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Summer Research Fellowship Programme of India's Science Academies

Simulating the impact of climate change on growth and yield of maize using CERES-Maize model under temperate Kashmir

Bilal Ahmad Lone

Division of Agronomy, Sher-e- Kashmir University of Agricultural Sciences and Technology of Kashmir 190025

Guided by:

Dr. Shivam Tipathi

IIT Kanpur

Abstract

Climate variability has been, and continues to be, the principal source of fluctuations in global food production in countries of the developing world and is of serious concern. Process-based models use simplified functions to express the interactions between crop growth and the major environmental factors that affect crops (i.e., climate, soils, and management), and many have been used in climate impact assessments. The average for 10 years of weather data from 1985 to 2010, the maximum temperature shows an increasing trend ranging from 18.5 oC to 20.5oC . This means there is an increase of 2oC within a span of 25 years. Decreasing trend with respect to precipitation was observed with the same data. The magnitude of decrease was from 925 mm to 650 mm of rainfall which is almost a decrease of 275 mm of rainfall in 25 years. Future climate for 2011-2090 from A1B scenario extracted from PRECIS run shows that overall maximum and minimum temperature increase by 5.39 oC (±1.76) and 5.08 oC(±1.37) also precipitation will decrease by 3094.72 mm to 2578.53 (±422.12). The objective of this study was to investigate the effects of climate variability and change on maize growth and yield of Srinagar, Kashmir. Two enhanced levels of temperature (maximum and minimum by 2 and 4 0C) and CO2 enhanced by 100 ppm & 200 ppm were used in this study with total combinations of 9 with one normal condition. Elevation of maximum and minimum temperature by 4oC during anthesis and maturity of maize was earlier 14 days with a deviation of 18%, and 26 days with a deviation of 20%, respectively. With an increase in temperature by 20C to 4 0C, alone or in combination with enhanced levels of CO2 by 100 and 200 ppm, the growth and yield of maize drastically declined with a reduction of about 40% in grain yield. Enhancement of CO2 alone at both the levels fails to show any significant impact on maize yield.

Keywords: DSSAT, Maize, Climate Change , Yield , Kashmir

INTRODUCTION

The effect of climate change on crop productivity is usually investigated by experimental methods using a growth chamber or by numerical methods using a crop simulation model. According to the IPCC Third Assessment Report (IPCC 2007), climate change is already happening, and will continue to happen even if global greenhouse gas emissions are curtailed.

Many studies document the implications of climate change for agriculture and pose a reasonable concern that climate change is at threat to poverty and sustainable development, especially in developing countries. Future crop production will be adapted to climate change by implementing alternative management practices and developing new genotypes that are adapted to future climatic conditions. Long term weather data of Kashmir valley revealed (​​Fig 1​​) that there is increasing trend in temperature. Average maximum temperature has increased by 1oC during the last 30 years. Consequently average minimum temperature has increased by 0.5oC. Precipitation trend is decreasing and erratic. Crop simulation models can be used in decision making in advance along with GIS in future effectively, saving time.

Maize known as the “Queen of Cereals” is the third most important cereal crop in India after rice and wheat and is cultivated on 8.11 million (m) ha with production of 19.73 (mt) with productivity of 2.41 t ha-1 (​Agricultural Research Data Book 2011​). Among the major crops of Jammu and Kashmir in terms of acreage maize is grown in area of 0.32 mha with the production of 0.63 mt (D.E.S, 2010-11). The average yield of 2.0 tha-1 (D.E.S, 2010-11) of this crop has also nearly doubled since the last decade. This increase in yield has been mainly achieved by increase in the area under high yielding varieties. However, the genetic potential of the improved varieties is at least three times of the present average yield of the state. Being an important cereal, over 85% of its production in the country is consumed directly as food in various forms, the chapatis is the common ‘preparation, whereas, roasted ears, pop corns and porridge are other important forms in which maize is consumed. Besides, it is also used for animal feeding, particularly for poultry, and in the starch industry. Green maize plants furnish a very succulent fodder during spring and monsoon particularly in North India. Maize is grown under a wide range of climatic conditions, mostly in warmer parts of the temperate region and areas of humid sub-tropical climate. It is grown practically at all altitudes except where it is too cold or the growing season is too short. The crop requires considerable moisture and warmth from the time of planting to the termination of the flowering period. 

Process-based crop models

Researchers first evaluated model performance using data from cropping systems currently used in their respective countries, then used the models to assess the potential impacts of climate change on their cropping systems using different climate scenarios. Use of crop simulation models would help in studying impacts of climate change on crops as well as identifying and prioritizing the management options for adapting/mitigating the climate change effects.

Process-based models use simplified functions to express the interactions between crop growth and the major environmental factors that affect crops (i.e., climate, soils, and management), and many have been used in climate impact assessments. Most were developed as tools in agricultural management, particularly for providing information on the optimal amounts of input (such as fertilizers, pesticides, and irrigation) and their optimal timing. Dynamic crop models are now available for most of the major crops. In each case, the aim is to predict the response of a given crop to specific climate, soil, and management factors governing production. Crop models have been used extensively to represent stakeholder’s management options (Rosenzweig and Iglesias 1998).

Background/Rationale

Simulating the impact of climate change on growth and yield of maize using CERES-Maize model under temperate Kashmir.

Using DSSAT, ​Jones and Thornton 2003​ simulated the impact of climate change on maize production in Africa and Latin America and predicted 10 % decrease in aggregate maize production by 2055. ​Alexandrov & Hoogenboom, 2000​ simulated the impact of climate variability for the major crops, including maize and winter wheat, and assessed possible adaptation measures for Bulgarian agriculture under an expected climate change. Keeping in view the importance of climate change for maize crop in Kashmir. Simulation studies carried out using DSSAT V.4.5 (CERES-Maize) model. The main objective of the study is “To access the impact of climate change on growth and yield of maize using CERES-Maize model DSSAT 4.5” with below mentioned environmental modifications.

Statement of the Problems

Environmental modifications assesed in the present investigation

Enviromental modifications studied
Environmental modificationTreatments (Climate change)
Max. temp. (oC)Min. temp. (oC)CO2 (ppm)
E1 (control observed weather data )NormalNormalNormal
E2+2+2Normal
E3+4+4Normal
E4NormalNormal480
E5+2+2480
E6+4+4480
E7NormalNormal580
E8+2+2580
E9+4+4580

Objectives of the Research

To assess the impact of climate change on maize growth and yields under temperate kashmir.

METHODOLOGY

DSSAT is a software package integrating the effects of soil, crop phenotype, weather and management options that allows users to ask "what if" type questions and simulate results by conducting, in minutes on a ktop computer, experiments which would consume a significant part of an agronomist's career. It has been in use for more than 15 years by researchers in over 100 countries. The DSSAT simulates growth, development and yield of a crop growing on a uniform area of land under prescribed or simulated management as well as the changes in soil, water, carbon, and nitrogen that take place under the cropping system over time. The ICASA/IBSNAT models have been used widely for evaluating climate impacts in agriculture at different levels ranging from individual sites to wide geographic areas (Rosenzweig et al., 1994; Rosenzweig and Iglesias 1998). This type of model structure is particularly useful in evaluating the adaptation of agricultural management to climate change. The DSSAT software includes all ICASA/IBSNAT models with an interface that allows output analysis. On the basis of the above observations, DSSAT 4.5 is selected to study environmental modifications on growth and yield of maize under temperate conditions of Kashmir.

Simulation models

Crop growth simulation models and biogeochemical and biophysical models have been very helpful in projecting the future crop and soil productivity. These models in connection with different GCM models predict the future agricultural practices that can adapt to different climate change scenarios. They can be used for different scenario-analyses to combat impact of climate change on agricultural production of the globe. Simulation models that are able to assess climate change impact on crop growth, yield and farm economy, still lack complete feedback structures. Only single aspects can be investigated. However, modelling the single aspect increases knowledge on the aspects of expectations from climate change, if interpreted carefully and in the context of the model‘s abilities. Simulation models are widely used to address "what if" type questions, such as, what if the climate changes, different irrigation or fertilization regimes are used, different sowing dates are used, different cultivars are used, etc. In addressing actual yield predictions required by governments, private corporations, or NGOs, different types of simulation models are used for solving these "what if" type questions. Here, capabilities of different simulation models will be discussed in assessing the impact of climate change on an agro-ecosystem and the potential mitigation and adaptation.

Assuming that an appropriate model is selected and a reference crop production scenario exists, simulating the effects of climate change mainly involves running the model for the weather and CO2 scenarios of interest. For a single site or region, the scenarios may be specified as fixed (e.g. an increase in daily mean temperature of 2°C) or relative (20% decrease in daily precipitation). These adjustments may be held constant over the crop cycle or varied. The choice depends on the objectives and the source of the climate change scenario. Because a season might be unrepresentative of long-term trends, simulations are usually run for 20 or more years. The requisite weather data may come from historical records or from weather generator software that reproduces the statistical properties of historic conditions ( ​Jones and Thornton 2003​).

Using DSSAT, Jones and Thornton 2003 simulated the impact of climate change on maize production in Africa and Latin America and predicted 10 % decrease in aggregate maize production by 2055. Alexandrov & Hoogenboom (2000) simulated the impact of climate variability for the major crops, including maize and winter wheat, and assessed possible adaptation measures for Bulgarian agriculture under an expected climate change. Keeping in view the importance of climate change for maize crop in Kashmir, simulation studies were carried out using DSSAT V.4.5 (CERES-Maize) model. The main objective of the study is “To access the impact of climate change on growth and yield of maize using CERES-Maize model DSSAT 4.5” with below mentioned environmental modifications.

The Environmental modifications in the study are as given in ​Table 1​.

RESULTS AND DISCUSSION

The location of the study is Shalimar Srinagar which is situated 16 km away from the city center that lies between 34.08 o N latitude and 74.830 E longitude at an altitude of 1587 meters above the mean sea level.

Input requirements to run CERESmaize model

For simulation of CERES-maize model, minimum data sets (MDS) on crop management, macro and micro-environmental parameters associated with weather, soil and crop are required as input. The Input data files of CERES-maize model are as per IBSNAT standard input/output formats and file structure cribed in DSSAT v 3 (​Hoogenboom et al 1999​).

Weather information

Daily weather data of Kashmir, Shalimar Srinagar (2015) was used with parameters such as solar radiation (MJ m-2 day-1), minimum and maximum air temperature (0C), and rainfall (mm). These daily weather data including site specific information, and other optional weather variables were collected and used for creating weather file (WTH) and running the CERES-maize model.

Soil information
SOILLOWERUPPERSATEXTRINITROOTBULKpHNO3NH4ORG
DEPTHLIMITLIMITSWSWSWDISTDENSC
cmcm3/cm3cm3/cm3cm3/cm3g/cm3ugN/gugN/g%
0- 50.2040.340.3920.1360.32211.456.911.21.22.19
15-May0.2040.340.3920.1360.32211.456.911.21.22.19
15- 250.2090.3450.390.1360.3220.751.457.211.21.21.21
25- 350.2090.3450.390.1360.3220.51.457.211.21.21.21
35- 500.1980.3350.390.1370.2810.351.49811.21.20.53
50- 650.1850.3230.3950.1380.2570.21.588.211.21.20.2
65- 800.1850.3230.3950.1380.2440.151.588.211.21.20.2
80- 990.2010.3280.4080.1270.2390.11.548.111.21.20.1
99-1220.1980.3250.410.1270.3250.051.588.20.010.010.09

The soil file already developed at Shalimar for DSSAT was used for running model.

Genetic coefficients were calibrated and below mentioned values were used in the model.

Genetic coefficients of maize cultivar of Shalimar Maize Composite 4
CoefficientUnitDefinitionValue
P1˚C dayThermal time from seedling emergence to the end of the juvenile phase280
P2DaysExtent to which development is delayed for each hour increase in photoperiod above the longest photoperiod at which development proceeds at a maximum rate (which is considered to be 12.5 h).0.30
P5˚C daysThermal time from silking to physiological maturity789
G2NumberMaximum possible number of kernels per plant.650
G3mg/dayKernel filling rate during the linear grain filling stage and under optimum conditions6.03
PHINT˚C dayPhyllochron interval; the interval in thermal time between successive leaf tip appearances48

Climate trends of the study area.

Weather data of Kashmir, Shalimar Srinagar was undertaken to observe the tends of maximum & minimum temperature and precipitation. It was observed that in the average of 10 years of weather data from 1985 to 2010, maximum temperature shows an increasing trend ranging from 18.5 oC to 20.5oC. This means there is an increase of 2oC within a span of 25 years. Decreasing trend was observed with respect to precipitation. The magnitude of decrease was from 925 mm to 650 mm of rainfall which is almost a decrease of 275 mm of rainfall in 25 years (​​Fig 1​​). Future climate for 2011-2090 from A1B scenario extracted from PRECIS run shows an overall maximum and minimum temperature increase by 5.39 0C (±1.76) and 5.08 0C(±1.37) 0C; also precipitation decrease by 3094.72 mm to 2578.53 (±422.12) mm (Muslim et al 2015).

Fig1Bilal.png
    Trend of 10 year average yearly mean of maximum temperature, minimum temperature and rainfall at Shalimar, Srinagar (J&K), India.

    Simulated effect of elevated ambient maximum and minimum temperature by 2 oC (E2) resulted in early anthesis of maize by 7 days. Further elevation of maximum and minimum temperature by 4oC (E4) anthesis of maize was earlier by 14 days with a deviation of 18 %. However elevation of CO2 both at +100 ppm and + 200 ppm alone or in combination with maximum and minimum temperature failed to show any impact on anthesis date (​​Table 4​​). Simulated effect elevated ambient maximum and minimum temperature by 2oC (E2) resulted early maturity of maize by 15 days. Further elevation of maximum and minimum temperature by 4oC (E4) shifted maturity of maize earlier by 26 days with a deviation of 2-0 %. However elevation of CO 2 both at +100 ppm and + 200 ppm alone or in combination with maximum and minimum temperature failed to show any significant impact on anthesis date.

    Simulated Days to Anthesis of Maize as function of enhanced levels of temperature and CO2
    Environmental modificationSimulated Days to AnthesisDeviation of Anthesis from normal%age of deviation
    E1 (control )80--
    E2 (Max, Min temp +2)737-9
    E3(Max, Min temp +4)6614-18
    E4 ( CO2 +100ppm8000
    E5 (Max, Min temp +2 and CO2 +100ppm)737-9
    E6(Max, Min temp +4 and CO2 +100ppm)6614-18
    E7( CO2 +200ppm)8000
    E8(Max, Min temp +2 and CO2 +200ppm)737-9
    E9(Max, Min temp +4 and CO2 +200ppm)6614-18
    Simulated Days to Maturity of Maize as function of enhanced levels of temperature and CO2
    Environmental modificationSimulated Days to MaturityDeviation in Maturity from normal%age of deviation
    E1 (control )131_-
    E2 (Max, Min temp +2)11615-11
    E3(Max, Min temp +4)10526-20
    E4 ( CO2 +100ppm13100
    E5 (Max, Min temp +2 and CO2+100ppm)11615-11
    E6(Max, Min temp +4 and CO2 +100ppm)10526-20
    E7( CO2 +200ppm)13100
    E8(Max, Min temp +2 and CO2+200ppm)11615-11
    E9(Max, Min temp +4 and CO2 +200ppm)10526-20
    Simulated Tops weight Grain weight and their deviation of Maize as a unction of enhanced levels of temperature and CO2
    Environmental modificationSimulated Tops weight kg/ha Deviation in Tops weight kg/ha (%)Simulated Grain weight kg/ha Deviation in Grain weight kg/ha (%)
    E1 (control )26479-4441-
    E2 (Max, Min temp +2)24343-83189-28
    E3(Max, Min temp +4)22231-162561-42
    E4 ( CO2 +100ppm)26935245733
    E5 (Max, Min temp +2 and CO2 +100ppm)24710-73278-26
    E6(Max, Min temp +4 and CO2 +100ppm)22615-152643-40
    E7( CO2 +200ppm)27172346445
    E8(Max, Min temp +2 and CO2 +200ppm)24916-63327-25
    E9(Max, Min temp +4 and CO2 +200ppm)22813-142687-39

    Maximum simulated tops and grain weight of 27172 Kg ha -1was recorded with (E7) at enhanced level of CO2 with 200 ppm followed by E4 (CO 2 +100ppm) with 26935 kg /ha i.e. when CO2 was enhanced by 100 ppm than normal. Magnitude of increase was 3% at 200 ppm enhanced CO2 level and 2 % at 100 ppm enhanced level. However with increase in temperature there was a decrease in tops weight when tried alone or with combination of CO 2. Least tops weight of 22231 kg/ha was recorded when temperature was increased by +4 0C with a deviation of -16% as compared to normal, which was closely followed by E6 (Max, Min temp +4 and CO 2 +100ppm) with 15 %. Enhanced level of temperature with + 2 0C alone or in combination with enhanced levels of CO2 showed only -5 to -6 % deviation in tops weight compared to the normal environment (​​Table 4​​, and ​​Fig 2​​).

    Fig3Bilal.png
      Deviation in tops weight % as function of change in temperature and CO2 levels.

      Maximum simulated grain weight of 4644 Kgha-1 was recorded with (E7) at enhanced level of CO2 enhanced by 200 ppm followed by (E4) i.e. when CO2 was enhanced by 100 ppm over the normal conditions with grain weight of 4573 kg/ha. Magnitude of increase was 5% at 200 ppm enhanced CO 2 level and 3 % at 100 ppm enhanced CO2 level. However enhanced levels of temperature show drastic decrease in grain yield.. When crop was tested at an enhanced level of max and min temperature E2 (Max, Min temp +20C) the grain yield recorded was 3189 kg/ha with a decrease in yield of 28 % (​​Fig 6​​). Furthermore with the increase in the temperature from 20C to 40C (both min and max) the magnitude of decrease was 42% with the grain yield of 2561 kg/ha. Our findings are in agreement with results reported in earlier literature (​Yi Zhang et al 2017​; ​Jones and Thornton 2003​; ; ​Ruane et al 2013​; ​Bassu et al., 2014​ ). Enhanced levels of maximum and minimum temperature by 2 0C and 40C in combination with 100ppm and 200 ppm enhanced the levels of CO2 . The magnitude of decrease was 26 %, 40% , 25% and 39% respectively (​​Table 6​​, ​​Fig 3​​)

      Fig4Bilal.png
        Percentage deviation in grain weight % due to change in temperature and CO2 levels
        Fig5Bilal.png
          Days to anthesis as function of change in temperature and CO 2 levels
          Fig6.png
            Days to Maturity as function of change in temperature and CO2 levels
            Fig7Bilal.png
              Grain weight Kg/ha as function of change in temperature and CO2 levels

              Simulation from CERES Maize model DSSAT 4.5, shows that increase in the temperature by 20C or 40C, alone or in combination with the enhanced levels of CO2 with 100 ppm and 200ppm, drastically reduces the grain yield of maize under temperate conditions of Shalimar, Kashmir. This may be due to the fact that at higher temperature the plants shift earlier from vegetative to reproductive phase ​( Fig 4 & 5​​), less number of days were taken for anthesis and maturity at higher levels of temperature, which causes more biomass but which lower partioning of drymatter towards reproductive phase, ultimately lowering grain yield.

              CONCLUSION AND RECOMMENDATIONS

              Climate change impacts on crop yield are often integrated with its effects on water productivity and soil water balance. Global warming will influence temperature and rainfall, which will directly affect soil moisture status and groundwater level. Crop yield is constrained to crop varieties and planting areas, soil degradation, growing climate and water availability during the crop growth period. With temperature increasing and precipitation fluctuating, water availability and crop production will decrease in the future. Using DSSAT 4.5 and assuming management practices continue as present, CERES maize model predicted that enhanced level of CO 2 upto 200 ppm will have insignificant impact on crop growth and yield. However increase in temperature by 20C to 4 0C alone or in combination with enhanced levels of CO2 by 100 and 200 ppm the growth and yield of maize will drastically decline with an reduction of about 40% in grain yield. Further studies should be carried out for authentications of these results.

              References

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              • V.A Alexandrov, G Hoogenboom, 2000, The impact of climate variability and change on crop yield in Bulgaria, Agricultural and Forest Meteorology, vol. 104, no. 4, pp. 315-327

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              • Hoogenboom, G., Wilkens, P.W. and Tsuji, G.Y. 1999. DSSAT v 3 Volume 4. University of Hawaii, Honolulu, Hawaii.

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              • Yi Zhang, Yanxia Zhao, Chunyi Wang, and Sining Chen. (2017) Using statistical model to simulate the impact of climate change on maize yield with climate and crop uncertainties.  Theoretical and Applied Climatology 130:3-4, 1065-1071. Online publication date: 22-Sep-2016.

              • Ruane, A.C., Cecil, L.D., Horton, R.M., Gordónd, R., McCollume, R., Browne, D., Killough, B., Goldberg, R., Greeley, A.P., Rosenzweig, C., 2013. Climate change impact uncertainties for maize in Panama: Farm information, climate projections, and yield sensitivities. Agric. For. Meteorol. 170, 132–145.

              • Bassu, et al., 2014. How do various maize crop models vary in their responses to climate change factors? Glob. Change Biol. 2014 (20), 2301–2320.http://dx.doi.org/10.1111/gcb.12520.

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