Cai, C. Z., Yang, H. Y., Zhang, L. and Wenfang Cao (2022). Potential yield of world rice under global warming based on the ARIMA-TR Model. Atmosphere 13: doi:10. 3390/atmos13081336.
Divya, K. L., Mhatre, P. H., Venkatasalam, E. P. and R. Sudha (2021). Crop simulation models as decision-supporting tools for sustainable potato production: A Review. Potato Res. 64: 387-419. doi:10.3390/atmos13081336.
Dong, J., Lu, H. B., Wang, Y. W., Ye, T. and Yuan, W. (2020). Estimating winter wheat yield based on a light use efficiency model and wheat variety data. ISPRS J. Photogramm. Remote Sens. 160: 18-32. doi:10.1016/j.isprsjprs.2019.12.005.
Espe, M. B., Yang, H. S., Cassman, K. G. et al. (2016). Estimating yield potential in temperate high-yielding, direct-seeded US rice production systems. Field Crops Res. 193: 123-32. doi:10.1016/j.fcr.2016.04.003.
Huang, M., Tang, Q. Y., Ao, H. J. and Zou, Y. B. (2017). Yield potential and stability in super hybrid rice and its production strategies. J. Integr. Agric. 16: 1009-017. doi:10.1016/S2095-3119(16)61535-6.
Jensen, L. (1990). Guidelines for the application of ARIMA models in time series. Res. Nurs. Health 13: 429-35.
Lai, Y. R., Pringle, M. J., Kopittke, P. et al. (2018). An empirical model for prediction of wheat yield, sing time-integrated Landsat NDVI. Int. J. Appl. Earth Observ. Geoinformation 72: 99-108. doi:10.1016/j.jag.2018.07.013.
Liu, Z. C., Xu, Z. J., Bi, R. et al., (2021). Estimation of winter wheat yield in arid and semiarid regions based on assimilated multi-source sentinel data and the CERES-wheat model. Sensors 21: doi:10.3390/s21041247.
Ma, X., Wu, W. and Zhang, Y. (2019). Improved GM (1,1) Model Based on Simpson Formula and its applications. J. Gray System 31: 33-46. doi:10.48550/arXiv.1908.03493.
Sagar Maitra, Upasana Sahoo, Masina Sairam, Harun I. Gitari, Esmaeil Rezaei-Chiyaneh, Martin Leonardo Battaglia and Akbar Hossain (2023). Cultivating sustainability: A comprehensive review on intercropping in a changing climate. Res. Crop. 24: 702-15.
Ojeda, J. J., Huth, N., Holzworth, D. et al. (2021). Assessing errors during simulation configuration in crop models-A global case study using APSIM-Potato. Ecol. Modell. 458: doi:10.1016/j.ecolmodel.2021.109703.
Poudel, M. R., Neupane, M. P., Paudel, H., Bhandari, R. Nyaupane, S., Dhakal, A. and Panthi, B. (2023). Agromorphological analysis of wheat (Triticum aestivum L.) genotypes under combined heat and drought stress conditions. Farm. Manage. 8: 72-80.
Setiyono, T. D., Quicho, E. D., Holecz, F. H. et al., (2019). Rice yield estimation using synthetic aperture radar (SAR) and the ORYZA crop growth model: development and application of the system in South and South-east Asian countries. Int. J. Remote Sens. 40: 8093-124. doi:10.1080/01431161.2018.1547457.
Singh, P. K., Singh, K. K., Bhan, S. C. and A. K. Baxla. (2016). Potential yield and yield gap analysis of rice (Oryza Saliva L) in eastern and north eastern regions of India using CERES-rice model. J. Agrometeorol. 17: 194-98. doi:10.54386/jam.v17i2.1005.
Singh, P. K., Singh, K. K., Singh, P. and R. Subramanian. (2017). Forecasting of wheat yield in various agro-climatic regions of Bihar by using CERES-Wheat model. J. Agrometeorol. 19: 346-49. doi:10.54386/jam.v19i4.604.
Sun, T., Hasegawa, T., Liu, B., et al., (2021). Current rice models underestimate yield losses from short-term heat stresses. Global Change Biol. 27: 402-16. doi:10.1111/gcb.15393.
Tian, L. Y., Li, Z. X., Huang, J. X., et al., (2013). Comparison of two optimization algorithms for estimating regional winter wheat yield by integrating MODIS leaf area index and world food studies model. Sensor Lett. 11: 1261-68. doi:10.1166/sl.2013.2871.
Turko, S. Y. (2023). Influence of weather conditions on the productive potential of pasture ecosystems in the south of Russia (Based on artificially created models). Res. Crop. 24: 774-78.
Wang, J. W., Zhang, J. H., Bai, Y., et al., (2020). Integrating remote sensing-based process model with environmental zonation scheme to estimate rice yield gap in Northeast China. Field Crops Res. 246: doi:10.1016/j.fcr.2019.107682.
Wang, L. Z. (2018). Rice yield potential, gaps and constraints during the past three decades in a climate-changing Northeast China. Agric. Forest Meteorol. 259: 173-83. doi:10.1016/j.agrformet.2018.04.023.
Wang, Y. L., Xu, XG., Huang, L. S., et al., (2019). An Improved CASA model for estimating winter wheat yield from remote sensing images. Remote Sens. 11: doi:10.3390/rs11091088.
Divya, K. L., Mhatre, P. H., Venkatasalam, E. P. and R. Sudha (2021). Crop simulation models as decision-supporting tools for sustainable potato production: A Review. Potato Res. 64: 387-419. doi:10.3390/atmos13081336.
Dong, J., Lu, H. B., Wang, Y. W., Ye, T. and Yuan, W. (2020). Estimating winter wheat yield based on a light use efficiency model and wheat variety data. ISPRS J. Photogramm. Remote Sens. 160: 18-32. doi:10.1016/j.isprsjprs.2019.12.005.
Espe, M. B., Yang, H. S., Cassman, K. G. et al. (2016). Estimating yield potential in temperate high-yielding, direct-seeded US rice production systems. Field Crops Res. 193: 123-32. doi:10.1016/j.fcr.2016.04.003.
Huang, M., Tang, Q. Y., Ao, H. J. and Zou, Y. B. (2017). Yield potential and stability in super hybrid rice and its production strategies. J. Integr. Agric. 16: 1009-017. doi:10.1016/S2095-3119(16)61535-6.
Jensen, L. (1990). Guidelines for the application of ARIMA models in time series. Res. Nurs. Health 13: 429-35.
Lai, Y. R., Pringle, M. J., Kopittke, P. et al. (2018). An empirical model for prediction of wheat yield, sing time-integrated Landsat NDVI. Int. J. Appl. Earth Observ. Geoinformation 72: 99-108. doi:10.1016/j.jag.2018.07.013.
Liu, Z. C., Xu, Z. J., Bi, R. et al., (2021). Estimation of winter wheat yield in arid and semiarid regions based on assimilated multi-source sentinel data and the CERES-wheat model. Sensors 21: doi:10.3390/s21041247.
Ma, X., Wu, W. and Zhang, Y. (2019). Improved GM (1,1) Model Based on Simpson Formula and its applications. J. Gray System 31: 33-46. doi:10.48550/arXiv.1908.03493.
Sagar Maitra, Upasana Sahoo, Masina Sairam, Harun I. Gitari, Esmaeil Rezaei-Chiyaneh, Martin Leonardo Battaglia and Akbar Hossain (2023). Cultivating sustainability: A comprehensive review on intercropping in a changing climate. Res. Crop. 24: 702-15.
Ojeda, J. J., Huth, N., Holzworth, D. et al. (2021). Assessing errors during simulation configuration in crop models-A global case study using APSIM-Potato. Ecol. Modell. 458: doi:10.1016/j.ecolmodel.2021.109703.
Poudel, M. R., Neupane, M. P., Paudel, H., Bhandari, R. Nyaupane, S., Dhakal, A. and Panthi, B. (2023). Agromorphological analysis of wheat (Triticum aestivum L.) genotypes under combined heat and drought stress conditions. Farm. Manage. 8: 72-80.
Setiyono, T. D., Quicho, E. D., Holecz, F. H. et al., (2019). Rice yield estimation using synthetic aperture radar (SAR) and the ORYZA crop growth model: development and application of the system in South and South-east Asian countries. Int. J. Remote Sens. 40: 8093-124. doi:10.1080/01431161.2018.1547457.
Singh, P. K., Singh, K. K., Bhan, S. C. and A. K. Baxla. (2016). Potential yield and yield gap analysis of rice (Oryza Saliva L) in eastern and north eastern regions of India using CERES-rice model. J. Agrometeorol. 17: 194-98. doi:10.54386/jam.v17i2.1005.
Singh, P. K., Singh, K. K., Singh, P. and R. Subramanian. (2017). Forecasting of wheat yield in various agro-climatic regions of Bihar by using CERES-Wheat model. J. Agrometeorol. 19: 346-49. doi:10.54386/jam.v19i4.604.
Sun, T., Hasegawa, T., Liu, B., et al., (2021). Current rice models underestimate yield losses from short-term heat stresses. Global Change Biol. 27: 402-16. doi:10.1111/gcb.15393.
Tian, L. Y., Li, Z. X., Huang, J. X., et al., (2013). Comparison of two optimization algorithms for estimating regional winter wheat yield by integrating MODIS leaf area index and world food studies model. Sensor Lett. 11: 1261-68. doi:10.1166/sl.2013.2871.
Turko, S. Y. (2023). Influence of weather conditions on the productive potential of pasture ecosystems in the south of Russia (Based on artificially created models). Res. Crop. 24: 774-78.
Wang, J. W., Zhang, J. H., Bai, Y., et al., (2020). Integrating remote sensing-based process model with environmental zonation scheme to estimate rice yield gap in Northeast China. Field Crops Res. 246: doi:10.1016/j.fcr.2019.107682.
Wang, L. Z. (2018). Rice yield potential, gaps and constraints during the past three decades in a climate-changing Northeast China. Agric. Forest Meteorol. 259: 173-83. doi:10.1016/j.agrformet.2018.04.023.
Wang, Y. L., Xu, XG., Huang, L. S., et al., (2019). An Improved CASA model for estimating winter wheat yield from remote sensing images. Remote Sens. 11: doi:10.3390/rs11091088.