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Journal of Integrative Agriculture  2023, Vol. 22 Issue (5): 1381-1395    DOI: 10.1016/j.jia.2022.08.018
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Novel models for simulating maize growth based on thermal time and photothermal units: Applications under various mulching practices

LIAO Zhen-qi1, ZHENG Jing1, 2, FAN Jun-liang1#, PEI Sheng-zhao1, DAI Yu-long1, ZHANG Fu-cang1, LI Zhi-jun1

1 Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Yangling 712100, P.R.China

2 Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, P.R.China

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摘要  玉米(Zeamays L.)是三大粮食作物之一,是碳水化合物的重要来源,对保障世界粮食安全具有重要意义。株高(H)、茎粗(SD)、叶面积指数(LAI)和干物质(DM)是影响玉米产量的重要生长参数。然而,作物生长模型中很少考虑温度和光照对玉米生长的综合效应。基于2015-2018年的大田试验数据,本文提出了10个基于修正Logistic生长方程(Mlog)和Mitscherlich生长方程(Mit)的玉米生长模型,用于模拟不同覆盖措施下玉米H、SD、LAI和DM。以累积生长度日(AGDD)、太阳热单位(HTU)、光热单位(PTU)或我们首次提出的光周期热单位(PPTU)作为模型的单一驱动因素; AGDD进一步结合累积实际日照时数(ASS)、我们首次提出的累积光周期响应(APR)或累积最大可能日照时数(ADL)作为模型的双重驱动因素。采用七个统计指标和一个全局性能指数评估模型模拟效果。研究结果表明,与不覆盖相比,三种覆盖方式显着提高了玉米的生长速率和生长峰值。在四个单因素驱动模型中,MlogPTU模型的整体性能最优,其次为MlogAGDD模型。MlogPPTU模型在模拟SD和LAI方面优于MlogAGDD模型。在所有十个模型中,MlogAGDD-APR模型的整体性能最优,其次为MlogAGDD-ASS模型。具体而言,MlogAGDD-APR模型模拟H和LAI效果最好,而MlogAGDD-ADL和MlogAGDD-ASS模型分别模拟SD和DM效果最好。综上所述,以AGDD和APR、ASS或ADL为双驱动因素的修正Logistic生长方程在模拟玉米生长方面优于常规的以AGDD为单驱动因子的修正Logistic生长模型。




Abstract  

Maize (Zea mays L.) is one of the three major food crops and an important source of carbohydrates for maintaining food security around the world.  Plant height (H), stem diameter (SD), leaf area index (LAI) and dry matter (DM) are important growth parameters that influence maize production.  However, the combined effect of temperature and light on maize growth is rarely considered in crop growth models.  Ten maize growth models based on the modified logistic growth equation (Mlog) and the Mitscherlich growth equation (Mit) were proposed to simulate the H, SD, LAI and DM of maize under different mulching practices based on experimental data from 2015–2018.  Either the accumulative growing degree-days (AGDD), helio thermal units (HTU), photothermal units (PTU) or photoperiod thermal units (PPTU, first proposed here) was used as a single driving factor in the models; or AGDD was combined with either accumulative actual solar hours (ASS), accumulative photoperiod response (APR, first proposed here) or accumulative maximum possible sunshine hours (ADL) as the dual driving factors in the models.  The model performances were evaluated using seven statistical indicators and a global performance index.  The results showed that the three mulching practices significantly increased the maize growth rates and the maximum values of the growth curves compared with non-mulching.  Among the four single factor-driven models, the overall performance of the MlogPTU Model was the best, followed by the MlogAGDD Model.  The MlogPPTU Model was better than the MlogAGDD Model in simulating SD and LAI.  Among the 10 models, the overall performance of the MlogAGDD–APR Model was the best, followed by the MlogAGDD–ASS Model.  Specifically, the MlogAGDD–APR Model performed the best in simulating H and LAI, while the MlogAGDD–ADL and MlogAGDD–ASS models performed the best in simulating SD and DM, respectively.  In conclusion, the modified logistic growth equations with AGDD and either APR, ASS or ADL as the dual driving factors outperformed the commonly used modified logistic growth model with AGDD as a single driving factor in simulating maize growth.

Keywords:  thermal time       accumulative growing degree-days       helio thermal units       photothermal units       growth model  
Received: 24 January 2022   Accepted: 17 March 2022
Fund: 

This study was funded by the National Natural Science Foundation of China (51879226) and the Chinese Universities Scientific Fund (2452020018).

 

About author:  #Correspondence FAN Jun-liang, E-mail: nwwfjl@163.com

Cite this article: 

LIAO Zhen-qi, ZHENG Jing, FAN Jun-liang, PEI Sheng-zhao, DAI Yu-long, ZHANG Fu-cang, LI Zhi-jun. 2023.

Novel models for simulating maize growth based on thermal time and photothermal units: Applications under various mulching practices . Journal of Integrative Agriculture, 22(5): 1381-1395.

Adams S R, Pearson S, Hadley P. 2001. Improving quantitative flowering models through a better understanding of the phases of photoperiod sensitivity. Journal of Experimental Botany, 52, 655–662.
Akyuz F A, Kandel H, Morlock D. 2017. Developing a growing degree day model for North Dakota and Northern Minnesota soybean. Agricultural and Forest Meteorology, 239, 134–140.
Bai T, Tao W, Zhang N, Chen Y, Mercatoris B. 2020. Growth simulation and yield prediction for perennial jujube fruit tree by integrating age into the WOFOST model. Journal of Integrative Agriculture, 19, 721–734.
Behar O, Khellaf A, Mohammedi K. 2015. Comparison of solar radiation models and their validation under Algerian climate - The case of direct irradiance. Energy Conversion and Management, 98, 236–251.
Birch C J, Hammer G L, Rickert K G. 1998. Temperature and photoperiod sensitivity of development in five cultivars of maize (Zea mays L.) from emergence to tassel initiation. Field Crops Research, 55, 93–107.
Bonhomme R, Derieux M, Edmeades G O. 1994. Flowering of diverse maize cultivars in relation to temperature and photoperiod in multilocation field trials. Crop Science, 34, 156–164.
Borchert R, Renner S S, Calle Z, Navarrete D, Tye A, Gautier L, Spichiger R, von Hildebrand P. 2005. Photoperiodic induction of synchronous flowering near the equator. Nature, 433, 627–629.
Brisson N, Gary C, Justes E, Roche R, Mary B, Ripoche D, Zimmer D, Sierra J, Bertuzzi P, Bussière F, Cabidoche Y M, Celliera P, Debaekea P, Gaudillèrea J P, Hénault C, Maraux F, Seguina B, Sinoquet H. 2003. An overview of the crop model STICS. European Journal of Agronomy, 18, 309–332.
Bu T T, Lu S J, Wang K, Dong L D, Li S L, Xie Q G, Xu X D, Cheng Q, Chen L Y, Fang C, Li H Y, Liu B H, Weller J L, Kong F J. 2021. A critical role of the soybean evening complex in the control of photoperiod sensitivity and adaptation. Proceedings of the National Academy of Sciences of the United States of America, 118, e2010241118.
Confalonieri R, Bregaglio S, Rosenmund A S, Acutis M, Savin I. 2011. A model for simulating the height of rice plants. European Journal of Agronomy, 34, 20–25.
Despotovic M, Nedic V, Despotovic D, Cvetanovic S. 2015. Review and statistical analysis of different global solar radiation sunshine models. Renewable and Sustainable Energy Reviews, 52, 1869–1880.
Van Diepen C V, Wolf J, Van Keulen H, Rappoldt C. 1989. WOFOST: A simulation model of crop production. Soil Use and Management, 5, 16–24.
Dong X, Guan L, Zhang P H, Liu X L, Li S J, Fu Z J, Qi Z Y, Qiu Z G, Jin C, Huang S B, Yang H. 2021. Responses of maize with different growth periods to heat stress around flowering and early grain filling. Agricultural and Forest Meteorology, 303, 108378.
Ellis R H, Summerfield R J, Edmeades G O, Roberts E H. 1992. Photoperiod, temperature, and the interval from sowing to tassel initiation in diverse cultivars of maize. Crop Science, 32, 1225–1232.
Fan J L, Wu L F, Zhang F C, Cai H J, Ma X, Bai H. 2019. Evaluation and development of empirical models for estimating daily and monthly mean daily diffuse horizontal solar radiation for different climatic regions of China. Renewable and Sustainable Energy Reviews, 105, 168–186.
Flynn D F B, Wolkovich E M. 2018. Temperature and photoperiod drive spring phenology across all species in a temperate forest community. New Phytologist, 219, 1353–1362.
Ghamghami M, Ghahreman N, Irannejad P, Ghorbani K. 2019. Comparison of data mining and GDD-based models in discrimination of maize phenology. International Journal of Plant Production, 13, 11–22.
Girijesh G K, Kumarswamy A S, Sreedhar S, Kumar M D, Vageesh T S, Rajashekarappa K S. 2011. Heat unit utilization of kharif maize in transitional zone of Karnataka. Journal of Agrometeorology, 13, 43–45.
Gueymard C A. 2014. A review of validation methodologies and statistical performance indexes for modeled solar radiation data: Towards a better bankability of solar projects. Renewable and Sustainable Energy Reviews, 39, 1024–1034. 
He H Y, Hu Q, Li R, Pan X B, Huang B X, He Q J. 2020. Regional gap in maize production, climate and resource utilization in China. Field Crops Research, 254, 107830.
van Ittersum M K, Leffelaar P A, van Keulen H, Kropff M J, Bastiaans L, Goudriaan J. 2003. On approaches and applications of the Wageningen crop models. European Journal of Agronomy, 18, 201–234.
Jiang T, Liu J, Gao Y J, Sun Z, Chen S, Yao N, Ma H J, Feng H, Yu Q, He J Q. 2020. Simulation of plant height of winter wheat under soil water stress using modified growth functions. Agricultural Water Management, 232, 106066.
Johal N, Kaur J, Kushwah A, Singh S. 2020. Comparative agroclimatic indices of desi and kabuli chickpea genotypes under irrigated and rainfed conditions. Journal of Food Legumes, 33, 77–81.
Jones J W, Hoogenboom G, Porter C H, Boote K J, Batchelor W D, Hunt L A, Wilkens P W, Singh U, Gijsmana, A J, Ritchie J T. 2003. The DSSAT cropping system model. European Journal of Agronomy, 18, 235–265.
Keating B A, Carberry P S, Hammer G L, Probert M E, Robertson M J, Holzworth D, Huth N I, Hargreaves J N G, Meinke H, Hochman Z, McLean G, Verburg K, Snow V, Dimes J P, Silburn M, Wang E, Brown S, Bristow K L, Asseng S, Chapman S, et al. 2003. An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy, 18, 267–288.
Kiniry J R, Ritchie J T, Musser R L, Flint E P, Iwig W C. 1983. The photoperiod sensitive interval in maize. Agronomy Journal, 75, 687–690.
Kukal M S, Irmak S. 2018. US agro-climate in 20th century: Growing degree days, first and last frost, growing season length, and impacts on crop yields. Scientific Reports, 8, 1–14.
Kumudini S, Andrade F, Boote K, Brown G, Dzotsi K, Edmeades G, Gocken T, Goodwin M, Halter A, Hammer G. 2014. Predicting maize phenology: Intercomparison of functions for developmental response to temperature. Agronomy Journal, 106, 2087–2097.
Li Z P, Song M D, Feng H. 2017. Dynamic characteristics of leaf area index and plant height of winter wheat influenced by irrigation and nitrogen coupling and their relationships with yield. Transactions of the Chinese Society of Agricultural Engineering, 33, 195–202. (in Chinese)
Lizaso J I, Batchelor W D, Boote K J, Westgate M E. 2005. Development of a leaf-level canopy assimilation model for CERES-Maize. Agronomy Journal, 97, 722–733.
Majumder D, Kingra P K, Kukal S S. 2016. Canopy temperature and heat use efficiency of spring maize under modified soil microenvironment. Annals of Agricultural Research, 37, 225–235.
Mishra A K, Ines A V M, Das N N. 2015. Anatomy of a local-scale drought: Application of assimilated remote sensing products, crop model, and statistical methods to an agricultural drought study. Journal of Hydrology, 526, 15–29.
Mitscherlich E A. 1919. Das Gesetz des Pflanzenwachstums. Landwirtschaftliche Jahrbücher, 53, 167–182. (in German)
Pullanagari R R, Dehghan-Shoar M, Yule I J, Bhatia N. 2021. Field spectroscopy of canopy nitrogen concentration in temperate grasslands using a convolutional neural network. Remote Sensing of Environment, 257, 112353.
Rodrigues N, Dufresnes C. 2017. Using conventional F-statistics to study unconventional sex-chromosome differentiation. PeerJ, 5, e3207.
Sandhu R, Irmak S. 2020. Performance assessment of Hybrid-Maize model for rainfed, limited and full irrigation conditions. Agricultural Water Management, 242, 106402.
Satish J V, Ajithkumar B, John L C, Vysakh A. 2017. Heat units requirement for different rice varieties in the central zone of kerala. Contemporary Research in India, 7, 1–6.
Sepaskhah A R, Fahandezh-Saadi S, Zand-Parsa S. 2011. Logistic model application for prediction of maize yield under water and nitrogen management. Agricultural Water Management, 99, 51–57.
Seymour N P, Edwards D G, Thompson J P. 2019. A dual rescaled Mitscherlich model of the simultaneous savings in phosphorus and zinc fertilizer from arbuscular mycorrhizal fungal colonization of linseed (Linum usitatissimum L.). Plant and Soil, 440, 97–118.
Singh A K, Tripathi P, Adhar S. 2008. Heat unit requirements for phenophases of wheat genotypes as influenced by sowing dates. Journal of Agrometeorology, 10, 209–212.
Steduto P, Hsiao T C, Raes D, Fereres E. 2009. AquaCrop - The FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agronomy Journal, 101, 426–437.
Stöckle C O, Donatelli M, Nelson R. 2003. CropSyst, a cropping systems simulation model. European Journal of Agronomy, 18, 289–307.
Subrahmaniyan K, Veeramani P, Harisudan C. 2018. Heat accumulation and soil properties as affected by transparent plastic mulch in Blackgram (Vigna mungo) doubled cropped with groundnut (Arachis hypogaea) in sequence under rainfed conditions in Tamil Nadu, India. Field Crops Research, 219, 43–54.
Subrahmaniyan K, Veeramani P, Zhou W. 2021. Does heat accumulation alter crop phenology, fibre yield and fibre properties of sunnhemp (Crotalaria juncea L.) genotypes with changing seasons? Journal of Integrative Agriculture, 20, 2395–2409.
Todorovic M, Albrizio R, Zivotic L, Saab M T A, Stöckle C, Steduto P. 2009. Assessment of AquaCrop, CropSyst, and WOFOST models in the simulation of sunflower growth under different water regimes. Agronomy Journal, 101, 509–521.
Tollenaar M, Hunter R B. 1983. A photoperiod and temperature sensitive period for leaf number of maize. Crop Science, 23, 457–460.
Tolley S, Yang Y, Mohammadi M. 2020. High-throughput phenotyping identifies plant growth differences under well-watered and drought treatments. Journal of Integrative Agriculture, 19, 2429–2438.
Wang K, Su L J, Wang Q J. 2021. Cotton growth model under drip irrigation with film mulching: A case study of Xinjiang, China. Agronomy Journal, 113, 2417–2436.
Wang X L. 1986. How to use logistic equation in dynamic simulation of dry matter production. Chinese Journal of Agrometeorology, 7, 14–19. (in Chinese)
Wang Z K, Zhao X N, Wu P T, Gao Y, Yang Q, Shen Y Y. 2017. Border row effects on light interception in wheat/maize strip intercropping systems. Field Crops Research, 214, 1–13.
Ware G O, Ohki K, Moon L C. 1982. The Mitscherlich plant growth model for determining critical nutrient deficiency levels. Agronomy Journal, 74, 88–91.
Wardhani W S, Kusumastuti P. 2014. Describing the height growth of corn using logistic and Gompertz model. AGRIVITA. Journal of Agricultural Science, 35, 237–241.
Warrington I J, Kanemasu E T. 1983. Corn growth response to temperature and photoperiod. I. Seedling emergence, tassel initiation, and anthesis. Agronomy Journal, 75, 749–754.
Williams J R, Jones C A, Kiniry J R, Spanel D A. 1989. The EPIC crop growth model. Transactions of the ASAE, 32, 497–511.
Wu L, Peng Y, Fan J, Wang Y, Huang G. 2021. A novel kernel extreme learning machine model coupled with K-means clustering and firefly algorithm for estimating monthly reference evapotranspiration in parallel computation. Agricultural Water Management, 245, 106624.
De Wit A, Boogaard H, Fumagalli D, Janssen S, Knapen R, van Kraalingen D, Supita I, van der Wijngaart R, van Diepen K. 2019. 25 years of the WOFOST cropping systems model. Agricultural Systems, 168, 154–167. 
Yin S Y, Li P C, Xu Y, Liu J, Yang T T, Wei J, Xu S H, Yu J J, Fang H M, Xue L, Hao D R, Yang Z F, Xu C W. 2020. Genetic and genomic analysis of the seed-filling process in maize based on a logistic model. Heredity, 124, 122–134.
Zhang Y L, Liu P, Zhang X X, Zheng Q, Chen M, Ge F, Li Z L, Sun W T, Guan Z G, Liang T H, Zheng Y, Tan X L, Zou C Y, Peng H W, Pan G T, Shen Y. 2018. Multi-locus genome-wide association study reveals the genetic architecture of stalk lodging resistance-related traits in maize. Frontiers in Plant Science, 9, 611.
Zhao Y, Mao X, Shukla M K. 2020. A modified SWAP model for soil water and heat dynamics and seed-maize growth under film mulching. Agricultural and Forest Meteorology, 292, 108127.
Zheng J, Fan J L, Zhang F C, Zhuang Q L. 2021a. Evapotranspiration partitioning and water productivity of rainfed maize under contrasting mulching conditions in Northwest China. Agricultural Water Management, 243, 106473.
Zheng J, Fan J L, Zou Y, Zhang F C, Guo J J, Yan S C, Zhuang Q L, Cui N B, Guo L. 2021b. Interactive effects of mulching practice and nitrogen rate on grain yield, water productivity, fertilizer use efficiency and greenhouse gas emissions of rainfed summer maize in northwest China. Agricultural Water Management, 248, 106778.
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