Interactions of Echinacea spp. Root Extracts and Alkylamides With the Endocannabinoid System and Peripheral Inflammatory Pain

Historical ethnobotanies of indigenous peoples of the North American prairies reveal treatment of many painful conditions by Echinacea spp. Recent evidence suggests a pharmacological basis for such use as the bioactivity of E. angustifolia and E. purpurea is mediated, in part, through activation of the endocannabinoid system (ECS). Whereas the cannabimimetic effects of individual echinacea products and alkylamides have been described, the activity of crude extracts has not been compared between cannabinoid (CB) receptors or across species or genotypes. Moreover, few studies have explored echinacea’s engagement of the ECS for historic treatments or new therapeutic applications in peripheral inflammatory pain. We hypothesized that 1) the in vitro effects of root extracts on CB receptor internalization would vary with species and phytochemistry, and that echinacea root extracts would reduce inflammatory pain in vivo through activation of the ECS. Root extracts of different E. angustifolia and E. purpurea accessions were prepared, analyzed by HPLC-DAD to quantify caffeic acid derivatives and alkylamides (AKA), and tested for agonist and antagonist activities using receptor redistribution assays. Linear regression of activity relative to phytochemistry identified predictive compounds that were assessed individually in redistribution assays. Extracts were evaluated in the Hargreaves model of chronic inflammatory pain in rats with co-administration of selective CB1/2 antagonists to gauge involvement of the ECS. CB receptor agonist activity varied among accessions of both species with linear regression revealing a significant relationship between CB1 activity and AKA2 for E angustifolia, and AKA 9 + 10 for E purpurea. CB2 activity was positively related with AKA 9 + 10 and total AKAs in E. angustifolia. Four isolated AKA demonstrated agonist activity in the CB2, but not CB1, assay. In the inflammatory pain model, oral administration of either E angustifolia or E. purpurea root extract produced dose-dependent analgesic effects that were partially reversed by co-administration of CB receptor antagonists. This study demonstrates that in vitro effects of crude echinacea root extracts on CB receptors is predicted by phytochemistry. In vivo, echinacea has potential applications for peripheral inflammatory pain such as arthritis and burns, reflecting the traditional uses of Indigenous North Americans.

Spatiotemporal change and attribution of potential evapotranspiration over China from 1901 to 2100

Global warming has accelerated surface water loss around the world. This study investigates in detail the change and attribution of potential evapotranspiration (PET) across China from 1901 to 2100 by the Hargreaves model, based on a 1-km temperature dataset downscaled from the low-spatial-resolution datasets using a Delta downscaling framework. Results showed that (1) relative to 1961–1990, PET increased by 0.62% in the historic period (1901–2017) and 6.43–12.89% for the future period (2018–2100), suggesting considerable future drying for China. Moreover, these increments had strong spatial variations and the largest increases were detected in high-elevation regions; (2) PET over entire China demonstrated a nonsignificant upward trend during the historic period and significant upward trends for the future period. For each period and GCM, significant upward PET trends occupied a much larger percent area than significant downward trends; (3) PET variations during the historic period were most sensitive to mean temperature (TMP), while in the future period it was more sensitive to maximum temperature (TMX), suggesting a change in the primary sensitivity factor due to global warming; (4) minimum temperature (TMN) made the largest contribution (45%) to PET variations during the historic period, while TMX had the largest contribution (36–40%) in the future period. Therefore, the primary contributing factor might transform from TMN to TMX under climate change; and (5) PET variations exhibited strong spatial heterogeneity, detected on fine geographic scales, due to the use of downscaled dataset. Overall, the results present a deep insight for planning coping strategies of global warming in China.

Evapotranspiration Response to Climate Change in Semi-Arid Areas: Using Random Forest as Multi-Model Ensemble Method

Large ensembles of climate models are increasingly available either as ensembles of opportunity or perturbed physics ensembles, providing a wealth of additional data that is potentially useful for improving adaptation strategies to climate change. In this work, we propose a framework to evaluate the predictive capacity of 11 multi-model ensemble methods (MMEs), including random forest (RF), to estimate reference evapotranspiration (ET0) using 10 AR5 models for the scenarios RCP4.5 and RCP8.5. The study was carried out in the Segura Hydrographic Demarcation (SE of Spain), a typical Mediterranean semiarid area. ET0 was estimated in the historical scenario (1970–2000) using a spatially calibrated Hargreaves model. MMEs obtained better results than any individual model for reproducing daily ET0. In validation, RF resulted more accurate than other MMEs (Kling–Gupta efficiency (KGE) M=0.903, SD=0.034 for KGE and M=3.17, SD=2.97 for absolute percent bias). A statistically significant positive trend was observed along the 21st century for RCP8.5, but this trend stabilizes in the middle of the century for RCP4.5. The observed spatial pattern shows a larger ET0 increase in headwaters and a smaller increase in the coast.

Improved Hargreaves Model Based on Multiple Intelligent Optimization Algorithms to Estimate Reference Crop Evapotranspiration in Humid Areas of Southwest China

Reference crop evapotranspiration (ET0) is an important indicator for precise regulation of crop water content, irrigation forecast formulation, and regional water resources management. The Hargreaves model (HG) is currently recognized as the simplest and most effective ET0 estimation model. To further improve the prediction accuracy of the HG model, this study is based on the data of 98 meteorological stations in southwest China (1961–2019), using artificial bee colony (ABC), differential evolution (DE) and particle swarm optimization (PSO) algorithms to calibrate the HG model globally. The standard ET0 value was calculated by FAO-56 Penman–Monteith (PM) model. We compare the calculation accuracy of 3 calibrated HG models and 4 empirical models commonly used (Hargreaves, Priestley–Taylor, Imark–Allen and Jensen–Hais). The main outcomes demonstrated that on a daily scale, the calibrated HG models (R2 range 0.74–0.98) are more accurate than 4 empirical models (R2 range 0.55–0.84), and ET0-PSO-HG has the best accuracy, followed by ET0-ABC-HG and ET0-DE-HG, with average R2 of 0.83, 0.82 and 0.80, average RRMSE of 0.23 mm/d, 0.25 mm/d and 0.26 mm/d, average MAE of 0.52 mm/d, 0.53 mm/d and 0.57 mm/d, and average GPI of 0.17, 0.05, and 0.04, respectively; on a monthly scale, ET0-PSO-HG also has the highest accuracy, followed by ET0-ABC-HG and ET0-DE-HG, with median R2 of 0.96, 0.95 and 0.94, median RRMSE of 0.16 mm/d, 0.17 mm/d and 0.18 mm/d respectively, median MAE of 0.46 mm/d, 0.50 mm/d, and 0.55 mm/d, median GPI of 1.12, 0.44 and 0.34, respectively. The calibrated HG models (relative error of less than 10.31%) are also better than the four empirical models (relative error greater than 16.60%). Overall, the PSO-HG model has the most accurate ET0 estimation on daily and monthly scales, and it can be suggested as the preferred model to predict ET0 in humid regions in southwest China regions.

REFERENCE EVAPOTRANSPIRATION THROUGH HARGREAVES METHOD USING THE SOLAR RADIATION ESTIMATION FOR GOIÁS STATE, BRAZIL

This study evaluated the Hargreaves model (HG) with seasonal adjustments of the calibration coefficient (Krs) of the radiation equation to estimate the reference evapotranspiration (ETo) in 23 weather stations in Goiás State, Brazil, in comparison to the Penman-Monteith FAO (PM-FAO) standard method. The models were evaluated using Pearson’s correlation coefficient, Willmott’s agreement index, relative error, absolute mean error and root mean square error. The Krs values ranged from 0.146 to 0.189 ° C-0.5, while ETo PM-FAO ranged from 3.68 to 4.79 mm d-1; ETo HG from 3.99 to 5.16 mm d-1 and ETo HG-Krs from 4.15 to 5.02 mm d-1 in the annual period. Seasonal adjustments resulted in values of 0.144 to 0.205 ° C-0.5 for the dry period, from April to September, and 0.144 to 0.146 ° C-0.5 for the rainy period, from October to March. The first quarter (summer), presented Krs values from 0.150 to 0.175 ° C-0.5; the second quarter (autumn), from 0.154 to 0.218 ° C-0.5; the third quarter (winter), from 0.139 to 0.206 ° C-0.5; and, finally, the fourth quarter (spring) of 0.141 to 0.166 ° C-0.5. Thus, the use of the seasonally adjusted model proved to be viable for the estimation of ETo, in view of the simplicity and its good adherence to the standard method.

Study of Different Evapotranspiration Models under Polyhouse and Open Environment in Tarai Region of Uttarakhand

The average of mean monthly ET0 estimated under polyhouse by FAO PM (benchmark) model was 39.44 mm, but that of the FAO Penman, Hargreaves Stanghellini, Priestley-Taylor and FAO Radiation models were 38.37, 18.18, 37.80, 48.17 and 53.87 mm respectively. Whereas, the average of mean monthly ETo estimated under open environment by FAO PM (benchmark) model was 116.34 mm, but that of the FAO Penman, Hargreaves Stanghellini, Priestley-Taylor and FAO Radiation models were 119.33, 133, 126.41, 113.17 and 117.37 mm respectively. The FAO Penman and Hargreaves model are found to be most and least appropriate models for estimating daily ET0 under polyhouse. Whereas, FAO Radiation and Stanghellini model observed to be most and the least appropriate models in an open environment for estimating daily ET0 under polyhouse for the Pantnagar Tarai condition of Uttarakhand.

Evapotranspiration Based Micro Irrigation Scheduling of Tomato Crop under Naturally Ventilated Polyhouse

The present study was undertaken to investigate the Evapotranspiration Based Micro Irrigation Scheduling of Tomato Crop under Naturally Ventilated Polyhouse, at experimental field of Department of Irrigation and Drainage Engineering, G. B. Pant University of Agriculture & Technology, Pantnagar, Uttarakhand during 2017-18. The average of mean monthly ET0 estimated under polyhouse by FAO-PM (benchmark) model was 39.44 mm, but that of the FAO Penman, Hargreaves, Stanghellini, Priestley-Taylor and FAO Radiation models were 38.37, 18.18, 37.80, 48.17 and 53.87 mm, respectively. Whereas, the average of mean monthly ET0 estimated under open environment by FAO-PM (benchmark) model was 116.34 mm, but that of the FAO Penman, Hargreaves, Stanghellini, Priestley-Taylor and FAO Radiation models were 119.33, 133, 126.41, 113.17 and 117.37 mm, respectively. The FAO Penman and Hargreaves model are found to be most and least appropriate models for estimating daily ET0 under polyhouse. Whereas, FAO Radiation and Stanghellini model observed to be most and the least appropriate models in open environment for estimating daily ET0 under polyhouse for the Pantnagar tarai condition of Uttarakhand. During the six month growing period, the average water requirement for tomato crop under polyhouse and open environments were 0.2149 and 0.2924 liter per day per plant, respectively, showing that the water requirement in the open environment was estimated as 30% higher than that of polyhouse. The experimental results also revealed that the treatment T2 (100% water application of ETc without mulch under polyhouse) recorded significant yield (18.97 kg/m2), water use efficiency (135.26 kg/m3) and maximum fruit weight (106.66 gm).

Effect of watershed subdivision and estimation of climatic variables on hydrological simulation with the swat model in semi-arid mediterranean basins

Los resultados de cualquier intento de modelización hidrológica dependen de la calidad y discretización de los datos de entrada. En este trabajo se evalúa cómo la utilización de diferentes capas de variables climáticas de entrada (precipitación y ETP) y diferentes esquemas de discretización de la cuenca influyen en los resultados de un modelo hidrológico bien conocido (SWAT). En concreto se prueban 4 conjuntos de variables de entrada y discretización. Los estadísticos utilizados para evaluar la exactitud obtenida con estos conjuntos son RMSE, PBIAS, NSE y r2. Los resultados muestran que el uso de mejores modelos para obtener series de ETP (Penman Monteith FAO o Hargreaves calibrado) mejora considerablemente la exactitud del modelo respecto a las series obtenidas con el modelo de Hargreaves sin calibrar (opción por defecto en SWAT). Sin embargo, no se aprecian diferencias entre los resultados obtenidos con Penman Monteith FAO y Hargreaves calibrado. Por otro lado, el uso de información climática distribuida mejora notablemente los resultados obtenidos con información agregada. Se observa también la necesidad de calibrar los parámetros de SWAT ya que los valores por defecto están optimizados para ambientes templados de EEUU. The results of hydrological modelling depend on the quality and spatial resolution of the input data. This paper evaluates how the use of different estimations of climatic variables input layers (precipitation and ET P ) and different basin discretization schemes influence affect the results of a contrasted hydrological model (SWAT). Specifically, 4 se ts of input and discretization variables are tested. The statistics used to evaluate the accuracy obtained with these sets are RMSE, PBIAS, NSE and r 2 . The results show that the use of better models to obtain ET P series (Penman Monteith FAO or calibrated Hargreaves) considerably improves the accuracy of the model compared to the series obtained with the uncalibrated Hargreaves model (default option in SWAT). However, there is no difference between the results obtained with Penman Monteith FAO and calibrate d Hargreaves. On the other hand, the use of distributed climate information significantly improves the results obtained with aggregated information. There is also a need to calibrate SWAT parameters as the default values are optimized for temperate environ ments in the USA.

Predictive Performance of Hargreaves Model in Estimating Solar Radiation in Yola, Adamawa State Nigeria Using Minimum Climatological Data

Mathematical models have been very useful in reducing challenges encountered by researchers due to the inability of having solar radiation data or lack of instrumental sites at every point on the Earth. This work aimed at investigating the prediction performance of Hargreaves-Samani’s model in estimating global solar radiation (GSR) out of the many other empirical models so far formulated for this purpose. This model basically uses maximum and minimum temperature data and basically used in mid-latitudes. The paper attempts to assess the predictive performance of Hargreaves-Samani’s model in the Savanna region using Yola as a case study. Estimated values of GSR from one month data adopted from the Meteorological station of the Department of Geography, Federal University of Technology, Yola, Nigeria was used for this purpose. Using this model shows a 95% index of agreement (IA) with the observed values; which suggests a good model performance and can also be used in estimating global solar radiation in the Savanna region particularly in areas with little or no such climatic data.