Big Data for Climate Smart Agriculture

Enhancing & Sustaining Rice Systems for Latin America and the World

Introduction

Rice is the most important food crop in the world providing more energy to humanity than any other food

source. Latin America and the Caribbean (LAC) is a net importer of rice mainly because LAC farmers lack

adequate knowledge and current information to adapt their cropping systems to increasingly variable climate.

Recent climate change analytical studies by the WBG revealed that LAC could benefit from a more suitable

future climate for rice. New approaches are now urgently required to provide farmers with updated, relevant,

and near real time climate and cropping system information to support them in rice cropping decision making

and to enhance their resilience to climate variability and eventual climate change.

Figure1: Rice as a supplier of energy in the human diet and Global Rice Yields over time.

Problem and Opportunity

Climate change is not only about long term temperature and rainfall trends. It has already altered the patterns

of climate variables, especially in terms of variability, that were reasonably well understood and predicted by

farmers and communities worldwide. For example, in Colombia rainfall and daily maximum and minimum

temperature patterns, have changed in each region and the climate is less and less predictable.

Agriculture is greatly influenced by climate. Any perturbation of the climate directly affects production if

farmers are not able to adapt their cropping system in time. In Colombia, national average rice yields have

dropped from 6 to 5 t/ha in less than five years without changes in crop location (soil types) or in management

(which is actually improving). Traditionally, farmers across the world have used calendar references to make

decisions on when and what to sow. Nowadays, due to the increased climate variability, however, this

traditional knowledge is increasingly ineffective and farmers lack information to make appropriate decisions in

a new fast moving environment.

The International Center for Tropical Research (CIAT) in Cali, Colombia has recently tested an innovative ‘Big

Data’ approach to create a dynamic Decision Support System for rice farmers in Colombia that involves:

(i) A two way information system based on a web platform and an android app to support field data

capture,

(ii) An artificial neural network (ANN),

(iii) Clustering of data techniques for rapid analyses of harvest monitoring data

(iv) A coupled platform for accessing climate information at daily intervals, and

(v) A cloud-hosted, relational (SQL) database.

How the Big Data Concept was Operationalized for Rice in Colombia

Nowadays smartphones, cloud-computing and other ICTs make it possible to capture, analyze and share large

amount of information very quickly. In Colombia,

a) FEDEARROZ have been collecting information on commercial harvests for almost 20 years in the main

rice producing areas of Colombia, mainly for economic-studies and productivity monitoring purposes.

On the other hand, IDEAM (Colombian national meteorology institute) have also been capturing

climate data through a nationwide network of weather stations.

b) We combined those two databases, relating each individual harvest event to its corresponding ~120

days climate sequence between sowing and harvest for five main climate variables: minimum

temperature, maximum temperature, precipitation, relative humidity and solar radiation at daily

intervals.

c) We analyzed the data using machine learning techniques such as Artificial Neural Networks (ANNs),

Random Forest, Clustering so as to unravel underlying correlations patterns between climate factors

and yield variability that could help us identifying the combination of factor that result in high yields.

d) The analysis of the commercial data coupled with weather data generated valuable insights for rice

producers. The identification of the main limiting factors in each region allows the farmers to

understand why they got high or low yields in past years. The characterization of

favorable/unfavorable climate sequences and the match with seasonal forecasts allows learning from

past experiences to anticipate what is coming and to give advice to farmers on what variety should

work better, and when is the optimum sowing date.

e) The tool has the potential to compensate the loss of traditional calendar-based cropping system

interventions by providing the farmers with relevant data-driven information for decision making on

what, when, where to sow and how to manage the crop.

In pilot tests with Colombian rice farmers in two provinces, the methodology generated valuable insights

about the local rice cropping system by using available commercial data at two locations in Colombia. To the

surprise of local farmers, the new data system generated advice against planting rice in that particular season

due to projected adverse climate impacts. Farmers that followed the advice saved seed, labor, and fertilizer

and water inputs. Farmers that did not heed the advice, harvested nothing and lost all their inputs! Based on

this pilot outcome, the CIAT team and local rice association partner (FEDEARROZ) were awarded a UN Big Data

award. [http://www.theguardian.com/global-development/2014/sep/30/colombia-rice-growers-climate-change]

Figure2: Transforming Latin America from a Rice Importing to a Rice Exporting Region

Scaling Out and Up the Pilot Big Data for Rice Approach Developed in Colombia

The next step is to include soil and crop management factors and to scale out and up, the Colombian Big Data

pilot system for rice farmers in other countries in Latin America. We propose to target the big rice producers

Argentina, Brazil, and Uruguay via the establishment of an integrated partnership with the major regional

‘end-user’ association that has extensive networks with rice producers and the associated supply chains

throughout Latin America - the Fund for irrigated rice in Latin America (FLAR). For more than 19 years, FLAR

has gathered rice production and processing related data from all main rice producing countries in LAC and

their respective supporting organizations.

Next Steps: To take the approach to a new country or a new crop, requires the following main steps:

Involve from the very beginning the community of targeted end users of the tool in the design

and conception. It provides valuable feedback and facilitates the final adoption.

Undertake an initial diagnostic of available data and needs for complementary data capture on

climate, soil management and yields.

Set up an analysis team able to handle the data and analyze it to generate relevant information

for crop optimization.

Carry out a pilot case-study to demonstrate the value of the approach.

Accompany the dissemination of the method to all potential users

Expected outcomes:

Foster a data capture culture in agriculture, using ICTs

Adoption of the approach in routine work of farmers and rural advisory services to move

towards data-driven agronomy and climate smart agriculture.

Expected impacts:

Reduce yield losses due to uninformed decision making,

Increased adaptive capacity for agriculture to climate change

More efficient crops (bridge the yield gap) for more food with less land and inputs

Revolutionize agricultural advisory and extension models by transitioning to a robust, near real

time data-driven agronomy, and more site-specific recommendations.

Adapt the system to other crops (e.g. maize, beans, and horticultural crops).

Explore pilots for African rice and fruit growers (West and East Africa)

Contacts:

CIAT Team (Cali, Colombia): Andy Jarvis (a.jarvis@cgiar.org) and Daniel Jimenez, Sylvain Delerce,

Armando Muñoz, Hugo Dorado, Juan Felipe Rodriguez, Victor Hugo Patiño.

World Bank Team (Washington, DC & Brazil): Erick Fernandes (efernandes@worldbank.org) and

Renato Nardello, Holger Kray, Diego Arias Carballo