Maize transpiration and water productivity of two irrigated fields with varying groundwater depths in an arid area

Wang, Xingwang; Guan, Huade; Huo, Zailin; Guo, Ping; Du, Jiali; Wang, Weishu

doi:10.1016/j.agrformet.2019.107849

Cited by 26 publications

(8 citation statements)

References 44 publications

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“…Our study found that VPD showed a week positive correlation with ETn (R = 0.277) and passed the significance test (p-value < 0.01). However, at VPD to about 10 hpa, ETn did not show a significant elevation with increasing VPD, and similar VPD threshold effects were observed in other studies [76][77][78].…”

Section: Differences In Random Forest Feature Importance Assessment A...supporting

confidence: 89%

Integrating Meteorological and Remote Sensing Data to Simulate Cropland Nocturnal Evapotranspiration Using Machine Learning

Huang,

Zhang,

Zhang

et al. 2024

Sustainability

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Evapotranspiration (ET) represents a significant component of the global water flux cycle, yet nocturnal evapotranspiration (ETn) is often neglected, leading to underestimation of global evapotranspiration. As for cropland, accurate modeling of ETn is essential for rational water management and is important for sustainable agriculture development. We used random forest (RF) to simulate ETn at 16 globally distributed cropland eddy covariance flux sites along with remote sensing and meteorological factors. The recursive feature elimination method was used to remove unimportant variables. We also simulated the ETn of C3 and C4 crops separately. The trained RF resulted in a determination coefficient (R2) (root mean square error (RMSE)) of 0.82 (7.30 W m−2) on the testing dataset. C3 and C4 crops on the testing dataset resulted in an R2 (RMSE) of 0.86 (5.59 W m−2) and 0.55 (4.86 W m−2) for the two types of crops. We also showed that net radiation is the dominant factor in regulating ETn, followed by 2 m horizontal wind speed and vapor pressure deficit (VPD), and these three meteorological factors showed a significant positive correlation with ETn. This research demonstrates that RF can simulate ETn from crops economically and accurately, providing a methodological basis for improving global ETn simulations.

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Section: Differences In Random Forest Feature Importance Assessment A...supporting

confidence: 89%

Integrating Meteorological and Remote Sensing Data to Simulate Cropland Nocturnal Evapotranspiration Using Machine Learning

Huang,

Zhang,

Zhang

et al. 2024

Sustainability

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“…Data were sampled at 10 Hz and recorded at 30 min intervals using the CR3000 data logger. R n was measured by a CNR4‐L10 net radiometer (Kipp & Zonen, Netherlands) installed at the height of 1 m above the maximum canopy (Wang, Guan, et al, 2020; Wang, Huo, et al, 2020). Initial eddy covariance data were post‐processed with the EddyPro (LI‐COR) software, including spike detection, frequency response corrections, conversion of sonic temperature into actual temperature (Schotanus et al, 1983), coordinate rotation using planar fit method and WPL (Webb‐Pearman‐Leuning) density correction (Webb et al, 1980).…”

Section: Methodsmentioning

confidence: 99%

“…The leaf area was manually measured using the scanning leaf area metre (Beijing Yaxinliyi Science and Technology Corp., China) at 10–15 days intervals. Leaf area index (LAI) was calculated by leaf area and planting density (Wang, Guan, et al, 2020; Wang, Huo, et al, 2020). The LAI was linearly interpolated between two measurements.…”

Section: Methodsmentioning

confidence: 99%

Variation and attribution of water use efficiency in sunflower and maize fields in an irrigated semi‐arid area

Wang

Huo

et al. 2021

Hydrological Processes

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Water use efficiency (WUE) links carbon and water exchanges between farmlands and the atmosphere. Understanding the variation and attribution of WUE is essential to reveal the physiological and ecological adaptation mechanisms of crops to the changing environment, and to better allocate, regulate and conserve water resources. However, few studies on the variation and attribution of WUE have been conducted in irrigated arid or semi-arid farmlands. Therefore, in this study, water and carbon fluxes were measured using eddy covariance systems in two farmlands (one sunflower field and one maize field) in a semi-arid irrigation district in China. It was found that the average WUE of sunflower during its full growth period was 1.72 g C kg −1 H 2 O, much lower than that of maize (4.07 g C kg −1 H 2 O). At each growth stage, the WUE of both crops were negatively correlated with vapour pressure deficit (VPD), net radiation (R n) and soil water content (SWC). The negative correlations could be attributed to the arid meteorological condition and the relatively abundant soil moisture due to irrigation and shallow groundwater levels. VPD was the main factor affecting WUE, followed by R n and SWC. It was also found that the response of WUE to crop leaf area index (LAI) and to canopy conductance (g c) depended on the VPD ranges: when VPD increased, the response of WUE to LAI and to g c decreased. Our findings could improve the understanding of the coupling effect of water and carbon fluxes over farmland ecosystems in arid and semi-arid irrigation areas and help improve agricultural production and save water resources in such areas.

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“…Finally, regarding the variation in a plant, we can find some studies that evaluate the hourly changes in measurable parameters of plants. Wang et al [ 15 ] measured the variation of plant temperature and vapour pressure deficit in maize plants every 30 min over three days in two periods of time. Their results indicate that the variation from a cloudy to clear day modifies the temporal variation.…”

Section: State Of the Artmentioning

confidence: 99%

Evaluating the Effects of Environmental Conditions on Sensed Parameters for Green Areas Monitoring and Smart Irrigation Systems

Mauri¹,

Parra

Yousfi³

et al. 2021

Sensors

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The irrigation of green areas in cities should be managed appropriately to ensure its sustainability. In large cities, not all green areas might be monitored simultaneously, and the data acquisition time can skew the gathered value. Our purpose is to evaluate which parameter has a lower hourly variation. We included soil parameters (soil temperature and moisture) and plant parameters (canopy temperature and vegetation indexes). Data were gathered at 5 different hours in 11 different experimental plots with variable irrigation and with different grass composition. The results indicate that soil moisture and Normalized Difference Vegetation Index are the sole parameters not affected by the data acquisition time. For soil moisture, the maximum difference was in experimental plot 4, with values of 21% at 10:45 AM and 27% at 8:45 AM. On the other hand, canopy temperature is the most affected parameter with a mean variation of 15 °C in the morning. The maximum variation was in experimental plot 8 with a 19 °C at 8:45 AM and 39 °C at 12:45 PM. Data acquisition time affected the correlation between soil moisture and canopy temperature. We can affirm that data acquisition time has to be included as a variability source. Finally, our conclusion indicates that it is vital to consider data acquisition time to ensure water distribution for irrigation in cities.

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Maize transpiration and water productivity of two irrigated fields with varying groundwater depths in an arid area

Cited by 26 publications

References 44 publications

Integrating Meteorological and Remote Sensing Data to Simulate Cropland Nocturnal Evapotranspiration Using Machine Learning

Integrating Meteorological and Remote Sensing Data to Simulate Cropland Nocturnal Evapotranspiration Using Machine Learning

Variation and attribution of water use efficiency in sunflower and maize fields in an irrigated semi‐arid area

Evaluating the Effects of Environmental Conditions on Sensed Parameters for Green Areas Monitoring and Smart Irrigation Systems

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