Abstract
Nowadays, outputs of the global climate models (GCMs) are extensively used as inputs for the crop simulation models (CSMs) to assess the impact of climate change on different agricultural crops. As the skills of GCMs and CSMs are not at per for decision-making at local scale, particularly in distract level due to the coarser resolution of GCMs and limited ability of representing the regional scenarios through crop models, so GCMs have to be evaluated to test its ability for reproducing the local scale climatic mean features and crop model has to be calibrated and validated with local scale experimental data before its application for climate change studies. The present chapter describes how the combined influence of climate and crop models are able to provide useful information for policy-making at subregional or local scale. First part, the centennial scale (1901–2000) district-wise rainfall change over West Bengal state for four distinct seasons and annual scale has been quantified using station data as well as GCM simulation. Based on the ability of GCMs to simulate observed rainfall, a group of better performing models is identified for North and South Bengal districts separately for using an input for any decision-making research. Percentage change (with respect to 1971–2000) of future annual monsoon and pre-monsoon season rainfall in different short-term (30 year) and long-term (100 year) periods revealed a decreasing trend of rainfall by 16–25% over South Bengal and 1–15% over North Bengal districts, while the winter and post-monsoon rainfall were projected to be increased by 60–117% and 1–15%, respectively, over North Bengal. Future temperature is always showing increasing trends in different time periods in different parts of the world as well as in Indian subcontinent. An attempt has been taken to investigate how the increasing trends of temperature, increasing/decreasing patterns of rainfall as well as elevated CO2 can alter the productivity of rice crop over the Gangetic West Bengal region through a crop simulation model of rice, popularly known as ‘ORYZA2000’. It is revealed that rice production decreases by about 10% with only 1 °C rise in temperature and current CO2 level, while it increases by 10% under 1 °C rise of temperature and doubling CO2 concentration. It is further noticed that productivity of rice will fall by 30% with a rise of temperature by 2 °C and doubling CO2 level.
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Akhter J, Das L, Deb A (2016) CMIP5 ensemble-based spatial rainfall projection over homogenous zones of India. Clim Dyn 49:1885. https://doi.org/10.1007/s00382-016-3409-8
Akhter J, Das L, Meher JK, Deb A (2018) Uncertainties and time of emergence of multi-model precipitation projection over homogeneous rainfall zones of India. Clim Dyn 50(9–10):3813–3831
Akponikpea PBI, Mineta J, Gerardc B, Defournya P, Bieldersa CL (2011) Spatial fields’ dispersion as a farmer strategy to reduce agro-climatic risk at the household level in pearl millet-based systems in the Sahel: a modeling perspective. Agric For Meteorol 151:215–227
Alagarswamy G, Singh P, Hoogenboom G, Wani SP, Pathak P, Virmani SM (2000) Evaluation and application of the CROPGRO-Soybean simulation model in vertic inceptisol. Agric Syst 63:19–32
Alocilja EC, Ritchie JT (1990) The application of SIMOPT2: RICE to evaluate profit and yield-risk in upland-rice production. Agric Syst 33:315–326
Arora VK (2006) Application of a rice growth and water balance model in an irrigated semi-arid subtropical environment. Agric Water Manag 83:51–57
Artacho P, Meza F, Antonio Alcalde J (2011) Evaluation of the Oryza 2000 rice growth model under nitrogen-limited conditions in an irrigated Mediterranean environment. Chile J Agric Res 71(1):23–33
Boling A, Tuong TP, Jatmiko SY, Burac MA (2004) Yield constraints of rainfed lowland rice in Central Java, Indonesia. Field Crop Res 90:351–360
Bouman BAM (2001) ORYZA 2000: modeling lowland rice, vol. 1. IRRI
Chakraborty PK, Das L (2016) Rainfall trend analysis and its future projection over Gangetic West Bengal (GWB) region of India during post-monsoon and winter season. J Appl Natr Sci 8(3):1152–1156
Das L, Lohar D (2005) Construction of climate change scenarios for a tropical monsoon region. Clim Res 30(1):39–52
Das L, Lohar D, Sadhukhan I, Khan SA, Saha A, Sarkar S (2007) Evaluation of the performance of ORYZA2000 and assessing the impact of climate change on rice production in Gangetic West Bengal. J Agrometeorol 9:1–10
Das L, Annan J, Hargreaves J, Emori S (2012) Improvements over three generations of climate model simulations for eastern India. Clim Res 51:201–216. https://doi.org/10.3354/cr01064
Das L, Meher JK, Dutta M (2016) Construction of rainfall change scenarios over the Chilka lagoon in India. Atmos Res 182:36–45
Das L, Dutta M, Mezghani A, Benestad RE (2017) Use of observed temperature statistics in ranking CMIP5 model performance over the Western Himalayan region of India. Int J Climatol 38:554. https://doi.org/10.1002/joc.5193
Das L, Prasad H, Meher JK (2018) 20th Century District-level Spatio-temporal annual rainfall changes over West Bengal. J Clim Chang 4(2):31–39
Dias MPNM, Navaratne CM, Weerasinghe KDN, Hettiarachchi RHAN (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
FAR (1990) Report prepared for intergovernmental panel on climate change by working group I. JT Houghton, GJ Jenkins, JJ Ephraums (eds.). Cambridge University Press, Cambridge/Great Britain/New York/Melbourne, pp 410
Gadgil S, Seshagiri Rao PR, Sridhar S (1999) Modelling impact of climate variability on rainfed groundnut. Curr Sci 76:557–569
Godwin DC, Meyer WS, Singh U (1994) Simulation of the effect of chilling injury and nitrogen supply on floret fertility and yield in rice. Aust J Exp Agric 34:921–926
Guhathakurta P, Rajeevan M (2008) Trends in the rainfall pattern over India. Int J Climatol 28(11):1453–1470
Hundal SS, Prabhjyot-Kaur (1997) Application of the CERES-Wheat model to yield predictions in the irrigated plains of the Indian Punjab. J Agric Sci 129:13–18
IBSNAT (1993) The international benchmark sites network for Agrotechnology transfer decade. Department of Agronomy and Soil Science, College of Tropical Agriculture and Human Resources, University of Hawaii, Honolulu
Jinghua W, Erda L (1996) The impacts of potential climate change and climate variability on simulated maize production in China. Water Air Soil Poll 92:75–85
Jintrawet A (1995) A decision support system for rapid assessment of lowland rice-based cropping alternatives in Thailand. Agric Syst 47:245–258
Jones JW, Tsuji GY, Hoogenboom G, Hunt LA, Thornton PK, Wilkens PW, Imamura DT, Bowen WT, Singh U (1998) Decision support system for agrotechnology transfer; DSSAT v3
Jones JW, Hoogenboom G, Porter CH, Boote KJ, Batchelor WD, Hunt LA, Wilkens PW, Singh U, Gijsman AJ, Ritchie JT (2003) The DSSAT cropping system model. Eur J Agron 18:235–265
Kaur P, Hundal SS (1999) Forecasting growth and yield of groundnut (Arachis hypogaea) with a dynamic simulation model ‘PNUTGRO’ under Punjab conditions. J Agric Sci 133:167–173
Krishnan P, Swain DK, Chandra Bhaskar B, Nayak SK, Dash RN (2007) Impact of elevated CO2 and temperature on rice yield and methods of adaptation as evaluated by crop simulation studies. Agric Ecosyst Environ 122:233–242
Lal M, Singh KK, Rathore LS, Srinivasan G, Saseendran SA (1998) Vulnerability of rice and wheat yields in NW India to future changes in climate. Agric For Meteorol 89:101–114
Lal M, Singh KK, Srinivasan G, Rathore LS, Naidu D, Tripathi CN (1999) Growth and yield responses of soybean in Madhya Pradesh, India to climate variability and change. Agric For Meteorol 93(1):53–70
Lohar D, Pal B (1995) The effect of irrigation on pre-monsoon season precipitation over South West Bengal, India. J Clim 8:2567–2570
Luo Y, Tebeest DO, Teng PS, Fabellar NG (1995) Simulation studies on risk analysis of rice leaf blast epidemics associated with global climate change in several Asian countries. J Biogeogr 22:673–678
Luo Y, Teng PS, Fabellar NG, TeBeest DO (1998) Risk analysis of yield losses caused by rice leaf blast associated with temperature changes above and below for five Asian countries. Agric Ecosyst Environ 68(3):197–205
Luo Y, Teng PS, Fabellar NG, Tebeest DO (1997) A rice-leaf blast combined model for simulation of epidemics and yield loss. Agric Syst 53(1):27–39
Majumder D, Das L (2018) Simulating the yield attributes of Boro rice under nitrogen and irrigation management at Mohanpur, West Bengal using ORYZA 2000. J Agrometeorol 20(1):xxx
Meher JK, Das L, Akhter J, Benestad RE, Mezghani A (2017) Performance of CMIP3 and CMIP5 GCMs to simulate observed rainfall characteristics over the Western Himalayan region. J Clim 30:7777. https://doi.org/10.1175/JCLI-D-16-0774.1
Mukherjee J, Singh G, Bal SK, Singh H, Kaur P (2011) Comparative evaluation of WOFOST and ORYZA2000 models in simulating growth and development of rice (Oryza sativa L.) in Punjab. J Agrometeorol 13(2):86–91
Naidu CV, Durgalakshmi K, Muni Krishna K, Ramalingeswara Rao S, Satyanarayana GC, Lakshminarayana P, Malleswara Rao L (2009) Is summer monsoon rainfall decreasing over India in the global warming era? J Geophys Res Atmos 114(D24)
Penning de Vries FWT (1977) Evaluation of simulation models in agriculture and biology: conclusions of a workshop. Agric Syst 2:99–105
Pinnschmidt HO, Batchelor WD, Teng PS (1995) Simulation of multiple species pest damage in rice using CERES-rice. Agric Syst 48:193–222
Randall DA, Wood RA, Bony S, Colman R, Fichefet T, Fyfe J, Kattsov V, Pitman A, Shukla J, Srinivasan J, Stouffer RJ, Sumi A, Taylor KE (2007) Cilmate models and their evaluation. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Climate Change 2007: the physical science basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, UK/New York
Raychaudhuri A, Das TK (2005) WB economy: some contemporary issues. Allied Publishers, Ahmedabad
Rupa Kumar K, Kumar KK, Ashrit RG, Patwardhan SK, Pant GB (2002) Climate change in India: observations and model projections. In: Shukla PR, Sharma SK, Ramana PV (eds) Climate change and India: issues, concerns and opportunities. Tata McGraw-Hill, New Delhi, pp 24–75
Sadhukhan I, Lohar D, Pal DK (2000) Pre-monsoon season rainfall variability over Gangetic WB and its neighbourhood, India. Int J Climatol 20(12):1485–1493
Sailaja B, Voleti SR, Subrahmanyam D, Nathawat MS, Rao NH (2013) Validation of Oryza2000 model under combined nitrogen and water limited situations. Indian J Plant Physiol 18(1):31–40
Salam MU, Jones JW, Kobayashi K (2001) Predicting nursery growth and transplanting shock in rice. Exp Agric 37:65–81
Sarkar R, Kar S (2006) Evaluation of management strategies for sustainable rice–wheat cropping system, using DSSAT seasonal analysis. J Agric Sci 144(5):421–434
Singh U, Godwin DC (1990) Modelling the impact of climate change on agricultural production in the South Pacific. In: Hughes PJ, McGregor G (eds) Global warming-related effects on agriculture and human health and comfort in the South Pacific. South Pacific Regional Environment Programme, New Guinea, pp 521–537
Singh P, Boote KJ, Virmani SM (1994) Evaluation of the groundnut model PNUTGRO for crop response to plant population and row spacing. Field Crop Res 39:163–170
Singh KK, Kumar R, Mall RK, Rathore LS, Sanker U, Gupta BRD (1999) Soybean (Glycine max) yield prediction from current and historical weather data using CROPGRO model. Indian J Agric Sci 69(9):639–643
Singh P, Alagarswamy G, Hoogenboom G, Pathak P, Wani SP, Virmani SM (1999a) Soybean/chickpea rotation on vetric inceptisols: 2. Long-term simulation of water balance and crop yields. Field Crop Res 63:225–236
Singh P, Alagarswamy G, Pathak P, Wani SP, Hoogenboom G, Virmani SM (1999b) Soybean/chickpea rotation on vertic inceptisols: 1. Effect of soil depth and landform on light interception, water balance and crop yields. Field Crop Res 63:211–224
Singh A, Saha S, Mondal S (2013) Modelling irrigated wheat production using the FAOaquacrop model in West Bengal, India, for sustainable agriculture. Irrig Drain 62(1):50–56
Singh PK, Singh KK, Rathore LS, Baxla VS, Gupta A, Gohain GB, Balasubramanian R, Singh RS, Mall RK (2016) Rice (Oryza sativa L.) yield gap using the CERES-rice model of climate variability for different agroclimatic zones of India. Curr Sci 110(3):406–413
Swain DK, Yadav A (2009) Simulating the impact of climate change on rice yield using CERES-Rice model. J Environ Inf 13(2):104–110
Swain DK, Herath S, Bhaskar BC, Krishnan P, Rao KS, Nayak SK, Dash RN (2007) Developing ORYZA 1N for medium-and long-duration rice. Agron J 99(2):428–440
Tsuji GY (1998) Network management and information dissemination for agrotechnology transfer. In: Tsuji GY, Hoogenboom G, Thornton PK (eds) Understanding options for agricultural production. Kluwer Academic Publishers, Dordrecht, pp 367–381
Uehara G (1998.) Synthesis) In: Tsuji GY, Hoogenboom G, Thornton PK (eds) Understanding options for agricultural production. Kluwer Academic Publishers, Dordrecht, pp 389–392
Van Keulen H, Wolf J (eds) (1986) Modelling of agricultural production: weather, soils and crops. Simulation Monographs. PUDOC, Wageningen
Wang B, LinHo (2002) Rainy season of the Asian–Pacific summer monsoon. J Clim 15(4):386–398
Yadav S, Li T, Humphreys E, Kukal SS (2011) Evaluation and application of ORYZA2000 for irrigation scheduling of puddled transplanted rice in North West India. Fuel En Abs 122(2):104–117
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Das, L. (2018). Understanding the Crop-Climate Interaction Using Process-Based Simulation Models. In: Bal, S., Mukherjee, J., Choudhury, B., Dhawan, A. (eds) Advances in Crop Environment Interaction. Springer, Singapore. https://doi.org/10.1007/978-981-13-1861-0_13
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