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Impact of climate change on food security in India: an evidence from autoregressive distributed lag model

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Abstract

Food security is a critical global issue, particularly in India, and it is further exacerbated by the challenges posed by climate change. This study aims to examine the influence of climate change on food security in India, utilizing annual time series data spanning from 1994 to 2019. Using the Autoregressive Distributed Lag (ARDL) method, the study investigates the relationship between climate variables, area under cultivation, population growth, agricultural subsidies, and food production. The findings indicate that expanding the cultivation area for food crops significantly enhances food security, with a 1% increase leading to a 2.4% increase in the short run and a 3.2% increase in the long run. Conversely, population growth has a detrimental effect on food security, with a 1% increase resulting in a decline of 3.8% in the short run and 7.8% in the long run. Climate variables also play a crucial role, as rising temperatures adversely impact food security, leading to a decrease of 1.2% in the short run and 1.7% in the long run for every 1% temperature increase. Rainfall, on the other hand, does not significantly affect food security in the long run, but a decrease in rainfall in the preceding period negatively impacts food security in the short run. Furthermore, agricultural subsidies, particularly fertilizer subsidies, impact food security positively in the short run but have adverse effects in the long run. The study highlights the importance of sustainable land management, temperature control measures, water reservation, and effective agricultural subsidies to address food security challenges. These findings provide valuable insights for policymakers in designing effective strategies to ensure food security in India.

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Fig. 1
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Fig. 3

Source: https://vikaspedia.in/agriculture/crop-production/weather-information/agro-climatic-zones-in-india accessed on 13th/June/2023 at 12.03 a.m

Fig. 4
Fig. 5

Source: Authors’ computations. CUSUM: cumulative sum. CUSUM SQ: cumulative sum of squares

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Data availability

The datasets used in the analysis are available in the Indiastat.com, RBI, EPWRPF, and World Development Indicators repository.

Notes

  1. The major crops production such as wheat, rice, maize, and soybean are susceptible to changes in mean air temperature in Southeast Asia (Liu et al., 2020). Similarly, an Indian study predicted a decline of 2.5% in wheat production compared to the base year 2013–14 due to climate change (Amita and Surender 2020). Such reductions in agricultural production are likely to exacerbate food insecurity levels in India.

  2. According to this report, drought-like situations occurred in India during the years 2002–03, 2009–10, 2014–15, and 2015–16 (GOI, 2016). These droughts have led to a decline in food consumption, forcing rural populations in Odisha, India, to adopt co** strategies (Sam et al., 2020). Unfortunately, such co** strategies often contribute to an increase in food insecurity levels.

  3. In fact, approximately 74% of districts in India are affected by this climate variability. Source: https://theprint.in/environment/74-of-indias-districts-prone-to-extreme-climate-like-droughts-floods-cyclones-says-study/756813/ Accessed on 8th /August/2022 at 11.44 p.m.

  4. Furthermore, the report highlights that during the period of 1986–2015, the temperature of the coldest night increased by approximately 0.4%, while the temperature of the warmest day rose by around 0.63% (Krishnan et al., 2020).

  5. The recent Food and Agriculture Organization (FAO) report shows that about 70 percent of the rural population relied on agricultural activities to provide life and livelihood. Besides, the report also revealed that 82 percent of the farmers are small and marginal. These small and marginal farmers often depend on the monsoon because rainfall is the primary water source for irrigation in agricultural activity. Due to natural and anthropogenic activities, climate change likely affected the temperature and rainfall. As a result, there is a chance of adverse impact on the agricultural sector, and hence, food security.

  6. This method can be used for the combination of stationary and non-stationary time series, which is most advantage comparative to the other time series modeling. It provides information on the short-run and long-run impact of the predictors of importance. It is efficient in small samples. Additionally, it also reports the error correction mechanism which shows the speed of adjustment to move from disequilibrium to the equilibrium in the short-run. Due to above importance, the present study used ARDL method to examine our objective.

  7. The information is obtained from https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups on 08th/Sept/2022 at 5.23 p.m.

  8. The information is obtained from following website https://www.india.gov.in/india-glance/profile on 08th/Sept/2022 at 4.35 p.m.

  9. The information is obtained from http://cwc.gov.in/sites/default/files/NRLD_04012017.pdf on 30th/Sept/2022 at 6.15 p.m.

  10. The order of integration in time series, denoted as "I(d),” refers to the number of times required to be differentiable to become stationary. Stationarity of time series refers to the certain condition fulfilment such as the mean, variance, and covariance should be time invariant. If the time series is stationary at level, it is represented as I(0) whereas if the time series is stationary at first difference, it is denoted as I(1).

  11. Cointegration between time series refers to the fact that the time series moves together in long-run without the divergency from each other.

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  1. Department of Economics, Maharaja Purna Chandra (Autonomous) College, Baripada- 757003, Odisha, India

    Biswabhusan Bhuyan

  2. Xavier Institute of Management, XIM University, Bhubaneswar-751013, Odisha, India

    Ranjan Kumar Mohanty

  3. Goa Institute of Management, Sanquelim- 403505, Goa, India

    Subhamitra Patra

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BB was involved in conception and design, data collection and analysis, first draft of the manuscript and revision. RKM contributed to data collection and analysis and provided comments to improve the manuscript. SP was involved in conception and design, drafting and provided comments to improve the manuscript. All authors read and approved the final manuscript.

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Bhuyan, B., Mohanty, R.K. & Patra, S. Impact of climate change on food security in India: an evidence from autoregressive distributed lag model. Environ Dev Sustain (2023). https://doi.org/10.1007/s10668-023-04139-3

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