Simulation of potato yield in temperate condition by the AquaCrop model

Evaluation of Aquacrop Model in Simulating Bitter Gourd Water Productivity Under Saline Irrigation

“…Simulation models are vital tools in order to improve different management approaches in arid and semi-arid zones for achieving maximal production with minimal irrigation (Toumi et al, 2016). In this context, various refined crop growth models including DSSAT, CropSyst, CROPWAT and APSIM have been developed for predicting crop yield in response to different agro-ecological environments for different crops (Smith, 1991;Hoogenboom et al, 1995;McCown et al, 1996;Maniruzzaman et al, 2015;Carlson et al, 2016;Razzaghi et al, 2017;Xu et al, 2019).…”

Assessment of nutritional water productivity and improvement strategies for traditional vegetables in South Africa

“…Mabhaudhi et al (2014) reported similar findings, whereby the actual observed Zr for Colocasia esculenta L. Schott (taro) was 0.45 m but a good match between measured and simulated values for biomass was found at 0.80 m. The model is limited in parameters for leafy vegetables. Studies reported the normalised water productivity (WP*) to range from 13 to 19 g m -2 for C3 crops (Malik et al, 2017;Montoya et al, 2016;Paredes et al, 2015;Razzaghi et al, 2017;Tavakoli et al, 2015) and from 30 to 34 g m -2 for C4 crops (Araya et al, 2016;Bello and Walker, 2016;Greaves and Wang, 2017;Paredes et al, 2014).…”

“…The contrast between Bello and Walker (2017) with our results might be that; (1) they did not account for the fact that amaranth was harvested repeatedly during the growing period, (2) the effective rooting depth was not measured but estimated from literature, and (3) they used an empirical equation to convert leaf area index (LAI) to CC, which might have underestimated CC. Studies by Greaves and Wang (2017), Pawar et al (2017), and Razzaghi et al (2017) showed that AquaCrop was capable of simulating CC for maize, cabbage and potato, but not under water-stressed conditions. The strength of AquaCrop is its ability to separate ETa into soil evaporation (Es) and transpiration (Tr) Raes et al, 2009;Steduto et al, 2009), which enables the assessment of productive (Tr) and non-productive (Es) water use.…”

“…Most studies (Bello and Walker, 2016;Bello and Walker, 2017;Mabhaudhi et al, 2014;Mirsafi et al, 2016;Montoya et al, 2016;Razzaghi et al, 2017) Renault and Wallender (2000), Van Halsema and Vincent (2012), and Wenhold et al (2012) that C4 crops are more productive than C3 crops per unit of water used. It is a challenge to compare WP from different locations because of varying climatic conditions, methods used in calculating WP, irrigation water management, soil fertility management, and vapour pressure deficit (Zwart and Bastiaanssen, 2004); other studies report the total amount of water applied, whereas others report ETa.…”

“…AquaCrop has been utilised in many studies (e.g. Abedinpour et al, 2012;Araya et al, 2016;Battisti et al, 2017;Bello and Walker, 2016;Greaves and Wang, 2017;Katerji et al, 2013;Mirsafi et al, 2016;Montoya et al, 2016;Mustafa et al, 2017;Paredes et al, 2014;Paredes et al, 2015;Pawar et al, 2017;Razzaghi et al, 2017;Tavakoli et al, 2015;Yuan et al, 2013) The rain-shelter has a rain sensor that triggers an electric motor during a rainfall event and the shelter automatically covers the experimental field. Therefore, the experiment experiences normal field conditions, except when it is raining (Mabhaudhi et al, 2014).…”