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Evaluation of options for increasing yield and water productivity of wheat in Punjab, India using the DSSAT-CSM-CERES-Wheat model

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  • Timsina, J.
  • Godwin, D.
  • Humphreys, E.
  • Yadvinder-Singh
  • Bijay-Singh
  • Kukal, S.S.
  • Smith, D.

Abstract

The DSSAT-CSM-CERES-Wheat V4.0 model was calibrated for yield and irrigation scheduling of wheat with 2004-2005 data and validated with 13 independent data sets from experiments conducted during 2002-2006 at the Punjab Agricultural University (PAU) farm, Ludhiana, and in a farmer's field near PAU at Phillaur, Punjab, India. Subsequently, the validated model was used to estimate long-term mean and variability of potential yield (Yp), drainage, runoff, evapo-transpiration (ET), crop water productivity (CWP), and irrigation water productivity (IWP) of wheat cv. PBW343 using 36 years (1970-1971 to 2005-2006) of historical weather data from Ludhiana. Seven sowing dates in fortnightly intervals, ranging from early October to early January, and three irrigation scheduling methods [soil water deficit (SWD)-based, growth stage-based, and ET-based] were evaluated. For the SWD-based scheduling, irrigation management depth was set to 75 cm with irrigation scheduled when SWD reached 50% to replace 100% of the deficit. For growth stage-based scheduling, irrigation was applied either only once at one of the key growth stages [crown root initiation (CRI), booting, flowering, and grain filling], twice (two stages in various combinations), thrice (three stages in various combinations), or four times (all four stages). For ET-driven irrigation, irrigations were scheduled based on cumulative net ETo (ETo-rain) since the previous irrigation, for a range of net ETo (25, 75, 125, 150, and 175 mm). Five main irrigation schedules (SWD-based, ET-driven with irrigation applied after accumulation of either 75 or 125 mm of ETo, i.e., ET75 or ET125, and growth stage-based with irrigation applied at CRI plus booting, or at CRI plus booting plus flowering stage) were chosen for detailed analysis of yield, water balance, and CWP and IWP. Nitrogen was non-limiting in all the simulations. Mean Yp across 36 years ranged from 5.2 t ha-1 (10 October sowing) to 6.4 t ha-1 (10 November sowing), with yield variations due to seasonal weather greater than variations across sowing dates. Yields under different irrigation scheduling, CWP and IWP were highest for 10 November sowing. Yields and CWP were higher for SWD and ET75-based irrigations on both soils, but IWP was higher for ET75-based irrigation on sandy loam and for ET150-based irrigation on loam. Simulation results suggest that yields, CWP, and IWP of PBW343 would be highest for sowing between late October and mid-November in the Indian Punjab. It is recommended that sowing be done within this planting period and that irrigation be applied based on the atmospheric demand and soil water status and not on the growth stage. Despite the potential limitations recognised with simulation results, we can conclude that DSSAT-CSM-CERES-Wheat V4.0 is a useful decision support system to help farmers to optimally schedule and manage irrigation in wheat grown in coarse-textured soils under declining groundwater table situations of the Indian Punjab. Further, the validated model and the simulation results can also be extrapolated to other areas with similar climatic and soil environments in Asia where crop, soil, weather, and management data are available.

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  • Timsina, J. & Godwin, D. & Humphreys, E. & Yadvinder-Singh & Bijay-Singh & Kukal, S.S. & Smith, D., 2008. "Evaluation of options for increasing yield and water productivity of wheat in Punjab, India using the DSSAT-CSM-CERES-Wheat model," Agricultural Water Management, Elsevier, vol. 95(9), pages 1099-1110, September.
    • Handle: RePEc:eee:agiwat:v:95:y:2008:i:9:p:1099-1110
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    7. Kadiyala, M.D.M. & Jones, J.W. & Mylavarapu, R.S. & Li, Y.C. & Reddy, M.D., 2015. "Identifying irrigation and nitrogen best management practices for aerobic rice–maize cropping system for semi-arid tropics using CERES-rice and maize models," Agricultural Water Management, Elsevier, vol. 149(C), pages 23-32.
    8. Attia, Ahmed & Rajan, Nithya & Xue, Qingwu & Nair, Shyam & Ibrahim, Amir & Hays, Dirk, 2016. "Application of DSSAT-CERES-Wheat model to simulate winter wheat response to irrigation management in the Texas High Plains," Agricultural Water Management, Elsevier, vol. 165(C), pages 50-60.
    9. Mahajan, G. & Bharaj, T.S. & Timsina, J., 2009. "Yield and water productivity of rice as affected by time of transplanting in Punjab, India," Agricultural Water Management, Elsevier, vol. 96(3), pages 525-532, March.
    10. Vashisht, B.B. & Jalota, S.K. & Ramteke, P. & Kaur, Ramandeep & Jayeswal, D.K., 2021. "Impact of rice (O. sativa L.) straw incorporation induced changes in soil physical and chemical properties on yield, water and nitrogen–balance and –use efficiency of wheat (T. aestivum L.) in rice–wh," Agricultural Systems, Elsevier, vol. 194(C).
    11. Jalota, S.K. & Jain, A.K. & Vashisht, B.B., 2018. "Minimize water deficit in wheat crop to ameliorate groundwater decline in rice-wheat cropping system," Agricultural Water Management, Elsevier, vol. 208(C), pages 261-267.
    12. Devkota, Mina & Devkota, Krishna Prasad & Kumar, Shiv, 2022. "Conservation agriculture improves agronomic, economic, and soil fertility indicators for a clay soil in a rainfed Mediterranean climate in Morocco," Agricultural Systems, Elsevier, vol. 201(C).
    13. Araya, A. & Kisekka, Isaya & Gowda, Prasanna H. & Prasad, P.V. Vara, 2017. "Evaluation of water-limited cropping systems in a semi-arid climate using DSSAT-CSM," Agricultural Systems, Elsevier, vol. 150(C), pages 86-98.
    14. Meena, Raj Pal & Karnam, Venkatesh & Tripathi, S.C. & Jha, Ankita & Sharma, R.K. & Singh, G.P., 2019. "Irrigation management strategies in wheat for efficient water use in the regions of depleting water resources," Agricultural Water Management, Elsevier, vol. 214(C), pages 38-46.
    15. Anshuman Gunawat & Devesh Sharma & Aditya Sharma & Swatantra Kumar Dubey, 2022. "Assessment of climate change impact and potential adaptation measures on wheat yield using the DSSAT model in the semi-arid environment," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(2), pages 2077-2096, March.
    16. Geerts, S. & Raes, D. & Garcia, M. & Taboada, C. & Miranda, R. & Cusicanqui, J. & Mhizha, T. & Vacher, J., 2009. "Modeling the potential for closing quinoa yield gaps under varying water availability in the Bolivian Altiplano," Agricultural Water Management, Elsevier, vol. 96(11), pages 1652-1658, November.
    17. Chunlei Wang & Liping Feng & Lu Wu & Chen Cheng & Yizhuo Li & Jintao Yan & Jiachen Gao & Fu Chen, 2020. "Assessment of Genotypes and Management Strategies to Improve Resilience of Winter Wheat Production," Sustainability, MDPI, vol. 12(4), pages 1-21, February.
    18. Si, Zhuanyun & Zain, Muhammad & Li, Shuang & Liu, Junming & Liang, Yueping & Gao, Yang & Duan, Aiwang, 2021. "Optimizing nitrogen application for drip-irrigated winter wheat using the DSSAT-CERES-Wheat model," Agricultural Water Management, Elsevier, vol. 244(C).

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