Applicability of medium range weather forecasts for yield prediction of wheat (Triticum aestivum) using CERES-wheat model in south-western Punjab
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Keywords:
CERES-Wheat model, Phenology, Sowing dates, Varieties, Wheat yieldAbstract
The study was carried out during the winter (rabi) season of 2021–22 at Punjab Agricultural University, Regional Research Station, Bathinda, Punjab to evaluate the predictability of wheat (Triticum aestivum L.) production using forecast scenarios that were gathered from the India Meteorological Department (IMD). CERES-wheat model was used to estimate crop phenology and wheat yield. The experiment was laid out in a split-plot design (SPD) with three replications. The main plot treatments included five sowing dates, viz. October 25, November 4, November 14, November 24 and December 4 with four sub-plot treatments of variety which were HD 3086, PBW 725, HD 2967 and PBW 658. The model’s output with R2/RMSE for emergence, anthesis and maturity by using actual and forecasted data having 0.38/3.37 and 0.48/2.63, 0.56/4.28 and 0.70/8.48 and 0.71/8.30 and 0.49/2.63, respectively. Moreover, the wheat yield with R2/RMSE values of 0.85/148.31 kg/ha for the actual data and 0.86/140.50 kg/ha for the simulated data showed good agreement between simulated and observed data. Model validation showed that simulated emergence, anthesis and maturity were deviated over observed by 1–3 days, 1–8 days, and 1–20 days, respectively, whereas anthesis, maturity and yield were overestimated. Additionally, the simulated wheat yield differed from the observed yield by 0.5–12%. Phenology and yield were found to have greater RMSE values, wider deviations between simulated and actual values and less connection with delayed sowing. For the wheat growing season (2013–21), rainfall, Tmax and Tmin weather forecast were employed, which were to assess the likelihood of wheat production at various sowing periods. The medium-range weather forecast and the actual weather data closely matched each other for wheat phenology and yield. The annual fluctuation in observed wheat yields as well as treatment-wise variations was more or less effectively reflected by the daily medium-range weather forecast data. The findings of the study are extremely valuable for directing decisions in the study area, figuring out the best time to sow wheat crop, choosing appropriate wheat varieties based on predicted conditions, scheduling irrigation at critical growth stages and applying fertiliser optimally to increase crop productivity and resource efficiency.
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References
Ali S A, Tedone L and De Mastro G. 2017. Climate variability impact on wheat production in Europe: Adaptation and mitigation strategies. (In) Quantification of Climate Variability, Adaptation and Mitigation for Agricultural Sustainability, pp. 251–321.
Andarzian B, Hoogenboom G, Bannayan M, Shirali M and Andarzian B. 2015. Determining optimum sowing date of wheat using CSM-CERES-Wheat model. Journal of the Saudi Society of Agricultural Sciences 14(2): 189–99.
Anonymous. 2022. Package of Practices for Kharif Crops, pp. 37–53. Punjab Agricultural University, Ludhiana, India.
Arshad M N, Ahmad A, Wajid S A, Jehanzeb M, Cheema M and Schwartz M W. 2017. Adapting DSSAT model for simulation of cotton yield for nitrogen levels and planting dates. Agronomy Journal 6(1): 2639–48.
Basso B, Liu L and Ritchie J T. 2016. A comprehensive review of the CERES-wheat,-maize and-rice models’ performances. Advances in Agronomy 136: 27–132.
Bisht H, Kumar B, Singh D K and Mishra A K. 2023. Sensitivity analysis of wheat cultivar HD2967 to weather parameters using CERES-wheat model. Journal of Agricultural Science and Technology 25(3): 661–72.
Boote K J, Jones J W and Hoogenboom G. 2018. Simulation of crop growth: CROPGRO model. (In) Agricultural Systems Modelling and Simulation, pp. 651–92.
Dhir A, Pal R K, Kingra P K and Kaur R. 2024. Climate-smart cotton (Gossypium herbaceum) crop production in Punjab: A comprehensive review of sustainable management practices. The Indian Journal of Agricultural Sciences 94(2): 119–28.
Dhir A, Pal R K, Kingra P K, Mishra S K and Sandhu S S. 2021. Cotton phenology and production response to sowing time, row orientation and plant spacing using CROPGRO-cotton model. Mausam 72(3): 627–34.
Endalew A A. 2019. Calibration and validation of CERES-wheat in DSSAT model for yield simulation under future climate in Adet, north-western Ethiopia. African Journal of Agricultural Research 14(8): 509–18.
Goswami P and Dutta G. 2020. Evaluation of DSSAT model (CERES rice) on rice production: A review. International Journal of Chemical Studies 8(5): 404–09.
Grover K S and Pal R K. 2018. Simulating the effect of sowing dates and different irrigation levels on wheat cultivars using DSSAT-CSM-CERES wheat model. Journal of Pharmacognosy and Phytochemistry 7(5): 1934–38.
Hoogenboom G, Jones J W, Wilkens P W, Porter C H, Boote K J, Hunt L A, Singh U, Lizaso J L, White J W, Gijsman A J and Tsuji G Y. 2010. Decision Support System for Agrotechnology Transfer (DSSAT) Version 4.5, University of Hawaii, Honolulu, Hawaii (CD ROM).
Kaur P, Sandhu S S, Kothiyal S and Kaur J. 2022. Determination of sowing window for wheat in Punjab, India using sensitized, calibrated and validated CERES-wheat model. Journal of Agricultural Physics 22(2): 215–27.
Kingra P K. 2016. Climate variability impacts on wheat productivity in central Punjab. Journal of Agrometeorology 18(1): 97–99.
Kumar M, Pannu R K, Singh R, Singh B, Dhaka A K and Rajeev. 2017. Prediction of growth and yield of late sown wheat using DSSAT model under western zone of Haryana. International Journal of Current Microbiology and Applied Sciences 6: 1687–96.
Kundathil C, Viswan H and Kumar P. 2023. Crop simulation modeling: A strategic tool in crop management. Journal of Food Chemistry and Nanotechnology 9: S342–58.
Liu J, Feng H, He J, Chen H, Ding D, Luo X and Dong Q G. 2019. Modeling wheat nutritional quality with a modified CERES-wheat model. European Journal of Agronomy 109: 125901.
Mohan D and Krishnappa G. 2020. Wheat improvement for growth and sustainability in yield under varying. (In) Improving Cereal Productivity through Climate Smart Practices, pp. 269. Sareen S, Sharma P, Singh C, Jasrotia P, Singh G P and Sarial A K (Eds). Woodhead Publishing, Elsevier Inc. United Kingdom.
Mohanty U C, Sinha P, Nageswara Rao M M, Swain D K. and Singh K K. 2024. Crop modelling and simulation concept. (In) Climate Risk Management in Agriculture: Monthly and Seasonal Forecast Application, pp. 183–224.
Pal R K, Tripathi P and Rao M M N. 2012. Validation of CERES-wheat model for growth parameters of wheat in eastern Uttar Pradesh. Environment and Ecology 4A: 1434–38.
Pal R K and Murty N S. 2013. Temperature effect on growth parameters of wheat (CV. PBW-343) using CERES-wheat model for different sowing dates in foot hills of Western Himalayas. Indian Journal of Agricultural Research 47: 78–82.
Pal R K, Rawat K S, Singh J and Murty N S. 2015. Evaluation of CSM-CERES-wheat in simulating wheat yield and its attributes with different sowing environments in Tarai region of Uttarakhand. Journal of Applied and Natural Science 7: 404–09.
Pal R K and Yadav B K. 2018. Effect of soil variability on seed cotton yield using CROPGRO-Cotton model for Bathinda district of Punjab. International Journal of Scientific Progress and Research 13: 339–42.
Pal R K, Kataria S K and Singh P. 2016. Response of seed cotton yield to temperature and solar radiation as simulated with CROPGRO-cotton model. International Journal of Scientific Progress and Research 11: 262–64.
Rani A, Bandyopadhyay K K and Krishnan P. 2017. Simulation of nitrogen uptake, nitrogen utilization efficiency and yield of wheat under tillage, residue and nitrogen management using DSSAT-CERES-wheat model. Indian Journal of Ecology 44(2): 167–78.
Sarabjit Singh. 2019. ‘Comparative testing of CERES-Wheat and InfoCrop-wheat models to predict and optimize wheat yields in Punjab’. MSc Thesis, Punjab Agricultural University, Ludhiana, Punjab, India.
Thorp K R, Barnes E M, Hunsaker D J, Kimball B A, White J W, Nazareth V J and Hoogenboom G. 2014. Evaluation of CSM-CROPGRO-cotton for simulating effects of management and climate change on cotton growth and evapotranspiration in an arid environment. American Society of Agricultural and Biological Engineers 57(6): 1627–16.
Timsina J and Humphreys E J A S. 2006. Performance of CERES-rice and CERES-wheat models in rice-wheat systems: A review. Agricultural Systems 90(1–3): 5–31.
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