Crop simulation model

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A Crop Simulation Model (CSM) is a simulation model that describes processes of crop growth and development as a function of weather conditions, soil conditions, and crop management.[1][2][3][4][5] Typically, such models estimate times that specific growth stages are attained, biomass of crop components (e.g., leaves, stems, roots and harvestable products) as they change over time, and similarly, changes in soil moisture and nutrient status.

Types of crop simulation models[edit]

Crop simulation models have been classified into three broad categories:[6][7]

  1. Statistical models: These typically rely on yield information for large areas (such as counties), and identify broad trends. The two main trends identified are a secular trend of a gradual increase in crop yield, and variation based on weather conditions. Statistical models are a significant improvement over naive historical predictions, but are not preferred for very fine-grained predictions.
  2. Mechanistic models: These attempt to use fundamental mechanisms of plant and soil processes to simulate specific outcomes. These involve fairly detailed and computation-intensive simulations. These models use a continuous evolution and simulating them previously requires a small time step.
  3. Functional models: These use simplified closed functional forms to simulate complex processes. They are computationally easier than mechanistic models, and can often give results that are of only somewhat worse accuracy. The Penman equation is an example of an equation that might be used as a component of a functional model. Functional models are typically run using a daily time step and the data is updated daily.[6]

Commonly used crop simulation models[edit]

See also[edit]

References[edit]

  1. ^ "What Are Crop Simulation Models?". Agricultural Research Service, United States Department of Agriculture. Retrieved May 23, 2014.
  2. ^ "Georgia Crop Simulation Model". Georgia Weather. Retrieved May 23, 2014.
  3. ^ a b "CropSyst". Retrieved May 23, 2014.
  4. ^ Hoogenboom, Gerrit; White, Jeffrey W.; Messina, Carlos D. (2004). "From genome to crop: integration through simulation modeling". Field Crops Research. 90: 145–163. doi:10.1016/j.fcr.2004.07.014.
  5. ^ Chakrabarti, B. "Crop Simulation Models" (PDF). Indian Agricultural Research Institute. Retrieved May 23, 2014.
  6. ^ a b Basso, Bruno; Cammarano, David; Carfagna, Elisabetta. "Review of Crop Yield Forecasting and Early Warning Systems" (PDF).
  7. ^ Dourado-Neto, D.; Teruel, D. A.; Reichart, K.; Nielsen, D. R.; Frizzone, J. A.; Bacchi, O. O. S. "Principles of crop modeling and simulation: I. uses of mathematical models in agricultural science".
  8. ^ 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.; McCown, R.L; Freebairn, D.M; Smith, C.J (2003-01-01). "An overview of APSIM, a model designed for farming systems simulation". European Journal of Agronomy. 18 (3–4): 267–288. doi:10.1016/S1161-0301(02)00108-9. ISSN 1161-0301.
  9. ^ Holzworth, Dean P.; Huth, Neil I.; Devoil, Peter G.; Zurcher, Eric J.; Herrmann, Neville I.; McLean, Greg; Chenu, Karine; Van Oosterom, Erik J.; Snow, Val; Murphy, Chris; Moore, Andrew D.; Brown, Hamish; Whish, Jeremy P.M.; Verrall, Shaun; Fainges, Justin; Bell, Lindsay W.; Peake, Allan S.; Poulton, Perry L.; Hochman, Zvi; Thorburn, Peter J.; Gaydon, Donald S.; Dalgliesh, Neal P.; Rodriguez, Daniel; Cox, Howard; Chapman, Scott; Doherty, Alastair; Teixeira, Edmar; Sharp, Joanna; Cichota, Rogerio; et al. (2014-12-01). "APSIM – Evolution towards a new generation of agricultural systems simulation". Environmental Modelling & Software. 62: 327–350. doi:10.1016/j.envsoft.2014.07.009. ISSN 1364-8152.
  10. ^ Holzworth, Dean; Huth, N.I.; Fainges, J.; Brown, H.; Zurcher, E.; Cichota, R.; Verrall, S.; Herrmann, N.I.; Zheng, B.; Snow, V. (2018-05-01). "APSIM Next Generation: Overcoming challenges in modernising a farming systems model". Environmental Modelling & Software. 103: 43–51. doi:10.1016/j.envsoft.2018.02.002. ISSN 1364-8152.