Modeling Tomato Water Productivity Using Aquacrop Model in Njoro Sub County, Nakuru, Kenya

Main Article Content

Hellen J. Sang
Raphael M. Wambua
James M. Raude


Aims: To model tomato water productivity under deficit sub – surface drip irrigation and grass mulch densities using Aquacrop model.

Study Design: The study was factorial experimental with twelve treatments.

Place and Duration of Study: Tatton Agriculture Park, Egerton University, Nakuru, Kenya between January to May 2019.

Methodology: Tomato (Lycopersicon esculentum mill) crop (Tylka F1) was used to determine the effect of deficit irrigation and mulching on its productivity. Aquacrop model was calibrated to simulate the tomato yield, biomass and water productivity. Aquacrop model was used to estimate the tomato water requirements, water productivity, yield and biomass under deficit irrigation and mulching. The study was carried out on 36 experimental plots measuring 2 by 2 m with the total area under study being 144 m2.

Results: The results showed a good correlation between the actual and simulated water productivity as determined by the Nash and Sutcliffe efficiency (NSE) of 0.00, Root Mean Square Error (RMSE) (%) of 0.04 and Coefficient of determination (R2) of 0.72.

Conclusion: The study calibrated Aquacrop model for simulating tomato crop water productivity in Njoro Sub County and showed that the model is a good estimator of tomato water productivity.

Water productivity, deficit irrigation, calibrated.

Article Details

How to Cite
Sang, H. J., Wambua, R. M., & Raude, J. M. (2020). Modeling Tomato Water Productivity Using Aquacrop Model in Njoro Sub County, Nakuru, Kenya. Journal of Engineering Research and Reports, 10(3), 1-13.
Original Research Article


Moore TJ, et al. Modeling in engineering: The role of representational fluency in students' conceptual understanding. Journal of Engineering Education. 2013;102(1):141-178.

Velten K. Mathematical modeling and simulation: Introduction for scientists and engineers. John Wiley & Sons; 2009.

Ling Y, Mahadevan S. Quantitative model validation techniques: New insights. Reliability Engineering & System Safety. 2013;111:217-231.

Heng LK, et al. Validating the FAO Aquacrop model for irrigated and water deficient field maize. Agronomy Journal. 2009;101(3):488-498.

Raes D, et al. FAO crop water productivity model to simulate yield response to water. Aquacrop Version. 2011;3:1-1.

Sibomana I, Aguyoh J, Opiyo A. Water stress affects growth and yield of container grown tomato (Lycopersicon esculentum Mill) plants. Gjbb. 2013;2(4):461-466.

Mainuri ZG, Owino JO. Effects of land use and management on aggregate stability and hydraulic conductivity of soils within River Njoro Watershed in Kenya. International Soil and Water Conservation Research. 2013;1(2):80-87.

Pettyjohn WR. Infiltration rate and hydraulic conductivity of sand-silt soils in the Piedmont physiographic region. Georgia Institute of Technology; 2014.

Grossman R, Reinsch T. 2.1 Bulk density and linear extensibility. Methods of soil analysis: Part 4 physical methods. 2002;methodsofsoilan 4:201-228.

Cresswell H, Green T, McKenzie N. The adequacy of pressure plate apparatus for determining soil water retention. Soil Science Society of America Journal. 2008;72(1):41-49.

Romano N, Santini A. 3.3.3 Field. Methods of Soil Analysis: Part 4 Physical Methods. 2002; methodsofsoilan 4:721-738.

Gee GW, Or D. 2.4 Particle-size analysis. Methods of soil analysis. Part. 2002;4(598):255-293.

Ouédraogo WAA, Gathenya JM, Raude JM. Projecting wet season rainfall extremes using regional climate models ensemble and the advanced delta change model: Impact on the streamflow peaks in Mkurumudzi catchment, Kenya. Hydrology. 2019;6(3):76.

Moriasi DN, et al. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions of the ASABE. 2007;50(3): 885-900.

Saad AM, Mohamed MG, El-Sanat GA. Evaluating Aquacrop model to improve crop water productivity at North Delta soils, Egypt. Advances in Applied Sciences Research. 2014;5(5):293-304.

Tran TN. Modelling yield response to deficit irrigation by Aquacrop in the mekong Delta, Vietnam. Ghent University; 2018.

Crosetto M, Tarantola S, Saltelli A. Sensitivity and uncertainty analysis in spatial modelling based on GIS. Agriculture, Ecosystems & Environment. 2000;81(1):71-79.

Hamby D. A review of techniques for parameter sensitivity analysis of environmental models. Environmental Monitoring and Assessment. 1994;32(2): 135-154.

Algharibi E, et al. Evaluation of field and greenhouse experiments with tomatoes using the Aquacrop model as a basis for improving water productivity. In International Conference on Water Resources and Environment Research; 2013.

Paredes P, et al. Performance assessment of the FAO Aquacrop model for soil water, soil evaporation, biomass and yield of soybeans in North China Plain. Agricultural Water Management. 2015;152:57-71.

Jin Xl, et al. Assessment of the Aquacrop model for use in simulation of irrigated winter wheat canopy cover, biomass and grain yield in the North China Plain. PloS One. 2014;9(1):e86938.

Abedinpour M, et al. Performance evaluation of Aquacrop model for maize crop in a semi-arid environment. Agricultural Water Management. 2012;110: 55-66.

Pawar G, Kale M, Lokhande J. Response of Aquacrop model to different irrigation schedules for irrigated cabbage. Agricultural Research. 2017;6(1):73-81.

Lievens E. Parameterization and testing of the FAO Aquacrop model to simulate yield response to water in North-eastern Thailand. Dissertação (Mestrado em Engenharia de Biociências): Ciência Agrícola; 2014.

Stricevic R, et al. Assessment of the FAO Aquacrop model in the simulation of rainfed and supplementally irrigated maize, sugar beet and sunflower. Agricultural Water Management. 2011;98(10):1615-1621.

Geerts S, et al. Simulating yield response of quinoa to water availability with Aquacrop. Agronomy Journal. 2009; 101(3):499-508.

Salemi H, et al. Application of Aquacrop model in deficit irrigation management of winter wheat in arid region. African Journal of Agricultural Research. 2011;610:2204-2215.