Estimation of Field Alfalfa Evapotranspiration in a Windy, Arid Environment

Evapotranspiration (ET) of center pivot irrigated alfalfa was studied in the windy, arid, Curlew Valley, Northern Box Elder County, Utah, during the summers of 2009 and 2010. ET was estimated using eddy covariance (EC) and surface renewal (SR) techniques. ET estimates from the EC and SR analyses were compared with estimates using ASCE Standardized Reference ET Equation, with both dual and mean crop coefficients. EC energy balance closure was 0.80, on average, in 2009 and 0.76 in 2010. The SR weighting parameter (α) was calculated through linear regression of EC and SR sensible heat flux estimates. Alpha was found to be 0.70 if EC energy balance closure was forced and 0.55 if closure was not forced. ET from SR analysis with α = 0.70 (ETSRα=0.70) was 409 mm in 2009 and 331 mm in 2010. ET from EC analysis with forced closure (ETECforced) was 390 mm in 2009 and 326 mm in 2010. In contrast, ETSRα=0.55 was 408 and 333 mm in 2009 and 2010, respectively, while ETECunforced was 315 and 251 mm in 2009 and 2010, respectively. Combined ETECforced and ETSRforced were compared with estimated crop ET from the ASCE Std. Eq. with both dual and mean crop coefficients (ETcDual and ETcm, respectively). ETcDual was 689 mm in 2009, as compared to ETcm and ETEC-SRforced, which were 677 and 617 mm, respectively. In 2010 ETcDual was 674 mm, with ETcm and ETEC-SRforced being 629 and 576 mm, respectively. The Kcm approach more closely approximated the estimated wet soil evaporation determined from the ETEC-SRforced for the measurement conditions and stated assumptions. ETEC-SR estimates were compared with irrigation application information to approximate field scale water balances. Effective precipitation plus net irrigation application (less wind drift and evaporation) were nearly equal to ETEC-SRforced for 2nd and 3rd crops of alfalfa in 2009 and 2010. No deep percolation was calculated using ETEC-SRforced; however, soil moisture measurements were not sufficient to verify that this was true. The water balances suggested that the fields were being underirrigated which may have caused salt accumulation in the soil, as evidenced by the low reported yields

Evaluation of a hybrid remote sensing evapotranspiration model for variable rate irrigation management

Accurate generation of spatial irrigation prescriptions is essential for implementation and evaluation of variable rate irrigation (VRI) technology. A hybrid remote sensing evapotranspiration (ET) model was evaluated for use in developing irrigation prescriptions for a VRI center pivot. The model is a combination of a two-source energy balance model and a reflectance based crop coefficient water balance model. Spatial ET and soil water depletion were modeled for a 10 km2 area consisting of rainfed and irrigated maize fields in eastern Nebraska for 2013. Multispectral images from Landsat 8 Operational Land Imager and Thermal Infrared Sensor were used as model input. Modeled net radiation and soil heat fluxes compared well with measurements from eddy covariance systems located within three fields in the study area. Modeled sensible heat flux did not compare well. Latent heat flux compared well for the only mid-summer image, but poorly for the one spring and two fall images. The water balance ET compared well with the two-source energy balance ET for irrigated maize, but not for dryland maize. Image frequency is thought to be a contributing factor in the poor performance of the water balance. In 2015 the hybrid model will be used to generate irrigation prescription maps for a VRI system located in the study area based on modeled soil moisture depletion. Future research will focus on model parameterization and utilize aerial imagery and satellite imagery from other sensors for improved image frequency. Note: this is a revision of the original paper correcting erroneous data where one of the flux sites was mistakenly analyzed as soybeans, when it was actually maize. Mean biased error signs have also been corrected

STING-dependent recognition of cyclic di-AMP mediates type I interferon responses during Chlamydia trachomatis infection.

UnlabelledSTING (stimulator of interferon [IFN] genes) initiates type I IFN responses in mammalian cells through the detection of microbial nucleic acids. The membrane-bound obligate intracellular bacterium Chlamydia trachomatis induces a STING-dependent type I IFN response in infected cells, yet the IFN-inducing ligand remains unknown. In this report, we provide evidence that Chlamydia synthesizes cyclic di-AMP (c-di-AMP), a nucleic acid metabolite not previously identified in Gram-negative bacteria, and that this metabolite is a prominent ligand for STING-mediated activation of IFN responses during infection. We used primary mouse lung fibroblasts and HEK293T cells to compare IFN-β responses to Chlamydia infection, c-di-AMP, and other type I IFN-inducing stimuli. Chlamydia infection and c-di-AMP treatment induced type I IFN responses in cells expressing STING but not in cells expressing STING variants that cannot sense cyclic dinucleotides but still respond to cytoplasmic DNA. The failure to induce a type I IFN response to Chlamydia and c-di-AMP correlated with the inability of STING to relocalize from the endoplasmic reticulum to cytoplasmic punctate signaling complexes required for IFN activation. We conclude that Chlamydia induces STING-mediated IFN responses through the detection of c-di-AMP in the host cell cytosol and propose that c-di-AMP is the ligand predominantly responsible for inducing such a response in Chlamydia-infected cells.ImportanceThis study shows that the Gram-negative obligate pathogen Chlamydia trachomatis, a major cause of pelvic inflammatory disease and infertility, synthesizes cyclic di-AMP (c-di-AMP), a nucleic acid metabolite that thus far has been described only in Gram-positive bacteria. We further provide evidence that the host cell employs an endoplasmic reticulum (ER)-localized cytoplasmic sensor, STING (stimulator of interferon [IFN] genes), to detect c-di-AMP synthesized by Chlamydia and induce a protective IFN response. This detection occurs even though Chlamydia is confined to a membrane-bound vacuole. This raises the possibility that the ER, an organelle that innervates the entire cytoplasm, is equipped with pattern recognition receptors that can directly survey membrane-bound pathogen-containing vacuoles for leaking microbe-specific metabolites to mount type I IFN responses required to control microbial infections

Variable Rate Irrigation of Maize and Soybean in West-Central Nebraska under Full and Deficit Irrigation

Variable rate irrigation (VRI) may improve center pivot irrigation management, including deficit irrigation. A remote-sensing-based evapotranspiration model was implemented with Landsat imagery to manage irrigations for a VRI equipped center pivot irrigated field located in West-Central Nebraska planted to maize in 2017 and soybean in 2018. In 2017, the study included VRI using the model, and uniform irrigation using neutron attenuation for full irrigation with no intended water stress (VRI-Full and Uniform-Full treatments, respectively). In 2018, two deficit irrigation treatments were added (VRI-Deficit and Uniform-Deficit, respectively) and the model was modified in an attempt to reduce water balance drift; model performance was promising, as it was executed unaided by measurements of soil water content throughout the season. VRI prescriptions did not correlate well with available water capacity (R2 \u3c 0.4); however, they correlated better with modeled ET in 2018 (R2 = 0. 69, VRI-Full; R2 = 0.55, VRI-Deficit). No significant differences were observed in total intended gross irrigation depth in 2017 (VRI-Full = 351mm, Uniform Full = 344). However, in 2018, VRI resulted in lower mean prescribed gross irrigation than the corresponding uniform treatments (VRI-Full = 265mm, Uniform Full = 282mm, VRI-Deficit = 234mm, and Uniform Deficit = 267mm). Notwithstanding the differences in prescribed irrigation (in 2018), VRI did not affect dry grain yield, with no statistically significant differences being found between any treatments in either year (F = 0.03, p = 0.87 in 2017; F = 0.00, p = 0.96 for VRI/Uniform and F = 0.01, p = 0.93 for Full/Deficit in 2018). Likewise, any reduction in irrigation application apparently did not result in detectable reductions in deep percolation potential or actual evapotranspiration. Additional research is needed to further vet the model as a deficit irrigation management tool. Suggested model improvements include a continuous function for water stress and an optimization routine in computing the basal crop coefficient

Evaluation of a Hybrid Reflectance-Based Crop Coefficient and Energy Balance Evapotranspiration Model for Irrigation Management

Accurate generation of spatial soil water maps is useful for many types of irrigation management. A hybrid remote sensing evapotranspiration (ET) model combining reflectance-based basal crop coefficients (Kcbrf) and a two-source energy balance (TSEB) model was modified and validated for use in real-time irrigation management. We modeled spatial ET for maize and soybean fields in eastern Nebraska for the 2011-2013 growing seasons. We used Landsat 5, 7, and 8 imagery as remote sensing inputs. In the TSEB, we used the Priestly-Taylor (PT) approximation for canopy latent heat flux, as in the original model formulations. We also used the Penman-Monteith (PM) approximation for comparison. We compared energy balance fluxes and computed ET with measurements from three eddy covariance systems within the study area. Net radiation was underestimated by the model when data from a local weather station were used as input, with mean bias error (MBE) of -33.8 to -40.9 W m-2. The measured incident solar radiation appeared to be biased low. The net radiation model performed more satisfactorily when data from the eddy covariance flux towers were input into the model, with MBE of 5.3 to 11.2 W m-2. We removed bias in the daily energy balance ET using a dimensionless multiplier that ranged from 0.89 to 0.99. The bias-corrected TSEB ET, using weather data from a local weather station and with local ground data in thermal infrared imagery corrections, had MBE = 0.09 mm d-1 (RMSE = 1.49 mm d-1) for PM and MBE = 0.04 mm d-1 (RMSE = 1.18 mm d-1) for PT. The hybrid model used statistical interpolation to combine the two ET estimates. We computed weighting factors for statistical interpolation to be 0.37 to 0.50 for the PM method and 0.56 to 0.64 for the PT method. Provisions were added to the model, including a real-time crop coefficient methodology, which allowed seasonal crop coefficients to be computed with relatively few remote sensing images. This methodology performed well when compared to basal crop coefficients computed using a full season of input imagery. Water balance ET compared favorably with the eddy covariance data after incorporating the TSEB ET. For a validation dataset, the magnitude of MBE decreased from -0.86 mm d-1 (RMSE = 1.37 mm d-1) for the Kcbrfalone to -0.45 mm d-1 (RMSE = 0.98 mm d-1) and -0.39 mm d-1 (RMSE = 0.95 mm d-1) with incorporation of the TSEB ET using the PM and PT methods, respectively. However, the magnitudes of MBE and RMSE were increased for a running average of daily computations in the full May-October periods. The hybrid model did not necessarily result in improved model performance. However, the water balance model is adaptable for real-time irrigation scheduling and may be combined with forecasted reference ET, although the low temporal frequency of satellite imagery is expected to be a challenge in real-time irrigation management

Calibration of a common shortwave multispectral camera system for quantitative agricultural applications

Unmanned aerial systems (UAS) for collecting multispectral imagery of agricultural fields are becoming more affordable and accessible. However, there is need to validate calibration of sensors on these systems when using them for quantitative analyses such as evapotranspiration, and other modeling for agricultural applications. The results of laboratory testing of a MicaSense (Seattle, WA, USA) RedEdge™ 3 multispectral camera and MicaSense Downwelling Light Sensor (irradiance sensor) system using a calibrated integrating sphere were presented. Responses of the camera and irradiance sensor were linear over many light levels and became non-linear at light levels below expected real-world, field conditions. Simple linear corrections should suffice for most light conditions encountered during the growing season. Using an irradiance sensor or similar system may not properly account for light variability in cloudy or partly cloudy conditions as also identified by others. A simple stand for aiding in reference panel imagining was also described, which may facilitate repetitive, consistent reference panel imaging

Cover Crops have Negligible Impact on Soil Water in Nebraska Maize–Soybean Rotation

One perceived cost of integrating winter cover cropping in maize (Zea mays L.) and soybean [Glycine max (L.) Merr.] rotation systems is the potential negative impact on soil water storage available for primary crop production. The objective of this 3-yr study was to evaluate the effects of winter cover crops on soil water storage and cover crop biomass production following no-till maize and soybean rotations. Locations were near Brule (west-central), Clay Center (south-central), Concord (northeast), and Mead (east-central), NE. Treatments included crop residue only (no cover crop) and a multi-species cover crop mix, both broadcast-seeded before primary crop harvest and drilled following harvest. Pre-harvest broadcast-seeded cereal rye (Secale cereale L.) was also included in the last year of the study because rye was observed to be the dominant component of the mix in spring biomass samples. Soil water content was monitored using neutron probe or gravimetric techniques. Mean aboveground cover crop biomass ranged from practically 0 to ~3,200 kg ha–1 across locations and cover crop treatments. Differences in the change in soil water storage between autumn and spring among treatments occurred in 4 of 20 location–rotation phase–years for the top 0.3 m of soil and 3 of 20 location–rotation phase–years for the 1.2-m soil profile. However, these differences were small (profile). In conclusion, winter cover crops did not have an effect on soil water content that would impact maize and soybean crop production

Spatial Irrigation Management Using Remote Sensing Water Balance Modeling and Soil Water Content Monitoring

Spatially informed irrigation management may improve the optimal use of water resources. Sub-field scale water balance modeling and measurement were studied in the context of irrigation management. A spatial remote-sensing-based evapotranspiration and soil water balance model was modified and validated for use in real-time irrigation management. The modeled ET compared well with eddy covariance data from eastern Nebraska. Placement and quantity of sub-field scale soil water content measurement locations was also studied. Variance reduction factor and temporal stability were used to analyze soil water content data from an eastern Nebraska field. No consistent predictor of soil water temporal stability patterns was identified. At least three monitoring locations were needed per irrigation management zone to adequately quantify the mean soil water content. The remote-sensing-based water balance model was used to manage irrigation in a field experiment. The research included an eastern Nebraska field in 2015 and 2016 and a western Nebraska field in 2016 for a total of 210 plot-years. The response of maize and soybean to irrigation using variations of the model were compared with responses from treatments using soil water content measurement and a rainfed treatment. The remote-sensing-based treatment prescribed more irrigation than the other treatments in all cases. Excessive modeled soil evaporation and insufficient drainage times were suspected causes of the model drift. Modifying evaporation and drainage reduced modeled soil water depletion error. None of the included response variables were significantly different between treatments in western Nebraska. In eastern Nebraska, treatment differences for maize and soybean included evapotranspiration and a combined variable including evapotranspiration and deep percolation. Both variables were greatest for the remote-sensing model when differences were found to be statistically significant. Differences in maize yield in 2015 were attributed to random error. Soybean yield was lowest for the remote-sensing-based treatment and greatest for rainfed, possibly because of overwatering and lodging. The model performed well considering that it did not include soil water content measurements during the season. Future work should improve the soil evaporation and drainage formulations, because of excessive precipitation and include aerial remote sensing imagery and soil water content measurement as model inputs

Site-specific irrigation management in a sub-humid climate using a spatial evapotranspiration model with satellite and airborne imagery

Variable Rate Irrigation (VRI) considers spatial variability in soil and plant characteristics to optimize irrigation management in agricultural fields. The advent of unmanned aircraft systems (UAS) creates an opportunity to utilize high-resolution (spatial and temporal) imagery into irrigation management due to decreasing costs, ease of operation, and reduction of regulatory constraints. This research aimed to evaluate the use of UAS data for VRI, and to quantify the potential of VRI in terms of relative crop and water response. Irrigation treatments were: (1) VRI using Landsat imagery (VRI-L), (2) VRI using UAS imagery (VRI-U), (3) uniform (U), and (4) rainfed (R). An updated remote-sensing-based evapotranspiration and water balance model, incorporating soil water measurements, was used to make prescriptions for the VRI treatments at a field site in eastern Nebraska. In 2017, the mean prescribed seasonal irrigation depth (Ip) for VRI-L was significantly greater (α=0.05) than the Ip for U for soybean. In 2018, Ip for soybean was greatest for VRI-U treatment followed by the U and VRI-L treatments, with all being significantly different from each other. No significant differences in Ip for maize were observed in 2017 or 2018. In all crop-year combinations, the VRI and U treatments had significantly greater evapotranspiration (ET) than the R treatment. Yield differences among treatments were not significant (except for rainfed maize compared to VRI-L in 2017). For maize in 2017, IWUE for VRI-L was comparable to the U treatment. The UAS imagery was a better match for the scale of crop management than Landsat imagery, particularly for thermal data. The multispectral UAS data was successfully used in the crop coefficient ET model for real-time irrigation, but using UAS to determine accurate canopy temperatures for surface energy balance modeling remains a challenge

Evaluation of a Hybrid Reflectance-Based Crop Coefficient and Energy Balance Evapotranspiration Model for Irrigation Management

Accurate generation of spatial soil water maps is useful for many types of irrigation management. A hybrid remote sensing evapotranspiration (ET) model combining reflectance-based basal crop coefficients (Kcbrf) and a two-source energy balance (TSEB) model was modified and validated for use in real-time irrigation management. We modeled spatial ET for maize and soybean fields in eastern Nebraska for the 2011-2013 growing seasons. We used Landsat 5, 7, and 8 imagery as remote sensing inputs. In the TSEB, we used the Priestly-Taylor (PT) approximation for canopy latent heat flux, as in the original model formulations. We also used the Penman-Monteith (PM) approximation for comparison. We compared energy balance fluxes and computed ET with measurements from three eddy covariance systems within the study area. Net radiation was underestimated by the model when data from a local weather station were used as input, with mean bias error (MBE) of -33.8 to -40.9 W m-2. The measured incident solar radiation appeared to be biased low. The net radiation model performed more satisfactorily when data from the eddy covariance flux towers were input into the model, with MBE of 5.3 to 11.2 W m-2. We removed bias in the daily energy balance ET using a dimensionless multiplier that ranged from 0.89 to 0.99. The bias-corrected TSEB ET, using weather data from a local weather station and with local ground data in thermal infrared imagery corrections, had MBE = 0.09 mm d-1 (RMSE = 1.49 mm d-1) for PM and MBE = 0.04 mm d-1 (RMSE = 1.18 mm d-1) for PT. The hybrid model used statistical interpolation to combine the two ET estimates. We computed weighting factors for statistical interpolation to be 0.37 to 0.50 for the PM method and 0.56 to 0.64 for the PT method. Provisions were added to the model, including a real-time crop coefficient methodology, which allowed seasonal crop coefficients to be computed with relatively few remote sensing images. This methodology performed well when compared to basal crop coefficients computed using a full season of input imagery. Water balance ET compared favorably with the eddy covariance data after incorporating the TSEB ET. For a validation dataset, the magnitude of MBE decreased from -0.86 mm d-1 (RMSE = 1.37 mm d-1) for the Kcbrfalone to -0.45 mm d-1 (RMSE = 0.98 mm d-1) and -0.39 mm d-1 (RMSE = 0.95 mm d-1) with incorporation of the TSEB ET using the PM and PT methods, respectively. However, the magnitudes of MBE and RMSE were increased for a running average of daily computations in the full May-October periods. The hybrid model did not necessarily result in improved model performance. However, the water balance model is adaptable for real-time irrigation scheduling and may be combined with forecasted reference ET, although the low temporal frequency of satellite imagery is expected to be a challenge in real-time irrigation management