Skip to main navigation menu Skip to main content Skip to site footer
Plant Science Today (PST)

Research Articles

Vol. 13 No. sp2 (2026): Recent Advances in Agriculture

Decision support system for agrotechnology transfer-crop environment resource synthesis model for developing genetic coefficients to simulate late-sown wheat crop for Indo-Gangetic Plain, India

DOI
https://doi.org/10.14719/pst.12520
Submitted
31 October 2025
Published
14-05-2026

Abstract

The Indo-Gangetic Plains (IGP) account for nearly 75 % of India’s wheat production; however, more than 40 % of farmers in the region practice delayed sowing of wheat, resulting in substantial yield reductions. The research aim were to  develop the genetic coefficient parameters and calibrate the model under late sown wheat growing region of IGP, in India. Decision support system for agrotechnology transfer (DSSAT) is a comprehensive software platform for the crop environment resource synthesis (CERES) wheat model. The CERES wheat model has been simulating crop growth and development in response to climate, soil, genotypes and management at various temporal and spatial scales. The required files of the model was prepared using the input data such as daily weather data, soil characteristics, crop management, crop phenology and productivity. The genetic coefficient codes were developed through the trail and error approach. The CERES wheat model was calibrated with two years data (i.e., 2008–2009 and 2009–2010) and validated through a separate data of 7 years long-term field experimental trail from 2011–12 to 2017–18. The model’s performance was evaluated using difference statistical indices i.e., root mean squared error (RMSE), normalised root mean squared error (NRMSE), mean absolute error (MAE) and D-index. Crop phenology (appearance of anthesis days and physiological maturity days) and productivity variables (grain yield and straw yield) were simulated using the calibrated model. The model effectively simulated trends in phenological stages namely appearance of anthesis days, physiological maturity, grain yield and straw yield for wheat cultivars i.e. HUW 243, HUW 510, Kundan and PBW 373. The values across all variables were indicated excellent agreement between simulated and observed data with deviations of less than 5 %, except for straw yield 9.15 %. The successfully calibrated model and the developed genetic coefficients provide a reliable tool for assessing the impact of environmental factors on late-sown wheat production in the region.

References

  1. 1. Worldometer. Countries in the world by population (2025) [Internet]. 2025 [cited 2026 Apr 22]. https://www.worldometers.info/world-population/population-by-country/
  2. 2. Office of the Registrar General and Census Commissioner, India. Census of India 2011: Provisional Population Totals. Ministry of Home Affairs, Government of India; 2011. http://censusindia.gov.in
  3. 3. NITI Aayog. Working Group Report on Crop Husbandry, Agriculture Inputs, Demand & Supply 2024. Government of India; 2024.
  4. 4. Singh RS, Patel C, Yadav MK, Singh KK. Yield forecasting of rice and wheat crops for eastern Uttar Pradesh. J Agrometeorol. 2014;16(2):199–202.
  5. 5. Erenstein O. Resource scarcity gradients in the post-Green Revolution Indo-Gangetic Plains: Implications for agricultural technology use and supply; 2009. https://ageconsearch.umn.edu/record/51569
  6. 6. Jha RK, Sattar A, Singh AK, Kundu MS, Tiwari RK, Singh AK, et al. Managing climatic risks in rice–wheat cropping system for enhanced productivity in middle Gangetic plains of India. Front Sustain Food Syst. 2023;7:1259528. https://doi.org/10.3389/fsufs.2023.1259528
  7. 7. Ingle SN, Mondal BP, Patel C, Prasad J. Leveraging remote sensing and geospatial technologies in soil and water resources mapping and characterization: A review. Agric Rev. 2025;46(5):703–11. https://doi.org/10.18805/ag. R-2789
  8. 8. Yadav MK, Patel C, Singh RS, Singh KK, Balasubramanian R, Mall RK, et al. Assessment of climate change impact on different pigeonpea maturity groups in North Indian conditions. J Agrometeorol. 2021;23(1):82–92.
  9. 9. Sattar A, Nanda G, Singh G, Jha RK, Bal SK. Responses of phenology, yield attributes and yield of wheat varieties under different sowing times in Indo-Gangetic Plains. Front Plant Sci. 2023;14:1224334. https://doi.org/10.3389/fpls.2023.1224334
  10. 10. Newport D, Lobell DB, Srivastava AK, Rao P, Umashaanker M, Malik RK, et al. Factors constraining timely sowing of wheat as an adaptation to climate change in Eastern India. Weather Clim Soc. 2020;12(3):515–28. https://doi.org/10.1175/WCAS-D-19-0122.1
  11. 11. Jat LK, Singh SK, Latare AM, Singh RS, Patel CB. Effect of dates of sowing and fertilizer on growth and yield of wheat (Triticum aestivum) in an Inceptisol of Varanasi. Indian J Agron. 2013;58(4):611–14.
  12. 12. Intergovernmental Panel on Climate Change (IPCC). Sixth Assessment Report (AR6). 2022. https://www.ipcc.ch/assessment-report/ar6/
  13. 13. Yadav MK, Singh RS, Singh KK, Mall RK, Patel CB, Yadav SK, et al. Assessment of climate change impact on productivity of different cereal crops in Varanasi, India. J Agrometeorol. 2015;17(2):179–84.
  14. 14. Patel C, Nema AK, Singh RS, Yadav MK, Singh KK, Singh SK, et al. Assessment of climate change impact on wheat crop using MarkSim GCM in Varanasi, Uttar Pradesh. J Agrometeorol. 2018;20(3):216–18.
  15. 15. Hickinbotham EJ, Ridley FA, Rushton SP, Pattison Z. 30 years of climate-related phenological research: Themes and trends. Int J Biometeorol. 2025;69(6):1459–73. https://doi.org/10.1007/s00484-025-02903-w
  16. 16. Patel C, Nema AK, Singh RS, Yadav MK, Singh SK, Singh M. Evaluation of DSSAT-CERES model for irrigation scheduling of wheat crop in Varanasi region of Uttar Pradesh. J Agrometeorol. 2017;19(2):120–24. https://doi.org/10.54386/jam.v19i2.683
  17. 17. Bisht H, Shaloo KB, Rajput J, Singh DK, Vishnoi L, Singh RN, et al. Impacts of climate change on phenology, yield and water productivity of wheat in a semi-arid region of India using the CERES-Wheat model. J Water Clim Change. 2024;15(10):5089. https://doi.org/10.2166/wcc.2024.139
  18. 18. Ech-chatir L, Er-Raki S, Rodriguez JC, Meddich A, Chehbouni A. Optimizing sowing date, fertilization and irrigation strategies for winter wheat in Tensift Al Haouz (Morocco) using the DSSAT-CERES-Wheat model. Agric Water Manag. 2025;312:109443. https://doi.org/10.1016/j.agwat.2025.109443
  19. 19. Si Z, Zain M, Li S, Liu J, Liang Y, Gao Y, et al. Optimizing nitrogen application for drip-irrigated winter wheat using the DSSAT-CERES-Wheat model. Agric Water Manag. 2021;244:106592. https://doi.org/10.1016/j.agwat.2020.106592
  20. 20. Singh RS, Singh KK, Mall RK, Yadav MK, Gupta JP. Climate of Varanasi. Agro-Meteorological Field Unit (AMFU), Department of Geophysics, Banaras Hindu University; 2012. p. 1–78.
  21. 21. Kumar V, Singh SP, Raha P. Organic sources, use of amino acid-based biostimulants and irrigation schedule on yield: Water use efficiency relationship on potato tuber. J Pharmacogn Phytochem. 2018;7(1):1255–259.
  22. 22. Loague K, Green RE. Statistical and graphical methods for evaluating solute transport models: Overview and application. J Contam Hydrol. 1991;7(1–2):51–73.
  23. 23. Ishaq RAF, Zhou G, Jing G, Shah SRA, Ali A, Imran M, et al. Geospatial robust wheat yield prediction using machine learning and integrated crop growth model and time-series satellite data. Remote Sens. 2025;17(7):1140. https://doi.org/10.3390/rs17071140
  24. 24. Yang JM, Yang JY, Liu S, Hoogenboom G. An evaluation of the statistical methods for testing the performance of crop models with observed data. Agric Syst. 2014;127:81–89.
  25. 25. Priesack E, Gayler S, Hartmann HP. The impact of crop growth sub-model choice on simulated water and nitrogen balances. Nutr Cycl Agroecosyst. 2006;75:1–13. https://doi.org/10.1007/s10705-006-9006-1
  26. 26. Zhao Q, Huo F, Li Y, Li Z, Wu L. Performance of an integrated modeling framework for spring wheat yield simulation in Saskatchewan, Canada. Agric For Meteorol. 2025;372:110700. https://doi.org/10.1016/j.agrformet.2025.110700
  27. 27. Willmott CJ. On the validation of models. Phys Geogr. 1981;2(2):184–94. https://doi.org/10.1080/02723646.1981.10642213
  28. 28. Hoogenboom G, Porter CH, Boote KJ, Shelia V, Wilkens PW, Singh U, et al. The DSSAT crop modeling ecosystem. In: Boote KJ, editor. Adv Crop Model Sustain Agric. Boca Raton: CRC Press; 2019. p. 173–216. https://www.taylorfrancis.com/books/edit/10.1201/9780429266591
  29. 29. Ritchie JT, Otter S. Description and performance of CERES-Wheat: A user-oriented wheat yield model. In: W.O. Willis, ARS Wheat Yield Project. Springfield (VA): National Technical Information Service; 1985. p. 159–75. https://cir.nii.ac.jp/crid/1573387449661175552
  30. 30. Jones JW, Hoogenboom G, Porter CH, Boote KJ, Batchelor WD, Hunt LA, et al. The DSSAT cropping system model. Eur J Agron. 2003;18(3–4):235–65. https://doi.org/10.1016/S1161-0301(02)00107-7
  31. 31. Racsko P, Szeidl L, Semenov M. A serial approach to local stochastic weather models. Ecol Model. 1991;57(1–2):27–41.
  32. 32. Hunt R, Hand DW, Hannah MA, Neal AM. Further responses to CO₂ enrichment in British herbaceous species. Funct Ecol. 1993;7(5):661–68. https://doi.org/10.2307/2390186
  33. 33. Otter S, Ritchie JT. Validation of the CERES-wheat model in diverse environments. In: Day W, Atkin RK, editors. Wheat Growth and Modelling. Boston (MA): Springer; 1985. p. 307–10. https://doi.org/10.1007/978-1-4899-3665-3_28
  34. 34. Parthasarathi T, Velu G, Jeyakumar P. Impact of crop heat units on growth and developmental physiology of future crop production: A review. Res Rev J Crop Sci Technol. 2013;2(1):1–11.
  35. 35. Klepeckas M, Januškaitienė I, Vagusevičienė I, Juknys R. Effects of different sowing time to phenology and yield of winter wheat. Agric Food Sci. 2020;29(4):346–58. https://doi.org/10.23986/afsci.90013
  36. 36. Liu H, Pequeno DN, Hernández-Ochoa IM, Krupnik TJ, Sonder K, Xiong W, et al. A consistent calibration across three wheat models to simulate wheat yield and phenology in China. Ecological Modelling. 2020;430:109132. https://doi.org/10.1016/j.ecolmodel.2020.109132
  37. 37. Bregaglio S, Ginaldi F, Raparelli E, Fila G, Bajocco S. Improving crop yield prediction accuracy by embedding phenological heterogeneity into model parameter sets. Agric Syst. 2023;209:103666. https://doi.org/10.1016/j.agsy.2023.103666
  38. 38. Asseng S, Ewert F, Martre P, Rötter RP, Lobell DB, Cammarano D. et al. Rising temperatures reduce global wheat production. Nat Clim Chang. 2015;5(2):143–4
  39. 39. Pei J, Tan S, Zou Y, Liao C, He Y, Wang J, et al. The role of phenology in crop yield prediction: Comparison of ground-based phenology and remotely sensed phenology. Agric For Meteorol. 2025;361:110340. https://doi.org/10.1016/j.agrformet.2024.110340
  40. 40. Chen S, He L, Cao Y, Wang R, Wu L, Wang Z, et al. Comparisons among four different upscaling strategies for cultivar genetic parameters in rainfed spring wheat phenology simulations with the DSSAT-CERES-Wheat model. Agric Water Manag. 2021;258:107181. https://doi.org/10.1016/j.agwat.2021.107181
  41. 41. Zheng Z, Cai H, Wang Z, Wang X. Simulation of climate change impacts on phenology and production of winter wheat in northwestern China using CERES-Wheat model. Atmosphere. 2020;11(7):681. https://doi.org/10.3390/atmos11070681
  42. 42. Singh J, Biswas B, Dhaliwal LK, Poddar R, Gharib AF, Raafat BM, et al. Performance evaluation of the DSSAT-CERES-wheat and WOFOST-wheat models under various agroclimatic conditions in northwest India. Theor Appl Climatol. 2025;156:250. https://doi.org/10.1007/s00704-025-05476-1
  43. 43. Jahan MAHS, Sen R, Ishtiaque S, Choudhury AK, Akhter S, Ahmed F, et al. Optimizing sowing window for wheat cultivation in Bangladesh using CERES-wheat crop simulation model. Agric Ecosyst Environ. 2018;258:23–29. https://doi.org/10.1016/j.agee.2018.02.008
  44. 44. Hussain T, Anothai J, Nualsri C, Ata-Ul-Karim ST, Duangpan S, Hussain N, et al. Assessment of CSM–CERES–rice as a decision support tool in the identification of high-yielding drought-tolerant upland rice genotypes. Agronomy. 2023;13(2):432. https://doi.org/10.3390/plants12213685
  45. 45. Srikanth K, Umamahesh NV. Assessing climate change impacts on rice yield under compound extreme events using DSSAT-CERES-Rice. Theor Appl Climatol. 2025;156(7):361. https://doi.org/10.1007/s00704-025-05599-5
  46. 46. Timsina J, Humphreys E. Performance of CERES-rice and CERES-wheat models in rice–wheat systems: A review. Agric Syst. 2006;90(1–3):5–31. https://doi.org/10.1016/j.agsy.2005.11.007
  47. 47. Ma J, Wang Y, Liu L, Cui B, Ding Y, Zhao Y. Research on the optimal water and fertilizer scheme for maize in a typical hydrological year based on the DSSAT model. Agronomy. 2025;15(5):1085. https://doi.org/10.3390/agronomy15051085
  48. 48. Hati KM, Biswas AK, Somasundaram J, Mohanty M, Singh RK, Sinha NK, et al. Soil organic carbon dynamics and carbon sequestration under conservation tillage in tropical vertisols. In: Ghosh PK, Mahanta SK, Mandal D, Mandal B, Ramakrishnan S, editors. Carbon management in tropical and sub-tropical terrestrial systems. Singapore: Springer; 2019. p. 201–12. https://doi.org/10.1007/978-981-13-9628-1_12
  49. 49. Zhao X, Virk AL, Ma ST, Kan ZR, Qi JY, Pu C, et al. Dynamics in soil organic carbon of wheat–maize dominant cropping system in the North China Plain under tillage and residue management. J Environ Manag. 2020;265:110549. https://doi.org/10.1016/j.jenvman.2020.110549

Downloads

Download data is not yet available.