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Dissecting regional changes in climate variables for crop management studies using probabilistic convolution neighbourhood technique over Kerala

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Abstract

In light of the rapidly growing agricultural sector, spotting signs of climate change is crucial. Even a slight change in climate behaviour can hinder crop development. As a result, it is necessary to investigate these climatic signals at a localised regional scale. The current methods mainly emphasise on outliers when identifying abnormalities in geographical and temporal data. This disregards the possibility of negative impacts brought on by climatic changes at each location. This paper introduces a novel mathematical approach, named the probabilistic convolution neighborhood technique (PCNT), that finds the temporal and spatial coherence at each point. Its ability to examine distribution at all grid points makes it easy to identify even a slight change in climatic variables. This new approach has been applied to study the variations in parameters, namely humidity, precipitation, mean, and maximum temperatures, that are collected from the Indian Monsoon Data Assimilation and Analysis (IMDAA) for the time period 1980 to 2020. The datasets are daily single-level datasets from which annual and seasonal (Kharif, Rabi, and Zaid) data are obtained. Spatio-temporal properties, along with inter-vicennial and inter-decadal variations of climatic variables with regions having low, high, and normal probability distributions, are studied. The findings showed a pattern in the temporal features between Kharif and Rabi. The mean temperature for the annual and seasonal data showed decreasing trends for tail diagnostics. The study brings out an understanding in the probable change in various metrics, which are crucial in the context of crop growth and agricultural practice.

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Data Availability

The datasets generated during and/or analysed during the current study are available in the RDS NCMRWF repository, https://rds.ncmrwf.gov.in/dashboard/download, and https://www.ecostat.kerala.gov.in/

Code Availability

Not applicable.

References

  • Abaje IB, Achiebo PJ, Matazu MB (2018) Spatio-temporal analysis of rainfall distribution in kaduna state, nigeria. Ghana J Geogr 10(1):1–21

    Google Scholar 

  • Abhinav M, Lazarus TP, Priyanga V, Kshama, A, et al. (2018) Impact of rainfall on the coconut productivity in kozhikode and malappuram districts of kerala. Curr Agric Res J 6(2):183–187. Retrieved from https://doi.org/10.12944/CARJ.6.2.07

  • Auffhammer M, Ramanathan V, Vincent JR (2012) Climate change, the monsoon, and rice yield in india. Climatic Change 111:411–424. Retrieved from https://doi.org/10.1007/s10584-011-0208-4

  • Ayoade JO (2002) Introduction to agroclimatology. Vintage Publisher, Ibadan

    Google Scholar 

  • Brenkert A, Malone E (2003) Vulnerability and resilience of india and indian states to climate change: a first-order approximation. Joint Global Change Res Institute 65

  • Caloiero T (2014) Analysis of daily rainfall concentration in new zealand. Natural Hazards 72(2):389–404. Retrieved from https://doi.org/10.1007/s11069-013-1015-1

  • Coscarelli R, Caloiero T (2012) Analysis of daily and monthly rainfall concentration in southern italy (calabria region). J Hydrol 416:145–156. Retrieved from https://doi.org/10.1016/j.jhydrol.2011.11.047

  • Daloz AS, Rydsaa JH, Hodnebrog Ø, Sillmann J, van Oort B, Mohr CW, . . . Zhang T (2021) Direct and indirect impacts of climate change on wheat yield in the indo-gangetic plain in india. J Agric Food Res 4:100132. Retrieved from https://doi.org/10.1016/j.jafr.2021.100132

  • Daners D (2019) Introduction to functional analysis. NSW 2006, Australia: School of Mathematics and Statistics, University of Sydney

  • Dash S, Nair AA, Kulkarni MA, Mohanty U (2011) Characteristic changes in the long and short spells of different rain intensities in india. Theoretical Appl Climatol 105(3):563–570. Retrieved from https://doi.org/10.1007/s00704-011-0416-x

  • Datta P, Behera B, et al. (2022) Climate change and indian agriculture: a systematic review of farmers’ perception, adaptation, and transformation. Environ Challenges 100543. Retrieved from https://doi.org/10.1016/j.envc.2022.100543

  • Department of Agriculture C, Welfare F (2019) At a glance 2019. PDF document

  • Dias M, Navaratne C, Weerasinghe K, Hettiarachchi R (2016) Application of dssat crop simulation model to identify the changes of rice growth and yield in nilwala river basin for mid-centuries under changing climatic conditions. Procedia Food Sci 6:159–163

    Article  Google Scholar 

  • Food and agricultural organisation, annual statistical report (Vol. 56). (2005)

  • Giridhar B, Raghavendra K, Singh DR, Kuriachen P (2022) Agricultural vulnerability to climate change: a case study of kerala

  • Gocic M, Trajkovic S (2014) Spatio-temporal patterns of precipitation in serbia. Theoretical Appl Climatol 117:419–431

    Article  Google Scholar 

  • Gohari A, Eslamian S, Abedi-Koupaei J, Bavani AM, Wang D, Madani K (2013) Climate change impacts on crop production in iran’s zayandeh-rud river basin. Sci Total Environ 442:405–419. Retrieved from https://doi.org/10.1016/j.scitotenv.2012.10.029

  • Goparaju L, Ahmad F (2019) Analysis of seasonal precipitation, potential evapotranspiration, aridity, future precipitation anomaly and major crops at district level of india. KN-Journal of Cartography Geographic Inf 69(2):143–154

    Article  Google Scholar 

  • Government of India (2015) Ministry of agriculture and farmers’ welfare. Website. Retrieved from https://www.india.gov.in/website-ministry-agriculture-farmers-welfare (Last accessed 5 April 2023)

  • Government of Kerala (1987) Economic review 1987. PDF document. Retrieved from https://spb.kerala.gov.in/sites/default/files/inline-files/1987.pdf

  • Güçlü YS (2020) Improved visualization for trend analysis by comparing with classical mann-kendall test and ita. J Hydrol 584:124674. Retrieved from https://doi.org/10.1016/j.jhydrol.2020.124674

  • Hosch WL (2022) Gamma distribution. Encyclopaedia Britannica, inc. Retrieved from https://www.britannica.com/science/gamma-distribution

  • Huang S, Huang Q, Zhang H, Chen Y, Leng G (2016) Spatio-temporal changes in precipitation, temperature and their possibly changing relationship: a case study in the wei river basin, china. Int J Climatol 36(3):1160–1169

    Article  Google Scholar 

  • INCCA (Indian Network for Climate Change Assessment) (2010) Indian network for climate change assessment, india: Greenhouse gas emissions (2007) INCCA. Ministry of Environment & Forests, India

    Google Scholar 

  • IPCC-TGICA A (2007) General guidelines on the use of scenario data for climate impact and adaptation assessment. Task Group on Data and Scenario Support for Impact and Climate Assessment (TGICA),Intergovernmental Panel on Climate Change

  • Ismaila U, Gana A, Tswanya N, Dogara D et al (2010) Cereals production in nigeria: problems, constraints and opportunities for betterment. African J Agric Res 5(12):1341–1350

    Google Scholar 

  • Jagannathan P, Bhalme H (1973) Changes in the pattern of distribution of southwest monsoon rainfall over india associated with sunspots. Monthly Weather Rev 101(9):691–700

    Article  Google Scholar 

  • Kamruzzaman M, Rahman AS, Ahmed MS, Kabir ME, Mazumder QH, Rahman MS, Jahan CS (2018) Spatio-temporal analysis of climatic variables in the western part of bangladesh. Environ Develop Sustainability 20:89–108

    Article  Google Scholar 

  • Kim S, Casper R (2013) Applications of convolution in image processing with matlab. University of Washington, 1–20

  • Koutsoyiannis D, Onof C (2001) Rainfall disaggregation using adjusting procedures on a poisson cluster model. J Hydrol 246(1–4):109–122

    Article  Google Scholar 

  • Krishna Kumar K, Rupa Kumar K, Ashrit R, Deshpande N, Hansen JW (2004) Climate impacts on indian agriculture. Int J Climatol: A J Royal Meteorological Soc 24(11):1375–1393

    Article  Google Scholar 

  • Lal P, Prakash A, Kumar A, Srivastava PK, Saikia P, Pandey A, . . . Khan M (2020) Evaluating the 2018 extreme flood hazard events in kerala, india. Remote Sens Lett 11(5):436–445. Retrieved from https://doi.org/10.1080/2150704X.2020.1730468

  • Leng G, Huang M (2017) Crop yield response to climate change varies with crop spatial distribution pattern. Sci Reports 7(1):1463

  • Lobell DB, Burke MB (2010) On the use of statistical models to predict crop yield responses to climate change. Agric Forest Meteorol 150(11):1443–1452

    Article  Google Scholar 

  • Lobell DB, Field CB (2007) Global scale climate–crop yield relationships and the impacts of recent warming. Environ Res Lett 2(1):014002

  • Ludwig J (2007) Image convolution. https://web.pdx.edu/jduh/courses/Archive/geog481w07/Students/Ludwig ImageConvolution.pdf. (Accessed on June 11, 2023)

  • Mahato A (2014) Climate change and its impact on agriculture. Int J Sci Res Publications 4(4):1

    Google Scholar 

  • Mathew MM, Sreelash K, Mathew M, Arulbalaji P, Padmalal D (2021) Spatiotemporal variability of rainfall and its effect on hydrological regime in a tropical monsoon-dominated domain of western ghats, india. J Hydrol: Regional Stud 36:100861. Retrieved from https://doi.org/10.1016/j.ejrh.2021.100861

  • Mishra B, Tripathi N (2010) Winter agricultural drought detection using modis imagery: a case study of nepal. Proceeding of the 3rd international conference on git for natural disaster management, chiang mai, thailand (pp. 19–20)

  • Mishra S, Mishra D, Santra GH et al (2016) Applications of machine learning techniques in agricultural crop production: a review paper. Indian J Sci Technol 9(38):1–14

    Article  Google Scholar 

  • Mitra A, Seshadri AK (2019) Detection of spatiotemporally coherent rainfall anomalies using markov random fields. Comput Geosci 122:45–53. Retrieved from https://doi.org/10.1016/j.cageo.2018.10.004

  • Motha RP, Baier W (2005) Impacts of present and future climate change and climate variability on agriculture in the temperate regions: North america. Climatic Change 70(1–2):137–164

    Article  Google Scholar 

  • Mozny M, Tolasz R, Nekovar J, Sparks T, Trnka M, Zalud Z (2009) The impact of climate change on the yield and quality of saaz hops in the czech republic. Agric Forest Meteorol 149(6-7):913–919. Retrieved from https://doi.org/10.1016/j.agrformet.2009.02.006

  • Mukherjee UK, Bagozzi BE, Chatterjee S (2023) A bayesian framework for studying climate anomalies and social conflicts. Environmetrics 34(2):e2778

    Article  Google Scholar 

  • Nambudiri, S. (2023, Feb). Mild temp rise unlikely to affect paddy, study kochi news - times of india. The Times of India. Retrieved from https://timesofindia.indiatimes.com/city/kochi/mild-temp-rise-unlikely-to-affect-paddy-study/articleshow/9771391

  • Nanzad L, Zhang J, Tuvdendorj B, Nabil M, Zhang S, Bai Y (2019) Ndvi anomaly for drought monitoring and its correlation with climate factors over mongolia from 2000 to 2016. J Environ 164:69–77

    Google Scholar 

  • Nath K, Jain R, Marwaha S, Arora A (2020) Identification of optimal crop plan using nature inspired metaheuristic algorithms. Indian J Agric Sci 90(8):1587–92. Retrieved from http://krishi.icar.gov.in/jspui/handle/123456789/47171

  • Nithya N (2013) Kerala’s agriculture: performance and challenges. Int J Phys Soc Sci 3(11):127

    Google Scholar 

  • Özdoǧan M (2011) Modeling the impacts of climate change on wheat yields in northwestern turkey. Agric Ecosyst Environ 141(1–2):1–12. Retrieved from https://doi.org/10.1016/j.agee.2011.02.001

  • Pant M, Thangaraj R, Rani D, Abraham A, Srivastava DK (2010) Estimation of optimal crop plan using nature inspired metaheuristics. World J Model Simulation 6(2):97–109

    Google Scholar 

  • Papalexiou SM, Koutsoyiannis D (2012) Entropy based derivation of probability distributions: a case study to daily rainfall. Adv Water Resour 45:51–57

    Article  Google Scholar 

  • Planning Commission, India (2007). Kerala development report. Academic Foundation. (Retrieved 30 May 2015)

  • Prăvălie R, Piticar A, Roṣca, B, Sfîcă, L, Bandoc, G, Tiscovschi, A, Patriche, C, (2019) Spatio-temporal changes of the climatic water balance in romania as a response to precipitation and reference evapotranspiration trends during 1961–2013. Catena 172:295–31

  • PscAriVukal (2020, May) Crops in kerala. Blog post. Retrieved from https://www.pscarivukal.com/2020/05/crops-kerala-psc-gk.html

  • Raj B, Singh, O (2012) Study of impacts of global warming on climate change: rise in sea level and disaster frequency. Global Warming - Impacts and Future Perspectives

  • Rani SI, Arulalan T, George JP, Rajagopal EN, Renshaw R, Maycock A, . . . Rajeevan M (2021, Jun) Imdaa: High-resolution satellite-era reanalysis for the indian monsoon region. Am Meteorolog Soc. Retrieved from https://journals.ametsoc.org/view/journals/clim/34/12/JCLI-D-20-0412.1.xml

  • Rao CR, Raju B, Rao AS, Rao K, Rao V, Ramachandran K, . . . others (2016) A district level assessment of vulnerability of indian agriculture to climate change. Curr Sci 1939–1946

  • Rao CS, Gopinath K, Prasad J, Singh A, et al. (2016) Climate resilient villages for sustainable food security in tropical india: concept, process, technologies, institutions, and impacts. Adv Agronomy 140:101–214. Retrieved from https://doi.org/10.1016/bs.agron.2016.06.003

  • Rao G, Kesava Rao A, Krishnakumar K, Gopakumar C (2009) Impact of climate change on food and plantation crops in the humid tropics of india. ISPRS Archives 38(8):W3

    Google Scholar 

  • Rao GV, Reddy KV, Srinivasan R, Sridhar V, Umamahesh N, Pratap D (2020) Spatio-temporal analysis of rainfall extremes in the flood prone nagavali and vamsadhara basins in eastern india. Weather Climate Extremes 29:100265

    Article  Google Scholar 

  • Ray DK, Gerber JS, MacDonald GK, West PC (2015) Climate variation explains a third of global crop yield variability. Nature Commun 6(1):1–9

    Article  Google Scholar 

  • Rial-Lovera K, Davies WP, Cannon ND (2017) Implications of climate change predictions for uk cropping and prospects for possible mitigation: a review of challenges and potential responses. J Sci Food Agric 97(1):17–32. Retrieved from https://doi.org/10.1002/jsfa.7767

  • Roudier P, Sultan B, Quirion P, Berg A (2011) The impact of future climate change on west african crop yields: what does the recent literature say? Global Environ Change 21(3):1073–1083. Retrieved from https://doi.org/10.1016/j.gloenvcha.2011.04.007

  • Roy AD (2015) Trend detection in temperature and rainfall over rajasthan during the last century. Asian J Res Soc Sci Human 5(2):12–26

    Google Scholar 

  • Sameer (2021, Jan) Image convolution from scratch. Analytics Vidhya. Retrieved from https://medium.com/analytics-vidhya/image-convolution-from-scratch-d99bf639c32a

  • Saranya S, Amudha T (2016) Crop planning optimization research–a detailed investigation. 2016 ieee international conference on advances in computer applications (icaca) (pp. 202–208). https://doi.org/10.1109/ICACA.2016.7887951

  • Sethi A, Lin C-Y, Madhavan I, Davis M, Alexander P, Eddleston M, Chang S-S (2022) Impact of regional bans of highly hazardous pesticides on agricultural yields: the case of kerala. Agric Food Security 11(1):1–13

    Google Scholar 

  • Sivajothi R, Karthikeyan K (2016) Analysis of monthly rainfall data prediction for change of economic environment in pampadumpara using gamma distribution. Res J Pharm Technol 9(9):1477–1482

    Article  Google Scholar 

  • Sneha H (2018, November 30) 2d convolution in image processing. All About Circuits. Retrieved from https://www.allaboutcircuits.com/technical-articles/two-dimensional-convolution-in-image-processing/

  • Song X, Zou X, Zhang C, Zhang J, Kong F (2019) Multiscale spatio-temporal changes of precipitation extremes in beijing-tianjin-hebei region, china during 1958–2017. Atmosphere 10(8):462

    Article  Google Scholar 

  • Sudha T (2011) Opportunities in participatory planning to evolve a land use policy for western ghats region in kerala. Department of Town and Country Planning, Government of Kerala

    Google Scholar 

  • Toros H (2012) Spatio-temporal precipitation change assessments over turkey. Int J Climatol 32(9):1310–1325

    Article  Google Scholar 

  • Varughese AR (2022, Apr) Agrarian distress in kuttanad a wake-up call for urgent climate adaptation. Retrieved from https://science.thewire.in/environment/kuttanad-agrarian-distress-climate-adaptation-urgent/

  • White JW, Hoogenboom G, Kimball BA, Wall GW (2011) Methodologies for simulating impacts of climate change on crop production. Field Crops Res 124(3):357–368

    Article  Google Scholar 

  • Wu Z, Schneider EK, Kirtman BP, Sarachik ES, Huang NE, Tucker CJ (2008) The modulated annual cycle: an alternative reference frame for climate anomalies. Climate Dynamics 31:823–841

    Article  Google Scholar 

  • You L, Rosegrant MW, Wood S, Sun D (2009) Impact of growing season temperature on wheat productivity in china. Agric Forest Meteorol 149(6-7):1009–1014. Retrieved from https://doi.org/10.1016/j.agrformet.2008.12.004

  • Zhang Q, Xu C-Y, Zhang Z, Chen YD, Liu C-l, Lin H (2008) Spatial and temporal variability of precipitation maxima during 1960–2005 in the yangtze river basin and possible association with large-scale circulation. J Hydrol 353(3-4):215–227. Retrieved from https://doi.org/10.1016/j.jhydrol.2007.11.023

  • Zhao J, Guo J, Mu J (2015) Exploring the relationships between climatic variables and climate-induced yield of spring maize in northeast china. Agricul Ecosyst Environ 207:79–90

    Article  Google Scholar 

  • Zhao N, Yue T, Li H, Zhang L, Yin X, Liu Y (2018) Spatio-temporal changes in precipitation over beijing-tianjin-hebei region, china. Atmospheric Res 202:156–168

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Acknowledgements

Authors gratefully acknowledge NCMRWF, Ministry of Earth Sciences, Government of India, for IMDAA reanalysis. IMDAA reanalysis was produced under the collaboration between UK Met Office, NCMRWF, and IMD with financial support from the Ministry of Earth Sciences, under the National Monsoon Mission programme

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  1. Department of Mathematics, Amrita School of Physical Sciences, Amrita Vishwa Vidyapeetham, Kochi Campus, 682024, India

    Chalissery Mincy Thomas & Archana Nair

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Both the authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Chalissery Mincy Thomas. The first draft of the manuscript was written by Chalissery Mincy Thomas and Archana Nair commented on previous versions of the manuscript. Archana Nair read and approved the final manuscript

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Thomas, C.M., Nair, A. Dissecting regional changes in climate variables for crop management studies using probabilistic convolution neighbourhood technique over Kerala. Theor Appl Climatol 154, 567–600 (2023). https://doi.org/10.1007/s00704-023-04573-3

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