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Understanding the climate effects on rice production using BigData
 

Understanding the climate effects on rice production using BigData

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Understanding the climate effects on rice production using BigData

Understanding the climate effects on rice production using BigData

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    Understanding the climate effects on rice production using BigData Understanding the climate effects on rice production using BigData Presentation Transcript

    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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