Since predicting the outcome of legal cases in the Supreme Court of the United States to the marketing of precision by tracking the habits of individual retailers and make purchase suggestions based on recognized patterns of preferences; the allocation of police resources / security through the ability to predict where and when crimes / surveys is likely to take place, the prediction of disease and pest outbreaks, to unmask prejudices of voters’ and predict election result – ask the team1 Obama Campaign – big data has been and is being applied to a wide range of human endeavors.
So when scientists team large data CIAT decided to put many years of data of rice production – combined with climate / weather data from those years – under the scrutiny of big data, they understood exactly what they had to do and what results they wanted to achieve. Production statistics of Colombian rice farmers have been on a downward trend since 2007 and from the beginning, the team recognized that the identification of previously unseen patterns in existing data could be the key to solving the puzzle .
The team also realized the enormity of the task they had made for themselves. On one hand, most of the data needed to carry out its task was not immediately available; would have to retrieve data from various agencies. Given that open access and sharing of data is still in its infancy in many places, some trust-building process would take, along with a good dose of patience to allay the genuine fears of the owners of the raw data and look their understanding of what the project aims to achieve.
After many months of hard work and crunching data, tenacity and patience paid off; hitherto hidden patterns began to appear. What’s Near Espinal rice cultivation in the country, the results revealed that the average minimum temperature during the ripening stage as the most important in terms of performance climate factor.
New observations, the team realized that the rice variety Cimarron Barinas mostly planted in this region is highly sensitive to low temperature and ripening stage shows a negative relationship with increasing minimum temperature – at the stage of maturation – showing an interesting break around 22⁰C. Therefore, it is not suitable for hot regions.
In the region of Saldaña, we found that the level of solar energy stored in plants irrigated rice to have a positive relationship with – and the most important impact on – performance. Although this connection has been known for rice scientists for decades, the novelty of the team’s results in this area is the discovery of the specific stage in which the impact of solar radiation on the performance is more important – ie during the maturation stage of the rice plants.
Exploration of existing data even with large data analysis, the team found many other interesting details; clustering of climatic sequences to reveal the most adapted varieties to identify the distribution of performance of each variety in the different groups. The team also built on this basis to predict the likelihood of events for the production period 06-08, 2014 in Villavicencio region and make recommendations on the best varieties for adaptation.
The import of these results is multiple and can be profound. For one, it will help farmers to make decisions that favor their adaptation to climate variability, eg adjusting the planting date to the maturation stage to coincide with the time of maximum accumulation of solar energy Saldana or adjusting the planting date to avoid the high temperatures during ripening in Espinal.
In addition, agricultural research will accelerate in terms of time spent and money spent, providing valuable information for breeders in the actual behavior of plant materials in the field. Similarly, it will help identify the best tickets to a breeding program of new cultivars. In general, significantly cut down the number of years required to obtain new improved varieties and facilitate better adaptation to climate change Colombian rice.
Already, farmers are beginning to reap the benefits of this research. In the North Coast (Caribbean) areas of rice cultivation, about 170 farmers growing rice 1800Ha including irrigation were able to avoid economic losses from following recommendations based on seasonal prediction generated by this analysis principles big data. Compared to their peers, who either did not have the information or followed in the conventional manner.
To this affect immediate project and its many possibilities of adaptation to climate change and the impact of performance for rice producers Colombia, the project has recently won the UN Climate Challenge Big Data Pulse. After a one-month assessment, the Commission and the Technical Advisory Board of the competition selected the CIAT team as one of two winners based on the unique innovation of “project that uses large volumes of data to drive climate action all around the world. ”
And indeed, in the future, the CIAT team intends to integrate land management variables and their model in order to increase their explanation and pattern-recognition powers and extend its use to scientists and farmers in other countries. Currently, he is working on the expansion of the initiative, from 2015, using a similar approach with rice farmers in Peru and Nicaragua through a partnership with the Latin American Fund for Irrigated Rice (FLAR).
But, meanwhile, already creaking and reduce mountains many years of flow data refreshing and increasingly useful data, the results will make it more adaptable to climate variability and Colombian farmers more competitive the country enters into agreements free trade – with the USA and other countries – in an increasingly globalized world with its liberalized markets.