Current Science

Open Access Open Access  Restricted Access Subscription Access

Dry Biomass Partitioning of Growth and Development in Wheat (Triticum aestivum L.) Crop Using CERES-Wheat in Different Agro Climatic Zones of India


Affiliations
1 Agromet Service Cell, India Meteorological Department, New Delhi 110 003, India
2 School of Climate Change and Agri Meteorology, Punjab Agricultural University, Ludhiana 141 004, India
3 Department of Agri Meteorology, CCSHAU, Hisar 125 004, India
4 Department of Geophysics, Banaras Hindu University, Varanasi 221 005, India
5 Department of Soil Science & Chemistry, College of Agriculture, Indore 452 001, India
 

The CERES-wheat crop growth simulation model has been calibrated and evaluated for two wheat cultivars (PBW 343 and PBW 542) for three sowing dates (30 October, 15 November and 30 November) during 2008-09 and 2009-10 to study partitioning of leaf, stem and grains at Ludhiana, Punjab, India. The experimental data and simulated model data were analysed on partitioning of leaf, stem and grains, and validated. It was found that the model closely simulated the field data from phenological events and biomass. Simulated biological and grain yield was in accordance with-field experiment crop yield within the acceptable range. The correlation coefficient between field-experiment and simulated yield data and biomass data varied significantly from 0.81 and 0.96. The model showed overestimation from field-experimental yield for both cultivars. The cultivar PBW 343 gave higher yield than cultivar PBW 542 on 15 November during both years. The model performance was evaluated and it was found that CERES-wheat model could predict growth parameters like days to anthesis and maturity, biomass and yield with reasonably good accuracy (error less than 8%) and also correlation coefficient between field-experimental and simulated yield data and biomass data varied from 0.94 and 0.95. The results showed that the correlation coefficient between simulated and districts yield varied from 0.41 to 0.78 and also significantly at all six selected districts. The results may be used to improve and evaluate the current practices of crop management at different growth stages of the crop to achieve better production potential.

Keywords

Biomass Partitioning, Genetic Coefficient, Phenology Stages, Soil Parameters.
User
Notifications
Font Size

  • Curtis, B. C., Rajaram S. and Macpherson, H. G., Bread wheat improvement and production. FAO, Plant protection and production series no. 30, 2002, p. 77.

  • Shpiler, L. and Blum, A., Differential reaction of wheat cultivars to hot environments. Euphytica, 1986, 35, 483–492.

  • O’Toole, J. C. and Stockle, C. D., The role of conceptual and simulation modeling in plant breeding. In Improvement and Management of Winter Cereals under Temperature, Drought and Salinity Stresses (eds Acevedo, E. et al.). Proc. ICARDA-INIA Symp., Cordoba, Spain, 26–29 October 1987, 1991, pp. 205–225.

  • Singh, N. and Sontakke, N. A., On climatic fluctuations and environmental changes of Indo-Gangetic plains, India. Clim. Chnage, 2002, 52, 287–313.

  • Curry, R. B., Peart, R. M., Jones, J. W., Boote, K. J. and Allen, L. H., Simulation as a tool for analysing crop response to climate change. Trans. ASAE, 1990, 33, 981–900.

  • Ryle, G. J. A., Arnott, R. A. and Powel, C. E., Distribution of dry weight between ischolar_main weight ratios in response to nitrogen: opinion. Plant Soil, 1981, 75, 75–97.

  • Ishita, A., Malik, C. P., Raheja, R. K. and Bhatia, D. S., Physiological and biochemical changes in fruit development of Brassica oxyrhima and Brissica toumefortii. Phytomorphology, 1998, 48, 399–404.

  • Sharma, K. D. and Pannu, R. K., Biomass accumulation and its mobilization in Indian mustard, Brassica juncea (L.) and Coss under moisture stress. J. Oilseeds Res., 2007, 24(2), 267–270.

  • Pannu, R. K., Singh, D. P., Singh, D. and Chaudhary, B. D., Contribution of plant parts of the total biomass as affected by environments in Indian mustard (Brassica juncea (L). Czern and Coss). Ann. Biol., 1996, 12, 368–376.

  • Pannu, R. K., Singh, D. P., Singh, D., Chaudhary, B. D. and Sharma, H. C., Partitioning co-efficient of plant parts under different growth stages and environments in Indian mustard (Brassica juncea (L). Czern and Coss). Haryana Agric. University J. Res., 1997, 27, 31–37.

  • Guhcy, A. and Trivedi, A. K., Dry matter accumulation and partitioning in chickpea under two moisture regimes. Madras Agric. J., 2001, 88(7–9), 484–486.

  • Kumar, A., Deshmukh, P. S., Kushwaha, S. R. and Kumari, S., Effect of terminal drought on biomass production, its partitioning and yield of chickpea genotypic. Ann. Agric. Res., New Scries, 2001, 33(3), 408–411.

  • Jones, J. W., Decision support systems for agricultural development, In System Approaches for Agricultural Development (eds Penning de Vries, F. W. T., Teng, P. S. and Metselaar, K.), Kluwer Academic Publishers, Dordrecht, The Netherlands, 1993, pp. 459-472.

  • IBSNAT (International Benchmark Sites Network for Agrotechnology Transfer), Decision Support System for Agrotechnology Transfer Version 3.0 User Guide, (DSSAT) v3.0), University of Hawaii, Honolulu, 1994.

  • Tsuji, G. Y., Uehara, G. and Balas, S. S., Decision support system for agro technology transfer, University of Hawaii, Honolulu, Hawaii, 1994.

  • Wilkerson, G. G., Jones, J. W., Boote, K. J., Ingram, K. T. and Mishoe, J. W., Modeling soybean growth for crop management. Trans. ASAE26, 1983, pp. 63–73.

  • Boote, K. J., Jones, J. W., Mishoe, J. W. and Wilkerson, G. G., Modeling growth and yield of groundnut. Proc., Int. Symp.on Agrometeorology of Groundnut, 21–26 August 1985, Niamey, NIGER.

  • Hoogenboom, G., Jones, J. W. and Boote, K. J., Modeling growth, development and yield of grain legumes using SOYGRO, PINTGRO and BEANGRO – a review. Trans. Am. Soc. Agric. Eng., 1992, 35(6), 2043–2056.

  • Jones, J. W., Jagatap, S. S. and Mishoe, J. W., Soybean development. In Modeling Soil and Plant Systems (eds Hanks, J. and Ritchie, J. T.), Am. Soc. Agron., Madison, WI, 1991, pp. 71–90.

  • Angstrom, A., Solar and terrestrial radiation. Q. J. Roy Meteorol. Soc., 1924, 50, 121–126.

  • Richardson, C. W., Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resour. Res., 1981, 17(1), 182–190.

  • Richardson, C. W., Weather simulation for crop management models. Trans. ASAE, 1985, 28(5), 1602–1606.

  • Geng, S., Penning de Vries, F. W. T. and Supit, I., A simple method for generating daily rainfall data. Agricul. For. Meteorol., 1986, 36, 363–376.

  • Geng, S., Auburn, J., Brandstetter, E. and Li, B., A program to simulate meteorological variables. Documentation for SIMMETEO. Agronomy Report No. 204. University of California, Davis Crop Extension, Davis, CA, 1988.

  • Singh, K. K., Baxla, A. K. and Singh, P. K., Genetic coefficient of different of crops like Wheat, Rice and Maize. Report of Agromet Advisory, IMD, New Delhi, 2012.

  • Hunt, L. A., Pararajasingham, S. Jones, J. W., Hoogenboom, G., Imamura, D. T. and Ogoshi, R. M., GENCAL software to facilitate the use of crop models to analyze field experiments. Agron. J. 1993, 85, 1090–1094.

  • Singh, P. K., Singh, K. K., Bhan, S. C., Baxla, A. K., Akhilesh Gupta, R., Balasubramanian and Rathore, L. S., Growth and yield prediction of Rice DSSAT v 4.5 Model for the climate conditions of South Alluvial Zone of Bihar (India). J. Agrometeorol., 2015, 17(2), 194–198.

  • Singh, K. K., Baxla, A. K., Singh, P. K., Reports of wheat yield predication at F2 level using CERES-wheat model for rabi 2014–15. ASC, IMD, New Delhi, 2015.

  • Kumar, A. and Sharma, K. D., Physiological responses and dry matter partitioning of summer mungbean (Vigna radiate L.) genotypes subjected to drought conditions. J. Agron. Crop Sci., 2009, 195, 270–277.

  • Muchow, R. C., Robertson, M. J. and Pengelly, B. C., Accumulation and partitioning of biomass and nitrogen by soybean, mungbean and cowpea under contrasting environmental conditions. Field Crops Res., 1993, 33, 13–36.

  • Lal, M., Singh, K. K., Rathore, L. S., Srinivasan, G. and Saseendran, S. A., Vulnerability of rice and wheat yields in NW-India to future Changes in climate. Agric. Forest Meterol., 1998, 89, 101–114.

  • Lal, M., Singh, K. K., Srinivasan, G., Rathore, L. S., Naidu, D. and Tripathi, C. N., Growth and yield response of soybean in Madhya Pradesh, India to climate variability and change. Agric. For. Meterol., 1999, 93, 53–70.

  • Jones, C. A. and Kiniry, J. R., CERES – maize: a simulation model of maize growth and development, Texas A&M University Press, College Station, Texas, USA, 1986, p. 194.

  • Ritchie, J. T. and Otter, S., Description and performance of CERES – wheat: a user-oriented wheat yield model. In ARS Wheat Yield Project. ARS-38. Natl. Tech. Info. Serv., Springfield, Missouri, 1985, pp. 159–175.

  • Singh, K. K., Baxla, A. K., Mall, R. K., Singh, P. K., Balasubramanian, R. and Garg, S., Wheat yield predication using CERES-Wheat model for decision support in agro-advisory. J. Vayu Mandal., 2010, 35&36(1-4), 2010, 97–109.

  • Singh, P. K., Singh, K. K., Baxla, A. K., Kumar, B., Bhan, S. C. and Rathore, L. S., Crop yield predication using–Rice v 4.5 model for the climate variability of different agroclimatic zone of south and north-west plane zone of Bihar (India). Mausam, 2014, 65(4), 529–538.

  • Singh, P. K., Singh, K. K., Baxla, A. K. and Rathore, L. S., Impact of climatic variability on wheat predication using DSSATv4.5 (CERES-Wheat) model for the different agroclimatic zones in India. Springer, 2015, 45, 55.

  • Singh, P. K. et al., Rice (Oryza sativa L.) yield gap using the CERES-rice model of climate variability for different Agroclimatic zones of India. Curr. Sci., 2016, 110(3), 405–413.

  • Singh, P. K., Singh, K. K., Bhan, S. C., Baxla, A. K., Akhilesh Gupta, Balasubramanian, and Rathore, L. S., Growth and yield prediction of Rice DSSAT v 4.5 Model for the climate conditions of South Alluvial Zone of Bihar (India). J. Agrometeorol., 2015, 17(2), 194–198.


Abstract Views: 315

PDF Views: 142




  • Dry Biomass Partitioning of Growth and Development in Wheat (Triticum aestivum L.) Crop Using CERES-Wheat in Different Agro Climatic Zones of India

Abstract Views: 315  |  PDF Views: 142

Authors

P. K. Singh
Agromet Service Cell, India Meteorological Department, New Delhi 110 003, India
K. K. Singh
Agromet Service Cell, India Meteorological Department, New Delhi 110 003, India
K. K. Gill
School of Climate Change and Agri Meteorology, Punjab Agricultural University, Ludhiana 141 004, India
Ram Niwas
Department of Agri Meteorology, CCSHAU, Hisar 125 004, India
R. S. Singh
Department of Geophysics, Banaras Hindu University, Varanasi 221 005, India
Sanjay Sharma
Department of Soil Science & Chemistry, College of Agriculture, Indore 452 001, India

Abstract


The CERES-wheat crop growth simulation model has been calibrated and evaluated for two wheat cultivars (PBW 343 and PBW 542) for three sowing dates (30 October, 15 November and 30 November) during 2008-09 and 2009-10 to study partitioning of leaf, stem and grains at Ludhiana, Punjab, India. The experimental data and simulated model data were analysed on partitioning of leaf, stem and grains, and validated. It was found that the model closely simulated the field data from phenological events and biomass. Simulated biological and grain yield was in accordance with-field experiment crop yield within the acceptable range. The correlation coefficient between field-experiment and simulated yield data and biomass data varied significantly from 0.81 and 0.96. The model showed overestimation from field-experimental yield for both cultivars. The cultivar PBW 343 gave higher yield than cultivar PBW 542 on 15 November during both years. The model performance was evaluated and it was found that CERES-wheat model could predict growth parameters like days to anthesis and maturity, biomass and yield with reasonably good accuracy (error less than 8%) and also correlation coefficient between field-experimental and simulated yield data and biomass data varied from 0.94 and 0.95. The results showed that the correlation coefficient between simulated and districts yield varied from 0.41 to 0.78 and also significantly at all six selected districts. The results may be used to improve and evaluate the current practices of crop management at different growth stages of the crop to achieve better production potential.

Keywords


Biomass Partitioning, Genetic Coefficient, Phenology Stages, Soil Parameters.

References





DOI: https://doi.org/10.18520/cs%2Fv113%2Fi04%2F752-766