Linking satellite imagery and crop modeling for integrated assessment of climate

change impacts on chickpea yields in Southern India Dakshina Murthy Kadiyala, Murali Gumma, Nedumaran S , Kumara Charyulu D, Moses Shyam, Piara Singh and MCS Bantilan

International Crops Research Institute for Semi-Arid Tropics (ICRISAT), Patancheru, Hyderabad, India-502324

Soil Data

• The soil properties required for crop yield modeling were derived using Anantapur soil map

developed by the National Bureau of Soil Science and Land use Planning (NBSS&LUP), Nagpur

• Soil physical and chemical properties such as texture, hydraulic parameters, bulk density, organic

matter and available N were estimated for each location based on the available soil profile data

and the expert knowledge

• Additional soil parameters, including the soil albedo, drainage constant, and runoff curve number

were also estimated based on the soil texture data from the generic soil database available in the

DSSAT-models

Simulation of crop yields

• The sequence analysis tool of DSSAT v4.5 was

used to simulate fallow-chickpea rotation in the

study regions using JG-11 as test cultivar

• Simulations were carried out for the baseline

climate (1980-2009) and the projected climate

change during midcentury period (2040-69) for

each site

Crop Domain Mapping

Background

Crop simulation models are valuable tools for evaluating potential effects of

environmental, biological and management factors on crop growth and

developments. These models need to be applied at larger scales in order to

be economically useful so that the effects of various alternate management

strategies across the watershed or the region could be analyzed. Linking

crop models with Remote sensing and Geographical Information System

(GIS) have demonstrated a strong feasibility of crop modeling applications at

a spatial scale. Most of the agricultural operations closely connected with

natural resources that have an obvious spatial character and GIS, which is

capable of using spatial data, can be very handy in environmental and

agricultural modeling. Decision Support System for Agro-technology Transfer

(DSSAT) model was used to simulate and understand the spatial variability

in crop yield response to (i) current climate, (ii) changed climate and (iii)

evaluate various agronomic adaptation options to increase its productivity

under climate change in chickpea dominant regions.

Modeling Framework

Climate data

• Thirty-years (1980-2010) of observed daily weather data were obtained

from the ANGR Agricultural University Agromet observatory located at

Anantapur, Nandyal and Ongole

• The baseline weather datasets were quality controlled and inspected for

outliers or anomalous values and if found, such values were adjusted and

corrected using bias corrected AgMERRA data

• In order to create a representative 30-year weather series for each

location, we utilized neighboring sites from the highly spatially resolved

WorldClim data, which is available historically as monthly values

• The GCM's used were, CCSM4, GFDL-ESM2G), HadGEM2-ES ,

MICROC5, MPI-ESM.-MR . The future simulated was that for mid-century

(2040-2070) under RCP8.5.Baseline CO2 level used was 361 ppm and

future was 571 ppm.

Management

• The chickpea growing regions in the state were concentrated in the four

districts and 810 sample location were surveyed to know the management

practices followed by the farmers.

• The management data was used as an input to DSSAT model to simulate

current future yields with and without adaptation option

Management practices

Anantapur Kadapa Kurnool Prakasam

FYM 0 0 0 0

Total ‘N’ per ha 40 39 64 98

Seed rate per ha 106 103 105 123

Plant Population per sq m

40 40 40 45

Sowing window 9/15 to 10/15

10/5 to 11/5

9/15 to 10/15

11/5 to 11/25

Irrigation - - - -

• Image pre-processing

• Ground data collection

• Ideal spectra generation

• Class spectra preparation

• Grouping of similar classes

through decision tree algorithms

• Final class names through

Spectral matching techniques

• Land use land cover

classes

• Spatial distribution of

Chickpea and areas

• Accuracy assessment

.

• .

800

1300

1800

2300

2800

3300

3800

Baseline CCSM4 GFDL HadGEM2 MICROC5 MPI-ESM.-MR AvgGCMs

Yie

lds

(kg/

ha)

800

1300

1800

2300

2800

3300

3800

Baseline CCSM4 GFDL HadGEM2 MICROC5 MPI-ESM.-MR AvgGCMs

Yie

lds

(kg

/ha)

Results

• The base yields (1980-2009) in study location

ranged between 150 to 2795 kg/ha showing

spatially heterogeneous across the chickpea

growing regions

• The results suggest that as compared to baseline

climate, the climate change by 2069 (Mid –

century period) may decrease the yield of

chickpea by 4.3 to 18.6% across various

locations tested

• Yield benefits with various adaptation options

reveled that application of one critical irrigation at

60 DAS found to be beneficial in increasing

chickpea yields under climate change.

Fig 1. Map showing the weather stations and farm climate locations

Fig 2. Soil map of the study region (source: NBSS&LUP, Nagpur)

Fig.3 Spatial distribution of chickpea yields as projected by two GCMs for mid-century period under RCP

8.5 compared to baseline

a. Baseline b. MIROC5 c. GFDL-ESM2G

Fig.4 Spatial distribution of chickpea yields as projected by two GCMs for mid-century period under RCP

8.5 compared to baseline receiving supplemental irrigation at 60 DAS

Fig 5. Box plots of baseline and future chickpea yields

during mid century period under RCP 8.5

With adaptation

With out adaptation