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Drought is one of the most devasting and frequent abiotic stresses in agriculture. While many morphological, biochemical and physiological indicators are being used to quantify plant drought responses, stomatal control, and hence the transpiration and photosynthesis regulation through it, is of particular importance in marking the plant capacity of balancing stress response and yield. Due to the difficulties in simultaneous, large-scale measurement of stomatal traits such as sensitivity and speed of stomatal closure under progressive soil drought, forward genetic mapping of these important behaviors has long been unavailable. The recent emerging phenomic technologies offer solutions to identify the water relations of whole plant and assay the stomatal regulation in a dynamic process at the population level. Here, we report high-throughput physiological phenotyping of water relations of 106 cowpea accessions under progressive drought stress, which, in combination of genome-wide association study (GWAS), enables genetic mapping of the complex, stomata-related drought responsive traits “critical soil water content” (θcri) and “slope of transpiration rate declining” (KTr). The 106 accessions showed large variations in θcri and KTr, indicating that they had broad spectrum of stomatal control in response to soil water deficit, which may confer them different levels of drought tolerance. Univariate GWAS identified six and fourteen significant SNPs associated with θcri and KTr, respectively. The detected SNPs distributed in nine chromosomes and accounted for 8.7–21% of the phenotypic variation, suggesting that both stomatal sensitivity to soil drought and the speed of stomatal closure to completion were controlled by multiple genes with moderate effects. Multivariate GWAS detected ten more significant SNPs in addition to confirming eight of the twenty SNPs as detected by univariate GWAS. Integrated, a final set of 30 significant SNPs associated with stomatal closure were reported. Taken together, our work, by combining phenomics and genetics, enables forward genetic mapping of the genetic architecture of stomatal traits related to drought tolerance, which not only provides a basis for molecular breeding of drought resistant cultivars of cowpea, but offers a new methodology to explore the genetic determinants of water budgeting in crops under stressful conditions in the phenomics era.

Drought is one of the most devasting and frequent abiotic stresses in agriculture. While many morphological, biochemical and physiological indicators are being used to quantify plant drought responses, stomatal control, and hence the transpiration and photosynthesis regulation through it, is of particular importance in marking the plant capacity of balancing stress response and yield. Due to the difficulties in simultaneous, large-scale measurement of stomatal traits such as sensitivity and speed of stomatal closure under progressive soil drought, forward genetic mapping of these important behaviors has long been unavailable. The recent emerging phenomic technologies offer solutions to identify the water relations of whole plant and assay the stomatal regulation in a dynamic process at the population level. Here, we report high-throughput physiological phenotyping of water relations of 106 cowpea accessions under progressive drought stress, which, in combination of genome-wide association study (GWAS), enables genetic mapping of the complex, stomata-related drought responsive traits "critical soil water content" (θcri) and "slope of transpiration rate declining" (KTr). The 106 accessions showed large variations in θcri and KTr, indicating that they had broad spectrum of stomatal control in response to soil water deficit, which may confer them different levels of drought tolerance. Univariate GWAS identified six and fourteen significant SNPs associated with θcri and KTr, respectively. The detected SNPs distributed in nine chromosomes and accounted for 8.7–21% of the phenotypic variation, suggesting that both stomatal sensitivity to soil drought and the speed of stomatal closure to completion were controlled by multiple genes with moderate effects. Multivariate GWAS detected ten more significant SNPs in addition to confirming eight of the twenty SNPs as detected by univariate GWAS. Integrated, a final set of 30 significant SNPs associated with stomatal closure were reported. Taken together, our work, by combining phenomics and genetics, enables forward genetic mapping of the genetic architecture of stomatal traits related to drought tolerance, which not only provides a basis for molecular breeding of drought resistant cultivars of cowpea, but offers a new methodology to explore the genetic determinants of water budgeting in crops under stressful conditions in the phenomics era. [ABSTRACT FROM AUTHOR]

Phenotyping, via remote and proximal sensing techniques, of the agronomic and physiological traits associated with yield potential and drought adaptation could contribute to improvements in breeding programs. In the present study, 384 genotypes of wheat (Triticum aestivum L.) were tested under fully irrigated (FI) and water stress (WS) conditions. The following traits were evaluated and assessed via spectral reflectance: Grain yield (GY), spikes per square meter (SM2), kernels per spike (KPS), thousand-kernel weight (TKW), chlorophyll content (SPAD), stem water soluble carbohydrate concentration and content (WSC and WSCC, respectively), carbon isotope discrimination (Δ13C), and leaf area index (LAI). The performances of spectral reflectance indices (SRIs), four regression algorithms (PCR, PLSR, ridge regression RR, and SVR), and three classification methods (PCA-LDA, PLS-DA, and kNN) were evaluated for the prediction of each trait. For the classification approaches, two classes were established for each trait: The lower 80% of the trait variability range (Class 1) and the remaining 20% (Class 2 or elite genotypes). Both the SRIs and regression methods performed better when data from FI and WS were combined. The traits that were best estimated by SRIs and regression methods were GY and Δ13C. For most traits and conditions, the estimations provided by RR and SVR were the same, or better than, those provided by the SRIs. PLS-DA showed the best performance among the categorical methods and, unlike the SRI and regression models, most traits were relatively well-classified within a specific hydric condition (FI or WS), proving that classification approach is an effective tool to be explored in future studies related to genotype selection.

Phenotyping, via remote and proximal sensing techniques, of the agronomic and physiological traits associated with yield potential and drought adaptation could contribute to improvements in breeding programs. In the present study, 384 genotypes of wheat (Triticum aestivum L.) were tested under fully irrigated (FI) and water stress (WS) conditions. The following traits were evaluated and assessed via spectral reflectance: Grain yield (GY), spikes per square meter (SM2), kernels per spike (KPS), thousand-kernel weight (TKW), chlorophyll content (SPAD), stem water soluble carbohydrate concentration and content (WSC and WSCC, respectively), carbon isotope discrimination (Δ13C), and leaf area index (LAI). The performances of spectral reflectance indices (SRIs), four regression algorithms (PCR, PLSR, ridge regression RR, and SVR), and three classification methods (PCA-LDA, PLS-DA, and kNN) were evaluated for the prediction of each trait. For the classification approaches, two classes were established for each trait: The lower 80% of the trait variability range (Class 1) and the remaining 20% (Class 2 or elite genotypes). Both the SRIs and regression methods performed better when data from FI and WS were combined. The traits that were best estimated by SRIs and regression methods were GY and Δ13C. For most traits and conditions, the estimations provided by RR and SVR were the same, or better than, those provided by the SRIs. PLS-DA showed the best performance among the categorical methods and, unlike the SRI and regression models, most traits were relatively well-classified within a specific hydric condition (FI or WS), proving that classification approach is an effective tool to be explored in future studies related to genotype selection. [ABSTRACT FROM AUTHOR]

By contrast with photometry (i.e., the measurement of light transmitted through a particle suspension), nephelometry is a direct method of measuring light scattered by particles in suspension. Since the scattered light intensity is directly proportional to the suspended particle concentration, nephelometry is a promising method for recording microbial growth and especially for studying filamentous fungi, which cannot be efficiently investigated through spectrophotometric assays. We describe herein for the first time a filamentous fungi–tailored procedure based on microscale liquid cultivation and automated nephelometric recording of growth, followed by extraction of relevant variables (lag time and growth rate) from the obtained growth curves. This microplate reader technique is applicable for the evaluation of antifungal activity and for large-scale phenotypic profiling.

This article describes public, free software that provides efficient exploratory analysis of high-resolution spectral reflectance data. Spectral reflectance data can suffer from problems such as poor signal to noise ratios in various wavebands or invalid measurements due to changes in incoming solar radiation or operator fatigue leading to poor orientation of sensors. Thus, exploratory data analysis is essential to identify appropriate data for further analyses. This software overcomes the problem that analysis tools such as Excel are cumbersome to use for the high number of wavelengths and samples typically acquired in these studies. The software, Spectral Knowledge (SK-UTALCA), was initially developed for plant breeding, but it is also suitable for other studies such as precision agriculture, crop protection, ecophysiology plant nutrition, and soil fertility. Various spectral reflectance indices (SRIs) are often used to relate crop characteristics to spectral data and the software is loaded with 255 SRIs which can be applied quickly to the data. This article describes the architecture and functions of SK-UTALCA and the features of the data that led to the development of each of its modules. [ABSTRACT FROM AUTHOR]

Lack of water and increase in ambient temperature, caused by climate change, are already affecting agriculture worldwide. These factors will affect the physiology and development of plants in general, including blueberry plants (Vaccinium spp.). With this in mind, six cultivars of highbush blueberry ( Vaccinium corymbosum L.) (‘Star’, ‘Bluecrisp’, ‘Jewel’, ‘Bluegold’, ‘Elliott’ and ‘Liberty’) and two rabbiteye cvs. ( Vaccinium ashei R.) (‘Bonita’ and ‘Powderblue’) were subjected to two water treatments: continuous irrigation (Full irrigation– FI ); and with a water deficit (only one third of the volume of water, water deficit– WD ). Both treatments were applied in two greenhouses one of which represented ambient conditions ( At ) and the other simulated heat stress conditions ( At + 10 °C). Measurements were made of chlorophyll fluorescence, stem water potential ( Ψs ), chlorophyll content, leaf temperature and SPAD. In At conditions, cultivars showed differences in most parameters of chlorophyll fluorescence, but only the quantum yield of energy conversion of non-photochemical quenching (Y(NPQ)) and Ψs were significant, along with interactions between cultivars and irrigation treatments. In addition, cultivars differed in the maximum rate of electron transport (ETR max ), IK and effective photochemical quantum yield of PSII (Y(II)), indicating differences in the efficiency of photosystem II (PSII). Under At + 10 °C conditions, there were significant interactions in the minimum fluorescence in the dark-adapted state ( F 0 ), ETR max , IK, Y(II), Ψs and photochemical quenching (qP and qL). Thus indicating that when subjected to the two combined stresses ( WD − At + 10 °C) the cultivars showed different responses in the efficiency and operation of PSII. The results of this study indicate that the fluorescence parameters provide a good tool for phenotyping in blueberry breeding programs and enable the detection and elimination of unwanted genotypes at the beginning of the selection process. [ABSTRACT FROM AUTHOR]

Chlorophyll and anthocyanin contents provide a valuable indicator of the status of a plant's physiology, but to be more widely utilized it needs to be assessed easily and non-destructively. This is particularly evident in terms of assessing and exploiting germplasm for plant-breeding programs. We report, for the first time, experiments with Fragaria chiloensis (L.) Duch. and the estimation of the effects of response to salinity stress (0, 30, and 60 mmol NaCl/L) in terms of these pigments content and gas exchange. It is shown that both pigments (which interestingly, themselves show a high correlation) give a good indication of stress response. Both pigments can be accurately predicted using spectral reflectance indices (SRI); however, the accuracy of the predictions was slightly improved using multilinear regression analysis models and genetic algorithm analysis. Specifically for chlorophyll content, unlike other species, the use of published SRI gave better indications of stress response than Normalized Difference Vegetation Index. The effect of salt on gas exchange is only evident at the highest concentration and some SRI gave better prediction performance than the known Photochemical Reflectance Index. This information will therefore be useful for identifying tolerant genotypes to salt stress for incorporation in breeding programs. [ABSTRACT FROM AUTHOR]