Evaluating The Application Of AquaCrop Modeling In Simulating Rice Crop Performance Under System Of Rice Intensification (SRI) Management

References

[1] Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop evapotranspiration: Guidelines for computing crop water requirements. Irrigation and Drainage Paper no. 56. FAO, Rome, pp 300.

[2] Bengtson, H., 2010. Open Channel Flow Measurement 2: The Parshall Flume, ed. Lamar Stonecypher. Bright Hub Engineering, Troy, NY. http://www.brighthubengineering.com/hydraulics- civil-engineering/61462-open-channel-flow- measurement-parshall-flumes/

[3] Emongόr, R.A., Mureithi, F.M., Ndirangu, S.N., Kitaka, D.M., Walele, B.M., 2009. The Rice Value Chain in Kenya with Reference to Rice Producers. Kenya Agricultural Research Institute, Nairobi, City, Kenya.

[4] FAO. 2016. Save and Grow: Maize, Rice and Wheat– A Guide to Sustainable Crop Production, 44-47. UN Food and Agriculture Organization, Rome.

[5] Heng, L.K., Hsiao, T., Evett, S., Howell, T., Steduto, P., 2009. Validating the FAO AquaCrop Model for irrigated and water deficient field maize. J. Agron. 101, 488-498.

[6] Herschy, R.W., 2014. Streamflow Measurement, 3 rd ed. CRC Press, Boca Raton, FL.

[7] Hsiao, T.C., Heng, L.K., Steduto, P., Rojas-Lara, B., Raes, D., and Fereres, E., 2009. AquaCrop-The FAO crop model to simulate yield response to water: III. Parameterization and testing for maize. J. Agron. 101, 448-459.

[8] Mati, B.M., Wanjogu, R.K., Odongo, B., Home, P.G., 2011. Introduction of System of Rice Intensification in Kenya: Experiences from Mwea Irrigation Scheme. Paddy Water Environ 9(1), 145-154.

[9] McCauley, G.N., Turner, F.T., 1979. Rice Production and Water Use Efficiency in Relation to Flood Period. In: Agronomy Abstracts. ASA, Madison, WI, p. 105.

[10] McCown, R.L., Freebairn, D.M., Smith, C.J., 2003. An overview of APSIM: A model designed for farming systems simulation. Eur. J. Agron. 18, 267-288.

[11] McDonald, A.J., Hobbs, P.R., Riha, S.J., 2006. Does the System of Rice Intensification out- perform conventional best management? A synopsis of the empirical record. J. Field Crops Res. 96, 31-36.

[12] Menete, M. Z. L., Brito, R. M. L., DeGloria, S.D., Famba, S., 2008. Evaluation of system of rice intensification (SRI) component practices and their synergies on salt-affected soils. J. Field Crops Res. 109, 34-44.

[13] Michael, A. M., 2008. Irrigation Theory and Practice, 2 nd edition. Vikas Publishing, New Delhi, India.

[14] Mishra, A., 2009. System of rice intensification (SRI): A quest for interactive science to mitigate the climate change vulnerability. IOP Conf. Series: Earth and Environmental Science 6: 242028.

[15] Ndiiri, J. A., Mati, B. M., Home, P.G., Odongo, B., Uphoff, N., 2012. Comparison of water savings of paddy rice under System of Rice Intensification (SRI) for growing rice in Mwea, Kenya. Intl. J. Curr. Res. Rev. 4(6), 63-73.

[16] Ndiiri, J. A., Mati, B. M., Home, P.G., Odongo, B., Uphoff, N., 2013. Paddy rice under the System of Rice Intensification: Benefit-cost analysis and other benefits in Mwea, Kenya. Agric. Water Mgmt. 129, 44-55

[17] Ndiiri, J. A., Uphoff, N., Mati, B. M., Home, P.G., Odongo, B., 2013. Water productivity under the System of Rice Intensification from experimental plots and farmer surveys in Mwea, Kenya. Taiw. Water Conserv. 61(4), 63-75.

[18] Ndiiri, J. A., Uphoff, N., Mati, B. M., Home, P.G., Odongo, B., 2017. Comparison of yields of paddy rice under the System of Rice Intensification (SRI) for growing rice in Mwea, Kenya. Amer. J. Plant Biol. 2(2), 49-60

[19] Raes, D., 2009. ETo calculator version 3.1. In: Evapotranspiration from Reference Surface. Land and Water Division, UN Food and Agriculture Organization, Rome Italy, 65 pp.

[20] Raes, D., Stedulo, P., Hsiao, C., Fereres, E., 2009. AquaCrop: The FAO crop model to simulate yield response to water: Reference Manual. UN Food and Agriculture Organization, Rome Italy.

[21] Raes, D., Steduto, P., Hsiao, T.C., Fereres, E., 2010. The FAO Crop Model to simulate yield response to water: II. Main algorithms and software description. J. Agron 101, 438-447.

[22] Saadati, Z., Pirmoradian, N., Rezaei, M., 2011. Calibration and Evaluation of AquaCrop Model in Rice growth simulation under different irrigation managements. ICID 21 st International Congress on Irrigation and Drainage, 15-23 October 2011, Tehrani, Iran. R.56.5/Poster/14

[23] Saxton, K.E., Rawls, W.J., 2006. Soil water characteristic estimates by texture and organic matter for hydrologic solutions. Soil Sci. Soc. Amer. J. 70, 1569-1578.

[24] Sheldrick, B.H., Wang, C., 1993. Particle Size Distribution. Pp 499-511. In: MR Carter (ed.), Soil Sampling and Methods of Analysis. Canadian Society of Soil Science. Lewis Publishers. Ann Arbor, MI.

[25] Sombroek, W.G., Braun, H.M.H., Van der Pouw, B.J.A., 1982. The exploratory soil map of Kenya and agro climatic zone map of Kenya scale 1:1million. Exploratory Soil Survey Report No. E1. Kenya Soil Survey, Nairobi.

[26] Steduto, P., Raes, D., Hsiao, T., Fereres, E., Heng, L., Izzi, G., Hoogeveen, J., 2009. AquaCrop: a new model for crop prediction under water deficit conditions. Options Méditerranéennes, Series A: 80, 285-292.

[27] Stoop, W.A., Uphoff, N., Kassam, A., 2002. A review of agricultural research issues raised by the system of rice intensification (SRI) from Madagascar: Opportunities for improving farming systems for resource-poor farmers. Agric. Syst. 71, 249-274.

[28] Thakur, A.K., Uphoff, N., 2017. How the System of Rice Intensification can contribute to climate- smart agriculture. Agronomy J. 109, 1-20.

[29] Wanjogu, R.K., Mugambi, G., Adoli, H.L., Shisanya, S.O., Tamura, M., 1995. Mwea Rice Production Manual. Mwea Irrigation Agricultural Development Project, National Irrigation Board Library, Nairobi.

[30] Willmott, C.J. 1982. Some comments on the evaluation of model performance. Bull. Amer. Meteor. Soc. 63, 1309–1313.

[31] World Bank Institute, 2008. System of Rice Intensification (SRI): Achieving More with Less— A New Way of Rice Cultivation; Overview of SRI - Improving Rice Productivity and Achieving Water Savings. WBI Multimedia Team, World Bank Institute, Washington, D.C.

@article{1701567,
      author = {Dr. Jackline Alinda Ndiiri, Prof. Norman Uphoff, Prof. Bancy M. Mati, Prof. Patrick G. Home, Dr. Benson Odongo},
      title = {Evaluating The Application Of AquaCrop Modeling In Simulating Rice Crop Performance Under System Of Rice Intensification (SRI) Management},
      journal = {Iconic Research And Engineering Journals},
      year = {2019},
      volume = {3},
      number = {2},
      pages = {655-666},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1701567.pdf},
      abstract = {Crop models can be useful as decision support tools for cropping system management, planning and policy analysis. Specifically, modeling of crop yield response to water applications can contribute significantly to the optimization of crop water productivity in irrigated rice cultivation, which consumes about 80% of total irrigation freshwater.Field experiments were conducted in 2010 and 2011 seasons at the Mwea Irrigation Agricultural Development (MIAD) Center located in the Mwea Irrigation Scheme (MIS), Kenya to compare yields of Basmati 370 variety grown under System of Rice Intensification (SRI) management with reduced water applications vs. conventional practices of continuous flooding (CF).AquaCrop version 4.0 was used to simulate total biomass, grain yields and canopy cover. Model performance was evaluated using index of agreement (d), root mean square error (RMSE), coefficient of efficiency (E), and coefficient of determination (R2). The findings indicated a high potential effectiveness of a calibrated AquaCrop model with a high degree of reliability: R2 =0.97 for biomass and 0.87 for canopy cover. There was also a good fit between the observed and simulated grain yields, with a deviation of only 4.2% and -5.7% under SRI and CF practices, respectively. That AquaCrop model could be applied successfully to estimate biomass, canopy cover and grain yields, with in Mwea irrigation scheme showed that it can be used as a decision support tool by scheme managers and farmers to explore various management options for maximizing yields.},
      keywords = {AquaCrop model; Water Productivity; Basmati 370 variety; Kenya; Mwea irrigation scheme; System of Rice Intensification},
      month = {August},
  }