Using seasonal forecasts to predict rice

yield for Nepal’s Terai

Prakash Jha1, Panos Athanasiadis2, Silvio Gualdi2, Vakhtang Shelia3, and Gerrit Hoogenboom3

1University of Venice CA’Foscari, Venice, Italy 2Centro Euro-Mediterraneo Sui Cambiamenti Climatici (CMCC), Bologna, Italy

3University of Florida, Gainesville, Florida, USA

16th EMS Annual Meeting & 11th European Conference on Applied Climatology (ECAC)12-16 September 2016

Trieste, Italy1

Objective, Method and DataTo assess the potential application of dynamical seasonal forecasts (SFs)

into dynamic crop models for predicting variation in rice yield

associated with local climate variability (inter-annual and intra-

seasonal) and for optimizing crop management.

SFs from CFS v2 (Saha et al., 2010) seasonal prediction system (SPS)

Crop model CERES-RICE of the DSSAT v4.6 (Hoogenboom et al.,

2015)

• DSSAT v4.6 simulates growth, yield, soil water and Nitrogen

balance on a daily basis using time-varying input data on soil,

weather, management and cultivar

• Minimum weather data requirement for DSSAT v4.6

• Daily precipitation, temperature (maximum and

minimum) and total incoming surface solar radiation for

a point

Crop yield prediction is important for food security related planning

By optimizing management farmers can get maximum output from

investment during the favorable weather years.

2

Seasonal prediction system (SPS)

• CFSv2 hindcasts are available from NCEP T126 (~100 km)

• CFSv2 hindcasts consist of a set of 9 months hindcasts from 1982-2010

• 24 ensemble members based on initial conditions

• 1 and 2-months lead time forecasts from July to Dec. are used

We are using NCEP CFSv2 SPS because of its good performance at the

South Asian region (Pokhrel et al.,2013)

Skill of SPS assessed against APHRODITE (Yatagai et al. 2012), GPCP

precipitation data (Adler et al., 2003) and ERA Interim (Dee et al., 2011)

3

Model evaluation (mean bias)

Tmax (CFS-ERA) Tmin (CFS-ERA) Srad (CFS-ERA)

JJAS 1983-2010 0C and MJ/m2/day4

Daily climatology for one grid point

15

35

1 31 61 91 121 151 181 211

0 C

100 106 112118 200 206212 218 300306 312 318400 406 412418 500 506512 518 600

June Dec

5

25

1 31 61 91 121 151 181 211

0C

100 106 112118 200 206212 218 300306 312 318400 406 412418 500 506512 518 600

June Dec

5

15

25

1 31 61 91 121 151 181 211M

J/m

2

100 106 112 118200 206 212 218300 306 312 318400 406 412 418500 506 512 518600 606 612 618ens NASA_POWER ERA_ssrd

June Dec

0.0

5.0

10.0

15.0

20.0

25.0

1 31 61 91 121 151 181 211

mm

Days100 106 112 118 200

206 212 218 300 306

312 318 400 406 412

418 500 506 512 518

600 606 612 618 ens

APHRO Station NASA

June Dec

Precip

Tmx

TminSrad

5

SPS predictive skill (ACC)

All JJAS 1983-2010. Correlation is between CFS/APHRO for precip and between CFS and ERA for all other variables

Precip Tmax

Tmin Srad

6

DSSAT-CERES-RICE model evaluation

1000

1500

2000

2500

3000

3500

4000

19831985198719891991199319951997199920012003200520072009

Yiel

d (

kg/h

a)

Year

Obs vs Simulated yield using weather station

Dist yield_JKP Sim_JKP Dist yield_BHW

Sim_BHW Dist yield_PAR Sim_PAR

1000

1500

2000

2500

3000

3500

4000

19

83

19

84

19

85

19

86

19

87

19

88

19

89

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

20

05

20

06

20

07

Yiel

d (

kg/h

a)

Obs vs simulated yield using ERA-Interim/APHRO

Dist yield_JKP sim_JKP Dist yield_BHW

sim_BHW Dist yield_PAR sim_PAR

JKP BHW PARW

Weather

station

-0.1 0 -0.3

Reanalysis -0.2 0 -0.2

Poor skill in simulating

district yield

Model simulation only for

one point and one

management information

Correl. between district and simulated yield

7

Yield simulations using Forecasts

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

yield_sim_by_obs_weather_vs_N_limited_CFS_yield

yield_sim_by_obs_weather_vs_N_unlimited_CFS_yield

dist_stat_yield_vs_N_limited_CFS_yield

dist_stat_yield_vs_N_unlimited_CFS_yield

Forecasts consists of all

1-month-lead from June-Dec

for 1983-2010

Forecast-only weather data

don’t have good skill

Therefore we used mixed weather and took

yield simulated using observed weather as

the reference yield for comparison

1. Correlation between district yield and simulated yield using CFS v2

2. Correlation between yield simulated using weather station data and

CFS v2 forecasts

8

Merging forecasts with observation

28

123

45678

910111213141516171819

20212223

242526

27

CFSv2 forecasts (1 or 2 months)

Obs. for 28 years

24 members

Weather station data(Until the prediction date)

Initialization ${date}*{UTC}

We have 24*28=672 different weather time series per year from every point of prediction

We are using real weather data till the forecast date, followed by 1 and 2

month lead forecasts and climatology for the remaining periods

Forecasts

MaturityJuly Sep DecAug

1 month2 monthsObserved Historical climate

Forecasts

Aug

1 month2 months

Observed Historical climate

Sep DecOct

1 month2 months

Observed

Sep DecOct

climate

Nov

planting

9

Bias in prediction combining forecasts and obs.

0

20

40

60

80

July 1st Aug 1st Sept 1st Oct 1st Nov 1st Dec 1st

%

Predicted from

Mean % Error (1-month lead forecasts)

1983 1984 1986 1987 1988 1889 1990

1991 1992 1993 1994 1995 1996 1997

1998 1999 2000 2001 2002 2003 2004

2005 2006 2007 2008 2009 2010

The sudden surge in 1992 ‘Oct 1st’ is related to the under-estimation of rain in

forecasts.

Errors in 2010/2009 ‘Aug/Sept 1st’ are related to the excessive dry years and cannot

be captured.

In 2007 less yield in obs. due to extreme rain, not captured in forecasts

0.0

20.0

40.0

60.0

80.0

July 1st Aug 1st Sept 1st Oct 1st Nov 1st

%

Mean % Error (2-month lead forecasts)

1983 1984 1986 1987 1988 1989 1990

1991 1992 1993 1994 1995 1996 1997

1998 1999 2000 2001 2002 2003 2004

2005 2006 2007 2008 2009 2010

10

Correlation of simulated yield using 1-month lead forecasts and obs. combined vs. only weather obs.

-0.2

0

0.2

0.4

0.6

0.8

1

Climatology July_1st Aug_1st Sept__1st Oct_1st Nov_1st Dec_1st

Predicted from

100 106 112 118 200 206 212 218 300 306 312 318

400 406 412 418 500 506 512 518 600 606 612 618

‘r’ is less when 2-month lead forecasts are used

11

CFS v2’s SFs daily data do not have skill to predict yield before the

actual planting starts.

Therefore, we used ENSO categories of weather for optimizing

management.

12

IOD-monsoonENSO-monsoon

Can models predict ENSO-Indian monsoon relation?

‘r’ = 0.8 for the CFS v2 simulated Nino 3.4 index seasonal (JJAS) anomaly and observed

data for the same.

‘r’=0.7 for the CMCC v1.513

ENSO-phases climatology (one-station in Nepal’s Terai)

0

100

200

300

400

500

600

700

800

June July Aug Sep Oct Nov Dec

mm

Rainfall

El Nino La Nina Neutral

14

Yields depend on planting dates, N fertilizer and ENSO phases

15

N leaching

16

Key findings

The hindcasts simulation with the CSM-CERES-Rice model shows that

yield can be predicted with a high degree of certainty a few months before

harvest using forecasts combined with climatology.

Therefore this approach can be useful in predicting yield operationally.

The applicability of this study is limited mainly by the quality of the

seasonal forecasts, lack of management information and interpolated soil

data.

By using ENSO phases forecasts, a net gross margin of US $96/ha can be

achieved for the increase in N fertilizer application to 90 kg/ha and by

planting on 14 June in El Nino years compared with the similar changes in

other years. 17