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Accurate estimates of actual crop evapotranspiration (ET) are important for optimal irrigation water management, especially in arid and semi-arid regions. Common ET sensing methods include Bowen Ratio, Eddy Covariance (EC), and scintillometers. Large weighing lysimeters are considered the ultimate standard for measurement of ET, however, they are expensive to install and maintain. Although EC and scintillometers are less costly and relatively portable, EC has known energy balance closure discrepancies. Previous scintillometer studies used EC for ground-truthing, but no studies considered weighing lysimeters. In this study, a Surface Layer Scintillometer (SLS) was evaluated for accuracy in determining ET as well as sensible and latent heat fluxes, as compared to a large weighing lysimeter in Bushland, TX. The SLS was installed over irrigated grain sorghum (Sorghum bicolor (L.) Moench) for the period 29 July-17 August 2015 and over grain corn (Zea mays L.) for the period 23 June-2 October 2016. Results showed poor correlation for sensible heat flux, but much better correlation with ET, with r2 values of 0.83 and 0.87 for hourly and daily ET, respectively. The accuracy of the SLS was comparable to other ET sensing instruments with an RMSE of 0.13 mm·h−1 (31%) for hourly ET; however, summing hourly values to a daily time step reduced the ET error to 14% (0.75 mm·d−1). This level of accuracy indicates that potential exists for the SLS to be used in some water management applications. As few studies have been conducted to evaluate the SLS for ET estimation, or in combination with lysimetric data, further evaluations would be beneficial to investigate the applicability of the SLS in water resources management.
irrigation, energy balance, water management, semi-arid regions
Evaluation of Sensible Heat Flux and Evapotranspiration Estimates Using a Surface Layer Scintillometer and a Large Weighing Lysimeter
Jerry E. Moorhead,1,* Gary W. Marek,1 Paul D. Colaizzi,1 Prasanna H. Gowda,2 Steven R. Evett,1 David K. Brauer,1 Thomas H. Marek,3 and Dana O. Porter4
Background and Aims High-temperature environments with >30 °C during flowering reduce boll retention and yield in cotton. Therefore, identification of cotton cultivars with high-temperature tolerance would be beneficial in both current and future climates.
• Methods Response to temperature (10-45 °C at 5 °C intervals) of pollen germination and pollen tube growth was quantified, and their relationship to cell membrane thermostability was studied in 12 cultivars. A principal component analysis was carried out to classify the genotypes for temperature tolerance.
• Key Results Pollen germination and pollen tube length of the cultivars ranged from 20 to 60 % and 411 to 903 µm, respectively. A modified bilinear model best described the response to temperature of pollen germination and pollen tube length. Cultivar variation existed for cardinal temperatures (Tmin, Topt and Tmax) of pollen germination percentage and pollen tube growth. Mean cardinal temperatures calculated from the bilinear model for the 12 cultivars were 15·0, 31·8 and 43·3 °C for pollen germination and 11·9, 28·6 and 42·9 °C for pollen tube length. No significant correlations were found between pollen parameters and leaf membrane thermostability. Cultivars were classified into four groups based on principal component analysis.
• Conclusions Based on principal component analysis, it is concluded that higher pollen germination percentages and longer pollen tubes under optimum conditions and with optimum temperatures above 32 °C for pollen germination would indicate tolerance to high temperature.
Cotton, Gossypium hirsutum, high temperature, cell membrane thermostability, principal component analysis, pollen germination, pollen tube, relative injury
Differences in in vitro Pollen Germination and Pollen Tube Growth of Cotton Cultivars in Response to High Temperature
V. G. KAKANI,1,* K. R. REDDY,1 S. KOTI,1 T. P. WALLACE,1 P. V. V. PRASAD,2 V. R. REDDY,3 and D. ZHAO4
Warm-season legumes have been receiving increased attention as forage resources in the southern United States and other countries. However, the near infrared spectroscopy (NIRS) technique has not been widely explored for predicting the forage quality of many of these legumes. The objective of this research was to assess the performance of NIRS in predicting the forage quality parameters of five warm-season legumes—guar (Cyamopsis tetragonoloba), tepary bean (Phaseolus acutifolius), pigeon pea (Cajanus cajan), soybean (Glycine max), and mothbean (Vigna aconitifolia)—using three machine learning techniques: partial least square (PLS), support vector machine (SVM), and Gaussian processes (GP). Additionally, the efficacy of global models in predicting forage quality was investigated. A set of 70 forage samples was used to develop species-based models for concentrations of crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), and in vitro true digestibility (IVTD) of guar and tepary bean forages, and CP and IVTD in pigeon pea and soybean. All species-based models were tested through 10-fold cross-validations, followed by external validations using 20 samples of each species. The global models for CP and IVTD of warm-season legumes were developed using a set of 150 random samples, including 30 samples for each of the five species. The global models were tested through 10-fold cross-validation, and external validation using five individual sets of 20 samples each for different legume species. Among techniques, PLS consistently performed best at calibrating (R2c = 0.94-0.98) all forage quality parameters in both species-based and global models. The SVM provided the most accurate predictions for guar and soybean crops, and global models, and both SVM and PLS performed better for tepary bean and pigeon pea forages. The global modeling approach that developed a single model for all five crops yielded sufficient accuracy (R2cv/R2v = 0.92-0.99) in predicting CP of the different legumes. However, the accuracy of predictions of in vitro true digestibility (IVTD) for the different legumes was variable (R2cv/R2v = 0.42-0.98). Machine learning algorithms like SVM could help develop robust NIRS-based models for predicting forage quality with a relatively small number of samples, and thus needs further attention in different NIRS based applications.
partial least square, support vector machine, Gaussian processes, soybean, pigeon pea, guar, tepary bean
Predicting Forage Quality of Warm-Season Legumes by Near Infrared Spectroscopy Coupled with Machine Learning Techniques
Gurjinder S. Baath,1,* Harpinder K. Baath,2 Prasanna H. Gowda,3 Johnson P. Thomas,2 Brian K. Northup,4 Srinivas C. Rao,4 and Hardeep Singh1