Skip to main content
main-content
Top

Hint

Swipe to navigate through the chapters of this book

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

share
SHARE

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.
Literature
go back to reference 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 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
go back to reference Asseng S, Foster I, Turner NC (2011) The impact of temperature variability on wheat yields. Glob Chang Biol 17:997–1012 CrossRef Asseng S, Foster I, Turner NC (2011) The impact of temperature variability on wheat yields. Glob Chang Biol 17:997–1012 CrossRef
go back to reference 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 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
go back to reference Bernstein L, Bosch P, Canziani O, Chen Z, Christ R, Riahi K (2008) IPCC-2007 Climate change 2007: synthesis report; IPCC, Geneva, Switzerland Bernstein L, Bosch P, Canziani O, Chen Z, Christ R, Riahi K (2008) IPCC-2007 Climate change 2007: synthesis report; IPCC, Geneva, Switzerland
go back to reference Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification, and regression trees, Boca Raton Chapman and Hall/CRC press Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification, and regression trees, Boca Raton Chapman and Hall/CRC press
go back to reference Chattopadhyay RP, Samui, Banerjee SK (2008) Effect of weather on growth and yield of cotton grown in the dry Chattopadhyay RP, Samui, Banerjee SK (2008) Effect of weather on growth and yield of cotton grown in the dry
go back to reference Cutler A, Cutler DR, Stevens JR (2012) Random forests. Ensemble machine learning, Zhang C, Ma Y (eds). Springer, pp 157–175 Cutler A, Cutler DR, Stevens JR (2012) Random forests. Ensemble machine learning, Zhang C, Ma Y (eds). Springer, pp 157–175
go back to reference 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 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
go back to reference 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 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
go back to reference 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 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
go back to reference 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) 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)
go back to reference Högy P, Fangmeier A (2008) Effects of elevated atmospheric CO 2 on grain quality of wheat. J Cereal Sci 48:580–591 CrossRef Högy P, Fangmeier A (2008) Effects of elevated atmospheric CO 2 on grain quality of wheat. J Cereal Sci 48:580–591 CrossRef
go back to reference 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 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
go back to reference 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 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
go back to reference 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, 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
go back to reference 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, 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
go back to reference 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 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
go back to reference Liaw A, Wiener M (2002) Classification and regression by random forest. R News 2(3):18–22 Liaw A, Wiener M (2002) Classification and regression by random forest. R News 2(3):18–22
go back to reference 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 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
go back to reference 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 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
go back to reference 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 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
go back to reference Muñoz Sabater J (2019) ERA5-Land monthly averaged data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS) Muñoz Sabater J (2019) ERA5-Land monthly averaged data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS)
go back to reference NATCOM, GOI (2008) India’s second national communication to the United Nations framework convention on climate change, work program NATCOM, GOI (2008) India’s second national communication to the United Nations framework convention on climate change, work program
go back to reference 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 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
go back to reference 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 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
go back to reference 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 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
go back to reference 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 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
go back to reference Rännar S, Lindgren F, Geladi P, Wold S (1994) J. Chemometrics 8:111–125 CrossRef Rännar S, Lindgren F, Geladi P, Wold S (1994) J. Chemometrics 8:111–125 CrossRef
go back to reference 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 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
go back to reference 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 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
go back to reference 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 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
go back to reference 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 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
go back to reference 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 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
go back to reference 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 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
go back to reference 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 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
go back to reference 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 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
go back to reference 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 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
go back to reference Wold S, Sjöström M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemomet Intell Lab Syst 58(2):109–130 CrossRef Wold S, Sjöström M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemomet Intell Lab Syst 58(2):109–130 CrossRef
go back to reference 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 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
go back to reference You L, Rosegrant MW, Wood S, Sun D (2009) Impact of growing season temperature on wheat productivity in China. Agric Meteorol 149:1009–1014 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
DOI
https://doi.org/10.1007/978-3-030-76116-5_15

Premium Partners