Actual evapotranspiration and dual crop coefficients for dry-seeded rice and hybrid maize grown with overhead sprinkler irrigation

Alberto, Ma. Carmelita R.; Quilty, James R.; Buresh, R. J.; Waßmann, Reiner; Haidar, Sam A; Correa, Teodoro Q.; Sandro, Joseph M.

doi:10.1016/j.agwat.2014.01.005

Cited by 86 publications

(45 citation statements)

References 67 publications

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“…Our study results are consistent with previous studies that show that the growing stage is a key factor for estimating K c in agricultural crops (Allen et al, 1998;Zhang et al, 2013;Alberto et al, 2014;Wei et al, 2015), fruit trees (Abrisqueta et al, 2013;Marsal et al, 2014b), salt grass (Bawazir et al, 2014), and Populus euphratica Oliv. forest (Hou et al, 2010).…”

Section: Modeling the Dynamics Of K Csupporting

confidence: 83%

Environmental controls on seasonal ecosystem evapotranspiration/potential evapotranspiration ratio as determined by the global eddy flux measurements

Liu

Sun

McNulty

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. The evapotranspiration / potential evapotranspiration (AET / PET) ratio is traditionally termed as the crop coefficient (K c ) and has been generally used as ecosystem evaporative stress index. In the current hydrology literature, K c has been widely used as a parameter to estimate crop water demand by water managers but has not been well examined for other types of ecosystems such as forests and other perennial vegetation. Understanding the seasonal dynamics of this variable for all ecosystems is important for projecting the ecohydrological responses to climate change and accurately quantifying water use at watershed to global scales. This study aimed at deriving monthly K c for multiple vegetation cover types and understanding its environmental controls by analyzing the accumulated global eddy flux (FLUXNET) data. We examined monthly K c data for seven vegetation covers, including open shrubland (OS), cropland (CRO), grassland (GRA), deciduous broad leaf forest (DBF), evergreen needle leaf forest (ENF), evergreen broad leaf forest (EBF), and mixed forest (MF), across 81 sites. We found that, except for evergreen forests (EBF and ENF), K c values had large seasonal variation across all land covers. The spatial variability of K c was well explained by latitude, suggesting site factors are a major control on K c . Seasonally, K c increased significantly with precipitation in the summer months, except in EBF. Moreover, leaf area index (LAI) significantly influenced monthly K c in all land covers, except in EBF. During the peak growing season, forests had the highest K c values, while croplands (CRO) had the lowest. We developed a series of multivariate linear monthly regression models for K c by land cover type and season using LAI, site latitude, and monthly precipitation as independent variables. The K c models are useful for understanding water stress in different ecosystems under climate change and variability as well as for estimating seasonal ET for large areas with mixed land covers.

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Section: Modeling the Dynamics Of K Csupporting

confidence: 83%

Environmental controls on seasonal ecosystem evapotranspiration/potential evapotranspiration ratio as determined by the global eddy flux measurements

Liu

Sun

McNulty

et al. 2017

Hydrol. Earth Syst. Sci.

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“…In agreement with ET 0 , mean daily ET c values during the mid-and late-season stages in 2010 were higher than in 2011 in the FI treatment (Table 4). The relatively high ET c values during these growth stages in 2010 are related to higher climatic parameters, especially higher R n (Alberto et al, 2014) since climatic conditions are the most dominant factors for ET c during the mid-and late-season growth stages (Singh and Bhakar, 2002). On the one hand, a larger intercepted R n in 2010 produced higher LAI than in 2011.…”

Section: Temporal Soil-water-plant Interactionsmentioning

confidence: 98%

“…The reduction in fresh water resources due to climate change and rapid growth of the world's population often leads to severe competition between industrial, municipal, and environmental users of water (Alberto et al, 2014). Agriculture is the biggest fresh water user all over the world and commands over 70% of the world's freshwater withdrawals (OECD, 2010).…”

Section: Introductionmentioning

confidence: 99%

A field-modeling study for assessing temporal variations of soil-water-crop interactions under water-saving irrigation strategies

Karandish

Šimůnek

2016

Agricultural Water Management

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a b s t r a c tSimulation models are useful tools that may help to improve our understanding of soil-water-plant interactions under innovative water-saving irrigation strategies. In this study, the HYDRUS-2D model was applied to evaluate the influence of deficit irrigation (DI) and partial root-zone drying (PRD) on maize water extractions during two cropping cycles of 2010 and 2011. The model was calibrated and validated using measured soil water content data (expressed as equivalent water depths). Reliable estimates of soil water content were provided by HYDRUS-2D, with root mean square error and mean bias error values of 2.3-5.11 and 1.63-4.93 mm, respectively. Root water uptake and maize grain yields were reduced by 13.2-28.8% and 13.6-52.8%, respectively, under different water-saving irrigation treatments compared to full irrigation. However, different root and water repartitions in the PRD treatment with a 25% reduction in the irrigation depth (PRD 75 ) improved soil water utilization and consequently, crop growth. Increased root water uptake (2.2-4.4 times higher than in other treatments) from the 60-100 cm soil depth in the PRD 75 treatment maintained a favorable daily evapotranspiration rate, resulting in no significant reduction in maize grain yield compared to full irrigation. Consequently, a 15.7-85% increase in water use efficiency for maize cultivation under PRD 75 ensured 25% water savings without threatening food security in the study area. It can be concluded that HYDRUS-2D can be successfully used to optimize water management under local water-stress conditions.

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“…For example, Iwakiri 1965 observed rice ET for 3 years using a lysimeter method and concluded that the year-to-year differences were attributed to growth conditions or measurement errors; however, there was no further investigation. More recently, Alberto et al 2011Alberto et al , 2013Alberto et al , 2014 measured rice ET using the eddy covariance method in a dry-seeded rice field in the Philippines for a few years, and Baldocchi et al 2016 andHatala et al 2012 reported rice ET for 6 years and 2 years, respectively, from a rice field in California, USA. Although these studies set a milestone in understanding long-term rice ET based on eddy covariance, other terrestrial eddy covariance studies demonstrated a need for decade data to determine inter-annual variations e.g., Oechel et al, 2000;Ueyama et al, 2014 .…”

Section: Introductionmentioning

confidence: 99%

Evapotranspiration in a rice paddy field over 13 crop years

Ikawa

Ono

Mano

et al. 2017

J. Agric. Meteorol.

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Evapotranspiration ET in rice paddy fields has been studied over the past half-century, but despite that long history of rice ET study, not much is known about their inter-annual variation. This study investigates ET in rice paddy fields during the growing season for 13 years 2002 -2014 at one of the longest flux monitoring sites in Japan. The cumulative ET estimated for each growing season 120 days after transplanting was 419 45 SD standard deviation mm/season 3.5 mm d 1 , which increased to 534 64 mm/season 4.4 mm d 1 when energy balance was forced toclose. This study determined that ET was correlated with meteorological conditions defined by potential evaporation E P , and the inter-annual variability of seasonal ET/E P coefficient of variation CV = 4.6 was less than half the variability of ET CV = 10.7 . For most years, the seasonal ET was reasonably estimated as the product of E P and the overall average ET/E P =0.89 , including years that experienced both extremely warm and cold summers. However, the errors in estimation were relatively large for 2008 which experienced high precipitation and 2012 which was characterized by an extremely low leaf area index . Although the seasonal ET/E P was relatively invariable among years, both the seasonal trends of ET and ET/E P greatly differed between years. The inter-annual variability in ET/E P was particularly high at the ripening stage defined as 91 -120 days after transplanting . The ET/E P at this stage was well correlated to precipitation, and the variation was possibly caused by evaporation from intercepted rainfall. The variation in ET/E P associated with precipitation at the ripening stage accounted for 21 of the inter-annual variation of seasonal ET/E P .

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Actual evapotranspiration and dual crop coefficients for dry-seeded rice and hybrid maize grown with overhead sprinkler irrigation

Cited by 86 publications

References 67 publications

Environmental controls on seasonal ecosystem evapotranspiration/potential evapotranspiration ratio as determined by the global eddy flux measurements

Environmental controls on seasonal ecosystem evapotranspiration/potential evapotranspiration ratio as determined by the global eddy flux measurements

A field-modeling study for assessing temporal variations of soil-water-crop interactions under water-saving irrigation strategies

Evapotranspiration in a rice paddy field over 13 crop years

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