ORIGINAL PAPER

Forecasting climate change impacts and evaluation of adaptationoptions for maize cropping in the hilly terrain of Himalayas:Sikkim, India

Proloy Deb & Sangam Shrestha & Mukand S Babel

Received: 8 January 2014 /Accepted: 13 August 2014# Springer-Verlag Wien 2014

Abstract An investigation was carried out to assess the cli-mate change impacts on rainfed maize yield using AquaCropand CERES–maize crop simulation models, and evaluation ofadaptation measures were performed for Sikkim state of India.Data related to crop phenology retrieved from the field exper-iments were used to calibrate and validate the crop models forthree representative sites. Climate projections of six globalcirculation models (ECHAM5, CCSM, HadCM3, CSIRO-MK3.0, CGCM3.1, and MIROC3.2) for scenarios A2 andB2 were bias-corrected at station scale by power law transfor-mation. Simulation results by the two crop models indicate asignificant declination in the yield of NLD-White variety ofmaize ranging from 4.7±1.4 to 20.4±7.2 % for the future timewindows under A2 scenario and 2.5±0.9 to 15.8±5.7% underB2 scenario relative to the yield simulated for the baselineperiod of 1991–2000. It is also observed that, for a particulartemperature, yield remarkably increases with escalated carbondioxide (CO2) concentration. On contrary, increase in temper-ature reduces the yield at a particular CO2 concentration. Theoverall decline in the yield under future climate scenarios canbe alleviated by early planting, appropriate nutrient manage-ment, introducing supplementary irrigation, and shifting toheat-tolerant varieties.

1 Introduction

Climate change has become the greatest environmental, so-cial, and economic threat for human beings on this planet.Contemporary studies on climate change in the Himalayanregion have shown significant change in temperature and

precipitation patterns (Mishra et al. 2013; Seetharam 2008;IPCC 2007a; Du Et al. 2004; Shrestha et al. 1999). Projectionstudies of climate variables based on different scenarios ofCO2 emission validates probable increase in temperature andambiguous precipitation pattern in the region with increasingand decreasing trends depending on locations (Shrestha et al.2013; Babel et al. 2013). Additional threat due to the extremeevents has become more intense and frequent which can posesevere impact on agriculture (Alcamo et al. 2007).

Agricultural sector is directly influenced by climatic vari-ables as the physiological processes of plants are directlylinked to climate inputs. The alteration of the magnitude andpattern of temperature and precipitation has affected the cropwater cycle by changing growth period, photosynthesis abil-ity, and increase in respiration. This has ultimately increasedwater stress on crops and accordingly the food security (Taoet al. 2003a, b). Studies have shown that change in tempera-ture of up to 2 °C in the dry seasons can substantially reducethe yield of crops (Lobells et al. 2008; O’Neill 2007).

Agriculture is the main economic activity of the people inSikkim and is practiced by 65 % of population. Despite of theundulating topography leading to difficulty in performingagricultural practices in East Sikkim, maize (Zea mays) is animportant crop which is grown in 55 % of the total cultivatedarea (GIAHS 2009). Rainfed agriculture is dominant inSikkim where water storage for irrigation is a persistent prob-lem due to physiographic complexity (Sikkimstat 2013).Overdependence of farmers on rainfed maize production hasled to a significant decline in production and productivity(ICIMOD 2010). Site-specific impact assessment studies athilly regions are highly complicated. Several studies apprais-ing future impacts of climate change on maize yield in eastIndian and Nepalese basins using various yield simulationmodel (Joshi and Chaturvedi 2013; Panda et al. 2012;Nayava and Gurung 2010) have shown a significant decreasewithout proper adaptation practice. Malla (2008) have shown

P. Deb (*) : S. Shrestha :M. S. BabelWater Engineering and Management, School of Engineering andTechnology, Asian Institute of Technology, P.O. Box 4 Klong Luang,Pathumthani 12120, Thailande-mail: debproloy@gmail.com

Theor Appl ClimatolDOI 10.1007/s00704-014-1262-4

that higher temperature is beneficial for C4 crops (maize) athigher mountains in Nepal; however, a significant loss ofgreater than 10 % is expected in hills and plains.

Recent studies implies that the best representation ofchanges in agricultural productivity due to climate changecan be achieved by multi-global climate models (GCM) anal-ysis (Berg et al. 2013; H glind et al. 2013; Wang et al. 2011;Yao et al. 2011). Tebaldi and Lobell (2008) endorsed proba-bilistic meta-data analysis to assess the uncertainties withinthe impacts of temperature and precipitation changes based onmulti-GCM simulations on crops at global scale. Additionaluncertainty is contributed by parameterization of the cropsimulation model which is usually ignored in impact assess-ment studies (Asseng et al. 2013; Hawkins et al. 2013).Techniques for using multiple GCM models are necessary tobe targeted at projection- or utility-based approaches for eval-uation of more appropriate adaptation strategies (Challinoret al. 2013).

In agriculture, role of adaptation is decisive as it can enor-mously reduce the magnitude of impacts of climate change(Reidsma et al. 2010). Babel et al. (2011) evaluated impacts ofclimate change on rice production in Thailand and empha-sized on the need of effective adaptation consisting of chang-ing sowing dates, change in fertilizer application rate, andchange in cultivars as a measure to counter the negativeimpacts. A study on super-ensemble-based probabilistic pro-jection approach was applied to project maize productivityand water use in North China plain by Tao and Zhang (2010).Results reveal early and late planting of temperature-sensitiveand high–temperature-tolerant varieties, respectively, are suit-able for effective adaptation. Several studies also have vali-dated that the suitable adaptation options are usually region-specific and needs to be appraised as per the location (Tao andZhang 2010; Bryan et al. 2009).

Despite the progress in assessment of climate change im-plication in agricultural production, there is a dearth of ex-haustive study to examine the long-term effects of climatechange on regional scale maize production for Himalayanregion. The present study focuses on potential impacts ofclimate change on maize production and evaluation of severalagro-adaptation measures to minimize the adverse impactsusing two crop simulation models in hilly Himalayan terrains.

2 Materials and methods

2.1 Study area and maize cultivation

The selected study sites lies from higher to lower lap ofHimalayas in the northeast part of India between latitudes27°5′–28°10′ N and longitudes 88°–88°55′ E. The altitudeof the selected sites ranges from 1,350 and 6,300 m abovemean sea level (amsl) (Fig. 1). Precipitation is dominating in

the monsoon season (May–September) which contributes85 % of total rainfall ranging from 2,200 to 3,900 mm.Absolute maximum temperature (Tmax) and minimum tem-perature (Tmin) varies from 17–24 °C and 9–13 °C, respec-tively. The study was conducted based on meteorological datafrom three stations of Indian Meteorological Department net-work. The selected regions represent the major agriculturalareas in the state: Mangan (1,350 m), Gangtok (3,800 m), andGeyzing (6,300 m). The major climatic characteristics of theregion {temperature, reference evapotranspiration (ETo) andprecipitation} for Gangtok station are shown in Fig. 2a and b.

Sowing of seed is mostly performed from mid-February tomid-March depending on light shower during planting.Location-specific planting of the seed depends on precipita-tion and the optimum temperature which is favorable for themaize in the region (Basnet et al. 2003). NLD-White compos-ite variety of maize is mostly grown in the region which has agrowing cycle of 128 days. It is essentially used for poultryfeed and self-consumption. It is also exported to other parts ofIndia and gives an excellent monetary return. Single croppingpractice is followed by local farmers due to unavailability ofwater for irrigation during dry months. The other compositevarieties of maize grown in the region includes Sethi Makai 2and NAC 6004.

2.2 Methodological flowchart

Outputs of two climate scenarios derived from the specialreport on emission scenarios (SRES), A2 and B2, from sixGCMs were bias-corrected for the representative stations toforecast the future precipitation, Tmax and Tmin. The futureclimate projection was done for three time periods 2021–2030, 2051–2060, and 2081–2090 (represented as 2025,2055, and 2085 s, respectively) and was compared relativeto baseline period of 1991–2000 (Fig. 3). The outputs of finerresolution climate variables were further used as input for twocalibrated crop simulation models AquaCrop 4.0 (Stedutoet al. 2009) and CERESS-Maize v4.0 (Hoogenboom et al.2012) in order to project the yield for the rainfed maize for allfuture time windows. Furthermore, evaluation of agro-adaptation measures namely altering the sowing date, changein the application rate of Farm Yard Manure (FYM), introduc-ing supplementary irrigation, and change in the cultivars werealso done by CERES-maize for both scenarios using all sixGCM projections to counteract the negative impacts of cli-mate change.

2.3 Meteorological data

Daily meteorological data (air temperature, precipitation, sun-shine hours, and average relative humidity) were collected forthe period of 1975–2010 for Gangtok station. However, dataavailable for Mangan and Geyzing were from 1983–2010 and

P. Deb et al.

1977–2010, respectively, and the corresponding ETo wascalculated by ETo calculator using Penman–Monteith equa-tion as its governing equation. The daily solar radiation for thestudy areas were calculated based on Angstrom formulae as inEq. 1.

Rs ¼ 0:25þ 0:5n

N

� �h iRa ð1Þ

Where Rs is solar radiation in megajoules per square meterper day, n and N are actual and maximum possible duration of

sunshine, respectively, in hours and Ra is extraterrestrial radi-ation (megajoules per square meter per day). N and Ra for theconsidered meteorological stations are derived from standardcharts provided in Allen et al. (1998).

2.4 Projection of future climate

Globally available general circulation models (GCMs) projectthe climatic variables for different emission scenarios at acoarse resolution, and their representativeness makes it

Fig. 1 Map of the study area withlocations of the three stationscontemplated in this study

Climate change impacts and adaptation options for maize cropping

difficult to use one particular GCM for future climate projec-tion and its impact assessment studies (Gohari et al. 2013).Additional studies have verified that considering multi-modelensembles is an efficient technique to consider all the potentialuncertainties considered in the GCM projections (Semenovand Stratonovitch 2010). Therefore, in the study, we consid-ered six GCMs to identify the potential climate change im-pacts on maize productivity in the study area (Table 1).

2.5 Bias correction of the climate variables

Although GCMs predict the future climate variables, themajor drawback associated with this them heir coarse resolu-tion outputs. Moreover, regional features like topography,vegetation, cloudiness, and local climate effects are not incor-porated by GCMs (IPCC 2007b), and therefore, to makeregional studies, it is necessary to transfer the coarse resolu-tion to a finer scale either by bias correction or by downscalingtechnique. Due to the spatial dependence of the biases intemperature and precipitation, performing bias correction isnecessary for station scale study. The bias correction of theGCM projections for the future climate considering A2 and

B2 climate scenarios was done for all the three stationsGeyzing, Gangtok, and Mangan. The biases from the temper-ature were removed by power law transformation theoremwhere the data are normally distributed. It uses the scalingand shifting of the mean and variance of the dataset (Leanderand Buishand 2007). For the stations, the corrected dailytemperature T* was done as in Eq. 2.

T� ¼ T þ σ Tobsð Þσ T areað Þ T−T

� �þ Tobs−T area

� � ð2Þ

where T* is the daily corrected temperature (degreesCelsius), T is the uncorrected daily temperature (degreesCelsius), Tobs is the observed daily temperature (degreesCelsius) for historical period, and Tarea is the correspondingaverage temperature (degrees Celsius) obtained from eachGCMs.

Furthermore, the bias correction for precipitation was doneby non-liner method of multi-day windows for correction ofcoefficient of variation (CV). The baseline period consideredfor correcting the future period dataset was 1977–2010, 1975–2010, and 1983–2010 for Geyzing, Gangtok, and Mangan,

(a) (b)Fig. 2 Observed a Tmax and Tmin, b monthly precipitation, and ETo for Gangtok station

Bias

correction

Projections from 6

GCMs

Historical observed

precipitation and

temperature

Future climatic variables

Maximum and minimum

temperature

Precipitation

Projection of maize yield

under future climate (with

adaptation)

Projection of maize yield

under future climate

Soil data

Crop data

Management data

Changes in management

practices as adaptation

measures

AquaCrop

v4.0

CERES-

Maize v4.0

Calibrated

AquaCrop Calibrated

CERES-Maize

Fig. 3 Methodological flowchart used in this study

P. Deb et al.

respectively. Each daily amount of precipitation P is trans-formed by power law equation to a corrected P* using theEq. 3.

P� ¼ aPb ð3Þ

The effect of sampling variability was reduced by deter-mining the parameters a and b with a distribution-free ap-proach for each 5-day period of the year in a window includ-ing 30 days before and after the considered 5-day period. Thevalue of b was determined such that the CVof the correcteddaily precipitation fits that of the observed daily precipitationin variability of multi-day amounts. Coefficient of variationwas calculated as Eq. 4.

CV ¼ σμ

ð4Þ

Where σ represents the standard deviation in the observeddata and μ is the mean value of the precipitation for anyparticular period. Standard deviation was calculated as Eq. 5.

σ ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1

n

Xi¼1

n

xi−μð Þ2s

ð5Þ

Where xi represents the daily values of amount of precip-itation. The mean was calculated as Eq. 6.

μ ¼ 1

n

Xi¼1

n

xi ð6Þ

The parameter a was calculated subsequently in order tomatch the mean of the transformed daily values to the ob-served mean.

aPb ¼ Pobs ð7Þ

Each block of 5 days had its own a and b parameters, whichwere same for each year. Thus, it was sectioned into 73 blocksof 5 days in a year. For every 5-day block, a different set of a

and b parameters were decided using the method explainedabove.

2.6 Other data

Experimental data on field trials for maize were collected fromIndian Council of Agricultural Research (ICAR) researchcomplex located at Mangan, Gangtok and Geyzing whichwas used for model calibration and validation. The fieldexperimental data of the cultivar NLD-White for the years2001 to 2005 was used for model calibration whereas that foryears 2006 to 2009 was applied for model validation(Table 2). Further data on experimental field trials for thecultivars Sethi Makai 2 and NAC 6004 were also collectedfrom ICAR Gangtok center for the calibration of the cropmodel in order to evaluate the adaptation strategies (Table 3).

Data on physical and chemical properties of soil werecollected from Land Development Department (LDD),Government of Sikkim. The dataset collected consists of soilclass, texture, structure, and chemical composition includingorganic carbon content, pH, potassium, nitrogen, carbon, andcation exchange capacity.

2.7 Crop models

In order to abate the uncertainty in crop model simulations,two cropmodels working on different principles were selectedfor this study. Prior to impact assessment, both models werecalibrated and validated with the experimental field data.

2.7.1 AquaCrop

AquaCrop is a yield response to water stress model for severalfield crops. It requires comparatively lesser input datathan the carbon-driven and radiation-driven model.Literature (Shrestha et al. 2014a, Shrestha et al. 2014b)confirms that the ability of the model to simulate yield atgood accuracy at different locations across the globemakes it exciting to use it for the study site. The soft-ware integrates four sub-gear modules, namely the soil,crop, climate, and management. The model requires a basic

Table 1 List of the six GCMsused in this study Name of GCM Country Center Source

ECHAM5/MPI-OM Germany Max-Plank Institute for Meteorology Roeckner et al. (2003)

CCSM3 USA University Corporation for AtmosphericResearch

Collins et al. (2005)

UKMO-HadCM3 UK UK Meteorological Office Gordon et al. (2000)

CSIRO-MK3.0 Australia Commonwealth Scientific and IndustrialResearch Organization (CSIRO)

Gordon et al. (2002)

CGCM3.1 Canada Canadian Centre for Climate Modelingand Analysis

McFarlane et al. (2005)

MIROC3.2 Japan Meteorological Research Institute K-1 model developers (2004)

Climate change impacts and adaptation options for maize cropping

climate data (Tmax and Tmin, precipitation, reference evapo-transpiration, and mean annual carbon dioxide CO2 concen-tration), planting and maturity dates, crop growth details alongwith the stages, crop management data, and soil properties. Itestimates the plant growth on a daily basis by calculating thedaily water balance, which includes water flux and change insoil moisture.

AquaCrop calculates above-ground biomass based on nor-malized water productivity and ratio of transpiration which isdetermined by canopy cover and reference evapotranspiration(Eq. 8). The yield is assumed as a function of reference harvestindex and above-ground biomass (Eq. 9).

B ¼ ksb �WP�∑ Tr=EToð Þ ð8Þ

Table 2 Crop growth character-istics for NLD-White cultivar ofmaize at the three locations usedfor crop model calibration andvalidation

Year Station Sowingdate

Maturitydate

No. of leavesat maturity

Yield(kg ha−1)

Biomass(kg ha−1)

Water productivitykg ha−1 mm−1)

2001 Geyzing 17 Feb 26 Jun 21 3,941 10,119 10.9

Gangtok 25 Feb 3 Jul 19 3,684 11,154 11.2

Mangan 15 Feb 22 Jun 22 4,056 11,521 12.4

2002 Geyzing 16 Feb 25 Jun 20 3,816 11,065 10.6

Gangtok 19 Feb 30 Jun 20 3,698 11,341 12.7

Mangan 26 Feb 6 Jul 24 4,156 12,056 11.4

2003 Geyzing 8 Feb 19 Jun 24 3,895 9,989 11.4

Gangtok 11 Feb 21 Jun 19 3,598 8,769 9.69

Mangan 9 Feb 19 Jun 22 4,214 11,541 11.8

2004 Geyzing 12 Feb 20 Jun 19 3,896 9,987 9.9

Gangtok 4 Mar 15 Jul 18 4,325 11,854 11.8

Mangan 13 Feb 20 Jun 22 4,169 11,235 10.5

2005 Geyzing 4 Mar 16 Jul 18 3,498 10,065 9.2

Gangtok 3 Mar 18 Jul 20 3,876 10,212 10.6

Mangan 15 Feb 23 Jun 22 4,245 11,336 11.4

2006 Geyzing 22 Feb 3 Jul 19 3,749 10,166 12.5

Gangtok 9 Feb 20 Jun 20 4,054 11,098 10.5

Mangan 16 Feb 23 Jun 23 4,312 12,044 13.2

2007 Geyzing 8 Feb 14 Jun 18 3,859 10,855 11.6

Gangtok 11 Feb 19 Jun 22 4,056 12,541 12.3

Mangan 22 Feb 1 Jul 21 3,975 10,849 9.6

2008 Geyzing 19 Feb 29 Jun 23 4,216 11,688 11.9

Gangtok 14 Feb 22 Jun 20 3,849 10,800 10.5

Mangan 6 Feb 15 Jun 19 3,575 10,277 8.9

2009 Geyzing 24 Feb 3 Jul 23 4,214 11,706 10.9

Gangtok 5 Mar 17 Jul 22 3,996 10,642 9.7

Mangan 8 Mar 18 Jul 19 3,757 10,844 10.2

Table 3 Crop growth character-istics for Sethi Makai 2 and NAC6004 cultivars of maize at Gang-tok station used for crop modelcalibration for evaluation of agro-adaptation measures

Year Cultivars Sowing date Maturity date Grain yield (kg ha−1) Biomass (kg ha−1)

1995 Sethi Makai 2 17 Feb 11 Jun 4,247 11,798

NAC 6004 12 Feb 16 May 4,902 12,424

1998 Sethi Makai 2 8 Feb 2 Jun 4,844 13,127

NAC 6004 9 Feb 15 May 5,329 14,623

1999 Sethi Makai 2 5 Mar 28 Jun 5,046 14,007

NAC 6004 2 Mar 5 Jun 5,174 13,942

2002 Sethi Makai 2 19 Feb 14 Jun 4,327 11,927

NAC 6004 19 Feb 26 May 4,822 12,379

P. Deb et al.

Y ¼ f Hi � HIo � B ð9Þ

where B is above-ground biomass in tonnes per hectare,WP* is the normalized water productivity (gets adjusted forCO2, synthesized yield production, and soil fertility), (Tr/ETo)is the ratio of crop transpiration and reference evapotranspira-tion, Y is yield in tonnes per hectare, fHi is the adjustmentfactor for heat and water stress, HIo is the reference harvestindex. More details on AquaCrop can be found on Stedutoet al. 2009 (2009); Raes et al. (2009).

2.7.2 CERES–maize

The Decision Support System for Agrotechnology Transfer(DSSAT) v 4.0 Cropping System Model (CSM) particularlyCERES–maize model was the other model chosen for theimpact assessment due to its wide area of application and itsrobustness to simulate crop output parameters (Jeffrey et al.2010). Themodel can simulate 27 crops with similar input andoutput files. It has been applied extensively to analyze theimpacts of climate change (Babel et al. 2011; Attri andRathore 2003; Tubiello et al. 2002).

DSSAT-CERES for maize is a dynamic simulation modelwhich works on the understanding of the physiological pro-cesses in the crop growth. Cultivar-specific genetic coefficientsare required in determining the growth and phenological de-velopment of a particular crop depending on photoperiod,thermal time, and drymatter partitioning. It assumes drymatteraccumulation as a function of photosynthetic active radiation;hence, solar radiation is the most essential input to the model.Actual dry biomass at a particular stage is a function of thestress coefficient of water or nitrogen stress and initial accu-mulated biomass and the actual leaf area index. More detailson DSSAT-CERES can be found in Hoogenboom et al. (2012)and Jones et al. (2003).

2.7.3 Crop model development

Due to large number of parameters in AquaCrop model, it wascalibrated by fine-tuning the most sensitive global-scale non-conservative parameters (Vanuytrecht et al. 2014) obtainedfrom the sensitivity analysis of the model for the Gangtok site(Shrestha et al. 2014a). The input values of 15 parameterswere adjusted by ±25 % relative to the default values, andsimulations were carried out keeping others constant. Theresponse of change in yield (in terms of percentage) for theyears 2005 to 2009 by altering the input parameters were usedto select the most sensitive parameters based on the criteriadefined by Greets et al. (2009) and were used to calibrate themodel for all the sites. The model calibration was done byadjusting the sensitive parameters in the order to simulate theyield, biomass yield, and the water productivity and compar-ing with the observed values for the years of 2001 to 2005.

The difference between the model predicted and experimentaldata was minimized by using trial-and-error approach whereone particular variable was taken as the reference variable andsubsequently adjusting the parameters which were supposedto clout the reference variable. The procedure was reiterated toget maximum coefficient of determination (R2), minimumroot mean square error (RMSE), and mean absolute error(MAE).

Calibration in case of CERES–maize, was also performedin similar procedure to AquaCrop except the adjustment wasdone for six cultivar-specific phenological coefficients. Thecoefficients were adjusted based on the reference values cal-culated by field experiments for years 2001 to 2005 in order tomatch the simulated outputs to the observed ones.

Validation of the models was done by simulating the out-puts (grain yield, total biomass, and water productivity forAquaCrop and grain yield, total biomass, and number ofleaves for CERES–maize) using the calibrated parameters toobtain the best goodness-of-fit for the period of 2006 to 2009.

2.7.4 Model evaluation criterion

The model simulations for the output variables during cali-bration and validation were compared with observed valuesfrom the experiments. The goodness of fit between observedand simulated values was corroborated by coefficient of de-termination (R2), root mean square error (RMSE), and meanbias error (MBE) which is calculated as in Eqs. 10, 11, and 12.

R2 ¼X

Si� Oi−X

Si�X

OiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXSi2 −

XSi

� �2�

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXOi2 −

XOi

� �2rs ð10Þ

RMSE ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1

n

Xi¼1

n

Si−Oið Þ2s

ð11Þ

MBE ¼ 1=nXi¼1

n

Si−Oið Þ ð12Þ

Where Si and Oi represent the simulated and the observedvalues of the variables.

2.8 Effect of incremental temperature and Carbon dioxide(CO2) levels at fixed intervals

An initial test was conducted with the calibrated CERES–maize model to observe the yield sensitivity to the changesin atmospheric temperature and CO2. Several studies on rice at

Climate change impacts and adaptation options for maize cropping

global scale have validated the increase in yield attributing toelevated CO2 concentration is adjusted according to tempera-ture increase at different locations (Kim et al. 2013; Babelet al. 2011; Krishnan et al. 2007). Furthermore, modelingexercises on other crops such as maize and soybean also haveeventually showed the positive implications of CO2 on maizeyield which gets nullified by the continuous increase in tem-perature (Ruiz-Vera et al. 2013; Leakey et al. 2006).

For the present study, yield was simulated for NLD-Whitecultivar of maize at Gangtok station with different combina-tions of CO2 concentrations (390, 400, 500, 600, 700, and800 ppm) with Tmax and Tmin (+1, +2, +3, +4, +5, and+6 °C) as suggested by Babel et al. (2011). Assumed A2 andB2 scenarios estimate an increase in temperature of 2.0 to5.4 °C and 1.4 to 3.8 °C, respectively, by the end of thecentury relative to the baseline period, whereas an ab-solute increase of 830 and 550 ppm CO2 concentrationfor the corresponding scenarios (IPCC 2007a) relative tothe current concentration of 390 ppm which was themajor reason behind the selection of the fictional scenariosfor this study.

2.9 Model simulation for future climate

Both calibrated models were used to simulate the future yieldof maize under the two scenarios and the six GCMs consid-ered. The relative changes in the yield, referring to yieldsimulated for the baseline climate, was used for the studyrather than absolute yields.

2.10 Agro-adaptation options

Several plausible adaptation strategies were evaluated to min-imize the negative impacts of climate change on maize pro-ductivity in the region under both climate scenarios. Alternate

sowing date, proper nutrient management, introducing sup-plementary irrigation practices, and broaching heat-tolerantvarieties were evaluated by the calibrated CERES–maizemodel. Simulations for different sowing dates ranging from−34 and +37 days at an interval of 7 days compared withpresent sowing date (15 February) was considered. Variousapplication rate of FYM from 50 to 160 % comparedwith recommended application rate (12 t/ha) was alsoconsidered for this study. Investigation of different irri-gation application rates (10, 20, 30, and 40 mm) at20 days interval starting from 15 days prior to tassellingwas done to find the optimum amount. In addition,literature also suggests that shifting from traditionalcultivars to heat-tolerant varieties also serves as an effectiveadaptation measure (Shrestha et al. 2014a; Rezaei et al. 2014).Simulations were also carried for the study site for other twoheat-resistant cultivars (Sethi Makai 2 and NAC 6004) underfuture climate.

2.11 Methodological limitations

The main assumption of this study includes that other climaticparameters, namely humidity and wind speed, will be unal-tered and similar to the historical periods. Another majorshortfall of this study is during the estimation of the yieldresponse to various temperatures and CO2 concentrations,while using a single crop model (CERES–maize). The limita-tion in AquaCrop model to consider incremental level of CO2

concentrations as its input is the attributing cause of thisshortcoming. In addition, due to the unavailability of observedfield experimental data at the three stations for the baselineperiod (1991–2000), simulation of the yield was done for thecorresponding period with the observed climatic data andcalibrated AquaCrop and CERES–maize models. The com-parison of yield for future time windows was done based on

Table 4 Simulated averagechanges in future Tmax atGeyzing, Gangtok, and Manganstations under A2 and B2 scenar-ios (±indicates the variance ofprojections by different GCMs)

Station Baseline(°C)

Time period(s)

Tmax (°C)

Scenario A2 Scenario B2

Tmax (°C) Change (°C) Tmax (°C) Change (°C)

Geyzing 17.63 2025 18.47±1.29 0.84 18.29±0.87 0.66

2055 18.96±1.52 1.33 18.63±1.05 1.00

2085 19.59±2.85 1.96 19.48±1.89 1.85

Gangtok 19.84 2025 20.58±1.36 0.74 20.54±1.02 0.70

2055 21.07±1.69 1.23 20.81±1.26 0.97

2085 21.82±2.98 1.98 21.43±2.75 1.59

Mangan 23.66 2025 24.36±1.56 0.70 23.92±1.21 0.26

2055 24.77±1.86 1.11 24.26±1.59 0.60

2085 25.29±3.29 1.63 24.81±3.01 1.15

P. Deb et al.

the average of the simulated yield by the two models for thebaseline period. Also, to reduce the complexity and volume ofresults, we have used only CERES–maize to evaluate theagro-adaptation measures. Nonetheless, despite of the consid-erations contemplated in the study, it is emphasized that thepresent study will be extremely useful in describing the pat-terns of climate and various plausible agro-adaptation mea-sures for Himalayan agriculture although the figures can prob-ably vary due to the limitations.

3 Results and discussion

3.1 Climate change impact on temperature

An increasing trend in annual temperature is observed in caseof all three stations for the future time windows. Observedhighest changes are +1.96, +1.98 and +1.63 °C at Geyzing,Gangtok, and Mangan, respectively, for Tmax in case of A2

scenario for 2085 s (Table 4). Similarly for B2 scenario, amaximum change of +1.85, +1.59, and +1.15 °C is observedfor the corresponding stations and period. This implieshigher altitude (Geyzing) is more susceptible to changesin Tmax relative to lower elevation (Mangan) underfuture climate. Additionally, it can also be observed thatthe variance in the projections due to multiple GCMsaggravates with future time periods from 2025 to 2085 s.Interestingly, higher variance can be observed for thelower elevation (Mangan) in case of both scenarioswhich is probably due to the inter-model projectionuncertainty provoking to the higher ambiguity for lowerelevations (Önol et al. 2014).

Analysis of Tmin suggests higher increase in magnitudecompared with Tmax for the corresponding time period, andstation. Again maximum observed change for Geyzing,Gangtok, andMangan are 2.31, 2.11, and 1.73 °C respectively(Table 5). The observed projection suggests a minor increasein Tmin is expected for Mangan in both A2 and B2 scenarios;

Table 5 Simulated averagechanges in future Tmin atGeyzing, Gangtok, and Manganstations under A2 and B2 scenar-ios (±indicates the variance ofprojections by different GCMs)

Station Baseline(°C)

Time period(s)

Tmin (°C)

Scenario A2 Scenario B2

Tmin (°C) Change (°C) Tmin (°C) Change (°C)

Geyzing 9.32 2025 9.87±0.85 0.55 9.71±0.52 0.39

2055 10.51±1.23 1.19 10.43±0.98 1.11

2085 11.63±1.96 2.31 11.28±1.29 1.96

Gangtok 10.85 2025 11.41±0.93 0.56 11.29±0.65 0.44

2055 12.21±1.39 1.36 11.84±1.15 0.99

2085 12.96±2.39 2.11 12.49±2.06 1.64

Mangan 12.73 2025 13.23±1.19 0.50 13.15±0.85 0.42

2055 13.88±1.66 1.15 13.62±1.49 0.89

2085 14.46±2.51 1.73 13.79±1.98 1.06

Table 6 Simulated averagechanges in future precipitation atGeyzing, Gangtok, and Manganstations under A2 and B2 scenar-ios (±indicates the variance ofprojections by different GCMs)

Station Baseline(mm)

Time period(s)

Precipitation (mm)

Scenario A2 Scenario B2

Prcp. (mm) Change (%) Prcp. (mm) Change (%)

Geyzing 3,933 2025 3,846±106 −2.2 3,996±96 1.6

2055 3,595±134 −8.6 3,756±129 −4.52085 3,229±159 −17.9 3,516±144 −10.6

Gangtok 3,707 2025 3,644±68 −1.7 3,907±75 5.4

2055 3,347±77 −9.7 3,381±84 −8.82085 2,869±116 −22.6 2,984±109 −19.5

Mangan 2,271 2025 2,262±44 −0.4 2,296±32 1.1

2055 2,400±78 5.7 2,405±52 5.9

2085 2,530±98 11.4 2,553±79 12.4

Climate change impacts and adaptation options for maize cropping

however, a remarkable increment is projected for Gangtokand Geyzing by all GCMs. Higher increase in Tmin con-tributes to increased growing degree day (GDD) leading toalteration of growing periods and yield (Elmore and Taylor

2011). Additionally, due to increase in Tmin, available soilmoisture is affected and thus exacerbate in moisture stress inrainfed agriculture which further affects the grain yield(Harrison et al. 2011).

(a)

(c)

(b)

Fig. 4 Projected future average monthly precipitation under A2 and B2 scenarios for a Geyzing, b Gangtok, and c Mangan stations, respectively, in2025, 2055, and 2085 s (Error bars represent the variance of the projected precipitation resulting from the six GCMs)

Table 7 Sensitivity analysis ofnon-conservative (user-defined)parameters in AquaCrop model atGangtok station

Input parameters Sc (+25 %) Sc (−25 %) Sensitivity level

Maximum effective rooting depth 18.26 16.11 High

Effect of canopy cover in the late season 1.23 2.11 Low

Soil surface covered by an individual seedling 0.41 0.62 Low

Number of plants per hectare 0.16 0.54 Low

Canopy growth coefficient (CGC) 22.31 30.12 High

Maximum canopy cover (CCx) 8.15 5.96 Moderate

Canopy decline coefficient (CDC) 18.26 16.12 High

Time from sowing to emergence 1.02 0.85 Low

Time from sowing to maximum rooting depth 0.41 0.36 Low

Time from sowing to start senescence 0.65 0.22 Low

Time from sowing to maturity 0.84 0.55 Low

Time from sowing to flowering 18.69 26.13 High

Length of the flowering stage 0.51 0.78 Low

Building up of the Harvest index 0.69 0.84 Low

Reference harvest index (HIo) (%) 6.51 8.45 Moderate

P. Deb et al.

3.2 Future precipitation

A declining trend in precipitation extending to 17.9 and10.6 % is observed for A2 and B2 scenarios, respectively, incase of Geyzing station during 2085 s whereas, 22.6 and19.5 % in case of Gangtok station for the correspondingscenarios and time period respectively (Table 6). On contrary,Mangan is estimated to have an increase in average annualprecipitation up to 11.4 and 12.4 % for the identical scenariosand time period relative to the baseline period. Interestingly, a

minor increase of 1.6 and 5.4 % is observed for 2025 s in caseof Geyzing and Gangtok stations, respectively, for B2 scenar-io. In addition, analysis of projected monthly precipitationsuggests a late shift in the onset of monsoon from March forbaseline period to May in 2025 and 2055 s and June incase of 2085 s for all the stations (Fig. 4). Furthermore, asignificant reduction in the monthly average precipitationcan also be observed for the cropping season whereas amomentous increase can be observed for the later monthsfor all time periods under future change. Moreover,

Table 8 Calibrated parameters of AquaCrop and CERES—maize for NLD-White cultivar of Maize for all three stations

Parameters Stations Units

Geyzing Gangtok Mangan

AquaCrop

Maximum effective rooting depth 0.93 0.94 0.98 m

Canopy growth coefficient (CGC) 0.23 0.26 0.22 per day

Maximum canopy cover (CCx) 0.91 0.95 0.89 (% ) depending onplant spacing

Canopy decline coefficient (CDC) 0.12 0.13 0.10 per day

Time from sowing to flowering (tasseling) 52 53 55 Calendar days

Reference Harvest Index (HIo) (%) 42 46 41 %

CERES—maize

P1a 274.6 273.5 271.6 Degree days

P2b 0.25 0.24 0.21 Days

P5c 821.3 816.9 815.2 Degree days

G2d 251.9 256.1 253.9 Unitless

G3e 7.3 7.4 7.1 mg/day

PHINTf 51.4 50.6 51.3 Degree days

a Thermal time from seeding to end of the juvenile phase (expressed in degree days above a base temperature of 8 °C) during which the plant is notaffected by photoperiodb Level to which development (expressed in days) is generally deleted for each hour increase in photoperiod above the longest photoperiod at whichdevelopment proceeds at a maximum rate (which is considered to be 12.5 h)c Thermal time from silking to physiological maturity (expressed in degree days above a base temperature of 8 °C)dMaximum possible kernel number per plante Kernel filling rate during the linear filling stage and under optimum conditions (milligrams per day)f Phylochorn interval; the interval in time (degree days) between successive leaf tip appearances

Table 9 Model error statistics during calibration and validation of AquaCrop for the three study sites

Stations Yield (kg ha−1) Biomass (kg ha−1) Water Productivity (kg ha−1 mm−1)

R2 RMSE MBE R2 RMSE MBE R2 RMSE MBE

Calibration Geyzing 0.87 282 −170 0.92 429 395 0.79 1.06 −0.38Gangtok 0.92 365 231 0.85 695 −542 0.82 1.15 0.77

Mangan 0.78 398 −269 0.93 546 −509 0.91 1.23 −0.63Validation Geyzing 0.82 262 −166 0.94 599 544 0.85 1.17 −0.49

Gangtok 0.86 395 −277 0.91 639 −608 0.82 1.09 0.76

Mangan 0.79 474 312 0.86 785 706 0.86 1.61 −0.86

Climate change impacts and adaptation options for maize cropping

although Mangan station is expected to have higher mag-nitude of precipitation, yet for the months of April to July(growing period), a reduction is observed contributing toan intense precipitation at the later part of the years. Thesetrends imply an early seasonal forecasting of precipitationwould be beneficial at station scale for better agriculturaland water management practices.

3.3 Crop model setup

3.3.1 Sensitivity analysis of AquaCrop model

The sensitivity analysis of the non-conservative parametersfor AquaCrop suggests a majority of the parameters haveinsignificant contribution in simulation of the maize yield. Itis observed that maximum effective rooting depth, canopygrowth coefficient (CGC), canopy decline coefficient(CDC), and time from sowing to flowering bestows highinfluence on maize yield simulation (Table 7). In addition,maize yield is also observed to be moderately sensitive tomaximum canopy cover (CCx) and reference harvest index(HIo).

3.3.2 Calibration and validation of crop models

Both crop models, AquaCrop and CERES–maize, were cali-brated based on 5 years of field experimental data from 2001–2005 (discussed earlier) for the three study sites. A minorvariation in the calibrated parameter values can be observedfor both models at the three stations (Table 8). The alternationin the local climatological and physiographic variables due tothe change in elevation can be attributed to the observedchanges (Kim et al. 2013).

Simulated maize output variables are observed to be ingood agreement with the field measured values during cali-bration for all three sites with R2, RMSE, and MBE rangingfrom 0.78 to 0.93, 236 to 398, and −269 to 231 kg ha−1,respectively, for the yield simulated by both models(Tables 9 and 10). Furthermore, in case of validation,

simulated yield also corresponds well with the measuredyield values with R2, RMSE, and MBE ranging from0.76 to 0.86, 262 to 474, and −277 to 312 kg ha−1 forboth crop models, respectively. In addition, the good-ness of fit for other output variables namely, biomass,water productivity for AquaCrop, and number of leavesare maturity for CERES–maize also replicates very sim-ilar to the observed values during calibration and vali-dation of the two models. Paired t tests demonstrate nostatistically significant contrast among the measured andthe simulated maize output variables for all the simulations bythe two models.

3.4 Yield simulation for the baseline climate

The simulation of yield for the baseline period using theobserved climate data suggests lower yield in the higherelevation, i.e., Geyzing (Table 11). In addition, simulation ofyield by both models suggests higher intra-year variability forthe Geyzing station relative to Gangtok and Mangan.The parameterization of model depending on localweather can probably be a contributing factor for theexisting deviation. A maximum yield of 4,268 kg ha−1

is observed for the Mangan site but a minimum of3,197 kg ha−1 for Geyzing site by CERES–maize. Theaverage of the simulated yield by the two models was used forthe estimation of the relative change in the maize yield for thefuture under climate change.

Table 10 Model error statistics during calibration and validation of CERES—maize for the three study sites

Stations Yield (kg ha−1) Biomass (kg ha−1) Number of leaves at maturity

R2 RMSE MBE R2 RMSE MBE R2 RMSE MBE

Calibration Geyzing 0.84 236 −113 0.87 385 −261 0.75 0.86 0.41

Gangtok 0.93 306 −189 0.95 469 385 0.86 1.06 −0.96Mangan 0.81 285 183 0.92 551 −254 0.98 0.76 0.54

Validation Geyzing 0.86 296 168 0.86 521 329 0.89 1.12 −0.63Gangtok 0.80 303 −178 0.81 547 −441 0.83 0.69 −0.42Mangan 0.76 385 296 0.77 766 516 0.92 1.16 0.45

Table 11 Maize yield with observed climate data for the baseline period1991–2000 using AquaCrop and CERES—maize (±indicates intra-yearvariance in the yield for the baseline period)

Geyzing yield(kg ha−1)

Gangtok yield(kg ha−1)

Mangan yield(kg ha−1)

Observed climate usingAquaCrop

3,256±336 3,768±239 4,017±112

Observed climate usingCERES—maize

3,197±412 3,806±168 4,268±196

P. Deb et al.

3.5 Model response to changes in temperature and carbondioxide (CO2) levels at fixed intervals

Simulated yield shows a curvilinear response to the variedCO2 concentrations and temperatures (Fig. 5). Consideringthe current level of CO2 concentration (390 ppm), the modelsimulates an average yield reduction of 3.72 % for a temper-ature increase of 1 °C. Additionally, for the correspondingCO2 concentration, an increase of 6 °C above ambient tem-perature results in a yield reduction of 27.54 %. The loss inyield can be attributed to the heat stress due to increase intemperature during the tassel initiation and silking phase inmaize. On contrary, an increase of 25.31 % in maize yield isobserved for the ambient temperature at 800 ppm CO2 con-centration. Moreover, with higher CO2 concentration andcorrespondingly temperature (+6 °C), a relative low increasein yield is observed (5.46 %) compared with that of ambienttemperature implying that, although higher CO2 uplifts thecrop growth rate by accelerating photosynthesis process, theheat injury caused by high temperature is inevitable. Similarresults in Thailand and India for rice were also obtained byBabel et al. (2011) and Krishnan et al. (2007), respectively.

3.6 Impact of predicted GCM scenarios on maize yield

Our simulation suggest a significant declination in the maizeyield under the future climate for Gangtok and Mangan sta-tions using both crop simulation models (Fig. 6a and b).Considering all the stations, an average declination of 4.7,10.2, and 12.8 % along with 2.5, 6.7, and 10.9 % in yield isobserved for 2025, 2055, and 2085 s relative to baseline yieldof 3.98 t ha−1 under A2 and B2 scenarios, respectively, byAquaCrop. On the other hand, CERES–maize model indicatesa reduction of 10.9, 14.7, and 20.4 % and 7.4, 11.5, and15.8 % for the corresponding periods and scenarios relativeto the baseline yield. Detailed consideration of the phenolog-ical processes, including growth stages and its response toclimatological factors, while calculating the maize yield inCERES–maize can be attributed to the lower yield simulationby CERES–maize. Interestingly, both models show a netincrease in maize productivity for Geyzing station.

Simulation of maize yield by both models for individualsites indicate a significant reduction of 33.9 to 44.2 % in yieldfor Mangan site, under A2 scenario by 2085 s. Similarly, forB2 scenario, the reduction is expected to be in range of 29.9 to37.1 % by the end of the century. A reduction in yield rangingfrom 8.1 to 18.2 % and 5.6 to 12.4 % is also observed for thecorresponding scenarios and time window in case of Gangtoksite. However, it is anticipated that the projected increase intemperature will be beneficial for maize yield in Geyzing site,since the elevated temperature will be favorable of maizeduring tasseling and silking phase. All the three sites areexpected to experience a lower temperature during the sowingperiod followed by an increase by the harvesting time (Fig. 7).Greater increase in the Tmax and Tmin relative to the thresh-old temperatures of 8.5 and 18.5 °C (Cicchino et al. 2010)during the tasselling phase leads to sterility of the flowers andtherefore hampers the silking stage in maize (Harrison et al.2011) for Gangtok and Mangan sites. However, in case of

-40

-30

-20

-10

0

10

20

30C

ha

ng

e in

yie

ld (

%)

CO2

concentration (ppm)

Current

1 degree

2 degree

3 degree

4 degree

5 degree

6 degree

Average

Fig. 5 Yield response for NLD-White cultivar of maize to variousincremental temperature and CO2 concentrations at Gangtok site

(a) (b)Fig. 6 Change in yield (%) of NLD-White cultivar ofmaize for the futureclimate under A2 and B2 scenarios at three stations relative to the baselineyield (Geyzing=3.64 t ha−1; Gangtok=3.96 t ha−1; Mangan=4.27 t ha−1;

average=3.98 t ha−1) simulated by a AquaCrop and b CERES—maizemodels (Error bars show the variance of change in maize yield due tofuture climate projections by different GCMs)

Climate change impacts and adaptation options for maize cropping

Geyzing, both extreme temperatures are suitable for thetasselling and silking phases, and hence, a probable increasein yield is expected. Similar reduced yield in maize was alsoobserved by Twine et al. (2013) for Midwest USA underelevated temperatures. A significant change of −9.5 % in yieldfor 2046–2065 has been observed by Tachie-Obeng et al.(2013) for Ghana based on simulation done from nineGCMs. Study done in China by Tao and Zhang (2010) alsoindicates a potential reduction of 13.2 to 19.1 % is expected inNorth China Plain during 2,050 s if proper adaptation is notbeing taken into consideration.

3.7 Adaptation measures to enhance the maize yield

Proxy crop management practices comprising different plant-ing dates, rate of FYM application, supplementary irrigation,and change in cultivar were investigated at Gangtok site asadaptation options to climate change.

3.7.1 Change in planting date

In order to evaluate the optimum date of sowing, series ofsimulations were done by CERES–maize model for 11thJanuary to 22nd March at 7 days interval period for each timewindows and scenarios (Fig. 8a and b). Among the varioussowing dates evaluated, for A2 scenario, the 1st of February isobserved to increase maize yield by 5 % for 2025 s. Inaddition for the corresponding scenario, shifting the plantingdate to 25th and 18th January can enhance yield by 13.4 and22.5% compared with yield for the baseline sowing date (15thFebruary) for 2055 and 2085 s, respectively. In case of B2scenario, the 1st of February is noted suitable for 2025 s withan increase of 12.5% in potential yield. Similarly for 2055 and2,080 s, planting on 25th January and 18th can increase theyield by 11.4 and 11 %, respectively, for the correspondingscenario. Early sowing of seeds leads to reduced evaporativedemand of the plants during the growth phase relative to theinitial growing date. Furthermore, it also helps to escape the

(a) (b)Fig. 7 Changes in monthly average a Tmin and b Tmax (°C) obtained from bias correction climate data for 2085 s for the growing season at the threestations (Error bars indicate the variance in the temperature projections by multiple GCMs considered)

(a) (b)Fig. 8 Change in maize yield of NLD-White cultivar with different sowing dates for a A2 and b B2 scenarios at Gangtok station relative to yieldobtained from current sowing date (Error bars show the variance in change of maize yield simulated for multiple GCM projections)

P. Deb et al.

critical stages (tasseling and silking) of the higher temperaturestress due to early sowing (Shrestha et al. 2014a).

Significant increase in maize yield ranging from 0.6to 12.5 % was observed by forward shifting of sowingdate by Moradi et al. (2013) in Iran. Similar improve-ments in maize yield was also observed by Babel andTuryatunga (2014) for Uganda under future climatechange projections. Their results suggest forward shiftingof sowing date to 16 days relative to current planting date canpotentially reduce the yield reduction by 4.9 to 43.3 % undervarious time periods and scenarios.

3.7.2 Change in FYM application

Simulation of maize yield under various FYM applicationrates under future climate suggests a reduction in input re-sponses to a higher yield. For the baseline period, an applica-tion of 110 % FYM compared with the current rate (12 t ha−1)can improve yield reasonably to 1.72 % relative to baseline

yield. Furthermore, for 2025 s under A2 scenario, curtailingthe application rate to 80 % can enhance the maize yield by7.5 % as compared with the yield simulated by present appli-cation rate for that period. However, for 2055 and 2085 s,60 % FYM application rate is found optimum with a probableincrease of 6.2 and 7.1 % for the corresponding scenario.An application rate of 80 %, 70 %, and 60 % for thecorresponding time windows can boost the yield by 2.6,7.7, and 5.9 %, respectively, for B2 scenario (Fig. 9aand b). Existing high organic matter content in the soilof Sikkim (Debnath et al. 2012) along with the presenttrend of higher input of FYM by the farmers is projected toenhance the accumulation of ions in the soil attributing toreduction in fertilizer use efficiency (Olesen et al. 2011). Inaddition, the concentration of the organic matter in soil isobserved to be directly proportional to the water holdingcapacity of the soil, and therefore, addition of further FYMin soil increases the probability of potential wilting of plants(Deb et al. 2013).

(a) (b)Fig. 9 Change in maize yield of NLD-White cultivar with differentapplication rates of FYM for a A2 and b B2 scenarios at Gangtok stationrelative to yield obtained from the current recommended application rate

(Error bars show the variance in change of maize yield simulated formultiple GCM projections)

(a) (b)Fig. 10 Effect of supplementary irrigation on maize yield for a A2 and b B2 scenarios at Gangtok station relative to rainfed maize cultivation (Errorbars show the variance in change of maize yield simulated for multiple GCM projections)

Climate change impacts and adaptation options for maize cropping

3.7.3 Effect of supplementary irrigation on grain yield

It is evident that Gangtok site is expected to have a potentialreduction of 22.6 and 19.5 % in precipitation for A2 and B2scenarios compared with the baseline period by the end of thetwenty-first century and consecutively a significant reductionin yield of rainfed maize is also observed. Unavailability ofcopious amount of water due to the traditional agriculturalpractice (rainfed) is also a contributing factor for the yield loss.However, introduction of supplementary irrigation system hasvalidated to increase in yield in many regions in the world(Thomas 2008). For the current study site under A2 scenario,simulation by CERES–maize suggests a quadric applicationof 20, 30, and 40 mm for 2025, 2055, and 2085 s during thegrowing period can boost the maize yield by 17.1, 20.7, and38%, respectively, compared with entirely rainfed cultivation.Withal, for B2 scenario, 30mm (four applications) is observedto be maximizing the yield by 12.6, 15.1, and 17.6 %, respec-tively, for the corresponding time windows (Fig. 10a and b).The ability of irrigationwater to adjust the canopy temperatureirrespective of outside air temperature can be attributed to theincreased yield despite of the potential heat stress (Shresthaet al. 2014a).

Several studies at global scale have already reported sup-plementary irrigation as an effective adaptation measure underclimate change. Babel and Turyatunga (2014) demonstratedaddition of 80 mm irrigation to rainfed agriculture can possi-bly enhance the maize yield by 42.1 and 28 % for A2 and B2scenarios, respectively, in western Uganda. Furthermore, a

gain in yield of 48.29 % was also observed by Moradi et al.(2013) for 2,080 s relative to the rainfed yield under A2scenario.

3.7.4 Change in maize variety

Fostering new maize cultivars which are consistent in devel-oping kernels at high temperature and water stress has provedto be an important agro-adaptation measure to mitigate cli-mate change (Tachie-Obeng et al. 2013). Calibration ofCERES–maize crop model was done by adjusting the geneticcoefficients of the two cultivars (Table 12) for the baselineclimate to match the observed yield. The grain yield underrainfed conditions for composite varieties Sethi Makai 2 andNAC 6004 were simulated for the three time windows. Bothcultivars resemble higher yield for all the time windowscompared with NLD-White under future climate. In addition,for all GCM projected values in future, the model shows ahigher yield relative to NLD-White. Furthermore, a potentialincrease in yield ranging from 17.18 to 66.07% can beachieved for 2085 s using heattolerant cultivars for A2 scenario(Fig. 11). Similarly, a boost of 44.14 to 37.11 % can beobtained for B2 scenario for the corresponding time window.The ability of these cultivars to change leaf orientation, tran-spirational cooling, and short-term stress avoidance benefitsthe plant to cope with the heat and water stress. Also, increasein yield by varying cultivar also has been observed byTingem and Rivington (2009) and Travasso et al. (2006) atSouth America.

4 Conclusions

The present study investigates the changes in the future cli-mate and its impacts on rainfed maize productivity at threesites (Geyzing, Gangtok, and Mangan) in Sikkim state of

(a) (b)

Fig. 11 Simulated yield of Sethi Makai 2 and NAC 6004 relative to NLD-White for Gangtok station under future climate for a A2 and b B2 scenarios(Error bars show the variance of maize yield across different GCM projections for the two cultivars)

Table 12 Phenological coefficients of maize cultivars grown in Sikkimused for calibration of CERES—maize to evaluate adaptation measures

Cultivars P1 P2 P5 G2 G3 PHINT

Sethi Makai 2 299 0.31 866.29 253 7.4 59

NAC 6004 262 0.29 823.14 287 6.9 57

P. Deb et al.

India. Temperature and precipitation for two scenarios A2 andB2 were derived from six GCMs (ECHAM5, CCSM,HadCM3, CSIRO-MK3.0, CGCM3.1, and MIROC3.2). Theoutputs were further bias-corrected by power law transformtechnique for spatially refining the data at station scale andwere used for the impact assessment. Bias correction of theclimate variables suggest an increase of up to 1.98 and 1.85 °Cfor Tmax under A2 and B2 scenarios at 2085 s. Similarly, aboost of 2.31 and 1.96 °C is observed for Tmin in the corre-sponding scenarios and time period, respectively. In addition,a significant reduction in average annual rainfall is observedfor Geyzing and Gangtok stations with a maximum declina-tion of 22.6 and 19.5% for 2085 s under A2 and B2 scenarios;on the contrary, Mangan station is expected to experience anincrease in precipitation by 11.4 and 12.4 % for the corre-sponding stations and time period.

Two crop simulation models AquaCrop and CERES–maize were used to analyze the impacts of climate changeon maize productivity. The models were calibrated and vali-dated for three study sites based on the field experiments. Dueto unavailability of observed field experimental data, simula-tion of yield for a decade (1991–2000) was done by using theobserved climatic data considered as the baseline periodand was used for the comparison of yield with the threefuture periods: 2021–2030 (2025 s), 2051–2060 (2055 s),and 2081–2090 (2085 s) for impact analysis. AquaCropsimulates an average reduction in yield ranging from−4.7±1.4 to −12.8±4.85 % and −2.5±0.9 to −10.9±3.9 %for A2 and B2 scenarios, respectively, for 2085 s. Withal, forCERES–maize, the yield reduction ranged from −10.9±4.1 to−20.4±7.2 % and −7.4±2.9 to −15.8±5.7 %, respectively, forcorresponding scenarios and time period. However, distinctivesite-specific study reveals climate change will enhancethe yield for higher altitude station (Geyzing) by −1.1±0.25to 4.2±1.52 % for the future time windows. Additionally,hypothetical scenarios created by using CERES–maize as-suming increased CO2 concentration and temperature indicatehigher CO2 is beneficial for maize productivity and in contrastelevated temperature is expected to reduce the yield.

To counterattack the negative impacts of climate change onmaize productivity, several adaptation strategies were evalu-ated by CERES–maize model. Our simulation shows thatforward shift in sowing of seeds can lead to increase in yieldranging from 5±1.5 to 22.5±4.2 % and 10.9±2.3 to12.9±1.6 % for A2 and B2 scenarios, respectively.Similarly reducing the FYM application rate can possi-bly enhance the yield from 6.2±2.0 to 7.5±2.2 % and2.6±0.85 % to 7.7±2.6 % for A2 and B2 scenarios, respec-tively. In addition for the corresponding scenarios, introduc-tion of supplementary irrigation can increase yield from 17.1to 38 % and 12.6 to 17.6 %. Cultivating heat-tolerant varietiesis another probable agro-adaptation option which can enhancethe productivity and production. Composite varieties NAC

6004 and Sethi Makai 2 are found to have minimum influenceto climate change and are therefore suggested for cultivation.The output of this study is of utmost importance, since it canbe helpful for the farmers to change their agricultural practicesand agricultural policy makers at the state level for sustainablemaize cultivation in the state.

Acknowledgment The authors are wholeheartedly thankful to IndianCouncil of Agricultural Research (ICAR), Sikkim center and IndianMeteorological Department (IMD) for providing the necessary data tocarry out this research study.

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