scientific goal of the project entitled “Climate monitoring and seasonal forecast for global crop production” (CLIM4CROP project H2020-MSCA-IF-2016-740073) was to explore how best to exploit seasonal forecasts for crop management decision making on a global scale making use of the latest advances in climate and crop sciences.
With this project, we have improved the knowledge on the limits in global datasets of climate observations relevant to agriculture and developed innovative statistical models to analyse the climate-driven impact on crops. In addition, the implementation of the project along with the advanced training gained at BSC have allowed the MSCA fellow Dr. Marco Turco to reach one of the most important milestones in his career: i.e. scientific independence. Indeed, the project PREDFIRE proposed by the fellow has been granted from the Spanish Ministry of Science, Innovation and Universities, that allows the fellow to become an autonomous scientist. The MSCA fellowship has undoubtedly impacted in attaining this job position.
The links between crops and heat/water stress drivers have been analysed to better understand their interactions and develop statistical models. These models will be used to explore the seasonal predictability for crop yields. We have used historical yield data of the four principal crops worldwide (maize, rice, soybean and wheat) from the Global Dataset of Historical Yield (Iizumi 2017), a recent developed gridded dataset never used before for this kind of study. Importantly, we established a collaboration with the developer of this dataset, Dr. Iizumi, an internationally recognized agro-meteorologist.
The historical data on crop and potential climate predictors have been used to calibrate parsimonious regression models, providing a computationally inexpensive alternative to process-based models that usually require as input several variables at very fine scale where seasonal forecasts are not yet skilful. The fellow was trained by staff of the JRC in the development of these models, follow and extend the models developed by Zampieri et al. (2017, 2018, 2019a) and Ceglar et al. (2018).
The novelties of the empirical model developed relies mainly in (i) taking into account the potential effect of antecedent (i.e. within the growing season) climate conditions on crop yield and (ii) calibrate the model to achieve out-of-sample crop predictions from the knowledge of the predictor data outside the period used to train the model, adopting a leave-one-out cross-validation method. That is, the calibration of the crop-climate models and their evaluation are done by using cross-validation in order to evaluate the predictions as if they were done operationally. Specifically, merging observational information (for the months previous to the harvest months) with seasonal forecasts for the rest of the crop growth calendar (e.g. anthesis and harvesting stages) is a special feature of our approach that can contributes to increase crop predictability, making the most of the best information available to the users. This is especially useful over areas where the performance of the dynamical forecast systems is still affected by significant errors (e.g. Europe). Preliminary results suggest that the climate-crop lagged relationships provide a substantial contribution to the development of a seasonal forecast system allowing more efficient crop management.
CLIM4CROP was planned in such a way as to offer a unique opportunity to reinforce and expand the multi-sectorial experience of the fellow, linking his background in meteorology and climate change studies and the theoretical and practical requirements for climate prediction applications in agriculture. Hence, this MSCA has also addressed the training objectives described in the proposal on seasonal prediction, where the expert guidance of the supervisor at BSC (F.J. Doblas-Reyes) has been essential for the successful implementation of the work plan. Also, the training offered by the collaborators of the JRC on crop yield impacts has allowed to learning about agriculture production and food security, and to widen his competences in the statistical aspects regarding climate-impact models. Also, scientific challenges in the context of CLIM4CROP, such as highly cooperative research, have ensured the two-way transfer of derived new knowledge strengthening the skills of all participants.