Open Access Open Access  Restricted Access Subscription Access

Crop modelling in agricultural crops


Affiliations
1 Centurion University of Technology and Management, Paralakhemundi 761 211, India; International Crops Research Institute for the Semi-Arid Tropics, Patancheru 502 324, India, India
2 International Crops Research Institute for the Semi-Arid Tropics, Patancheru 502 324, India, India
3 Centurion University of Technology and Management, Paralakhemundi 761 211, India, India
 

With limited land resources and a growing population, agricultural output is under considerable strain. New technology is necessary for overcoming these issues and advising farmers, legislators and other decision-makers on adopting sustainable agriculture despite global climate variations. This has led to the crop simulation models that illustrate crop growth and development processes as a function of climate, soil and crop management. They also support agricultural agronomy (yield estimate, biomass, etc.), pest control, breeding and natural resource management. This study examines crop modelling for agricultural production planning and field-level management strategies. These can help researchers comprehend the significance of crop modelling for scenario-building and provide field-level suggestions by analysing future conditions and strategic activities to minimize the predicted negative influence and maximize the projected positive effect. The limitations and potential directions of crop modelling improvement have also been highlighted in this study

Keywords

Climate change, crop models, management strategies, sustainable agriculture, yield estimation.
User
Notifications
Font Size

  • Guiteras, R., The impact of climate change on Indian agriculture, 2009; http://econdse.org/wpcontent/uploads/2014/04/guiteras_cli- mate_change_indian_agriculture_sep_2009.pdf
  • Debaeke, P. and Aboudrare, A., Adaptation of crop management to water-limited environments. Eur. J. Agron., 2004, 21, 433–446.
  • Kumar, R., Singh, K. K., Gupta, B. R. D., Mall, R. K. and Rai, S. K., Simulation modeling on the basis of soybean yield and man-agement data. National Centre for Medium Range Weather Fore-casting, New Delhi, 2002, pp. 103–107.
  • Darko, O. P., Yeboah, S., Addy, S. N. T., Amponsah, S. and Dan-quah, E. O., Crop modelling: a tool for agricultural research – a review. J. Agric. Res. Dev., 2013, 2(1), 1–6.
  • Andarzian, B. M., Bannayan, P., Steduto, H., Mazraeh, M. E., Barati, A. and Rahnarna, N., Validation and testing of the Aqua-Crop model under full and deficit irrigated model for canola. Agron. J., 2011, 103, 1610–1618.
  • Matthews, R. B., Rivington, M., Muhammed, S., Newton, A. C., and Hallett, P. D., Adapting crops and cropping systems to future climates to ensure food security: the role of crop modelling. Global Food Secur., 2013, 2, 24–28; doi:10.1016/j.gfs.2012.11.009.
  • Hamid, J., Farahani, Gabriella, I. and Theib, Y., Parameterization and evaluation of the AquaCrop model for full and deficit irrigated cotton. Agron. J., 2009, 101, 469–476.
  • Radha Krishna Murthy, V., Crop growth modelling and its appli-cations in agricultural meteorology. In Satellite Remote Sensing and GIS Applications in Agricultural Meteorology, World Mete-orological Organisation, Switzerland, 2003, pp. 235–261.
  • De Wit, C. T., Transpiration and crop yields. Agricultural research report/Netherlands, Institute of Biological and Chemical Research on Field Crops and Herbage, 1958, vol. 59, p. 64.
  • Slatyer, R. O., Agricultural climatology of the Yass valley. CSIRO Aust. Div. Land Res. Reg. Surv. Tech., Paper No. 13, 1960.
  • Duncan, W. G., Loomis, R. S., Williams, W. A. and Hanau, R., A model for simulating photosynthesis in plant communities. Hil-gardia, 1967, 38(4), 181–205.
  • Loomis, R. S., Rabbinge, R. and Ng, E., Explanatory models in crop physiology. Annu. Rev. Plant Physiol., 1979, 30, 339–367.
  • Jones, J. W., Hesketh, J. D., Kamprath, E. J. and Bowen, H. D., Development of a nitrogen balance for cotton growth models: a first approximation. Crop Sci., 1974, 14(4), 541–546.
  • McKinion, J. M., Baker, D. N., Whisler, F. D. and Lambert, J. R., Applications of the GOSSYM/COMAX system for cotton crop management. Agric. Syst., 1989, 31, 55–65.
  • De Wit, C. T., Brouwer, R. and Penning de Vries, F. W. T., The simulation of photosynthetic systems, in prediction and measure-ment of photosynthetic productivity. In Proceedings of Interna-tional Biological Program/Plant Production Technical Meeting (ed. Setlik, I.), PUDOC, Wageningen, The Netherlands, 1970.
  • Wilkerson, G. G., Jones, J. W., Boote, K. J., Ingram, K. T. and Mishoe, J. W., Modeling soybean growth for crop management. Trans. Am. Soc. Mech. Eng., 1983, 26, 63–73.
  • Acock Williams, R. L., Durkin, C. O. and Stapper, M., A simple model of rice yield response to N fertilizer and its use as a decision support system. In Temperate Rice Conference (eds Humphrets, E. et al.), Yanco Agricultural Institute, Yanco, New South Wales, USA, 1994.
  • Wilkerson Xie, Y., Kiniry, J. R., Nedbalek, V. and Rosenthal, W. D., Maize and sorghum simulations with CERES-maize, sorghum, and almanac under water-limiting conditions. Agron. J., 2001, 93(5), 1148–1155.
  • Penning de Vries, F. W. T., van Laar, H. H. and Kropff, M. J. (eds), Simulation and Systems Analysis for Rice Production (SARP), PUDOC, Waneningen, The Netherlands, 1991, p. 369.
  • Ritchie, J. T. and Otter, S., Description and performance of CERES-wheat: a user-oriented wheat yield model. In Wheat Yield Project, ARS-38. National Technical Information Service, Spring-field, Missouri, USA, 1985, pp. 159–175.
  • Jones, J. W. and Kiniry Ritchie, J. T., Soil water balance and plant stress. In Understanding Options for Agricultural Production (eds Tsuji, G. Y., Hoogenboom, G. and Thornton, P. K.), Kluwer Aca-demic, Dordrecht, The Netherlands, 1988, pp. 41–54.
  • Boote, K. J., Jones, J. W., Hoogenboom, G., Wilkerson, G. G. and Jagtap, S. S., Peanut Crop Growth Simulation Model, User’s Guide, Florida Agricultural Experiment Station, University of Florida, Gainesville, Florida, USA, 1989.
  • Singh, U., Brink, J. E., Thornton, P. K. and Christianson, C. B., Linking crop models with a geographic information system to as-sist decision-making: a prototype for the Indian semiarid tropics, Paper IFDC-P-19, International Fertilizer Development Center, Muscle Shoals, AL, USA, 1993.
  • Kropff, M. J., Van Laar, H. H. and Matthews, R. B., ORYZA1: an eco-physiological model for irrigated rice production. SARP Research Proceedings, ABDLO, Wageningen, The Netherlands, 1994.
  • Aggarwal, P. K., Kalra, N., Singh, A. K. and Sinha, S. K., Analysing the limitations set by climatic factors, genotype, water and nitro-gen availability on productivity of wheat: I the model documenta-tion, parameterization and validation. Field Crops Res., 1994, 38, 73–91.
  • Yin, X. and Qi, C., Studies on the rice growth calendar model (RICAM) and its application, Acta Agron. Sin., 2006, 20, 339–346.
  • Graf, B., Rakotobe, O., Zahner, P., Delucchi, V. and Gutierrez, A. P., Simulation model for the dynamics of rice growth and develop-ment: Part I – the carbon balance. Agric. Syst., 1990, 32, 341–365.
  • Hoogenboom, G. et al., The DSSAT crop modeling ecosystem. In Advances in Crop Modeling for a Sustainable Agriculture (ed. Boote, K. J.), Burleigh Dodds Science Publishing, Cambridge, UK, 2019, pp. 173–216.
  • Jones, J. W., Hoogenboom, G., Porter, C. H., Boote, K. J., Batch-elor, W. D., Hunt, L. A. and Ritchie, J. T., The DSSAT cropping system model. Eur. J. Agron., 2003, 18, 235–265.
  • Boote Ritchie, J. T., Singh, U., Godwin, D. C. and Bowen, W. T., Cereal growth, development and yield. In Understanding Options for Agricultural Production (eds Tsuji, G. Y., Hoogenboom, G. and Thornton, P. K.), Kluwer Academic, Dordrecht, The Nether-lands, 1998, pp. 79–98.
  • Basak, J. K., Titumir, R. A. M., Biswas, J. K. and Mohinuzzaman, M., Impacts of temperature and carbon dioxide on rice yield in Bangladesh. Bangladesh Rice J., 2013, 17(1&2), 15–25.
  • Basso, B., Liu, L. and Ritchie, J. T., A comprehensive review of the CERES-wheat, -maize and -rice models performances. Adv. Agron., 2016, 136, 27–132.
  • Liu, S., Yang, J. Y., Zhang, X. Y., Drury, C. F., Reynolds, W. D. and Hoogenboom, G., Modelling crop yield, soil water content and soil temperature for a soybean–maize rotation under conven-tional and conservation tillage systems in Northeast China. Agric. Water Manage., 2013, 123, 32–44.
  • Mubeen, M., Ahmad, A., Wajid, A. and Bakhsh, A., Evaluating different irrigation scheduling criteria for autumn-sown maize un-der semi-arid environment. Pak. J. Bot., 2013, 45(4), 1293–1298.
  • Mubeen, M., Ahmad, A., Wajid, A., Khaliq, T. and Bakhsh, A., Evaluating CSM-CERES-maize model for irrigation scheduling in semi-arid conditions of Punjab, Pakistan. Int. J. Agric. Biol., 2013, 15, 1–10.
  • Mubeen, M. et al., Effect of growth stage-based irrigation sched-ules on biomass accumulation and resource use efficiency of wheat cultivars. Am. J. Plant Sci., 2013, 4, 1435–1442.
  • Surendran, U., Sivakumar, K., Gopalakrishnan, M. and Murugap-pan, V., Modeling based fertilizer prescription using Nutmon-Toolbox and DSSAT for soils of semi-arid tropics in India. Libyan Agric. Res. Center J. Int., 2010, 4, 221–230.
  • Hasegawa, H., Bryant, D. C. and Denison, R. F., Testing CERES model predictions of crop growth and N dynamics, in cropping systems with leguminous green manures in a Mediterranean cli-mate. Field Crops Res., 2000, 67, 239–255.
  • Johnen, T., Boettcher, U. and Kage, H., Variable thermal time of the double ridge to flag leaf emergence phase improves the predic-tive quality of a CERES-Wheat type phenology model. Comput. Electron. Agric., 2012, 89, 62–69.
  • Otegui, M. E., Ruiz, R. A. and Petruzzi, D., Modeling hybrid and sowing date effects on potential grain yield of maize in a humid temperate region. Field Crops Res., 1996, 47, 167–174.
  • Timsina, J. and Humphreys, E., Performance of CERES-rice and CERES-wheat models in rice–wheat systems: a review. Agric. Syst., 2006, 90, 5–31.
  • Bachelet, D. and Gay, C. A., The impacts of climate change on rice yield: a comparison of four model performances. Ecol. Modell., 1993, 65, 71–93.
  • Rosenzweig, C. and Parry, M. L., Potential impact of climate change on world food supply. Nature, 1994, 367, 133–138.
  • Godwin, D. C. and Singh, U., Nitrogen balance and crop response to nitrogen in upland and lowland cropping systems. In Under-standing Options for Agricultural Production (eds Tsuji, G. Y., Hoogenboom, G. and Thornton, P. K.), Kluwer Academic, Dor-drecht, The Netherlands, 1998, pp. 55–78.
  • Ahmad, S. et al., Application of the CSM–CERES-rice model for evaluation of plant density and nitrogen management of fine transplanted rice for an irrigated semiarid environment. Precis. Agric., 2011; doi:10.1007/s11119-011-9238-1.
  • Yang, J. M., Yang, J. Y., Dou, S., Yang, X. M. and Hoogenboom, G., Simulating the effect of long-term fertilization on maize yield and soil C/N dynamics in northeastern China using DSSAT and CENTURY-based soil model. Nutr. Cycl. Agroecosyst., 2013, 95, 287–303.
  • Ahmed, I., Ur Rahman, M. H., Ahmed, S., Hussain, J., Ullah, A. and Judge, J., Assessing the Impact of climate variability on maize using simulation modeling under semi-arid environment of Pun-jab, Pakistan. Environ. Sci. Pollut. Res., 2018, 25, 28413–28430; https://doi.org/10.1007/s11356-018-2884-3.
  • Mojtaba, R. A., Mahmoud, R. S. and Vazifedoust, M., Improving agricultural management in a large-scale paddy field by using re-motely sensed data in the CERES-rice model. Irrig. Drain., 2016, 65, 224–228.
  • Wikarmpapraharn, C. and Kositsakulchai, E., Evaluation of ORYZA2000 and CERES-rice models under potential growth condition in the Central Plain of Thailand. Thai J. Agric. Sci., 2010, 43(1), 17–29.
  • Yang, Y., Watanabe, M., Zhang, X., Zhang, J., Wang, Q. and Hayashi, S., Optimizing irrigation management for wheat to reduce groundwater depletion in the piedmont region of the Taihang Mountains in the North China Plain. Agric. Water Manage., 2006, 82, 25–44; https://doi.org/10.1016/j.agwat.2005.07.020
  • Jones, J. W., Hoogenboom, G., Porter, C. H., Boote, K. J., Batchelor, W. D., Hunt, L. A. and Ritchie, J. T., The DSSAT cropping sys-tem model. Eur. J. Agron., 2003, 18, 235–265.
  • Zhang, D. et al., DSSAT-CERES-wheat model to optimize plant density and nitrogen best management practices. Nutr. Cycl. Agro-Ecosyst., 2019, 114, 19–32.
  • Zhuanyun, S., Muhammad, Z., Shuang, L., Junming, L., Yueping, L., Yang, G. and Aiwang, D., Optimizing nitrogen application for drip-irrigated winter wheat using the DSSAT-CERES-wheat model. Agric. Water Manage., 2021, 244, 106–592.
  • Ahmed, M., Akram, M. N., Asim, M., Aslam, M., Hassan, F. U., Higgins, S. and Hoogenboom, G., Calibration and validation of APSIM-wheat and CERES-wheat for spring wheat under rainfed conditions. Comput. Electron. Agric., 2016, 123, 384–401.
  • Dar, E. A., Brar, A. S., Mishra, S. K. and Singh, K. B., Simulating response of wheat to timing and depth of irrigation water in drip irrigation system using CERES-wheat model. Field Crops Res., 2017, 214, 149–163.
  • Ji, J., Cai, H., He, J. and Wang, H., Performance evaluation of CERES-wheat model in Guanzhong Plain of Northwest China. Agric. Water Manage., 2014, 144, 1–10.
  • Patel, H. R., Patel, G. G., Shroff, J. C., Pandey, V., Shekh, A. M., Vadodaria, R. P. and Bhatt, B. K., Calibration and validation of CERES-wheat model for wheat in middle Gujarat region. J. Agro-Meteorol., 2010, 12, 114–117.
  • Hundal, S. S. and Kaur, P., Application of the CERES-wheat model to yield predictions the irrigated planes of the Indian Pun-jab. J. Agric. Sci. Cambridge, 1997, 29, 13–18.
  • Timsina, J., Godwin, D., Humphreys, E., Yadvinder-Singh, Bijay-Singh, Kukal, S. S. and Smith, D., Evaluation of options for increasing yield and water productivity of wheat in Punjab, India using the DSSAT-CSM-CERES-wheat model. Agric. Water Manage., 2008, 95, 1099–1110.
  • Kisekka, I., Aguilar, J. P., Rogers, D., Holman, J., O’Brian, D. and Klock, N., Assessing deficit irrigation strategies for corn using simulation. In ASABE/IA Irrigation Symposium: Emerging Tech-nologies for Sustainable Irrigation – A Tribute to the Career of Terry Howell. Conference Proceedings, American Society of Ag-ricultural and Biological Engineers, US, 2015, pp. 1–28.
  • Singh, A. K., Tripathy, R. and Chopra, U. K., Evaluation of CERES-wheat and CropSyst models for water–nitrogen interac-tions in wheat crop. Agric. Water Manage., 2008, 95, 776–786.
  • Corbeels, M., Guillaume, C., Samir, M. and Christian, T., Perfor-mance and sensitivity of the DSSAT crop growth model in simu-lating maize yield under conservation agriculture. Eur. J. Agron., 2016, 76, 41–53; https://doi.org/10.1016/j.eja.2016.02.001.
  • De Jonge, K. C., Ascough, J. C., Ahmadi, M., Andales, A. A. and Arabi, M., Global sensitivity and uncertainty analysis of a dynamic agroecosystem model under different irrigation treatments. Ecol. Model., 2012, 231, 113–125.
  • Ben Nouna, B., Katerji, N. and Mastrorilli, M., Using the CERES-maize model in a semi-arid Mediterranean environment. Evalua-tion of model performance. Eur. J. Agron., 2000, 13(4), 309–322.
  • Mastrorilli, M., Katerji, N. and Nouna, B. B., Using the CERES-maize model in a semiarid Mediterranean environment – valida-tion of three revised versions. Eur. J. Agron., 2003, 19, 125–134.
  • Liu, H. L. et al., Using the DSSAT-CERES-maize model to simu-late crop yield and nitrogen cycling in fields under long-term con-tinuous maize production. Nutr. Cycl. Agroecosyst., 2011, 89, 313–328.
  • Adnan Adnan, A., Jibrin Jibrin, M., Kamara Alpha, Y., Abdul-rahman Bassam, L., Shaibu Abdulwahab, S. and Garba Ismail, I., CERES-maize model for determining the optimum planting dates of early maturing maize varieties in northern Nigeria. Front. Plant Sci., 2017, 8, 1118.
  • Kumar, A., Pandey, V., Shekh, A. M., Dixit, K. and Kumar, M., Evaluation of CROPGRO-soybean (Glycine max. [L.] (Merrill) model under varying environment condition. Am.–Euras. J. Agron., 2008, 1, 34–40.
  • Paknejad, F., Farahani, P., Ilkaee, M. N. and Fazeli, F., Simulation of soybean growth under sowing date management by the CROPGRO model. Am. J. Agric. Biol. Sci., 2012, 7, 143–149.
  • Mall, R. K., Lal, M., Bhatia, V. S., Rathore, L. S. and Singh, R., Mitigating climate change impact on soybean productivity in India: a simulation study. Agric. For. Meteorol., 2004, 121, 113–125.
  • Vrishali, D., Salunke, C. and Akmanchi, A., Estimation of soybean growth and yield by the CROPGRO-soybean model. Technology Report 2.04, Indian Council of Agriculture Research, New Delhi, 2004.
  • Patil, D. D., Pandey, V. and Patel, H. R., Effect of intra-seasonal variation in temperature and rainfall on seed yield of pigeon pea cultivars using the CROPGRO model. J. Agrometeorol., 2018, 20, 286–292.
  • Debjani, H., Rabindra Kumar, P., Srivastava, R. K. and Shyamal, K., Evaluation of the CROPGRO-peanut model in simulating appro-priate sowing date and phosphorus fertilizer application rate for peanut in a subtropical region of eastern India. Crop J., 2017, 5, 317–325.
  • Mubeen, M., Ahmad, A., Hammad, H. M., Awais, M., Farid, H. U. and Saleem, M., Evaluating the climate change impact on water use efficiency of cotton–wheat in semi-arid conditions using DSSAT model. J. Water Climate Change, 2020, 11(4), 1661–1675.
  • Mall, R. K., Lal, M., Bhatia, V. S., Rathore, L. S. and Singh, R., Mitigating climate change impact on soybean productivity in India: a simulation study. Agric. For. Meteorol., 2004, 121, 113–125.
  • Bhuvaneswari, K., Geethalakshmi, V., Lakshmanan, A., Anbha-zhagan, R. and Nagothu Udaya Sekhar, D., Climate change impact assessment and developing adaptation strategies for rice crop in western zone of Tamil Nadu. J. Agrometeorol., 2014, 16(1), 38–44.
  • Dadhwal, V. K., Crop growth and productivity monitoring and simulation using remote sensing and GIS. Satellite Remote Sens-ing and GIS Applications in Agricultural Meteorology, World Me-teorological Organisation, Switzerland, 2005, pp. 263–289.
  • Gumma, M. K. et al., Assimilation of remote sensing data into crop growth model for yield estimation: J. Indian Soc. Remote Sensing, 2022; https://doi.org/10.1007/s12524-021-01341-6.
  • Sudharsan, D. et al., Evaluation of weather-based rice yield models, India. Int. J. Biometeorol., 2012; doi:10.1007/s00484-012-0538-6.
  • Bouman, S. B. A. M., Kropff, M. J., Tuong, T. P., Wopereis, M. C. S., Ten Berge, H. F. M. and Van Laar, H. H., ORYZA2000: modelling lowland rice. International Rice Research Institute, Los Banos, Philippines and Wageningen University and Research Cen-tre, Wageningen, The Netherlands, 2001.
  • Li, T., Angeles, O., Marcaida, M., Manalo, E., Manalili, M. P., Radanielson, A. and Mohanty, S., From ORYZA2000 to ORYZA (v3): an improved simulation model for rice in drought and nitrogen-deficient environments. Agric. For. Meteorol., 2017, 237–238, 246–256; https://doi.org/10.1016/j.agrformet.2017.02.025.
  • Espe, M. B., Yang, H., Cassman, K. G., Guilpart, N., Sharifi, H. and Linquist, B. A., Estimating yield potential in temperate high-yielding, direct-seeded US rice production system. Field Crops Res., 2016, 193, 123–132.
  • Yuan, S., Peng, S. and Li, T., Evaluation and application of the ORYZA rice model under different crop managements with high-yielding rice cultivars in central China. Field Crops Res., 2017, 212(1), 115–125; doi:10.1016/j.fcr.2017.07.010.
  • Radanielson, M. et al., Varietal improvement options for higher rice productivity in salt affected areas using crop modelling. Field Crops Res., 2018, 229, 29–36.
  • Wang, W. et al., Responses of rice yield, irrigation water require-ment and water use efficiency to climate change in China: histori-cal simulation and future projections. Agric. Water Manage., 2014, 146, 249–261; ISSN 0378-3774; https://doi.org/10.1016/ j.agwat.2014.08.019.
  • Arora, V. K., Application of a rice growth and water balance model in an irrigated semi-arid subtropical environment. Agric. Water Manage., 2006, 83, 51–57.
  • Luo, Y., Jiang, Y., Peng, S., Cui, Y., Khan, S., Yalong, L. and Weiguang, W., Hindcasting the effects of climate change on rice yields, irrigation requirements, and water productivity. Paddy Water Environ., 2015, 13, 81–89; https://doi.org/10.1007/s10333-013-0409-8.
  • Zhang, D., Wang, H., Li, D., Li, H., Ju, H., Li, R., Batchelor, W. and Li, Y., DSSAT–CERES-wheat model to optimize plant density and nitrogen best management practices. Nut. Cycl. Agro-Ecosyst., 2019, 114, 19–32.
  • Xu, C., Wu, W. and Ge, Q., Impact assessment of climate change on rice yields using the ORYZA model in the Sichuan Basin, Chi-na. Int. J. Climatol., 2018, 38(D18).
  • Lu, B., Kun, Y., Zhiming, W., Jing, W. and Jie, S., Adaptability evaluation of ORYZA (v3) for single-cropped rice under different establishment techniques in eastern China. Agron. J., 2020, 112, 2741–2758.
  • Soundharajan, B. and Sudheer, K. P., Sensitivity analysis and auto-calibration of Oryza2000 using simulation-optimization frame-work. Paddy Water Environ., 2013, 11(1–4), 59–71; doi:10.1007/ s10333-011-0293-z.
  • Cown, R. L., Hammer, G. L., Hargreaves, J. N. G., Holzworth, D. P. and Freebairn, M., APSIM – a novel software system for model development, model testing and simulation in agricultural systems research. Agric. Syst., 1996, 50, 255–271.
  • Van Oosterom E. J., Carberry, P. S., Hargreaves, J. N. G. and Oleary, G. J., Simulating growth development and yield of tillering pearl millet II. Simulation of canopy development. Field Crops Res., 2001, 72(1), 67–91.
  • Akponikpe, P. B. I., Michels, K. and Bielders, C. L., Integrated nutrient management of pearl millet in the Sahel using combined application of cattle manure, crop residues and mineral fertilizer. Exp. Agric., 2008, 46(4), 333–334.
  • Farre, I., Robertson, M. J., Walton, G. H. and Asseng, S., Simulating phenology and yield response of canola to sowing date in western Australia using the APSIM model. Aust. J. Agric. Res., 2002, 53, 1155–1164.
  • Gaydon, D. S. et al., Rice in cropping systems – modelling transi-tions between flooded and non-flooded soil environments. Eur. J. Agron., 2012, 39, 9–24; https://doi.org/10.1016/j.eja.2012.01.003.
  • Gaydon, D. S., Probert, M. E., Buresh, R. J., Meinke, H. and Timsina, J., Modelling the role of algae in rice crop nutrition and soil organic carbon maintenance. Eur. J. Agron., 2012, 39, 35–43; https:// doi.org/10.1016/j.eja.2012.01.004.
  • Keating, B. A. et al., An overview of APSIM, a model designed for farming systems simulate. Eur. J. Agron., 2003, 18, 267– 288.
  • Meinke, H., Rabbinge, R., Hammer, G. L., Van, K. and Jamieson, P. D., Improving wheat simulation capabilities in Australia from a cropping systems perspective. II. Testing simulation capabilities of wheat growth. Eur. J. Agron., 1998, 2, 83–99.
  • Yunusa, I. A. M., Bellotti, W. D., Moore, A. D., Probert, M. E., Baldock, J. A. and Miyan, S. M., An exploratory evaluation of APSIM to simulate growth and yield processes for winter cereals in rotation systems in South Australia. Aust. J. Exp. Agric., 2004, 44, 787–800.
  • Asseng, S., Fillery, I. R. P., Anderson, G. C., Dolling, P. J., Dunin, F. X. and Keating, B. A., Use of the APSIM wheat model to predict yield, drainage and NO3 leaching in a deep sand. Austr. J. Agric. Res., 1998, 49, 363–377.
  • Asseng, S., Van Keulen, H. and Stol, W., Performance and application of the APSIM wheat model in the Netherlands. Eur. J. Agron., 2000, 12(1), 37–54; https://doi.org/10.1016/S1161-0301(99)00044-1.
  • Balwinder Singh, Gaydon, D. S., Humphreys, E. and Eberbach, P. L., The effects of mulch and irrigation management on wheat in Punjab, India – evaluation of APSIM model. Fields Crop Res., 2011, 124, 1–13.
  • Dilla, A., Smethurst, P. J., Barry, K., Parsons, D. and Denboba, M., Potential of the APSIM model to simulate impacts of shading on maize productivity. Agrofor. Syst., 2018, 92(6), 1699–1709.
  • De Silva, S. H. N. P., Takahashi, T. and Okada, K., Evaluation of APSIM-wheat to simulate the response of yield and grain protein content to nitrogen application on an Andosol in Japan. Plant Prod. Sci., 2021; doi:10.1080/1343943X.2021.1883989.
  • Huth, N. I., Thorburn, P. J. and Radford, B. J., Impacts of fertilizers and legumes on N2O and CO2 emissions from soils in subtropical agriculture systems: a simulation study. Agric. Ecosyst. Environ., 2010, 136, 351–357.
  • Aggarwal, P. K. et al., InfoCrop: a dynamic simulation model for the assessment of crop yields, losses due to pests, and environmental impact of agro-ecosystems in tropical environments. II. Perfor-mance of the model. Agric. Syst., 2006, 89, 47–67.
  • Bhatia, A. and Aggarwal, P. K., Simulating greenhouse gas emis-sions from Indian rice fields using the InfoCrop model. Int. Rice Res. Notes, 2007, 32(1), 38–40.
  • Ebrayi, K. N., Pathak, H., Kalra, N., Bhatia, A. and Jain, N., Simula-tion of nitrogen dynamics in soil using InfoCrop model. Environ. Monit. Assess., 2007, 13(1), 451–465.
  • Bandyopadhyay, K. K., Chopra, U. K., Pradhan, S., Krishnan, P. and Ranjan, R., Simulation of grain yield, seasonal evapotranspiration, global warming potential and yield gap analysis of wheat under varied water and nitrogen management practices using InfoCrop model. Agric. Res., 2020, 9(2), 277–290.
  • Boomiraj, K., Chakrabarti, B., Aggarwal, P. K., Choudhary, R. and Chander, S., Assessing the vulnerability of Indian mustard to climate change. Agric. Ecosyst. Environ., 2010, 138, 265–273.
  • Dubey, R., Pathaka, H., Chakrabarti, B., Singh, S., Gupta, D. K. and Hari. R. C., Impact of terminal heat stress on wheat yield in India and options for adaptation. Agricul. Syst., 2020, 181, 102826.
  • Fagodiya, R. K., Pathak, H., Bhatia, A., Kumar, A., Singh, S. D., Jain, N. and Harith, R., Simulation of maize (Zea mays L.) yield under alternative nitrogen fertilization using InfoCrop-maize model. Biochem. Cell. Arch., 2017, 17(1), 65–71.
  • Akula, B. and Sheikh, A. M., Field calibration and evaluation of crop simulation model InfoCrop to estimate wheat yields. J. Agro-meterol., 2005, 7(2), 199–207.
  • Tarun, A., Chakravarty, N. V. K. and Saxena, R., Growth and yield prediction in mustard using InfoCrop simulation model. J. Agrometeorol., 2009, 11(2), 156–161.
  • Krishnan, P., Swain, D. K., Chandra Bhaskar, B., Nayak, S. K. and Dash, R. N., Impact of elevated CO2 and temperature on rice yield and methods of adaptation as evaluated by crop simulation studies. Agric. Ecosyst. Environ., 2007, 122, 233–242.
  • Choudharay, D., Patel, H. R. and Pandey, V., Evaluation of adap-tation strategies under A2 climate change scenario using InfoCrop model for kharif maize in middle Gujarat region. J. Agrometeorol., 2015, 17(1), 98–101.
  • Srivastava, A., Naresh Kumar, S. and Aggarwal, P. K., Assessment on vulnerability of sorghum to climate change in India. Agric. Eco-syst. Environ., 2010, 138, 160–169.
  • Hebbar, K. B., Venugopalan, M. V., Prakash, A. H. and Aggarwal, P. K., Simulating the impacts of climate change on cotton production in India. Climatic Change, 2013, 118, 701–713.
  • Geerts, S. et al., Simulating yield response to water of quinoa (Chenopodium quinoa Willd.) with FAO-AquaCrop. Agron. J., 2009, 101, 499–508.
  • Kumar, P., Sarangi, A., Singh, D. K. and Parihar, S. S., Evaluation of AquaCrop model in predicting wheat yield and water producti-vity under irrigated saline regimes. Irrig. Drain., 2014, 63, 474–487; doi:https://doi.org/10.1002/ird.1841.
  • Gebreselassie, Y., Mekonen, A. and Kassa, T., Field experimenta-tion-based simulation of yield response of maize crop to deficit irriga-tion using AquaCrop model, Arba Minch, Ethiopia. Afr. J. Agric. Res., 2015, 10(4), 269–280.
  • Heng, L. K., Hsiao, T. C., Evett, S., Howell, T. and Steduto, P., Validating the FAO AquaCrop model for irrigated and water defi-cient field maize. Agron. J., 2009, 101, 488–498.
  • Gallardo, H. F., Waldo, O. B., Hector, F. M., Ernesto, S. I. and Enrique, M. S., Simulation of corn (Zea mays L.) yield in northern Sinoloa using the AquaCrop model. Agron. J., 2013, 47(4), 347–359.
  • Abedinpour, M. and Sarangi, A., Deficit irrigation and nitrogen effects on maize growth in semi-arid environment. World Appl. Sci. J., 2013, 21(11), 1687–1692.
  • Ahmed, M. S., Marwa, G. M. and Gamal, A. El-Sanat., Evaluating AquaCrop model to improve crop water productivity on North Delta soils, Egypt. Adv. Appl. Sci. Res., 2014, 5(5), 293–304.
  • Van, H. et al., A semi-quantitative approach for modelling crop response to soil fertility: evaluation of the AquaCrop procedure. J. Agric. Sci., 2014, 5, 25–32.
  • Andarzian, B. M., Bannayan, P., Steduto, H., Mazraeh, M. E., Barati, A. and Rahnarna, M., Validation and testing of the Aqua-Crop model under full and deficit irrigated model for Canola. Agron. J., 2011, 103, 1610–1618.
  • Ngetich, K. F., Raes, D., Shisanya, C. A., Mugwe, J., Mucheru, M., Mugendi, D. N. and Diels, J., Calibration and validation of Aqua-Crop model for maize in sub-humid and semiarid regions of cen-tral highlands of Kenya. In Third RUFORUM Biennial Meeting, Entebbe, Uganda, 24–28 September 2012.
  • Stricevic, R., Cosic, M., Djurovic, N., Pejic, B. and Maksimovic, L., Assessment of the FAO AquaCrop model in the simulation of rainfed and supplementary irrigated maize, sugarbeet and sun-flower. Agric. Water Manage., 2011, 98, 1615–1621.
  • Steduto, P., Hsiao, T., Raes, C. D. and Fereres, E., AquaCrop – the FAO crop model to simulate yield response to water: I. Con-cepts and underlying principles. Agron. J., 2009, 101, 426–437.
  • Heidariniya, M., Naseri, A. A., Boroumandnasab, S., Moshkabadi, B. S. and Nasrolahi, A. H., Evaluation of AquaCrop model application in irrigation management of cotton. World Rural Obs., 2012, 4, 55–59.
  • Sethi, R. R. et al., Simulating paddy crop response to irrigation us-ing FAO AquaCrop model: a case study. J. Food, Agric. Environ., 2016, 2, 99–103.
  • Farai, M. S., Michael, M., Talent, M. and David, C., Prediction of yield and biomass productions: a remedy to climate change in semiarid regions of Zimbabwe. Int. J. Adv. Agric. Res., 2013, 1, 14–21.
  • Allen, R. G. et al., A recommendation on standardized surface re-sistance for hourly calculation of reference ET0 by the FAO 56 Penman–Monteith method. Agric. Water Manage., 2006, 81, 1–22.
  • Cai, J., Liu, Y., Lei, T. and Pereira, L. S., Estimating reference evapotranspiration with the FAO Penman–Monteith equation us-ing daily weather forecast messages. Agric. For. Meteorol., 2007, 145(1), 22–35.
  • Lopez-Urreaa, R., Montoroa, A., Manasa, F., Lopez-Fustera, P. and Fereres, E., Evapotranspiration and crop coefficients from lysi-meter measurements of mature Tempranillo wine grapes. Agric. Water Manage., 2012, 112, 13–20.
  • Smith, M., CROPWAT – a computer program for irrigation plan-ning and management. FAO Irrigation and Drainage Paper 52, FAO, Rome, Italy, 1992, p. 46.
  • George, B., Shende, S. and Raghuwanshi, N., Development and testing of an irrigation scheduling model. Agric. Water Manage., 2000, 46(2), 121–136.
  • Anadranistakis, M., Liakatas, A., Kerkides, P. and Rizos, S., Crop water requirements model tested for crops grown in Greece. Agric. Water Manage., 2000, 45(3), 297–316.
  • Sheng-Feng, K., Shin-Shen, H. and Chen-Wuing, L., Estimation of irrigation water requirements with derived crop coefficients for upland and paddy crops in Chia Nan Irrigation Association, Tai-wan. Agric. Water Manage., 1998, 82(6), 433–451.
  • Wahaj, R., Marauxet, F. and Munoz, G., Actual crop water use in project countries: a synthesis at the regional level. The GEF funded project: Climate Change Impacts on and Adaptation of Agroeco-logical Systems in Africa, Africa, 2007, pp. 1–50.
  • Kang, S., Payne, W. A., Evett, S. R., Stewart, B. A. and Robinson, C. A., Simulation of winter wheat evapotranspiration in Texas and Henan using three models of differing complexity. Agric. Water Manage., 2009, 96, 167–178.
  • Nazeer, M., Simulation of maize crop under irrigated and rainfed conditions with CROPWAT model. J. Agric. Biol. Sci., 2009, 4(2), 68–73.
  • Mimi, Z. A. and Jamous, S. A., Climate change and agricultural water demand impacts and adaptations. Afr. J. Environ. Sci. Technol., 2010, 4(4), 183–191.
  • Stancalie, G., Marica, A. and Toulios, L., Using earth observation data and CROPWAT model to estimate the actual crop evapotran-spiration. Phys. Chem. Earth, 2010, 35, 25–30.
  • Mhashu, S. V., Yield response to water function and simulation of deficit irrigation scheduling of sugarcane estate in Zimbabwe using CROPWAT 8.0 and CLIMWAT 2.0. Master’s thesis, University of Florence, Faculty of Agriculture, Italy, 2007.
  • Smith, M. and Kivumbi, D., Calculation procedure use of the FAO CROPWAT model in deficit irrigation studies. FAO, Rome, Italy, 2006.
  • FAO, CROPWAT software. Food and Agriculture Organization, Land and Water Division, Rome, Italy, 2009; http://www.fao.org/ nr/water/infores_databases_cropwat.
  • Ganesh Babu, R., Veeranna, J., Raja Kumar, K. N. and Bhaskara Rao, I., Estimation of water requirement for different crops using CROPWAT model in Anantapur region. Asian J. Environ. Sci., 2014, 9(2), 75–79.
  • Srinivasulu, A., Satyanarayana, T. V., Ravi Kumar, M. and Sai Sudha, J. L. N., Crop water requirement in comparison to actual applied in some canal commands of Krishna Western Delta. J. Agric. Eng., 2003, 40(4), 43–50.
  • Khandelwal, M. K., Gupta, S. K. and Tyagi, N. K., Mismatch between canal water supply and demand in Ukai–Kakrapar irrigation. Water-logging and Soil Salinity in Ukai-Kakrapar Command-Causes and Remedial Measures, Walmi (Anand), India, 1996.
  • Nivesh, S., Kashyap, P. S. and Saran, B., Irrigation water require-ment modelling using CROPWAT model: Balangir district, Odisha. Pharma Innov. J., 2019, 8(12), 185–188.
  • Chowdhury, S., Al-Zahrani, M. and Abbas, A., Implications of climate change on crop water requirements in arid region: an ex-ample of Al-Jouf, Saudi Arabia. J. King Saud Univ. – Eng. Sci., 2016, 28, 21–31.
  • Lal, M., Singh, K. K., Rathore, L. S., Srinivasan, G. and Saseen-dran, S. A., Vulnerability of rice and wheat yields in NW India to future changes in climate. Agric. For. Meteorol., 1998, 89, 1–13.
  • Salam, H. E., Salwan, A. A. and Nadhir, Al-Ansari, Crop water requirements and irrigation schedules for some major crops in Southern Iraq. Water, 2019, 11, 756; doi:10.3390/w11040756.
  • Surendran, U., Sushanth, C. M., Mammen, G. and Joseph, E. J., Modelling the crop water requirement using FAO-CROPWAT and assessment of water resources for sustainable water resource man-agement: a case study in Palakkad district of humid tropical Kerala, India. Aquat. Procedia, 2015, 4, 1211–1219.
  • Surendran, U., Sushanth, C. M., Mammen, G. and Joseph, E. J., FAO-CROPWAT model-based estimation of crop water need and appraisal of water resources for sustainable water resource man-agement: pilot study for Kollam district – humid tropical region of Kerala, India. Curr. Sci., 2017, 112(1) 76–86.
  • Ayushi Trivedi, S. K., Pyasi, S. K. and Galkate, R. V., Estimation of evapotranspiration using CROPWAT 8.0 model for Shipra River Basin in Madhya Pradesh. Int. J. Curr. Microbiol. Appl. Sci., 2018, 7(5), 1248–1259.
  • Ravishankar, Pandey, D., Sinha, J., Sahu, G. S. and Singh, K. K., Calibration and validation of the canal simulation model: a case study on Nawagarh distributary of Janjgir Branch Canal, district Janjgir-Champa (Chhattisgarh, India). J. Pharmacogn. Phyto-chem., 2018, 7, 6–13.
  • Jyotsna, R. K., Crop water requirement and irrigation scheduling of some selected crops using CROPWAT 8.0. A case study of Khadakwasla dam Irrigation project. Int. J. Civ. Eng. Technol., 2017, 8(5), 342–349.
  • Bhat, S. A., Pandit, B. A., Khan, J. N., Kumar, R. and Jan, R., Water requirements and irrigation scheduling of maize crop using CROPWAT model. Int. J. Curr. Microbiol. Appl. Sci., 2017, 6(11), 23–26.
  • Abirdew, S., Mamo, G. and Mengesha, M., Determination of crop water requirements for maize in Abshege Woreda, Gurage Zone, Ethiopia. J. Earth Sci. Climatic Change, 2018, 9, 1.
  • Suryadi, E., Ruswandi, D., Dwiratna, S. and Boy Macklin Pareira Prawiranegara, Crop water requirements analysis using Cropwat 8.0 software in maize intercropping with rice and soybean. Int. J. Adv. Sci. Eng. Inf. Technol., 2019, 9(4).
  • Saif Ud Din, Al-Rumikhani, Y. A. and Sajid Latif, M., Use of re-mote sensing and agrometeorology for irrigation management in arid lands: a case study from Northwestern Saudi Arabia. J. Envi-ron. Hydrol., 2004, 12(9), 14.
  • Vozhehov, R. A., Lavrynenko, Y. O., Kokovikhin, S. V., Lykhovyd, P. V., Biliaieva, I. M., Drobitko, A. V. and Nesterchuk, V. V., Assessment of the CROPWAT 8.0 software reliability for evapotranspiration and crop water requirements calculations. J. Water Land Dev., 2018, 39, 147–152.
  • Bouraima, A. K., Weihua, Z. and Chaofu, W., Irrigation water re-quirements of rice using CROPWAT model in northern Benin. Int. J. Agric. Biol. Eng., 2015, 8(2), 58–64.
  • Song, L., Oeurng, C. and Hornbuckle, J., Assessment of rice water requirement by using CROPWAT model. In The 15th Science Council of Asia Board Meeting and International Symposium, 2015.
  • Rose, N., Sankaranarayanan, Pande, S. K. and Das, D., Application of FAO-CROPWAT software for modelling irrigation schedule of rice in Rwanda. Rwanda J. Agric. Sci., 2019, 1(1), 7–13.
  • Kumari, S., Irrigation scheduling using CROPWAT. International Conference Proceeding of ICCCT, International Conference on Communication and Computational Technologies, December 2017.
  • Mehanuddin, H., Nikhitha, G. R., Prapthishree, K. S., Praveen, L. B. and Manasa, H. G., Study on water requirement of selected crops and irrigation scheduling using CROPWAT 8.0. Int. J. Innov. Res. Sci., Eng. Technol., 2018, 7(4), 10–14.
  • Navatha, N., Roja, M. and Umareddy, R., Estimation of crop water requirement of maize and cotton using FAO CROPWAT 8.0 model in Jagtial district. Int. J. Ecol. Environ. Sci., 2020, 2(4), 718–724.
  • Roja, M., Deepthi, C. H. and Devender Reddy, M., Estimation of crop water requirement of sunflower crop using FAO CROPWAT 8.0 model for north coastal Andhra Pradesh. Agro-Econ.: Int. J., 2021, 7(2), 13–18.
  • Saravanan, K. and Saravanan, R., Determination of water require-ments of main crops in the tank irrigation command area using CROPWAT 8.0. Int. J. Interdiscip. Multidiscip. Stud., 2014, 1(5), 266–272.
  • Roja, M., Navatha, N., Devender Reddy, M. and Deepthi, Ch., Estima-tion of crop water requirement of groundnut crop using FAO CROPWAT 8.0 model. Agro Econ. – Int. J., 2020, 7(2), 35–40.
  • Banerjee, S., Chatterjee, S., Sarkar, S. and Jena, S., Projecting fu-ture crop evapotranspiration and irrigation requirement of potato in lower Gangetic Plains of India using the CROPWAT 8.0 model. Eur. Potato J., 2016, 59(4); doi:10.1007/s11540-016-9327-7.
  • Nithya, K. B. and Shivapur, A. V., Study on water requirement of selected crops under the Tarikere command area using CROPWAT. Irrig. Drain. Syst. Eng., 2016, 5(1), 1000153; http://dx.doi.org/ 10.4172/2168-9768.1000153.
  • Onyancha, D. M., Gachene, C. K. K. and Kironchi, G., FAO CROPWAT model-based estimation of the crop water requirement of major crops in Mwala, Machakos county. Res. J. Ecol., 2017, 4(2), 1–11.

Abstract Views: 396

PDF Views: 163




  • Crop modelling in agricultural crops

Abstract Views: 396  |  PDF Views: 163

Authors

M. Roja
Centurion University of Technology and Management, Paralakhemundi 761 211, India; International Crops Research Institute for the Semi-Arid Tropics, Patancheru 502 324, India, India
M. K. Gumma
International Crops Research Institute for the Semi-Arid Tropics, Patancheru 502 324, India, India
M. D. Reddy
Centurion University of Technology and Management, Paralakhemundi 761 211, India, India

Abstract


With limited land resources and a growing population, agricultural output is under considerable strain. New technology is necessary for overcoming these issues and advising farmers, legislators and other decision-makers on adopting sustainable agriculture despite global climate variations. This has led to the crop simulation models that illustrate crop growth and development processes as a function of climate, soil and crop management. They also support agricultural agronomy (yield estimate, biomass, etc.), pest control, breeding and natural resource management. This study examines crop modelling for agricultural production planning and field-level management strategies. These can help researchers comprehend the significance of crop modelling for scenario-building and provide field-level suggestions by analysing future conditions and strategic activities to minimize the predicted negative influence and maximize the projected positive effect. The limitations and potential directions of crop modelling improvement have also been highlighted in this study

Keywords


Climate change, crop models, management strategies, sustainable agriculture, yield estimation.

References





DOI: https://doi.org/10.18520/cs%2Fv124%2Fi8%2F910-920