Modification, Calibration and Validation of APSIM to Suit Maize (Zeamays L.) Production System: A Case of Nkango Irrigation Scheme in Malawi
American Journal of Agriculture and Forestry
Volume 2, Issue 6-1, December 2014, Pages: 1-11
Received: Oct. 22, 2014;
Accepted: Nov. 10, 2014;
Published: Nov. 12, 2014
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John Mthandi, Department of Agri. Eng. and Land Planning, Sokoine University of Agriculture, Morogoro, Tanzania
Fredrick C. Kahimba, Department of Agri. Eng. and Land Planning, Sokoine University of Agriculture, Morogoro, Tanzania
Andrew K. P. R. Tarimo, Department of Agri. Eng. and Land Planning, Sokoine University of Agriculture, Morogoro, Tanzania
Baandah. A. Salim, Department of Agri. Eng. and Land Planning, Sokoine University of Agriculture, Morogoro, Tanzania
Max W. Lowole, Department of Crop Science, Bunda College of Agriculture, Lilongwe, Malawi
Nitrogen (N) is the most important nutrient in maize production and its availability can affect the production potential of maize. Availability of nitrogen in soil largely varies with place and time. Models are some of practical methods used to evaluate and monitor availability and impact of nitrogen on maize production; APSIM is one of such models. APSIM has several modules that have different functionalities and one of such modules is SoilWat module. The study modified SoilWat module by incorporating Nitrogen Distribution model. Trial and error method was used in the calibration of the nitrogen distribution model that was incorporated in the APSIM model as subroutine. The initial values of nitrogen distribution were obtained from literature and these values formed the basis for development of the model. After development of model using parameters obtained from literature review, field experiment was conducted to collect data to be used in redefining the model. The simulated nitrogen distribution was compared with values obtained from the field experiment and their mean differences were initially high but the process was repeated until the mean difference was small. In field experiment, the study had two factor, each with four regimes. The Triscan Sensor (EnviroScan, Sentek Pty Ltd, Stepney, Australia) was used to measure total nitrogen concentration at lateral distances and vertical depths. Primary soil samples were collected and analysed at Bunda College Laboratory. The study inferred that Soil water percolates down to underlying layer only when proceeded layers are satisfied i.e. has reached its field capacity, above which excess water is left free to percolates down the soil profile. Before water arriving in last layer it had to satisfy the above-lying soil profiles. The study has shown that increase of nitrogen contents in underlying layers corresponds with decrease of the same in top layers due to advection movement. Consequently, the increases of soil water in a specific layer correspond to decrease of nitrogen content in that particular layer. The study has shown that APSIM under predicted during the latter stage of the maize growing season and over predicted in the early stage of the growing season, and it overestimates soil water contents in soil profile.
Fredrick C. Kahimba,
Andrew K. P. R. Tarimo,
Baandah. A. Salim,
Max W. Lowole,
Modification, Calibration and Validation of APSIM to Suit Maize (Zeamays L.) Production System: A Case of Nkango Irrigation Scheme in Malawi, American Journal of Agriculture and Forestry. Special Issue: Agriculture Ecosystem and Environment.
Vol. 2, No. 6-1,
2014, pp. 1-11.
Uribelarrea, M., Crafts-Brandner, S.J., Below, F.E., 2009. Physiological N response of field-grown maize hybrids (Zea mays L.) with divergent yield potential and grain protein concentration. Plant and Soil, 316, 151-160.
Mullins, G.L., Alley, S.E., Reeves, D.W., 1998. Tropical maize response to nitrogen and starter fertilizer under strip and conventional tillage systems in southern Alabama. Soil Till. Res. 45, 1-15.
Halvorson, A.D., Mosier, A.R., Reule, C.A., Bausch, W.C., 2006. Nitrogen and tillage effects on irrigated continuous corn yields. Agron. J. 98, 63-71.
Eck, H.V., 1984. Irrigated corn yield response to nitrogen and water. Agron. J. 76, 421-428.
Al-Kaisi, M.M., and X. Yin. 2003. Effects of nitrogen rate, irrigation rate, and plant population on corn yield and water use efficiency. Agron. J. 95: 1475 – 1482.
Devienne-Barret, F., Justes, E., Machet, J.M., Mary, B., 2000. Integrated control of nitrate uptake by crop growth rate and soil nitrate availability under field conditions. Ann. Bot. 86, 995-1005.
Russell, A.E., Laird, D.A., Mallarino, A.P., 2006. Nitrogen fertilization and cropping system impacts on soil quality in midwestern mollisols. Soil Sci. Soc. Am. J. 70, 249-255.
Addiscott, T.M. 1995. Modelling the fate of crop nutrients in the environment: Problems of scale and complexity. European Journal of Agronomy 4: 413417.
Jury, W.A., Gardner, W.R. and Gardner, W.H. 1991. Soil physics. 5th edition. John Wiley and Sons. New York.
Van Genuchten, M.Th., Leij, F.J., Yates, S.R., 1991. The RETC code for quantifying the hydraulic functions of unsaturated soils. USAEPA Rep. 600/2-91/065. R.S. Kerr Environmental Research Laboratory. Ada, OK 74820.
Vitousek, P.M., Hattenschwiler, S., Olander, L. and Allison, S. 2002. Nitrogen and nature. Ambio. 31: 97-101
Hutson, J.L. and Wagenet, R.J. 1993. A pragramatic field scale approach for modelling pesticides. Journal of Environmental Quality 22: 494–499.
Hanson, J.D., Rojas, K.W. and Shaffer, M.J. 1999. Calibrating the root zone water quality model. Agronomy Journal. J. 91:171–177.
Ahuja, L.R., Rojas, K.W., Hanson, J.D., Shaffer, M.J. and Ma L. (eds.). 2000. Root zone water quality model: Modelling management effects on water quality and crop production. Water Resources Publications LLC, Highlands Ranch, Co. 372 pp.
Shaffer, M. J., Halvorson, A. D. and Pierce, F. J. 1991. Nitrate leaching and economic analysis package (NLEAP): Model description and application. Pages 285–322. In R. F. Folet et al. (eds.). Managing nitrogen for groundwater quality and farm profitability. SSSA, Madison WI.
Simunek, J., Sejna, M. and van Genuchten, M.Th. 1999. The HYDRUS-2D software package for simulating two-dimensional movement of water, heat, and multiple solutes in variably saturated media. Version 2.0, IGWMC - TPS - 53, International Ground Water Modeling Center, Colorado School of Mines, Golden, Colorado, 251pp.
McCown, R.L., Hammer, G.L., Hargreaves, J.N.G., Holzworth, D.P. and Freebairn, D.N. (1996). APSIM: A novel software system for model development, model testing, and simulation in agricultural research. Agricultural Systems 50: 255-271.
Probert, M.E., J.P. Dimes, B.A. Keating, R.C. Dalal, and W.M. Strong. 1998.APSIM’s water and nitrogen molecules and simulation of the dynamics of water and nitrogen in fallow systems. Agricultural systems, 56, 1-28,
Sharp, J.M., Thomas S.M, Brown, H.E. 2011. A validation of APSIM nitrogen balance and leaching predictions. The New Zealand Institute for Plant & Food Research Limited, Private Bag 4704, Christchurch, 8140, New Zealand.
Allen, R.G., L.S. Pereira, D. Raes, and M. Smith. 1998. Crop evapotranspiration. Guidelines for computing crop water requirements. Irrig. And Drainage Paper no. 56.
Salim, B.A. (1999). Modelling and Measurement of Soil Moisture Content Based on a Remote Sensing Method for Application in Semi-Arid Tropics. PhD dissertation. Institut fur Landtechik der Rheinischen Friedrich-Wilhelms-Universitaet Bonn, pp. 148-149.
Ministry of Agriculture and Food Security. (2011). Annual reports and notes. Lilongwe, Malawi.
FOASTAT (2000) Maize. http://www.fao.org/nr/water/cropinfo_maize.html