Soil water and salinity dynamics under sprinkler irrigated almond exposed to a varied salinity stress at different growth stages

Phogat, V.; Pitt, Tim; Cox, Jim; Šimůnek, Jirka; Skewes, M.A.

doi:10.1016/j.agwat.2018.01.018

Cited by 37 publications

(26 citation statements)

References 55 publications

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“…The validation period resulted in mean RMSE = 0.008 m 3 m −3 (from 0.005 to 0.01 m 3 •m −3 ) and mean R 2 = 0.81 (from 0.69 to 0.93) for soil water, and mean RMSE = 0.28 dSm −1 (from 0.07 to 0.6 dSm −1 ) and mean R 2 = 0.77 (from 0.65 to 0.85) for ECsw (Table 3). Former studies applying different versions of the HYDRUS model for simulating water content and non-reactive solute transport reported a similar range of goodness-of-fit indicators for water content [25,[28][29][30] and for salinity [23,50]. Note that calibration goodness-of-fit indicators were slightly better than those obtained for validation (Table 3) a common occurrence according to Phogat et al [50].…”

Section: Model Calibration and Validationsupporting

confidence: 53%

“…Former studies applying different versions of the HYDRUS model for simulating water content and non-reactive solute transport reported a similar range of goodness-of-fit indicators for water content [25,[28][29][30] and for salinity [23,50]. Note that calibration goodness-of-fit indicators were slightly better than those obtained for validation (Table 3) a common occurrence according to Phogat et al [50]. The main cause is probably linked to the fact that for the calibration period, irrigation frequency was higher, during the mid-crop season, than it was for the validation period occurring at the crop season end.…”

Section: Model Calibration and Validationmentioning

confidence: 88%

“…The model overestimated (e.g., soil layer 50-100 cm) or underestimated salinity contents. Phogat et al [50] experienced similar difficulties when modelling salinity using HYDRUS-2D and noted that it was more difficult than simulating water flow especially for low values of water contents. This could be explained by diverse geochemical processes occurring when irrigation water infiltrates the vadose zone such as dissolution/precipitation, adsorption/desorption [23], and rock water interaction [20].…”

Section: Model Calibration and Validationmentioning

confidence: 99%

See 2 more Smart Citations

Modelling the Impact on Root Water Uptake and Solute Return Flow of Different Drip Irrigation Regimes with Brackish Water

Slama

Zemni

Bouksila

et al. 2019

Water

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Water scarcity and quality degradation represent real threats to economic, social, and environmental development of arid and semi-arid regions. Drip irrigation associated to Deficit Irrigation (DI) has been investigated as a water saving technique. Yet its environmental impacts on soil and groundwater need to be gone into in depth especially when using brackish irrigation water. Soil water content and salinity were monitored in a fully drip irrigated potato plot with brackish water (4.45 dSm−1) in semi-arid Tunisia. The HYDRUS-1D model was used to investigate the effects of different irrigation regimes (deficit irrigation (T1R, 70% ETc), full irrigation (T2R, 100% ETc), and farmer’s schedule (T3R, 237% ETc) on root water uptake, root zone salinity, and solute return flows to groundwater. The simulated values of soil water content (θ) and electrical conductivity of soil solution (ECsw) were in good agreement with the observation values, as indicated by mean RMSE values (≤0.008 m3·m−3, and ≤0.28 dSm−1 for soil water content and ECsw respectively). The results of the different simulation treatments showed that relative yield accounted for 54%, 70%, and 85.5% of the potential maximal value when both water and solute stress were considered for deficit, full. and farmer’s irrigation, respectively. Root zone salinity was the lowest and root water uptake was the same with and without solute stress for the treatment corresponding to the farmer’s irrigation schedule (273% ETc). Solute return flows reaching the groundwater were the highest for T3R after two subsequent rainfall seasons. Beyond the water efficiency of DI with brackish water, long term studies need to focus on its impact on soil and groundwater salinization risks under changing climate conditions.

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Section: Model Calibration and Validationsupporting

confidence: 53%

Section: Model Calibration and Validationmentioning

confidence: 88%

Section: Model Calibration and Validationmentioning

confidence: 99%

See 1 more Smart Citation

Modelling the Impact on Root Water Uptake and Solute Return Flow of Different Drip Irrigation Regimes with Brackish Water

Slama

Zemni

Bouksila

et al. 2019

Water

View full text Add to dashboard Cite

show abstract

“…This study's soil salinity had an average range of 2.4-3.7 dS/m. This range is above the salinity threshold that Mass and Hoffman (1977) described (Phogat et al 2018) for adequate yield (Table 4).…”

Section: Percent Of Full Yield Potentialmentioning

confidence: 78%

“…In summary, the aforementioned salinity studies have shown that salinity affects not only water uptake (Phogat et al 2018), but also yield (Franco et al 2000). Phogat et al (2018) found that even if salinity levels in the soil were constant, water uptake was affected. Under these high salinity conditions, a reduction of evapotranspiration is expected.…”

Section: Percent Of Full Yield Potentialmentioning

confidence: 91%

Effect of Evapotranspiration Rate on Almond Yield in California

Serrano¹

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Effect of Evapotranspiration Rate on Almond Yield in California Dafne Isaac SerranoSince 2011, California has been under drought conditions. These conditions have not only affected water availability for farmers, but also production. California's second most valuable crop, almonds, has been affected by drought conditions. This study used three models (Model 1-3) to describe almond yield variability from year to year and almond yield variability within a year in Kern County, CA. The study evaluated 185 almond farms that were classified in three locations (east side, west side and north west side). The years of the study were 2011 (wet year) and 2013-2015 (drought condition years). Model 1 determined a functional regression between almond yield and annual evapotranspiration during the 4 years of the study. The R 2 was 7.9%, meaning low association between both variables and high unexplained variability (92.1%). Model 2 evaluated year to year variation. A regression function between almond yield and annual evapotranspiration after adjusting for location, precipitation, chilling hours and year was made. The R 2 of this model 62.6%, and all the variables used had a p<0.05. The R 2 was higher than Model 1; however, there was high unexplained variability (47.4%). Model 3 evaluated within-year variation. A regression function between almond yield and annual evapotranspiration after adjusting for tree age and location (east, west and northwest side) was made for each year (2011 and 2013 -2015). Coefficient of variation of evapotranspiration and soil available water storage were analyzed as additional variables in Model 3; however, they were not introduced in Model 3 due to the low increase in R 2 in each year (<2%). The R 2 of Model 3 for each year were, 60.4%, 49.7%, 53.8% and v 53.2% for the years 2011, 2013-2015, respectively. Model 3 also had high unexplained almond yield variability in each year (39.6%-50.3%). This high unexplained variability leads to introduce additional variables to the functional regression model for further studies. Identifying these additional variables and having a functional regression model with high R 2 would lead to understand how low evapotranspiration could potentially lead to a positive response on yield in drought conditions; thus, making farmers improve water use efficiency and hence, lowering production cost. However, the high unexplained variability clearly indicates that evapotranspiration is only one of many factors that influence yield. If improved yield is an important outcome, future studies must examine large-scale almond-producing farms with multiple agricultural system variables.

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Modeling the effect of wastewater irrigation on soil salinity using a SALT‐DNDC model

et al. 2021

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Wastewater has been widely reclaimed to irrigate crops where freshwater resources are scarce. Therefore, predicting the impacts of wastewater irrigation on soil moisture and soil salinity is critical for sustainable wastewater irrigation management. In this study, the denitrification-decomposition (DNDC) model was modified to couple wastewater irrigation with a water balance equation (SALT-DNDC). Secondly, the SALT-DNDC model was verified against the measured soil moisture, temperature, and nitrous oxide emission during the barley-growing season at Lethbridge, Alberta, Canada. Third, the SALT-DNDC model was used to predict the effects of one-time and split wastewater irrigation with varying quantity and quality on transpiration and soil salinity. The results showed that split irrigation of wastewater with an electrical conductivity of 6 dS m À1 reduced the peak soil salinity from 52-55 dS m À1 to a range of 8-20 dS m À1 , compared to one-time irrigation. Therefore, the split irrigation of wastewater could substantially reduce peak soil salinity. In this regard, optimal split wastewater irrigation with elevated salt concentration can limit soil salinity to acceptable salt tolerance levels for crop growth. The SALT-DNDC model can simulate dynamics of split irrigation wastewater and offers a new tool for assessing the effects of wastewater reuse on soil salinity.

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Soil water and salinity dynamics under sprinkler irrigated almond exposed to a varied salinity stress at different growth stages

Cited by 37 publications

References 55 publications

Modelling the Impact on Root Water Uptake and Solute Return Flow of Different Drip Irrigation Regimes with Brackish Water

Modelling the Impact on Root Water Uptake and Solute Return Flow of Different Drip Irrigation Regimes with Brackish Water

Effect of Evapotranspiration Rate on Almond Yield in California

Modeling the effect of wastewater irrigation on soil salinity using a SALT‐DNDC model

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