Impact
of Climate Change on Paddy Yield in Bastar District of Chhattisgarh
Devendra
Kumar Kurrey*, Hulas Pathak
Department
of Agricultural Economics, Indira Gandhi Krishi Vishwavidyalaya, Raipur
Chhattisgarh, India.
ABSTRACT
Agriculture
and forest product collection are the economic foundations of the Bastar
district. Paddy and maize are widely grown in the Bastar region. Paddy is grown
in 76 percent of the gross cropped area in Bastar district. To assess the
impact of climate change on paddy productivity a simple ordinary least square (OLS) Regression analysis have been analysed.
Observation revealed that the rainfall
during the month of June
to September affected the crop yield positively, Monsoon rainfall
affects more the crop yield negatively. Bastar district is mainly rainfed and
only 2.14 percent area near to the water
resources can be irrigated, the lack of irrigation effects the crop yield
greatly hence, any increase in
rainfall in the germination and booting period (June to August) will
help in increase the productivity of crop. The month of or before harvesting if gets rain, it can destroy
the standing crop and cause
huge lose to the production.An increase in annual maximum Temperature
affects the paddy crop productivity negatively whereas productivity increases
with every per cent increase with the annual
minimum Temperature. Paddy is a tropical and subtropical crop hence, the result
had positive relationship with maximum Temperature, Excessive rainfall can negatively
or positively impact crop yield.The study suggests to develop such varieties
which can withstand with minimum Temperature.
Keywords:
Paddy,
Climate Change, Rainfall, Temperature, Regression.
1. INTRODUCTION
India’ s agriculture is more dependent
on monsoon from the ancient
periods. Any change in
monsoon trend drastically affects agriculture. Even the increasing Temperature is affecting the Indian
agriculture. In the Indo-Gangetic Plain, these
pre-monsoon changes will primarily affect the wheat crop (more than 0.5oC increase in time slice 2010-2039; IPCC 2007). In the states of Jharkhand, Odisha and Chhattisgarh alone, rice production
losses during severe droughts (about one year
in five) average about 40 per cent of total production, with an estimated value of $800 million (Pandey, 2007). Increase in CO2 to 550 ppm increases
yields of rice, wheat, legumes and
oilseeds by 10-20 per cent. A 1oC increase in Temperature may reduce yields of wheat, soybean,
mustard, groundnut, and potato by 3-7 per cent. Recent studies carried
out at the Indian Agricultural Research Institute indicated the possibility of losing 4-5
million tons of wheat production in the future
with every rise of 1oC Temperature throughout the growing period. The impacts of rainfall variability on agriculture,
food security, livelihoods and human mobility in the Janjgir-Champa district of
Chhattisgarh. They tested the hypothesis
that human migration is a major coping mechanism against climate variability.
The study confirmed the hypothesis that a coordination mechanism exists between
rainfall changes (i.e. erratic rainfall patterns in terms of delayed monsoons,
seasonal shifts, drought and floods) and livelihood and food security of a
number of farmers and farm labourers in the research site. Because there is
only a single annual harvest of paddy rice (practiced as monoculture) in the
research site, which is partly due to the non-availability of water for a
second crop, marginal farmers and farm laborers are left with very few options
in finding sufficient employment in and around their villages.(Muralia and
Afifi 2014).The changes in rainfall and
the effect of changes in rainfall on agriculture in Haveri district. Compound
growth rate, correlation, and functional analyses were used to analyze the
data. The results showed that the area under paddy crop in the district had
decreased, and the production and productivity of paddy crop had increased.
They also revealed a higher positive correlation between rainfall and paddy
production than between maize and groundnut, as indicated by a correlation
coefficient of 0.80 for paddy crop and rainfall production.( Patil et al.
2015)
Paddy is
most important in Chhattisgarh, which accounts for around 86 percent of the
gross cropped area in the state. In all three Agro-climatic zones, farmers
livelihoods are mainly paddy based. Agriculture and forest product
collecting are the economic foundations of the Bastar district. Paddy and maize
are the most important crops in agriculture, while wheat, jowar, kodo kutki,
gram, tur, urad, sesame, niger, and mustard are also cultivated. the effects
and implications of climate change
will sooner or later
impact agriculture as well as human life, whether
it is agriculture, water or the climate
as a whole, people's
livelihoods will be in danger of food, water, life-threatening climatic clematises such as droughts from floods,
etc. There is a need to take meaningful steps from every part of
the world to monitor
climate-effecting activities.
2. MATERIAL
AND METHODS
Understanding
of climate of a region is essential to planning for crop production technologies, sowing practices, irrigation
requirements and other care and
management methods. Humidity, Temperature and rainfall are the key drivers of agricultural produce as they
are responsible for crop growth. Each crop
requires a different type of climate demand from germination to the ripening
stage, and any change in climate
during any stage affects the crop. Therefore, in this study, data related to rainfall, temperature and
irrigation facilities were gathered from Indian Meteorological Department and
Directorate of Economics and Statistics, Government of India. The positive impacts of climate change on crops are beneficial
for farmers but the negative impact affects
the productivity of crop as
well farmers' livelihood.This
study used panel data from 2000-2020, which included 21 time series
observations, to analyse the results. A total of 15 climate related observations
of climate related and other independent variables were inputted correctly into
GRETL SOFTWARE. Due to the fact that the area of major crops is deciding
factors for selection of crops that covers atleast 75 percent of the district.
It is believed that atleast 75 percent covered area represent the district's
agricultural production. Cobb-Douglas production function or the OLS Log form
is used to better understand the relationship between dependent and independent
variables.
Table
1 Weather variables that influence the crop Productivity
IR
|
Irrigated area under ith crop (ha)
|
PIR
|
Proportion of irrigated area under ith crop
|
Ai
|
Area of the
ith crop (ha)
|
R
|
Rainfall (mm)
|
R1
|
Rainfall in January
|
R2
|
Rainfall in February
|
R3
|
Rainfall in March
|
R4
|
Rainfall in April
|
R5
|
Rainfall in May
|
R6
|
Rainfall in June
|
R7
|
Rainfall in July
|
R8
|
Rainfall in August
|
R9
|
Rainfall in September
|
R10
|
Rainfall in October
|
R11
|
Rainfall in November
|
R12
|
Rainfall in December
|
T
|
Temperature (0 c)
|
Tmax1
|
Maximum Temperature in January
|
Tmax2
|
Maximum Temperature in February
|
Tmax3
|
Maximum Temperature in March
|
Tmax4
|
Maximum Temperature in April
|
Tmax5
|
Maximum Temperature in May
|
Tmax6
|
Maximum Temperature in June
|
Tmax7
|
Maximum Temperature in July
|
Tmax8
|
Maximum Temperature in August
|
Tmax9
|
Maximum Temperature in September
|
Tmax10
|
Maximum Temperature in October
|
Tmax11
|
Maximum Temperature in November
|
Tmax12
|
Maximum Temperature in December
|
Tmin1
|
Minimum Temperature in January
|
Tmin2
|
Minimum Temperature in February
|
Tmin3
|
Minimum Temperature in March
|
Tmin4
|
Minimum Temperature in April
|
Tmin5
|
Minimum Temperature in May
|
Tmin6
|
Minimum Temperature in June
|
Tmin7
|
Minimum Temperature in July
|
Tmin8
|
Minimum Temperature in August
|
Tmin9
|
Minimum Temperature in September
|
Tmin10
|
Minimum Temperature in October
|
Tmin11
|
Minimum Temperature in November
|
Tmin12
|
Minimum Temperature in December
|
|
|
3. RESULT AND DISCUSSIONS
Paddy is currently one of the most important crops in
Bastar district, covering a large area. Due to the water-intensive nature of
the crop and the lack of irrigation and other important amenities in the
region, however, the crop's yield is fairly low.the section is divided into
following subsections:
3.1 Impact of area,
rainfall and temperature on paddy yield
in Bastar district
The equation of
the dependent variable as estimated yield of paddy and 15 independent variables
of area, irrigated area, rainfall, and maximum Temperature. Standard errors are
presented in parentheses. The lowest percentage of standard error was observed
in rainfall in June, and maximum temperature data were less consistent. The
total no of observations was 21 for its 21 years of climatic data set. R square
was calculated around 0.820.
The fitted model
has an R2 value of 82 percent, indicating that the model can explain the
variance. Equation 1 summarises the findings of the Cobb-Douglas analysis of
the district's paddy yield.
Equation 1 Log
equation for area, rainfall, and temperature against paddy yield
^l_Yield = 139 - 0.522*l_AreaofCrops - 2.63*l_ +
2.07*l_Irrigatedarea - 8.09*l_GrossCroppedArea
(31.8)
(0.226)
(0.697)
(2.03)
+ 0.975*l_RF6
+ 1.91*l_RF7 + 1.52*l_RF8 + 0.527*l_RF9 - 7.31*l_Monsoon
(0.273) (0.437) (0.421) (0.178) (1.91)
+
4.52*l_AnnualRF - 35.1*l_TMINSUMMER - 4.00*l_TMINWINTER + 29.2*l_TMINANNUAL +
13.8*l_TMAXSUMMER
(1.40) (10.8) (5.24) (13.3)
(4.51)
-
5.46*l_TMAXWINTER - 6.52*l_TMAXANNUAL
(3.94) (6.66)
T = 21, R-squared = 0.820 (standard errors in
parentheses)
Interestingly, Increase
in area under paddy seems to decrease
the productivity of paddy. The argument
behind the decrease is the low production of
the crop when generally, if
the area of a crop rises but its yield does not, its productivity will
decrease. Table 1 revealed that the rainfall
during the month of June
to September affected the crop yield positively, Monsoon rainfall
affects more the crop yield negatively. Bastar district is mainly rainfed and
only 2.14 percent area near to the water
resources can be irrigated, the lack of irrigation effects the crop yield
greatly hence, any increase in
rainfall in the germination and booting period (June to August) will
help in increase the productivity of crop. The month of or before harvesting if gets rain, it can destroy
the standing crop and cause
huge lose to the production.
An increase in
annual maximum Temperature affects the paddy crop productivity negatively
whereas productivity increases with every per cent increase with the annual
minimum Temperature. Rana and Randhawa (2013) reported the effect of rainfall on agriculture
in Himachal Pradesh. They used long-term data, related to average annual
rainfall, area, and production of major food crops such as rice, wheat, maize,
and barley collected for the period 2001–2012. The results showed a positive
correlation between rainfall and production as well as area of all crops. The
study indicated that rainfall is the major factor affecting production as well
as the area sown under all the crops in Himachal Pradesh.
Model 1: OLS, using
observations 2000-2020 (T = 21)
Dependent variable: l_PADDY Yield
HAC standard errors, bandwidth 2 (Bartlett kernel)
Table
1 Model 1 Time series data set of area
rainfall temperature against paddy yield in Bastar district
|
Coefficient
|
Std. Error
|
t-ratio
|
p-value
|
|
const
|
138.517
|
31.8354
|
4.351
|
0.0121
|
**
|
l_AreaofCrops
|
−0.522130
|
0.226036
|
−2.310
|
0.0820
|
*
|
l_Irrigatedarea
|
2.06783
|
0.438569
|
4.715
|
0.0092
|
***
|
l_GrossCroppedArea
|
−8.09449
|
2.02911
|
−3.989
|
0.0163
|
**
|
l_RF6
|
0.974601
|
0.272589
|
3.575
|
0.0233
|
**
|
l_RF7
|
1.91042
|
0.436630
|
4.375
|
0.0119
|
**
|
l_RF8
|
1.52313
|
0.420526
|
3.622
|
0.0223
|
**
|
l_RF9
|
0.527493
|
0.178128
|
2.961
|
0.0415
|
**
|
l_Monsoon
|
−7.31103
|
1.91118
|
−3.825
|
0.0187
|
**
|
l_AnnualRF
|
4.52169
|
1.40107
|
3.227
|
0.0321
|
**
|
l_TMINSUMMER
|
−35.1050
|
10.7598
|
−3.263
|
0.0310
|
**
|
l_TMINWINTER
|
−3.99833
|
5.24023
|
−0.7630
|
0.4880
|
|
l_TMINANNUAL
|
29.2099
|
13.3404
|
2.190
|
0.0937
|
*
|
l_TMAXSUMMER
|
13.7610
|
4.50528
|
3.054
|
0.0379
|
**
|
l_TMAXWINTER
|
−5.46238
|
3.93847
|
−1.387
|
0.2377
|
|
l_TMAXANNUAL
|
−6.52488
|
6.66213
|
−0.9794
|
0.3828
|
|
*10 Percent , ** 5 Percent, ***1 Percent
Significance Level,
Mean dependent
var
|
4.903385
|
|
S.D. dependent
var
|
0.340118
|
Sum squared
resid
|
0.416370
|
|
S.E. of
regression
|
0.322634
|
R-squared
|
0.820035
|
|
Adjusted
R-squared
|
0.100173
|
F(16, 4)
|
49.97093
|
|
P-value(F)
|
0.000872
|
Log-likelihood
|
11.36968
|
|
Akaike criterion
|
11.26064
|
Schwarz
criterion
|
29.01752
|
|
Hannan-Quinn
|
15.11434
|
rho
|
−0.435814
|
|
Durbin-Watson
|
2.796839
|
Source:
Derived from analysis
Equation
2 presents the equation of the dependent variable as estimated paddy yield and
16 independent variables temperature. Standard errors are presented in
parentheses. The total no of observations was 21 for its 21 years of climatic
data set. R square was calculated around 77.10 percent. Grover
and Upadhaya (2014) reported climate change and it is impact on paddy
productivity in Ludhiana District of Punjab using long term data of 1972-2010. They observed significant increase in average minimum temperature in the range
of 1.4-2.1°C for all months in kharif season. The average maximum
temperature has also increased, but only in the month of August (0.5°C), while
it decreased for the months of June (1.4°C) and September (0.6°C). They also
observed increase in the maximum temperature and its negative impact on paddy
productivity, while increase in the minimum temperature, rainfall and relative
humidity has non-significant positive impact.
Equation
2 Log equation minimum and maximum Temperature against paddy yield
^l_Yield = 1.02 + 1.98*l_TMIN3 - 5.68*l_TMIN4 - 10.0*l_TMIN5 +
8.63*l_TMIN6
(14.5) (2.32) (2.57) (2.68) (5.54)
- 3.09*l_TMIN10 + 1.61*l_TMIN11
- 1.44*l_TMIN12 - 1.35*l_TMIN1 + 2.11*l_TMAX3
(1.14) (1.09) (0.880) (0.708) (1.83)
+ 3.49*l_TMAX4 + 8.58*l_TMAX5 -
3.98*l_TMAX6 - 7.84*l_TMAX10 + 6.51*l_TMAX11
(1.97) (2.75) (3.36) (2.04) (2.67)
- 0.884*l_TMAX12 + 1.06*l_TMAX1
(1.78) (1.62)
T = 21, R-squared = 0.771
(standard errors in parentheses)
The
rise in every degree minimum Temperature in month of April, May and October is observed to be affecting the crop most
negatively as 5.67, 10.01 and 3.09 percent, Respectively. The increase in
maximum Temperature in the month of May, October and November is found to have most negative
impact on crop yield, with every one per cent
increase, the productivity will decrease by 8.57, 7.87 and 6.05 per cent, Respectively. A study revealed that the impact of climate variability on crop yield in
different agro-climatic zones of Tamil Nadu. The results indicated that the
yields of paddy, maize, cotton, and sugarcane increased due to rising
temperatures, whereas, banana was benefited due to temperature variation. They
observed that the rainfall had negatively affected the maize crop. Yields of
most crops were found to be negatively associated with the intensity of
rainfall. They suggested the need for taking suitable weather-crop insurance
policy to reduce financial loss to farmers (Arumugam et al. 2014)
Model 2: OLS, using observations 2000-2020 (T = 21)
Dependent variable: l_PADDY Yield
HAC standard errors, bandwidth 2 (Bartlett kernel)
Table
2 Model 2 Time series data det of minimum and maximum Temperature against paddy
yield in Bastar district
|
Coefficient
|
Std. Error
|
t-ratio
|
p-value
|
|
const
|
1.02271
|
14.5288
|
0.07039
|
0.9473
|
|
l_TMIN3
|
1.97821
|
2.31779
|
0.8535
|
0.4415
|
|
l_TMIN4
|
−5.67536
|
2.57395
|
−2.205
|
0.0921
|
*
|
l_TMIN5
|
−10.0148
|
2.67720
|
−3.741
|
0.0201
|
**
|
l_TMIN6
|
8.62680
|
5.54017
|
1.557
|
0.1944
|
|
l_TMIN10
|
−3.09087
|
1.14499
|
−2.699
|
0.0541
|
*
|
l_TMIN11
|
1.61069
|
1.08697
|
1.482
|
0.2125
|
|
l_TMIN12
|
−1.44020
|
0.879798
|
−1.637
|
0.1770
|
|
l_TMIN1
|
−1.34563
|
0.707617
|
−1.902
|
0.1300
|
|
l_TMAX3
|
2.11390
|
1.83456
|
1.152
|
0.3134
|
|
l_TMAX4
|
3.49323
|
1.97219
|
1.771
|
0.1512
|
|
l_TMAX5
|
8.57527
|
2.75277
|
3.115
|
0.0357
|
**
|
l_TMAX6
|
−3.98325
|
3.35727
|
−1.186
|
0.3011
|
|
l_TMAX10
|
−7.84264
|
2.04281
|
−3.839
|
0.0185
|
**
|
l_TMAX11
|
6.50786
|
2.67046
|
2.437
|
0.0714
|
*
|
l_TMAX12
|
−0.884337
|
1.77723
|
−0.4976
|
0.6449
|
|
l_TMAX1
|
1.06319
|
1.62155
|
0.6557
|
0.5478
|
|
*10 Percent , ** 5 Percent, ***1 Percent
Significance Level,
Mean dependent
var
|
4.903385
|
|
S.D. dependent
var
|
0.340118
|
Sum squared
resid
|
0.530877
|
|
S.E. of regression
|
0.364307
|
R-squared
|
0.770542
|
|
Adjusted
R-squared
|
-0.147292
|
F(16, 4)
|
79.21190
|
|
P-value(F)
|
0.000351
|
Log-likelihood
|
8.818633
|
|
Akaike criterion
|
16.36273
|
Schwarz
criterion
|
34.11961
|
|
Hannan-Quinn
|
20.21643
|
rho
|
0.032593
|
|
Durbin-Watson
|
1.877703
|
Source:
Derived from calculation
Murthy et al. (2012) reported the impact of climate change on crop
yields in India for 5 major Indian crops–rice, wheat, sorghum, cotton and
sugarcane, using data from 1961 to 2010. The results showed that there was no
significant effect of temperature and precipitation trends on major crop yields
over the study period. They emphasized the importance of error measurement,
when predicting the results. They suggested that adaptation can play a vital
role in mitigating the adverse effects of climate change.
COCLUSION
Due to the small
sized and scattered land holdings,
in bastar districts ,The agriculture
is dependent on paddy production in the districts, paddy crop provides a good
minimum support price in the state but an uncertain and unstable climatic condition, causing a markable
loss in the paddy productivity. As the plateau
and hilly regions are more sensitive and more affected by climate
change. Crop diversification like cultivation of coarse cereals such as
maize, jowar, and minor millets such as Kodo Kutki, ragi, etc. would be the
beneficial in the districts. According
to the climatic conditions of these regions, agricultural scientists should
develop varieties of minor millets and maize. In parts of Bastar Plateau regions, there is a
slight change in the cropping pattern due to an increase in temperature or less
rainfall in the monsoon, as the area under maize is increasing due to
reasonable price and market availability. Mostly stability has been found in
the temperature of Bastar, due to which constant temperature-demanding crops
can be promoted in this region. The government of Chhattisgarh should
implement such a policy, to prohibit the unnecessary digging of tube
wells. In areas with tubewells, there is
an immediate need to work on groundwater recharge and borewell recharge
policies.
ACKNOWLEDGEMENT
Without the
outstanding assistance of my supervisor, Dr. Hulas Pathak, this work and the
research supporting it would not have been possible. i want to express my
gratitude to Dr. V.K. Choudhary (Head of Department, Agricultural Economics)
inspired and helped me stay on track with my work.
REFERENCES
Arumugama, S.,
Ashokb, K. R., Kulshreshthac, S. N., Vellanganya, I. and Govindasamya, R.
(2014). Does Climate Variability Influence Crop Yield? — A Case Study of Major
Crops in Tamil Nadu. Agricultural Economics Research Review, 24(1): 61-71.
Grover, D. K. and Upadhya, D. (2014). Changing Climate Pattern and Its
Impact on Paddy Productivity in Ludhiana District of Punjab. Indian Journal of
Agricultural Economics, 69(1): 150-162.
IPCC,
2007: Climate Change 2007: Impacts, adaptation and vulnerability.
Parry, M.L., Canziani, O., Palutikof, J.P., van der Linden, P.J., and Hanson,
C.E.(Eds.) Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge
University Press
Karmokar, J.,
Islam, M. A., Hassan, M. R. and Billah, M. M. (2020). Impact of seasonal climatic
variability on rice yield in Bangladesh. Journal of Agrometeorology 22(2):
165-171
Pandey,
S. 2007. Economic costs of drought and
rice farmers' coping mechanisms.
International Rice Research News,
32: 5-11
Rana, I. and Randhawa, S. S. (2013). Changing Trends of
Monsson in Himachal Pradesh. H.P. State Council for Science Technology &
Environment, Pp. 4-14.
Narayanan, AL.,
Balasubramanian, TN., Chellamuthu. V. and Kumar, J. S. (2003). Identification
of efficient rice cropping zone for union territory of Pondicherry, Madras
Agricultural Journal, 90: 729-730.
Mahmood, N.,
Ahmad, B., Hassan, S. and Bakhsh, K. (2012). Impact of temperature and
precipitation on rice productivity in rice-wheat cropping system of Punjab
province. The Journal of Animal & Plant Sciences, 22(4): 993-997.
Moorthy, A., Buermann, W. and Rajagopal, D. (2012). The Impact of Climate
Change on Crop Yields in India from 1961 to 2010. Available online: http://www.environment.ucla.edu/media/files/3-Climate-change-and-Crop-Yields-in-India-%282%29-54-ylq.pdf (accessed on 8 May 2014) Pp. 1-43.
Murali, J. &
Afifi, T. (2014). Rainfall variability, food security and human mobility in the
Janjgir-Champa district of Chhattisgarh state, India. Climate and Development,
6: 28-37.