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Assessing the Impact of Climate and Technological Factors on Rice Productivity in India, Slides of Agricultural policy

Gokhale Institute of Politics and EconomicsAgricultural policy

This research paper examines the impact of climate change and technological factors on rice productivity in india. It analyzes the long-term and short-term effects of climate variables such as temperature, rainfall, and co2 emissions on rice yield. The study also investigates the role of technological factors like fertilizer consumption, irrigation, and area under cultivation in influencing rice productivity. The paper concludes that while climate change poses significant challenges to rice production, technological advancements and policy interventions can mitigate these challenges and enhance rice productivity in india.

Typology: Slides

2023/2024

Available from 01/28/2025

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Author: Kiran Bhowmik, Sekharan Das and Subhrabaran Das
Assessing the Impact of Climate and
Technological Factors on Rice
Productivity in India
Submitted to
AERC Platinum Jubilee Conference on “Sustainability of Agricultural
Sector: Issues, Challenges and Policy Matrix”
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Download Assessing the Impact of Climate and Technological Factors on Rice Productivity in India and more Slides Agricultural policy in PDF only on Docsity!

Author: Kiran Bhowmik, Sekharan Das and Subhrabaran Das

Assessing the Impact of Climate and Technological Factors on Rice Productivity in India

Submitted to

AERC Platinum Jubilee Conference on “Sustainability of Agricultural

Sector: Issues, Challenges and Policy Matrix”

  • (^) Climate change is the most serious issue confronting the world today. Climate change refers to long-term

changes in temperature and weather patterns.

  • (^) The change in climate is natural due variations in the solar cycle and global warming. But one of the primary

causes of climate change is increase in global population growth. It leads to the increase in human needs day

by day. In order to meet those needs, we humans engaging in various activities, are the primary driver of

climate change. Use of fossil fuels such as coal, oil and gas are also the root cause of the climate change.

  • (^) Agriculture is likely to be exposed to climate change the most because of its direct dependence on factors like

temperature and rainfall. The continuous increase in uncertainties of rainfall and increase in temperature and

their variability, occurrence of climatic extremes is expected to harm crop productivity and production.

  • (^) Farming production and productivity are entirely dependent on climatic conditions, and farming activities are

carried out by selecting crops that are specific to climate, soil type, resource availability, and so on. As

temperatures rise, different cropping may also be affected, so climate has a significant impact on agriculture.

INTRODUCTION

Author Objective

Methodolog

y

Findings

Abbas (2020) Exploring robust short run and long run relationship between cultivated area, temperature change, fertilizer input and production of cotton Auto regressive distributive lag model (ARDL)

  • Both in short and long-run average temperature change has a positive but insignificant effect on cotton production which means a rise in temperature doesn't affect cotton yield.
  • Area and fertilizer have a significant positive effect on cotton production. Warsame et al. (2021) The study focuses on to examine and explore the impacts of global climate change on agricultural output Auto regressive distributive lag model (ARDL)
  • CO2 emissions, land area under cereal crops, fertilizer consumption, and energy consumption have a positive and significant impact on agricultural output.
  • Temperature and rainfall have a negative impact.

Contd.

Author Objective Methodology Findings

Bharadwaj et al. (2022) To examine the climatic change and its impact on the productivity of highly demanding agricultural crops. Fully modified ordinary least square and Dynamic ordinary least square, pooled mean group

  • Rice yield is positively affected by minimum temperature and cultivated area, but it is negatively affected by maximum temperature and rainfall.
  • Conversely, the wheat yield is positively affected by the minimum temperature and the cultivated area and maximum temperature and rainfall negatively affect it. Guntukula & Goyari (2020) Examining the climatic effects across crop yield and variability especially focused on rice, cotton, jowar and groundnut. Just and Pope stochastic production function
  • The effect of climate change varies among crops. Rice, cotton and groundnut yields are adversely affected by maximum temperature while these crops are positively affected by minimum temperature.
  • On the other hand, cotton and groundnut yields are adversely affected by rainfall.
  • (^) In Indian Context :

OBJECTIVE

  • (^) To examine the change in climate in India during last 30 years.
  • (^) To examine the impact of both climatic factors and non-climatic i.e Technological factors on rice productivity in India DATA & METHODOLOGY
  • (^) Ministry of Agriculture & Farmers Welfare, Govt. of India, Directorate of Economics and Statistics and FAO are the major data sources for the study.
  • (^) This study uses a time series data framework over 30 years from 1990 to 2020.
  • (^) To explore the short as well as long run effect of climatic variation on average productivity of rice in India an ARDL (Autoregressive distributive lag ) model was used in the study.

1990 1995 2000 2005 2010 2015 2020 2025 0

1

2

3

**Temperature variability 1990 1995 2000 2005 2010 2015 2020 2025

7

Rainfall variability 1985 1990 1995 2000 2005 2010 2015 2020 2025**

**Average temperature 1985 1990 1995 2000 2005 2010 2015 2020 2025

7

avegare rainfall Fig. 1: Climate trend**

Where;  (^) represents the coefficients of short run dynamics  (^) stands for the coefficients of long-run in the same model.  (^) is lag operator of first difference and is the error term in the model. The null & the alternative hypothesis are formulated in the following:

Descriptive Statistics Rice Yield Fertilizers Irrigation Rainfall Temperature Mean 2113 200.97 55.2568 1139.999 25. Median 2079 183.98 55.23 1120.200 25. Maximum 2705 293.69 63.1684 1327 26. Minimum 1740 115.68 45.55 972.80 25. Std. Dev 291.54 58.7519 4.99 95.09 0. Skewness 0.4143 0.0546 -0.3745 0.271 0. Kurtosis 2.0171 1.4911 2.1333 2.28 2. Jarque-Bera 2.1347 2.9561 1.6948 1.0577 0. Probability 0.34 0.22 0.4285 0.5892 0. Sum 65503 6230.180 1712.963 35339.96 795. Sum Sq. Dev. 2550020 103553.9 749.119 271268.8 1. Observations 31 31 31 31 31 Table 1: Details of the Summary of Descriptive Statistics Source: Authors’ calculation from the compiled data set.

Function F-statistic 12.417* Critical value bounds Lower Bound Upper Bound Significance I(0) I(1) 5 per cent 2.86 4. 1 per cent 2.45 3. Table 3. ARDL Co-integration results for yield of rice Source: Authors’ calculation from the compiled data set. Note: ‘’, ‘’ and ‘**’ are significant at 1%, 5% and 10% level

Estimates & Adjustment Variables Dependent Variable:^ Coefficient^ Std. Error^ t-statistic^ Probability LNRY ADJ LNRY(-1) -0.6738* 0.1447 -4.66 0. Long run dynamics LNFERC 0.3179* 0.0927 3.43 0. LNIRRI 0.7250** 0.3118 2.32 0. LNRAIN 16.6155** 6.5218 2.55 0. LNTEMP -16.1084** 6.4715 -2.49 0. Short run dynamics D(LNFERC) -0.1200 0.1063 -1.13 0. D(LNFERC(-1)) -0.6826* 0.1124 -6.07 0. D(LNIRRI) -0.1443 0.2805 -0.51 0. D(LNRAIN) -4.4354*** 2.1511 -2.06 0. D(LNTEMP) 4.1658*** 2.1609 1.93 0. D((LNTEMP(-1)) -0.1769* 0.0427 -4.14 0. D(LNTEMP(-2)) -0.0566*** 0.0318 -1.78 0. Constant 49.8919 16.8265 2.97 0. Goodness of Fit R-squared = 0.9881, Adjusted R-squared = 0.9719, F-statistics = 60.90 and Probability (F-statistics) = 0. Source: Authors’ calculation from the compiled data set. Note: ‘’, ‘’ and ‘**’ are significant at 1%, 5% and 10% level. Table 4. Long Run and short-run results based on the ARDL model on rice production

    • 0 5 10 15 00 02 04 06 08 10 12 14 16 18 20 CUSUM 5 % Significance -0. -0.

00 02 04 06 08 10 12 14 16 18 20 CUSUM of Square s 5 % Significance Fig.2: Stability of the ARDL model

Conclusion & Policy Suggestion

  • (^) The result also confirms the presence of co-integration. The factors considered in the study are collectively influencing average productivity of rice over the long run.
  • (^) In the long run, the average productivity of rice increases if the farmers use more fertilizers and sufficient irrigation or there is sufficient rainfall happens; but it will negatively affected by temperature.
  • (^) In the short run the previous year’s fertilizers consumption has a significant negative impact on average productivity of the crop rice where rainfall and temperature also have significant effects in the short run.
  • (^) The scheme-based irrigation system, known as the Pradhan Mantri Krishi Sinchayee Yojana (PMKSY), was introduced in India in 2015-16 with the goal of enhancing sufficient access to water on farms and expanding cultivable area under assured irrigation over time.

THANK YOU