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Evaluation of CERES-Maize model for simulating maize phenology, grain yield, soil–water, evapotranspiration, and water productivity under different nitrogen levels and rainfed, limited, and full irrigation conditions

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Abstract

The CERES-Maize model performance was investigated in simulating maize phenology, grain yield, soil–water, evapotranspiration, and water productivity under different irrigation and nitrogen (N) levels under a variable rate lateral (linear)-move sprinkler irrigation system. The irrigation levels were rainfed, full irrigation treatment (FIT) and 75% FIT. The N levels were 0, 84, 140, 196, and 252 kg/ha. The field experiment was conducted in the form of split plots with the irrigation levels as the main treatment and N levels as a sub-main treatment. The root mean squared error (RMSE), normalized RMSE (RMSEn), R2, T test, and model prediction error (Pe) statistics were used to evaluate the performance and accuracy of the model. Calibration of the model was done using the data of 2011 and 2012 and validation of the model was conducted for 2013 and 2014 by considering days after planting to flowering (DAPF), days after planting to maturity (DAPM), grain yield, crop evapotranspiration (ETc), water productivity (WP), and soil water content (SWC). The DAPF simulations based on the average values of Pe (0%), RMSE (2 days), and RMSEn (3%) and the DAPM simulation results based on the average values of Pe (2%), RMSE (4 days), and RMSEn (3%) showed that the model had an acceptable accuracy. In calibration years, RMSE, RMSEn, and R2, respectively, were 0.57 ton/ha, 5%, and 0.91; and in validation years, the same statistics, respectively, were 0.86 ton/ha, 10% and 0.94, indicating good performance of the model in estimating the grain yield. Good accuracy was observed in the estimation of ETc and WP. In most cases, the model accuracy was greatest for 75% FIT and FIT treatments than the stressed conditions in the rainfed treatment. The model accuracy can be enhanced by improving the model coefficients in response to low levels of water and N supply. R2 values obtained in rainfed (0.83), 75% FIT (0.81) and FIT (0.67) treatments in calibration years and R2 values in rainfed (0.75), 75% FIT (0.77) and FIT (0.86) treatments in validation years showed that the model predicted the SWC relatively well. The comparison of ETc values with respect to N levels showed that there was no considerable difference between levels of N applications impact(s) on the ETc magnitude in the rainfed treatment. Comparison of different levels of N in rainfed and FIT showed that the application of 252 kg/ha of N resulted in 2.37 kg/m3 and 2.56 kg/m3 of WP, respectively, which was significantly different from other levels of N fertilizer applications. In general, CERES-Maize model can be a useful tool for predicting plant phenology, grain yield, ETc, WP, and SWC for the conditions similar to those presented in this research. The CERES-Maize model can provide valuable data and information for sustainable maize production by examining the long-term grain yield and WP, which can be beneficial to growers, advisors, and stakeholders to enhance the maize production efficiency by accounting for irrigation and N management strategies.

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Data availability

The datasets used in this research are available from the corresponding author Professor Suat Irmak (sfi5068@psu.edu) upon a reasoable request.

Abbreviations

CERES:

Crop Estimation through Resource and Environment Synthesis

ETc:

Reference evapotranspiration

FIT:

Full irrigation treatment

RMSE:

Root mean squared error

References

  • Adnan A, Diels J, Jibrin J, Kamara A, Shaibu A, Craufurd P, Menkir A (2020) CERES-Maize model for simulating genotype-by-environment interaction of maize and its stability in the dry and wet savannas of Nigeria. Field Crops Res 253:107826

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Aluoch SO, Li Z, Li X, Hu C, Mburu DM, Yang J, Su H (2022) Effect of mineral N fertilizer and organic input on maize yield and soil water content for assessing optimal N and irrigation rates in central Kenya. Field Crops Res 277:108420

    Article  Google Scholar 

  • Amiri E, Irmak S, Araji HA (2022) Assessment of CERES-Maize model in simulating maize growth, yield and soil water content under rainfed, limited and full irrigation. Agric Water Manage 259:107271

    Article  Google Scholar 

  • Amouzoua KA, Naabb JB, Lamersa JPA, Beckerc M (2018) CERES-Maize and CERES-Sorghum for modeling growth, nitrogen and phosphorus uptake, and soil moisture dynamics in the dry savanna of West Africa. Field Crops Res 217:134–149

    Article  Google Scholar 

  • Andrea MC, Boote KJ, Sentelhas PC, Romanelli TL (2018) Variability and limitations of maize production in Brazil: potential yield, water-limited yield and yield gaps. Agric Syst 165:264–273

    Article  Google Scholar 

  • Attia A, Govind A, Qureshi AS, Feike T, Rizk MS, Shabana MM, Kheir AM (2022) Coupling process-based models and machine learning algorithms for predicting yield and evapotranspiration of maize in arid environments. Water 14(22):3647

    Article  CAS  Google Scholar 

  • 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 8:207–217

    Article  Google Scholar 

  • Basso B, Liu L, Ritchie JT (2016) A comprehensive review of the CERES-wheat, -maize and-rice models’ performances. Adv Agron 136:27–132

    Article  Google Scholar 

  • Bazkiaee PA, Kamkar B, Amiri E, Kazemi H, Rezaei M, López-Bernal A (2022) The rice yield gap estimation using integrated system approaches: a case study – Guilan province, Iran. Int J Environ Sci Technol:1–14

  • Chi YX, Gao F, Muhammad I, Huang JH, Zhou XB (2022) Effect of water conditions and nitrogen application on maize growth, carbon accumulation and metabolism of maize plant in subtropical regions. Arch Agron Soil Sci:1–15

  • Chisanga CB, Phiri E, Shepande C, Sichingabula H (2015) Evaluating CERES-Maize model using planting dates and nitrogen fertilizer in Zambia. J Agric Sci 7(3):1–19

    Google Scholar 

  • Chisanga CB, Phiri E, Chinene VR (2021) Evaluating APSIM-and-DSSAT-CERES-maize models under rainfed conditions using Zambian rainfed maize cultivars. Nitrogen 2(4):392–414

    Article  CAS  Google Scholar 

  • Corbeels M, Berre D, Rusinamhodzi L, Lopez-Ridaura S (2018) Can we use crop modelling for identifying climate change adaptation options? Agric for Meteorol 256:46–52

    Article  Google Scholar 

  • Dokoohaki H, Gheysari M, Mousavi SF, Zand-Parsa S, Miguez FE, Archontoulis SV, Hoogenboom G (2016) Coupling and testing a new soil water module in DSSAT CERES-Maize model for maize production under semi-arid condition. Agric Water Manage 163:90–99

    Article  Google Scholar 

  • Dokoohaki H, Gheysari M, Mousavi SF, Hoogenboom G (2017) Effects of different irrigation regimes on soil moisture availability evaluated by CSM-CERES-Maize model under semi-arid condition. Ecohydrol Hydrobiol 17(3):207–216

    Article  Google Scholar 

  • Fang Q, Ma L, Harmel RD, Yu Q, Sima MW, Bartling PNS, Doherty J (2019) Uncertainty of CERES-Maize calibration under different irrigation strategies using PEST optimization algorithm. Agronomy 9(5):241

    Article  CAS  Google Scholar 

  • FAO (2021) Food and agric. Org. of the United Nations. http://www.fao.org/index_en.htm/. 1/1/2022

  • Fu C, Wang J, Gong S, Zhang Y, Wang C, Mo Y (2020) Optimization of irrigation and fertilization of drip-irrigated corn in the chernozem area of north-east China based on the CERES-Maize model. Irrig Drain 69(4):714–731

    Article  Google Scholar 

  • Gungula DT, Kling JG, Togun AO (2003) CERES-Maize predictions of maize phenology under nitrogen-stressed conditions in Nigeria. Agron J 5:892–899

    Article  Google Scholar 

  • He J, Dukes MD, Hochmuth GJ, Jones JW, Graham WD (2012) Identifying irrigation and nitrogen best management practices for sweet corn production on sandy soils using CERES-Maize model. Agric Water Manag 109:61–70

    Article  Google Scholar 

  • Hoogenboom G, Porter C, Shelia V, Boote K, Hoogenboom U, White J, Hunt L, Ogoshi R, Lizaso J, Koo J (2017) Decision Support System for Agrotechnology Transfer (DSSAT) Ver. 4.7. DSSAT Foundation, Gainesville

    Google Scholar 

  • Irmak S (2015a) Inter-annual variation in long-term center pivot-irrigated maize evapotranspiration (ET) and various water productivity response indices: part I. Grain yield, actual and basal ET, irrigation-yield production functions, ET-yield production functions, and yield response factors. J Irrig Drain Eng 141(5):1–17. 04014068. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000825

  • Irmak S (2015b) Inter-annual variation in long-term center pivot-irrigated maize evapotranspiration (ET) and various water productivity response indices: part II. Irrigation water use efficiency (IWUE), crop WUE, evapotranspiration WUE, irrigation-evapotranspiration use efficiency, and precipitation use efficiency. J Irrig Drain Eng 141(5):1–11. 04014069. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000826

  • Irmak S, Sandhu R, Kukal MS (2021) Multi-model projections of trade-offs between irrigated and rainfed maize yields under changing climate and future emission scenarios. Agric Water Manag. https://doi.org/10.1016/j.agwat.2021.107344

    Article  Google Scholar 

  • Jones JW, Hoogenboom G, Porter CH, Boote KJ, Batchelor WD, Hunt LA, Ritchie JT (2003) The DSSAT cropping system model. Eur J Agron 18(3–4):235–265

    Article  Google Scholar 

  • Kaur R, Arora VK (2018) Assessing spring maize responses to irrigation and nitrogen regimes in north-west India using CERES-Maize model. Agric Water Manage 209:171–177

    Article  Google Scholar 

  • Kent C, Pope E, Thompson V, Lewis K, Scaife AA, Dunstone N (2017) Using climate model simulations to assess the current climate risk to maize production. Env Res Lett 12(5):054012

    Article  Google Scholar 

  • Kipkulei HK, Bellingrath-Kimura SD, Lana M, Ghazaryan G, Baatz R, Boitt M, Sieber S (2022) Assessment of maize yield response to agricultural management strategies using the DSSAT–CERES-Maize model in Trans Nzoia County in Kenya. Int. J. Plant Prod. 16(4):557–577

    Article  Google Scholar 

  • Liben FM, Wortmann CS, Yang H, Tadesse T, Stewart ZP, Wegary D, Mupangwa W (2021) Nitrogen response functions targeted to technology extrapolation domains in Ethiopia using CERES-maize. Agronomy J 113(1):436–450

    Article  CAS  Google Scholar 

  • Liu HL, Yang JY, Ping HE, Bai YL, Jin JY, Drury CF, Hoogenboom G (2012) Optimizing parameters of CSM-CERES-Maize model to improve simulation performance of maize growth and nitrogen uptake in northeast China. J Integr Agric 11:1898–1913

    Article  CAS  Google Scholar 

  • Liu S, Yang JY, Drury CF, Liu HL, Reynolds WD (2014) Simulating maize (Zea mays L.) growth and yield, soil nitrogen concentration, and soil water content for a long-term cropping experiment in Ontario Canada. Can J Soil Sci 94(3):435–452

    Article  CAS  Google Scholar 

  • Ma H, Wang J, Liu T, Guo Y, Zhou Y, Yang T, Sun C (2023) Time series global sensitivity analysis of genetic parameters of CERES-maize model under water stresses at different growth stages. Agric Water Manage 275:108027

    Article  Google Scholar 

  • Malik W, Isla R, Dechmi F (2019) DSSAT-CERES-maize modelling to improve irrigation and nitrogen management practices under Mediterranean conditions. Agric Water Manage 213:298–308

    Article  Google Scholar 

  • Marek GW, Marek TH, Evett SR, Bell JM, Colaizzi PD, Brauer DK, Howell TA (2020) Comparison of lysimeter-derived crop coefficients for legacy and modern drought-tolerant maize hybrids in the Texas High Plains. Trans ASABE 63(5):1243–1257

    Article  Google Scholar 

  • Menefee D, Rajan N, Cui S, Bagavathiannan M, Schnell R, West J (2021) Simulation of dryland maize growth and evapotranspiration using DSSAT-CERES-Maize model. Agronomy J 113:1317–1332. https://doi.org/10.1002/agj2.20524

    Article  Google Scholar 

  • Nasim W, Ahmad A, Belhouchette H, Fahad S, Hoogenboom G (2016) Evaluation of the OILCROP-SUN model for sunflower hybrids under different agro-meteorological conditions of Punjab—Pakistan. Field Crop Res 188:17–30

    Article  Google Scholar 

  • Paredes P, Rodrigues GC, Alves I, Pereira LS (2014) Partitioning evapotranspiration, yield prediction and economic returns of maize under various irrigation management strategies. Agric Water Manage 135:27–39

    Article  Google Scholar 

  • Payero JO, Tarkalson DD, Irmak S, Davison D, Petersen JL (2009) Effect of timing of a deficit-irrigation allocation on corn evapotranspiration, yield, water use efficiency and dry mass. Agric Water Manage 96(10):1387–1397

    Article  Google Scholar 

  • Ran H, Kang S, Hu X, Li S, Wang W, Liu F (2020) Capability of a solar energy-driven crop model for simulating water consumption and yield of maize and its comparison with a water-driven crop model. Agric for Meteorol 287:107955

    Article  Google Scholar 

  • Ritchie JT (1998) Soil water balance and plant stress. In: Tsuji GY, Hoogenboom G, Thornton PK (eds) Understanding options for agricultural production. Kluwer Academic Publishers, Dordrecht, pp 41–54

    Chapter  Google Scholar 

  • Ritchie JT, Basso B (2008) Water use efficiency is not constant when crop water supply is adequate or fixed: the role of agronomic management. Eur J Agron 28(3):273–281

    Article  Google Scholar 

  • Rudnick D, Irmak S (2013) Impact of water and nitrogen management strategies on maize yield and water productivity indices under linear-move sprinkler irrigation. Trans ASABE 56(5):1769–1783

    Google Scholar 

  • Rudnick D, Irmak S, Ferguson R, Shaver T, Djaman K, Slater G, Bereuter A, Ward N, Francis D, Schmer M, Wienhold B, van Donk S (2016) Economic return vs crop water productivity of maize for various nitrogen rates under full irrigation, limited irrigation, and rainfed settings in south central Nebraska. J Irrig Drain Eng 142(6):1–12. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001023

    Article  Google Scholar 

  • Rugira P, Ma J, Zheng L, Wu C, Liu E (2021) Application of DSSAT CERES-maize to identify the optimum irrigation management and sowing dates on improving maize yield in Northern China. Agronomy 11(04):674

    Article  Google Scholar 

  • Saddique Q, Cai H, Ishaque W, Chen H, Chau HW, Chattha MU, He J (2019) Optimizing the sowing date and irrigation strategy to improve maize yield by using CERES (crop estimation through resource and environment synthesis)-maize model. Agronomy 9(2):109

    Article  CAS  Google Scholar 

  • Sandhu R, Irmak S (2020) Performance assessment of Hybrid-Maize model for rainfed, limited and full irrigation conditions. Agric Water Manage 242:106402

    Article  Google Scholar 

  • Saseendran SA, Ma L, Nielsen DC, Ahuja LR (2005) Simulation of planting date effects on maize performance in Eastern Colorado using CERES and RZWQM. Agron J 97:58–71

    Article  Google Scholar 

  • Sima MW, Fang QX, Qi Z, Yu Q (2020) Direct assimilation of measured soil water content in Root Zone Water Quality Model calibration for deficit-irrigated maize. Agron J 112(2):844–860

    Article  Google Scholar 

  • Soler CMT, Sentelhas PC, Hoogenboom G (2007) Application of the CSM-CERES-Maize model for planting date evaluation and yield forecasting for maize grown off-season in a subtropical environment. Europ J Agronomy 27:165–177

    Article  Google Scholar 

  • Song L, Jin J (2020) Improving CERES-Maize for simulating maize growth and yield under water stress conditions. Eur J Agron 117:126072

    Article  Google Scholar 

  • Todorovic M, Albrizio R et al (2009) Assessment of AquaCrop, CropSyst, and WOFOST models in the simulation of sunflower growth under different water regimes. Agron J 101(3):509–521. https://doi.org/10.2134/agronj2008.0166s

    Article  Google Scholar 

  • Vilayvong S, Banterng P, Patanothai A, Pannangpetch K (2015) CSM-CERES-rice model to determine management strategies for lowland rice production. Scientia AGRICOLA 72(3):229–236

    Article  Google Scholar 

  • Wang Y, Guo F, Shen H, Xing X, Ma X (2021) Global sensitivity analysis and evaluation of the DSSAT model for summer maize (Zea mays L.) under irrigation and fertilizer stress. Int J Plant Prod 15(4):523–539

    Article  Google Scholar 

  • Wang Y, Huang D, Zhao L, Shen H, Xing X, Ma X (2022) The distributed CERES-Maize model with crop parameters determined through data assimilation assists in regional irrigation schedule optimization. Comput Electron Agric 202:107425

    Article  Google Scholar 

  • Webber H, Gaiser T, Ewert F (2014) What role can crop models play in supporting climate change adaptation decisions to enhance food security in Sub-Saharan Africa? Agric Sys 127:161–177

    Article  Google Scholar 

  • Xu J, Cai H, Wang X, Ma C, Lu Y, Ding Y, Saddique Q (2020) Exploring optimal irrigation and nitrogen fertilization in a winter wheat-summer maize rotation system for improving crop yield and reducing water and nitrogen leaching. Agric Water Manage 228:105904

    Article  Google Scholar 

  • Zheng H, Ying H, Yin Y, Wang Y, He G, Bian Q, Yang Q (2019) Irrigation leads to greater maize yield at higher water productivity and lower environmental costs: a global meta-analysis. Agric Ecosyst & Environ 273:62–69

    Article  CAS  Google Scholar 

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Acknowledgments

The work presented in this paper is a part of a long-term research that investigates the fundamentals of coupled irrigation water and nitrogen management strategies on grain yield, water productivity, evapotranspiration, evaporation and transpiration dynamics, yield production functions, soil-water dynamics and yield response factors, and other productivity variables and environmental relationships for different cropping systems in the Irmak Research Laboratory. The authors thanks Daran Rudnick (former MS and Ph.D. student under the supervision of Professor Suat Irmak) and Matthew Drudik (former research technician and MS student under the supervision of Professor Suat Irmak) for their work, time and assistance with the experiment and field data collection.

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S.I. and E.A. developed the concepts; S.I., E.A., P.A.B., and H.A.A. conducted analyses and wrote the manuscript; S.I. did the revisions; S.I. secured resources for research and conducted the research and data collection.

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Correspondence to Suat Irmak.

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Irmak, S., Amiri, E., Bazkiaee, P.A. et al. Evaluation of CERES-Maize model for simulating maize phenology, grain yield, soil–water, evapotranspiration, and water productivity under different nitrogen levels and rainfed, limited, and full irrigation conditions. Irrig Sci 42, 551–573 (2024). https://doi.org/10.1007/s00271-023-00909-z

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