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Author(s): Devendra Kumar Kurrey*, Hulas Pathak

Email(s): devendrakurrey95@gmail.com

Address: Department of Agricultural Economics, Indira Gandhi Krishi Vishwavidyalaya, Raipur Chhattisgarh, India.
Department of Agricultural Economics, Indira Gandhi Krishi Vishwavidyalaya, Raipur Chhattisgarh, India.

*Corresponding Author: devendrakurrey95@gmail.com

Published In:   Volume - 36,      Issue - 1,     Year - 2023


Cite this article:
Kurrey and Pathak (2023). Impact of Climate Change on Paddy Yield in Bastar District of Chhattisgarh. Journal of Ravishankar University (Part-B: Science), 36(1), pp. 58-65.



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.

*Corresponding Author: devendrakurrey95@gmail.com

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.



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