Assessment of DSSAT and AquaCrop models to simulate soybean and maize yield under water stress conditions
Abstract
Aim of study: To evaluate the performance of DSSAT and AquaCrop models in the estimation of soybean and grain maize yield under water stress conditions in a semi-arid region.
Area of study: Kermanshah, Iran.
Material and methods: AquaCrop and DSSAT were assessed to simulate soybean and maize. Both models were calibrated using field data. Field experiments were performed in a randomized complete block design with eight and four irrigation treatments for soybeans and maize, respectively with three replications. Measures of Normalized Root Mean Square Error (nRMSE) and Nash-Sutcliffe Model Efficiency were used to evaluate the accuracy of the models. For this purpose, simulated values of leaf area index / green crop canopy, grain yield, biomass, and soil moisture were compared with measured data.
Main results: Results indicated that the CROPGRO-Soybean in DSSAT software simulated more accurate crop growth of soybean than AquaCrop. The average nRMSE of the DSSAT model for estimating soil moisture, leaf area index, grain yield, and biomass were 6%, 14%, 16% and 20%, respectively. For maize, AquaCrop simulated crop growth more reliably than CERES-maize. The average nRMSE of 3%, 10%, 13% and 27% of the Aquacrop model in simulating the parameters of soil moisture, green crop canopy, biomass, and grain yield.
Research highlights: Considering the better performance of AquaCrop for maize and DSSAT for soybean in the study area, it is not possible to propose a specific model to simulate the growth of all crops in a region.
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References
Abedinpour M, Sarangi A, 2018. Evaluation of DSSAT-CERES model for maize under different water and nitrogen levels. Pertanika J Sci Technol 26(4): 1605-1618.
Adeboye OB, Schultz B, Adekalu KO, Prasad K, 2017. Modelling of response of the growth and yield of soybean to full and deficit irrigation by using Aquacrop. Irrig Drain 66(2): 192-205. https://doi.org/10.1002/ird.2073
Adeboye OB, Schultz B, Adekalu KO, Prasad KC, 2019. Performance evaluation of AquaCrop in simulating soil water storage, yield, and water productivity of rainfed soybeans (Glycine max L. Merr) in Ile-Ife, Nigeria. Agr Water Manage 213: 1130-1146. https://doi.org/10.1016/j.agwat.2018.11.006
Agri-PERI, 2017. National Water Document. APERDRI, Tehran, Iran [in Persian].
Ahmadi SH, Mosallaeepour E, Kamgar-Haghighi AA, Sepaskhah AR, 2015. Modeling maize yield and soil water content with AquaCrop under full and deficit irrigation managements. Water Resour Manag 29: 2837-2853. https://doi.org/10.1007/s11269-015-0973-3
Ahmadpour A, 2013. Estimation of maize crop yield under various irrigation management using WOFOST and Aqua Crop models in Kermanshah. MSc. Thesis, Razi University, Kermanshah, Iran. [in Persian].
Ahmadpour A, Farhadi Bansouleh B, Ghobadi M, 2017. Effects of deficit irrigation on growth trend, quantity and quality characteristics of maize in Kermanshah. J Water Soil Resour Conserv 6(3): 99-112. [in Persian].
Allen RG, Pereira LS, Raes D, Smith M, 1998. Crop evapotranspiration-Guidelines for computing crop water requirements. FAO Irrig Drain Paper 56, FAO, Rome, p. 300
Andarzian B, Bannayan M, Steduto P, Mazraeh H, Barati M, Barati M, et al., 2011. Validation and testing of the AquaCrop model under full and deficit irrigated wheat production in Iran. Agr Water Manage 100(1): 1-8. https://doi.org/10.1016/j.agwat.2011.08.023
Araya A, Keesstra S, Stroosnijder L, 2010. Simulating yield response to water of Teff (Eragrostis tef) with FAO's AquaCrop model. Field Crops Res 116(1-2): 196-204. https://doi.org/10.1016/j.fcr.2009.12.010
Babel MS, Deb P, Soni P, 2019. Performance evaluation of AquaCrop and DSSAT-CERES for maize under different irrigation and manure application rates in the Himalayan region of India. Agric Res 8(2): 207-217. https://doi.org/10.1007/s40003-018-0366-y
Battisti R, Sentelhas PC, Boote KJ, 2017. Inter-comparison of performance of soybean crop simulation models and their ensemble in southern Brazil. Field Crops Res 200: 28-37. https://doi.org/10.1016/j.fcr.2016.10.004
Boote KJ, Jones JW, Hoogenboom G, 2018. Simulation of crop growth: CROPGRO model, agricultural systems modeling and simulation. CRC Press, pp: 651-692. https://doi.org/10.1201/9781482269765-18
Brevedan R, Egli D, 2003. Short periods of water stress during seed filling, leaf senescence, and yield of soybean. Crop Sci 43(6): 2083-2088. https://doi.org/10.2135/cropsci2003.2083
Castañeda-Vera A, Leffelaar PA, Álvaro-Fuentes J, Cantero-Martínez C, Mínguez M, 2015. Selecting crop models for decision making in wheat insurance. Eur J Agron 68: 97-116. https://doi.org/10.1016/j.eja.2015.04.008
de Wit A, Boogaard H, Fumagalli D, Janssen S, Knapen R, van Kraalingen D, et al., 2019. 25 years of the WOFOST cropping systems model. Agric Syst 168: 154-167. https://doi.org/10.1016/j.agsy.2018.06.018
Dehghan Moroozeh A, 2019. Yield estimation and water requirements for soybean and maize under climate change by using crop growth simulation models in Kermanshah. Ph.D. Thesis, Razi University, Kermanshah, Iran. [in Persian].
Dogan E, Kirnak H, Copur O, 2007. Deficit irrigations during soybean reproductive stages and CROPGRO-soybean simulations under semi-arid climatic conditions. Field Crops Res 103(2): 154-159. https://doi.org/10.1016/j.fcr.2007.05.009
Doorenbos J, Kassam A, 1979. Yield response to water. FAO Irrig Drain Paper No. 33, Rome, p. 257 https://doi.org/10.1016/B978-0-08-025675-7.50021-2
Esmaili M, 2014. Estimation the effects of water deficit irrigation on soybean crop yield in Kermanshah under climate change scenarios using AquaCrop model. MSc. Thesis, Razi University, Kermanshah, Iran. [in Persian].
Esmaeili M, Farhadi Bansouleh B, Ghobadi M, 2015. Effects of deficit irrigation on quantity and quality of Soybean crop yield in Kermanshah region. J Water Soil 29(3): 551-559. [in Persian].
García-Vila M, Fereres E, Mateos L, Orgaz F, Steduto P, 2009. Deficit irrigation optimization of cotton with AquaCrop. Agron J 101(3): 477-487. https://doi.org/10.2134/agronj2008.0179s
Giménez L, Paredes P, Pereira LS, 2017. Water use and yield of soybean under various irrigation regimes and severe water stress. Application of AquaCrop and SIMDualKc models. Water 9(6): 393. https://doi.org/10.3390/w9060393
Hammad HM, Abbas F, Ahmad A, Farhad W, Anothai J, Hoogenboom G, 2018. Predicting water and nitrogen requirements for maize under semi-arid conditions using the CSM-CERES-Maize model. Eur J Agron 100: 56-66. https://doi.org/10.1016/j.eja.2017.10.008
Hellal F, Mansour H, Abdel-Hady M, El-Sayed S, Abdelly C, 2019. Assessment water productivity of barley varieties under water stress by AquaCrop model. AIMS Agric Food 4(3): 501-517. https://doi.org/10.3934/agrfood.2019.3.501
Heng LK, Hsiao T, Evett S, Howell T, Steduto P, 2009. Validating the FAO AquaCrop model for irrigated and water deficient field maize. Agron J 101(3): 488-498. https://doi.org/10.2134/agronj2008.0029xs
Hoogenboom G, Porter CH, Boote KJ, Shelia V, Wilkens PW, Singh U, et al., 2019. The DSSAT crop modeling ecosystem. In: Advances in crop modelling for a sustainable agriculture. Burleigh Dodds Sci Publ, pp: 173-216.
Jamieson P, Porter J, Wilson D, 1991. A test of the computer simulation model ARCWHEAT1 on wheat crops grown in New Zealand. Field Crops Res 27(4): 337-350. https://doi.org/10.1016/0378-4290(91)90040-3
Johnson AI, 1962. Methods of measuring soil moisture in the field. US Department of the Interior, US Geological Survey.
Jones C, Kiniry J, 1986. A simulation model of maize growth and development. Texas A & M University Press, College Station.
Jones JW, Hoogenboom G, Porter CH, Boote KJ, Batchelor WD, Hunt LA, et al., 2003. The DSSAT cropping system model. Eur J Agron 18(3-4): 235-265. https://doi.org/10.1016/S1161-0301(02)00107-7
Kumar A, Pandey V, Shekh A, Dixit S, Kumar M, 2008. Evaluation of cropgro-soybean (Glycine max. L. Merrill) model under varying environment condition. Am-Euras J Agr 1(2): 34-40.
Liu H, Yang J, Drury CA, Reynolds W, Tan C, Bai Y, et al., 2011. Using the DSSAT-CERES-Maize model to simulate crop yield and nitrogen cycling in fields under long-term continuous maize production. Nutr Cycl Agroecosyst 89: 313-328. https://doi.org/10.1007/s10705-010-9396-y
Malik A, Shakir AS, Ajmal M, Khan MJ, Khan TA, 2017. Assessment of AquaCrop model in simulating sugar beet canopy cover, biomass and root yield under different irrigation and field management practices in semi-arid regions of Pakistan. Water Resour Manag 31: 4275-4292. https://doi.org/10.1007/s11269-017-1745-z
Malik W, Isla R, Dechmi F, 2019. DSSAT-CERES-maize modelling to improve irrigation and nitrogen management practices under Mediterranean conditions. Agr Water Manage 213: 298-308. https://doi.org/10.1016/j.agwat.2018.10.022
Mibulo T, Kiggundu N, 2018. Evaluation of FAO AquaCrop model for simulating rainfed maize growth and yields in Uganda. Agronomy 8(11): 238. https://doi.org/10.3390/agronomy8110238
Mirsafi ZS, Sepaskhah AR, Ahmadi SH, Kamgar-Haghighi AA, 2016. Assessment of AquaCrop model for simulating growth and yield of saffron (Crocus sativus L.). Sci Hortic 211: 343-351. https://doi.org/10.1016/j.scienta.2016.09.020
Mirzaee M, 2013. Effects of deficit irrigation on maize crop yield in Mahidasht region using crop growing simulation models. Ms.C., Razi University, Kermansh, Iran. [in Persian].
Mkhabela MS, Bullock PR, 2012. Performance of the FAO AquaCrop model for wheat grain yield and soil moisture simulation in Western Canada. Agr Water Manage 110: 16-24. https://doi.org/10.1016/j.agwat.2012.03.009
Ovando G, Sayago S, Bocco M, 2018. Evaluating accuracy of DSSAT model for soybean yield estimation using satellite weather data. ISPRS J Photogramm Remote Sens 138: 208-217. https://doi.org/10.1016/j.isprsjprs.2018.02.015
Paredes P, Wei Z, Liu Y, Xu D, Xin Y, Zhang B, et al., 2015. Performance assessment of the FAO AquaCrop model for soil water, soil evaporation, biomass and yield of soybeans in North China Plain. Agr Water Manage 152: 57-71. https://doi.org/10.1016/j.agwat.2014.12.007
Raes D, Steduto P, Hsiao TC, Fereres E, 2009. AquaCrop-the FAO crop model to simulate yield response to water: II. Main algorithms and software description. Agron J 101(3): 438-447. https://doi.org/10.2134/agronj2008.0140s
Ranjbar A, Rahimikhoob A, Ebrahimian H, Varavipour M, 2019. Assessment of the AquaCrop model for simulating maize response to different nitrogen stresses under semi-arid climate. Commun Soil Sci Plant Anal 50(22): 2899-2912. https://doi.org/10.1080/00103624.2019.1689254
Sandhu R, Irmak S, 2019. Assessment of AquaCrop model in simulating maize canopy cover, soil-water, evapotranspiration, yield, and water productivity for different planting dates and densities under irrigated and rainfed conditions. Agr Water Manage 224: 105753. https://doi.org/10.1016/j.agwat.2019.105753
Sharda V, Gowda PH, Marek G, Kisekka I, Ray C, Adhikari P, 2019. Simulating the impacts of irrigation levels on soybean production in Texas high plains to manage diminishing groundwater levels. J Am Water Resour Assoc 55(1): 56-69. https://doi.org/10.1111/1752-1688.12720
Sinclair TR, Kitani S, Hinson K, Bruniard J, Horie T, 1991. Soybean flowering date: linear and logistic models based on temperature and photoperiod. Crop Sci 31(3): 786-790. https://doi.org/10.2135/cropsci1991.0011183X003100030049x
Soltani A, Hoogenboom G, 2007. Assessing crop management options with crop simulation models based on generated weather data. Field Crops Res 103(3): 198-207. https://doi.org/10.1016/j.fcr.2007.06.003
Steduto P, Hsiao TC, Raes D, Fereres E, 2009. AquaCrop-The FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agron J 101(3): 426-437. https://doi.org/10.2134/agronj2008.0139s
Teixeira WWR, Battisti R, Sentelhas PC, de Moraes MF, de Oliveira Junior A, 2019. Uncertainty assessment of soya bean yield gaps using DSSAT-CSM-CROPGRO-Soybean calibrated by cultivar maturity groups. J Agron Crop Sci 205(5): 533-544. https://doi.org/10.1111/jac.12343
Yang JM, Yang JY, Liu S, Hoogenboom G, 2014. An evaluation of the statistical methods for testing the performance of crop models with observed data. Agric Syst 127: 81-89. https://doi.org/10.1016/j.agsy.2014.01.008
Ziaii G, Babazadeh H, Abbasi F, Kaveh F, 2014. Evaluation of the AquaCrop and CERES-Maize models in assessment of soil water balance and maize yield. Iran J Soil Water Res 45(4): 435-445. [in Persian].
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