Aerosol and agriculture

1. Impact of Climate and Aerosols on wheat crop in India
Presented by:
Dr. Geetika Sonkar
Presentation on
Presentation for Asst. Prof. at Ram Manohar Lohia Awadh University, Awadh Dated: 28/03/2022

2. Background of the study .
 Agriculture is the major sector for the country’s economy though it faces many challenges
in an era of growing population and changing climate.
 It is projected that the world population will reach to 8.6 billion in 2030 and 9.8 billion in
2050 that will add maximally to India’s expected population to increase up to 1.5 billion
by 2030 and 1.7 billion in 2050 (UN, 2017).
 Over, the past decades, rapid urbanization and economic development in India, has led to
an increase in air pollution, which pose serious environmental problems. However, the
effects on crop production have not been systematically studied.
 Increasing air pollution in the region is significantly altering radiative processes and
thereby potentially affecting the crops photosynthesis and yield.
 Aerosols are the collection of airborne solid or liquid particles. These may
influence climate directly through scattering and absorbing radiation, and indirectly by
acting as cloud condensation nuclei or modifying the optical properties and lifetime of
clouds.
 The attenuation of solar radiation by atmospheric aerosols simultaneously decreases the
amount of radiation and increase the fraction of radiation which is diffuse.
 Decrease in solar radiation will result in lower photosynthetic rates and ultimately affect
the crop production.
 Very limited studies have been reported on to quantify the impact of aerosols on crop
production. Therefore, the present study is an attempt to quantify the influence of weather
and aerosols on wheat production over wheat growing zones of India
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3/27/2022
Presentation for Assistant Professor at Ram
Manohar Lohia Awadh University, Awadh

3. Study Area
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Fig. 1. Description of wheat crop growing zones across India. (a) population density of India, (b) area under production, (c) wheat production
and (d) wheat yield. Note. All data were averaged over 1986e2015. Population data is the curtsey of Indian census (MHA, 2011).
White space indicates no/limited data.
3/27/2022
Presentation for Assistant Professor at Ram
Manohar Lohia Awadh University, Awadh

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Fig. 2. Spatial distribution of (a) temperature maximum (Tmax), (b) temperature minimum (Tmin), (c) solar radiation (Srad) and (d) aerosol
optical depth (AOD) in the growing season across the wheat cropping zones. Note. Spatial comparison of Tmax, Tmin and Srad were averaged
over 30 years (1986e2015, all inclusive) and 15 years for MODIS-AOD (2001-2015, all inclusive) only for the wheat growing season
•An overall increase in Tmax varying from 0.002 to 0.029 C year1 was noted, highest over the southern region (PZ: 0.029 C
year1) followed by northern part (NHZ: 0.023 C year1).
•An overall increase in Tmin for all the cropping zones was also noted varying from 0.016 to 0.029 C year1, except for the
PZ (0.008 C year1).
•A gradual increase in Srad varying from 0.013 to 0.027MJm2 day1 over all the wheat growing zones was noted except over
NHZ (0.047 MJm2 day1) and NEPZ (0.028 MJm2 day1)
3/27/2022
Presentation for Assistant Professor at Ram
Manohar Lohia Awadh University, Awadh

5. Approach .
Dynamic Modeling
Approach
CERES-Wheat
Calibration and
Validation
Calculation of
Direct and Diffuse
radiation using
NCAR TUV
radiation Model
Total PAR and Diffuse
fraction from TUV model
Change in crop yield
Crop Model Run
Statistical Modeling
Approach
Multicollinearity Test
“Variance Inflation Factor”
Autocorrelation test
“Durbin-Watson statistics”
Linear Regression Modelling
“GLM”
Influence of aerosol
on crop
Partial Derivation of weather
variables
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3/27/2022
Presentation for Assistant Professor at Ram
Manohar Lohia Awadh University, Awadh

6. Approach 1
Statistical Modeling
Checking
multicollinearity
between climate
variables
 Assessed the multicollinearity using Pearson’s correlation test and variance inflation factor
 The multicollinearity is considered when correlation is =>7 and vif >5(among two variables only 1 will
be considered as predictor for regression
 The test was performed using “Performance Analytics” and “fmsb”package in R version 3.4.3
Autocorrelation
within climate
variables
 Serial or Autocorrelation was checked in the residuals of regression analysis using Durbin
Watson test (D-W). This test was done in “Car” package in R version 3.4.3.
 The default lag time used was 1-time unit. The Durbin Watson statistics varies from 0 to 4, where,
2 refers no autocorrelation, 0 to <2 positive autocorrelation and >2 to 4 negative auto
correlation.
 To remove the autocorrelation we used “cochrane.orcutt” test for variable transformation”
Linear
Regression
Modelling
 Linear regression of logged yield against climate variables.
 As the yield did not follow Gaussian distribution (normal distribution), therefore, the
log – transformation was done to make it normally distributed.
Log (Ydt) = cd + β1Tmaxdt + β2Tmindt + β3Sraddt + β4Raindt+ γt + udt
Impact of
aerosol on crop
yield
 performed single predictor regression where AOD is taken as
independent predictor and maximum & minimum temperature
and solar radiation as a dependent variable.
Yt = cd + βAODdt + γt + udt
 Using partial derivatives from two regression equation estimated
effect of AOD on crop yield
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3/27/2022
Presentation for Assistant Professor at Ram
Manohar Lohia Awadh University, Awadh

7. Table 2: Influence of aerosol loading on Wheat Yield
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Parameters NHZ NEPZ NWPZ CPZ PZ All India
Tmax 3.03 -0.07 1.65*** 1.06 -0.19 0.69
Tmin 0.47 0.39** -0.56 0.16 0.34 -0.73
Srad 3.08** -0.56 1.18 -0.15 -1.23* -0.10
No. of Observations 57885 141525 108990 129536 53010 33456
District fixed effects Yes Yes Yes Yes Yes Yes
Linear Time trend Yes Yes Yes Yes Yes Yes
dlogy/dAOD -0.23 -0.03 -0.03 -0.07 -0.04 -0.01
(Significance level: * 0.05, **0.01, *** 0.001)
Overall, the influence of aerosol loading on wheat yield was negative for all India (1%) and varied within a range of
23% (over NHZ) to 3% (over NEPZ, NWPZ). The spatial influence of aerosols varied considerably as with
corresponding increase in aerosols.
Overall, the study brings out the conclusive evidence of negative impact of rising temperature on wheat yield
across India, which we found spatially inconsistent and highly uncertain when integrated with the compounding
effect of aerosols loading.
3/27/2022
Presentation for Assistant Professor at Ram
Manohar Lohia Awadh University, Awadh

8. Latitude
Longitude
AOD & COD
Surface Elevation
Altitude
Wavelength
Zenith Angle Surface Reflectivity Pressure at Surface
Total Ozone
Column
Single Scattering
Albedo
NCAR –TUV
Radiation Model
Direct & Diffuse Radiation
Assumptions:
AOD = 0.01, 0.05, 0.1, 0.5, 1.0, 1.5, 2.0
COD = 1 (Clear Sky Scenario); COD = 5 (Overcast Sky Scenario)
2 Stream Mode
DSSAT
CERES-Wheat Crop Model
•Calibration
•Validation
Daily Weather Data:
•Total PAR,
•Diffuse Fraction
•Rainfall
•Temp Max & Min
Crop Yield
under various assumptions
Approach 2
Dynamic Modeling
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3/27/2022
Presentation for Assistant Professor at Ram
Manohar Lohia Awadh University, Awadh

9. DSSAT CROP MODEL
(CERES-Wheat)
Input parameters
Crop Management Data
Cultivar Data
Daily Weather Data
Soil Data
Output
 Grain Yield, Anthesis, LAI,
Physiological Maturity, Straw Yield, Harvest
Index, Product wt, Final leaf number, Final
shoot number, Canopy (tops) wt, Vegetative
wt, Assimilate wt, N uptake, Product N, &
Others….
 Soil-Plant- Atmosphere, Soil Evaporation
 Soil Inorganic Nitrogen daily output
 Evapotranspiration
Calibration & Validation
(Genetic Coefficient Estimation
using GENCALC)
Wheat Variety: HUW-234 & HUW-468
Methodology .
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10. Evaluation of CERES Wheat Crop Model
The calibration and validation result showed that CERES-Wheat model is able to simulate the
attributes for the crop cultivar with good accuracy.
 DSSAT CERES-Wheat was calibrated and validated for wheat cultivars for the IGP region indicated
that model satisfactorily simulate the yield and other attributes of observed crop data.
 This can be adopted for prediction of crop growth, phenology, water management, potential and
attainable yields under different climatic conditions over the region.
 This evaluated model is used for the further analysis of the present study
Fig 3: Evaluation of CERES –Wheat model performance using different crop parameters
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11. Table 2: Genetic coefficient derived for experimental wheat cultivars used in CERES-Wheat
crop model
VAR# VAR-NAME ECO# P1V P1D P5 G1 G2 G3 PHINT
IN0016 HUW-234 DSWH04 20 65 750 25 42 1.5 99
IN0017 HUW-468 DSWH04 25 70 780 22 40 1 95
VAR# Identification code or number for a specific cultivar
VAR-NAME Name of cultivar
ECO# Ecotype code for this cultivar points to the ecotype in the ECO file.
P1V It represents the vernalization coefficient.
P1D It represents the photoperiod response of cultivar as photoperiod coefficient.
P5 Time period in GDD (0C) during grain filling (3-4 days after flowering) stage.
G1 Potential kernel number coefficient as estimated from the number of kernel per unit canopy weight (less lead
at anthesis.
G2 Single grain weight (mg) under optimum growing conditions.
G3 Tillering coefficient under ideal conditions i.e. weight of mature tiller including grain (g dwt) under non-
stressed conditions.
PHINT It represents the time interval calculated as growing degree days (GDD) between successive appearances of
leaf tip.
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12. 12
Key Findings
• Effects of climate variables and aerosol loading on wheat yield were analyzed with
reasonable confidence that effect of rising temperature on wheat yield was negative for India,
which has been spatially inconsistent and varied slightly among different temperature
matrices.
• Overall, for India, we found 7% decrease in wheat yield with 1 C rise in Tmean, projected
almost equally for marginal impact of Tmax (4%) and Tmin (3%). However, such impact
varied considerably for different crop growing zones.
• Likewise, 1 C rise in Tmean and Tmax was most detrimental over peninsular zone and the
central plain zone. While loss of wheat yield for 1 C rise in Tmin was most prominent over
north-eastern plain zone. This certainly indicate that the influence of change in
temperature is not consistent and necessitates regional adaptation practices.
• Although the research concludes the possibility of considerable reduction in wheat yield in
lieu of changing climate across India, the ‘food basket of India’ Indo-Gangetic plain reveals
better adapted to changing climate and exhibit comparatively minimum yield loss compared
to other crop growing zones.
3/27/2022
Presentation for Assistant Professor at Ram
Manohar Lohia Awadh University, Awadh

13. 13
Thank You
3/27/2022
Presentation for Assistant Professor at Ram
Manohar Lohia Awadh University, Awadh