Article abstract

Journal of Agricultural and Crop Research

Research Article | Published December 2017 | Volume 5, Issue 6, pp. 96-107

 

Evaluating the CERES-Rice model under dry season irrigated rice in Bangladesh: Calibration and validation

 


 

 

M Maniruzzaman*

JC Biswas

MB Hosain

MM Haque

UA Naher

A Biswas

AK Choudhury

S Akhter

F Ahmed

MM Rahman

N Kalra

 

Email Author


 

     Irrigation and Water Management Division, Bangladesh Rice Research Institute (BRRI), Gazipur-1701, Bangladesh.






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Citation: Maniruzzaman M, Biswas JC, Hossain MB, Haque MM, Naher UA, Biswas A, Choudhury AK, Akhter S, Ahmed F, Rahman MM, Kalra N (2017). Evaluating the CERES-Rice model under dry season irrigated rice in Bangladesh: Calibration and validation. J. Agric. Crop Res. 5(6): 96-107.

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 Abstract 


Crop Environment Resource Synthesis-Rice (CERES-Rice) model was calibrated and validated for major rice varieties suitable for growing in the dry season. Yield performances for BRRI dhan28, BRRI dhan29 and BRRI dhan58 were tested at Gazipur, Rangpur, Rajshahi, Barisal, Comilla and Habiganj districts of Bangladesh under recommended agronomic management practices, respectively. Nitrogen rates (0, 40, 80, 120, 160 and 200 kg ha-1) and varying sowing date experiments were conducted at Gazipur (latitude: 23° 45' N, longitude: 90° 22' E, elevation: 8.4 m amsl) during 2012/2013 and 2013/2014. Seeding interval was 15 days starting from 15 October in 2012 and was extended up to 30 January in 2013. The genetic coefficients were developed based on data from multi-locations yield trials and date of sowing and fertilizer management trials at Gazipur. The model performance was evaluated using prediction error (Pe), coefficient of determination (R2), normalized root means squared error (NRSME) and Willmott’s index of agreement (d). The model calibration yielded 0.81<R2<0.99, 0.81<NRMSE<10.74, and 0.81<d<0.97 in simulating grain yields, biomass and growth durations, respectively. The model validation yielded 0.60<R2<0.95, 1.72<NRMSE<5.99, and 0.86<d<0.98 in simulating grain yields, biomass and growth durations. During calibration, the prediction errors for average grain yield, biomass and growth duration varied from 3.46 to 4.80%, 10.20 to 15.39% and 0.92 to 2.25%, respectively indicating satisfying model performances. The changes in simulated results compared to observe values varied from -1.82 to 9.12% for grain yield, 5.38% to 18.82% for biomass production and -4.67 to 4.87% for growth duration depending on tested varieties. Thus, CERES-Rice model is ready for its use in climate change impacts and variabilities on rice production.

Keywords  CERES-Rice model   calibration   validation   model application   yield   Bangladesh  

 

 

 

 References 

 

Abedinpour M, Sarangi A, Rajput TBS, Singh M, Pathak H, Ahmad T (2012). Performance evaluation of AquaCrop model for maize crop in a semi-arid environment. Agric. Water Manage. 110:55-66.

Alociljha EC, Ritchie JT (1991). A model for the phenology of rice. In: Hodges T. (Ed.), Predicting Crop Phenology. CRC Press, Boca Raton, FL, USA, pp. 181-189.

Amien I, Rejekingrum P, Pramudia A, Susanti E (1996). Effects of inter-annual climate variability and climate change on rice yield in Java, Indonesia. Water Air Soil Pollut. 92: 29-39 and soil management with application of the AquaCrop model. Trans. ASABE, 55(3):839-848.

Araya A, Habtu S, Hadgu KM, Kebede A, Dejene T (2010a). Test of AquaCrop model in simulating biomass and yield of water deficit and irrigated barley (Hordeum vulgare). Agric. Water Manage. 97:1838-1846.

Araya A, Keesstra SD, Stroosnijder L (2010b). Simulating yield response to water of Teff (Eragrostis tef) with FAO AquaCrop model. Agric. Water Manage. 116:196-204.

Baer BD, Meyer WS, Erskine D (1994). Possible effects of global climate change on wheat and rice production in Australia. In: Rosenzweig C, Iglesias A (Eds.), Implications of Climate Change for International Agriculture: Crop Modeling Study. United States Environmental Protection Agency, Australia, pp. 1-14.

BBS (Bangladesh Bureau of Statistics) (2016). Yearbook of Agricultural Statistics of Bangladesh. Government of Bangladesh, Dhaka, p. 390.

Biswas JC, Sarker UK, Sattar SA (2001). Performance of some BRRI varieties planted year round. Bangladesh Rice J. 10(1&2):55-59.

BRRI (Bangladesh Rice Research Institute) (2012). Annual Report of Bangladesh Rice Research Institute 2009-2010. BRRI, Gazipur-1701, Bangladesh, p. 357.

Doorenbos J, Pruitt WO (1977). Irrigation and Drainage Food and Agricultural Organization, Rome.

Escano CR, Buendia L (1994). Climate impact assessment for agriculture in the Philippines: simulation of rice yield under climate change scenarios. In: Rosenzweig C, Iglesias A (Eds.), Implications of Climate Change for International Agriculture: Crop Modeling Study. United States Environmental Protection Agency, Philippines, 24:1-13.

Guerra LC, Bhuiyan SI, Tuong TP, Barker R (1998). Producing more rice with less water from irrigated systems. SWIM Paper 5. IWMI/IRRI, Colombo, Sri Lanka, p. 24.

Heng LK, Hsiao TC, Evett S, Howell T, Steduto P (2009). Validating the FAO AquaCrop model for irrigated and water deficit field maize. Agron. J. 101:488-498.

Hsiao TC, Heng LK, Steduto P, Rojas-Lara B, Raes D, Fereres E (2009). AquaCrop - The FAO crop model to simulate yield response to water. III. Parameterization and testing for maize. Agron. J. 101:448-459.

Hunt LA, Ritchie JT, Teng PS, Boote KJ (1989). “Genetic coefficients for the IBSNAT crop models,” in Agronomy Abstract, ASA, Madison, Wis, USA. pp. 16:17.

Iqbal MA, Shen Y, Stricevic R, Pie H, Sun H, Amiri E, Penas A, Rio SD (2014). Evaluation of the FAO AquaCrop model for winter wheat on the North China Plain under deficit irrigation from field experiment to regional yield simulation. Agric. Water Manage. 135:61-72.

IRRI (International Rice Research Institute) (2012). IRRI Annual Report, Los Banos, the Philippines.

Jeong H, Jang T, Seong C, Park S (2014). Assessing nitrogen fertilizer rates and split applications using the DSSAT model for rice irrigated with urban wastewater. Agric. Water Manage. 141:1-9.

Jin Z, Ge D, Chen H, Zheng X (1995). Assessing impacts of climate change on rice production: strategies for adaptation in southern China. In: Peng S, Ingram KT, Neue HU, Ziska LH (Eds.), Climate Change and Rice. International Rice Research Institute, Manilla, Philippines, pp. 303-313.

Kropff MJ, Williams RL, Horie T, Angus JF, Singh U, Centeno HG, Cassman KG (1994). Predicting yield potential of rice in different environments. In: Humphreys et al. (Eds.), Temperate Rice – Achievements and Potential. Proceedings of Temperate Rice Conference, Yanco, NSW, Australia, pp. 657-663.

Liu HL, Yang JY, Tan CS, Drury CF, Reynolds WD, Zhang TQ, Bai YL, Jin J (2011). Simulating water content, crop yield and Nitrate-N loss under free and controlled tile drainage with subsurface irrigation using DSSAT model. Agric. Water Manage. 98:1105-1111.

Liu S, Yang JY, Zhang XY, Drury CF, Reynolds WD, Hoogenboom G (2013). Modelling crop yield, soil water content and soil temperature for a soybean-maize rotation under conventional and conservation tillage systems in Northeast China. Agric. Water Manage. 123:32-44.

Mahmood R, Meo M, Legates DR, Morrissey ML (2003). The CERES-Rice model-based estimates of potential monsoon season rainfed rice productivity in Bangladesh. The Professional Geographer, 55(2):259-273. DOI: 10.1111/0033-0124.5502013.

Mall RK, Aggarwal PK (2002). Climate change and rice yields in diverse agro-environments of India. I. Evaluation of impact assessment models. Climate Change 52:315-330.

Matthews RB, Wassmann R, Buendia LV, Knox JW (2000). Using a crop/soil simulation model and GIS techniques to assess methane emissions from rice fields in Asia. II. Model validation and sensitivity analysis. Nutr. Cycl. Agroecosyst. 58:161-177.

Mkhabela MS, Paul RB (2012). Performance of the FAO AquaCrop model for wheat grain yield and soil moisture simulation in Western Canada. Agric. Water Manage. 110:16-24.

Moriasi DN, Arnold JG, Liew MWV, Bingner RL, Harmel RD, Veith TL (2007). Model evaluation guidelines for systemic quantification of accuracy in watershed simulations. Trans. ASABE, 50:885-900.

Ortiz-Monasterio JI, Dhillon SS, Fischer RA (1994). Date of sowing effects on grain yield and yield components of irrigated spring wheat cultivars and relationship with radiation and temperature in Ludhiana, India. Field Crops Res. 37:169-184.

Pathak H, Li C, Wassmann R (2005). Greenhouse gas emissions from Indian rice fields: calibration and upscaling using the DNDC model. Biogeosci. Discus. 2(1):77-102.

Pathak H, Timsina J, Humphreys E, Godwin DC, Bijay-Singh, Shukla AK, Singh U, Matthews RB (2004). Simulation of rice crop performance and water and N dynamics, and methane emissions for rice in northwest India using CERES-Rice model. CSIRO Land and Water Technical Report 23/04. CSIRO Land and Water, Griffith, NSW 2680, Australia, p.111. Available from: www.clw.csiro.au/publications/technical2004.

Postal S (1997). Last oasis: facing water scarcity. New York (USA): Norton and Company, p. 239. Priestley CHB, Taylor RJ (1972). On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly Weather Review 100:81-92.

Raes D, Steduto P, Hsiao TC, Fereres E (2012). Reference Manual of AquaCrop Model. Chapter 2, Users Guide, FAO Land and Water Division, Rome Italy, p. 164.

Randhawa AS, Dhillon SS, Singh W (1981). Productivity of wheat varieties, as influence by time of sowing. J. Res. Punjub Agric. Univ. 18:227-233.

Rao GSLHV, Sebastian S, Subash N (2002). Crop growth simulation models of rice under humid climates. Available from: http:///www.commonorthwestealthknowledge.north-east/MetCD/Chapter7/C7P06.htm.

Ritchie JT (1972). Model for predicting evaporation from a row crop with complete cover. Water Resourc. Res. 8:1204-1213.

Ritchie JT, Alocilija EC, Uehara G (1986). IBSNAT/CERES Rice Model Agro-technology Transfer, 3:1-5.

Ritchie JT, Alocilja EC, Singh U, Uehara G (1987). IBSNAT and the CERES-Rice model. Weather and Rice, Proceedings of the International Workshop on the Impact of Weather Parameters on Growth and Yield of Rice, 7–10 April, 1986. International Rice Research Institute, Manilla, Philippines, pp. 271-281.

Ritchie, JT (1998). Soil water balance and plant water stress. In: Tsugi, GY, Hoogenboom, G, Thornton PK (Eds.). Understanding Options for Agricultural Production. Kluwer Academic Publishers, pp. 41-54.

Saseendran SA, Hubbard KG, Singh KK, Mendiratta N, Rathore LS, Singh SV (1998a). Optimum transplanting dates for rice in Kerala, India, determined using both CERES v3.0 and ClimProb. Agron. J. 90:185-190.

Saseendran SA, Singh KK, Rathore LS, Rao GSLHVP, Mendiratta N, Lakshmi Narayan K, Singh SV (1998b). Evaluation of the CERES-Rice v.3.0 model for the climate conditions of the state of Kerala, India. Meterol. Appl. 5:385-392.

Seino H (1994). Implication of climate change for Japanese agriculture: evaluation by simulation of rice, wheat and maize growth. In: Rosenzweig C, Iglesias A (Eds.), Implication for Climate Change for International Agriculture: Crop Modelling Study. United States Environmental Protection Agency, Japan, pp. 1-18.

Singh U, Godwin DC, Ritchie JT (1988). Modeling growth and development of rice under upland lowland conditions. Agronomy Abs. 80:27.

Timsina J, Humphreys E (2006). Performance of CERES-Rice and CERES-Wheat models in rice-wheat systems: A review. Agric. Systems. 90:5-31.

Timsina J, Singh U, Badaruddin M, Meisner C (1998). Cultivar, nitrogen, and moisture effects on a RW sequence: experimentation and simulation. Agron. J. 90:119-130.

Timsina J, Singh U, Singh Y, Lansigan FP (1995). Addressing sustainability of RW systems: testing and applications of CERES and SUCROS models. In: Proceedings of the International Rice Research Conference, pp. 13-17 February 1995. IRRI, Los Banos, Philippines, pp. 633-656.

Tongyai C (1994). Impact of climate change on simulated rice production in Thailand. In: Rosenzweig C, Iglesias A (Eds.), Implications of Climate Change for International Agriculture: Crop Modeling Study. United States Environmental Protection Agency, Thailand, pp. 1-13.

Tsuji GY, Uehara G, Balas G (1994). DSSAT version 3, vols. 1–3. University of Hawaii, Hawaii. Willmott, CJ (1984). On the evaluation of model performance in physical geography, In Spatial Statistics and Models, Gaile GL, Willmott CJ (Eds.). D. Reidel: Boston, pp. 443-460.

Zeleke KT, David L, Raymond C (2011). Calibration and testing of FAO AquaCrop model for canola. Agron. J. 103:1610-1618.