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Systems Modeling

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Systems Modeling

Abstract

The agricultural systems have complex interactions with the surrounding environment and soil, and better understanding is possible through computer application. The interactions between systems and environment are so complex that one cannot quantify their cumulative affects without application of latest computing tools. The solar radiations, temperature, photoperiod, humidity, and wind are some of the important environmental variables which interact with agricultural system. These variables should be considered with importance for understanding the agricultural system on scientific basis. The light required is for photosynthesis and photoperiod, humidity for determination of water loss, and wind to transfer water vapors and gases to and from plants. The model converts qualitative data into quantitative to give out quantitative predictions to the theories which can be compared very easily in the real world. There is rich future for systems modeling, and it can open new frontiers and helps in the agroecological transitions of agriculture. Plants and crops should be considered as holobionts (individual host and its microbial community). In system modeling, the environmental variables are linked to various physiological processes to predict the crop responses with a given set of environmental conditions. The increased ozone concentration in the environment also damages the crop, and these impacts should be considered during model development. Similarly, application of different models at different scales is presented which could help to understand the mechanisms in qualitative and quantitative way. Last but not least, the concept of digital agriculture and its linkage with modeling were elaborated. In general the chapter discusses in detail the type, methods of measurement along with mathematical representation, terminologies, and their impact on the various processes of the plants.

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References

  • Ahmad S, Abbas G, Fatima Z, Khan RJ, Anjum MA, Ahmed M, Khan MA, Porter CH, Hoogenboom G (2017) Quantification of the impacts of climate warming and crop management on canola phenology in Punjab, Pakistan. J Agron Crop Sci 203(5):442–452. https://doi.org/10.1111/jac.12206

    Article  Google Scholar 

  • Ahmad S, Abbas G, Ahmed M, Fatima Z, Anjum MA, Rasul G, Khan MA, Hoogenboom G (2019) Climate warming and management impact on the change of phenology of the rice-wheat cropping system in Punjab, Pakistan. Field Crop Res 230:46–61. https://doi.org/10.1016/j.fcr.2018.10.008

    Article  Google Scholar 

  • Ahmed M, Aslam MA, Hassan FU, Asif M, Hayat R (2014) Use of APSIM to model nitrogen use efficiency of rain-fed wheat. Int J Agric Biol 16:461–470

    CAS  Google Scholar 

  • Ahmed M, Akram MN, Asim M, Aslam M, F-u H, Higgins S, Stöckle CO, Hoogenboom G (2016) Calibration and validation of APSIM-wheat and CERES-wheat for spring wheat under rainfed conditions: models evaluation and application. Comput Electron Agric 123:384–401. https://doi.org/10.1016/j.compag.2016.03.015

    Article  Google Scholar 

  • Ahmed M, Stöckle CO, Nelson R, Higgins S (2017) Assessment of climate change and atmospheric CO2 impact on winter wheat in the Pacific northwest using a multimodel ensemble. Front Ecol Evol 5(51). https://doi.org/10.3389/fevo.2017.00051

  • Ahmed M, Ijaz W, Ahmad S (2018) Adapting and evaluating APSIM-SoilP-wheat model for response to phosphorus under rainfed conditions of Pakistan. J Plant Nutr 41(16):2069–2084. https://doi.org/10.1080/01904167.2018.1485933

    Article  CAS  Google Scholar 

  • Ahmed M, Stöckle CO, Nelson R, Higgins S, Ahmad S, Raza MA (2019) Novel multimodel ensemble approach to evaluate the sole effect of elevated CO2 on winter wheat productivity. Sci Rep 9(1):7813. https://doi.org/10.1038/s41598-019-44251-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Alagarswamy G, Ritchie JT (1991) Phasic development in CERES-sorghum model. In: Hodges T (ed) Predicting crop phenology. CRC Press, Boca Raton, pp 143–152

    Google Scholar 

  • Aslam MA, Ahmed M, Hayat R (2017a) Modeling nitrogen use efficiency under changing climate. In: Ahmed M, Stockle CO (eds) Quantification of climate variability, adaptation and mitigation for agricultural sustainability. Springer International Publishing, Cham, pp 71–90. https://doi.org/10.1007/978-3-319-32059-5_4

  • Aslam MA, Ahmed M, Stöckle CO, Higgins SS, Hassan FU, Hayat R (2017b) Can growing degree days and photoperiod predict spring wheat phenology? Front Environ Sci 5

    Google Scholar 

  • Avnery S, Mauzerall DL, Liu J, Horowitz LW (2011) Global crop yield reductions due to surface ozone exposure: 2. Year 2030 potential crop production losses and economic damage under two scenarios of O3 pollution. Atmos Environ 45(13):2297–2309. https://doi.org/10.1016/j.atmosenv.2011.01.002

    Article  CAS  Google Scholar 

  • Bird R, Hulstrom R (1981) A simplified clear sky model for direct and diffuse insolation on horizontal surfaces, SERI. TR. Solar Energy Research Institute, Golden, CO, pp 642–761

    Google Scholar 

  • Blackman FF (1905) Optima and limiting factors. Ann Bot 19:281–296

    Google Scholar 

  • Boysen Jensen P (1932) Die Stoffproduktion der Pflanzen. Gustav Fischer, Jena

    Google Scholar 

  • Brisson N, Mary B, Ripoche D, Jeuffroy MH, Ruget F, Nicoullaud B, Gate P, Devienne-barret F, Antonioletti R, Durr C, Richard G, Beaudoin N, Recous S, Tayot X, Plenet D, Cellier P, Machet J-M, Meynard JM, Delécolle R (1998) STICS: a generic model for the simulation of crops and their water and nitrogen balances. I. Theory and parameterization applied to wheat and corn. Agronomie 18:311–346

    Google Scholar 

  • Brisson N, Gary C, Justes E, Roche R, Mary B, Ripoche D, Zimmer D, Sierra J, Bertuzzi P, Burger P, Bussière F, Cabidoche YM, Cellier P, Debaeke P, Gaudillère JP, Hénault C, Maraux F, Seguin B, Sinoquet H (2003) An overview of the crop model stics. Eur J Agron 18:309–332

    Google Scholar 

  • Caldeira CF, Jeanguenin L, Chaumont F, Tardieu F (2014) Circadian rhythms of hydraulic conductance and growth are enhanced by drought and improve plant performance. Nat Commun 5(1):5365. https://doi.org/10.1038/ncomms6365

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Campbell GS, Norman JM (2012) An introduction to environmental biophysics. Springer, New York

    Google Scholar 

  • Carruthers TJB, Longstaff BJ, Dennison WC, Abal EG, Aioi K (2001) Chapter 19: Measurement of light penetration in relation to seagrass. In: Short FT, Coles RG (eds) Global seagrass research methods. Elsevier Science, Amsterdam

    Google Scholar 

  • Coucheney E, Buis S, Launay M, Constantin J, Mary B, García de Cortázar-Atauri I, Ripoche D, Beaudoin N, Ruget F, Andrianarisoa KS, Le Bas C, Justes E, Léonard J (2015) Accuracy, robustness and behavior of the STICS soil–crop model for plant, water and nitrogen outputs: evaluation over a wide range of agro-environmental conditions in France. Environ Model Softw 64:177–190

    Google Scholar 

  • De Pury DGG, Farquhar GD (1997) Simple scaling of photosynthesis from leaves to canopies without the errors of big-leaf models. Plant Cell Environ 20:537–557

    Google Scholar 

  • Devi MJ, Reddy VR (2018) Transpiration response of cotton to vapor pressure deficit and its relationship with stomatal traits. Front Plant Sci 9

    Google Scholar 

  • Emberson LD, Pleijel H, Ainsworth EA, van den Berg M, Ren W, Osborne S, Mills G, Pandey D, Dentener F, Büker P, Ewert F, Koeble R, Van Dingenen R (2018) Ozone effects on crops and consideration in crop models. Eur J Agron 100:19–34. https://doi.org/10.1016/j.eja.2018.06.002

    Article  CAS  Google Scholar 

  • Folliard A, Traoré PCS, Vaksmann M, Kouressy M (2004) Modeling of sorghum response to photoperiod: a threshold–hyperbolic approach. Field Crop Res 89:59–70

    Google Scholar 

  • Granier A, Huc R, Barigah S (1996) Transpiration of natural rain forest and its dependence on climatic factors. Agric For Meteorol 78:19–29

    Google Scholar 

  • Hammer G, Wright G (1994) A theoretical analysis of nitrogen and radiation effects on radiation use efficiency in peanut. Aust J Agric Res 45:575–589

    Google Scholar 

  • Hammer G, Cooper M, Tardieu F, Welch S, Walsh B, Van Eeuwijk F, Chapman S, Podlich D (2006) Models for navigating biological complexity in breeding improved crop plants. Trends Plant Sci 11:587–593

    CAS  PubMed  Google Scholar 

  • Hammer GL, Van Oosterom E, Mclean G, Chapman SC, Broad I, Harland P, Muchow RC (2010) Adapting APSIM to model the physiology and genetics of complex adaptive traits in field crops. J Exp Bot 61:2185–2202

    CAS  PubMed  Google Scholar 

  • Holzworth DP, Huth NI, Devoil PG, Zurcher EJ, Herrmann NI, Mclean G, Chenu K, Van Oosterom EJ, Snow V, Murphy C, Moore AD, Brown H, Whish JPM, Verrall S, Fainges J, Bell LW, Peake AS, Poulton PL, Hochman Z, Thorburn PJ, Gaydon DS, Dalgliesh NP, Rodriguez D, Cox H, Chapman S, Doherty A, Teixeira E, Sharp J, Cichota R, Vogeler I, Li FY, Wang E, Hammer GL, Robertson MJ, Dimes JP, Whitbread AM, Hunt J, Van Rees H, Mcclelland T, Carberry PS, Hargreaves JNG, Macleod N, Mcdonald C, Harsdorf J, Wedgwood S, Keating BA (2014) APSIM – evolution towards a new generation of agricultural systems simulation. Environ Model Softw 62:327–350

    Google Scholar 

  • Ijaz W, Ahmed M, Asim M, Aslam M (2017) Models to study phosphorous dynamics under changing climate. In: Ahmed M, Stockle CO (eds) Quantification of climate variability, adaptation and mitigation for agricultural sustainability. Springer International Publishing, Cham, pp 371–386. https://doi.org/10.1007/978-3-319-32059-5_15

  • Jabeen M, Gabriel HF, Ahmed M, Mahboob MA, Iqbal J (2017) Studying impact of climate change on wheat yield by using DSSAT and GIS: a case study of Pothwar region. In: Ahmed M, Stockle CO (eds) Quantification of climate variability, adaptation and mitigation for agricultural sustainability. Springer International Publishing, Cham, pp 387–411. https://doi.org/10.1007/978-3-319-32059-5_16

    Chapter  Google Scholar 

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

    Google Scholar 

  • Kempes CP, West GB, Crowell K, Girvan M (2011) Predicting maximum tree heights and other traits from allometric scaling and resource limitations. PLoS One 6:e20551

    CAS  PubMed  PubMed Central  Google Scholar 

  • Landsberg J, Sands P (2011a) Chapter 2: Weather and energy balance. Elsevier, Terrestrial Ecology

    Google Scholar 

  • Landsberg J, Sands P (2011b) Chapter 3: Physiological processes. In: Terrestrial ecology. Elsevier, Oxford

    Google Scholar 

  • Lindquist JL, Arkebauer TJ, Walters DT, Cassman KG, Dobermann A (2005) Maize radiation use efficiency under optimal growth conditions. Agron J 97:72–78

    Google Scholar 

  • Lobell DB, Hammer GL, Chenu K, Zheng B, Mclean G, Chapman SC (2015) The shifting influence of drought and heat stress for crops in Northeast Australia. Glob Chang Biol 21:4115–4127

    PubMed  Google Scholar 

  • Loomis RS, Williams WA (1963) Maximum crop productivity: an Extimate1. Crop Sci 3(1):cropsci1963.0011183X000300010021x. https://doi.org/10.2135/cropsci1963.0011183X000300010021x

    Article  Google Scholar 

  • Louca S, Scranton MI, Taylor GT, Astor YM, Crowe SA, Doebeli M (2019) Circumventing kinetics in biogeochemical modeling. Proc Natl Acad Sci 116(23):11329–11338. https://doi.org/10.1073/pnas.1819883116

    Article  CAS  PubMed  Google Scholar 

  • Mairhofer S, Zappala S, Tracy SR, Sturrock C, Bennett M, Mooney SJ, Pridmore T (2012) RooTrak: automated recovery of three-dimensional plant root architecture in soil from X-ray microcomputed tomography images using visual tracking. Plant Physiol 158(2):561–569. https://doi.org/10.1104/pp.111.186221

    Article  CAS  PubMed  Google Scholar 

  • Maskell EJ (1928) Experimental researches on vegetable assimilation and respiration. XVIII.—the relation between stomatal opening and assimilation.—a critical study of assimilation rates and porometer rates in leaves of Cherry Laurel. Proc R Soc Lond Ser B 102:488–533

    Google Scholar 

  • Millet EJ, Kruijer W, Coupel-Ledru A, Alvarez Prado S, Cabrera-Bosquet L, Lacube S, Charcosset A, Welcker C, van Eeuwijk F, Tardieu F (2019) Genomic prediction of maize yield across European environmental conditions. Nat Genet 51(6):952–956. https://doi.org/10.1038/s41588-019-0414-y

    Article  CAS  PubMed  Google Scholar 

  • Monsi M, Saeki T (1953) Über den Lichtfaktor in den Pflanzengesellschaften und seine Bedeutung für die Stoffproduktion. Jpn J Bot 14:22–52

    Google Scholar 

  • Monteith JL, Unsworth MH (2013) Chapter 13 – steady-state heat balance: (i) Water surfaces, soil, and vegetation. In: Monteith JL, Unsworth MH (eds) Principles of environmental physics, 4th edn. Academic Press, Boston, pp 217–247. https://doi.org/10.1016/B978-0-12-386910-4.00013-5

    Chapter  Google Scholar 

  • Oxford Dictionary of English (2010) Oxford University Press. ISBN: 9780199571123. https://doi.org/10.1093/acref/9780199571123.001.0001

  • Pradal C, Fournier C, Valduriez P, Cohen-Boulakia S (2015) OpenAlea: scientific workflows combining data analysis and simulation. Paper presented at the Proceedings of the 27th international conference on Scientific and Statistical Database Management, La Jolla, CA

    Google Scholar 

  • Reed K, Hamerly E, Dinger B, Jarvis P (1976) An analytical model for field measurement of photosynthesis. J Appl Ecol 13:925–942

    Google Scholar 

  • Shuttleworth WJ (2007) Putting the ‘vap’ into evaporation. Hydrol Earth Syst Sci 11:1–35

    Google Scholar 

  • Sinclair TR, Muchow RC (1999) Radiation use efficiency. In: SPARKS DL (ed) Advances in agronomy. Academic, New York

    Google Scholar 

  • Sinclair TR, Shiraiwa T, Hammer GL (1992) Variation in crop radiation-use efficiency with increased diffuse radiation. Crop Sci 32:1281–1284

    Google Scholar 

  • StÖckle CO, Donatelli M, Nelson R (2003) CropSyst, a cropping systems simulation model. Eur J Agron 18:289–307

    Google Scholar 

  • Tardieu F, Simonneau T, Muller B (2018) The physiological basis of drought tolerance in crop plants: a scenario-dependent probabilistic approach. Annu Rev Plant Biol 69(1):733–759. https://doi.org/10.1146/annurev-arplant-042817-040218

    Article  CAS  PubMed  Google Scholar 

  • Thomas B (2003) Regulators of growth photoperiodism. In: Thomas B (ed) Encyclopedia of applied plant sciences. Elsevier, Oxford

    Google Scholar 

  • Thomas B, Vince-Prue D (1997) Photoperiodism in plants. Academic, San Diego

    Google Scholar 

  • Thornley JH (1976) Mathematical models in plant physiology. Academic, London

    Google Scholar 

  • Thornley JHM (1998) Dynamic model of leaf photosynthesis with acclimation to light and nitrogen. Ann Bot 81:421–430

    Google Scholar 

  • Thornley JH, France J (2007) Mathematical models in agriculture: quantitative methods for the plant, animal and ecological sciences. CABI, Cambridge, MA

    Google Scholar 

  • Van Dingenen R, Dentener FJ, Raes F, Krol MC, Emberson L, Cofala J (2009) The global impact of ozone on agricultural crop yields under current and future air quality legislation. Atmos Environ 43(3):604–618. https://doi.org/10.1016/j.atmosenv.2008.10.033

    Article  CAS  Google Scholar 

  • Vialet-Chabrand SRM, Matthews JSA, McAusland L, Blatt MR, Griffiths H, Lawson T (2017) Temporal dynamics of stomatal behavior: modeling and implications for photosynthesis and water use. Plant Physiol 174(2):603–613. https://doi.org/10.1104/pp.17.00125

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wang E, Engel T (1998) Simulation of phenological development of wheat crops. Agric Syst 58(1):1–24. https://doi.org/10.1016/S0308-521X(98)00028-6

    Article  Google Scholar 

  • Wang S, Grant RF, Verseghy DL, Black TA (2001) Modelling plant carbon and nitrogen dynamics of a boreal aspen forest in CLASS – the Canadian Land Surface Scheme. Ecol Model 142:135–154

    CAS  Google Scholar 

  • Wu S, Wang X, Reddy U, Sun H, Bao K, Gao L, Mao L, Patel T, Ortiz C, Abburi VL (2019) Genome of ‘Charleston Gray’, the principal American watermelon cultivar, and genetic characterization of 1,365 accessions in the US National Plant Germplasm System watermelon collection. Plant Biotechnol J 17(12):2246–2258

    CAS  PubMed  PubMed Central  Google Scholar 

  • Yan W, Hunt LA (1999) An equation for modelling the temperature response of plants using only the cardinal temperatures. Ann Bot 84:607–614

    Google Scholar 

  • Yan W, Wallace DH, Ross J (1996) A model of photoperiod × Temperature interaction effects on plant development. Crit Rev Plant Sci 15(1):63–96. https://doi.org/10.1080/07352689609701936

    Article  Google Scholar 

  • Yan W, Wallace DH (1998) Simulation and prediction of plant phenology for five crops based on photoperiod × temperature interaction. Ann Bot 81:705–716

    Google Scholar 

  • Ye Z-P (2007) A new model for relationship between irradiance and the rate of photosynthesis in Oryza sativa. Photosynthetica 45:637–640

    CAS  Google Scholar 

  • Yin X, van Laar HH (2005) Crop systems dynamics: an ecophysiological simulation model for genotype-by-environment interactions. Wageningen Academic Publishers, Wageningen

    Google Scholar 

  • Yin X, Kropff MJ, Mclaren G, Visperas RM (1995) A nonlinear model for crop development as a function of temperature. Agric For Meteorol 77:1–16

    Google Scholar 

  • Zheng B, Chenu K, Doherty A, Doherty T, Chapman L (2014) The APSIM-wheat module (7.5 R3008). In: APSRU Toowoomba, Australia

    Google Scholar 

  • Zhou G, Wang Q (2018) A new nonlinear method for calculating growing degree days. Sci Rep 8:10149

    PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. Department of Agricultural Research for Northern Sweden, Swedish University of Agricultural Sciences, Umeå, Sweden

    Mukhtar Ahmed

  2. Department of Agronomy, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi, Pakistan

    Mukhtar Ahmed

  3. Department of Agronomy, Faculty of Agricultural Sciences and Technology, Bahauddin Zakariya University, Multan, Pakistan

    Shakeel Ahmad

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  1. Mukhtar Ahmed
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Correspondence to Mukhtar Ahmed .

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  1. Department of Agricultural Research for Northern Sweden, Swedish University of Agricultural Sciences, Umeå, Sweden, Department of Agronomy, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi, Pakistan

    Mukhtar Ahmed

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Ahmed, M., Ahmad, S. (2020). Systems Modeling. In: Ahmed, M. (eds) Systems Modeling. Springer, Singapore. https://doi.org/10.1007/978-981-15-4728-7_1

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