Performance of the FAO AquaCrop model for wheat grain yield and soil moisture simulation in Western Canada

Mkhabela, M.S.; Bullock, Paul

doi:10.1016/j.agwat.2012.03.009

Cited by 122 publications

(86 citation statements)

References 42 publications

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“…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.

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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.

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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.

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“…For example, WOFOST (WOrld FOod STudies) which was used by EU Joint Research Centre's Monitoring Agricultural Resources (MARS) Unit Mission, requires data for about 40 input parameters [43]. Food and Agriculture Organizaions (FAO) AquaCrop model requires a relatively smaller number of parameters and variables to run [44]. Every crop growth model requires information regarding crop management, soil, and weather, at least data for the temperature, phasic development, and growing degree days [45].…”

Section: Data Requirementsmentioning

confidence: 99%

Contribution of Remote Sensing on Crop Models: A Review

et al. 2018

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Abstract:Crop growth models simulate the relationship between plants and the environment to predict the expected yield for applications such as crop management and agronomic decision making, as well as to study the potential impacts of climate change on food security. A major limitation of crop growth models is the lack of spatial information on the actual conditions of each field or region. Remote sensing can provide the missing spatial information required by crop models for improved yield prediction. This paper reviews the most recent information about remote sensing data and their contribution to crop growth models. It reviews the main types, applications, limitations and advantages of remote sensing data and crop models. It examines the main methods by which remote sensing data and crop growth models can be combined. As the spatial resolution of most remote sensing data varies from sub-meter to 1 km, the issue of selecting the appropriate scale is examined in conjunction with their temporal resolution. The expected future trends are discussed, considering the new and planned remote sensing platforms, emergent applications of crop models and their expected improvement to incorporate automatically the increasingly available remotely sensed products.

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“…AquaCrop is a point-based, water-driven model that has been widely used (Stricevic et al 2011;Abrha et al 2012;Mkhabela and Bullock 2012;Shrestha et al 2013;Mhizha et al 2014) and allows simulation under future conditions, including elevated [CO 2 ] . Impacts for winter wheat were assessed with the Sirius model 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 Raes et al (2009) and Jamieson et al (1998), respectively.…”

Section: Impact Assessmentmentioning

confidence: 99%

Regional and global climate projections increase mid-century yield variability and crop productivity in Belgium

2015

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The impact of mid-century climatic changes on crop productivity of winter wheat, maize, potato and sugar beet was assessed for a temperate maritime climate in the Flemish Region, Belgium. Climatic projections of multiple regional and global climate models (RCMs from the EU-ENSEMBLES project and GCMs from the Coupled Model Intercomparison Project phase 3) were stochastically downscaled by the LARS-WG weather generator for use in the crop models AquaCrop and Sirius. Primarily positive effects on mean yield were simulated. Crops benefitted from elevated CO 2., and from more radiation interception if the cropping period was adapted in response to higher temperatures. However, increased productivity was linked with increased susceptibility to water stress and greater inter-annual yield variability, particularly with adapted management. Impacts differed among and within ensembles of climate models, and among crops and environments. Although RCMs may be more suitable for local impact assessments than GCMs, inter-ensemble differences and contingent wider ranges of impacts with GCM projections found in this study indicate that applying RCMs driven by a limited number of GCMs alone would not give the full range of possible impacts. Further, this study suggests that the simulated inter-model variation can be larger than spatial variation within the region. These findings advocate the use of both GCM and RCM ensembles in assessments where temperature and precipitation are central, such as for crop production.

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Performance of the FAO AquaCrop model for wheat grain yield and soil moisture simulation in Western Canada

Cited by 122 publications

References 42 publications

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

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

Contribution of Remote Sensing on Crop Models: A Review

Regional and global climate projections increase mid-century yield variability and crop productivity in Belgium

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