Variable upper and lower crop water stress index baselines for corn and soybean

Payero, José O.; Irmak, Suat

doi:10.1007/s00271-006-0031-2

Cited by 72 publications

(37 citation statements)

References 32 publications

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“…Jackson et al (1981) developed the crop water stress index (CWSI), which is the most popular evaluation method and is applied to fields in the USA (Payero & Irmak, 2006). However, the canopy surface temperature and microclimate of the canopy, including the air temperature, humidity, net radiation and wind velocity, must be measured.…”

Section: Water Stress Index Of Soybean Based On the Difference In Canmentioning

confidence: 99%

Evaluation of Soil Moisture Status in the Field to Improve the Production of Tanbaguro Soybeans

Homma¹

2011

Soybean - Applications and Technology

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Section: Water Stress Index Of Soybean Based On the Difference In Canmentioning

confidence: 99%

Evaluation of Soil Moisture Status in the Field to Improve the Production of Tanbaguro Soybeans

Homma¹

2011

Soybean - Applications and Technology

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“…Practical application of the CWSI has been limited by the difficulty of estimating without actually measuring T nws and T dry (Maes and Steppe, 2012). Experimental determination of a crop specific constant for T nws and T dry relative to ambient air temperature has not been fruitful due to the poorly understood and complex influences of environmental conditions on the soil-plant-air continuum Jones, 1999Jones, , 2004Payero and Irmak, 2006). In the original development and application of the CWSI, T nws and T dry were experimentally determined with T nws correlated with VPD to account for climatic effects on T canopy measurements.…”

Section: Introductionmentioning

confidence: 99%

“…(2)- (4) (Coombe, 1995). (Payero and Irmak, 2006). Irmak et al (2000) determined in corn that T dry was 4.6-5.1 • C above air temperature and, in several subsequent studies with crops other than corn (including grapevines), a value of air temperature plus 5.0 • C has been used for T dry in Eq.…”

Section: Introductionmentioning

confidence: 99%

“…Regression equations have been the most promising, practical approach used to estimate T nws and T dry for the CWSI. However, regression, by necessity, simplifies complex, unknown interactions into a priori or assumed multiple linear or nonlinear relationships (Payero and Irmak, 2006). Artificial Neural Networks (NN) have been used successfully to model complex, unknown relationships and predict physical conditions, such as evapotranspiration (Kumar et al, 2002;Bhakar et al, 2006;Trajkovic et al, 2003) and many other water resource applications (ASCE, 2000).…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of neural network modeling to predict non-water-stressed leaf temperature in wine grape for calculation of crop water stress index

King

Shellie

2016

Agricultural Water Management

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a b s t r a c tPrecision irrigation management of wine grape requires a reliable method to easily quantify and monitor vine water status to allow effective manipulation of plant water stress in response to water demand, cultivar management and producer objective. Mild to moderate water stress is desirable in wine grape in determined phenological periods for controlling vine vigor and optimizing fruit yield and quality according to producer preferences and objectives. The traditional leaf temperature based crop water stress index (CWSI) for monitoring plant water status has not been widely used for irrigated crops in general partly because of the need to know well-watered and non-transpiring leaf temperatures under identical environmental conditions. In this study, leaf temperature of vines irrigated at rates of 35, 70 or 100% of estimated evapotranspiration demand (ET c ) under warm, semiarid field conditions in southwestern Idaho USA was monitored from berry development through fruit harvest in 2013 and 2014. Neural network (NN) models were developed based on meteorological measurements to predict well-watered leaf temperature of wine grape cultivars 'Syrah' and 'Malbec' (Vitis vinifera L.). Input variables for the cultivar specific NN models with lowest mean squared error were 15-min average values for air temperature, relative humidity, solar radiation and wind speed collected within ±90 min of solar noon (13:00 and 15:00 MDT). Correlation coefficients between NN predicted and measured well-watered leaf temperature were 0.93 and 0.89 for 'Syrah' and 'Malbec', respectively. Mean squared error and mean average error for the NN models were 1.07 and 0.82• C for 'Syrah' and 1.30, and 0.98 • C for 'Malbec', respectively. The NN models predicted well-watered leaf temperature with significantly less variability than traditional multiple linear regression using the same input variables. Non-transpiring leaf temperature was estimated as air temperature plus 15• C based on maximum temperatures measured for vines irrigated at 35% (ET c ). Daily mean CWSI calculated using NN estimated well-watered leaf temperatures between 13:00 and 15:00 MDT and air temperature plus 15• C for non-transpiring leaf temperature consistently differentiated between deficit irrigation amounts, irrigation events, and rainfall. The methodology used to calculate a daily CWSI for wine grape in this study provided a daily indicator of vine water status that could be automated for use as a decision-support tool in a precision irrigation system.Published by Elsevier B.V.

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“…This approach has been based on the fact that water-stressed crops tend to have a higher canopy temperature than non-stressed crops, which has long been proposed as a way of scheduling irrigation [7] [8] [9] [10] [11]. Despite some success, the use of canopy temperature for irrigation scheduling can have significant limitations [12] [13], especially for incomplete crop canopies and for humid environments. Therefore, its practical application among commercial farmers is also still very limited.…”

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confidence: 99%

Development of a Low-Cost Internet-of-Things (IoT) System for Monitoring Soil Water Potential Using Watermark 200SS Sensors

Payero

Mirzakhani-Nafchi

Khalilian

et al. 2017

AIT

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Soil moisture monitoring is one of the methods that farmers can use for irrigation scheduling. Many sensor types and data logging systems have been developed for this purpose over the years, but their widespread adoption in practical irrigation scheduling is still limited due to a variety of factors. Important factors limiting adoption of soil moisture sensing technology by farmers include high cost and difficulties in timely data collection and interpretation. Recent developments in open source microcontrollers (such as Arduino), wireless communication, and Internet-of-Things (IoT) technologies offer opportunities for reducing cost and facilitating timely data collection, visualization, and interpretation for farmers. Therefore, the objective of this study was to develop and test a low-cost IoT system for soil moisture monitoring using Watermark 200SS sensors. The system uses Arduino-based microcontrollers and data from the field sensors (End Nodes) are communicated wirelessly using LoRa radios to a receiver (Coordinator), which connects to the Internet via WiFi and sends the data to an open-source website (ThingSpeak.com) where the data can be visualized and further analyzed using Matlab. The system was successfully tested under field conditions by installing Watermark sensors at four depths in a wheat field. The system described here could contribute to widespread adoption of easy-to-use and affordable moisture sensing technologies among farmers.

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Variable upper and lower crop water stress index baselines for corn and soybean

Cited by 72 publications

References 32 publications

Evaluation of Soil Moisture Status in the Field to Improve the Production of Tanbaguro Soybeans

Evaluation of Soil Moisture Status in the Field to Improve the Production of Tanbaguro Soybeans

Evaluation of neural network modeling to predict non-water-stressed leaf temperature in wine grape for calculation of crop water stress index

Development of a Low-Cost Internet-of-Things (IoT) System for Monitoring Soil Water Potential Using Watermark 200SS Sensors

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