Abstract
In this chapter, we will discuss issues related to the development of a general UML model that covers a large class of similar models, the class of water-balance and irrigation-scheduling models [19, 22, 36, 60, 71]. Many irrigation-scheduling and water-balance models have been developed and published in the past. These models have been used for both research purposes and as management tools.
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Addiscott, T.M., Wagenet, R.J.: Concepts of solute leaching in soils: a review of modeling approaches. J. Soil Sci. 36, 411–424 (1985)
Beck, H., Jackson, J.: Florida automated weather network In: Proceedings of the Seventh International Conference on Computers in Agriculture, pp. 595–601. Orlando, FL, ASAE, St. Joseph, MI (1998)
Chopart, J.L., Vauclin, M.: Water balance estimation model: field test and sensitivity analysis. Soil Sci. Soc. Am. J. 54, 1377–1384 (1990)
Clemente, R.S., De Jong, R., Hayhoe, H.N., Reynolds, W.D., Hares, M.: Testing and comparison of three unsaturated soil water flow models. Agric. Water Manag. 25, 135–152 (1994)
Eagleson, P.S.: Climate, soil and vegetation. A simplified model of soil moisture movement in the liquid phase. Water Resour. Res. 14, 722–730 (1978)
George, B.A., Shende, S.A., Raghuwanshi, N.S.: Development and testing of an irrigation scheduling model. Agric. Water Manag. 46, 121–136 (2000)
Hearn, A.B.: OZCOT, a simulation model for cotton crop management. Agric. Syst. 44, 257–299 (1994)
Laurenson, M., Kiura, T., Ninomiya, S.: Providing agricultural models with mediated access to heterogeneous weather databases. Appl. Eng. Agric. 17, 617–625 (2002)
Leib, B.G., Elliott, T.V., Matthews, G.: WISE: a web-linked and producer oriented program for irrigation scheduling. Comput. Electron. Agric. 33, 1–6 (2001)
Maraux, F., Lafolie, F., Bruckler, L.: Comparison between mechanistic and functional models for estimating soil water balance: deterministic and stochastic approaches. Agric. Water Manag. 38, 1–20 (1998)
Olejnik, J., Eulenstein, F., Kedziora, A., Werner, A.: Evaluation of a water balance model using data for bare soil and crop surfaces in Middle Europe. Agric. For. Meteorol. 106, 105–116 (2001)
Papajorgji, P., Shatar, T.: Using the unified modelling language to develop soil water-balance and irrigation-scheduling models. Environ. Model. Softw. 19, 451–459 (2004)
Ritchie, J.T.: Soil water balance and plant water stress. In: Tsuji, G.Y., Hoogenboom, G., Thornton, P.K. (eds.) Understanding Options for Agricultural Production. Kluwer, Dordrecht (1998)
Ritchie, J.T., Otter, S.: Description and performance of CERESwheat: a user-oriented wheat yield model, In: Muchow, R.C., Bellamy, J.A. (eds.) Climate Risk in Crop Production: Models and Management for the Semiarid Tropics and Subtropics. CAB International, Wallingford (1985)
Wegehenkel, M.: Test of a modelling system for simulating water balances and plant growth using various different complex approaches. Ecol. Model. 129, 39–64 (2000)
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Papajorgji, P.J., Pardalos, P.M. (2014). Soil Water-Balance and Irrigation-Scheduling Models: A Case Study. In: Software Engineering Techniques Applied to Agricultural Systems. Springer Optimization and Its Applications, vol 93. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7463-1_13
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DOI: https://doi.org/10.1007/978-1-4899-7463-1_13
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