Does Climate Change Asymmetrically Affect Rice Productivity In India? New Insight From NARDL Cointegration Approach

: 14 This article attempt to answer the question "whether the dynamic relationship between climate change and rice 15 productivity is symmetrical or asymmetrical" using data from 1990-2017 in India. First, we test the symmetrical and 16 long-run dynamic relationship using the Autoregressive Distributed Lags (ARDL) model and test the asymmetrical 17 and cointegration relationship based on Nonlinear Auto-Regressive Distributed Lag (NARDL) technique. The results 18 of the ARDL model indicates that no symmetrical relationship between the variables in long-run. Whereas outcomes 19 of the NARDL bound test reveal that there is long-run asymmetrical impact of climate change on rice productivity. 20 The positive and negative shock of climate change has affected the rice productivity by different magnitude in India. 21 The Wald statistics confirm asymmetric relationship between rice productivity and climate change in the long-run 22 while only short-run asymmetrical relationships exist between rainfall and rice productivity in India. 23 Furthermore, dynamic multipliers indicate that negative component of rainfall and temperature has a dominant effect 24 over the positive component on rice productivity. To the best of the author's knowledge, no studies have been done to 25 assess both symmetrical and asymmetrical dynamic relationships between climate change and rice productivity using 26 ARDL and NARDL cointegration approaches in India's context. This study will help frame the environmental policies 27 and strategy to cope with climate change in India's agriculture productivity.

crop production and food security in the long in South Asia. Swaminathan & Kesavan (2012) stated that climate 49 change adversely affected the food production and also location could be change of main food producing areas.

50
The developing nations are more vulnerable than developed countries due to more extensive dependence on agriculture 51 sector for livelihood, lack of technological advancement and lack of adaptation policies of climate change on 52 agriculture production (Praveen & Sharma, 2020;Warsame, 2021). However, Chandio et al., (2021)

57
Similarly, Coulibaly et.al, (2020) concluded that temperature and drought are the main factors which negatively affect 58 agriculture productivity. The Indian agriculture sector is the most sensitive and exposed area to climate change due to 59 less adaptive capacity to cope with it (Guntukula, 2019). Investigating the impact of climate change on agriculture 60 productivity is of immense importance because more than 50 % population of India primarily depends on agricultural 61 activities for their livelihoods (Pattanayak & Kumar, 2013). As the changes in environmental factors such as 62 temperature, precipitation, CO2, and rainfall pattern directly affect agriculture productivity (Res et al., 1998), it is 63 indispensable to examine the effect of changes in climatic conditions on agriculture productivity.

64
In India, More than 60 per cent population mainly depends on the agriculture and its allied sectors and Climate change 65 may be the effect of food security by hampering agriculture productivity are not only from one-way but also from 66 multiple-ways. However, the impact of climate change is across the globe and its adverse effects are likely to be more 67 severe under Indian agro-ecological conditions, and climate models predict the severe effect of climate change on the 68 agriculture sector (Bahl, 2015). Clime change has significantly affected the agricultural productivity and foods supply 69 which will be a threat to food security in the country (Moses et al., 2015). The emerging adverse impacts of climatic 70 changes will put pressure on crop productivity particularly rice because they are more sensitive to variation due to 71 climate change and its associated factors (Bahl, 2015). Given the sensitivity of rice to environmental change, especially 72 those related to temperature increases and extended drought periods, copping with the future worldwide demand for 73 rice seems a troublesome task. Furthermore, changes in the length of the growing period because of temperature 74 increments will influence rice yield as well as will move cultivating frameworks from rice towards more appropriate 75 crops with adequate temperature optima (Korres et al., 2017).

76
India delivers a predominantly remarkable case study for examining how rice may respond to climate change. India 77 is the first largest exportable country of rice in the World which is counted 9.8 million tonnes followed by Thailand 78 (7.5 million tonnes), Vietnam (6.5 million tonnes), Pakistan (4.6 million tonnes) and the USA (3.1 million tonnes). In 79 Asia, India is the second producer after China, followed by Indonesia, Vietnam and Thailand (see Figure 1).

108
The rest of the paper is framed in the following manner. Section 2 discusses the existing literature and several

142
However, annual mean temperature has a positive impact on the yield of wheat, coarse cereals and pulse except for 143 rice while rainfall has a positive impact on rice, coarse cereals and pulse except for wheat in India.

144
In contrast, Mitra (2014) and Pal & Mitra (2018) investigated the nonlinear relationship between climate change and 145 crop productivity in India. Mitra (2014) found no asymmetric relationship between rainfall and food grain in India 146 and observed that average rainfall has a greater impact on food grain production than below-average rain. In contrast,

165
Crop modelling, Ricardian and econometric approach, these are three approaches to measure the effect of climate 166 change on agricultural productivity. Table 1 shows the history of literature in which researchers used above-mentioned 167 methods to analyse the impact of climate change on agricultural productivity across the globe.  Table 3, which reveal that all the variables are normally distributed.

189
Employing recently developed and advanced technique NARDL to assess the asymmetrical and non-linearity impact

272
To choose the maximum lag, we applied the general to a specific technique. Maximum lag of dependent and 273 explanatory variables are 2 (p=q=2) choose by the Akaike and Schwarz information criteria. Table 8 shows the results

274
of the asymmetrical relationship between the variables.      Table 9 represents the results of the asymmetrical short-run impact on rice productivity. The results infer that the     Table 11 shows various diagnostic results such as heteroscedasticity, serial correlation, normality, and Ramsey

317
RESET Test. P-value indicates there is no problem in the model, and data is normally distributed.

368
This study's outcomes may be vital for planning and strategy for policymakers to adopt appropriate environmental 369 policies and modern technology regarding precise climate forecasting, and precautionary and direct actions are also 370 expected to create and support an improved water system framework. In a nutshell, crop-specific research should be 371 conducted to highlight environmental problems. The Government should also take the initiative to cope with climate 372 change's harmful effects on agriculture's productivity. The Government should provide better irrigation facilities such as water canal, tube wells, and government can subsidise the electricity to cope with deficiency of rainfall, enhancing 374 rice production in India. To cope with climate change, the Government should set-up a team of two or three people to 375 give guidance and proper knowledge regarding the different aspects of climate change phenomenon at the block level 376 in India.