Several natural and anthropogenic activities mainly escalate the demand for water and food and develop implications for their availability. Hence, it is necessary to analyse the natural and man-made changes for managing resources precisely. This study focuses on assessing the trends of climatic factors such as rainfall and temperature using the Mann–Kendall (MK) test and innovative trend analysis (ITA) for the agricultural cropping seasons. The findings revealed that a statistically insignificant increasing trend in rainfall is observed for all three cropping seasons (i.e.) Kuruvai/Samba (June–September), Thaladi/Navarai (October–January), and Sornavari (February–May), while the mean temperature shows a significantly increasing trend for all three cropping seasons. The results obtained using the MK test are compared with those of the ITA test to identify the reliable trend technique for climatic factor analysis of small regions. To manage the impacts of climate change and develop sustainable conditions in agriculture, this micro-level study assists the decision makers to prefer suitable trend analysis techniques and helps in identifying the climate change adaptation strategies for small regions. Also, this study is helpful for rural farmers to increase their adaptability conditions with regard to the climate change impacts as they are the frontline victims of climate change.

  • Climate change is one of the important challenges the world is currently facing and highly affects the environment and ecosystems.

  • The changes in climatic factors directly cause great concern to the agriculture sector.

  • To reduce the consequences of climatic variability on agriculture, it is necessary to analyse the rainfall and temperature trends to maintain agricultural sustainability.

Climate change is one of the important challenges the world is currently facing and highly affects the environment and ecosystems. The intensity of climatic changes varies with the geographical locations and anthropogenic activities (Marie et al. 2021). After the globally increasing population, one of the main causes of water and food scarcity is the global climate changes resulting in an average increased temperature and declining rainfall (Gebremicheal et al. 2014). All these parameters eventually influence the agricultural activities. However, agriculture is a prime sustenance of livelihood in India, where half of the population depends on agriculture and its allied sectors (Baanu et al. 2022). Therefore, it is necessary to observe and analyse climatic data, since almost all the crops and agricultural products and by-products are directly linked with the temperature and rainfall (Abeysingha et al. 2014). Due to the persistent rise in water demand, longer production times and lower farm earnings, the agriculture sector is directly affected by climate change and also confronts resource limitations. Therefore, alternate adaptation strategies are required for the conventional systems to overcome climate change issues. The analysis of climatic fluctuations is essential to combat climate crisis, maintain agricultural sustainability, and enhance the farmers' livelihood.

Several researchers have carried out the analysis of the trends in climatic changes. One of the greatest techniques for analysing rainfall and temperature trends is the Mann–Kendal (MK) test (Mann 1945; Kendall 1975), which may also be used as the best water management tool for effective watershed protection (Tabari & Talaee 2011; Jain & Kumar 2012; Nouri et al. 2017; Alhaji et al. 2018; Chand et al. 2020). Jain & Kumar (2012) described the direction and strength of trends using the MK test and Sen's slope estimator. Chand et al. (2020) examined the temperature trends persisting on the Seonath River Basin and found that the summer season had the strongest warming trends, followed by the winter, pre-monsoon, and post-monsoon seasons. The temperature trend increases over time, which might bring about extreme weather events due to climate change, and the rainfall trends are significantly decreasing in many regions and increasing in a few (Palaniswami & Muthiah 2018; Alemu & Dioha 2020; Isabella et al. 2020; Tossou et al. 2021). This rainfall and temperature variability results in the rise in frequency of extreme weather events, also affecting crop production, increased weed and insect proliferation, and groundwater depletion, and their detrimental implications can go beyond the effects of changing rainfall and temperature (Sathischandra et al. 2014; Siddig et al. 2020; Tabari 2020). To reduce the consequences of climatic variability on agriculture, the study recommends the analysis of rainfall and temperature trends to maintain agricultural sustainability.

Even though the MK test is frequently used in hydrological trend analysis, earlier reports suggested that the existence of autocorrelation (the degree to which the time series resembles a lagged version of itself over subsequent time intervals) can affect the capacity to detect trends using MK and Sen's slope (Pastagia & Mehta 2022). Perhaps to overcome the setbacks of the MK test, Sen (2012) created a novel technique called the innovative trend analysis (ITA), to identify the trends in climatic, hydrological, and air pollution variables (Mallick et al. 2021). ITA has gained popularity as a statistical approach since it is independent of sample size and distribution, and another key benefit is that it displays trends in graphical form (Gedefaw et al. 2018). The ITA approach evaluates the data points that are with the 1:1 line in a Cartesian grid.

Based on the literature, it is evident that these researchers either used MK test or ITA test to interpret the various factors regarding climatic change. But no detailed work was reported about the effect of rainfall and temperature during cropping seasons pertaining to the area of study chosen using MK and ITA tests. In this work, it is planned to use the MK and ITA approach to investigate the trends in rainfall and temperature regarding various cropping seasons in a taluk (a subdivision of a district) and compare them to evaluate the most reliable technique for small-scale regions. The findings of the study would help the policymakers and rural farmers increase their preparedness for climate change and assess sustainable solutions. It is a micro-level representation of large regions that examines the patterns of seasonal rainfall and temperature variability using the trend analysis tool and evaluates the most preferred technique.

Study area

The proposed study area (Figure 1) is the Rajapalayam taluk in the Virudhunagar district of Tamil Nadu, India. It is situated on a low-lying plain near the eastern foot of the Western Ghats. The geographical coordinates of the region are 9.4515°N and 77.5543°E and it depends mainly on the northeast monsoon. The taluk has a population of 130,442 of which 64,765 are men and 65,677 are women per the report released by Census India 2011. The total area of Rajapalayam is 465.83 km2 with a population density of 746/km2. Of the total population of 1,942,288, 44.55% lives in urban areas and 55.45% lives in rural areas per the Census 2011 data.
Figure 1

Location of the study area.

Figure 1

Location of the study area.

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The climate is generally hot and dry and the annual average temperature ranges between 22 and 40 °C. The region receives an average annual rainfall between 724 and 913 mm/year. The major soil type in the area is red loamy and black clay soil with cotton, paddy, and maize as the most predominant crops. The observed rainfall and temperature data are divided into three important categories based on the state's agricultural cropping seasons; Kuruvai/Samba (June–September), Thaladi/Navarai (October–January), and Sornavari (February–May) seasons.

Data collection

Climatic data such as rainfall and temperature available for 2007–2021, i.e., for a 15-year period, were acquired from the Public Works Department (PWD) of Rajapalayam taluk, Virudhunagar district, and the work flow diagram is presented (Figure 2). The mean annual, monthly, and seasonal rainfall (Table 1) and the mean temperatures (Table 2) of the study area were calculated from the observed data to study the trends.
Table 1

Monthly rainfall data of the taluk for the years 2007–2021

YearMean monthly rainfall (mm)
Annual rainfall (mm)Cropping seasons
JanFebMarAprMayJunJulAugSepOctNovDecKuruvai (Jun–Sep)Thaladi (Oct–Jan)Sornavari (Feb–May)
2007 27.20 0.00 0.00 70.50 30.00 31.00 0.00 32.20 18.70 204.20 111.20 133.00 658.00 81.90 475.60 100.50 
2008 0.00 20.00 382.50 40.00 33.40 4.20 35.00 168.60 21.00 262.60 144.60 16.60 1128.50 228.80 423.80 475.90 
2009 29.20 0.00 95.80 17.20 85.20 0.00 0.00 0.00 70.60 11.80 198.70 25.40 533.90 70.60 265.10 198.20 
2010 0.00 0.00 0.00 21.40 60.60 23.60 18.40 0.00 79.40 380.10 229.20 120.30 933.00 121.40 729.60 82.00 
2011 0.00 39.00 22.00 139.20 1.10 11.40 0.00 46.80 53.00 206.00 342.60 8.60 869.70 111.20 557.20 201.30 
2012 6.40 0.00 0.00 57.00 134.60 1.50 72.40 0.00 33.60 221.20 16.80 1.00 544.50 107.50 245.40 191.60 
2013 1.40 67.20 57.00 23.40 15.40 24.80 0.00 193.30 98.60 229.40 187.40 53.80 951.70 316.70 472.00 163.00 
2014 2.60 6.60 44.60 0.00 94.80 27.80 6.20 143.20 59.00 188.80 160.60 32.20 766.40 236.20 384.20 146.00 
2015 15.80 5.00 28.40 66.60 146.70 27.60 18.00 50.20 109.60 82.00 459.60 120.80 1130.30 205.40 678.20 246.70 
2016 0.00 0.00 0.00 14.00 72.00 0.00 34.80 2.60 6.00 84.00 66.60 80.00 360.00 43.40 230.60 86.00 
2017 38.40 10.40 47.80 35.00 165.20 1.80 15.00 70.30 148.80 66.60 93.00 132.40 824.70 235.90 330.40 258.40 
2018 0.00 33.00 20.60 56.20 135.90 7.10 49.10 12.30 194.40 158.70 92.40 7.60 767.30 262.90 258.70 245.70 
2019 4.00 3.00 0.00 50.00 63.00 86.00 0.00 87.00 196.00 191.00 75.00 56.00 811.00 369.00 326.00 116.00 
2020 0.00 0.00 0.00 20.00 234.00 24.00 77.00 69.00 61.00 155.30 306.70 96.90 1043.90 231.00 558.90 254.00 
2021 69.00 0.00 8.00 13.00 40.00 16.00 66.00 8.50 35.00 98.00 443.00 164.00 960.50 125.50 774.00 61.00 
YearMean monthly rainfall (mm)
Annual rainfall (mm)Cropping seasons
JanFebMarAprMayJunJulAugSepOctNovDecKuruvai (Jun–Sep)Thaladi (Oct–Jan)Sornavari (Feb–May)
2007 27.20 0.00 0.00 70.50 30.00 31.00 0.00 32.20 18.70 204.20 111.20 133.00 658.00 81.90 475.60 100.50 
2008 0.00 20.00 382.50 40.00 33.40 4.20 35.00 168.60 21.00 262.60 144.60 16.60 1128.50 228.80 423.80 475.90 
2009 29.20 0.00 95.80 17.20 85.20 0.00 0.00 0.00 70.60 11.80 198.70 25.40 533.90 70.60 265.10 198.20 
2010 0.00 0.00 0.00 21.40 60.60 23.60 18.40 0.00 79.40 380.10 229.20 120.30 933.00 121.40 729.60 82.00 
2011 0.00 39.00 22.00 139.20 1.10 11.40 0.00 46.80 53.00 206.00 342.60 8.60 869.70 111.20 557.20 201.30 
2012 6.40 0.00 0.00 57.00 134.60 1.50 72.40 0.00 33.60 221.20 16.80 1.00 544.50 107.50 245.40 191.60 
2013 1.40 67.20 57.00 23.40 15.40 24.80 0.00 193.30 98.60 229.40 187.40 53.80 951.70 316.70 472.00 163.00 
2014 2.60 6.60 44.60 0.00 94.80 27.80 6.20 143.20 59.00 188.80 160.60 32.20 766.40 236.20 384.20 146.00 
2015 15.80 5.00 28.40 66.60 146.70 27.60 18.00 50.20 109.60 82.00 459.60 120.80 1130.30 205.40 678.20 246.70 
2016 0.00 0.00 0.00 14.00 72.00 0.00 34.80 2.60 6.00 84.00 66.60 80.00 360.00 43.40 230.60 86.00 
2017 38.40 10.40 47.80 35.00 165.20 1.80 15.00 70.30 148.80 66.60 93.00 132.40 824.70 235.90 330.40 258.40 
2018 0.00 33.00 20.60 56.20 135.90 7.10 49.10 12.30 194.40 158.70 92.40 7.60 767.30 262.90 258.70 245.70 
2019 4.00 3.00 0.00 50.00 63.00 86.00 0.00 87.00 196.00 191.00 75.00 56.00 811.00 369.00 326.00 116.00 
2020 0.00 0.00 0.00 20.00 234.00 24.00 77.00 69.00 61.00 155.30 306.70 96.90 1043.90 231.00 558.90 254.00 
2021 69.00 0.00 8.00 13.00 40.00 16.00 66.00 8.50 35.00 98.00 443.00 164.00 960.50 125.50 774.00 61.00 
Table 2

Monthly temperature data of the taluk for the years 2007–2021

YearMean monthly temperature (°C)
Average annual temperatureCropping seasons
JanFebMarAprMayJunJulyAugSepOctNovDecKuruvai (Jun–Sep)Thaladi (Oct–Jan)Sornavari (Feb–May)
2006 30.40 33.04 34.00 35.6 35.81 35.70 35.00 35.00 33.70 31.75 29.36 29.19 33.21 139.40 120.70 138.45 
2007 29.77 31.82 34.62 34.6 36.11 34.78 34.13 34.52 34.52 32.70 30.35 28.87 33.07 137.95 121.69 137.15 
2008 30.73 31.55 31.16 33.85 35.48 34.48 34.06 33.15 34.57 31.87 31.46 28.61 32.58 136.26 122.67 132.04 
2009 30.01 31.79 33.73 34.86 35.73 34.97 34.26 34.52 34.22 34.21 29.55 29.98 33.15 137.97 123.75 136.11 
2010 30.80 32.79 35.06 35.96 35.86 35.73 33.97 35.02 34.59 33.70 31.43 29.74 33.72 139.31 125.67 139.67 
2011 30.56 30.92 34.02 34.98 35.14 35.35 35.36 35.06 34.78 34.18 33.07 33.62 33.92 140.55 131.43 135.06 
2012 33.27 33.98 34.83 35.05 35.71 35.63 35.64 35.57 35.36 33.56 34.36 33.62 34.72 142.20 134.81 139.57 
2013 34.22 33.48 34.15 35.39 35.5 35.23 35.27 35.28 35.10 34.55 34.44 34.33 34.75 140.88 137.54 138.52 
2014 34.27 34.32 33.87 35.07 34.39 35.53 35.36 35.09 34.91 34.23 34.95 34.06 34.67 140.89 137.51 137.65 
2015 34.94 35.34 35.42 35.46 35.56 35.4 36.08 35.66 35.36 34.94 32.74 32.07 34.91 142.5 134.69 141.78 
2016 35.00 35.5 35.57 36.67 36.54 36.67 34.71 35.75 36.03 36.30 33.00 32.16 35.33 143.16 136.46 144.28 
2017 32.30 33.12 35.75 40.1 38.68 38.75 39.59 36.59 34.57 34.24 32.49 31.14 35.61 149.50 130.17 147.65 
2018 32.66 34.34 37.17 37.64 36.61 36.42 36.66 36.23 37.67 32.25 31.76 32.75 35.18 146.98 129.42 145.76 
2019 33.10 35.77 37.98 39.46 41.13 39.06 38.65 36.19 32.81 33.85 31.94 29.88 35.82 146.71 128.77 154.34 
2020 33.57 35.01 37.96 39.17 38.24 38.24 35.68 32.57 30.65 31.90 29.95 29.63 34.38 137.14 125.05 150.38 
YearMean monthly temperature (°C)
Average annual temperatureCropping seasons
JanFebMarAprMayJunJulyAugSepOctNovDecKuruvai (Jun–Sep)Thaladi (Oct–Jan)Sornavari (Feb–May)
2006 30.40 33.04 34.00 35.6 35.81 35.70 35.00 35.00 33.70 31.75 29.36 29.19 33.21 139.40 120.70 138.45 
2007 29.77 31.82 34.62 34.6 36.11 34.78 34.13 34.52 34.52 32.70 30.35 28.87 33.07 137.95 121.69 137.15 
2008 30.73 31.55 31.16 33.85 35.48 34.48 34.06 33.15 34.57 31.87 31.46 28.61 32.58 136.26 122.67 132.04 
2009 30.01 31.79 33.73 34.86 35.73 34.97 34.26 34.52 34.22 34.21 29.55 29.98 33.15 137.97 123.75 136.11 
2010 30.80 32.79 35.06 35.96 35.86 35.73 33.97 35.02 34.59 33.70 31.43 29.74 33.72 139.31 125.67 139.67 
2011 30.56 30.92 34.02 34.98 35.14 35.35 35.36 35.06 34.78 34.18 33.07 33.62 33.92 140.55 131.43 135.06 
2012 33.27 33.98 34.83 35.05 35.71 35.63 35.64 35.57 35.36 33.56 34.36 33.62 34.72 142.20 134.81 139.57 
2013 34.22 33.48 34.15 35.39 35.5 35.23 35.27 35.28 35.10 34.55 34.44 34.33 34.75 140.88 137.54 138.52 
2014 34.27 34.32 33.87 35.07 34.39 35.53 35.36 35.09 34.91 34.23 34.95 34.06 34.67 140.89 137.51 137.65 
2015 34.94 35.34 35.42 35.46 35.56 35.4 36.08 35.66 35.36 34.94 32.74 32.07 34.91 142.5 134.69 141.78 
2016 35.00 35.5 35.57 36.67 36.54 36.67 34.71 35.75 36.03 36.30 33.00 32.16 35.33 143.16 136.46 144.28 
2017 32.30 33.12 35.75 40.1 38.68 38.75 39.59 36.59 34.57 34.24 32.49 31.14 35.61 149.50 130.17 147.65 
2018 32.66 34.34 37.17 37.64 36.61 36.42 36.66 36.23 37.67 32.25 31.76 32.75 35.18 146.98 129.42 145.76 
2019 33.10 35.77 37.98 39.46 41.13 39.06 38.65 36.19 32.81 33.85 31.94 29.88 35.82 146.71 128.77 154.34 
2020 33.57 35.01 37.96 39.17 38.24 38.24 35.68 32.57 30.65 31.90 29.95 29.63 34.38 137.14 125.05 150.38 
Figure 2

Workflow diagram of the present study.

Figure 2

Workflow diagram of the present study.

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Trend analysis

The trend is a long-term variation of climatic variables over a longer period. Trend analysis explains the relationship of the variables over a period. Trends in climatic factors can be analysed using MK and ITA test. Non-parametric Mann–Kendall (MK) test has been successfully used in studies to analyse monotonic trends for rainfall, temperature, and streamflow (Mondal et al. 2012). It is a statistical technique used to examine the fluctuations of spatial regions and temporal trends of hydroclimatic data. The trends in temperature data are analysed using MK and Sen slope estimator test in the XLSTAT software program (XLSTAT; Asfaw et al. 2018; Panda & Sahu 2019; Bharani Baanu & Jinesh Babu 2024), which is used to analyse, evaluate, and forecast the trends of variables and can pictorially represent trends. The MK test statistic (S) equation is as follows (Equations (1)–(4)):
formula
(1)
formula
(2)
formula
(3)
formula
(4)
where Xi and Xj are the time series observations. The test hypothesis includes the null hypothesis (H0: no trend is observed) and alternate hypothesis (H1: Trend is observed). The probability value () conveys the existence of the trend and its significance: no trend if = 0; significant trend if < 0.05; and insignificant trend if > 0.05. In a like manner, Z conveys the nature of the trend: increasing trend if Z > 0; and decreasing trend if Z < 0.

The variable denotes the variance, n is the number of data points, g is the number of tied groups (a tied group is a set of sample data having the same value), and tp is the number of data points in the pth group.

The variable Z is the statistic normal distribution value; if the values are positive, there is an increasing trend in the climatological time series, and if negative, then the trend is decreasing in the time series.

The linear slope () and magnitude ( of changing trends are found using the Sen slope estimator test. The Qi values represent the positive and negative values of the slope indicating an upward trend or downward trend. Sen's slope equation is as follows (Equations (5)–(7)):
formula
(5)
formula
(6)
formula
(7)
where Xj and Xk are the consecutive data values of series in years j and k. The magnitude of the trend ( represents the amount of change per year.
As mentioned previously, the ITA technique was created by Sen (2012) for trend analysis of hydroclimatic data. The first step is to divide the data from the time series into two equal subseries and the second step is to sort the data from smallest to largest. The third step is plotting the data against one another, which results in a scatter of points along the 1:1 (45°) line in the grid point system (Figure 3). The approach is based on the observation that when two-time series are equal, the scatter points above the 1:1 line indicate the increasing trend and the scatter points below indicate the decreasing trend, or they can be divided into three verbal clusters for a more comprehensive interpretation: low, medium, and high.
Figure 3

ITA technique for the observed data.

Figure 3

ITA technique for the observed data.

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Trend analysis of rainfall and temperature using MK test and ITA test

The spatial and temporal variations in climate, unpredictable rainfall events, and an increase in global temperature all have a substantial impact on agricultural productivity (Gebremicheal et al. 2014; Chombo et al. 2020). They reduce crop yield and nutritional quality thereby decreasing food availability and accessibility, finally contributing to food insecurity. According to Ochieng et al. (2016), climatic change and variability have caused a significant drop in agricultural production across the globe. There is a developing need to mitigate the water scarcity and food insecurity problems caused by climate change impacts to create resilient and equitable food systems. This can be achieved by analysing the trends of climatic factors and identifying the proper adaptation strategy, which is crucial for achieving agricultural sustainability. The results of the MK trend test of rainfall and temperature are tabulated in Tables 3 and 4. The fluctuation in rainfall spanning over a period of 15 years is schematically shown in Figure 4.
Table 3

Mann–Kendall test for rainfall data analysis

Cropping seasonsMann–Kendall test
Probability p valueSen's slope valueTrend
Kuruvai/Samba (JunSep) 0.218 2.28 Insignificant increasing trend 
Thaladi/Navarai (Oct–Jan) 0.915 0.15 Insignificant increasing trend 
Sornavari (Feb–May) 0.276 1.611 Insignificant increasing trend 
Cropping seasonsMann–Kendall test
Probability p valueSen's slope valueTrend
Kuruvai/Samba (JunSep) 0.218 2.28 Insignificant increasing trend 
Thaladi/Navarai (Oct–Jan) 0.915 0.15 Insignificant increasing trend 
Sornavari (Feb–May) 0.276 1.611 Insignificant increasing trend 
Table 4

Mann–Kendall test for temperature data analysis

Cropping seasonsMann–Kendall test
Probability p valueSen's slope valueTrend
Kuruvai/Samba (JunSep) 0.004* 0.76 Significantly increasing trend 
Thaladi/Navarai (Oct–Jan) 0.013* 0.90 Significantly increasing trend 
Sornavari (Feb–May) 0.006* 0.85 Significantly increasing trend 
Cropping seasonsMann–Kendall test
Probability p valueSen's slope valueTrend
Kuruvai/Samba (JunSep) 0.004* 0.76 Significantly increasing trend 
Thaladi/Navarai (Oct–Jan) 0.013* 0.90 Significantly increasing trend 
Sornavari (Feb–May) 0.006* 0.85 Significantly increasing trend 

*Statistically significant.

Figure 4

Mean monthly rainfall of the area.

Figure 4

Mean monthly rainfall of the area.

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From Figure 4 it is evident that the rainfall pattern in the study area does not follow a uniform distribution pattern; the rainfall concentration is higher in few months and lower in most other months with an average annual rainfall of 818.89 mm, which is comparatively less than the state's average rainfall of 960 mm.

From the results of the trend analysis, it is observed that the taluk has an insignificant increasing rainfall trend and a significantly increasing temperature trend. This implies that even though there is an increasing rainfall trend in the region, simultaneously the temperature also increases during the cropping seasons. The results are in alignment with the results of Bharani Baanu & Jinesh Babu (2024). Similarly, the MK test results showed an increasing trend in precipitation, which can be considered as an increase in the supply of water for agriculture in the region through surface or groundwater.

The mean monthly temperatures recorded in the area are graphically shown in Figure 5, which appears like a Gaussian distribution with a maximum temperature of 31 °C as the peak temperature found in the month of May and an average annual temperature of 29.74 °C.
Figure 5

Mean monthly temperature of the area.

Figure 5

Mean monthly temperature of the area.

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The temperature concurrently shows a significantly increasing trend by the MK test. The increase in temperature has a direct impact on the water demand for irrigation and crop water requirement. An increase in temperature results in an increase in crop evapotranspiration, which will in turn increase the irrigation water requirement. Therefore, the demand–supply gap must be measured by estimating the water available for agriculture from rainfall and the crop water requirement. In case the demand is higher than the supply, water scarcity arises, which in turn affects crop growth and agricultural productivity. This can be counteracted through proper climate change agriculture adaptation strategies such as adopting water harvesting technologies, use of wastewater for agriculture, planting short duration crop varieties, and developing integrated water resources management.

Trends in rainfall during the cropping seasons are analysed using the ITA technique. The results for the Kuruvai/Samba (June–September), Thaladi/Navarai (October–January), and Sornavari (February–May) seasons are as follows (Figures 68).
Figure 6

ITA trend of rainfall for the Kuruvai/Samba cropping season.

Figure 6

ITA trend of rainfall for the Kuruvai/Samba cropping season.

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Figure 7

ITA trend of rainfall for Thaladi/Navarai cropping season.

Figure 7

ITA trend of rainfall for Thaladi/Navarai cropping season.

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Figure 8

ITA trend of rainfall for Sornavari cropping season.

Figure 8

ITA trend of rainfall for Sornavari cropping season.

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From the ITA of rainfall, it is observed that during the Kuruvai/Samba (Figure 6) and Sornavari seasons (Figure 8), the trends are increasing with more data points falling above the trendline, but for the Thaladi/Navarai season (Figure 7), the trend is decreasing. This clearly states that the MK test and ITA test are not in concordance with each other for rainfall trend analysis.

While analysing the temperature trends of the cropping seasons using the ITA technique (Figures 911), it is noted that all three cropping seasons show increasing temperature trends, which is in agreement with the MK test.
Figure 9

ITA trend of temperature for Kuruvai/Samba cropping season.

Figure 9

ITA trend of temperature for Kuruvai/Samba cropping season.

Close modal
Figure 10

ITA trend of temperature for Thaladi/Navarai cropping season.

Figure 10

ITA trend of temperature for Thaladi/Navarai cropping season.

Close modal
Figure 11

ITA trend of temperature for Sornavari cropping season.

Figure 11

ITA trend of temperature for Sornavari cropping season.

Close modal

It is found that the temperature data (Table 5) for all three cropping seasons are similar, but while comparing the rainfall trends (Table 6), Kuruvai/Samba and Sornavari cropping seasons show increasing trends in both methods, while the Thaladi/Navarai season shows a decreasing trend in the ITA technique indicating a decrease in rainfall trend over a period. The Thaladi/Navarai season (October–January) is the most predominant cropping and rainfall season of the region per the observed climatic data with a rainfall record of 443 mm, which is nearly half of the district's average annual rainfall of 960 mm. This season favours the growing of short, medium, and long duration crop varieties with the cropping period of 120–150 days. The increasing trend in rainfall could increase the crop growth and yield, while the increasing temperature trend assists in developing higher temperature-tolerant crop varieties. In this study, it is found that the MK test is the most reliable technique compared with the ITA technique for the analyses of small areas.

Table 5

Comparison between MK test and ITA test for temperature

Cropping seasonsMann–Kendall testInnovative trend analysis test
Kuruvai/Samba (JunSep) Increasing trend Increasing trend 
Thaladi/Navarai (OctJan) Increasing trend Increasing trend 
Sornavari (Feb–May) Increasing trend Increasing trend 
Cropping seasonsMann–Kendall testInnovative trend analysis test
Kuruvai/Samba (JunSep) Increasing trend Increasing trend 
Thaladi/Navarai (OctJan) Increasing trend Increasing trend 
Sornavari (Feb–May) Increasing trend Increasing trend 
Table 6

Comparison between MK test and ITA test for rainfall

Cropping seasonsMann–Kendall testInnovative trend analysis test
Kuruvai/Samba (JunSep) Increasing trend Increasing trend 
Thaladi/Navarai (OctJan) Increasing trend Decreasing trend 
Sornavari (FebMay) Increasing trend Increasing trend 
Cropping seasonsMann–Kendall testInnovative trend analysis test
Kuruvai/Samba (JunSep) Increasing trend Increasing trend 
Thaladi/Navarai (OctJan) Increasing trend Decreasing trend 
Sornavari (FebMay) Increasing trend Increasing trend 

In this study, trends of rainfall and temperature over the Rajapalayam taluk for the period of 2007–2021 (15 years) were examined by the application of both MK and ITA techniques. The comparison of the MK and ITA tests drives to the understanding of the characteristics of both the methods. Both the methods are found suitable for the analysis of climate trends. The application of MK test has the ability to detect nonlinear trends and shows robustness to outliers, but it also has certain setbacks such as the detection of trends monotonically, existence of serial autocorrelation which may result in erroneous trends, and data sensitivity. Also, the MK test does not find the maximum and minimum of detected trends. The ITA technique detects both monotonic and non-monotonic trends. The method detects trends in different levels of a given time series, such as the lows and highs. This method can be applied regardless of distribution assumptions, size, and serial correlation. ITA has also been used to determine trends for different climate factors in a graphical representation, providing a comprehensive understanding of climate events over the period. The results of both techniques were compared to identify the most reliable technique for small-scale regions. Climate change impacts such as a rise in temperature in the study area would possibly affect the water supply in the region and increase the irrigation water demand.

The study shows that, on a yearly basis, rainfall and temperature have an increasing trend. In particular, the results of the ITA technique indicate an increasing rainfall trend in the Kuruvai/Samba (June–September) and Sornavari (March–May) cropping seasons but a decreasing trend in the Thaladi/Navarai (October–January) season. The results detected an increasing trend for temperature in all three cropping seasons. During the predominant cropping season, the rainfall has a decreasing trend by the ITA technique, which does not agree with the MK test. Increase in temperature affects the plant development such as growth, early maturity, higher evapotranspiration, and finally alters the metabolic activities of plants. In such conditions, growing higher-temperature-tolerant crops could be a possible adaptation measure. The most common responses to climate change include crop variety changes, inter- and multiple-crop planting, water harvesting technology utilisation, greywater use, and simultaneous use of surface and groundwater, wastewater aquifer recharging, and so on. In addition, adopting soil and water conservation methods for increasing water productivity and mitigating the effects of climate change, understanding farmers’ perceptions towards climate change and elucidating the necessity and preparedness towards climate change, and the establishment of early warning systems will possibly reduce the impacts of climate change in the area.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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