Phenylpropanoid Metabolism in Phaseolus vulgaris during Growth under Severe Drought.

Drought limits the growth and development of Phaseolus vulgaris L. (known as common bean). Common bean plants contain various phenylpropanoids, but it is not known whether the levels of these metabolites are altered by drought. Here, BT6 and BT44, two white bean recombinant inbred lines (RILs), were cultivated under severe drought. Their respective growth and phenylpropanoid profiles were compared to those of well-irrigated plants. Both RILs accumulated much less biomass in their vegetative parts with severe drought, which was associated with more phaseollin and phaseollinisoflavan in their roots relative to well-irrigated plants. A sustained accumulation of coumestrol was evident in BT44 roots with drought. Transient alterations in the leaf profiles of various phenolic acids occurred in drought-stressed BT6 and BT44 plants, including the respective accumulation of two separate caftaric acid isomers and coutaric acid (isomer 1) relative to well-irrigated plants. A sustained rise in fertaric acid was observed in BT44 with drought stress, whereas the greater amount relative to well-watered plants was transient in BT6. Apart from kaempferol diglucoside (isomer 2), the concentrations of most leaf flavonol glycosides were not altered with drought. Overall, fine tuning of leaf and root phenylpropanoid profiles occurs in white bean plants subjected to severe drought.

A novel method for irrigating plants, tracking water use, and imposing water deficits in controlled environments.

The study of genomic control of drought tolerance in crops requires techniques to impose well defined and consistent levels of drought stress and efficiently measure single-plant water use for hundreds of experimental units over timescales of several months. Traditional gravimetric methods are extremely labor intensive or require expensive technology, and are subject to other errors. This study demonstrates a low-cost, passive, bottom-watered system that is easily scaled for high-throughput phenotyping. The soil water content in the pots is controlled by altering the water table height in an underlying wicking bed via a float valve. The resulting soil moisture profile is then maintained passively as water withdrawn by the plant is replaced by upward movement of water from the wicking bed, which is fed from a reservoir via the float valve. The single-plant water use can be directly measured over time intervals from one to several days by observing the water level in the reservoir. Using this method, four different drought stress levels were induced in pots containing soybean (Glycine max (L.) Merr.), producing four statistically distinct groups for shoot dry weight and seed yield, as well as clear treatment effects for other relevant parameters, including root:shoot dry weight ratio, pod number, cumulative water use, and water use efficiency. This system has a broad range of applications, and should increase feasibility of high-throughput phenotyping efforts for plant drought tolerance traits.

A Physio-Morphological Trait-Based Approach for Breeding Drought Tolerant Wheat.

In the past, there have been drought events in different parts of the world, which have negatively influenced the productivity and production of various crops including wheat (Triticum aestivum L.), one of the world's three important cereal crops. Breeding new high yielding drought-tolerant wheat varieties is a research priority specifically in regions where climate change is predicted to result in more drought conditions. Commonly in breeding for drought tolerance, grain yield is the basis for selection, but it is a complex, late-stage trait, affected by many factors aside from drought. A strategy that evaluates genotypes for physiological responses to drought at earlier growth stages may be more targeted to drought and time efficient. Such an approach may be enabled by recent advances in high-throughput phenotyping platforms (HTPPs). In addition, the success of new genomic and molecular approaches rely on the quality of phenotypic data which is utilized to dissect the genetics of complex traits such as drought tolerance. Therefore, the first objective of this review is to describe the growth-stage based physio-morphological traits that could be targeted by breeders to develop drought-tolerant wheat genotypes. The second objective is to describe recent advances in high throughput phenotyping of drought tolerance related physio-morphological traits primarily under field conditions. We discuss how these strategies can be integrated into a comprehensive breeding program to mitigate the impacts of climate change. The review concludes that there is a need for comprehensive high throughput phenotyping of physio-morphological traits that is growth stage-based to improve the efficiency of breeding drought-tolerant wheat.

Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean.

Recent substantial advances in high-throughput field phenotyping have provided plant breeders with affordable and efficient tools for evaluating a large number of genotypes for important agronomic traits at early growth stages. Nevertheless, the implementation of large datasets generated by high-throughput phenotyping tools such as hyperspectral reflectance in cultivar development programs is still challenging due to the essential need for intensive knowledge in computational and statistical analyses. In this study, the robustness of three common machine learning (ML) algorithms, multilayer perceptron (MLP), support vector machine (SVM), and random forest (RF), were evaluated for predicting soybean (Glycine max) seed yield using hyperspectral reflectance. For this aim, the hyperspectral reflectance data for the whole spectra ranged from 395 to 1005 nm, which were collected at the R4 and R5 growth stages on 250 soybean genotypes grown in four environments. The recursive feature elimination (RFE) approach was performed to reduce the dimensionality of the hyperspectral reflectance data and select variables with the largest importance values. The results indicated that R5 is more informative stage for measuring hyperspectral reflectance to predict seed yields. The 395 nm reflectance band was also identified as the high ranked band in predicting the soybean seed yield. By considering either full or selected variables as the input variables, the ML algorithms were evaluated individually and combined-version using the ensemble-stacking (E-S) method to predict the soybean yield. The RF algorithm had the highest performance with a value of 84% yield classification accuracy among all the individual tested algorithms. Therefore, by selecting RF as the metaClassifier for E-S method, the prediction accuracy increased to 0.93, using all variables, and 0.87, using selected variables showing the success of using E-S as one of the ensemble techniques. This study demonstrated that soybean breeders could implement E-S algorithm using either the full or selected spectra reflectance to select the high-yielding soybean genotypes, among a large number of genotypes, at early growth stages.

Assessing stomatal and non-stomatal limitations to carbon assimilation under progressive drought in peanut (Arachis hypogaea L.).

Drought is known to limit carbon assimilation in plants. However, it has been debated whether photosynthesis is primarily inhibited by stomatal or non-stomatal factors. This research assessed the underlying limitations to photosynthesis in peanuts (Arachis hypogaea L.) grown under progressive drought. Specifically, field-grown peanut plants were exposed to either well-watered or drought-stressed conditions during flowering. Measurements included survey measurements of gas exchange, chlorophyll fluorescence, PSII thermotolerance, pigment content, and rapid A-Ci response (RACiR) assessments. Drought significantly decreased stomatal conductance with consequent declines in photosynthesis (AN), actual quantum yield of PSII, and electron transport rate (ETR). Pigment contents were variable and depended on stress severity. Stomatal closure on stressed plants resulted in higher leaf temperatures, but Fv/Fm and PSII thermotolerance were only slightly affected by drought. A strong, hyperbolic relationship was observed between stomatal conductance, AN, and ETR. However, when RACiR analysis was conducted, drought significantly decreased AN at Ci values comparable to drought-stressed plants, indicating non-stomatal limitations to AN. The maximum rate of carboxylation and maximum electron transport rate were severely limited by drought, and chloroplast CO2 concentration (CC) declined substantially under drought along with a comparable increase in partitioning of electron flow to photorespiration. Thus, while stomatal conductance may be a viable reference indicator of water deficit stress in peanut, we conclude that declines in AN were largely due to non-stomatal (diffusional and metabolic) limitations. Additionally, this is the first study to apply the rapid A-Ci response method to peanut, with comparable results to traditional A-Ci methods.

Estimating photosynthetic electron transport via chlorophyll fluorometry without Photosystem II light saturation.

Estimates of thylakoid electron transport rates (J(e)) from chlorophyll fluorometry are often used in combination with leaf gas exchange measurements to provide detailed information about photosynthetic activity of leaves in situ. Estimating J(e) requires accurate determination of the quantum efficiency of Photosystem II (Phi(P)), which in turn requires momentary light saturation of the Photosystem II light harvesting complex to induce the maximum fluorescence signal (F(M)'). In practice, full saturation is often difficult to achieve, especially when incident photosynthetic photon flux density (Q) is high and energy is effectively dissipated by non-photochemical quenching. In the present work, a method for estimating the true F(M)' under high Q was developed, using multiple light pulses of varying intensity (Q'). The form of the expected relationship between the apparent F(M)' and Q' was derived from theoretical considerations. This allowed the true F(M)' at infinite Q' to be estimated from linear regression. Using a commercially available leaf gas exchange/ chlorophyll fluorescence measurement system, J(e) was compared to gross photosynthetic CO(2) assimilation (A(G)) under conditions where the relationship between J(e) and A(G) was expected to be linear. Both in C(4) leaves (Zea mays) in ambient air and also in C(3) leaves (Gossypium hirsutum) under non-photorespiratory conditions the apparent ratio between J(e) and A(G) declined at high Q when Phi(P) was calculated from F(M)' measured simply using the highest available saturating pulse intensity. When F(M)' was determined using the multiple pulse / linear regression technique, the expected relationship between J(e) and A(G) at high Q was restored, indicating that the Phi(P) estimate was improved. This method of determining F(M)' should prove useful for verifying when saturating pulse intensities are sufficient, and for accurately determining Phi(P) when they are not.