2011
DOI: 10.2134/agronj2011.0150
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Calibration and Testing of the FAO AquaCrop Model for Canola

Abstract: or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. C anola (Brassica napus L.) is an oil seed crop that con-ABSTRACT Water being a major limiting factor in crop production, prediction of the growth and yield response of crop to water is important. Field experiments were conducted in 2009 and 2010 at Wagga Wagga (Australia) to calibrate and validate a water productivity model AquaCrop for canola (Brassica napus L.)… Show more

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Cited by 87 publications
(50 citation statements)
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“…This could be because of the fact that AquaCrop simplifies the process of crop senescence, especially the process of CC decrease [36]. Furthermore, the biomass increases of the foxtail millet beyond 63 days after sowing might also have contributed to the underestimation [37]. Bello et al [13] found similar underestimation of biomass in the simulation for pearl millet, and pointed out that this result was due to the biomass accumulation increased beyond 60 days after sowing.…”
Section: Discussionmentioning
confidence: 90%
“…This could be because of the fact that AquaCrop simplifies the process of crop senescence, especially the process of CC decrease [36]. Furthermore, the biomass increases of the foxtail millet beyond 63 days after sowing might also have contributed to the underestimation [37]. Bello et al [13] found similar underestimation of biomass in the simulation for pearl millet, and pointed out that this result was due to the biomass accumulation increased beyond 60 days after sowing.…”
Section: Discussionmentioning
confidence: 90%
“…Zeleke et al [27] note that while E is an index of predictive performance, d indicates the degree to which predicted and measured values show similar deviation from the measured mean. It is a dimensionless quantity and ranges from 0 to 1, where 0 describes complete disagreement and 1 indicates perfect model agreement.…”
Section: Model Evaluationmentioning
confidence: 99%
“…Araya et al (2010a) simulated barley grain yield by using AquaCrop and reported that the simulated grain yield deviated from the observed yield within a range of −13% to 15%. Zeleke et al (2011) simulated total biomass and grain yield for canola (B. napus L.) using AquaCrop model and reported a <10% difference between observed and simulated values. The value of NRMSE was 3.34% indicating an excellent agreement between the predicted and observed grain yield data.…”
Section: Model Validationmentioning
confidence: 99%
“…The model is aimed for use by broad range of users like engineers, economists, extension specialists and water managers at various levels ). The AquaCrop model has been tested by several researchers (Baumhardt et al, 2009;Heng et al, 2009;Alizadeh et al, 2010;Araya et al, 2010a;Araya et al, 2010b;Andarzian et al, 2011;Stricevic et al, 2011;Zeleke et al, 2011;Zinyengere et al, 2011;Abedinpour et al, 2012;Mkhabela and Bullock 2012) around the globe under diverse environmental conditions and their results indicate the wide applicability of the model under diverse climatic conditions. Therefore, a study was conducted, in order to verify these findings and to develop irrigation schedule with higher IWP, along with similar grain yield, by using Food and Agriculture Organisation (FAO) AquaCrop model version 3.1 Plus for rice transplanted in puddled field in northwest India.…”
Section: Introductionmentioning
confidence: 99%