Defining the Crop and Climate Modelling Team at CIAT
Coffee with: Patricia Moreno
Patricia Moreno works as Scientific Support with the Crop and Climate Modelling team at CIAT. She has used this ‘coffee with’ to define the team, to describe the models they work with, the opportunities she sees, the challenges they face and the collaboration of the team with other teams in CIAT.
It is hard to define the Crop and Climate Modelling team at CIAT in one sentence. I can give it a try though: our work is basically about how to generate impact. While doing our research, we work with a wide variety of models and touch upon many themes in CIAT. Therefore you could say that we are a very cross-cutting group.
If we talk about climate modelling, for seasonal forecasting, the main tool we use is CPT (Climate Predictability Tool) at IRI. If we are modelling a long term impact, we use all the available data from the models and scenarios in accordance with IPCC. Our group also works on different methodologies of climate data analysis, where we develop scripts and tools. For this, we use tools such as Rclimtool and the Analogues tool.
Related with crop modelling, we use both deterministic and statistical models with different complexity levels. For the deterministic approach we work with DSSAT (Decision Support System for Agrotechnology Transfer), APSIM (The Agricultural Production Systems sIMulator) and GLAM (General Large-Area Model for annual crops). Furthermore we work with simpler niche models such as EcoCrop. For our statistical approaches, we work jointly with the group working on Big Data (Daniel Jimenez and his team), and do we use models such as MaxEnt.
While doing our research, we work mostly with CIAT crops: beans, rice, cassava and forages. Furthermore we work on additional crops such as coffee and maize, with some general suitability analysis for more than 25 crops. Finally do we work on the development and improvement of models on cassava and forages.
Another aspect of our research is the evaluation of different technologies and their impact. One example of these technologies is the use of different varieties, and through the modifications of parameters, the creation of virtual crops and ideotypes. For all of those parameters modifications it is important to take in account the physiological knowledge of the crop and the constant help from the breeders. Finally, we evaluate other management practices and environmental conditions answering the ‘what if’ questions. With this, our big challenge exists in the evaluation of the performance of models principally developed in temperate climate, including tropical varieties, and finding the right set of parameters.
There are various final users of our products; most of them are related to instititutions on decision and policy making.
It is not easy to define the members of our group, because as a crosscutting team there are some people working directly with other specific themes, where they use climate and crop modelling approaches. Recently, Sharon Gourdji came to be the coordinator of our interdisciplinary team. The members of this team are Carlos Navarro, David Arango, Lizeth Llanos, Camilo Barrios, Jefferson Rodriguez, Patricia Alvarez, Mayra Toro, Diego Obando, Diana Giraldo, Julián Ramirez, Louis Parker and Ulrike Rippke.
Other people working in climate and crop modelling are Jesus Martinez, Beatriz Rodriguez, Oriana Ovalle, Stephania Carmona, Antonio Pantoja and Juan ‘el Paisa’. Despite the fact that they work with different teams, we still learn a lot from each other. Finally, we work closely with the Crop Wild Relatives team, eventhough they work mostly with MaxEnt to evaluate the distribution of crop wild relatives.
Most of our connections with other teams in DAPA and CIAT are because our outputs serve as inputs for the other teams. For example, Bernardo Creamer will use our information for the Global Futures project to be able to measure the economic impact on a global level.
Our great challenge now is to work with the Knowledge Management team of CIAT, and especially with Impact Pathways. I would love to see that there is a better way to make sure that our information actually will create direct benefits to the farmer: that our models and forecasts get translated into something useful for the daily life of the farmer.