Poster presented at CSA Global science conference in Montpellier (2015). Daniel Jimenez , Sylvain Delerce, Hugo Andres Dorado, Maria Camila Rebolledo , Gabriel Garces, Edgar Torres

1. Climate-smart, site-specific agriculture: reducing uncertainty on when,
where and how to grow rice in Colombia
Daniel Jimenéz1 , Sylvain Delerce1, Hugo Andrés Dorado1, Maria Camila Rebolledo1 , Gabriel Garcés2, Edgar Torres1
1 International Center for Tropical Agriculture (CIAT), Cali, Colombia www.ciat.cgiar.org . 2Colombian National Rice Growers Association (FEDEARROZ), Bogotá, Colombia www.fedearroz.com.co
Contacts: d.jimenez@cgiar.org or s.delerce@cgiar.org
We used Conditional Inference Forest (CIF) models as introduced in (Strobl, 2008) to assess the
climatic variables importance in each region-cultivar. The specific relationship between main factor
and output variable was characterized using partial dependence plots. We used the Dynamic Time
Wrapping (DTW) distance for the clustering of climate patterns as it allowed us to work directly on
weather series. All analysis were run under R environment 3.1.2, using party, caret, and dtw
packages.
Limitingfactors
Traditional calendar landmarks are still used to make climate-related decisions such as what crop to
grow, where and when. Such approach is being challenged by the increasing climate variability
(Timmerman, 1999) that makes climate less predictable. In the last five years, national average rice
yields have dropped in Colombia (from 6t/ha in irrigated rice before 2009 to 5t/ha today) and rice
growers have not managed to recover since then.
New approaches are required to provide growers associations and farmers with updated and relevant
information that can support the decision making process and make them more resilient to climate
variability.
Novel use of ICTs and the possibility of following the principles of Big-Data for capturing, analyzing
and sharing large amounts of information in agriculture offer an alternative to approaches based on
small scale studies.
Explained
variability
We merged three pre-existing commercial cropping
events databases from Fedearroz conserving the
following variables:
Cultivarresponseto
limitingfactors
Inside each cluster, cultivars performed differently. Under cluster 6 conditions, no significant
differences appear between cultivars: no signal in the data. Under cluster 5 conditions, cultivar
Lagunas significantly yield less than cultivars F60 or F473.
We combined the two information based on the
actual sowing and harvest dates of each record.
Nine indicators were calculated for each growth
stage. These were our input variables for
empirical modelling of yield variability.
We obtained original daily weather records from
IDEAM for 5 variables:
The variable exhibit a non-linear
negative relationship with a break
point around 22°C
The variable exhibit a positive relationship
indicating that frequent rains are required
to achieve higher yields.
Potential
users
of the results
Farmers
Efficient - Optimize
management to bridge
the yield gap
Resilient - get relevant
information on climate
patterns to make better
decisions on what crop to
grow, when and where
Extensionists,
Rural advisors
Modern – take
advantage of data-driven
approach to extract more
value form data, and
make site-specific
recommendations
Breeders
Specific - benefit from
direct feedback on the
actual behavior of
cultivars in commercial
field conditions to speed-
up breeding processes
and design site-specific
cultivars
Models performed differently depending on the cultivar. The R-squared commonly
interpreted as an indication of the fraction of explained variance varied from 4.5% for
Lagunas to 59.2% for Cimarron Barinas. On average, models explain between 30-40% of the
variance, which coincide with (Ray, 2015). Differences between cultivars give an idea of their
relative sensitivity to climate conditions. Main limiting factors also changed from one cultivar
to another giving a glimpse at the cultivar specific G*E interaction.
Cultivar F174 in Villavicencio
134 observations | R2= 28.8%
Cultivar Lagunas in Saldaña | 187 observations |R2= 4.5%
Climate patterns
exhibit different
potentials for rice
cropping.
Yield objective
may be adjust on
farms according
to the forecasted
pattern.
Favorable/unfavorable
climatepatterns
120 days daily weather for 5 variables
Locality Variety Cropping system
Sowing & harvest dates Yield
Maximum T°C Minimum T°C
Precipitation Relative humidity
Solar energy
Cultivar Cimarron Barinas in Espinal
180 observations | R2= 59.2%
Main limiting factor’s partial plot:
Average minimum temperature in
ripening stage
Main limiting factor’s partial plot:
Frequency of significant (>10mm)
rainfalls in vegetative stage
Yield
Yield
• The re-analysis of desegregated commercial crops data and its combination with daily weather records series
proved to be able to quickly generate useful insights for farmers, rural advisors and breeders:
o main climatic limiting factors were identified for each cultivar in different regions,
o The relationships of predictors with yield were characterized using partial plots
o Favorable and unfavorable climate patterns were detected and differences could be observed in cultivars
performances
• Results of the climate patterns clustering can be matched to seasonal forecasts, learning form past
experiences to anticipate near future.
• This study is the first step towards a complete data-driven decision support system for farmers in Latin
America that will also include soil and crop management factors.• Ray, D. K., Gerber, J. S., Macdonald, G. K., & West, P. C. (2015). Climate variation explains a third of
global crop yield variability. Nature Communications, 6, 1–9. doi:10.1038/ncomms6989
• Strobl, C., Boulesteix, A., Kneib, T., Augustin, T., & Zeileis, A. (2008). Conditional variable
importance for random forests. BMC Bioinformatics, 11, 1–11. doi:10.1186/1471-2105-9-307
• Timmermann, A., Oberhuber, J., Bacher, A., & Esch, M. (1999). Increased El Niño frequency in a
climate model forced by future greenhouse warming. Nature, 398(June 1982), 1996–1999.
The authors acknowledge the willingness of Fedearroz and IDEAM to share their data and the Colombian
Ministry of agriculture and CCAFS for funding this study.
Introduction
Results
Data
Methods
Conclusions
References
Acknowledgments