Assessment of the FAO AquaCrop model in the simulation of rainfed and supplementally irrigated maize, sugar beet and sunflower

Stričević, Ružica; Ćosić, Marija; Djurović, Nevenka; Pejić, Borivoj; Maksimović, Livija

doi:10.1016/j.agwat.2011.05.011

Cited by 127 publications

(88 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The model was validated for a wide range of environmental conditions, namely, extraordinarily high evapotranspiration and wind speed in Bushland, Texas, rainy weather and sandy soil in Gainesville, Florida, and semiarid conditions in Zaragoza, Spain ). Many papers have reported the application of AquaCrop in simulating various management scenarios for many crops including maize , Stricevic et al, 2011), quinoa (Geerts et al, 2009a, cotton (Garcia-Vila et al, 2009), sunflower (Todorovic et al, 2009, Stricevic et al, 2011 and sugar beet (Stricevic et al, 2011) with success. Against such background, Aquacrop is expected to be potentially suitable for simulating maize yield response to water availability in the semi-arid conditions of Zimbabwe, although reported studies are lacking.…”

Section: Introductionmentioning

confidence: 99%

Use of the FAO AquaCrop model in developing sowing guidelines for rainfed maize in Zimbabwe

Mhizha

Geerts²,

Vanuytrecht³

et al. 2014

WSA

View full text Add to dashboard Cite

This paper presents a procedure in which the water-driven water productivity model AquaCrop was fine-tuned and validated for maize for the local conditions in Zimbabwe and then applied to develop sowing management options for decision support. Data from experiments of 2 seasons in Harare and from 5 other sites around Zimbabwe were used for the local calibration and validation of AquaCrop. Model parameters such as the reference harvest index (HI o ); the canopy growth coefficient (CGC); early canopy decline and normalised biomass water productivity (WP b * ) were adjusted during model calibration. Model performance was satisfactory after calibration with a Nash-Sutcliffe model efficiency parameter (EF = 0.81), RMSE = 15% and R 2 = 0.86 upon validation. To develop sowing guidelines, historical climate series from 13 meteorological stations around Zimbabwe were used to simulate maize yield for 6 consecutive sowing dates determined according to criteria applicable in Zimbabwe. Three varieties and typical shallow and deep soil types were considered in the simulation scenarios. The simulated yield was analysed by an optimisation procedure to select the optimum sowing time that maximised long-term mean yield. Results showed that highest yields depended on the climate of the site (rainfall availability), variety (length of growing cycle) and soil depth (soil water storage capacity). The late variety gave higher mean yields for all sowing dates in the maize belt. Staggered sowing is recommended as a way of combating the effects of rainfall variability and as an answer to labour constraints.

show abstract

Section: Introductionmentioning

confidence: 99%

Use of the FAO AquaCrop model in developing sowing guidelines for rainfed maize in Zimbabwe

Mhizha

Geerts²,

Vanuytrecht³

et al. 2014

WSA

View full text Add to dashboard Cite

show abstract

“…The model performance on simulating crop growth and water use has been well tested for a variety of crop types under diverse environmental conditions (e.g. Kumar et al, 2014;Jin et al, 2014;Abedinpour et al, 2012;Mkhabela and Bullock, 2012;Andarzian et al, 2011;Stricevic et al, 2011;Heng et al, 2009;Farahani et al, 2009;García-vila et al, 2009). AquaCrop has been applied in WF accounting at field (Chukalla et al, 2015), river basin (Zhuo et al, 2016a), and national level (Zhuo et al, 2016b) at high spatial resolution.…”

Section: Estimating Consumptive Wf Of Growing a Cropmentioning

confidence: 99%

Benchmark levels for the consumptive water footprint of crop production for different environmental conditions: a case study for winter wheat in China

Zhuo

Mekonnen

Hoekstra

2016

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Meeting growing food demands while simultaneously shrinking the water footprint (WF) of agricultural production is one of the greatest societal challenges. Benchmarks for the WF of crop production can serve as a reference and be helpful in setting WF reduction targets. The consumptive WF of crops, the consumption of rainwater stored in the soil (green WF), and the consumption of irrigation water (blue WF) over the crop growing period varies spatially and temporally depending on environmental factors like climate and soil. The study explores which environmental factors should be distinguished when determining benchmark levels for the consumptive WF of crops. Hereto we determine benchmark levels for the consumptive WF of winter wheat production in China for all separate years in the period 1961-2008, for rain-fed vs. irrigated croplands, for wet vs. dry years, for warm vs. cold years, for four different soil classes, and for two different climate zones. We simulate consumptive WFs of winter wheat production with the crop water productivity model AquaCrop at a 5 by 5 arcmin resolution, accounting for water stress only. The results show that (i) benchmark levels determined for individual years for the country as a whole remain within a range of ±20 % around long-term mean levels over 1961-2008, (ii) the WF benchmarks for irrigated winter wheat are 8-10 % larger than those for rain-fed winter wheat, (iii) WF benchmarks for wet years are 1-3 % smaller than for dry years, (iv) WF benchmarks for warm years are 7-8 % smaller than for cold years, (v) WF benchmarks differ by about 10-12 % across different soil texture classes, and (vi) WF benchmarks for the humid zone are 26-31 % smaller than for the arid zone, which has relatively higher reference evapotranspiration in general and lower yields in rain-fed fields. We conclude that when determining benchmark levels for the consumptive WF of a crop, it is useful to primarily distinguish between different climate zones. If actual consumptive WFs of winter wheat throughout China were reduced to the benchmark levels set by the best 25 % of Chinese winter wheat production (1224 m 3 t −1 for arid areas and 841 m 3 t −1 for humid areas), the water saving in an average year would be 53 % of the current water consumption at winter wheat fields in China. The majority of the yield increase and associated improvement in water productivity can be achieved in southern China.

show abstract

“…AquaCrop is developed from revision of 'FAO Irrigation and Drainage Paper No. 33 Yield Response to Water' (Doorenbos and Kassam, 1979).The capacity of AquaCrop model in simulating the yield in response to water is proved by various researchers (Araya et al 2010a, Heng et al, 2009Stricevic et al, 2011;Abedinpour et al, 2012, Andarzian et al, 2011.The use of these models can assist in evaluating and reducing time intensive and expensive field tests (Whisler et al, 1986). Model results with regards to crop performance, management and yield estimates will help decision makers to decide which management system is suited best for a particular field, by estimating the yield and crop water productivity optimum.…”

Section: Introductionmentioning

confidence: 99%

Analysis of AquaCrop Model for Potato Crop under Different Irrigation Levels

Tayade¹,

Kothari²,

Bhakar³

et al. 2018

Int.J.Curr.Microbiol.App.Sci

View full text Add to dashboard Cite

Assessment of the FAO AquaCrop model in the simulation of rainfed and supplementally irrigated maize, sugar beet and sunflower

Cited by 127 publications

References 18 publications

Use of the FAO AquaCrop model in developing sowing guidelines for rainfed maize in Zimbabwe

Use of the FAO AquaCrop model in developing sowing guidelines for rainfed maize in Zimbabwe

Benchmark levels for the consumptive water footprint of crop production for different environmental conditions: a case study for winter wheat in China

Analysis of AquaCrop Model for Potato Crop under Different Irrigation Levels

Contact Info

Product

Resources

About