Understanding the climate effects on rice production using BigData

1. Understanding the climate effects on
rice production using BigData
Daniel Jiménez, Sylvain Delerce, Hugo Dorado,
Camila Rebolledo, Edgar Torres
Big Data
www.ciat.cgiar.org
Agricultura Eco-Eficiente para Reducir la Pobreza

2. Context
•Within the framework “Convenio MADR-CIAT” climate change
project
•As part of the adaptation strategy – SSA
+
+
Climate
Soil
Crop management
=
productivity/ha
•Crop sector (FEDEAROZ) holds a lot of information on climate and
productivity
•Empirical hypothesis of FEDEARROZ needed to be proven

3. Objectives
To:
•Evaluate multivariate modeling techniques (parametric
and non-parametric) to determine their suitability as
tools for modeling the response of rice to variation in
climate
•Provide de crop sector with scientific evidence of the
effect of climate on rice productivity
•Identify the combination of factors that lead to high
productivities

4. Methods
•Regressions (Linear & Non-linear)
Obs
Climate
Yield/
Plot
1
X1
X2
X3
X4…Xn
Y1
2
X1
X2
X3
X4…Xn
Y2
3
X1
X2
X3
X4…Xn
Ordinary least squares - linear
Y3
Yield/Plot= temp (b1) + rainfall (b2) …+ (B)
•ANNs – Non-linear
4
Y4
…..
500
X1
X2
X3
X4…Xn
Yn

5. Hypotesis
Yield variation in Saldaña (research station) is associated with climate
Plot
Sowing
Harvest
time
a cropping
event in rice = about 120 days
Climate series for all variables

6. Crop sector (FEDEARROZ) -> Sharing information and
obtaining new insights
Multivariate analysis for Saldaña (research station ): cropping events (2010 to 2012),
variety FEDEARROZ 733
% de varianza explicada
Fedearroz 733
12.00
N = 98
10.43
10.00
8.00
6.20
6.00
6.03
4.78
4.00
3.76
3.74
1.92
2.00
0.00
Non- lineal
Variables profiles
Ener_accu
Tmax
9000
9000
7500
Rendimiento
Rendimiento
7500
6000
4500
3000
6000
4500
3000
1500
1500
0
34
35
36
37
Tmax
38
39
0
52000 53000 54000 55000 56000 57000 58000
Ener_accut

7. Crop sector (FEDEARROZ) -> Sharing information and
obtaining new insights
Multivariate analysis for Saldaña (research station ): cropping events (2010 to
2012), con por variedad
Climate (%)
+ Soil (%) + Crop management (%) =
% de varianza explicada
Fedearroz 733
12.00
FEDEARROZ 733, 37% of productivity
variation explained
10.43
10.00
8.00
6.20
6.03
6.00
4.78
4.00
10.00
8.00
3.76
3.74
1.92
2.00
0.00
Lagunas
% de varianza explicada
N = 98
productivity/plot
N = 112
8.05
6.57
6.00
4.00
2.00
0.00
3.53
1.26
1.03
0.94
0.50
Lagunas, 22% of productivity
variation explained

8. Rice
Analysis based on phenological stages in Saldaña : multidiciplinary work!
Siembra
VEG
FLOR
FLOR
Ini Pan
Ini Pan
Cosecha
VEG
Cómo aumentar la predicción?
Variedad 1
Variedad 2
Vegetative stage
Panicle initiation
Flowering
Grain filling

9. Crop sector (FEDEARROZ) -> Sharing information -> CIAT working
together -> obtaining new insights!!!
Analysis based on phenological stages in Saldaña (research station) (FEDEARROZ) - N= 329
(cropping events)
20
17.76
Variable profile (Eneraccu_llen – Radiation)
Varianza explicada
15
We explained more than 40
% of productivity variation
of rice
10
6.03
5
3.06 2.74
2.56
1.87 1.56 1.51 1.46
1.38 1.31
0.85 0.69 0.53 0.53 0.50
0
• The crop sector can suggest to farmers the best date for planting
• By assessing the same approach in other stations (enviroments) – New insights for
future breeding
• Adaptation strategy for climate change

10. Conclusions and perspectives
•The analytical tools used demonstrated that variation of
rice productivity in Saldaña can be associated with
climate
•Optimization of the crop system- Site-specific conditions
(germplasm, environment, crop management)
•As long as the information is available it can be applied in
any other region