Guar responses to temperature: Estimation of cardinal temperatures and photosynthetic parameters

Baath, Gurjinder S.; Kakani, Vijaya Gopal; Gowda, Prasanna H.; Rocateli, Alexandre C.; Northup, Brian K.; Singh, Hardeep; Katta, Jhansy R.

doi:10.1016/j.indcrop.2019.111940

Cited by 19 publications

(12 citation statements)

References 48 publications

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“…Decreasing ѱ resulted in the consumption of water potential energy and as a result, the supply of water is limited to seed [ 36 ]. Our germination findings against ѱ can be validated with the studies of [ 16 ] and [ 19 ] for watermelon and zucchini. The GR (g) showed a linear decreasing correlation with T up to T s ≤ T o at constant ѱ , which is in agreement with the TT data on Hordeum sponataneum [ 37 ].…”

Section: Discussionsupporting

confidence: 78%

“…However, it is significant that each taxon requires a certain range of T s for seed germination called cardinal temperatures [ 15 ]. These are the minimum or base temperature ( T b ), maximum or ceiling temperature ( T c ) at which germination rate is minimum and optimum temperature ( T o ) at which maximum germination occurs in a short period [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

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Using mathematical models to evaluate germination rate and seedlings length of chickpea seed (Cicer arietinum L.) to osmotic stress at cardinal temperatures

et al. 2021

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Cicer arietinum is the 3rd most important cool season legume crop growing in vast arid and semi-arid regions of the world. A lab experiment was designed using hydrothermal time model (HTT) to investigate the chickpea seed germination (SG) behavior, cardinal temperatures and germination responses across fluctuating temperatures (Ts) and water potentials (Ψs). Seeds of chickpea var. NIFA 1995 were germinated at six constant Ts (7, 14, 21, 28, 35 and 42°C) each having the following five water potentials: 0, -0.2, -0.4–0.6 and -0.8 MPa. Germination percentage (G%) decreased significantly at (*P ≤ 0.05) from 86.7% at 28°C in -0.2 MPa to 10% in -0.2 MPa at 7°C. The germination rate (GR = 1/t50) against different T percentiles exhibited that linear increase was observed in the GR pattern above and below the To. Based on the confidence intervals of the model coefficients and (R2: 0.96), the average cardinal temperatures were 4.7, 23 and 44.2°C for the base (Tb), optimal (To) and ceiling (Tc) temperatures respectively. θT1 value was observed maximum at 28°C in -0.2 MPa and decreases with decreasing Ψ (-0.8 MPa). In comparison with control, the θT2 value was also highest in -0.2 MPa at 28°C. The thermal time (TT) concept is well fitted to germination fraction data in distilled water with an R2 value increasing 0.972. The hydro time constant (θH) increased with an increase in T to To and then decreased when T>To. The ѱb(50) irregularly varied with increasing T, σΨb was also recorded lowest (0.166 MPa) at 28°C and highest (0.457 MPa) at 7°C. Based on the statistical analysis, cardinal temperatures, hydrothermal time constant (θHTT) and germination findings the HTT gives an insight into the interactive effect of T and Ψ on seed germination time courses under varying environmental conditions.

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Section: Discussionsupporting

confidence: 78%

Section: Introductionmentioning

confidence: 99%

Using mathematical models to evaluate germination rate and seedlings length of chickpea seed (Cicer arietinum L.) to osmotic stress at cardinal temperatures

et al. 2021

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“…In comparison to ADF, the NDF concentration of tepary samples were less accurately predicted by all three techniques (Table 3). Similar differences between prediction accuracy of ADF and NDF were also noticed for guar in this study, and also reported earlier in NIRS studies involving Brassica napus [26], Lolium multiflorum [11], and Oryza sativa [27]. Though PLS performed better at predicting IVTD in tepary bean compared to other two, all three techniques resulted in relatively low prediction accuracy with R 2 cv and R 2 v ranging between 0.75-0.79 and 0.75-0.88, respectively.…”

Section: Parametersupporting

confidence: 92%

Predicting Forage Quality of Warm-Season Legumes by Near Infrared Spectroscopy Coupled with Machine Learning Techniques

Baath

Gowda

et al. 2020

Sensors

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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.

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“…Guar [ Cyamopsis tetragonoloba (L.) Taub.] is another heat and drought tolerant, multi‐purpose legume known for its range of industrial uses (Singla, Grover, Angadi, Schutte, & VanLeeuwen, 2016; Baath et al., 2020). Past research conducted in SGP suggested that different guar cultivars were capable of generating high N (325 g kg −1 ), digestible biomass if harvested during vegetative growth (Rao & Northup, 2013).…”

Section: Introductionmentioning

confidence: 99%

Summer forage capabilities of tepary bean and guar in the southern Great Plains

et al. 2020

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Low nutritive value of perennial warm‐season grasses often causes limitation to stocker cattle production in the U.S. southern Great Plains (SGP). Exploration of novel legumes capable of generating nutritious summer forage is required to fill forage deficit periods. This 2‐yr field experiment compared the seasonal changes in forage biomass, leaf/stem ratio, and chemical composition of three varieties each of tepary bean [Phaseolus acutifolius (A.) Gray] and guar [Cyamopsis tetragonoloba (L.) Taub.] to soybean [Glycine max (L.) Merr.]. Tepary bean cultivar Black outperformed soybean and guar varieties by producing 18–48% greater aboveground dry matter (ADM) amounts (6537 kg ha−1) with a leaf/stem ratio of 3.1 at 65 days after planting (DAP). The ADM and leaf nutritive value of soybean were not surpassed by the other five tested legumes, but had the least digestible stems with an in vitro true digestibility (IVTD) of 529–581 g kg−1 in both years. Guar varieties maintained a lower leaf/stem ratio (1.3–1.5 kg kg−1) throughout the growing season, which could limit its value for grazing compared to other legumes. We concluded tepary bean could serve as an alternate forage option to soybean for producers in the SGP. Tepary bean possessed the greatest capabilities for producing adequate biomass yields with superior and consistent nutritive value when grown as a summer forage. Future research should focus on defining management practices for growing tepary bean in extensive production settings for grazing or hay.

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Guar responses to temperature: Estimation of cardinal temperatures and photosynthetic parameters

Cited by 19 publications

References 48 publications

Using mathematical models to evaluate germination rate and seedlings length of chickpea seed (Cicer arietinum L.) to osmotic stress at cardinal temperatures

Using mathematical models to evaluate germination rate and seedlings length of chickpea seed (Cicer arietinum L.) to osmotic stress at cardinal temperatures

Predicting Forage Quality of Warm-Season Legumes by Near Infrared Spectroscopy Coupled with Machine Learning Techniques

Summer forage capabilities of tepary bean and guar in the southern Great Plains

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