Response of AquaCrop Model to Different Irrigation Schedules for Irrigated Cabbage

Abstract

Agricultural sector faces the challenge to produce more food with less water by increasing crop water productivity. As such, the question of improving the present level of crop water productivity in general and for irrigation in particular assumes a great significance in perspective water resource planning. This study was undertaken to improve water productivity, i.e., 'more crop per drop.' In this study response of cabbage to different irrigation schedules under mulch and non-mulch condition using calibrated AquaCrop model was evaluated. AquaCrop is a crop model that simulates yield response to water developed by FAO and is appropriate to consider effects where water is the limiting factor for crop production. AquaCrop was calibrated for cabbage (Brassica oleracea var. capitata), a leafy vegetable. Simulations were carried out for the period November 27, 2013, to February 19, 2014 with T 1 , T 2 , T 3 (treatments irrigation scheduling at 50 % moisture depletion of available water capacity under black, silver, and no polyethylene mulch with drip irrigation) and T 4 , T 5 , T 6 (irrigation scheduling at 100 % evapotranspiration under black, silver, and no polyethylene mulch with drip irrigation). The model provided excellent simulation of canopy and yield. The harvest index was observed as 80 % for cabbage. Formulated Schedule with S 1 , S 2 , S 3 , S 4 , and S 5 (irrigation schedule at 110, 90, 80, 70, and 60 % ET c ) and S 6 , S 7 , S 8 , S 9 , and S 10 (mulch ? Irrigation schedule at 110, 90, 80, 70 and 60 % ET c ). S 8 saved 25.19 % water with only 4.63 % reduction in the yield of cabbage head compared to control and resulted in water use efficiency as 6.05 kg m -3 . Thus S 8 is recommended to be used for cabbage production, to water productivity.

Modeling Tomato Water Productivity Using Aquacrop Model in Njoro Sub County, Nakuru, Kenya

“…The performance of the model was found to be good as described by the of 0.91. The results also concur with those of Pawar, Kale [23] who carried out a research in India where the model was able to simulate biomass with a NSE of 0.96. A study by Lievens [24] carried out in North Eastern Thailand revealed that Aquacrop model was able to relate well the simulated to the actual biomass of sweet corn with a root mean square error (RMSE) of 0.56.…”

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

“…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.…”

“…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. For example, Pawar et al (2017) reported WPETa of cabbage ranging from 50 to 69 kg ha -1 mm -1 , however, the denominator was total irrigation water applied. Wenhold et al (2012) benchmarked WPETa of leafy vegetables using a dataset (AGB per ETa) that was derived from different literature sources and reported values ranging from 2 to 90 kg ha -1 mm -1 for selected leafy vegetables.…”

“…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).…”

Examination of Sugarcane Yield by Simulating Aqua Crop to Overcome the Irrigation Deficiency

“…Sampling can be conducted from 120 cm soil depth [3]. Usually soil profile study was allowed to demonstrate the soil characteristics at experimental field and determined the analysis of physicochemical belongings [5]. In this model, the condition of soil water in the lower, middle and upper positions has been measured as the input of the AquaCrop model in order to predict the water use efficiency of the sugarcane [6].…”