CERES-Rice model: Calibration, evaluation and application

Journal of Agrometeorology 9(2) : 138 - 148 (Dec. 2007)

CERES-Rice model: Calibration, evaluation and applicationfor solar radiation stress assessment on rice production

DILLIP KUMAR SWAIN*, SRIKANTHA HERATH1, SANJOY SAHA andRABINDRA NATH DASH

Central Rice Research Institute, Cuttack-753006, India1Environment and Sustainable Development, United Nations University, Tokyo, JAPAN

ABSTRACT

CERES–Rice model (DSSAT v. 4.0) was calibrated and evaluated forcultivar IR 36 at Cuttack, Orissa using experimental data of wet seasons(June-December) 2001 and 2002. The model accurately predictedphenological events i.e. flowering and maturity date. The simulated grainyield at different N levels was in close agreement with experimental grainyield. Application of the model for solar radiation stress assessment due toAtmospheric Brown Clouds on the same site during dry season (January-May) on historical weather data (1983-2002) revealed a reduction in ricegrain yield by 4% with reduction of incident solar radiation by 30% under non-fertilized condition. Compared to non-fertilized condition, grain yield reductionwas higher up to 12% with similar solar radiation stress under high rates ofN application (120 kg N ha-1). The reduction in grain yield is associated withlower grain formation.

Key words: CERES-rice model, calibration, evaluation, solar radiationstress, rice grain yield

A recent international study under theIndian Ocean Experiment (INDOEX) hasrevealed that a brown haze, a pollutant as aresult of biomass burning and industrialemission pervaded particularly duringDecember to April (dry season) over southAsian region and the tropical Indian Ocean,Arabian Sea and Bay of Bengal. Thisbrown haze is named as AtmosphericBrown Cloud. The most direct effect ofAtmospheric Brown Cloud is a significantreduction in the solar radiation reaching the

surface, which results in reduction ofagricultural productivity, reduction in theprecipitation efficiency by inhibiting theformation of larger raindrop size particles,and adverse health effect (UNEP, 2002).The reduction in photo synthetically activesolar radiation is a major concern to Asia,the largest agricultural continent with 60 –90% of the world’s agricultural population(Fu et. al., 1998) and producer of 80 to 90%of world’s rice. Data on radiationmeasurements over several stations in India

* Present address : IIT, Kharagpur, Email : [email protected]

Journal of Agrometeorology/ceety/19

Dec 2007] 140CERES-RICE MODEL EVALUATION

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Fig. 1 : Daily rainfall, solar radiation, maximum and minimum temperature ofthe experimental site in India for the year 2001 and 2002

25 cm with rainwater throughout theexperiment during the year 2001. Plantsamples were collected at active tillering,panicle initiation, flowering and maturitystages of the variety following Thyagarajanet al. (1995) in order to reduce error insampling. After collection, the plant sampleswere cleaned to remove surfacecontamination, then separated into stems(leaf sheath + stem), leaves and panicles.The samples were oven dried to stopenzymatic reactions and to remove moisture.The dry weight of the samples was recordedtill constancy. The total dry matterproduction at all stages of crop growth was

determined. The data recorded from thisexperiment was used for calibration ofgenotype coefficients.

The average rainfall of the region is1421 mm with standard deviation of 262 mm(Directorate of Agriculture and FoodProduction, 2003). About 81% of the totalrainfall is received during monsoon months(June to September). During the wet season2001, the rice crop was grown without anywater stress. The weather for both the yearsare shown in Fig. 1. Rainfall during the year2001 was 2250 mm and during 2002, it wasonly 950 mm. The soil was sandy clay loam



comparisons that involve predictions of thetime of occurrence of discrete events duringthe life cycle (e.g., flowering date, maturitydate), or of yield or biomass at maturity.Regression analysis (Huda, 1988) wasapplied to a set of simulated and observeddata for evaluation purpose. With such anapproach, good predictability is indicated ifthe regression slope is near 1.0, if theintercept is close to 0, and if the R2 value ishigh. The experimental data pertaining tophenological events, biomass and grain yieldof the year 2001 at 40 kg N level and of theyear 2002 at both N levels (40 N and 80 N)were used for model evaluation.

The model was used to simulate theeffect of varying incident solar radiationlevels on crop behavior of cultivar IR 36 atN application levels 0, 40, 80 and 120 kg Nha-1 during dry season (January-May) atCuttack, India. Rice yield was estimatedwith corresponding variation in temperature.The maximum/minimum temperature andsolar radiation are normally distributed andthe correlation of each variable may bedescr ibed by a first-order linearautoregressive model (Richardson, 1982).The relation between incident solar radiationand temperature was obtained by plottingcurves (Fig. 2) for mean weather data ofpast 20 years (1983-2002). The relation ofincident solar radiation was stronger withmaximum temperature than with minimumtemperature. With reduction of incidentradiation by 10 %, not much variation inmaximum temperature was noted.Whereas increased reduction of incidentradiation by 20% and 30% decreased the

maximum temperature considerably. Hencethe impact of reduction in incident solarradiation was used to simulate rice grainyield. The crop was planted on 1st February2002 using 25 days old seedling with 20 cmrow spacing and planting density was 99plants m-2. The crop was irrigated as andwhen required.

RESULTS AND DISCUSSION

Model calibration and evaluation

The calibrated genotype coefficientsfor the rice cultivar IR 36 are given in Table2. The genotype coefficients at differentphases indicated that the temperaturerequired for vegetative stage is higher thanfor the reproductive stage. The model wasevaluated with reference to phenologicaloccurrence of anthesis and physiologicalmaturity days after transplantation, biomassand grain yield.

A comparison between observed andsimulated phenological events (Table 3)shows that the model predicted both anthesisand maturity days almost accurately. Theweather of 2002 is highly contrasted fromthat of 2001, with respect to quantum ofrainfall received. A good match betweenobserved and simulated phenological eventsin varied weather condition reflects theconsistency in model performance. Thesepredictions are important because floweringis the most critical stage of rice crop andstresses for moisture or nutrients at thisstage cause massive reduction in grain yield(Saseendran et. al., 1998).



Table 2: Calibrated genetic coefficients for the rice cultivar IR 36 used for model evaluationand application

GDD, growing degree days (0C)

Genetic coefficients (abbreviations) Value Juvenile phase (P1), GDD 474 Critical photo period (P2O), h 11.7 Photoperiod sensitivity (P2R), GDD h-1 90 Ripening phase (P5), GDD 385 Potential spikelet number (G1) 69 Single grain weight (G2), g 0.023 Potential tillering habit 1.0 Temperature tolerance 1.0

Table 3: Comparison of observed and simulated days to anthesis and maturity aftertransplantation of rice cultivar IR 36 at different N application levels during wetsseasons 2001 and 2002

N40 and N80 indicate the N application levels at 40 and 80 kg per hectare

Anthesis Maturity N levels Observed Simulated Observed Simulated Wet season 2001

N40 57 57 84 84 N80 (Calibration) 57 57 84 84

Wet season 2002 N40 53 55 83 82 N80 53 55 83 82

Table 4: Comparison of experimental and simulated grain yield (t ha-1) of rice cultivar IR36 at different N application levels during wets seasons 2001 and 2002

N levels Experimental Simulation Wet season 2001

N40 4.36 4.41 N80 (Calibration) 5.43 5.66

Wet season 2002 N40 3.40 3.38 N80 3.75 3.90

N40 and N80 indicate the N application levels at 40 and 80 kg per hectare



approximation has been made to relate Amto N concentration expressed per leaf areaunit because that determined the amount ofchlorophyll per unit area (Penning de Vrieset al., 1990). Am varies between 10-50 kgCO2 ha-1 leaf h-1 for rice depending on leafN concentration and photo syntheticallyactive radiation (PAR). For lower leaf Nconcentration (1.5% N), Am reaches 10 kgCO2 ha-1 leaf h-1 at a level of lower radiation(PAR, 250 J m-2 s-1). However, Am reaches50 kg CO2 ha-1 leaf h-1 only at a higherradiation level (PAR, 400 J m-2 s-1). Further,intensity of reduction in Am due to limitationsin PAR is more evident at high Nconcentration (Ehleringer, and Pearcy,1983). The most probable effect of limitedPAR will be a substantial reduction in grainyield at high leaf N concentration ascompared to lower leaf N concentration.We observed reduction in yield to the extentof 12 % at high N application rates withPAR limitations, which was due tointeraction of high leaf N concentration andlimited PAR. Panicle differentiation beginsa 42-day critical sunlight-requiring period(Ronald and Ralph, 2002) and any variationin radiation level beyond this causessignificant reduction in grain yield. Thepresent simulation study also reflects lowergrain number under incident solar radiationdepletion (Fig. 5) in both fertilized and non-fertilized condition.

CONCLUSION

The CERES-Rice (DSSAT v. 4.0) wasable to simulate phenological events andgrain yield with high accuracy under varied

weather condition. The model providesinsights about the response mechanism todifferent N management and variousweather conditions. As the CERES-Ricemodel is found to be capable of predictingthe crop yield fairly well, it can serve as atool in assessing yield potential of alternatetechnologies and management practices toavoid the yield loss due to reduced solarradiation incidence. The adaptationstrategies for climatic change scenarios canbe evaluated through the model.

ACKNOWLEDGEMENT

The World Bank funded NationalAgricultural Technology Project (ProjectNo. NATP-RRPS 25) and Japan Societyfor the Promotion of Science are gratefullyacknowledged for financial assistance inexecution of this research work.

REFERENCES

Directorate of Agriculture and FoodProduction. 2003. Orissa AgriculturalStatistics, 2000-2001, Directorate ofAgriculture and Food Production,Orissa, Bhubaneswar, India. 3 pp.

Ehleringer, J, and Pearcy, R.W. 1983.Variation in quantum yield for CO2uptake among C3 and C4 plants. PlantPhysiol., 73, 555-559

Fu, C.B., Kim, J.W., and Zhao, Z.C. 1998.Preliminary assessment of impacts ofglobal change on Asia. Asian Changein the Context of Global ClimaticChange, Galloway, J. N. & Melillo,Jerry M. (Eds.), Cambridge University



on Fertilizer Usage in the Tropics,Kuala Lumpur, Malaysia, 24-27 Aug.1992. Malaysian Society of SoilScience, Kuala Lumpur, Malaysia.

Stansel, J.W. 1975. In Six Decades of RiceResearch in Texas (Texas AgriculturalExperiment Station, College Station)

Thiyagarajan, T.M., Sivasamy, R., andBudhar, M.N. 1995. Procedure forcollecting plant samples at differentgrowth stages of transplanted ricecrop. In SARP Research Proceedings:Nitrogen management studies in

irrigated rice by Thiyagarajan et al.(ed). International Rice ResearchInstitute, Los Banos, Philippines, 18April – 6 May, 1994, pp 99-102.

Uehara, G. and Tsuji, G.Y. 1993. TheIBSNAT project. In Penning de Vrieset al., (ed.) Systems approaches foragricultural development. KluwerAcademic Publ., Dordrecht, theNetherlands. p. 505-513

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CERES-Rice model: Calibration, evaluation and application