Optimal Irrigation Scheduling and Crop Production Functions Development using AquaCrop and TOMLAB

Ilya Ioslovich, Raphael Linker

2015

Abstract

Water stress is one of the most influential factors contributing to crop yield loss. The importance of the irrigation constantly increases because of water scarcity and growing demand for agricultural production worldwide. Previously, an approach using empirical water production functions and analytic optimal control methodology has been developed for optimal irrigation scheduling. Such an approach based on numerical optimal control is an alternative to common irrigation scheduling based on agronomy practice. Nowadays, more complex dynamic crop simulation models, such as the FAO AquaCrop model, predict crop responses to different irrigation strategies and climates. The state variables of the AquaCrop model include crop characteristics, such as biomass, and soil water content in up to 12 soil layers. In this paper the numerical optimal control scheme for irrigation scheduling and crop water production function development is described and demonstrated using this model and the TOMLAB optimization library. Maize crop in Foggia, Italy, for season of the year 2000, is used as an illustrative case study.

References

  1. Amir, I. and Fisher, F. (2000). Response of near optimal agricultural production to water policies. Agricultural Systems, 64:115-130.
  2. Garcia-Villa, M. and Fereres, E. (2012). Combining the simulation crop model AquaCrop with an economic model for the optimisation of irrigation management at farm level. European Journal of Agronomy, 36:21- 31.
  3. Geerts, S., Raes, D., and Garcia, M. (2010). Using AquaCrop to derive deficit irrigation schedules. Agricultural Water Management, 98:213-216.
  4. Geerts, S., Raes, D., Garcia, M., Miranda, R., Cusicanqui, J., A., Taboada, C., Mendoza, J., Huanca, R., Mamani, A., Condori, O., Mamani, J., Morales, B., Osco, V., and Steduto, P. (2009). Simulating yield response of Quinoa to water availability with AquaCrop. Agronomy Journal, 101:499-508.
  5. Heng, L. K., Hsiao, T., C., S, E., Howell, T., and P, S. (2009). Validating the FAO AcuaCrop model for irrigated and water deficient field maize. Agronomy Journal, 101:488-498.
  6. Holmstrom, K., Goran, A., O., and Edvall M., M. (2007). Users Guide for TOMLAB/OQNLP. http://tomopt.com/tomlab/products/oqnlp/.
  7. Ioslovich, I., Borshchevsky, M., and Gutman, P.-O. (2012). On optimal irrigation scheduling. Dynamics of Continuous, Discrete and Impulsive Systems, Series B: Applications and Algorithms, 19:303-310.
  8. Linker, R. and Ioslovich, I. (2015). A multi-year simulation study of optimal and sub-optimal irrigation of maize in Kansas. In 2015 ASABE Annual International Meeting in New Orleans, Louisiana, USA, July 26-July 29. ASABE Online Technical Library.
  9. Linker, R., Sylaos, G., and Ioslovich, I. (2013). Optimization of irrigation scheduling using genetic algorithms and AcuaCrop: a case study for cotton in Northern Greece. In Proceedings of the International Conference on Agriculture Science and Environmental Engineering (ICASEE 2013), DVD. December 19-20, Beijing, China, paper ICASEE 132117.
  10. Mkhabela, M., S. and Bullock, P., R. (2012). Performance of the FAO AcroCrop model for wheat grain yield and soil moisture simulation in Western Canada. Agricultural Water Management, 110:16-24.
  11. Oweis, T., Rodrigues, P., N., and Pereira, L., S. (2003). Tools for Drought Mitigation in Mediterranean regions. Simulation of Supplemental Irrigation Strategies for Wheat in near East to Cope with Water Scarcity, pages 259-272. Kluwer Academic Publishers.
  12. Shani, U., Tsur, Y., and Zemel, A. (2004). Optimal dynamic irrigation schemes. Optimal Control Applications and methods, 25:91-106.
  13. Shani, U., Tsur, Y., Zemel, A., and Zilberman, D. (2009). Irrigation production functions with water-capital substitution. Agricultural Economics, 40:55-66.
  14. Steduto, P., Hsiao, T., C., Raes, D., and Ferereset, E. (2009). AcuaCrop the FAO crop model to simulate yield responce to water: I. concepts and underlying principles. Agronomy Journal, 101:426-437.
  15. Xiangxiang, W., Quanjiu, W., Jun, F., and Quiping, F. (2013). Evaluation of the AquaCrop model for simulating the impact of water deficits and different irrigation regimes on the biomass and yield of winter wheat grown on China's Loess Plateau. Agricultural Water Management, 129:95-104.
Download


Paper Citation


in Harvard Style

Ioslovich I. and Linker R. (2015). Optimal Irrigation Scheduling and Crop Production Functions Development using AquaCrop and TOMLAB . In Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-122-9, pages 49-52. DOI: 10.5220/0005501700490052


in Bibtex Style

@conference{icinco15,
author={Ilya Ioslovich and Raphael Linker},
title={Optimal Irrigation Scheduling and Crop Production Functions Development using AquaCrop and TOMLAB},
booktitle={Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2015},
pages={49-52},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005501700490052},
isbn={978-989-758-122-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Optimal Irrigation Scheduling and Crop Production Functions Development using AquaCrop and TOMLAB
SN - 978-989-758-122-9
AU - Ioslovich I.
AU - Linker R.
PY - 2015
SP - 49
EP - 52
DO - 10.5220/0005501700490052