Evaluation of different soil water potential by field capacity threshold in combination with a triggered irrigation module

https://doi.org/10.17221/189/2014-SWRCitation:Marković M., Filipović V., Legović T., Josipović M., Tadić V. (2015): Evaluation of different soil water potential by field capacity threshold in combination with a triggered irrigation module. Soil & Water Res., 10: 164-171.
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Irrigation efficiency improvement requires optimization of its parameters like irrigation scheduling, threshold and amount of water usage. If these parameters are not satisfactorily optimized, negative consequences for the plant-soil system can occur with decreased yield and hence economic viability of the agricultural production. Numerical modelling represents an efficient, i.e. simple and fast method for optimizing and testing different irrigation scenarios. In this study HYDRUS-1D model assuming single- and dual-porosity systems was used to evaluate a triggered irrigation module for irrigation scheduling in maize/soybean cropping trials. Irrigation treatment consisted of two irrigation regimes (A2 = 60–100% field capacity (FC) and A3 = 80–100% FC) and control plot (A1) without irrigation. The model showed a very good fit to the measured data with satisfactory model efficiency values of 0.77, 0.69, and 0.93 (single-porosity model) and 0.84, 0.67, and 0.92 (dual-porosity model) for A1, A2, and A3 plots, respectively. The single-porosity model gave a slightly better fit in the irrigated plots while the dual-porosity model gave better performance in the control plot. This inconsistency between the two approaches is due to the manual irrigation triggering and uncertainty in field data timing collection. Using the triggered irrigation module provided more irrigation events during maize and soybean crop rotation and consequently increased cumulative amounts of irrigated water. However, that increase resulted in more water available in the root zone during high evapotranspiration period. The HYDRUS code can be used to optimize irrigation threshold values further by assuming different scenarios (e.g. different irrigation threshold or scheduling) or a different crop.
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