# Sustainable Management of Water Resources in Supplementary Irrigation Management
## 4 Discussion  
The amount of rainfall during the January-July period was only 30 mm lower than the long-term average (LTA) and was well-distributed through the growing season. Further, the amount of rainfall during winter and early springtime was above the LTA, which provided a considerable amount of SWC for initial growth (Figure 3b). The SWC was near the FWC (readily available water) up to the beginning of the month of June, when there was a rainfall event. Twenty-four millimiters of rainfall in one single day provided enough water for the pea crop till the end of the growth, so there was no need to irrigate. At the time when SWC was low, the pea crop was in a development stage when there was no need for additional water (end of the month of June and the beginning of the month of July). As a result of such a growing condition, the year with a sufficient amount of rainfall for pea growing and lack of irrigation water, the sensors' laboratory testing was performed to test the sensor performance in a full range of moisture. In general, Watermark and tensiometer generated larger MD than TDR and overestimated SWC, while the TDR underestimated SWC (both for field and laboratory calibration) with minor differences in MD for both soil depths (Table 2). [PERSON] et al. [9] also found that the Watermark sensor overestimated SWC in sandy loam soil. Furthermore, Watermark and tensiometer performed differently at different soil depths and after the rainfall event. The best performance of the Watermark sensor was \(\geq\)20 cm (Table 2). A conclusion from previous research [12] was that the main factor for irrigation efficiency is to properly determine the optimum level for Watermark sensor installation. The sensor installation at \(\geq\)20 cm due to the impact of groundwater level on sensor performance is proposed. As is presented in Figure 1, in this study, the groundwater was below 2 m and therefore had no impact on SWC in the top layer nor in sensor performance. Furthermore, in this study, we noticed the poor response of the Watermark sensor to wetting and drying processes in 10 and 45 cm of soil depth. This is also confirmed by [PERSON] and [PERSON] [35] and [PERSON] and [PERSON] [36]. The author claims that the Watermark does not respond properly to rapid drying or partial rewetting of the soil, showing hysteretic behavior, which consequently may lead to incorrect estimation of the actual soil water status in these situations. As for the tensiometer, the best performance was at 20 cm with the emphasis on the fact that maintenance (servicing) was required. [PERSON] and [PERSON] [37] have also stated that tensiometers tend to require more maintenance compared to solid-state sensors such as Granular Matrix Sensors (GMS) or Time Domain Reflectometry (TDR) sensors. As is presented in Table 2, the sensor performance was soil-depth-dependent. The performance of calibration equations was tested with RMSE analysis for both field and laboratory calibration as well, where good performance was shown with RMSE close to 0. For example, the Watermark sensor showed the best performance of the calibration equation in 20 cm soil layer with a value of RMSE 1.7. The RMSE value increased as the soil layer deepened (20 cm = 1.7; 45 cm = 2.8). The highest RMSE was for a 10 cm soil layer, which indicates that the Watermark sensor has difficulty capturing the changes in SWC in the upper soil layer. Since there was no large variation in SWC, this poor response of the Watermark sensor could be because of the lack of adequate contact between the sensor and cracking soil. [PERSON] et al. [38] claim that poor contact between sensor and soil will increase mud (slurry). The author also claims that when the slurry dries, it will crack and move away from the soil, creating space between the sensor and the soil. Similar to the Watermark calibration equation, the lowest RMSE for tensiometer was in topsoil layer (20 cm = 3.2; 45 cm = 5.0).