Skip to main content
SpringerLink
Log in

Rainfall and crop modeling-based water stress assessment for rainfed maize cultivation in peninsular India

  • Original Paper
  • Published:
Theoretical and Applied Climatology Aims and scope Submit manuscript

Abstract

Water stress due to uneven rainfall distribution causes a significant impact on the agricultural production of monsoon-dependent peninsular India. In the present study, water stress assessment for rainfed maize crop is carried out for kharif (June–October) and rabi (October–February) cropping seasons which coincide with two major Indian monsoons. Rainfall analysis (1976–2010) shows that the kharif season receives sufficient weekly rainfall (28 ± 32 mm) during 26th–39th standard meteorological weeks (SMWs) from southwest monsoon, whereas the rabi season experiences a major portion of its weekly rainfall due to northeast monsoon between the 42nd and 51st SMW (31 ± 42 mm). The later weeks experience minimal rainfall (5.5 ± 15 mm) and thus expose the late sown maize crops to a severe water stress during its maturity stage. Wet and dry spell analyses reveal a substantial increase in the rainfall intensity over the last few decades. However, the distribution of rainfall shows a striking decrease in the number of wet spells, with prolonged dry spells in both seasons. Weekly rainfall classification shows that the flowering and maturity stages of kharif maize (33rd–39th SMWs) can suffer around 30–40% of the total water stress. In the case of rabi maize, the analysis reveals that a shift in the sowing time from the existing 42nd SMW (16–22 October) to the 40th SMW (1–7 October) can avoid terminal water stress. Further, AquaCrop modeling results show that one or two minimal irrigations during the flowering and maturity stages (33rd–39th SMWs) of kharif maize positively avoid the mild water stress exposure. Similarly, rabi maize requires an additional two or three lifesaving irrigations during its flowering and maturity stages (48th–53rd SMWs) to improve productivity. Effective crop planning with appropriate sowing time, short duration crop, and high yielding drought-resistant varieties will allow for better utilization of the monsoon rain, thus reducing water stress with an increase in rainfed maize productivity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Abedinpour M, Sarangi A, Rajput TBS et al (2012) Performance evaluation of AquaCrop model for maize crop in a semi-arid environment. Agric Water Manag 110:55–66. doi:10.1016/j.agwat.2012.04.001

    Article  Google Scholar 

  • Adamson GCD, Nash DJ (2013) Long-term variability in the date of monsoon onset over western India. Clim Dyn 40:2589–2603. doi:10.1007/s00382-012-1494-x

    Article  Google Scholar 

  • Auffhammer M, Ramanathan V, Vincent JR (2012) Climate change, the monsoon, and rice yield in India. Clim Chang 111:411–424. doi:10.1007/s10584-011-0208-4

    Article  Google Scholar 

  • Barron J, Rockström J, Gichuki F, Hatibu N (2003) Dry spell analysis and maize yields for two semi-arid locations in East Africa. Agric For Meteorol 117:23–37. doi:10.1016/S0168-1923(03)00037-6

    Article  Google Scholar 

  • Byjesh K, Kumar SN, Aggarwal PK (2010) Simulating impacts, potential adaptation and vulnerability of maize to climate change in India. Mitig Adapt Strateg Glob Chang 15:413–431. doi:10.1007/s11027-010-9224-3

    Article  Google Scholar 

  • Çakir R (2004) Effect of water stress at different development stages on vegetative and reproductive growth of corn. F Crop Res 89:1–16. doi:10.1016/j.fcr.2004.01.005

    Article  Google Scholar 

  • Choudhury BU, Singh AK, Pradhan S (2013) Estimation of crop coefficients of dry-seeded irrigated rice-wheat rotation on raised beds by field water balance method in the Indo-Gangetic plains, India. Agric Water Manag 123:20–31. doi:10.1016/j.agwat.2013.03.006

    Article  Google Scholar 

  • Dash SK, Kulkarni MA, Mohanty UC, Prasad K (2009) Changes in the characteristics of rain events in India. J Geophys Res. doi:10.1029/2008JD010572

    Google Scholar 

  • Farré I, Faci JM (2006) Comparative response of maize (Zea mays L.) and sorghum (Sorghum bicolor L. Moench) to deficit irrigation in a Mediterranean environment. Agric Water Manag 83:135–143. doi:10.1016/j.agwat.2005.11.001

    Article  Google Scholar 

  • Ge T, Sui F, Bai L et al (2012) Effects of water stress on growth, biomass partitioning, and water-use efficiency in summer maize (Zea mays L.) throughout the growth cycle. Acta Physiol Plant 34:1043–1053. doi:10.1007/s11738-011-0901-y

    Article  Google Scholar 

  • Ghosh S, Luniya V, Gupta A (2009) Trend analysis of Indian summermonsoon rainfall at different spatial scales. Atmos Sci Lett 10:285–290. doi:10.1002/asl

    Google Scholar 

  • GOI (2012) State of Indian agriculture 2012-13. 247.

  • GOI (2015) Director’s review 2014-15.

  • Hellin J, Shiferaw B, Cairns JE et al (2012) Climate change and food security in the developing world: potential of maize and wheat research to expand options for adaptation and mitigation. J Dev Agric Econ 4:311–321. doi:10.5897/JDAE11.112

    Google Scholar 

  • Heng LK, Hsiao T, Evett S et al (2009) Validating the FAO aquacrop model for irrigated and water deficient field maize. Agron J 101:488–498. doi:10.2134/agronj2008.0029xs

    Article  Google Scholar 

  • Jat M, Singh R, Balyan J, Jain L (2005) Analysis of weekly rainfall for crop planning in Udaipur region. J Agric Eng 42:35–41

    Google Scholar 

  • Joseph PV, Gokulapalan B, Nair A, Wilson SS (2013) Variability of summer monsoon rainfall in India on inter-annual and decadal time scales. Atmos Ocean Sci Lett 6:398–403. doi:10.3878/j.issn.1674-2834.13.0044.1

    Article  Google Scholar 

  • Kendall M (1975) Rank correlation methods. Charles Griffin, London

  • Krishna Kumar K, Rupa Kumar K, Ashrit RG et al (2004) Climate impacts on Indian agriculture. Int J Climatol 24:1375–1393. doi:10.1002/joc.1081

    Article  Google Scholar 

  • Krishnamurthy CKB, Lall U, Kwon HH (2009) Changing frequency and intensity of rainfall extremes over India from 1951 to 2003. J Clim 22:4737–4746. doi:10.1175/2009JCLI2896.1

    Article  Google Scholar 

  • Kumar R, Srinivas K, Sivaramane N (2013) Assessment of the maize situation, outlook and investment opportunities in India. Country Report—Regional Assessment Asia (MAIZE-CRP), National Academy of Agricultural Research Management, Hyderabad, India.

  • Lobell DB, Burke MB (2008) Why are agricultural impacts of climate change so uncertain? The importance of temperature relative to precipitation. Environ Res Lett 3:34007. doi:10.1088/1748-9326/3/3/034007

    Article  Google Scholar 

  • Lobell DB, Burke MB, Tebaldi C et al (2008) Prioritizing climate change adaptation needs for food security in 2030. Science 319(80):607–610

    Article  Google Scholar 

  • Mall RK, Singh R, Gupta A et al (2006) Impact of climate change on Indian agriculture: a review. Clim Chang 78:445–478. doi:10.1007/s10584-005-9042-x

    Article  Google Scholar 

  • Mandal S, Choudhury BU, Mondal M, Bej S (2013) Trend analysis of weather variables in Sagar Island, West Bengal, India: a long-term perspective (1982-2010). Curr Sci 105:947–953

    Google Scholar 

  • Mandal S, Choudhury BU, Satpati LN (2015) Monsoon variability, crop water requirement, and crop planning for kharif rice in Sagar Island, India. Int J Biometeorol 59:1891–1903. doi:10.1007/s00484-015-0995-9

    Article  Google Scholar 

  • Manivasagam VS, Nagarajan R (2017) Assessing the supplementary irrigation for improving crop productivity in water stress region using spatial hydrological model. Geocarto Int 32:1–17. doi:10.1080/10106049.2015.1120355

    Article  Google Scholar 

  • Mann HB (1945) Nonparametric tests against trend. Author(s): Henry B. Mann Source: Econometrica 13:245–259.

  • Mcdermid S, Gowtham R, Bhuvaneswari K et al (2016) The impacts of climate change on Tamil Nadu rainfed maize production: a multi-model approach to identify sensitivities and uncertainties. Curr Sci. doi:10.18520/cs/v110/i7/1257-1271

    Google Scholar 

  • Menon A, Levermann A, Schewe J et al (2013a) Consistent increase in Indian monsoon rainfall and its variability across CMIP-5 models. Earth Syst Dyn 4:287–300. doi:10.5194/esd-4-287-2013

    Article  Google Scholar 

  • Menon A, Levermann A, Schewe J (2013b) Enhanced future variability during India’s rainy season. Geophys Res Lett 40:3242–3247. doi:10.1002/grl.50583

    Article  Google Scholar 

  • Mugalavai EM, Kipkorir EC, Raes D, Rao MS (2008) Analysis of rainfall onset, cessation and length of growing season for western Kenya. Agric For Meteorol 148:1123–1135. doi:10.1016/j.agrformet.2008.02.013

    Article  Google Scholar 

  • Mushore T, Manatsa D, Pedzisai E et al (2016) Investigating the implications of meteorological indicators of seasonal rainfall performance on maize yield in a rain-fed agricultural system: case study of Mt. Darwin District in Zimbabwe. Theor Appl Climatol. doi:10.1007/s00704-016-1838-2

    Google Scholar 

  • Nyakudya IW, Stroosnijder L (2011) Water management options based on rainfall analysis for rainfed maize (Zea mays L.) production in Rushinga District, Zimbabwe. Agric Water Manag 98:1649–1659. doi:10.1016/j.agwat.2011.06.002

    Article  Google Scholar 

  • Omoyo NN, Wakhungu J, Otengi S (2015) Effects of climate variability on maize yield in the arid and semi arid lands of lower eastern Kenya. Agric Food Secur 4:8–21. doi:10.1186/s40066-015-0028-2

    Article  Google Scholar 

  • Pandey RK, Maranville JW, Admou A (2000) Deficit irrigation and nitrogen effects on maize in a Sahelian environment. I. Grain yield and yield components. Agric Water Manag 46:1–13. doi:10.1016/S0378-3774(00)00073-1

    Article  Google Scholar 

  • Paredes P, de Melo-Abreu JP, Alves I, Pereira LS (2014) Assessing the performance of the FAO AquaCrop model to estimate maize yields and water use under full and deficit irrigation with focus on model parameterization. Agric Water Manag 144:81–97

    Article  Google Scholar 

  • Pathak H, Wassmann R (2009) Quantitative evaluation of climatic variability and risks for wheat yield in India. Clim Chang 93:157–175. doi:10.1007/s10584-008-9463-4

    Article  Google Scholar 

  • Prasanna V (2014) Impact of monsoon rainfall on the total foodgrain yield over India. J Earth Syst Sci 123:1129–1145. doi:10.1007/s12040-014-0444-x

    Article  Google Scholar 

  • Preethi B, Revadekar JV (2013) Kharif foodgrain yield and daily summer monsoon precipitation over India. Int J Climatol 33:1978–1986. doi:10.1002/joc.3565

    Article  Google Scholar 

  • Raes D, Steduto P, Hsiao TC, Fereres E (2009) Aquacrop—the FAO crop model to simulate yield response to water: II. Main algorithms and software description. Agron J 101:438–447

    Article  Google Scholar 

  • Raes D, Steduto P, Hsiao TC, Fereres E (2012) Aquacrop reference manual version 4.0. Chapter 3: Calculation procedures.

  • Revadekar JV, Preethi B (2012) Statistical analysis of the relationship between summer monsoon precipitation extremes and foodgrain yield over India. Int J Climatol 32:419–429. doi:10.1002/joc.2282

    Article  Google Scholar 

  • Rowhani P, Lobell DB, Linderman M, Ramankutty N (2011) Climate variability and crop production in Tanzania. Agric For Meteorol 151:449–460. doi:10.1016/j.agrformet.2010.12.002

    Article  Google Scholar 

  • Selvaraju R (2003) Impact of El Nino—southern oscillation on Indian foodgrain production. Int J Climatol 23:187–206. doi:10.1002/joc.869

    Article  Google Scholar 

  • Sen PK (1968) Estimates of the regression coefficient based on Kendall’s tau. J Am Stat Assoc 63:1379–1389. doi:10.2307/2285891

    Article  Google Scholar 

  • Siderius C, Biemans H, Van Walsum PEV et al (2016) Flexible strategies for coping with rainfall variability: seasonal adjustments in cropped area in the Ganges basin. PLoS One 11:1–23. doi:10.1371/journal.pone.0149397

    Article  Google Scholar 

  • Singh N, Ranade A (2010) The wet and dry spells across India during 1951–2007. J Hydrometeorol 11:26–45. doi:10.1175/2009JHM1161.1

    Article  Google Scholar 

  • Singh N, Rajendran A, Shekhar M, et al (2012) Rabi maize opportunities & challenges. Directorate of Maize Research, Pusa Campus, New Delhi; 110 012

  • Steduto P, Hsiao TC, Raes D, Fereres E (2009) AquaCrop—the FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agron J 101:426–437

    Article  Google Scholar 

  • Stricevic R, Cosic M, Djurovic N et al (2011) Assessment of the FAO AquaCrop model in the simulation of rainfed and supplementally irrigated maize, sugar beet and sunflower. Agric Water Manag 98:1615–1621

    Article  Google Scholar 

  • Subash N, Gangwar B (2014) Statistical analysis of Indian rainfall and rice productivity anomalies over the last decades. Int J Climatol 34:2378–2392. doi:10.1002/joc.3845

    Article  Google Scholar 

  • Subash N, Ram Mohan HS (2011) Trend detection in rainfall and evaluation of standardized precipitation index as a drought assessment index for rice-wheat productivity over IGR in India. Int J Climatol 31:1694–1709. doi:10.1002/joc.2188

    Google Scholar 

  • Subash N, Singh SS, Priya N (2011) Extreme rainfall indices and its impact on rice productivity—a case study over sub-humid climatic environment. Agric Water Manag 98:1373–1387. doi:10.1016/j.agwat.2011.04.003

    Article  Google Scholar 

  • Tongwane M, Moeletsi M (2015) Intra-seasonal rainfall variability during the maize growing season in the northern lowlands of Lesotho. Theor Appl Climatol 120:575–585. doi:10.1007/s00704-014-1183-2

    Article  Google Scholar 

  • Wang P, Song X, Han D et al (2012) Determination of evaporation, transpiration and deep percolation of summer corn and winter wheat after irrigation. Agric Water Manag 105:32–37. doi:10.1016/j.agwat.2011.12.024

    Article  Google Scholar 

Download references

Acknowledgements

The data used in this study were obtained from the India Meteorological Department (IMD), Government of India, and Hydrology Project (Surface Water), Water Resources Department, Government of Maharashtra, India. The authors would like to thank the reviewers for the constructive comments and suggestions to improve the quality of the paper.

Author information

Authors and Affiliations

  1. Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, 400076, India

    V. S. Manivasagam & R. Nagarajan

Authors
  1. V. S. Manivasagam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Nagarajan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to V. S. Manivasagam.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Manivasagam, V.S., Nagarajan, R. Rainfall and crop modeling-based water stress assessment for rainfed maize cultivation in peninsular India. Theor Appl Climatol 132, 529–542 (2018). https://doi.org/10.1007/s00704-017-2101-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00704-017-2101-1

Search

Navigation