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2021 | OriginalPaper | Chapter

Impact of Climate Change on Crop Production and Its Consequences on Human Health

Authors : Gopal Krishna, Mahfooz Alam, Rabi N. Sahoo, Chandrashekhar Biradar

Published in: Recent Technologies for Disaster Management and Risk Reduction

Publisher: Springer International Publishing

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Abstract

Climate change and water availability directly impact the agricultural practices around the globe. Humans are dependent on agriculture for their food needs. Changing climatic conditions like the delayed onset of monsoon, less rainfall, and increasing temperature are profoundly impacting agriculture practices. Therefore, this has a direct or indirect impact on human health. According to FAO, only 12% of global land is available for crop production and that too is not increasing proportionately with the continuously increasing world population. Climatic stresses may cause insufficiency to food security. Hence, this type of study is quite significant for decision making. In the Indian scenario, approximately 61% of the total net sown area is rainfed. In this huge proportion of rainfed areas, there are many types of changing climatic conditions that impact crop productivity. Geoinformation science-based big data encompasses enormous opportunities for addressing issues like climate modeling, its impact on crops, and production alteration. This chapter discusses the real-world problems, their impacts as well as the way forward for sustainable solutions by utilizing the strength of remote sensing big data. Here, time series analysis, machine learning-based predictive multivariate modeling approaches have been exploited to detect the climatic impact on crop production for rice and cotton crops of the highest productive districts of Maharashtra state. After delineation of climatic implications on agriculture, its consequences on human health issues have been discussed. The ML-driven partial least squares regression (PLSR) technique was proved better over other investigated techniques. The results of this study illustrate that with the rise in temperature and rainfall during 2050, cotton production is projected to decline by 1–35%, whereas rice production looks to be increased by 0.4–20% by nullifying high temperature with excess rainfall except for one district.

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previous chapter Human Health Hazards and Risks in the Agriculture Sector
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Literature
Adler RF, Huffman GJ, Chang A, Ferraro R, Xie P, Janowiak J, Rudolf B, Schneider U, Curtis S, Bolvin D, Gruber A, Susskind J, Arkin P, Nelkin EJ (2003) The version 2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979-Present). J Hydrometeor 4(6):1147–1167 CrossRef
Asseng S, Foster I, Turner NC (2011) The impact of temperature variability on wheat yields. Glob Chang Biol 17:997–1012 CrossRef
Asner G, Martin R (2008) Spectral and chemical analysis of tropical forests: scaling from leaf to canopy levels. Remote Sens Env 112(10):3958–3970
Bernstein L, Bosch P, Canziani O, Chen Z, Christ R, Riahi K (2008) IPCC-2007 Climate change 2007: synthesis report; IPCC, Geneva, Switzerland
Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification, and regression trees, Boca Raton Chapman and Hall/CRC press
Chattopadhyay RP, Samui, Banerjee SK (2008) Effect of weather on growth and yield of cotton grown in the dry
Cutler A, Cutler DR, Stevens JR (2012) Random forests. Ensemble machine learning, Zhang C, Ma Y (eds). Springer, pp 157–175
Darvishzadeh R, Skidmore A, Schlerf M, Atzberger C, Corsi F, Cho M (2008) LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements. ISPRS J Photogrammetry Rem Sens 63(4):409–426
Das B, Sahoo RN, Pargal S, Krishna G, Verma R, Chinnusamy V, Sehgal VK, Gupta VK (2017) Comparison of different uni- and multi-variate techniques for monitoring leaf water status as an indicator of water-deficit stress in wheat through spectroscopy. Biosyst Eng 160:69–83 CrossRef
Guo G, Mu G (2013) Joint estimation of age, gender and ethnicity: CCA vs. PLS. In: Proceedings of the paper presented at the 2013 10th IEEE international conference and workshops on automatic face and gesture recognition (FG), IEEE, Shanghai
Hebbar K, Venugopalan M, Prakash A, Aggarwal P (2013) Simulating the impacts of climate change on cotton production in India. Clim Change 118(3–4): 701–713(13)
Högy P, Fangmeier A (2008) Effects of elevated atmospheric CO 2 on grain quality of wheat. J Cereal Sci 48:580–591 CrossRef
IPCC (2007) Climate change 2007: impacts, adaptation and vulnerability. In: Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hanson CE (eds) Contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change, Cambridge University Press, Cambridge, UK, 976 pp
Kelkar SM, Kulkarni A, Rao KK (2020) Impact of climate variability and change on crop productivity in Maharashtra, India. Current Sci 118(8):1235–1245
Krishna G, Sahoo RN, Pradhan S, Ahmad T, Sahoo PM (2017) Hyperspectral satellite data analysis for pure pixels extraction and evaluation of advanced classifier algorithms for LULC classification. Earth Sci Inf 11(2):159–170
Krishna G, Sahoo RN, Singh P, Bajpai V, Patra H, Kumar S, Sahoo PM (2019a) Comparison of various modelling approaches for water deficit stress monitoring in rice crop through hyperspectral remote sensing. Agric Water Manag 213:231–244
Krishna G, Sahoo RN, Singh P, Patra H, Bajpai V, Das B, Kumar S, Dhandapani R, Vishwakarma C, Pal M et al (2019b) Application of thermal imaging and hyperspectral remote sensing for crop water deficit stress monitoring. Geocarto Int 1–14
Liaw A, Wiener M (2002) Classification and regression by random forest. R News 2(3):18–22
Li L, Ustin SL, Riano D (2007) Retrieval of fresh leaf fuel moisture content using genetic algorithm partial least squares (GA-PLS) modeling. IEEE Geosci Remote Sens Lett 4(2):216–220
Mirzaie M, Darvishzadeh R, Shakiba A, Matkan AA, Atzberger C, Skidmore A (2014) Comparative analysis of different uni- and multi-variate methods for estimation of vegetation water content using hyper-spectral measurements. Int J Appl Earth Obs Geoinf 26:1–11 CrossRef
Mishra VN, Prasad R, Kumar P, Srivastava PK, Rai PK (2017) A knowledge based decision tree approach for mapping spatial distribution of rice crop using C-band SAR derived information. J Appl Remote Sens 11(4):46003-1–18. E-ISSN: 1931-3195
Muñoz Sabater J (2019) ERA5-Land monthly averaged data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS)
NATCOM, GOI (2008) India’s second national communication to the United Nations framework convention on climate change, work program
Nistor MM, Ronchetti F, Corsini A, Cheval S, Dumitrescu A, Rai PK, Petrea D, Dezsi S (2017b) Crop evapotranspiration variation under climate change in South East Europe during 1991–2050. Carpathian J Environ Sci 12(2): 571–582. ISSN Online: 1844-489X
Peng S, Huang J, Sheehy JE et al (2004) Rice yields decline with higher night temperature from global warming. Proc Natl Acad Sci USA 101:9971–9975 CrossRef
Ramoelo A, Skidmore AK, Schlerf M, Mathieu R, Heitkönig IMA (2011) Water-removed spectra increase the retrieval accuracy when estimating savanna grass nitrogen and phosphorus concentrations. ISPRS J Photogrammetry Remote Sens 66(4):408–417
Rai PK, Singh P, Mishra VN, Shahi AP, Singh A, Sajan B (2019) Remote sensing based analysis on environmental and climatic impact on resources in Alaska region: a review has been accepted in (ed. Book) Alaska: social, economics, and environment, 2nd edn by Nistor MM. NOVA Science Publisher, New York
Rännar S, Lindgren F, Geladi P, Wold S (1994) J. Chemometrics 8:111–125 CrossRef
Rodríguez-Galiano VF, Chica Olmo M, Abarca Hernández F, Atkinson PM, Jeganathan C (2012) Random forest classification of mediterranean land cover using multi-seasonal imagery and multi-seasonal texture. Remote Sens Environ 121:93–107
Sahoo RN, Viswanathan C, Krishna G, Das B, Goel S, Dhandapani RD, Kumar S, Viswakarma C, Swain P, Dash S (2019) Next generation phenotyping for developing climate resilient rice varieties. ORYZA Int J Rice 56:92–105
Saseendran SA, Singh KK, Rathore LS, Singh SV, Sinha SK (2000) Effects of climate change on rice production in the tropical humid climate of Kerala, India. Clim Change 44:495–514 CrossRef
Schlenker W, Roberts MJ (2009) Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. Proc Natl Acad Sci 106:15594–15598
Solomon S (2007) Climate change 2007—the physical science basis: working group I contribution to the fourth assessment report of the IPCC, vol 4. Cambridge University Press, Cambridge
Thrasher B, Maurer EP, McKellar C, Duffy PB (2012) Technical note: bias correcting climate model simulated daily temperature extremes with quantile mapping. Hydrol Earth Syst Sci 16(9):3309–3314 CrossRef
Wang J, Vanga SK, Saxena R, Orsat V, Raghavan V (2018) Effect of climate change on the yield of cereal crops: a review. Climate 6:41 CrossRef
Waske B, Benediktsson JA, Árnason K, Sveinsson JR (2009) Mapping of hyperspectral AVIRIS data using machine-learning algorithms Can. J Remote Sens 35:106–116
Wold H (1982) Soft modeling: the basic design and some extensions. In: Joreskog KG, Wold HOA (eds) Proceedings of the paper presented at the systems under indirect observation, Amsterdam
Wold S, Sjöström M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemomet Intell Lab Syst 58(2):109–130 CrossRef
Yao F, Huang J, Cui K, Nie L, Xiang J, Liu X, Wu W, Chen M, Peng S (2012) Agronomic performance of high-yielding rice variety grown under alternate wetting and drying irrigation. Field Crops Res 126:16–22 CrossRef
You L, Rosegrant MW, Wood S, Sun D (2009) Impact of growing season temperature on wheat productivity in China. Agric Meteorol 149:1009–1014
Metadata
Title
Impact of Climate Change on Crop Production and Its Consequences on Human Health
Authors
Gopal Krishna
Mahfooz Alam
Rabi N. Sahoo
Chandrashekhar Biradar
Copyright Year
2021
Book
Recent Technologies for Disaster Management and Risk Reduction

Print ISBN: 978-3-030-76115-8

Electronic ISBN: 978-3-030-76116-5

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-76116-5_15