Quantifying crop water stress factors from soil water measurements in a limited irrigation experiment

2015 ◽  
Vol 137 ◽  
pp. 191-205 ◽  
Author(s):  
S.A. Saseendran ◽  
T.J. Trout ◽  
L.R. Ahuja ◽  
L. Ma ◽  
G.S. McMaster ◽  
...  
2020 ◽  
Author(s):  
Angela Morales Santos ◽  
Reinhard Nolz

<p>Sustainable irrigation water management is expected to accurately meet crop water requirements in order to avoid stress and, consequently, yield reduction, and at the same time avoid losses of water and nutrients due to deep percolation and leaching. Sensors to monitor soil water status and plant water status (in terms of canopy temperature) can help planning irrigation with respect to time and amounts accordingly. The presented study aimed at quantifying and comparing crop water stress of soybeans irrigated by means of different irrigation systems under subhumid conditions.</p><p>The study site was located in Obersiebenbrunn, Lower Austria, about 30 km east of Vienna. The region is characterized by a mean temperature of 10.5°C with increasing trend due to climate change and mean annual precipitation of 550 mm. The investigations covered the vegetation period of soybean in 2018, from planting in April to harvest in September. Measurement data included precipitation, air temperature, relative humidity and wind velocity. The experimental field of 120x120 m<sup>2</sup> has been divided into four sub-areas: a plot of 14x120 m<sup>2</sup> with drip irrigation (DI), 14x120 m<sup>2</sup> without irrigation (NI), 36x120 m<sup>2</sup> with sprinkler irrigation (SI), and 56x120 m<sup>2</sup> irrigated with a hose reel boom with nozzles (BI). A total of 128, 187 and 114 mm of water were applied in three irrigation events in the plots DI, SI and BI, respectively. Soil water content was monitored in 10 cm depth (HydraProbe, Stevens Water) and matric potential was monitored in 20, 40 and 60 cm depth (Watermark, Irrometer). Canopy temperature was measured every 15 minutes using infrared thermometers (IRT; SI-411, Apogee Instruments). The IRTs were installed with an inclination of 45° at 1.8 m height above ground. Canopy temperature-based water stress indices for irrigation scheduling have been successfully applied in arid environments, but their use is limited in humid areas due to low vapor pressure deficit (VPD). To quantify stress in our study, the Crop Water Stress Index (CWSI) was calculated for each plot and compared to the index resulting from the Degrees Above Canopy Threshold (DACT) method. Unlike the CWSI, the DACT method does not consider VPD to provide a stress index nor requires clear sky conditions. The purpose of the comparison was to revise an alternative method to the CWSI that can be applied in a humid environment.</p><p>CWSI behaved similar for the four sub-areas. As expected, CWSI ≥ 1 during dry periods (representing severe stress) and it decreased considerably after precipitation or irrigation (representing no stress). The plot with overall lower stress was BI, producing the highest yield of the four plots. Results show that DACT may be a more suitable index since all it requires is canopy temperature values and has strong relationship with soil water measurements. Nevertheless, attention must be paid when defining canopy temperature thresholds. Further investigations include the development and test of a decision support system for irrigation scheduling combining both, plant-based and soil water status indicators for water use efficiency analysis.</p>


Agronomy ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1377
Author(s):  
Jeffrey D. Svedin ◽  
Ruth Kerry ◽  
Neil C. Hansen ◽  
Bryan G. Hopkins

Addressing within-field and within-season variability of crop water stress is critical for spatially variable irrigation. This study measures interactions between spatially variable soil properties and temporally variable crop water dynamics; and whether modelling soil water depletion is an effective approach to guide variable-rate irrigation (VRI). Energy and water balance equations were used to model crop water stress at 85 locations within a 22 ha field of winter wheat (Triticum aestivum L.) under uniform and spatially variable irrigation. Significant within-field variability of soil water holding capacity (SWHC; 145–360 mm 1.2 m−1), soil electrical conductivity (0.22–49 mS m−1), spring soil water (314–471 mm 1.2 m−1), and the onset of crop water stress were observed. Topographic features and modelled onset of crop water stress were significant predictors of crop yield while soil moisture at spring green-up, elevation, and soil electrical conductivity were significant predictors of the onset of crop water stress. These results show that modelling soil water depletion can be an effective scheduling tool in VRI. Irrigation zones and scheduling efforts should consider expanding to include temporally dynamic factors, including spring soil water content and the onset of crop water stress.


2020 ◽  
Vol 63 (5) ◽  
pp. 1217-1231
Author(s):  
Bruno P. Lena ◽  
Brenda V. Ortiz ◽  
Andres F. Jiménez-Lópe ◽  
Álvaro Sanz-Sáez ◽  
Susan A. O’Shaughnessy ◽  
...  

HighlightsCorn response to irrigation was influenced by the precipitation distribution in 2018 and 2019, and that impacted the response of CWSI as an irrigation scheduling signaling method.CWSI was sensitive to changes in soil water storage, increasing due to crop evapotranspiration and decreasing after a precipitation or irrigation event.In 2018, both seasonal CWSI and yield were not different among the irrigation treatments, while in 2019, seasonal CWSI and yield were all statistically different among the treatments evaluated.Post analysis of canopy and air temperature indicated that the temperature-time threshold (TTT) method might not appropriately signal crop water stress in a humid environment.Abstract. Irrigation scheduling based on the crop water stress index (CWSI) and temperature-time threshold (TTT) methods is promising for semi-arid and arid climates. The objective of this study was to investigate if CWSI and TTT methods could be used as irrigation signaling tools for a humid environment in the southeastern U.S. Corn canopy temperature data were collected in Alabama in 2018 and 2019 using infrared leaf temperature sensors on a fully irrigated treatment and on two limited irrigation treatments. A set of three soil water sensors installed at 0.15, 0.3, and 0.6 m soil depth were used to prescribe irrigation time and amount. CWSI was sensitive to precipitation, irrigation, and plant water uptake. No statistical differences in CWSI or yield among the three irrigation levels were found in 2018 when precipitation was well distributed during the season. In contrast, during 2019 both CWSI and yield differed significantly among the three irrigation treatments. Precipitation events in 2019 were sparse compared to 2018; therefore, irrigation promoted greater differences in water availability between treatments. Inconsistencies observed in potential irrigation signaling using the TTT method with or without the inclusion of a limiting relative humidity algorithm indicate that the TTT method may not be a reliable irrigation signaling tool for humid environments. Keywords: Corn yield, Crop water stress index, Irrigation scheduling, Limiting relative humidity, Soil water depletion, Temperature-time threshold.


HortScience ◽  
2004 ◽  
Vol 39 (2) ◽  
pp. 276-279 ◽  
Author(s):  
Maria Victoria Cremona ◽  
Hartmut Stützel ◽  
Henning Kage

Two-year field experiments were carried out to evaluate the suitability of crop water stress index (CWSI) as a basis for irrigation scheduling of kohlrabi (Brassica oleracea L. var. gongylodes) by comparison with irrigation scheduling based on total soil water content (SWC). In the first year, irrigation scheduling when CWSI exceeded 0.3 resulted in more frequent water applications, but the total amount of irrigation water given was lower compared to irrigation when SWC fell below 70%. Kohlrabi tuber fresh weight at harvest was similar in both scheduling treatments, leading to 25% higher irrigation water use efficiency in the CWSI-scheduled plots. In the second year, three threshold levels, i.e., 0.2 and 80%, 0.4 and 60%, and 0.6 and 40% of CWSI and SWC, respectively, were investigated. At the level of highest water supply (CWSI = 0.2 and SWC = 80%), the total amount of water supplied was less in the CWSI but the number of irrigations was higher than in the SWC plots. The CWSI-based approach may be a method for irrigation scheduling of vegetables under temperate conditions. The higher irrigation frequency required would make this method particularly suitable in combination with irrigation system that allow frequent applications, i.e., in drip irrigation. To improve the method, a coupling with a soil water balance model seems promising.


Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1117
Author(s):  
Anatoly Mikhailovich Zeyliger ◽  
Olga Sergeevna Ermolaeva

In the past few decades, combinations of remote sensing technologies with ground-based methods have become available for use at the level of irrigated fields. These approaches allow an evaluation of crop water stress dynamics and irrigation water use efficiency. In this study, remotely sensed and ground-based data were used to develop a method of crop water stress assessment and analysis. Input datasets of this method were based on the results of ground-based and satellite monitoring in 2012. Required datasets were collected for 19 irrigated alfalfa crops in the second year of growth at three study sites located in Saratovskoe Zavolzhie (Saratov Oblast, Russia). Collected datasets were applied to calculate the dynamics of daily crop water stress coefficients for all studied crops, thereby characterizing the efficiency of crop irrigation. Accordingly, data on the crop yield of three harvests were used. An analysis of the results revealed a linear relationship between the crop yield of three cuts and the average value of the water stress coefficient. Further application of this method may be directed toward analyzing the effectiveness of irrigation practices and the operational management of agricultural crop irrigation.


2013 ◽  
Vol 118 ◽  
pp. 79-86 ◽  
Author(s):  
N. Agam ◽  
Y. Cohen ◽  
J.A.J. Berni ◽  
V. Alchanatis ◽  
D. Kool ◽  
...  

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