Winter wheat grain yield and water use efficiency in wide-precision planting pattern under deficit irrigation in North China Plain

Li, Quanqi; Bian, Chengyue; Liu, Xinhui; Ma, Chunyan; Liu, Quan‐Ru

doi:10.1016/j.agwat.2015.02.004

Cited by 64 publications

(57 citation statements)

References 21 publications

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“…The rainfall was significantly low (<250 mm) throughout the winter wheat growing season of this study period, which limited grain yield. Water saving technology [63], proper plant density [64], minimum tillage [65], and other agronomic and breeding approaches would be advantageous to reduce water consumption of winter wheat. Furthermore, the evapotranspiration sequence was 1H1Y < 3H2Y < 2H1Y OPT < 2H1Y FP .…”

Section: Effect Of Different Cropping Systems On Grain Yield Water Amentioning

confidence: 99%

Modeling Water and Nitrogen Balance of Different Cropping Systems in the North China Plain

Leghari

Liang

et al. 2019

Agronomy

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The North China Plain (NCP) is experiencing serious groundwater level decline and groundwater nitrate contamination due to excessive water pumping and application of nitrogen (N) fertilizer. In this study, grain yield, water and N use efficiencies under different cropping systems including two harvests in 1 year (winter wheat-summer maize) based on farmer (2H1Y) FP and optimized practices (2H1Y) OPT , three harvests in 2 years (winter wheat-summer maize-spring maize, 3H2Y), and one harvest in 1 year (spring maize, 1H1Y) were evaluated using the water-heat-carbon-nitrogen simulator (WHCNS) model. The 2H1Y FP system was maintained with 100% irrigation and fertilizer, while crop water requirement and N demand for other cropping systems were optimized and managed by soil testing. In addition, a scenario analysis was also performed under the interaction of linearly increasing and decreasing N rates, and irrigation levels.Results showed that the model performed well with simulated soil water content, soil N concentration, leaf area index, dry matter, and grain yield. Statistically acceptable ranges of root mean square error, Nash-Sutcliffe model efficiency, index of agreement values close to 1, and strong correlation coefficients existed between simulated and observed values. We concluded that replacing the prevalent 2H1Y FP with 1H1Y would be ecofriendly at the cost of some grain yield decline. This cropping system had the highest average water use (2.1 kg m −3 ) and N use efficiencies (4.8 kg kg -1 ) on reduced water (56.64%) and N (81.36%) inputs than 2H1Y FP . Whereas 3H2Y showed insignificant results in terms of grain yield, and 2H1Y FP was unsustainable. The 2H1Y FP system consumed a total of 745 mm irrigation and 1100 kg N ha -1 in two years. When farming practices were optimized for two harvests in 1 year system (2H1Y) OPT , then grain yield improved and water (18.12%) plus N (61.82%) consumptions were minimized. There was an ample amount of N saved, but water conservation was still unsatisfactory. However, considering the results of scenario analyses, it is recommended that winter wheat would be cultivated at <200 mm irrigation by reducing one irrigation event.Since the 1970s, the annual use of the two-harvest cropping system greatly increased the total grain output. However, it has also caused severe groundwater decline [7,8], because both crops generally have high water requirements to complete their life cycle [9,10]. Crop water requirement does not meet the balance between groundwater recharge through rain and evapotranspiration (ET) loss from plant and soil [11]. An estimated amount of more than 450 mm yr −1 irrigation water is required to maintain high crop yield under the conventional double cropping system [12], which dominatingly depends on pumping of groundwater, and there is no access to irrigation from the river source. Thus, the groundwater is almost the only source of irrigation [13]. In the last 20 years, sustained water pumping has adversely affected aquifers. Consequently, the groundwater leve...

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Section: Effect Of Different Cropping Systems On Grain Yield Water Amentioning

confidence: 99%

Modeling Water and Nitrogen Balance of Different Cropping Systems in the North China Plain

Leghari

Liang

et al. 2019

Agronomy

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“…Serious water shortages force people to implement deficit irrigation. Researchers have reported that this irrigation method can effectively improve water use efficiency without many adverse effects on yield (Han, Ren, Gao, Yan, & Li, 2017;Li, Bian, Liu, Ma, & Liu, 2015).…”

mentioning

confidence: 99%

Modelling root water uptake under deficit irrigation and rewetting in Northwest China

et al. 2020

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The spatial and temporal distribution of root water uptake (RWU) under deficit irrigation are critical factors for crop growth. The SWAP (soil–water–atmosphere–plant) model was applied to analyze the pattern of RWU for winter wheat (Triticum aestivum L.) under three irrigation levels: no water deficit (100% evapotranspiration [ET]), moderate water deficit (80% ET) and severe water deficit (60% ET). The 2–yr experiments indicated that SWAP was highly accurate (mean relative error [MRE] <21.7%, root mean square error [RMSE] <0.07 cm3 cm−3) in simulating the soil water content (SWC). Root water uptake was significantly (P < 0.01) different in the 0‐ to 60‐cm soil layer. The 0‐ to 60‐cm soil layer was the main source of RWU, and the average value accounted for 89.4% of the total root zone. Water stress had the greatest adverse effect on heading to grain filling, reducing RWU by 0.0026 cm3 cm−3 d−1. The critical SWC was 67.9% of the field capacity, when the RWU dropped to 95% of the control treatment. After rewetting, compensation and hysteresis effects on RWU were observed. The ranking of RWU recovery ability after rewetting was: emergence to jointing > jointing to heading > grain filling to maturity > heading to grain filling. Recovery time of RWU was 2 to 11 d and gradually increased with growth stage. The simplified RWU model established using path analysis and regression performed well (R2 = 0.836; P < 0.01) for RWU. This provided a more convenient way to accurately estimate RWU with fewer variables.

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“…One of the possible reasons is that the sunflower field is irrigated more frequently (Ren et al, ) and the value is maintained at a higher level. The soil moisture of winter wheat is also at a higher value in April and May due to the effects of irrigation (Q. Li et al, ; H. Y. Sun et al, ).…”

Section: Resultsmentioning

confidence: 99%

Estimation of Surface Soil Moisture With Downscaled Land Surface Temperatures Using a Data Fusion Approach for Heterogeneous Agricultural Land

Bai

Long

Yan

2019

Water Resources Research

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Field-scale surface soil moisture (SSM, 0-10 cm), which is closely linked with land surface temperature (LST), is particularly important to agricultural water resource management. Active and passive microwave remote sensing-based SSM retrievals on the order of kilometer squared resolutions are difficult to apply to heterogeneous agricultural land surfaces that may need SSM data at a resolution of 30 m. In this study, the High-resolution Urban Thermal Sharpener and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model were applied to downscale optical and thermal remote sensing data simultaneously by blending Landsat and MODIS red-near infrared-LST data, with the ultimate goal to generate field-scale SSM values from the trapezoidal approach. To evaluate the performance of the downscaled LST E (based on the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model method) and SSM, an irrigation district (Area 1) in Inner Mongolia and an irrigation district in the North China Plain (Area 2) with varying spatial heterogeneity were selected as the testbeds. Results indicated that the downscaled LST E was highly consistent with synchronous Landsat LST H and in situ LST measurements in Area 1, with the root-mean-square error ranging from 0.73 to 2.75 K. Compared with the MODIS SSM, the average root-mean-square error of the downscaled SSM improved from 0.048 to 0.038 cm 3 /cm 3 for both areas. The downscaled LST E and SSM developed in this study enhance the spatiotemporal resolutions of the SSM estimates, maximizing the potential of remotely sensed information for agricultural water resource management.Plain Language Summary Field-scale (30 m) surface soil moisture (SSM), closely linked with land surface temperature (LST), is particularly important for agricultural water resource management, such as for assessment of agricultural droughts, optimization of irrigation schedules and improvement of water use efficiency, particularly in the heterogeneous agricultural land. Here, the High resolution Urban Thermal Sharpener (HUTS) and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) were jointly adopted to downscale optical and thermal remote sensing data simultaneously by blending Landsat and MODIS Red-Near infrared-LST data, with the ultimate goal to generate field-scale SSM values from the downscaled Red-Near infrared-LST remote sensing data using the theoretical trapezoidal approach. The field-scale LST and SSM developed in this study maximize the potential of remotely sensed information, improve both the spatial and temporal resolutions of SSM, and provide more valuable information on heterogeneous land surfaces for agricultural water resource management. Key Points: • Downscaled LSTE (30 m), closely linked with SSM, can be acquired jointly using HUTS and ESTARFM methods • Downscaled field-scale SSM (30 m) well represents the actual soil moisture than MODIS SSM in heterogeneous land • Downscaled Red-Near infrared-LSTE improves both the spatial and temporal resolution of SSM

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Winter wheat grain yield and water use efficiency in wide-precision planting pattern under deficit irrigation in North China Plain

Cited by 64 publications

References 21 publications

Modeling Water and Nitrogen Balance of Different Cropping Systems in the North China Plain

Modeling Water and Nitrogen Balance of Different Cropping Systems in the North China Plain

Modelling root water uptake under deficit irrigation and rewetting in Northwest China

Estimation of Surface Soil Moisture With Downscaled Land Surface Temperatures Using a Data Fusion Approach for Heterogeneous Agricultural Land

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