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1 Sreelash, K.; Buis, S.; Sekhar, M.; Ruiz, L.; Tomer, S. K.; Guerif, M. 2017. Estimation of available water capacity components of two-layered soils using crop model inversion: effect of crop type and water regime. Journal of Hydrology, 546:166-178. [doi: https://doi.org/10.1016/j.jhydrol.2016.12.049]
Water holding capacity ; Water availability ; Estimation ; Soil water content ; Soil hydraulic properties ; Layered soils ; Soil moisture ; Field capacity ; Wilting point ; Water stress ; Crop management ; Models ; Sensitivity analysis ; Leaf Area Index ; Maize ; Sorghum ; Sunflowers ; Turmeric ; Remote sensing ; Catchment areas / South India / Berambadi Catchment
(Location: IWMI HQ Call no: e-copy only Record No: H048041)
(1.43 MB)
Characterization of the soil water reservoir is critical for understanding the interactions between crops and their environment and the impacts of land use and environmental changes on the hydrology of agricultural catchments especially in tropical context. Recent studies have shown that inversion of crop models is a powerful tool for retrieving information on root zone properties. Increasing availability of remotely sensed soil and vegetation observations makes it well suited for large scale applications. The potential of this methodology has however never been properly evaluated on extensive experimental datasets and previous studies suggested that the quality of estimation of soil hydraulic properties may vary depending on agro-environmental situations. The objective of this study was to evaluate this approach on an extensive field experiment. The dataset covered four crops (sunflower, sorghum, turmeric, maize) grown on different soils and several years in South India. The components of AWC (available water capacity) namely soil water content at field capacity and wilting point, and soil depth of two-layered soils were estimated by inversion of the crop model STICS with the GLUE (generalized likelihood uncertainty estimation) approach using observations of surface soil moisture (SSM; typically from 0 to 10 cm deep) and leaf area index (LAI), which are attainable from radar remote sensing in tropical regions with frequent cloudy conditions. The results showed that the quality of parameter estimation largely depends on the hydric regime and its interaction with crop type. A mean relative absolute error of 5% for field capacity of surface layer, 10% for field capacity of root zone, 15% for wilting point of surface layer and root zone, and 20% for soil depth can be obtained in favorable conditions. A few observations of SSM (during wet and dry soil moisture periods) and LAI (within water stress periods) were sufficient to significantly improve the estimation of AWC components. These results show the potential of crop model inversion for estimating the AWC components of two-layered soils and may guide the sampling of representative years and fields to use this technique for mapping soil properties that are relevant for distributed hydrological modelling.
2 Jabro, J. D.; Stevens, W. B.; Iversen, W. M.; Allen, B. L.; Sainju, U. M. 2020. Irrigation scheduling based on wireless sensors output and soil-water characteristic curve in two soils. Sensors, 20(5):1336. (Special issue: Soil Moisture Sensors for Irrigation Management) [doi: https://doi.org/10.3390/s20051336]
Irrigation Scheduling ; Soil water characteristics ; Soil water content ; Soil water potential ; Wilting point ; Water availability ; Sandy loam soils ; Clay loam soils ; Monitoring ; Rain ; Sensors / USA / North Dakota / Montana
(Location: IWMI HQ Call no: e-copy only Record No: H049690)
(1.53 MB) (1.53 MB)
Data-driven irrigation planning can optimize crop yield and reduce adverse impacts on surface and ground water quality. We evaluated an irrigation scheduling strategy based on soil matric potentials recorded by wireless Watermark (WM) sensors installed in sandy loam and clay loam soils and soil-water characteristic curve data. Five wireless WM nodes (IRROmesh) were installed at each location, where each node consisted of three WM sensors that were installed at 15, 30, and 60 cm depths in the crop rows. Soil moisture contents, at field capacity and permanent wilting points, were determined from soil-water characteristic curves and were approximately 23% and 11% for a sandy loam, and 35% and 17% for a clay loam, respectively. The field capacity level which occurs shortly after an irrigation event was considered the upper point of soil moisture content, and the lower point was the maximum soil water depletion level at 50% of plant available water capacity in the root zone, depending on crop type, root depth, growth stage and soil type. The lower thresholds of soil moisture content to trigger an irrigation event were 17% and 26% in the sandy loam and clay loam soils, respectively. The corresponding soil water potential readings from the WM sensors to initiate irrigation events were approximately 60 kPa and 105 kPa for sandy loam, and clay loam soils, respectively. Watermark sensors can be successfully used for irrigation scheduling by simply setting two levels of moisture content using soil-water characteristic curve data. Further, the wireless system can help farmers and irrigators monitor real-time moisture content in the soil root zone of their crops and determine irrigation scheduling remotely without time consuming, manual data logging and frequent visits to the field
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