Models in Irrigation and Water Management

Chapter

Abstract

In some cases, we need to know the knowledge of specific process within a complex system of interacting and interdependent phenomena, and then need to reintegrate such knowledge to obtain a comprehensive and accurate solution of the phenomena. Model is ideal to integrate the complex system and to obtain the answer if the condition is. It is merely a useful tool in obtaining answers in the choice of a decision or policy.

Keywords

Biomass Clay Porosity Shrinkage Photosynthesis 

References

  1. Addiscott TM, Whitemore AP (1987) Computer simulation of changes in soil mineral nitrogen and crop nitrogen during autumn, winter and spring. J Agric Sci Camb, 109:141–157CrossRefGoogle Scholar
  2. Ali MH (2000) Behavior study of cracking clay soil with special emphasis on water relations. M. Engg. Thesis, Submitted to the Department of Civil & Environmental Engineering, The University of Melbourne, AustraliaGoogle Scholar
  3. Ali MH (2005a) CropET0: a computer model to estimate reference evapotranspiration from climatic data. Bangladesh J Agric Eng 16(1 & 2):25–37Google Scholar
  4. Ali MH (2005b) E-STAT: a computer program to perform statistical analysis of experimental data. J Bangladesh Agric Univ 3(1):133–138Google Scholar
  5. Ali MH, Amin MGM (2006) ‘AmanGrow’: a simulation model based on weather parameters for predicting transplanted ‘Aman rice’ production in Bangladesh. Ind J Agril Sci 76(1):50–51Google Scholar
  6. Ali MH, Hoque MR, Hassan AA, Khair MA (2007) Effects of deficit irrigation on wheat yield, water productivity and economic return. Agric Water Manage 92:151–161CrossRefGoogle Scholar
  7. Allen R, Pereira L, Raes D, Smith M (1998) Crop evapotranspiration (guidelines for computing crop water requirements). FAO Irrigation and Drainage Paper No 56. Rome, Italy, p 300Google Scholar
  8. APSIM Initiative (2010) APSIM – agricultural production systems simulator. http://www.apsim.info. Accessed on Mar 10, 2010
  9. Bronswijk JJB (1988) Modeling of water balance, cracking and subsidence of clay soils. J Hydrol 97:199–212CrossRefGoogle Scholar
  10. Chandran KP, Prajneshu (2004) Modeling the effect of sunshine and temperature on rice tiller production using non-parametric regression. Ind J Agril Sci 74(10):563–565Google Scholar
  11. Clarke D (1998) CropWat for windows: user guide. FAO, Rome, p 23Google Scholar
  12. de Wit CT (1958) Transpiration and crop yields. Agric Res Rep. 64.6, Pudoc, Wegeningen, 88 ppGoogle Scholar
  13. De Laat PJM (1980) Model for unsaturated flow above a shallow water-table. Applied to a regional sub-surface flow problem. PUDOC, Doctoral thesis, Wageningen, The Netherlands, p 126Google Scholar
  14. De Laat PJM (1995) Design and operation of a subsurface irrigation scheme with must. In Pereira, LS, van den Broek, BJ, Kabat, P, Allen RG (eds) Cropwater-simulation models in practice. Wageningen Pers, The Netherlands, pp 123–140Google Scholar
  15. Donnigan AS (1983) Model predictions vs. field observations: the model validation/testing process. In: Swannand RL, Eschenroder A (eds) Fate of chemicals in the environment. American Chemical Society Symposium Series 225. ACS, Washington, DC, pp 151–171CrossRefGoogle Scholar
  16. Doorenbos J, Kassam AH (1979) Yield response to water. FAO Irrigation and Drainage Paper No. 33, FAO, RomeGoogle Scholar
  17. Feddes RA, Kowalik PJ, Zaradny H (1978) Simulation of field water use and crop yield. Simulation Monographs. PUDOC, Wageningen, The Netherlands, p 189Google Scholar
  18. Fox MS (1981) An organizational view of distributed systems. IEEE Trans Syst Man Cybernet 11:70–80CrossRefGoogle Scholar
  19. Gabrielle B, Kengni L (1996) Analysis and field-evaluation of the CERES model’s soil components: nitrogen transfer and transformations. Soil Sci Soc Am J 60:142–149CrossRefGoogle Scholar
  20. Granger OE (1980) The impact of climatic variation on the yield of selected crops in three California countries. Agril Meteor 22:367–386CrossRefGoogle Scholar
  21. Hanks RJ (1974) Model for predicting plant yield as influenced by water use. Agron J 66:660–665CrossRefGoogle Scholar
  22. Hexem RW, Heady EO (1978) Water production functions for irrigated agriculture. Iowa State University Press, Ames, IA, p 215.Google Scholar
  23. Howell TA, Hiler EA (1975) Optimization of water use efficiency under high frequency irrigation. I. Evapotranspiration and yield relationship. Trans ASAE 18(5):873–878Google Scholar
  24. Jarvis N (1994) The MACRO model (Version 3.1): technical description and sample simulations. Monograph, Department of Soil Sciences, Reports and Dissertations – 19, Swedish University of Agricultural Sciences, UppsalaGoogle Scholar
  25. Jensen ME (1968) Water consumption by agricultural plants. In: Kozlowski TT (ed) Water deficit and plant growth, vol 2, Academic press, New York, pp 1–22Google Scholar
  26. Lecina S, Martinez-Cob A, Perez PJ, Villalobos FJ, Baselga JJ (2003) Fixed versus variable bulk canopy resistance for reference ET estimation using the Penman-Monteith equation under semi-arid conditions. Agril Water Manage 60:181–198CrossRefGoogle Scholar
  27. Loague K, Green RE (1991) Statistical and graphical methods for evaluating solute transport models: overview and application. J Contam Hydrol 7:51–73CrossRefGoogle Scholar
  28. Loague KM, Yost RS, Green RE, Liang TC (1989) Uncertainty in a pesticide leaching assessment for Hawai. J Contamin Hydrol 4:139–161CrossRefGoogle Scholar
  29. Mapp HP, Eidman VR (1978) Simulation of soil-water-crop yield systems: the potential for economic analyses. South J Agric Econ 7(1):47–53Google Scholar
  30. Martin DL, Watts DG, Gulley JR (1984) Model and production function for irrigation management. J Irrig Drain Eng, ASCE 110(2):149–164CrossRefGoogle Scholar
  31. McCown RL, Hammer GL, Hargreaves JNG, Hozworth DP, Freebairn DM (1996) APSIM: a novel software system for model development, model testing and simulation in agricultural systems research. Agric Syst 50:255–271CrossRefGoogle Scholar
  32. Minhas BS, Parikh KS, Srinivasan TN (1974) Towards the structure of a production function for wheat yields with dated inputs of irrigation water. Water Resour Res 10:383–393CrossRefGoogle Scholar
  33. Mogensen VO, Jensen HE, Rab MA (1985) Grain yield, yield components, drought sensitivity and water use efficiency of spring wheat subjected to water stress at various growth stages. Irrig Sci 6:131–140Google Scholar
  34. Monteith JL (1981) Evaporation and surface temperature. Quart J R Meteor Soc 107:1–27CrossRefGoogle Scholar
  35. Musick JT, Jones OR, Stewart BA, Dusek DA (1994) Water-yield relationships for irrigated and dryland wheat in the U.S. Southern Plains. Agron J 86:980–986Google Scholar
  36. Oostindie K, Bronswijk JJB (1992) FLOCR – a simulation model for the calculation of water balance, cracking and surface subsidence of clay soils. Report 47, Agricultural Research Department, The Winand Staring Centre for Integrated Land, Soil and Water Research, Wageningen, The NetherlandsGoogle Scholar
  37. Parthasarathy BK, Kumar BR, Munot AA (1992) Forecast of rainy-season food grain production based on monsoon rainfall. Ind J Agril Sci 62(1):1–8Google Scholar
  38. Quadir DA, Khan TMA, Hossain MA, Iqbal A (2003) Study of climate variability and its impact of rice yield in Bangladesh SAARC. J Agric 1:69–83Google Scholar
  39. Raes D (2002) UPFLOW – water movement in a soil profile from a shallow water table to the top soil (capillary rise). Reference manual version 2.1, Department of Land Management, K.U. Leuven University, BelgiumGoogle Scholar
  40. Raes D, Deproost P (2003) Model to assess water movement from a shallow water table to the root zone. Agric Water Manage 62:79–91CrossRefGoogle Scholar
  41. Rao NH, Sarma PBS, Chander S (1988) Irrigation scheduling under a limited water supply. Agric Water Manage 15:165–175CrossRefGoogle Scholar
  42. Rao NH, Sarma PBS, Chandar S (1990) Optimal multicrop allocation of seasonal and intra-seasonal irrigation water. Water Reour Res 26:551–559CrossRefGoogle Scholar
  43. Retta A, Vanderlip RL, Higgin RA, Moshier LJ (1996) Application of SORKAM to simulate shattercane growth using forage sorghum. Agron J 88:596–601CrossRefGoogle Scholar
  44. Singh P, Aggarwal MC (1986) Improving irrigation water use efficiency and yield of cotton by different agrotechniques. In: Proceedings of the National Seminar, water management the key to development of agriculture. Indian National Science Academy, New Delhi (1986)Google Scholar
  45. Singh NT, Singh R, Mahajan PS, Vig AC (1979) Influence of supplemental irrigation and pre-sowing soil water storage on wheat. Agron J 71:401–404Google Scholar
  46. Smith M, Allen R, Monteith JL, Perrier A, Pereira LS, Segeren A (1992) Expert consultation on revision of FAO methodologies for crop water requirements. Food and Agriculture Organization of the United Nation (Land and Water Development Division), Rome, p 60Google Scholar
  47. Stewart JI, Hagan EM (1973) Functions to predict effects of crop after deficit. J Irrig Drain Div, ASCE, 99(IR4):429–439Google Scholar
  48. Stewart JI, Hagen RM, Pruitt WO, Hanks RJ, Denilson RE, Franklin WT, Jackson EB (1977) Optimizing crop production through control of water, salinity levels. Utah Water Research Laboratory PRWG 151, Logan, Utah, 191 ppGoogle Scholar
  49. Stockle CO, Martin SA, Campbell GS (1994) CropSyst, acropping systems simulation model: water/nitrogen budgets and crop yield. Agric Syst 46(3):335–359CrossRefGoogle Scholar
  50. Stockle CO, Nelson RL (1994) CropSyst user’s manual (version 1.0). Biological Systems Engineering Department, Washington State University, Pullman, WAGoogle Scholar
  51. Stockle CO, Nelson RL (2000) CropSyst user’s manual (version 3.0). Biological Systems Engineering Department, Washington State University, Pullman, WAGoogle Scholar
  52. Tsakiris GP (1982) A method for applying crop sensitivity factors in irrigation scheduling. Agric Water Manage 5(4):335–343CrossRefGoogle Scholar
  53. Tsuji GY, Uehara G, Balas S (eds) (1994) DSSAT v.3. University of Hawaii, HonoluluGoogle Scholar
  54. Willmott CJ (1982) Some comments on the evaluation of model performance. Am Meteorol Soc Bull 63:1309–1313CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Agricultural Engineering DivisionBangladesh Institute of Nuclear Agriculture (BINA)MymensinghBangladesh

Personalised recommendations