1. Introduction
Climate change imposes critical pressure on agricultural production that is already struggling to feed the burgeoning world population. The increasing temperature and changing precipitation patterns coupled with increasing extreme events have negatively affected crop yield around the globe and specifically in the lower latitudinal regions of the world (Dinu 2019). Several studies (Chandio et al. 2020; Guntukula 2019; Prăvălie et al. 2020) predict a substantial impact of changing climate on crop yield and productivity in the long run in Asia, one of the major rice producers of the world. Temperature rise has been predicted to reduce the rice yield by as much as 7% (Shabbir et al. 2020). Coastal regions have been identified as particularly vulnerable to these changing climatic patterns (Allen et al. 2018). Along with sea level rise, low-lying coastal systems are likely to be affected due to increased risks from extreme weather events (IPCC 2011) unleashing agrarian crises. Impacts on agricultural production have a serious bearing on food security on a local as well as regional scale, as low-income rural communities, including subsistence farmers, are expected to be more at the receiving end (Nooghabi et al. 2022). The coastal regions of South Asia are dominated by such populations (Chandio et al. 2020) and therefore suffer from food and nutritional insecurities due to the changing temperature and rainfall regime. The low adaptive capacity of these populations would further push them into the poverty tunnel (Reddy et al. 2021). Assessment of the impact of climate change on agriculture and food security in formulating a socially inclusive adaptation framework is therefore a major policy concern.
The coastal stretch of India that falls within the state of West Bengal is inhabited by almost 13.2 million (Ghosh et al. 2011) people who are directly dependent on agriculture as the primary source of livelihood. Recent studies have shown that the agricultural system in this region is already threatened by saltwater intrusion into coastal aquifers (Giri 2022), waterlogging (Ghosh and Mistri 2020a), embankment breaching (Ghosh and Mistri 2020b), and various agronomic problems. The changing climate is acting as a tipping factor. However, except for one generalized study by Chatterjee et al. (2016), not much is known about the pattern of changing climate in this region, which is particularly vulnerable due to climate extremes. The population residing in this region has a high poverty index value (Planning and Development Department, Government of Balochistan 2011), and any climate-induced perturbation will have a serious bearing on their livelihood and food security. Furthermore, there have been significant changes in agricultural land use in this region (Mondal 2012; Ojha and Chakrabarty 2018), with increased conversion of farmlands to aquaculture. Not only is there a critical research gap in establishing a connection between the changing climatic regimes and this land conversion in this region, there is also an overall knowledge gap regarding the critical impacts of the changing climatic parameters on the rain-fed agricultural system and agricultural production in the area. Not much is known about its consequences on the livelihood of the economically poor subsistence farmers. Therefore, an assessment of the extent to which the role of climate uncertainties and extremes has contributed to the agricultural distress needs to be explored, and this can best be done in a participatory way, as attempted in this study.
Various statistical methods have been used to bring out trends in longitudinal climate data, including nonparametric tests, which have been preferred (Chingombe et al. 2005) due to their robustness against missing values, tied values, nonnormality, and nonlinearity. The Mann–Kendall (MK) test is a widely accepted nonparametric test (Atta-ur-Rahman and Dawood 2017; Basarir et al. 2018; Nyikadzino et al. 2020; Solaimani et al. 2021) that tends to detect whether any monotonic trend is present in time series data (Kendall 1970). Contrarily, for the East Indian coast, several previous studies (Barnwal and Kotani 2013; Mishra and Sahu 2014; Sikder and Xiaoying 2014) have used panel analyses and cross-sectional analyses to examine the impact of changing climate on agriculture, whereas other studies (Bhuvaneswari et al. 2014; Kalra et al. 2007; Ramachandran et al. 2017) have adopted a biophysical process-based agroecological modeling approach. However, we argue that all these analytical frameworks provide only a partial understanding of the dynamics of agricultural land-use patterns in response to changing climatic regimes in a relatively broader spatial and social context. From this aspect, a satellite-based remote sensing approach plays a pivotal role in understanding the agronomic dynamics and changes in an agricultural system or the agricultural landscape at both spatial and temporal scales (Estes et al. 1978; Glenn et al. 2011). However, we emphasize that even though more efficient, any such study without proper ground truthing at the community level can be futile and will not be able to help in any policy formulation for the stakeholders. This can be done by making a thorough assessment of the risk perception of the communities inhabiting the risk-prone area. Therefore, it is necessary to triangulate the empirical knowledge base of the farming communities with the findings from the statistical as well as remotely sensed geospatial techniques to arrive at a comprehensive understanding of the impact of the changing climate on the agricultural structure. This study, therefore, is an attempt to investigate the change in the climatic pattern in the coastal blocks of the East Medinipur district of West Bengal to understand the extent to which the agronomic and land-use changes are caused by the changing climate patterns by combining statistical, geospatial, and empirical methods. The major objectives of this study are as follows:
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To analyze the temporal changes in the climatic regimes (especially temperature and rainfall regimes) along the coastal blocks of the East Medinipur district.
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To depict the spatiotemporal changes in the land-use pattern, with special reference to agricultural land use coupled with the changes in the land surface indices [normalized difference vegetation index (NDVI) and normalized difference vegetation index (NDWI)] and land surface temperature (LST) within the agricultural land use in the aforesaid region.
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To analyze the perception of the farming community regarding the biophysical as well as socioeconomic impacts of the changing climate on the different facets of the rain-fed paddy cultivation system and the associated consequences on land use in exploring the underlying causal relationship of climate change, agricultural land use, and the agricultural system.
2. Study area
The East Medinipur district is located in the southern part of West Bengal, in the coastal saline agroclimatic zone, and the study has been conducted along the two community development (CD) coastal blocks, namely, Ramnagar-I and Ramnagar-II (Fig. 1). The latitudinal extension of the area is 21°37′–21°47′N, and the longitudinal extension is 87°25′–87°46′E, and the study area is bordered by the Bay of Bengal in the south. Orissa is located in the western part; other CD blocks (e.g., Egra-I, Egra-II, and Contai-I) of the East Medinipur districts are located along the northwestern, northern, and eastern parts, respectively. The Pichaboni River borders the eastern part of the study area.
(a) Location of the study area, (b) false-color composite satellite image of the study area, and (c) distribution of different types of agricultural workers in the two blocks of the study area.
Citation: Weather, Climate, and Society 15, 3; 10.1175/WCAS-D-21-0175.1
The area is a low-lying plain. Some parts are even lower than the MSL. It actually consists of the littoral tract, which lies at the head of the Bay of Bengal (O’Malley 1911). Soil is fertile enough to cultivate paddies, but due to the proximity to the sea, sometimes the salinity of the soil is increased. Soil pH along the two CD blocks mostly ranges from 6.0 to 7.0, making it slightly acidic to neutral in nature, whereas the southwestern part of the Ramnagar-I block has slightly alkaline (pH > 7.0) soil. Most parts of the CD blocks have a medium (0.5%–0.75%) to low (<0.5%) amount of organic carbon, a very low amount of phosphorus (up to 45 kg ha−1), and a medium (150–340 kg ha−1) to high (>340 kg ha−1) amount of potassium (Sahu 2014). Agriculture and fishing are the two major sources of income for the inhabitants of the study area. Among the people engaged in agriculture, 42.53% and 37.21% are agricultural laborers and marginal farmers (farmers possess less than 1 ha of farming land), respectively, in the Ramnagar-I block, whereas the amounts are almost 44.73% and 35.13%, respectively, in the Ramnagar-II block (Department of Planning and Statistics 2014) (Fig. 1). Such a major concentration of farmers in the lesser hierarchical level of an agroeconomic sector certainly implies the lesser economic capability of the farming community of the study area, which in turn promotes the fact that any means of perturbation in the agricultural system will certainly lead to a greater degree of disruption in the livelihood as well as in the nutritional status of the farming community.
3. Methods
The entire study has been conducted using an integrated method of combining statistical techniques and remote sensing–based physical science approaches as well as questionnaire-based social science approaches. Long-term climatic data and remote sensing–based satellite images have been used as secondary data, whereas a questionnaire-based perception study of farmers has been taken as primary data in the study. A statistics-based trend analysis approach, on one hand, helps to delineate the long-term temporal changes in the climatic regimes, whereas a remote sensing–based multidated analysis of satellite images fosters the spatiotemporal changes of different dimensions of the land surface (e.g., land use, land surface indices, and land surface temperature), which, in turn, reflects the impact of changing climatic regimes on the land surface, specifically on the agricultural lands. Moreover, the questionnaire-based analysis of farmers’ perceptions helps to validate the results from statistical as well as geospatial analyses and gain an affirmed and comprehensive perception of the situation. The detailed methodological framework of the triangulation of statistics-, remote sensing–, and questionnaire-based approaches (Fig. 2) has been discussed in the following subsections.
Methodological framework of the study.
Citation: Weather, Climate, and Society 15, 3; 10.1175/WCAS-D-21-0175.1
a. Statistical assessment of longitudinal climatic data
Longitudinal gridded data of maximum temperature, minimum temperature, and rainfall (at a grid resolution of 1° × 1°) spanning 70 years (1951–2020) were downloaded from the website of the Indian Meteorological Department (IMD) (https://www.imdpune.gov.in/). The particular cell that contains the entire study area and its nearest IMD surface monitoring station (located at Digha) was selected, and the temporal dataset of 70 years was extracted using codes in a Python programming environment. The data were analyzed seasonally, decadally, and at 30-yr intervals using time series analyses. The Mann–Kendall trend test (Mann 1945) and Sen’s slope estimation (Sen 1968) techniques were used in the RStudio environment to discern trends in the time series data. The MK test is a nonparametric test to determine whether there exists any statistically significant monotonic trend in time series data using Kendall’s test statistic (Kendall 1970), whereas Sen’s slope examines the magnitude of such a trend. The effectiveness of using the MK test lies in its ability to avert problems raised due to data skew (Smith 2000), and it has been persuasively used in several studies (Gadedjisso-Tossou et al. 2020; Gocic and Trajkovic 2013; Nyikadzino et al. 2020; Solaimani et al. 2021) to determine trends in climatic time series data. The null hypothesis (H0) in the MK test predicts the absence of a significant trend in time series data, whereas the alternate hypothesis (Ha) assumes the existence of a monotonic trend (Kendall 1970). Prior to the application of the MK test, the presence of autocorrelation or serial correlation in the time series data of the climatic parameters is tested. Whenever a significant serial correlation is found, a prewhitening technique is applied to nullify the effect (Fuenzalida and Rosenbluth 1990). In this study, the MK test has been applied to the raw climatic data since no serial correlation was observed in any of the climatic parameters.
b. Land-use change detection through remote sensing
Cloud-free remotely sensed satellite images of 2000, 2008, and 2018 during the month of November were downloaded from the USGS EarthExplorer repository. Images from Landsat-5 TM (2000, 2008) and Landsat-8 OLI/TIRS (2018) sensors were selected for the study. Multispectral bands of both sensors have a spatial resolution of 30 m, which can suitably depict the spatiotemporal dynamics of the land surface. Moreover, the Landsat-5 TM sensor obtains six multispectral and one thermal band and the Landsat-8 OLI/TIRS sensor has nine multispectral and two thermal bands of different electromagnetic wavelengths. Therefore, both sensors can perfectly differentiate various land-use patterns using the multispectral bands as well as delineate the land surface temperature using the thermal bands. November was chosen for downloading images because November–December is the harvesting period for the rain-fed kharif (aman) crop in the region, is generally cloud free, and therefore suitably and clearly reflects the preharvest cropping land use. Downloaded images were radiometrically calibrated and atmospherically corrected using the FLAASH module in the ENVI 5.3 environment to omit the effect of atmospheric aerosols on the refraction of radiant energy (Pimentel et al. 2014). These calibrated images were then clipped as per the project area and were used for classification using the ArcGIS 10.2.2 environment using the supervised classification method. Supervised classification iteratively makes predictions from the training datasets, which the user assigns based on the differences in the reflectance of different bands and the user’s prior knowledge. For the image classification, six land-use classes were selected (i.e., vegetative land, agricultural land, agricultural fallow land, commercial fishing ground, marshy land, and other land use [land for drying of fish]). Training samples were selected for each of the training classes based on the nature of the spectral signature (Lillesand and Kiefer 1979) and were then classified using the maximum likelihood classifier (MLC) algorithm. For each classified image, the κ coefficient (which measures the statistical significance of the classification matrix) value remains greater than 0.80, which can be considered a substantially acceptable result. Further, the areal extensions of land-use classes were analyzed statistically.
c. Extraction of NDVI and NDWI and LST of agricultural pixels
Agricultural land use, extracted from the classified images, was further studied in detail using some band-oriented indices (e.g., NDVI and NDWI). NDVI is an indicator of vegetation health measured from its chlorophyll b content, while NDWI indicates the water content of the vegetation. In this study, NDVI and NDWI have been used to measure the harvest readiness of the paddy [the more harvest ready, the less chlorophyll b in leaves and therefore the lower the NDVI and the less water content in leaves (i.e., lower NDWI)]. Measured at a particular time of the year, these two values would indicate how harvest ready the crop is at that juncture. LST, on the other hand, depicts the surface temperature distribution of the pixels, and the LST of agricultural pixels will certainly help in understanding the impact of change in the ambient temperature on the surface environment of the agricultural fields. Detailed calculations for NDVI, NDWI, and LST are discussed in the appendix.
d. Perception of farmers regarding changing climate and its impact on agriculture
The primary data used in this study were the perception of the farmers on the changing climatic pattern in the area and the impact of these changes on the agricultural system. A total of 228 farmers from the two blocks (104 from the Ramnagar-I block and 124 from the Ramnagar-II block) were selected through purposive sampling and were interviewed using a bilingual (in English and in the local Bengali language) semistructured questionnaire between July 2019 and April 2022. The survey schedule was interrupted between March 2020 and November 2021 due to the outbreak of the first and second waves of the COVID-19 pandemic; hence, the actual survey was done between July 2019 and March 2020 and March 2022 and April 2022. Dolisca et al. (2007) and Tran and Perry (2003) discussed the effectiveness of the purposive sampling technique to segregate the target populations of their respective studies. Because the vulnerability of the farmers residing nearer to the coast is greater than that of farmers residing farther from it, 50% of the respondent farmers selected reside within a distance of 0–2 km from the coast, 30% reside within 2–4 km, and the rest reside more than 4 km. Adequate written consent (both in English and the native Bengali language) of the farmers was taken before engaging them in the survey. No farmer’s name or personal details have been disclosed in the entire study to maintain the anonymity of the farmers. Post facto approval from the ethics committee of the university has also been obtained.
Along with personal information (age, academic qualification, economic possessions, farming experience), information on agricultural possessions (amount of land, type of land), and information on the practiced agricultural system (type of farming, type of crops produced, type of seeds used, type and amount of fertilizer used, amount of pesticide used, cropping calendar), perceptions of the farmers were also collected based on a five-point Likert scale (highly increase, increase, no change, decrease, highly decrease) based on the following two major aspects:
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Whether the farmers think the climatic parameters (e.g., temperature; rainfall during sowing, growing, and harvesting periods; rainfall intensity and erraticism; dry spell; cyclone; storm surge) in the project area have changed in the last 30 years.
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If changed, how far such changes have affected production, soil salinity and fertility, amount of pest attacks, and especially the overall agricultural system.
4. Results
a. Analysis of climatic scenario
The climatic regime in East Medinipur started to change noticeably after the 1950s, and the change has intensified since 1991. The yearly average minimum temperature has increased at a rate of 0.008°C yr−1 since 1991 (Fig. 3b), and the change is even more pronounced (0.02°C yr−1) for the yearly average maximum temperature (Fig. 3a). The yearly average postmonsoon temperature has also increased since 1991 at 0.01°C yr−1 (Fig. 3c).
Trends of (a) yearly average maximum temperature, (b) yearly average minimum temperature, and (c) average postmonsoon maximum temperature (inset: trend of average postmonsoon maximum temperature from 1951 to 2020) from 1991 to 2020.
Citation: Weather, Climate, and Society 15, 3; 10.1175/WCAS-D-21-0175.1
The two major components of the precipitation regime [e.g., monthly wet-day frequency and monthly intensity of rainfall (rainfall/rainy day)] show an interesting trend. The number of wet days in the months of June, July, August, and September has consecutively decreased from 1991, which has resulted in an overall reduction in wet days in the monsoon season (Fig. 5). Moreover, rainfall intensity in the premonsoon and monsoon seasons has also decreased since 1991 (Figs. 4b,c). On the other hand, rainfall intensity postmonsoon and even in the winter season has risen at rates of 0.05 and 0.07 mm day−1 yr−1, respectively, since 1991 (Figs. 4d,a). The pattern of change in the seasonal precipitation regime certainly depicts a shift in the onset of the monsoon in the region.
Trends of monthly rainfall intensity for (a) winter, (b) premonsoon, (c) monsoon, and (d) postmonsoon seasons from 1991 to 2020.
Citation: Weather, Climate, and Society 15, 3; 10.1175/WCAS-D-21-0175.1
Trends of monthly wet-day frequency for (a) June, (b) July, (c) August, (d) September, and (e) monsoon season from 1991 to 2020.
Citation: Weather, Climate, and Society 15, 3; 10.1175/WCAS-D-21-0175.1
The Mann–Kendall test and Sen’s slope estimation revealed that the yearly average maximum temperature of July, August, September, October, and the monsoon season has increased most significantly (p < 0.01) over seven decades (1951–2020), whereas the increase is less significant in the case of November (p < 0.05) and the postmonsoon season (p < 0.1) (Table 1). On the other hand, the yearly average minimum temperature in July, November, and the monsoon season showed a significant (p < 0.1) increasing trend from 1951 to 2020, with Sen’s slope values of 0.005°, 0.013°, and 0.004°C yr−1, respectively (Table 1). The MK test certainly portrays monsoonal and postmonsoonal increases in the temperature regime in the project region.
MK test with Sen’s slope estimation showing the monotonic trend of different climatic parameters from 1951 to 2020. One, two, or three asterisks indicate that the result is significant at the 90%, 95%, or 99% confidence level, respectively.
b. Analysis of land-use classification
The supervised classified images (Fig. 6) of 2000, 2008, and 2018 reflect some typical features of land-use change in the region during the last 18 years. As Table 2 shows, 4.63% and 3.4% of agricultural land has been transformed into fallow land and vegetative land, respectively, from 2000 to 2008. The result is more significant from 2008 to 2018 (Table 3), where 9.56% and 5.12% of agricultural land has been converted to fallow land and vegetative land, respectively. As a result, the agricultural land use in the area fell from 16.85% in 2000 to 11.79% in the year 2018. Concomitantly, the extent of agricultural fallow land rose from 17.46% in 2000 to 26.27% in 2018. This land-use shift from agricultural land to fallow land or land under seminatural vegetation is most prominent in the northeast, northwest, and central parts of the region.
Spatiotemporal distribution of different land-use classes in the study area for 2000, 2008, and 2018.
Citation: Weather, Climate, and Society 15, 3; 10.1175/WCAS-D-21-0175.1
Land-use transition matrix showing the transformation of different land-use classes from 2000 to 2008.
The extent of marshy land (fish cultivation dykes within farm holdings) is quite dynamic in the region in the three time periods. Conversion of 3.65% and 2.43% of marshy land into agricultural land and agricultural fallow land, respectively, was observed from 2000 to 2008 (Table 2), whereas 2.07% of agricultural land and 3.38% of fallow land have been converted into marshy land from 2008 to 2018 (Table 3), making an overall 2.66% increase in the area of marshy land. As apparent from Fig. 6, this is a prominent feature along the right bank of the Pichaboni River (bordering the extreme east of the study area). The commercial fishing ground has also increased by 1.38% due to substantial transformation from “other land use” (fish-drying land) in 2000–08, whereas it decreased by 1.17% mostly by being transformed into “other land use” (1.68%) (Fig. 6).
c. Analysis of NDVI and NDWI of agricultural land
The maximum, minimum, and mean values of both NDVI and NDWI for November in the agricultural land increased in successive years (Table 4). The mean NDVI and NDWI values in 2000 were −0.09 and −0.16, respectively, whereas they became 0.09 and −0.07, respectively, in 2008 and further increased in 2018 (0.15 and 0.10, respectively). This indicates that crops are increasingly less harvest ready or are younger in November in successive years, and that implies a shift in the cropping calendar.
Yearwise distribution of minimum, maximum, mean, and standard deviation values of NDVI and NDWI.
d. Analysis of LST of agricultural land
The LST of the agricultural fields rose slightly between 2008 and 2018 (Fig. 7). The maximum, minimum, and mean LSTs for 2008 were 25.11°, 21.62°, and 23.03°C, respectively, whereas these became 26.03°, 21.95°, and 23.23°C, respectively, in 2018. The mode value of the LST for 2008 was almost 22.80°C, and it became almost 23.2°C in 2018.
Distribution of LST of pixels containing agricultural land use in 2008 and 2018.
Citation: Weather, Climate, and Society 15, 3; 10.1175/WCAS-D-21-0175.1
e. Analysis of farmers’ perception
Agriculture is one of the prime economic activities in the study area and has been practiced for centuries. Therefore, all the respondent farmers possess more than 15–20 years of farming experience. Of the respondent farmers, 42.9% and 28.6% possess less than 0.27 ha (2 bigha in the native units) and less than 0.67 ha (5 bigha) of agricultural land, respectively, which reflects the dominance of economically worse-off marginal farmers in the study area. Because of the lack of irrigation facilities, almost 83.27% of the respondent farmers cultivate only aman (rain fed) paddies and do not go for boro (irrigation based) paddy cultivation. All of the farmers use high-yielding variety (HYV) cultivars commonly known as dudher swar, swarna masuri, pratiksha, and santoshi (in the native language), to name a few. Farmers usually apply fertilizer three times to the land during the entire cropping season (i.e., before the initiation of the transplantation period, during the growing period, and just before the milking period).
From the farmers’ perception, it is observed that most of them believed that the temperature has changed (50.75% believed it has “highly increased” and 31.69% believed it has “increased”) during the last 30 years (Fig. 8); 81.55% of the respondents believed that dry spells have increased, whereas 14.29% and 79.76% believed that erratic rainfall has “highly increased” and “increased,” respectively, during the aman paddy cultivation season; 30.71% of respondents felt the frequency of cyclones has “highly increased,” while 56% of the farmers felt the occurrence of cyclones has “increased” over the last 30 years; and 19.48% and 70.67% of the farmers believed seawater intrusion has highly increased and increased, respectively (Fig. 8).
Farmers’ perception on changing nature of climatic elements and the impact on agriculture in the last 30 years (1991–2020).
Citation: Weather, Climate, and Society 15, 3; 10.1175/WCAS-D-21-0175.1
The impacts of the above are evident by the fact that 80.26% of the farmers have delayed their sowing period of the aman crop by almost a month (from July to August). The remaining 19.74%, who still have not shifted their cropping calendar, expressed that they are at risk of crop failure. Moreover, farmers also complained of crop loss from harvest-ready plant lodging and waterlogging due to heavy rain. Farmers also reported an increase in expenditure on labor costs to save the plants, which cut into the profit. Regarding pest attacks on their crops, 44.31% and 45.57% of farmers believed they have highly increased and increased, respectively, in the last 30 years (Fig. 8); 63.26% of the farmers felt that soil salinity has increased and 72.74% believed soil fertility has declined in the last 30 years.
5. Discussion
Climate change and climate variability cause severe uncertainties in agriculture, and both the magnitude and intensity of this are far greater in a coastal region. In the study region, there has been a relative increase in cyclones, sea level rise, and storm surges in the recent past, which has severely impacted the agricultural system. The present study clearly brings out these changes in agriculture and land use in the study region and attributes them to the changing climate.
There seems to be a clear indication of a significant increase in the temperature regime in the project region. The temperature keeps rising from the monsoon (i.e., June) and continues to rise almost throughout the year. Such an increasing trend in temperature for India has also been reported by Kothawale et al. (2010). A postmonsoon increase in the temperature found in our study was also corroborated earlier by Dash et al. (2007). This increase in the temperature, especially during the paddy crop spikelet formation and the flowering stage, has a severe negative impact on rice production. Several studies (Abbas and Mayo 2021; Srivani et al. 2007) have shown that the increase in minimum temperature results in a reduced number of tillers and therefore a fall in productivity. Furthermore, the decrease in rainfall intensity and the number of rainy days in the premonsoon and monsoon seasons combined with the increasing temperature reduces the available moisture content in the soil, which, in turn, impacts the stem elongation process, hampering production. The increase in temperature during the reproductive and ripening stages also impacts the production negatively, as mentioned by Abbas and Mayo (2021). This increase in the ambient temperature consequently increases the LST, which was also reported earlier for East Medinipur (Das Malakar 2020). It can certainly be assumed that the significant yearly increase in the temperature regime has resulted in an increase of almost 0.2°C in the mean LST of the agricultural fields from 2008 to 2018. Besides the phenology of rice plants, one more significant impact of this increasing temperature regime is the increase in pest attacks, which has also been corroborated by the farmers. Studies (Karuppaiah and Sujayanad 2012; Das et al. 2015) have predicted that a lower increase in temperature up to 35°C will favorably affect the survival and growth rate of almost all the pests and insects affecting rice plants. A study by Deutsch et al. (2018) predicted that production loss of rice due to pest attacks can be almost 59% in South and Southeast Asia in a warming climate. The same critical scenario is also reflected by the farmers’ perception in this study. Almost 44.31% and 45.57% of the respondent farmers in our study expressed a “high increase” and “increase” in pest attacks, respectively, in the last 30 years. Consequently, the consumption of pesticides has also subsequently increased over the years, further inflating the cost of farming and hence farm profitability.
The analysis of the rainfall regime clearly indicates a delayed arrival of the monsoon and a rainfall peak postmonsoon. Such a delayed arrival of the monsoon in South Bengal has been reported earlier by Chatterjee et al. (2016), whereas Hazra (2002) reported a marginal rise in postmonsoon rainfall in the study region from 1970 to 2005. Such an increase in rainfall during the ripening phase (i.e., November) causes muddy water within the rice fields, which blocks the pores in the plants, thus hampering respiration and photosynthesis and thereby reducing production (Balasubramanian et al. 2004).
Reduction in farm profitability has clearly led to land conversion. The conversion rate of agricultural fallow land to agricultural land has subsequently decreased (4.99% in 2000–08 and 2.11% in 2008–18), whereas the opposite has taken place (4.63% in 2000–08 and 9.56% in 2008–18). Such systematic duality in the land-use conversion scenario certainly reveals the tendency of decreasing agricultural operations. During our survey, farmers reported that under an increasing temperature and erratic rainfall regime, maintaining the traditional cost-effective cropping practice has become uncertain, forcing them to opt out of agriculture and into a nonagricultural livelihood. Conversion of farmlands to fishing dykes seems to be the alternative for which farmers are opting. In 2000–08, 1.44% of agricultural land and 4.90% of agricultural fallow lands were converted into aquacultural marshy lands (Table 2), whereas the amounts became 2.07% and 3.38%, respectively, in 2008–18 (Table 3). This change also is an occupational shift (i.e., from farming to aquaculture). Unscientific means of transformation of cultivated lands into aquacultural lands have been quite well observed in the case of the West Bengal coast (Dutta et al. 2016; Rajakumari et al. 2020) as well as on the Bangladesh coast (Ali 2006; Gurung et al. 2016; Morshed et al. 2020). According to the farmers, this shift is mainly due to farming stress caused by shifts in the rainfall pattern as well as to short-term economic profit from aquaculture. Mondal et al. (2013), Ojha and Chakrabarty (2018), Rajakumari et al. (2020), and Samanta and Paul (2016) reported similar transformation of cropland into aquaculture farms in the East Medinipur coastal region. A detrimental effect of aquaculture on surrounding agricultural land (i.e., seepage of saline water in the nearest agricultural land, affecting productivity; increase in soil pH and soil salinity of nearby agricultural land) in the study area was reported earlier by Ojha and Chakrabarty (2018). The “high increase” and “increase” of soil salinity have also been noted by almost 25.43% and 63.26% of respondent farmers, respectively, in this study. Previous reports from the study area implicate the inadequacy of irrigation facilities as a factor in land transformation to aquaculture (Mondal 2012). However, no previous study has shown climate change to be a reason behind such land transformation, as is clearly evident from the present study.
Consecutively, as the estimation of NDVI is solely dependent on the chlorophyll content of plants, this is a good indicator of the phenology of plants (Boori et al. 2020). The nature of the spectral signature in the near-infrared (NIR) region steeply increases in the growing stage of the plant, leading to an increase in NDVI, whereas it falls as the plant ages (Boori et al. 2020; Boschetti et al. 2009; Pan et al. 2015). On the other hand, NDWI, which estimates the water content of the plants, has also been identified as a good indicator of the growth stage of plants by Gao (1996). Younger paddy plants show greater NDWI values when compared with plants in other phenological phases (Delbart et al. 2005; Tornos et al. 2015). As evident from increasingly the higher NDVI and NDWI values of the agricultural field in our study area in November (traditional harvesting period) (Fig. 9), it is clear that the crops are increasingly not as harvest ready at that period as they used to be. Our analyses of the climatic and the land-use data clearly indicate this shift in the cropping period (i.e., the crop calendar is underpinned by the climatic shift). Additionally, the Pearson’s correlation coefficient value between the LST and NDVI of November increases tremendously from 2008 to 2018 (−0.18 in 2008 and −0.73 in 2018). The negative relationship between LST and NDVI has been used as an index for soil moisture content in several studies (Carlson et al. 1994; Price 1990; Son et al. 2012; Sun and Kafatos 2007). High LST causes increased evapotranspiration that results in a decrease in soil moisture and consequently a decline in NDVI (Carlson et al. 1994; Price 1990). The increasing degree of negative correlation between LST and NDVI as observed in November indicates that the crop is becoming more water stressed in November, which may further lead to crop sterility (Julia and Dingkuhn 2013) and affect production.
(top) NDVI and (bottom) NDWI across the study region in 2000, 2008, and 2018.
Citation: Weather, Climate, and Society 15, 3; 10.1175/WCAS-D-21-0175.1
These findings are further corroborated by the community-based perception survey. As a larger proportion of the farmers residing in the study area are small and marginal farmers, they follow agriculture mainly as a yearly source of staple food and sell a smaller portion of production for economic profit. Hence, the impact of changing climatic parameters is critically affecting the agricultural system, which, in turn, is affecting food and nutritional security. According to the farmers, maintaining the traditional crop calendar has become challenging due to the prominent shift in the onset of the monsoon in July. Traditionally, the seedbeds are prepared in July. Because of the late arrival of the monsoon, this has now shifted by almost a month due to the unavailability of irrigation at this time. This is all the more true for the subsistence farmers, who cannot afford groundwater and are dependent solely on the monsoon. Due to their weaker economic ability, farmers tend to avoid buying rice for daily consumption to lower the household expenditure; hence, although they are trying to maintain the agricultural system with their best efforts, a sense of fear and anxiety over crop loss and lesser yield due to climatic perturbations is increasing with each passing year. As reported by the farmers, this shift has, however, further added to the farmers’ plight since the rise in the postmonsoon rainfall (as observed in our analysis of the rainfall trends) and the effects of cyclones are damaging the harvest-ready standing crops. Although the low-yielding local varieties of paddy seeds (locally known as swarna masuri, tangra, malabati, etc.) bestow a more stable production relative to the HYV seeds during drought-prone or flood-prone situations, farmers insist on using the HYV ones to meet the deficit of production of past years. Hence, during consecutive years of greater climatic variability, the situation becomes worse.
Moreover, farmers have also reported an increase in the seawater intrusion in the region. Several studies (Gopinath et al. 2016; Narayan et al. 2007; Sappa et al. 2015) corroborate the same along the East Indian coast. Therefore, farmers living in comparatively low land areas with worse drainage systems are trying to shift their prime livelihood from agriculture to other off-farm activities. Most of the young members of the farming households are fleeing to the urban center of Kolkata or other states of India (especially Kerala and Tamil Nadu) for job opportunities, which is also changing the demographic characteristics of the region. Almost 92.38% of the respondent farmers in the study are older than 35 years. A report by FAO (2017) has adroitly mentioned that climate change intensifies the socioeconomic drivers of rural migration, and such a situation is rightly observed in this study. As this study shows, the older people are converting their agricultural land into commercial fishing grounds or marshy lands for prawn cultivation, which is also seen from the land-use maps. Although this practice of prawn cultivation is somewhat economically beneficial, it increases the salinity of the surrounding lands. In such a scenario, rice production along the surrounding agricultural lands gets decreased, and farmers also tend to convert those lands into fishing grounds. Such conversion, however, may not be ecologically sustainable ultimately.
Last, but not least, the farmers have also corroborated the indifferent attitude of the local rural institutions (locally known as the gram panchayet) regarding the distressed situation of agriculture due to the changing climate. No means of training or agricultural extension services are being provided by the local rural bodies; henceforth, whatever the decisions the farmers take to revive the agricultural system in a changing climatic regime are completely intuitive and lack a structured scientific premise. Therefore, a sense of institutional noncompliance is predominant among the farmers, which, in turn, is contributing to an undercurrent of political unrest in the region. Such sociopolitical issues evolving due to agronomic distresses in changing climatic regimes should be analyzed in more detail in future studies for better comprehension.
6. Conclusions
The climatic components are the most important factors that regulate the performance of rain-fed paddy cultivation, and in the Indian scenario, the sheer impact of climatic perturbation on the production and yield of rain-fed paddies is well observed in different studies (Barnwal and Kotani 2013; Bhuvaneswari et al. 2014; Srivani et al. 2007). Moreover, the additional burden of increasing cyclonic activities coupled with seawater intrusion has made the situation more complex in coastal areas. The present study, therefore, tries to examine the changing pattern of the climatic elements (especially temperature and rainfall) in a coastal part of eastern India and its impact on the proficiency of the agricultural system. Furthermore, without completely depending on data-centric quantitative methodologies, this study tries to triangulate statistical approaches, remote sensing approaches, and community perception–based approaches to gain a holistic comprehension of the situation.
There seems to be a clear and statistically significant shift in the monsoonal rainfall pattern and temperature regime (especially in the postmonsoon season). This affects rice productivity due to its impacts on crop plant respiration and photosynthesis, tilling, and pest resurgence. Moreover, the increasing nature of the NDVI and NDWI of agricultural pixels in the month of November certainly depicts a shift in the crop calendar in the region. The impact of this production loss, changing cropping calendar, and consequent reduction of farm profitability has led to farmland conversion to aquaculture dykes. Interestingly, all the geospatial results and predicted impact of the changing climate on agrological dynamics are corroborated by the farmer perception survey, and it was found that the idea of changing climatic components is quite prominent among most of the respondent farmers. The plausible impacts of the changing climate on agriculture (i.e., crop loss, pest resurgence, and increased soil salinity due to seawater intrusion) have also been corroborated by the farmers. Moreover, the increase in the economic burden of agricultural practice due to the sheer effects of changing climatic regimes has been reflected in the farmers’ survey. Last, but not least, the accession in the trend of off-farm activities among the young household members of the farming community and the tendency toward converting agricultural lands into fishing activities have also been substantiated by the farmers. This study, therefore, underscores the effectiveness of an integrated approach that brings together statistics-, remote sensing–, and community perception–based information and thereby keeps out any possible bias. Such an integrated methodological framework is completely new in terms of the study area, and it tries to contribute to the present-day multifaceted and multidisciplinary approach in the discourse of climate change studies.
Although this study tries to coalesce physical science and social science approaches, it certainly is not devoid of limitations. The study was interrupted due to the sudden outbreak of the COVID-19 pandemic; hence, the field surveys, as well as the compilation of the data, became more time consuming. Freely available multispectral images (Landsat-5 TM and Landsat-8 OLI/TIRS) with a spatial resolution of almost 30 m have been used in the study, whereas images with a finer spatial resolution (i.e., LISS-IV images with almost 5-m spatial resolution) could have been a better choice for a more precise analysis of agricultural pixels. However, the study did not have any budgetary allocation for such paid datasets. Finally, the availability of microscale (block level) secondary data on temporal change in agricultural production and other related datasets on agricultural practice (e.g., consumption of fertilizer and pesticide, area-specific differentiation on types of fertilizers used) is scarce in the region. Analysis of such data could culminate in more insights into the study.
The key findings of the study that emerged from our triangulated approach call for immediate policy interventions in the region. Changes in the climatic parameters coupled with increasing cyclones and seawater intrusion are quite pronounced in the region, and the farming community is also aware of these facts. Moreover, the sheer impacts of such changes on different facets of the agronomic system are also manifested in the farmers’ perception. Therefore, a participatory management approach should be the best possible strategy, where the knowledge from scientists and policy makers and the perception of the farmers should be integrated to formulate a comprehensive policy framework. More microlevel agricultural planning is also an urgent need to cope with the area-specific agricultural problems in a changing climatic domain. More climate-specific agricultural insurance and compensation schemes and more agricultural extension programs to brainstorm how to move the farming communities toward sustainable climate-smart agricultural practices should be introduced by the government and local administrative bodies, which, in turn, will ensure nutritional security, and thus, a sense of social security can also be developed among the farming community. Such a sense of social security may act as a psychological boost among the farming community, and thus, the rigorous decremental trend in agricultural practice can also be reversed. Thus, organized and collocated efforts from different stakeholders could emerge as a probable aid to sustain the agricultural system under a changing climatic paradigm on the eastern coast of India, and such a framework could also be simulated as a management tool in a similar regional setup, especially in coastal areas of developing countries.
Acknowledgments.
The authors express their sincere gratitude to all the farmers who gave their valuable time and effort by participating in the primary survey. The authors also thank the anonymous reviewers for their valuable comments and suggestions that have helped to improve the quality of the paper. Post hoc ethical approval for the study (Reference CUIEC/03/13/2022-23) has been obtained from the “Institutional Ethical Committee for Bio Medical and Health Research Involving Human Participants, University of Calcutta,” dated 8 March 2023. This study was funded by the Department of Science and Technology and Biotechnology, government of West Bengal. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Data availability statement.
All longitudinal climatic data are freely available from the official website of the Indian Meteorological Department (https://www.imdpune.gov.in/lrfindex.php), and satellite images are also freely available from the USGS EarthExplorer repository (https://earthexplorer.usgs.gov/). Because of ethical and confidentiality concerns, questionnaire-based survey data cannot be made available.
APPENDIX
Calculations for Remote Sensing–Based Analyses
a. Calculations adopted for NDVI and NDWI
b. Calculations adopted for LST
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