climate yield estimation

8/12/2019 Climate Yield Estimation

1/19

Climate Yield Estimation

Charumathi Raja, Intern, SSD, IRRI


2/19

Outline

Inspiration and RationaleDataProposed ModelEmpirical Methodology: Regression MethodsInitial Results: Nepal, ThailandLimitationsSteps Forward


3/19

InspirationPredicting the Quality and Prices of Bordeaux Wines Simple quantitative model to explain the factors influencing winevintage quality

Aims to predict the quality and prices of wines using data that isavailable during the growing season

Method: Exploits variations in weather and quality of grapes as anatural experiment

1 2 _

3 _ 4 _ _

5 _ _

ln _ _ int Avei growing seasonit

Ave Ave growing season pre growing season

Avend growing season

it p Age of V age Temp

Rainfall Rainfall

Rainfall

6 _ _ e Ave

end growing s ea son i t Temp e


4/19

RationaleInevitable trade-off between accuracy,cost and timingWith a relatively simple, yet sufficientlycomprehensive model

Can consider a broad spatialdistribution on climates effects onyieldReduced costs, longer lead timespossible with climate-based yieldestimation to complement existingapproaches of rice yield predictions

Implications of impending weatherconditions better analyzed andacted on

Initial stages yet could be used for avariety of purposes

Map 7: Relationship between SST and RainfallFirst Linear Combination

Map 1: Rainfall Climatic Groups SSA

Source : Trends in Rainfall and Economic Growth in Africa: ANeglected Cause of the Growth Tragedy (Cobos et al, 2008)
http://www.google.com.ph/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&docid=n5-KGvWmzRssOM&tbnid=uAN_vEWAPUm-dM:&ved=0CAUQjRw&url=http://stats.stackexchange.com/questions/16489/what-is-the-statistical-justification-of-interpolation&ei=4FuZU-6PNcSulQX0jICQBw&psig=AFQjCNGVSccgdB8LwFxTeg8xrtoI0t9VjA&ust=1402645741800776


5/19

Data

Yield data Area, production, yield data across a large number ofyears, over a range of countriesYield data on an annual-, season-, ecosystem(irrigated/rain-fed) basis

Climate dataMonthly weather variables that can be uniquely matchedto administrative units in the yield data sets

Crop CalendarsRegion-specific growing seasons, number of seasonsplantedMajor activities in each month i.e peak harvest, peakplanting


6/19

Proposed Model

Stages inCropCalendar

Peak Planting Mid_Season . Mid_Season Mid_Season= Ripening PeakHarvest


7/19

Proposed Model

CropCal

PeakPlanting

Mid_Season . Mid_Season Mid_Season= Ripening Peak Harvest

Weather in Vegetative and Reproductive Stage Weather in Ripening Stage

Annual / SeasonalYield


8/19

Creation of Growth-Stage Specific WeatherVariables

Months ofVegetative Stage

Month of RipeningStage

id year month cld dtr frs pet pre tmp

2104 1950 1 399.5722 176.2032 2926.995 13.2567 223.5241 -38.56682104 1950 2 434.1123 169.4599 2637.861 17.3422 336.9305 -41.22462104 1950 3 398.6952 165.9144 2784.765 25.0267 611.0161 -6.54012104 1950 4 284.1604 158.9144 2299.813 34.369 147.1176 47.20322104 1950 5 566.8021 149.631 1687.139 37.5989 811.9733 87.51872104 1950 6 707 128.6043 887.0054 36.0856 2400.123 106.8075

ISO NAME_0 NAME_1 HASC Y_ALL_TOT_1970 Y_ALL_TOT_1971PHL Philippine Abra PH.AB 1.11 1.27PHL Philippine Agusan d PH.AN 1.04 1.09PHL Philippine Agusan d PH.AS 1.23PHL Philippine Aklan PH.AK 0.94 1PHL Philippine Albay PH.AL 1.57 1.82PHL Philippine Antique PH.AQ 1.64 1.25

ISO COUNTRY REGION SUB_REGION HASC ID Jan Feb Mar Apr May Jun Jul Aug S Oct Nov DecPHL Philippines ARMM Basilan PH.BS 1969 PeakP s s PeakH PHL Philippines ARMM Lanao del Sur PH.LS 2000 PeakP s s s PeakH PHL Philippines ARMM Maguindanao PH.MG 2030 PeakP s s PeakH PHL Philippines ARMM Sulu PH.SU 1956 PeakP s s PeakH PHL Philippines ARMM Tawi-Tawi PH.TT 2028 PeakP s s PeakH PHL Philippines CAR Abra PH.AB 1957 PeakP s s s PeakH

Geography Season 1 dates


9/19

Empirical Methodology

Panel data analysis with fixed effectsEmpirical specification:

0 1 , 2 ,

3 , 4

ln ln _ ) ln _ )

ln _ ) ln _

Ave Avevegetative it reproductive it it

Avevegetative it

p Min Temperature Min Temperature

Solar Radiation Solar

,

5 , 6 ,

)

ln _ inf ) ln _ inf )

Avereproductive it

Ave Avevegetative it reproductive it

i

Radiation

Total Ra all Total Ra all

e

it


10/19

Initial Results: Nepal

Variables (1) Tmin (2): Add Radiation (3) Add Rainfall (4): Random Effects

Minimum Temperature (Veg - Season 1) -0.216*** 0.576* 0.534* 0.138(0.12) (0.11) (0.11) (0.11)

Minimum Temperature (Ripe- Season 1) 1.205* 0.786* 0.817* 0.338*(0.12) (0.13) (0.14) (0.12)

Solar Radiation (Veg - Season 1) -0.434* - 0.466* -0.621*(0.05) (0.06) (0.05)

Solar Radiation (Ripe- Season 1) 0.155* 0.196* 0.174*(0.05) (0.05) (0.05)

Total Rainfall (Veg- Season 1) - 0.021 -0.045*(0.02) (0.02)

Total Rainfall (Ripe - Season 1) 0.027** 0.029**(0.01) (0.01)

R-sqr 0.040 0.138 0.140Number of Observations 3066 2117 2117 2117Number of Regions 73 73 73 73

*** p


11/19

Variables (3) Add Rainfall (5) Add S-2 vars (6) Add Harvest Vars (7): Only regions with Season 1

Minimum Temperature (Veg - Season 1) 0.534* 3.447* 0.347* 0.208(0.11) (0.51) (0.12) (0.13)

Minimum Temperature (Ripe- Season 1) 0.817* 1.176* 0.739* 0.752*(0.14) (0.37) (0.14) (0.15)

Solar Radiation (Veg - Season 1) - 0.466* 0.076 - 0.440* -0.503*(0.06) (0.15) (0.06) (0.07)

Solar Radiation (Ripe- Season 1) 0.196* 0.005 0.201* 0.202***(0.05) (0.12) (0.05) (0.06)

Total Rainfall (Veg- Season 1) - 0.021 0.059* - 0.024 - 0.035***(0.02) (0.03) (0.02) (0.02)

Total Rainfall (Ripe - Season 1) 0.027** 0.025 0.027** 0.028***(0.01) (0.03) (0.01) (0.01)

Minimum Temperature (Veg - Season 2) 0.084 (0.07)

Minimum Temperature (Ripe - Season 2) 0.063

(0.10) Solar Radiation (Veg - Season 2) - 0.289**

(0.12)Solar Radiation (Ripe - Season 2) - 0.561*

(0.17)Total Rainfall (Veg - Season 2) 0.011**

(0.01)Total Rainfall (Ripe - Season 2) - 0.035*

(0.01)Minimum Temperature (Harvest - Season 1) 0.287* 0.231*(0.07) (0.07)

Solar Radiation (Harvest - Season 1) - 0.111*** - 0.045(0.06) (0.07)

Total Rainfall (Harvest - Season 1) - 0.009** - 0.004(0.00) (0.01)

R-sqr 0.140 0.432 0.150 0.148Number of Observations 2117 300 2114 1797Number of Regions 73 12 73 61*** p


12/19

Initial Results: Thailand (Season 1 Dry)Variables (1) Tmin (2): Add Radiation (3) Add Rainfall

Minimum Temperature (Veg - Season 1) 1.909*** 1.078*** 1.165***(0.29) (0.27) (0.27)

Minimum Temperature (Ripe- Season 1) 1.515*** 1.839*** 1.756***(0.24) (0.22) (0.22)

Solar Radiation (Veg - Season 1) - 1.128*** - 1.060***(0.06) (0.06)

Solar Radiation (Ripe- Season 1) 0.065 0.016(0.05) (0.06)

Total Rainfall (Veg- Season 1) 0.050***(0.02)

Total Rainfall (Ripe - Season 1) - 0.012*(0.01)

R-sqr 0.096 0.247 0.253Number of Observations 1791 1777 1776

Number of Regions 76 76 76


13/19

Initial Results: Thailand (Season 2 Wet)Variables (1) Tmin (2): Add Radiation (3) Add Rainfall

Minimum Temperature (Veg - Season 1) 0.434 0.161 0.178(0.37) (0.37) (0.37)

Minimum Temperature (Ripe- Season 1) 0.517* 0.571*** 0.537**(0.31) (0.31) (0.31)

Solar Radiation (Veg - Season 1) -0.410* -0.380*(0.08) (0.09)

Solar Radiation (Ripe- Season 1) -0.050 -0.110(0.07) (0.08)

Total Rainfall (Veg- Season 1) 0.018(0.02)

Total Rainfall (Ripe - Season 1) -0.017***(0.01)

R-sqr 0.006 0.022 0.025Number of Observations 1542 1542 1542Number of Regions 75 75 75


14/19

Variables (3) Add Rainfall (4): Random Effects (6) Add Harvest VarsMinimum Temperature (Veg - Season 1) 1.165* 0.986* 1.050*

(0.27) (0.26) (0.28)Minimum Temperature (Ripe- Season 1) 1.756* 1.245* 1.546*

(0.22) (0.22) (0.23)Solar Radiation (Veg - Season 1) - 1.060* -1.050* -1.026*

(0.06) (0.06) (0.06)Solar Radiation (Ripe- Season 1) 0.016 -0.027 -0.006

(0.06) (0.06) (0.06)Total Rainfall (Veg- Season 1) 0.050* 0.061* 0.052*

(0.02) (0.02) (0.02)Total Rainfall (Ripe - Season 1) - 0.012*** -0.018* -0.007

(0.01) (0.01) (0.01)Minimum Temperature (Harvest - Season 1) 0.378*

(0.11)

Solar Radiation (Harvest - Season 1) -0.024(0.06)

Total Rainfall (Harvest - Season 1) -0.008**(0.00)

R-sqr 0.253 0.258Number of Observations 1776 1776 1770Number of Regions 76 76 76

Initial Results: Thailand (Season 1 Dry)


15/19

Variables (3) Add Rainfall (4): Random Effects (6) Add Harvest VarsMinimum Temperature (Veg - Season 1) 0.178 0.660*** 0.065

(0.37) (0.34) (0.39)Minimum Temperature (Ripe- Season 1) 0.537** -0.042 0.702**

(0.31) (0.28) (0.33)Solar Radiation (Veg - Season 1) -0.380* -0.347* -0.393*

(0.09) (0.09) (0.09)Solar Radiation (Ripe- Season 1) -0.110 -0.207* -0.083

(0.08) (0.08) (0.09)Total Rainfall (Veg- Season 1) 0.018 0.035 0.016

(0.02) (0.02) (0.02)Total Rainfall (Ripe - Season 1) -0.017*** -0.022** -0.015

(0.01) (0.01) (0.01)Minimum Temperature (Harvest - Season 1) -0.163

(0.16)

Solar Radiation (Harvest - Season 1) -0.030(0.09)

Total Rainfall (Harvest - Season 1) -0.005(0.01)

R-sqr 0.025 0.027Number of Observations 1542 1542 1536Number of Regions 75 75 75

Initial Results: Thailand (Season 2 Wet)


16/19

Limitations so far

Non-linearity in the effect of climatic conditions onyieldInability to control for different varieties, andshort/medium/long growing seasons explicitly

Large amount of heterogeneity in models suited todifferent countries, regionsDifferent climate variables - important for differentgrowth stages of the plant


17/19

Steps Forward

Evolution of the model as more case studies aredoneTest the Predictive Strength of the modelCompare models results to Crop Models estimates

Model with Probabilities i.e Relative quality ofseasonControl for extreme weather conditions ( e.g drought,flooding)

*** Establishing a balance betweencomprehensiveness and over-parameterisation


18/19

Why Its Really More Fun in The Philippines


19/19

Thank you!

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