Evaluation of crop water stress index and leaf water potential for differentially irrigated quinoa with surface and subsurface drip systems

Abstract

A 2-year field experiment was conducted with the objectives to evaluate the physiological and yield response of quinoa cv Titicaca to various deficit irrigation strategies applied with surface drip (SD) and subsurface drip systems (SSD) under the Mediterranean climatic conditions in 2016 and 2017. The treatments consisted of regulated deficit irrigation (RDI), partial root-zone drying (PRD 50 ), conventional deficit irrigations (DI 50 , DI 75 ) and full irrigation (FI) under SD and SSD. A rainfed treatment was also included. The experimental design was split plots with four replications. DI 75 and DI 50 received 75 and 50% of FI, respectively. PRD 50 plots received 50% of FI, but from alternative laterals in each application. RDI received 50% of FI during vegetative stage until flowering, then received 100% of water requirement. The results indicated that RDI resulted in water saving of 23 and 21% for surface drip (SD) and SSD systems, respectively, and RDI produced statistically similar yield to FI treatment in both experimental years. DI 75 treatment resulted in water savings of 16% for both drip methods in the first year and 10 and 25% for SD and SSD systems, respectively, in the second year. Thus, RDI and DI 75 treatments appear to be good alternative to FI for sustainable quinoa production in the Mediterranean environmental conditions. Greater leaf water potential (LWP) and smaller crop water stress index (CWSI) values were measured in FI plots under both drip systems than deficit irrigation treatment plots. Significant second-order polynomial relations were determined between CWSI and LWP for the drip systems. Leaf area index (LAI), LWP decreased and CWSI increased as the drought increased. CWSI correlated significantly (P < 0.01) and negatively with grain yield, dry matter yield, LAI, and mean soil water content indicating that grain yield of quinoa declined with increasing CWSI values. All these relations are best described by significant second-order polynomial equations. The results revealed that quinoa should be irrigated at LWP values between − 1.35 and − 1.60 MPa, and average CWSI value of approximately 0.35 for high yields.

Yield and water productivity response of quinoa to various deficit irrigation regimes applied with surface and subsurface drip systems

“…More information regarding SWC variations in the different treatments in the experimental years have been given in previous publication by Bozkurt Çolak et al . (2020).…”

“…Drought stress during the vegetative growth stage leads to deep root development, and without stress conditions for the rest of the growing season allowed the plant to be able to optimize its photosynthesis and carbon translocation (Geertz et al ., 2008; Jacobsen et al ., 2009; Hirich et al ., 2014). Greater midday leaf water potential (LWP) values were observed in FI treatment plots than the DI treatment plots under both drip systems (data are not shown, details can be found in Bozkurt Çolak et al ., 2020). RDI and DI 75 treatments had greater LWP values than DI 50 and PRD 50 treatments since RDI and DI 75 received more irrigation water than DI 50 and PRD 50 treatment.…”

“…We also observed slightly higher LWP values in SSD plots than in the SD plots, however, the difference between the two irrigation systems was not significant. Generally, LWP values decreased towards the end of season in comparison to the beginning of the season (Bozkurt Çolak et al ., 2020). Towards the end of the growing season, SWC reached closer to the wilting point in DI 50 , PRD 50 and RF treatments.…”

Efecto de diferentes niveles de evapotranspiración sobre área foliar, temperatura superficial, potencial hídrico y rendimiento en sorgo forrajero

“…Los coef icientes de determinación (r 2 ) se presentaron en un rango de 0.89 a 0.96. Resultados similares fueron reportados por Bozkurt et al (2021) en el cultivo de Quinoa (Chenopodium quinoa willd) quienes reportaron correlaciones de tipo polinomial entre IAF y el índice de estrés hídrico, donde los mayores valores de IAF se encontraron en el tratamiento de menor estrés por agua.…”

Application of artificial intelligence-based modelling for the prediction of crop water stress index

“…CWSI ranges from 0 to 1, with 0 corresponding to wellwatered and 1 corresponding to non-transpiring conditions of the plant under investigation. Numerous studies documented in the scientific literature on the application of CWSI in different crops under diverse agro-climates (Kirnak et al 2019;Alghory 2019;Anda et al 2020;Kumar et al 2020b;Bozkurt Çolak et al 2021) CWSI is generally computed using either the empirical approach (Idso et al 1981) or the theoretical approach (Jackson et al 1981). Both approaches rely on upper baseline canopy temperature (Tc-dry) and lower baseline canopy temperature (Tc-ww).…”