Modeling the response of dry bean yield to irrigation water availability controlled by watershed hydrology

Mompremier, R.; Her, Younggu; Hoogenboom, Gerrit; Migliaccio, Kati W.; Muñoz‐Carpena, Rafael; Brym, Zachary; Colbert, Raphael Wesly; Jeune, Wesly

doi:10.1016/j.agwat.2020.106429

Cited by 10 publications

(14 citation statements)

References 46 publications

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“…42 SWAT model SWAT is capable of simulating the hydrological cycle at a watershed scale. SWAT has been widely used, especially for research into the influence of land-use pattern and climate change 15,35,43 ; it can be used to simulate runoff, evapotranspiration, and groundwater table dynamics with a high level of accuracy. Subbasins were identified according to Digital Elevation Model (DEM) and river systems, which were then divided into different hydrologic response units (HRUs).…”

Section: Plus Modelmentioning

confidence: 99%

“…Five stations in the irrigation district were used: Wugong (WG), Fengxiang (FX), Pucheng (PC), Xianyang (XY), and Yongshou (YS). The following equations from Mompremier et al 15 were used to determine irrigation water availability according to runoff: where WA is irrigation water availability (m 3 /day); SD is runoff discharge (m 3 /s); and IE is irrigation efficiency. Then the irrigation water availability was then converted into a form that can be used in the APSIM model, according to another equation from Mompremier et al 15 :…”

Section: Framework Constructionmentioning

confidence: 99%

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A framework for assessing the impacts of land‐use/cover change and climate change on wheat productivity under 1.5 and 2.0 °C warming at watershed scale

Sun,

Wang

2024

J Sci Food Agric

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BackgroundIrrigation is used extensively to enhance grain production and ensure food security. Many studies have used crop models and global climate models to study the variation of irrigated crop yield in the context of climate change. But most considered the influence of direct climate change but neglected the influence of irrigation water availability, which is affected by land‐use/cover change (LUCC) and indirect climate change, on irrigated crop yield. This study therefore developed a framework including Patch‐generating Land Use Simulation model, Soil and Water Assessment Tool, Agricultural Production Systems sImulator Model, and global climate models for exploring the impacts of LUCC, direct climate change, and indirect climate change on wheat yield in a typical watershed.ResultsBoth LUCC and climate change caused increased runoff from October to May, and thus increased the irrigation water availability, by 51.6 mm and 30.7 mm per growing season under 1.5°C and 2.0°C warming, respectively. The combined influence of LUCC, direct, and indirect climate change increased wheat yield by about 18.5% and 15.5% in the context of 1.5°C and 2.0°C warming, respectively. The relative contribution of LUCC, indirect climate change and direct climate change to yield was 4.7%, 41.2%, and 54.1% under 1.5°C warming, and 13.1%, 28.7%, and 58.2% under 2.0°C warming, respectively.ConclusionWe suggest that changes in irrigation water availability should be considered from a watershed perspective when simulating the influence of climate change on crop yield, especially regional crop production estimation.This article is protected by copyright. All rights reserved.

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Section: Plus Modelmentioning

confidence: 99%

Section: Framework Constructionmentioning

confidence: 99%

A framework for assessing the impacts of land‐use/cover change and climate change on wheat productivity under 1.5 and 2.0 °C warming at watershed scale

Sun,

Wang

2024

J Sci Food Agric

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“…The historical daily data of maximum (T max ) and minimum (T min ) temperature, precipitation (P), wind speed (W S ) and dew point (T Dew ) from 1990 to 2016 on a 0.5 × 0.5-degree grid were retrieved from NASA Prediction of Worldwide Energy Resources (https://power. larc.nasa.gov/data-access-viewer/, accessed on 15 April 2022) [45,46]. Potato yield data from 1990 to 2016 were collected from the agriculture directorates of the governorates, Economic Affairs Sector, Ministry of Agriculture and Land Reclamation.…”

Section: Data Sourcesmentioning

confidence: 99%

“…WF is divided into three classifications: green, blue and grey water [45]. Following the terminology of Hoekstra [28], the water footprint of the crop season (WFc) is the sum of the green (WF green ) and blue (WF blue ) components and is generally expressed in m 3 ton −1 , which is equivalent to L kg −1 as:…”

Section: Water Footprint Calculationsmentioning

confidence: 99%

Winter Potato Water Footprint Response to Climate Change in Egypt

et al. 2022

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The limited amount of freshwater is the most important challenge facing Egypt due to increasing population and climate change. The objective of this study was to investigate how climatic change affects the winter potato water footprint at the Nile Delta covering 10 governorates from 1990 to 2016. Winter potato evapotranspiration (ETC) was calculated based on daily climate variables of minimum temperature, maximum temperature, wind speed and relative humidity during the growing season (October–February). The Mann–Kendall test was applied to determine the trend of climatic variables, crop evapotranspiration and water footprint. The results showed that the highest precipitation values were registered in the northwest governorates (Alexandria followed by Kafr El-Sheikh). The potato water footprint decreased from 170 m3 ton−1 in 1990 to 120 m3 ton−1 in 2016. The blue-water footprint contributed more than 75% of the total; the remainder came from the green-water footprint. The findings from this research can help government and policy makers better understand the impact of climate change on potato crop yield and to enhance sustainable water management in Egypt’s major crop-producing regions to alleviate water scarcity.

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“…Puntel et al [19] reported that the Agricultural Production Systems sIMulator (APSIM) effectively simulated (S) the long-term effects of different nitrogen rates on corn yield and winter wheat-summer maize yields in central Iowa, USA. Examples of others are given by Zhao et al [20], Li et al [21], Lu et al [22], Kothari et al [23], Marek et al [24], Masasi et al [25], Mompremier et al [26], Attia et al [27] and Spivey et al [28]. Long-term simulations of agricultural management strategies on crop yield are mainly concentrated in semi-arid and arid regions.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Irrigation Strategies to Improve Water Use Efficiency of Cotton in Northwest China Using RZWQM2

Chen

Feng

et al. 2022

Agriculture

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Irrigated cotton (Gossypium hirsutum L.) is produced mainly in Northwest China, where groundwater is heavily used. To alleviate water scarcity and increase regional economic benefits, a four-year (2016–2019) field experiment was conducted in Qira Oasis, Xingjiang Province, to evaluate irrigation water use efficiency (IWUE) in cotton production using the Root Zone Water Quality Model (RZWQM2), that was calibrated and validated using volumetric soil water content (θ), soil temperature (Tsoil°) and plant transpiration (T), along with cotton growth and yield data collected from full and deficit irrigation experimental plots managed with a newly developed Decision Support System for Irrigation Scheduling (DSSIS). In the validation phase, RZWQM2 adequately simulated (S) topsoil θ and Tsoil°, as well as cotton growth (average index of agreement (IOA) > 0.76). Relative root mean squared error (RRMSE) and percent bias (PBIAS) of cotton seed yield were 8% and 2.5%, respectively, during calibration, and 20% and −10.3% during validation. The cotton crop’s (M) T was well S (−18% < PBIAS < 14% and IOA > 0.95) for both full and deficit irrigation fields. The validated RZWQM2 model was subsequently run with seven irrigation scenarios with 850 to 350 mm water (Irr850, Irr750, Irr700, Irr650, Irr550, Irr450, and Irr350) and long-term (1990–2019) weather data to determine the best IWUE. Simulation results showed that the Irr650 treatment generated the greatest cotton seed yield (4.09 Mg ha−1) and net income (US $3165 ha−1), while the Irr550 treatment achieved the greatest IWUE (6.53 kg ha−1 mm−1) and net water production (0.94 $ m−3). These results provided farmers guidelines to adopt deficit irrigation strategies.

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Modeling the response of dry bean yield to irrigation water availability controlled by watershed hydrology

Cited by 10 publications

References 46 publications

A framework for assessing the impacts of land‐use/cover change and climate change on wheat productivity under 1.5 and 2.0 °C warming at watershed scale

A framework for assessing the impacts of land‐use/cover change and climate change on wheat productivity under 1.5 and 2.0 °C warming at watershed scale

Winter Potato Water Footprint Response to Climate Change in Egypt

Optimizing Irrigation Strategies to Improve Water Use Efficiency of Cotton in Northwest China Using RZWQM2

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