Inter-relationships between water depletion and temperature differential in row crop canopies in a sub-humid climate

Singh, Jasreman; Ge, Yufeng; Heeren, Derek M.; Walter‐Shea, Elizabeth A.; Neale, Christopher M. U.; Irmak, Suat; Woldt, Wayne; Bai, Geng; Bhatti, Sandeep; Maguire, Mitchell S.

doi:10.1016/j.agwat.2021.107061

Cited by 6 publications

(4 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The larger the amount of available water to answer crop water demand, the higher the expectable soybean grain yield. In accordance with the results of Singh et al [55] in Nebraska (41 • N), for the period of 2018 and 2019, a linear equation described the relationship between seasonal mean CWSI and average yield [kg m −2 ] in soybean (y = −0.4663x + 0.5644; R 2 = 0.6671; RMSE = 0.252; p < 0.001). In this study, each 0.1 increase in CWSI above 0.19 at about solar noon would on average reduce the soybean grain yield by about 0.50 kg m −2 in 2017-2019, which is 9.9, 18.0 and 13.1% of the yield for WW, RO, and P, respectively, when combining data for all seasons.…”

Section: Discussionsupporting

confidence: 87%

“…Similar CWSI values of 0.17 and 0.22 (2-year-study) have also been reported for fully irrigated soybean by Candogan et al [48] in Turkey (40° N). CWSI concurred with a value of 0.2 for fully irrigated soybean, published by Nielsen [55] in the Central Great Plains Research Station, USA (41° N). Over the three-season study, water stress under flowering and rainfed conditions increased the CWSIt (RO = 0.52 ± 0.11; p = 0.39 ± 0.16), reducing yield by about half.…”

Section: Discussionsupporting

confidence: 79%

See 1 more Smart Citation

The Application of a Self-Organizing Model for the Estimation of Crop Water Stress Index (CWSI) in Soybean with Different Watering Levels

Anda

Simon-Gáspár

Soós

2021

Water

View full text Add to dashboard Cite

A field experiment was conducted with soybean to observe evapotranspiration (ET) and crop water stress index (CWSI) with three watering levels at Keszthely, Hungary, during the growing seasons 2017–2020. The three different watering levels were rainfed, unlimited, and water stress in flowering. Traditional and converted evapotranspirometers documented water stress levels in two soybean varieties (Sinara, Sigalia), with differing water demands. ET totals with no significant differences between varieties varied from 291.9 to 694.9 mm in dry, and from 205.5 to 615.6 mm in wet seasons. Theoretical CWSI, CWSIt was computed using the method of Jackson. One of the seasons, the wet 2020 had to be excluded from the CWSIt analysis because of uncertain canopy temperature, Tc data. Seasonal mean CWSIt and Tc were inversely related to water use efficiency. An unsupervised Kohonen self-organizing map (K-SOM) was developed to predict the CWSI, CWSIp based on easily accessible meteorological variables and Tc. In the prediction, the CWSIp of three watering levels and two varieties covered a wide range of index values. The results suggest that CWSIp modelling with the minimum amount of input data provided opportunity for reliable CWSIp predictions in every water treatment (R2 = 0.935–0.953; RMSE = 0.033–0.068 mm, MAE = 0.026–0.158, NSE = 0.336–0.901, SI = 0.095–0.182) that could be useful in water stress management of soybean. However, highly variable weather conditions in the mild continental climate of Hungary might limit the potential of CWSI application. The results in the study suggest that a less than 450 mm seasonal precipitation caused yield reduction. Therefore, a 100–160 mm additional water use could be recommended during the dry growing seasons of the country. The 150 year-long local meteorological data indicated that 6 growing seasons out of 10 are short of precipitation in rainfed soybean.

show abstract

Section: Discussionsupporting

confidence: 87%

Section: Discussionsupporting

confidence: 79%

The Application of a Self-Organizing Model for the Estimation of Crop Water Stress Index (CWSI) in Soybean with Different Watering Levels

Anda

Simon-Gáspár

Soós

2021

Water

View full text Add to dashboard Cite

show abstract

“…In addition, the scope of IoT has increased recently in agricultural activities such as farming, planting, and animal rearing [17,39]. The current study proposes a UAS-enabled data ferrying system that uses narrow band-internet of things technology, i.e., the LoRa (long-range radio) technology, as a means of monitoring water and plant status in an agricultural setting, building on previously published work on the real-time monitoring and calibration of soil water content sensor [35]; and developing inter-relationships between soil water depletion and crop canopy temperature differential [36], and; sensor-based irrigation management of maize and soybean [37]. The LoRa and ZigBee technology offer various advantages such as lower power consumption and data transmission over longer distances in comparison to other wireless technologies or protocols used in agricultural applications, such as WiFi, Bluetooth, GPRS/3G/4G, and SigFox [23].…”

Section: Discussionmentioning

confidence: 99%

“…SI-111 infrared radiometer (Apogee Instruments, Inc., Logan, UT, USA) measures emitted infrared radiation (within an atmospheric window of 8-14 µm) from which target surface temperature is remotely determined. The infrared radiometer (also known as an infra-red thermometer or IRTs) monitors the maize field surface temperature continuously every 15 min as an average with a sampling frequency of 5 s [36,37].…”

Section: Si-111 Infrared Radiometermentioning

confidence: 99%

Unmanned Aerial System-Based Data Ferrying over a Sensor Node Station Network in Maize

Singh

Heeren

et al. 2022

Sensors

Self Cite

View full text Add to dashboard Cite

Agriculture is considered a hotspot for wireless sensor network (WSN) facilities as they could potentially contribute towards improving on-farm management and food crop yields. This study proposes six designs of unmanned aerial system (UAS)-enabled data ferries with the intent of communicating with stationary sensor node stations in maize. Based on selection criteria and constraints, a proposed UAS data ferrying design was shortlisted from which a field experiment was conducted for two growing seasons to investigate the adoptability of the selected design along with an established WSN system. A data ferry platform comprised of a transceiver radio, a mini-laptop, and a battery was constructed and mounted on the UAS. Real-time monitoring of soil and temperature parameters was enabled through the node stations with data retrieved by the UAS data ferrying. The design was validated by establishing communication at different heights (31 m, 61 m, and 122 m) and lateral distances (0 m, 38 m, and 76 m) from the node stations. The communication success rate (CSR) was higher at a height of 31 m and within a lateral distance of 38 m from the node station. Lower communication was accredited to potential interference from the maize canopy and water losses from the maize canopy.

show abstract

Crop water stress index computation approaches and their sensitivity to soil water dynamics

Katimbo

Rudnick

DeJonge

et al. 2022

Agricultural Water Management

View full text Add to dashboard Cite

Inter-relationships between water depletion and temperature differential in row crop canopies in a sub-humid climate

Cited by 6 publications

References 46 publications

The Application of a Self-Organizing Model for the Estimation of Crop Water Stress Index (CWSI) in Soybean with Different Watering Levels

The Application of a Self-Organizing Model for the Estimation of Crop Water Stress Index (CWSI) in Soybean with Different Watering Levels

Unmanned Aerial System-Based Data Ferrying over a Sensor Node Station Network in Maize

Crop water stress index computation approaches and their sensitivity to soil water dynamics

Contact Info

Product

Resources

About