Post on 03-Sep-2020
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