The objective of this paper was to assess the performance of Hydrus-2D model to simulate the effects of different on-farm irrigation strategies applied on potato crop. The ability of the model to simulate the stress coefficient (K_s), obtained as the ratio between actual and maximum transpiration, and to define the productive function of potato crop under the semi-arid conditions of central Tunisia were also evaluated. Experiments were carried out on potato crop under full (FI) and deficit irrigation (DI) and two different water qualities supplied by means of a subsurface drip irrigation system. Results evidenced that the model, despite some discrepancies locally observed, can fairly accurately predict soil water contents and electrical conductivity around buried emitters. Furthermore, under water and salt stress conditions, “measured” K_s, based on crop water stress index (CWSI) obtained on thermal images, resulted in a good correlation with the corresponding estimated by the model (R² = 0.8). The database collected during the three growth seasons also allowed the definition of the crop productive function represented by a linear relationship between the relative yield loss and K_s. This function represents a useful guidelines for the sustainable use of irrigation water in countries characterized by a semi-arid climate and a limited availability of water for irrigation.

Under semi-arid conditions, irrigated agriculture faces hard competition for water. It is against this backdrop that appropriate management of irrigation techniques and water resources becomes a major concern. This study investigated the effect of surface (SDI) and subsurface drip irrigation (SSDI) with domestic treated wastewater (TWW) and fresh water (FW) on soil water dynamics, salinity, yield, and mineral nutrition of okra. The experimental design was set-up based on two adjacent plots according to the water quality: Fresh Water (FW) T₁ and domestic Treated Wastewater (TWW) T₂. Results showed that measured soil water contents (SWCs), under TWW treatment (T₂), were greater than their corresponding measurements under FW (T₁), and in particular at 35 cm depth. Meanwhile, for both water qualities, soil Electrical Conductivity (EC) registered at 5 cm depth was higher than those measured at 35 cm, with values ranging from 0.14 to 0.36 mS·cm⁻¹ and from 0.20 to 0.47 mS·cm^−m for T₁ and T₂, respectively. Regarding crop yield, a statistically significant increase (p = 0.05) in okra fresh yield was observed when TWW was used. Fresh yield in SDI was 2.55 t·ha⁻¹ and 3.9 t·ha⁻¹ in T₁ and T₂, respectively. Nevertheless, results indicated that lateral depth did not significantly affect okra fresh yield. Moreover, a significant higher irrigation water productivity (WP_irrig) with TWW (1.08 ± 0.26 and 1.23 ± 0.18 kg m⁻¹) was observed, which was nearly double those obtained with FW (0.72 ± 0.33 to 0.78 ± 0.18 kg m⁻¹). Appropriate use of SSDI with TWW stands as an irrigation management technique to improve yield and irrigation water productivity of okra crops.

Water supplies have been decreasing in several semi-arid regions, and it is therefore necessary to adopt irrigation strategies aimed at maximizing water use efficiency. In this paper, the effects of saline and deficit irrigation on water use efficiency and on potato crop response, based on observations of soil and plant water status, were investigated. Experiments were carried out in Central Tunisia, by monitoring potato crop growth during two seasons in four distinct treatments (T1–T4), represented by two different irrigation doses and two water qualities. For irrigation scheduling purposes, thresholds of soil matric potential, soil water content and Crop Water Stress Index (CWSI) were identified with the aim to quantify the effects of water and/or salinity stress on the achievable yield. Experiments allowed verifying that crop yield is strongly affected by the seasonal amount and quality of applied water. Despite differences of crop yield between treatments T2, T3 and T4 not being statistically significant (P < 0.05), crop yield varied between 26.3 t/ha (T3 in 2015) to 16.3 t/ha (T4 in 2015). However, crop yield decline of 17.0 t/ha and 12.0 t/ha per each 100 mm decrease of applied water were observed under the application of water electrical conductivity of 1.6 dS/m and 4.1 dS/m respectively. On the other hand, an increase of 1.0 dS/m in water electrical conductivity caused a yield decline rate of about 10%. The results achieved showed that under the semi-arid climate of Tunisia, potato crop irrigation should be scheduled to avoid water deficit; however, the possibility to reduce water supply can be envisaged when water availability is limited, but with the awareness to accept the shortage of production. Finally, when saline water is the only source available to the farm, it is necessary to avoid the reduction of irrigation doses, to prevent excessive salt accumulation in the root zone with unavoidable effects on crop yield.

As a semi-arid Mediterranean country, Tunisia is affected by the impacts of climate change, particularly the coastal regions like the Cap-Bon. Irregular rainfall, rising temperatures and the recurrence of extreme events are all indicators that affect ecosystems and populations and make them more vulnerable to the influence of climatic variables. Therefore, an analysis of the trends of climate variables can contribute to facilitating the development of effective adaptation strategies. In this matter, this study was conducted to assess the homogeneity and trends of minimum and maximum air temperature (Tmin and Tmax) and precipitation (P) in the Cap-Bon region. Daily data were collected from the meteorological station of Nabeul for the period of 1982–2020. Pettitt and SNHT tests for homogeneity were applied to identify the breakpoints in multi-time scales of Tmax, Tmin and P data series. The Mann–Kendall (MK) test was used to detect the change in the time-series trend. A modified Mann–Kendall (mMK) test was used to remove the autocorrelation effect from the data series. Both the MK and mMK tests were used at the 5% significant level. The magnitude of the climatic trend was estimated using the non-parametric Sen’s slope estimator. Contrary to Tmin and P, the results of the homogeneity tests revealed the existence of significant breakpoints in the annual, seasonal and monthly Tmax time series. For most cases, the breakpoint occurred around the year 2000. For Tmin, significant breakpoints were recorded in March and April, while a significant shift in the P time series was detected in December. The Mann–Kendall results show a significant warming trend in annual Tmax, with magnitudes equal to 0.065 and 0.045 °C/year before and after the breakpoint, respectively. Nevertheless, non-significant tendencies were observed in the annual Tmin and P time series. On the monthly time scale, Tmax exhibited a significant upward trend in June and August, before the observed breakpoints, with Sen’s slope values equal to 0.065 and 0.045 °C/year, respectively. Regarding the Tmin data, a significant positive trend was observed in July at a rate of 0.033 °C/year.

Systematic biases in general circulation models (GCM) and regional climate models (RCM) impede their direct use in climate change impact research. Hence, the bias correction of GCM-RCMs outputs is a primary step in such studies. This study compares the potential of two bias correction methods (the method from the third phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3) and Detrended Quantile Matching (DQM)) applied to the raw outputs of daily data of minimum and maximum air temperatures and precipitation, in the Cap-Bon region, from eight GCM-RCM combinations. The outputs of GCM/RCM combinations were acquired from the European branch of the coordinated regional climate downscaling experiment (EURO-CORDEX) dataset for historical periods and under two representative concentration pathway (RCP4.5 and RCP8.5) scenarios. Furthermore, the best combination of bias correction/GCM-RCM was used to assess the impact of climate change on reference evapotranspiration (ET₀). Numerous statistical indicators were considered to evaluate the performance of the bias correction/historical GCM-RCMs compared to the observed data. Trends of the Hargreaves–Samani_ET₀ model during the historical and projected periods were determined using the TFPMK method. A comparison of the bias correction methods revealed that, for all the studied model combinations, ISIMIP3 performs better in reducing biases in monthly precipitation. However, for Tmax and Tmin, the biases are greatly removed when the DQM bias correction method is applied. In general, better results were obtained when the HadCCLM model was used. Before applying bias correction, the set of used GCM-RCMs projected reductions in precipitation for most of the months compared to the reference period (1982–2006). However, Tmin and Tmax are expected to increase in all months and for the three studied periods. Hargreaves–Samani ET₀ values obtained from the best combination (DQM/ HadCCLM) show that RCP8.5 (2075–2098) will exhibit the highest annual ET₀ increase compared to the RCP4.5 scenario and the other periods, with a change rate equal to 11.85% compared to the historical period. Regarding spring and summer seasons, the change rates of ET₀ are expected to reach 10.44 and 18.07%, respectively, under RCP8.5 (2075–2098). This study shows that the model can be used to determine long-term trends in ET₀ patterns for diverse purposes, such as water resources planning, agricultural crop management and irrigation scheduling in the Cap-Bon region.

Internet of Things (IoT) based precision irrigation system has proven to be promising tool for optimizing water use and crop production. In this study, two irrigation water-saving techniques, double-line drip irrigation (DI) and partial-root zone drying (PRD) were assessed on 27-year-old...