Assessing Aquacrop model for pearlmillet (Pennisetum glaucum) under in-situ water conservation in a rainfed semi-arid environment

SUMAN KUMAR; SUSAMA SUDHISHRI; ANCHAL DASS; MANOJ KHANNA; V K SEHGAL; NEELAM PATEL

doi:10.56093/ijas.v89i8.92875

Authors

SUMAN KUMAR Ex M Sc Student, ICAR-Indian Agricultural Research Institute, New Delhi 110012 India
SUSAMA SUDHISHRI Principal Scientist, ICAR-Indian Agricultural Research Institute, New Delhi 110012 India
ANCHAL DASS Principal Scientist, Division of Agronomy, ICAR-IARI, New Delhi
MANOJ KHANNA Principal Scientist, ICAR-Indian Agricultural Research Institute, New Delhi 110012 India
V K SEHGAL Principal Scientist, Division of Agricultural Physics, ICAR-IARI, New Delhi
NEELAM PATEL Principal Scientist, ICAR-Indian Agricultural Research Institute, New Delhi 110012 India

https://doi.org/10.56093/ijas.v89i8.92875

Keywords:

AquaCrop, In-situ water conservation, Pearlmillet yield, Simulation, Soil moisture dynamics

Abstract

AquaCrop (v4. 0) model has been commonly used to simulate the crop yield under irrigated environments, but this model has not yet been calibrated and validated for simulating the yield of pearlmillet (Pennisetum glaucum (L.) R. Br.) under in-situ water conservation. Thus, a three year (2011âˆ’13) field experiment was conducted at ICAR-Indian Agricultural Research Institute, New Delhi with six in-situ water conservation treatments,viz. trench-cum-bund (TCB: 20 cm depth of trench, 20 cm height of bund), bund (30 cm height), ridge-and-furrow (R&F: 15 cm height), skip-row (SR: 3:1) planting, basin-tillage (BT: 45 cm Ã— 45 cm) and control (no-water conservation). Calibration of AquaCrop (v 4. 0) model for pearlmillet grain yield and total soil moisture content was done using experimental data of 2011 and validated separately for 2012 (deficit rainfall year) and 2013 (excess rainfall year) data. For the year (2012), absolute prediction errors of grain yield were 1.7, 8.7, 14.1, 14.9, 3.3 and 7.3% for BT, R&F, TCB, bunds, SR and control, respectively, whereas Nash Sutcliffe Efficiency, root mean square error and coefficient of determination were 0.95, 0.07 and 0.96, respectively during calibration period and 0.73, 0.15 and 0.93 during validation period of deficit year. Thus, model predictions were satisfactoryfor less rainfall (<600 mm) year. Coefficient of determination R2 (0.4 to 1.0) was better for soil moisture simulation during dry-spell periods and for BT practice. The validated AquaCrop model can be used for prediction of pearlmillet yield and soil moisture under different water conservation practices in a semi-arid environment.

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References

Bitri M,Grazhdani S and Ahmeti A. 2014. Validation of the AquaCrop model for full and deficit irrigated potato production in environmental condition of Koreça zone, south-eastern Albania. International Journal of Innovative Research in Science, Engineering and Technology 3(5): 12013–19.

Dass A, Nain A S,Sudhishri S and Chandra S. 2012. Simulation of maturity duration and productivity of two rice varieties under system of rice intensification using DSSAT v 4. 5/CERES Rice model. Journal of Agrometeorology 14(1): 26–30. DOI: https://doi.org/10.54386/jam.v14i1.1374

FAO. 2009. How to feed the world in 2050. Issue brief from the high-level expert forum held in Rome, 12-13 October. FAO, Rome, Italy.

Hamid J F, Gabriella I and Theib Y O. 2009. Parameterization and evaluation of the AquaCrop model for full and deficit irrigated cotton. Agronomy Journal 101: 469–76. DOI: https://doi.org/10.2134/agronj2008.0182s

Hsiao T C,Heng L K,Steduto P, Rojas-Lara B, Raes D and Fereres E. 2009. AquaCrop—the FAO crop model to simulate yield response to water III: Parameterization and testing for maize. Agronomy Journal 101: 448–59. DOI: https://doi.org/10.2134/agronj2008.0218s

Jones C A and Kiniry J R. 1986. Ceres-N Maize: a simulation model of maize growth and development. Texas A&M University Press, College Station, Temple, TX, pp. 49–111.

Mohammad Ali, Sudhishri Susama, Singh Man, Das T K, Sharma V K and Dwivedi Neeta. 2018. Performance evaluation of Aqua Crop model for conservation agriculture based direct seeded rice (Oryza sativa). Indian Journal of Agricultural Sciences 88 (3): 379–86.

Panigrahi B and Panda N S. 2003. Field test of a soil water balance simulation model. Agricultural Water Management 58: 223–40. DOI: https://doi.org/10.1016/S0378-3774(02)00082-3

Paredes P, Melo-Abreu J P de, Alves I and Pareira L S. 2014. Assessing the performance of the FAO AquaCrop model to estimate maize yields and water use under full and deficit irrigation with focus on model parameterization. Agricultural Water Management 144: 81–97. DOI: https://doi.org/10.1016/j.agwat.2014.06.002

Raes D, Steduto P, Hsiao T C and Fereres E. 2009. AquaCrop— the FAO crop model to simulate yield response to water II. Main algorithms and soft ware description. Agronomy Journal 101: 438–47. DOI: https://doi.org/10.2134/agronj2008.0140s

Rockström J and Barron J. 2007. Water productivity in rainfed systems: overview of challenges and analysis of opportunities in water scarcity prone savannahs. Irrigation Science 25: 299–311. DOI: https://doi.org/10.1007/s00271-007-0062-3

Rockström J, Barron J and Fox P. 2003. Water productivity in rainfed agriculture: challenges and opportunities for smallholder farmers in drought-prone tropical agroecosystems (eds. ). Kijne J W, Barker R and Molden D Water productivity in agriculture: Limits and Opportunities for Improvement. International Water Management Institute (IWMI),Colombo. DOI: https://doi.org/10.1079/9780851996691.0145

Simba Malvern Farai,Mubvuma Michael, Murwendo Talent and Chikodzi David. 2013. Prediction of yield and biomass productions: A remedy to climate change in semi-arid regions of Zimbabwe. International Journal of Advance Agricultural Research 1: 14–21.

Steduto P, Hsiao T C, Raes D and Fereres E. 2009. AquaCrop— the FAO Crop Model to Simulate Yield Response to Water I. Concepts and Underlying Principles. Agronomy Journal 101: 426–37. DOI: https://doi.org/10.2134/agronj2008.0139s

Stockle C O, Donatelli M and Nelson R. 2003. CropSyst a cropping systems simulation model. European Journal of Agronomy 18: 289–307. DOI: https://doi.org/10.1016/S1161-0301(02)00109-0

Sudhishri S, Kumar A and Singh J K. 2016. Comparative evaluation of neural network and regression based models to simulate runoff and sediment yield in an outer Himalayan watershed. Journal of Agricultural Science and Technology 18: 681–94.

Sudhishri S, Dass A and Choudhury P R. 2014. Combating climate change through water resource conserving technologies. Indian farming 64(6): 2–7.

Sudhishri S and Dass A. 2014. Change in climate water balance and agro-climatic situational analysis for crop planning in southern Odisha. Annals of Agricultural Research 35(3): 249–58.

Sudhishri S and Dass A. 2012. Study on the impact and adoption of soil and water conservation technologies in eastern ghats of India. Journal of Agricultural Engineering 49 (1): 51–59.

Sudhishri S, Dass A and Paikaray N K. 2007. Water balance studies and strategies for combating water deficit in upper Kolab catchment of Orissa. Hydrology Journal 30 (3&4): 103–16.

Sudhishri S and Patnaik U S. 2004. Analysis of water balance for Kokriguda watershed. Journal ofApplied Hydrology 17: (2&3): 34–39.

Todorovic M, Albrizio R, Zivotic L, Saab M T A, Stockle C and Steduto P. 2009. Assessment of AquaCrop, CropSyst, and WOFOST Models in the simulation of sunflower growth under different water regimes. Agronomy Journal 101(3): 509–21. DOI: https://doi.org/10.2134/agronj2008.0166s

Zeleke Tilahun, Ketema Luckett and David Cowley Raymond. 2011. Calibration and testing of the FAO AquaCrop model for canola. Agronomy Journal 103(6): 1610–19. DOI: https://doi.org/10.2134/agronj2011.0150