Phenotyping for drought tolerance of crops in the genomics era

Milletomics: a metabolomics centered integrated omics approach toward genetic progression

Producing alternative staple foods like millet will be essential to feeding ten billion people by 2050. The increased demand for millet is driving researchers to improve its genetic variation. Millets include protein, dietary fiber, phenolic substances, and flavonoid components. Its climate resilience makes millet an appealing crop for agronomic sustainability. Integrative omics technologies could potentially identify and develop millets with desirable phenotypes that may have high agronomic value. Millets' salinity and drought tolerance have been enhanced using transcriptomics. In foxtail, finger, and pearl millet, proteomics has discovered salt-tolerant protein, phytohormone-focused protein, and drought tolerance. Metabolomics studies have revealed that certain metabolic pathways including those involving lignin, flavonoids, phenylpropanoid, and lysophospholipids are critical for many processes, including seed germination, photosynthesis, energy metabolism, and the synthesis of bioactive chemicals necessary for drought tolerance. Metabolomics integration with other omics revealed metabolome engineering and trait-specific metabolite creation. Integrated metabolomics and ionomics are still in the development stage, but they could potentially assist in comprehending the pathway of ionomers to control nutrient levels and biofortify millet. Epigenomic analysis has shown alterations in DNA methylation patterns and chromatin structure in foxtail and pearl millets in response to abiotic stress. Whole-genome sequencing utilizing next-generation sequencing is the most proficient method for finding stress-induced phytoconstituent genes. New genome sequencing enables novel biotechnological interventions including genome-wide association, mutation-based research, and other omics approaches. Millets can breed more effectively by employing next-generation sequencing and genotyping by sequencing, which may mitigate climate change. Millet marker-assisted breeding has advanced with high-throughput markers and combined genotyping technologies.

Dissecting the Superior Drivers for the Simultaneous Improvement of Fiber Quality and Yield Under Drought Stress Via Genome-Wide Artificial Introgressions of Gossypium barbadense into Gossypium hirsutum

Global water scarcity and extreme weather intensify drought stress, significantly reducing cotton yield and quality worldwide. Drought treatments are conducted using a population of chromosome segment substitution lines generated from E22 (G. hirsutum) and 3-79 (G. barbadense) as parental lines either show superior yields or fiber quality under both control and drought conditions. Fourteen datasets, covering 4 yields and 4 quality traits, are compiled and assessed for drought resistance using the drought resistance coefficient (DRC) and membership function value of drought resistance (MFVD). Genome-wide association studies, linkage analysis, and bulked segregant analysis are combined to analyze the DR-related QTL. A total of 121 significant QTL are identified by DRC and MFVD of the 8 traits. CRISPR/Cas9 and virus-induced gene silencing techniques verified DRR1 and DRT1 as pivotal genes in regulating drought resistant of cotton, with hap3-79 exhibiting greater drought resistance than hapE22 concerning DRR1 and DRT1. Moreover, 14 markers with superior yield and fiber quality are selected for drought treatment. This study offers valuable insights into yield and fiber quality variations between G. hirsutum and G. barbadense amid drought, providing crucial theoretical and technological backing for developing cotton varieties resilient to drought, with high yield and superior fiber quality.

Breeding for improved digestibility and processing of lignocellulosic biomass in Zea mays

Forage maize is a versatile crop extensively utilized for animal nutrition in agriculture and holds promise as a valuable resource for the production of fermentable sugars in the biorefinery sector. Within this context, the carbohydrate fraction of the lignocellulosic biomass undergoes deconstruction during ruminal digestion and the saccharification process. However, the cell wall's natural resistance towards enzymatic degradation poses a significant challenge during both processes. This so-called biomass recalcitrance is primarily attributed to the presence of lignin and ferulates in the cell walls. Consequently, maize varieties with a reduced lignin or ferulate content or an altered lignin composition can have important beneficial effects on cell wall digestibility. Considerable efforts in genetic improvement have been dedicated towards enhancing cell wall digestibility, benefiting agriculture, the biorefinery sector and the environment. In part I of this paper, we review conventional and advanced breeding methods used in the genetic improvement of maize germplasm. In part II, we zoom in on maize mutants with altered lignin for improved digestibility and biomass processing.

Dissecting the effect of heat stress on durum wheat under field conditions

Introduction

Heat stress negatively affects wheat production in several ways, mainly by reducing growth rate, photosynthetic capacity and reducing spike fertility. Modeling stress response means analyzing simultaneous relationships among traits affecting the whole plant response and determinants of grain yield. The aim of this study was to dissect the diverse impacts of heat stress on key yield traits and to identify the most promising sources of alleles for heat tolerance.

Methods

We evaluated a diverse durum wheat panel of 183 cultivars and breeding lines from worldwide, for their response to long-term heat stress under field conditions (HS) with respect to non stress conditions (NS), considering phenological traits, grain yield (GY) and its components as a function of the timing of heat stress and climatic covariates. We investigated the relationships among plant and environmental variables by means of a structural equation model (SEM) and Genetic SEM (GSEM).

Results

Over two years of experiments at CENEB, CIMMYT, the effects of HS were particularly pronounced for the normalized difference vegetation index, NDVI (-51.3%), kernel weight per spike, KWS (-40.5%), grain filling period, GFP (-38.7%), and GY (-56.6%). Average temperatures around anthesis were negatively correlated with GY, thousand kernel weight TKW and test weight TWT, but also with spike density, a trait determined before heading/anthesis. Under HS, the correlation between the three major determinants of GY, i.e., fertile spike density, spike fertility and kernel size, were of noticeable magnitude. NDVI measured at medium milk-soft dough stage under HS was correlated with both spike fertility and grain weight while under NS it was less predictive of grain weight but still highly correlated with spike fertility. GSEM modeling suggested that the causal model of performance under HS directly involves genetic effects on GY, NDVI, KWS and HD.

Discussion

We identified consistently suitable sources of genetic resistance to heat stress to be used in different durum wheat pre-breeding programs. Among those, Desert Durums and CIMMYT'80 germplasm showed the highest degree of adaptation and capacity to yield under high temperatures and can be considered as a valuable source of alleles for adaptation to breed new HS resilient cultivars.

Review of applications of artificial intelligence (AI) methods in crop research

Sophisticated and modern crop improvement techniques can bridge the gap for feeding the ever-increasing population. Artificial intelligence (AI) refers to the simulation of human intelligence in machines, which refers to the application of computational algorithms, machine learning (ML) and deep learning (DL) techniques. This is aimed to generalise patterns and relationships from historical data, employing various mathematical optimisation techniques thus making prediction models for facilitating selection of superior genotypes. These techniques are less resource intensive and can solve the problem based on the analysis of large-scale phenotypic datasets. ML for genomic selection (GS) uses high-throughput genotyping technologies to gather genetic information on a large number of markers across the genome. The prediction of GS models is based on the mathematical relation between genotypic and phenotypic data from the training population. ML techniques have emerged as powerful tools for genome editing through analysing large-scale genomic data and facilitating the development of accurate prediction models. Precise phenotyping is a prerequisite to advance crop breeding for solving agricultural production-related issues. ML algorithms can solve this problem through generating predictive models, based on the analysis of large-scale phenotypic datasets. DL models also have the potential reliability of precise phenotyping. This review provides a comprehensive overview on various ML and DL models, their applications, potential to enhance the efficiency, specificity and safety towards advanced crop improvement protocols such as genomic selection, genome editing, along with phenotypic prediction to promote accelerated breeding.

Genetic mapping reveals the complex genetic architecture controlling slow canopy wilting in soybean

In soybean [Glycine max (L.) Merr.], drought stress is the leading cause of yield loss from abiotic stress in rain-fed US growing areas. Only 10% of the US soybean production is irrigated; therefore, plants must possess physiological mechanisms to tolerate drought stress. Slow canopy wilting is a physiological trait that is observed in a few exotic plant introductions (PIs) and may lead to yield improvement under drought stress. Canopy wilting of 130 recombinant inbred lines (RILs) derived from Hutcheson × PI 471938 grown under drought stress was visually evaluated and genotyped with the SoySNP6K BeadChip. Over four years, field evaluations of canopy wilting were conducted under rainfed conditions at three locations across the US (Georgia, Kansas, and North Carolina). Due to the variation in weather among locations and years, the phenotypic data were collected from seven environments. Substantial variation in canopy wilting was observed among the genotypes in the RIL population across environments. Three QTLs were identified for canopy wilting from the RIL population using composite interval mapping on chromosomes (Chrs) 2, 8, and 9 based on combined environmental analyses. These QTLs inherited the favorable alleles from PI 471938 and accounted for 11, 10, and 14% of phenotypic variation, respectively. A list of 106 candidate genes were narrowed down for these three QTLs based on the published information. The QTLs identified through this research can be used as targets for further investigation to understand the mechanisms of slow canopy wilting. These QTLs could be deployed to improve drought tolerance through a targeted selection of the genomic regions from PI 471938.

The Quest for Reliable Drought Stress Screening in Tetraploid Wheat (Triticum turgidum spp.) Seedlings: Why MDA Quantification after Treatment with 10% PEG-6000 Falls Short

Drought stress poses significant productivity challenges to wheat. Several studies suggest that lower malondialdehyde (MDA) content may be a promising trait to identify drought-tolerant wheat genotypes. However, the optimal polyethylene glycol (PEG-6000) concentration for screening seedlings for drought tolerance based on MDA quantification is not clear. The aim of this study was to verify whether a 10% (w/v) PEG-6000 concentration-induced water stress was reliable for discriminating between twenty-two drought-susceptible and drought-tolerant tetraploid wheat (Triticum turgidum spp. durum, turanicum, and carthlicum) accessions based on MDA quantification. To do so, its correlation with morpho-physiological traits, notoriously related to seedling drought tolerance, i.e., Seedling Vigour Index and Seedling Water Content, was evaluated. Results showed that MDA content was not a reliable biomarker for drought tolerance, as it did not correlate significantly with the aforementioned morpho-physiological traits, which showed, on the contrary, high positive correlation with each other. Combining our study with the cited literature, it clearly emerges that different wheat genotypes have different "water stress thresholds", highlighting that using a 10% PEG-6000 concentration for screening wheat seedlings for drought tolerance based on MDA quantification is not reliable. Given the conflicting results in the literature, this study provides important insights for selecting appropriate methods for evaluating wheat seedling drought tolerance.

Genetic Basis of Potato Tuber Defects and Identification of Heat-Tolerant Clones

Heat stress during the potato growing season reduces tuber marketable yield and quality. Tuber quality deterioration includes external (heat sprouts, chained tubers, knobs) and internal (vascular discoloration, hollow heart, internal heat necrosis) tuber defects, as well as a reduction in their specific gravity and increases in reducing sugars that result in suboptimal (darker) processed products (french fries and chips). Successfully cultivating potatoes under heat-stress conditions requires planting heat-tolerant varieties that can produce high yields of marketable tubers, few external and internal tuber defects, high specific gravity, and low reducing sugars (in the case of processing potatoes). Heat tolerance is a complex trait, and understanding its genetic basis will aid in developing heat-tolerant potato varieties. A panel of 217 diverse potato clones was evaluated for yield and quality attributes in Dalhart (2019 and 2020) and Springlake (2020 and 2021), Texas, and genotyped with the Infinium 22 K V3 Potato Array. A genome-wide association study was performed to identify genomic regions associated with heat-tolerance traits using the GWASpoly package. Quantitative trait loci were identified on chromosomes 1, 3, 4, 6, 8, and 11 for external defects and on chromosomes 1, 2, 3, 10, and 11 for internal defects. Yield-related quantitative trait loci were detected on chromosomes 1, 6, and 10 pertaining to the average tuber weight and tuber number per plant. Genomic-estimated breeding values were calculated using the StageWise package. Clones with low genomic-estimated breeding values for tuber defects were identified as donors of good traits to improve heat tolerance. The identified genomic regions associated with heat-tolerance attributes and the genomic-estimated breeding values will be helpful to develop new potato cultivars with enhanced heat tolerance in potatoes.

Genomic regions of durum wheat involved in water productivity

Durum wheat is a staple food in the Mediterranean Basin, mostly cultivated under rainfed conditions. As such, the crop is often exposed to moisture stress. Therefore, the identification of genetic factors controlling the capacity of genotypes to convert moisture into grain yield (i.e., water productivity) is quintessential to stabilize production despite climatic variations. A global panel of 384 accessions was tested across 18 Mediterranean environments (in Morocco, Lebanon, and Jordan) representing a vast range of moisture levels. The accessions were assigned to water responsiveness classes, with genotypes 'Responsive to Low Moisture' reaching an average +1.5 kg ha-1 mm-1 yield advantage. Genome wide association studies revealed that six loci explained most of this variation. A second validation panel tested under moisture stress confirmed that carrying the positive allele at three loci on chromosomes 1B, 2A, and 7B generated an average water productivity gain of +2.2 kg ha-1 mm-1. These three loci were tagged by kompetitive allele specific PCR (KASP) markers, and these were used to screen a third independent validation panel composed of elites tested across moisture stressed sites. The three KASP combined predicted up to 10% of the variation for grain yield at 60% accuracy. These loci are now ready for molecular pyramiding and transfer across cultivars to improve the moisture conversion of durum wheat.

Assessment of genetic variation among wheat genotypes for drought tolerance utilizing microsatellite markers and morpho-physiological characteristics

Drought is a major abiotic stress that severely limits sustainable wheat (Triticum aestivum L.) productivity via morphological and physio-biochemical alterations of cellular processes. The complex nature and polygenic control of drought tolerance traits make breeding tolerant genotypes quite challenging. However, naturally occurring variabilities among wheat germplasm resources could potentially help combating drought. The present study was conducted to assess the drought tolerance of 18 Bangladeshi hexaploid wheat genotypes, focusing on the identification of potent sources of diversity by combining microsatellite markers, also known as single sequence repeat markers, and morpho-physiological characteristics that might help accelerating wheat crop improvement programs. Initially, the genotypes were evaluated using 25 microsatellite markers followed by an on-field evaluation of 7 morphological traits (plant height, spike number, spike length, grains per spike, 1000-grain weight, grain yield, biological yield) and 6 physiological traits (SPAD value, membrane stability index, leaf relative water content, proline content, canopy temperature depression, and leaf K+ ion content). The field-trial was conducted in a factorial fashion of 18 wheat genotypes and two water regimes (control and drought) following a split-plot randomized complete block design. Regardless of genotype, drought was significantly damaging for all the tested traits; however, substantial variability in drought stress tolerance was evident among the genotypes. Spike length, 1000-grain weight, SPAD value, leaf relative water content, canopy temperature depression, proline content, and potassium (K+) ion content were the most representative of drought-induced growth and yield impairments and also correlated well with the contrasting ability of genotypic tolerance. Microsatellite markers amplified 244 alleles exhibiting 79% genetic diversity. Out of 25 markers, 23 was highly polymorphic showing 77% average polymorphism. Morpho-physiological trait-based hierarchical clustering and microsatellite marker-based neighbor-jointing clustering both revealed three genotypic clusters with 71% co-linearity between them. In both cases, the genotypes Kanchan, BAW-1147, BINA Gom 1, BARI Gom 22, BARI Gom 26, and BARI Gom 33 were found to be comparatively more tolerant than the other tested genotypes, showing potential for cultivation in water-deficit environments. The findings of this study would contribute to the present understanding of drought tolerance in wheat and would provide a basis for future genotype selection for drought-tolerant wheat breeding programs.

Variability in drought stress response in a panel of 100 faba bean genotypes

Faba bean is an important protein crop for food and feed worldwide and provides a range of advantages in crop rotations. Its limited use in modern agriculture is mainly due to the high fluctuations in yield. A well known limiting factor in most legumes, and particularly in faba bean, is the high sensitivity to water shortage, which is further aggravated by climate change. The present study was undertaken to exploit the genetic variation in drought stress response in a faba bean collection of 100 accessions with diverse origins and to assess selection criteria for identifying drought tolerant genotypes. Physiological, phenological and yield related traits evaluated under drought or water-sufficient conditions responded significantly to the end-terminated drought stress. Comparison of yield relations showed the advantage of using a stress tolerance index (STI) to identify genotypes combining high yield potential with high stress yield. With regard to physiological traits, SPAD (chlorophyll content) values were significantly related to yield as well as to STI, while the other traits also contributed to different extents to variation in yield formation. Among the yield related traits, seeds per plant proved to be the most important trait followed by pods per plant. Interestingly, the eight genotypes with the best STI performance use different strategies to cope with drought stress.

Early-Stage Detection of Biotic and Abiotic Stress on Plants by Chlorophyll Fluorescence Imaging Analysis

Most agricultural land, as a result of climate change, experiences severe stress that significantly reduces agricultural yields. Crop sensing by imaging techniques allows early-stage detection of biotic or abiotic stress to avoid damage and significant yield losses. Among the top certified imaging techniques for plant stress detection is chlorophyll a fluorescence imaging, which can evaluate spatiotemporal leaf changes, permitting the pre-symptomatic monitoring of plant physiological status long before any visible symptoms develop, allowing for high-throughput assessment. Here, we review different examples of how chlorophyll a fluorescence imaging analysis can be used to evaluate biotic and abiotic stress. Chlorophyll a is able to detect biotic stress as early as 15 min after Spodoptera exigua feeding, or 30 min after Botrytis cinerea application on tomato plants, or on the onset of water-deficit stress, and thus has potential for early stress detection. Chlorophyll fluorescence (ChlF) analysis is a rapid, non-invasive, easy to perform, low-cost, and highly sensitive method that can estimate photosynthetic performance and detect the influence of diverse stresses on plants. In terms of ChlF parameters, the fraction of open photosystem II (PSII) reaction centers (qp) can be used for early stress detection, since it has been found in many recent studies to be the most accurate and appropriate indicator for ChlF-based screening of the impact of environmental stress on plants.

The potentialities of omics resources for millet improvement

Millets are nutrient-rich (nutri-rich) cereals with climate resilience attributes. However, its full productive potential is not realized due to the lack of a focused yield improvement approach, as evidenced by the available literature. Also, the lack of well-characterized genomic resources significantly limits millet improvement. But the recent availability of genomic data and advancement in omics tools has shown its enormous potential to enhance the efficiency and precision faced by conventional breeding in millet improvement. The development of high throughput genotyping platforms based on next-generation sequencing (NGS) has provided a low-cost method for genomic information, specifically for neglected nutri-rich cereals with the availability of a limited number of reference genome sequences. NGS has created new avenues for millet biotechnological interventions such as mutation-based study, GWAS, GS, and other omics technologies. The simultaneous discovery of high-throughput markers and multiplexed genotyping platform has aggressively aided marker-assisted breeding for millet improvement. Therefore, omics technology offers excellent opportunities to explore and combine useful variations for targeted traits that could impart high nutritional value to high-yielding cultivars under changing climatic conditions. In millet improvement, an in-depth account of NGS, integrating genomics data with different biotechnology tools, is reviewed in this context.

The Era of Plant Breeding: Conventional Breeding to Genomics-assisted Breeding for Crop Improvement

Plant breeding has made a significant contribution to increasing agricultural production. Conventional breeding based on phenotypic selection is not effective for crop improvement. Because phenotype is considerably influenced by environmental factors, which will affect the selection of breeding materials for crop improvement. The past two decades have seen tremendous progress in plant breeding research. Especially the availability of high-throughput molecular markers followed by genomic-assisted approaches significantly contributed to advancing plant breeding. Integration of speed breeding with genomic and phenomic facilities allowed rapid quantitative trait loci (QTL)/gene identifications and ultimately accelerated crop improvement programs. The advances in sequencing technology helps to understand the genome organization of many crops and helped with genomic selection in crop breeding. Plant breeding has gradually changed from phenotype-to-genotype-based to genotype-to-phenotype-based selection. High-throughput phenomic platforms have played a significant role in the modern breeding program and are considered an essential part of precision breeding. In this review, we discuss the rapid advance in plant breeding technology for efficient crop improvements and provide details on various approaches/platforms that are helpful for crop improvement. This review will help researchers understand the recent developments in crop breeding and improvements.

Analysis of the Differentially Expressed Proteins and Metabolic Pathways of Honeybush (Cyclopia subternata) in Response to Water Deficit Stress

Honeybush (Cyclopia spp.) is a rich source of antioxidant properties and phenolic compounds. Water availability plays a crucial role in plant metabolic processes, and it contributes to overall quality. Thus, this study aimed to investigate changes in molecular functions, cellular components, and biological processes of Cyclopia subternata exposed to different water stress conditions, which include well-watered (as Control, T1), semi-water stressed (T2), and water-deprived (T3) potted plants. Samples were also collected from a well-watered commercial farm first cultivated in 2013 (T13) and then cultivated in 2017 (T17) and 2019 (T19). Differentially expressed proteins extracted from C. subternata leaves were identified using LC-MS/MS spectrometry. A total of 11 differentially expressed proteins (DEPs) were identified using Fisher's exact test (p < 0.00100). Only α-glucan phosphorylase was found to be statistically common between T17 and T19 (p < 0.00100). Notably, α-glucan phosphorylase was upregulated in the older vegetation (T17) and downregulated in T19 by 1.41-fold. This result suggests that α-glucan phosphorylase was needed in T17 to support the metabolic pathway. In T19, five DEPs were upregulated, while the other six were downregulated. Based on gene ontology, the DEPs in the stressed plant were associated with cellular and metabolic processes, response to stimulus, binding, catalytic activity, and cellular anatomical entity. Differentially expressed proteins were clustered based on the Kyoto Encyclopedia of Genes and Genomes (KEGG), and sequences were linked to metabolic pathways via enzyme code and KEGG ortholog. Most proteins were involved in photosynthesis, phenylpropanoid biosynthesis, thiamine, and purine metabolism. This study revealed the presence of trans-cinnamate 4-monooxygenase, an intermediate for the biosynthesis of a large number of substances, such as phenylpropanoids and flavonoids.

Plant Tolerance to Drought Stress with Emphasis on Wheat

Environmental stresses, such as drought, have negative effects on crop yield. Drought is a stress whose impact tends to increase in some critical regions. However, the worldwide population is continuously increasing and climate change may affect its food supply in the upcoming years. Therefore, there is an ongoing effort to understand the molecular processes that may contribute to improving drought tolerance of strategic crops. These investigations should contribute to delivering drought-tolerant cultivars by selective breeding. For this reason, it is worthwhile to review regularly the literature concerning the molecular mechanisms and technologies that could facilitate gene pyramiding for drought tolerance. This review summarizes achievements obtained using QTL mapping, genomics, synteny, epigenetics, and transgenics for the selective breeding of drought-tolerant wheat cultivars. Synthetic apomixis combined with the msh1 mutation opens the way to induce and stabilize epigenomes in crops, which offers the potential of accelerating selective breeding for drought tolerance in arid and semi-arid regions.

TSWIFT: Tower Spectrometer on Wheels for Investigating Frequent Timeseries for high-throughput phenotyping of vegetation physiology

BACKGROUND

Remote sensing instruments enable high-throughput phenotyping of plant traits and stress resilience across scale. Spatial (handheld devices, towers, drones, airborne, and satellites) and temporal (continuous or intermittent) tradeoffs can enable or constrain plant science applications. Here, we describe the technical details of TSWIFT (Tower Spectrometer on Wheels for Investigating Frequent Timeseries), a mobile tower-based hyperspectral remote sensing system for continuous monitoring of spectral reflectance across visible-near infrared regions with the capacity to resolve solar-induced fluorescence (SIF).

RESULTS

We demonstrate potential applications for monitoring short-term (diurnal) and long-term (seasonal) variation of vegetation for high-throughput phenotyping applications. We deployed TSWIFT in a field experiment of 300 common bean genotypes in two treatments: control (irrigated) and drought (terminal drought). We evaluated the normalized difference vegetation index (NDVI), photochemical reflectance index (PRI), and SIF, as well as the coefficient of variation (CV) across the visible-near infrared spectral range (400 to 900 nm). NDVI tracked structural variation early in the growing season, following initial plant growth and development. PRI and SIF were more dynamic, exhibiting variation diurnally and seasonally, enabling quantification of genotypic variation in physiological response to drought conditions. Beyond vegetation indices, CV of hyperspectral reflectance showed the most variability across genotypes, treatment, and time in the visible and red-edge spectral regions.

CONCLUSIONS

TSWIFT enables continuous and automated monitoring of hyperspectral reflectance for assessing variation in plant structure and function at high spatial and temporal resolutions for high-throughput phenotyping. Mobile, tower-based systems like this can provide short- and long-term datasets to assess genotypic and/or management responses to the environment, and ultimately enable the spectral prediction of resource-use efficiency, stress resilience, productivity and yield.

Genome-wide association studies of Striga resistance in extra-early maturing quality protein maize inbred lines

Identification of genes associated with Striga resistance is invaluable for accelerating genetic gains in breeding for Striga resistance in maize. We conducted a genome-wide association study to identify genomic regions associated with grain yield and other agronomic traits under artificial Striga field infestation. One hundred and forty-one extra-early quality protein maize inbred lines were phenotyped for key agronomic traits. The inbred lines were also genotyped using 49,185 DArTseq markers from which 8,143 were retained for population structure analysis and genome wide-association study. Cluster analysis and population structure revealed the presence of 3 well-defined genetic groups. Using the mixed linear model, 22 SNP markers were identified to be significantly associated with grain yield, Striga damage at 10 weeks after planting, number of emerged Striga plants at 8 and 10 weeks after planting and ear aspect. The identified SNP markers would be useful for breeders for marker-assisted selection to accelerate the genetic enhancement of maize for Striga resistance in sub-Saharan Africa after validation.

Marker-trait association analyses revealed major novel QTLs for grain yield and related traits in durum wheat

The growing global demand for wheat for food is rising due to the influence of population growth and climate change. The dissection of complex traits by employing a genome-wide association study (GWAS) allows the identification of DNA markers associated with complex traits to improve the productivity of crops. We used GWAS with 10,045 single nucleotide polymorphism (SNP) markers to search for genomic regions associated with grain yield and related traits based on diverse panels of Ethiopian durum wheat. In Ethiopia, multi-environment trials of the genotypes were carried out at five locations. The genotyping was conducted using the 25k Illumina Wheat SNP array to explore population structure, linkage disequilibrium (LD), and marker-trait associations (MTAs). For GWAS, the multi-locus Fixed and Random Model Circulating Probability Unification (FarmCPU) model was applied. Broad-sense heritability estimates were high, ranging from 0.63 (for grain yield) to 0.97 (for thousand-kernel weight). The population structure based on principal component analysis, and model-based cluster analysis revealed two genetically distinct clusters with limited admixtures. The LD among SNPs declined within the range of 2.02-10.04 Mbp with an average of 4.28 Mbp. The GWAS scan based on the mean performance of the genotypes across the environments identified 44 significant MTAs across the chromosomes. Twenty-six of these MTAs are novel, whereas the remaining 18 were previously reported and confirmed in this study. We also identified candidate genes for the novel loci potentially regulating the traits. Hence, this study highlights the significance of the Ethiopian durum wheat gene pool for improving durum wheat globally. Furthermore, a breeding strategy focusing on accumulating favorable alleles at these loci could improve durum wheat production in the East African highlands and elsewhere.

Hyperspectral Remote Sensing for Phenotyping the Physiological Drought Response of Common and Tepary Bean

Proximal remote sensing offers a powerful tool for high-throughput phenotyping of plants for assessing stress response. Bean plants, an important legume for human consumption, are often grown in regions with limited rainfall and irrigation and are therefore bred to further enhance drought tolerance. We assessed physiological (stomatal conductance and predawn and midday leaf water potential) and ground- and tower-based hyperspectral remote sensing (400 to 2,400 nm and 400 to 900 nm, respectively) measurements to evaluate drought response in 12 common bean and 4 tepary bean genotypes across 3 field campaigns (1 predrought and 2 post-drought). Hyperspectral data in partial least squares regression models predicted these physiological traits (R ² = 0.20 to 0.55; root mean square percent error 16% to 31%). Furthermore, ground-based partial least squares regression models successfully ranked genotypic drought responses similar to the physiologically based ranks. This study demonstrates applications of high-resolution hyperspectral remote sensing for predicting plant traits and phenotyping drought response across genotypes for vegetation monitoring and breeding population screening.

Proof of concept and early development stage of market-oriented high iron and zinc rice expressing dicot ferritin and rice nicotianamine synthase genes

Micronutrient deficiencies such as iron (Fe), zinc (Zn), and vitamin A, constitute a severe global public health phenomenon. Over half of preschool children and two-thirds of nonpregnant women of reproductive age worldwide have micronutrient deficiencies. Biofortification is a cost-effective strategy that comprises a meaningful and sustainable means of addressing this issue by delivering micronutrients through staple foods to populations with limited access to diverse diets and other nutritional interventions. Here, we report on the proof-of-concept and early development stage of a collection of biofortified rice events with a high density of Fe and Zn in polished grains that have been pursued further to advance development for product release. In total, eight constructs were developed specifically expressing dicot ferritins and the rice nicotianamine synthase 2 (OsNAS2) gene under different combinations of promoters. A large-scale transformation of these constructs to Bangladesh and Philippines commercial indica cultivars and subsequent molecular screening and confined field evaluations resulted in the identification of a pool of ten events with Fe and Zn concentrations in polished grains of up to 11 μg g-1 and up to 37 μg g-1, respectively. The latter has the potential to reduce the prevalence of inadequate Zn intake for women of childbearing age in Bangladesh and in the Philippines by 30% and 50%, respectively, compared to the current prevalence. To our knowledge, this is the first potential biotechnology public-sector product that adopts the product cycle phase-gated approach, routinely applied in the private sector.

Potential abiotic stress targets for modern genetic manipulation

Research into crop yield and resilience has underpinned global food security, evident in yields tripling in the past 5 decades. The challenges that global agriculture now faces are not just to feed 10+ billion people within a generation, but to do so under a harsher, more variable, and less predictable climate, and in many cases with less water, more expensive inputs, and declining soil quality. The challenges of climate change are not simply to breed for a "hotter drier climate," but to enable resilience to floods and droughts and frosts and heat waves, possibly even within a single growing season. How well we prepare for the coming decades of climate variability will depend on our ability to modify current practices, innovate with novel breeding methods, and communicate and work with farming communities to ensure viability and profitability. Here we define how future climates will impact farming systems and growing seasons, thereby identifying the traits and practices needed and including exemplars being implemented and developed. Critically, this review will also consider societal perspectives and public engagement about emerging technologies for climate resilience, with participatory approaches presented as the best approach.

Development of bread wheat (Triticum aestivum L) variety HD3411 following marker-assisted backcross breeding for drought tolerance

Marker-assisted backcross breeding enables selective insertion of targeted traits into the genome to improve yield, quality, and stress resistance in wheat. In the current investigation, we transferred four drought tolerance quantitative trait loci (QTLs) controlling traits, viz canopy temperature, normalized difference vegetative index, chlorophyll content, and grain yield from the drought-tolerant donor line, C306, into a popular high-yielding, drought-sensitive variety, HD2733. Marker-assisted selection coupled with stringent phenotypic screening was used to advance each generation. This study resulted in 23 improved lines carrying combinations of four drought tolerance QTLs with a range of 85.35%-95.79% background recovery. The backcross-derived lines gave a higher yield under moisture-deficit stress conditions compared with the recipient parent. They also showed higher phenotypic mean values for physiological traits and stability characteristics of HD2733. A promising genotype, HD3411, derived from this cross was identified for release after national multi-location coordinating trials under the All India Coordinated Wheat Improvement Project. Our study is a prime example of the advantages of precision breeding using integrating markers and phenotypic selection to develop new cultivars with desirable traits like drought tolerance.

Environment-oriented selection criteria to overcome controversies in breeding for drought resistance in wheat

Wheat is one of the most important cereal crops, representing a fundamental source of calories and protein for the global human population. Drought stress (DS) is a widespread phenomenon, already affecting large wheat-growing areas worldwide, and a major threat for cereal productivity, resulting in consistent losses in average grain yield (GY). Climate change is projected to exacerbate DS incidence and severity by increasing temperatures and changing rainfall patterns. Estimating that wheat production has to substantially increase to guarantee food security to a demographically expanding human population, the need for breeding programs focused on improving wheat drought resistance is manifest. Drought occurrence, in terms of time of appearance, duration, frequency, and severity, along the plant's life cycle varies significantly among different environments and different agricultural years, making it difficult to identify reliable phenological, morphological, and functional traits to be used as effective breeding tools. The situation is further complicated by the presence of confounding factors, e.g., other concomitant abiotic stresses, in an open-field context. Consequently, the relationship between morpho-functional traits and GY under water deficit is often contradictory; moreover, controversies have emerged not only on which traits are to be preferred, but also on how one specific trait should be desired. In this review, we attempt to identify the possible causes of these disputes and propose the most suitable selection criteria in different target environments and, thus, the best trait combinations for breeders in different drought contexts. In fact, an environment-oriented approach could be a valuable solution to overcome controversies in identifying the proper selection criteria for improving wheat drought resistance.

Potato (Solanum tuberosum L.) Plant Shoot and Root Changes under Abiotic Stresses-Yield Response

During the growing season, potato plants are often exposed to soil drought, frequently accompanied by heat stress, which results in crop losses. In our experiment, the impact of these stresses, both separately and simultaneously, on the above-ground, on the root, and on the tuber mass was assessed. Four potato cultivars were tested. In vitro plants were planted in plastic tubes. Four treatments were used: control-optimal irrigation and temperature (22/18 °C), drought stress, high temperature stress (38/25 °C), and drought and high temperature stresses combined. The stresses were applied for two weeks during the tuberization phase. Both stresses caused changes in plant morphology. Drought stress had a greater impact on these changes than high temperatures. The biggest changes, however, took place when both stresses were applied simultaneously. Under all stresses, a decrease in tuber yield was found. The largest decrease was recorded in the case of applying both stresses simultaneously, while the smallest one was in the case of high temperature stress in relation to a control condition. Among the morphological parameters studied, the mass of the root system and its share in the entire biomass of the plant had the greatest impact on the decrease in yield. This mainly concerned drought stress.

PEG-induced physiological drought for screening winter wheat genotypes sensitivity - integrated biochemical and chlorophyll a fluorescence analysis

This study aimed to screen different winter wheat genotypes at the onset of metabolic changes induced by water deficit to comprehend possible adaptive features of photosynthetic apparatus function and structure to physiological drought. The drought treatment was the most influential variable affecting plant growth and relative water content, and genotype variability determined with what intensity varieties of winter wheat seedlings responded to water deficit. PEG-induced drought, as expected, changed phenomenological energy fluxes and the efficiency with which an electron is transferred to final PSI acceptors. Based on the effect size, fluorescence parameters were grouped to represent photochemical parameters, that is, the donor and acceptor side of PSII (PC1); the thermal phase of the photosynthetic process, or the electron flow around PSI, and the chain of electrons between PSII and PSI (PC2); and phenomenological energy fluxes per cross-section (PC3). Furthermore, four distinct clusters of genotypes were discerned based on their response to imposed physiological drought, and integrated analysis enabled an explanation of their reactions' specificity. The most reliable JIP-test parameters for detecting and comparing the drought impact among tested genotypes were the variable fluorescence at K, L, I step, and PI_TOT. To conclude, developing and improving screening methods for identifying and evaluating functional relationships of relevant characteristics that are useful for acclimation, acclimatization, and adaptation to different types of drought stress can contribute to the progress in breeding research of winter wheat drought-tolerant lines.

Leaf Count Aided Novel Framework for Rice (Oryza sativa L.) Genotypes Discrimination in Phenomics: Leveraging Computer Vision and Deep Learning Applications

Drought is a detrimental factor to gaining higher yields in rice (Oryza sativa L.), especially amid the rising occurrence of drought across the globe. To combat this situation, it is essential to develop novel drought-resilient varieties. Therefore, screening of drought-adaptive genotypes is required with high precision and high throughput. In contemporary emerging science, high throughput plant phenotyping (HTPP) is a crucial technology that attempts to break the bottleneck of traditional phenotyping. In traditional phenotyping, screening significant genotypes is a tedious task and prone to human error while measuring various plant traits. In contrast, owing to the potential advantage of HTPP over traditional phenotyping, image-based traits, also known as i-traits, were used in our study to discriminate 110 genotypes grown for genome-wide association study experiments under controlled (well-watered), and drought-stress (limited water) conditions, under a phenomics experiment in a controlled environment with RGB images. Our proposed framework non-destructively estimated drought-adaptive plant traits from the images, such as the number of leaves, convex hull, plant-aspect ratio (plant spread), and similarly associated geometrical and morphological traits for analyzing and discriminating genotypes. The results showed that a single trait, the number of leaves, can also be used for discriminating genotypes. This critical drought-adaptive trait was associated with plant size, architecture, and biomass. In this work, the number of leaves and other characteristics were estimated non-destructively from top view images of the rice plant for each genotype. The estimation of the number of leaves for each rice plant was conducted with the deep learning model, YOLO (You Only Look Once). The leaves were counted by detecting corresponding visible leaf tips in the rice plant. The detection accuracy was 86-92% for dense to moderate spread large plants, and 98% for sparse spread small plants. With this framework, the susceptible genotypes (MTU1010, PUSA-1121 and similar genotypes) and drought-resistant genotypes (Heera, Anjali, Dular and similar genotypes) were grouped in the core set with a respective group of drought-susceptible and drought-tolerant genotypes based on the number of leaves, and the leaves' emergence during the peak drought-stress period. Moreover, it was found that the number of leaves was significantly associated with other pertinent morphological, physiological and geometrical traits. Other geometrical traits were measured from the RGB images with the help of computer vision.

Productivity and Feed Quality Performance of Napier Grass (Cenchrus purpureus) Genotypes Growing under Different Soil Moisture Levels

In the semi-arid and arid environments of Sub-Sharan Africa, forage availability throughout the year is insufficient and highly limited during the dry seasons due to limited precipitation. Thus, the identification of drought stress-tolerant forage cultivars is one of the main activities in forage development programs. In this study, Napier grass (Cenchrus purpureus), an important forage crop in Eastern and Central Africa that is broadly adapted to produce across tropical environments, was evaluated for its water use efficiency and production performance under field drought stress conditions. Eighty-four Napier grass genotypes were evaluated for their drought stress tolerance from 2018 to 2020 using agro-morphological and feed quality traits under two soil moisture stress regimes during the dry season, i.e., moderate (MWS) and severe (SWS) water stress conditions, and under rainfed conditions in the wet season (wet). Overall, the results indicated the existence of genotype variation for the traits studied. In general, the growth and productivity of the genotypes declined under SWS compared to MWS conditions. High biomass-yielding genotypes with enhanced WUE were consistently observed across harvests in each soil moisture stress regime. In addition, the top biomass-yielding genotypes produced the highest annual crude protein yield, indicating the possibility of developing high-feed-quality Napier grass genotypes for drought stress environments.

Natural variation further increases resilience of sorghum bred for chronically drought-prone environments

Drought stress is one of the major constraints for crop production in the Sahel region of Africa. Here, we explore the potential to use natural genetic variation to build on the inherent drought tolerance of an elite sorghum cultivar, Teshale, that has been bred for Ethiopian conditions including chronic drought. We evaluated a backcross nested-association mapping population using 12 diverse founder lines crossed with Teshale under three drought-prone environments in Ethiopia. All 12 populations averaged higher head exsertion and lower leaf senescence than the recurrent parent in the two most stressful environments, reflecting new drought resilience mechanisms from the donors. A total of 154 quantitative trait loci (QTLs) were detected for eight drought-responsive traits, and their validity was supported by the fact that 113 (73.4%) overlapped with QTLs previously detected for the same traits, concentrated in regions previously associated with 'stay-green' traits. Allele effects showed that some favourable alleles are already present in the Ethiopian cultivar; however, the exotic donors offer rich scope for increasing drought resilience. Using model-selected SNPs associated with the eight traits identified in this study and three in a companion study, phenotypic prediction accuracies for grain yield were equivalent to genome-wide SNPs and were significantly better than random SNPs, indicating that the selected traits are predictive of sorghum grain yield.

Comparing gradient boosting machine and Bayesian threshold BLUP for genome-based prediction of categorical traits in wheat breeding

Genomic selection (GS) is a predictive methodology that is changing plant breeding. Genomic selection trains a statistical machine-learning model using available phenotypic and genotypic data with which predictions are performed for individuals that were only genotyped. For this reason, some statistical machine-learning methods are being implemented in GS, but in order to improve the selection of new genotypes early in the prediction process, the exploration of new statistical machine-learning algorithms must continue. In this paper, we performed a benchmarking study between the Bayesian threshold genomic best linear unbiased predictor model (TGBLUP; popular in GS) and the gradient boosting machine (GBM). This comparison was done using four real wheat (Triticum aestivum L.) data sets with categorical traits measured in terms of two metrics: the proportion of cases correctly classified (PCCC) and the Kappa coefficient in the testing set. Under 10 random partitions with four different sizes of testing proportions (20, 40, 60, and 80%), we compared the two algorithms and found that in three of the four data sets, the GBM outperformed the TGBLUP model in terms of both metrics (PCCC and Kappa coefficient). In the larger data sets (Data Sets 3 and 4), the gain in terms of prediction accuracy of the GBM was considerably significant. For this reason, we encourage more research using the GBM in GS to evaluate its virtues in terms of prediction performance in the context of GS.

A comparative analysis of genomic and phenomic predictions of growth-related traits in 3-way coffee hybrids

Genomic prediction has revolutionized crop breeding despite remaining issues of transferability of models to unseen environmental conditions and environments. Usage of endophenotypes rather than genomic markers leads to the possibility of building phenomic prediction models that can account, in part, for this challenge. Here, we compare and contrast genomic prediction and phenomic prediction models for 3 growth-related traits, namely, leaf count, tree height, and trunk diameter, from 2 coffee 3-way hybrid populations exposed to a series of treatment-inducing environmental conditions. The models are based on 7 different statistical methods built with genomic markers and ChlF data used as predictors. This comparative analysis demonstrates that the best-performing phenomic prediction models show higher predictability than the best genomic prediction models for the considered traits and environments in the vast majority of comparisons within 3-way hybrid populations. In addition, we show that phenomic prediction models are transferrable between conditions but to a lower extent between populations and we conclude that chlorophyll a fluorescence data can serve as alternative predictors in statistical models of coffee hybrid performance. Future directions will explore their combination with other endophenotypes to further improve the prediction of growth-related traits for crops.

Integrated Approach in Genomic Selection to Accelerate Genetic Gain in Sugarcane

Marker-assisted selection (MAS) has been widely used in the last few decades in plant breeding programs for the mapping and introgression of genes for economically important traits, which has enabled the development of a number of superior cultivars in different crops. In sugarcane, which is the most important source for sugar and bioethanol, marker development work was initiated long ago; however, marker-assisted breeding in sugarcane has been lagging, mainly due to its large complex genome, high levels of polyploidy and heterozygosity, varied number of chromosomes, and use of low/medium-density markers. Genomic selection (GS) is a proven technology in animal breeding and has recently been incorporated in plant breeding programs. GS is a potential tool for the rapid selection of superior genotypes and accelerating breeding cycle. However, its full potential could be realized by an integrated approach combining high-throughput phenotyping, genotyping, machine learning, and speed breeding with genomic selection. For better understanding of GS integration, we comprehensively discuss the concept of genetic gain through the breeder’s equation, GS methodology, prediction models, current status of GS in sugarcane, challenges of prediction accuracy, challenges of GS in sugarcane, integrated GS, high-throughput phenotyping (HTP), high-throughput genotyping (HTG), machine learning, and speed breeding followed by its prospective applications in sugarcane improvement.

Utilization of Spectral Indices for High-Throughput Phenotyping

The conventional plant breeding evaluation of large sets of plant phenotypes with precision and speed is very challenging. Thus, consistent, automated, multifaceted, and high-throughput phenotyping (HTP) technologies are becoming increasingly significant as tools to aid conventional breeding programs to develop genetically improved crops. With rapid technological advancement, various vegetation indices (VIs) have been developed. These VI-based imaging approaches, linked with artificial intelligence and a variety of remote sensing applications, provide high-throughput evaluations, particularly in the field of precision agriculture. VIs can be used to analyze and predict different quantitative and qualitative aspects of vegetation. Here, we provide an overview of the various VIs used in agricultural research, focusing on those that are often employed for crop or vegetation evaluation, because that has a linear relationship to crop output, which is frequently utilized in crop chlorophyll, health, moisture, and production predictions. In addition, the following aspects are here described: the importance of VIs in crop research and precision agriculture, their utilization in HTP, recent photogrammetry technology, mapping, and geographic information system software integrated with unmanned aerial vehicles and its key features. Finally, we discuss the challenges and future perspectives of HTP technologies and propose approaches for the development of new tools to assess plants' agronomic traits and data-driven HTP resolutions for precision breeding.

OsMLP423 Is a Positive Regulator of Tolerance to Drought and Salt Stresses in Rice

Rice (Oryza sativa L.) is one of the main food crops for human survival, and its yield is often restricted by abiotic stresses. Drought and soil salinity are among the most damaging abiotic stresses affecting today's agriculture. Given the importance of abscisic acid (ABA) in plant growth and abiotic stress responses, it is very important to identify new genes involved in ABA signal transduction. We screened a drought-inducing gene containing about 158 amino acid residues from the transcriptome library of rice exposed to drought treatment, and we found ABA-related cis-acting elements and multiple drought-stress-related cis-acting elements in its promoter sequence. The results of real-time PCR showed that OsMLP423 was strongly induced by drought and salt stresses. The physiological and biochemical phenotype analysis of transgenic plants confirmed that overexpression of OsMLP423 enhanced the tolerance to drought and salt stresses in rice. The expression of OsMLP423-GFP fusion protein indicated that OsMLP423 was located in both the cell membrane system and nucleus. Compared with the wild type, the overexpressed OsMLP423 showed enhanced sensitivity to ABA. Physiological analyses showed that the overexpression of OsMLP423 may regulate the water loss efficiency and ABA-responsive gene expression of rice plants under drought and salt stresses, and it reduces membrane damage and the accumulation of reactive oxygen species. These results indicate that OsMLP423 is a positive regulator of drought and salinity tolerance in rice, governing the tolerance of rice to abiotic stresses through an ABA-dependent pathway. Therefore, this study provides a new insight into the physiological and molecular mechanisms of OsMLP423-mediated ABA signal transduction participating in drought and salt stresses.

Genome-wide association studies identify putative pleiotropic locus mediating drought tolerance in sorghum

Drought is a key constraint on plant productivity and threat to food security. Sorghum (Sorghum bicolor L. Moench), a global staple food and forage crop, is among the most drought-adapted cereal crops, but its adaptation is not yet well understood. This study aims to better understand the genetic basis of preflowering drought in sorghum and identify loci underlying variation in water use and yield components under drought. A panel of 219 diverse sorghum from West Africa was phenotyped for yield components and water use in an outdoor large-tube lysimeter system under well-watered (WW) versus a preflowering drought water-stressed (WS) treatment. The experimental system was validated based on characteristic drought response in international drought tolerant check genotypes and genome-wide association studies (GWAS) that mapped the major height locus at QHT7.1 and Dw3. GWAS further identified marker trait associations (MTAs) for drought-related traits (plant height, flowering time, forage biomass, grain weight, water use) that each explained 7-70% of phenotypic variance. Most MTAs for drought-related traits correspond to loci not previously reported, but some MTA for forage biomass and grain weight under WS co-localized with staygreen post-flowering drought tolerance loci (Stg3a and Stg4). A globally common allele at S7_50055849 is associated with several yield components under drought, suggesting that it tags a major pleiotropic variant controlling assimilate partitioning to grain versus vegetative biomass. The GWAS revealed oligogenic variants for drought tolerance in sorghum landraces, which could be used as trait predictive markers for improved drought adaptation.

Meta-QTL analysis and candidate genes identification for various abiotic stresses in maize (Zea mays L.) and their implications in breeding programs

Global climate change leads to the concurrence of a number of abiotic stresses including moisture stress (drought, waterlogging), temperature stress (heat, cold), and salinity stress, which are the major factors affecting maize production. To develop abiotic stress tolerance in maize, many quantitative trait loci (QTL) have been identified, but very few of them have been utilized successfully in breeding programs. In this context, the meta-QTL analysis of the reported QTL will enable the identification of stable/real QTL which will pave a reliable way to introgress these QTL into elite cultivars through marker-assisted selection. In this study, a total of 542 QTL were summarized from 33 published studies for tolerance to different abiotic stresses in maize to conduct meta-QTL analysis using BiomercatorV4.2.3. Among those, only 244 major QTL with more than 10% phenotypic variance were preferably utilised to carry out meta-QTL analysis. In total, 32 meta-QTL possessing 1907 candidate genes were detected for different abiotic stresses over diverse genetic and environmental backgrounds. The MQTL2.1, 5.1, 5.2, 5.6, 7.1, 9.1, and 9.2 control different stress-related traits for combined abiotic stress tolerance. The candidate genes for important transcription factor families such as ERF, MYB, bZIP, bHLH, NAC, LRR, ZF, MAPK, HSP, peroxidase, and WRKY have been detected for different stress tolerances. The identified meta-QTL are valuable for future climate-resilient maize breeding programs and functional validation of candidate genes studies, which will help to deepen our understanding of the complexity of these abiotic stresses.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-022-01294-9.

Detection of rare nematode resistance Rhg1 haplotypes in Glycine soja and a novel Rhg1 α-SNAP

This study pursued the hypothesis that wild plant germplasm accessions carrying alleles of interest can be identified using available single nucleotide polymorphism (SNP) genotypes for particular alleles of other (unlinked) genes that contribute to the trait of interest. The soybean cyst nematode (SCN, Heterodera glycines [HG]) resistance locus Rhg1 is widely used in farmed soybean [Glycine max (L.) Merr.]. The two known resistance-conferring haplotypes, rhg1-a and rhg1-b, typically contain three or seven to 10 tandemly duplicated Rhg1 segments, respectively. Each Rhg1 repeat carries four genes, including Glyma.18G022500, which encodes unusual isoforms of the vesicle-trafficking chaperone α-SNAP. Using SoySNP50K data for NSFRAN07 allele presence, we discovered a new Rhg1 haplotype, rhg1-ds, in six accessions of wild soybean, Glycine soja Siebold & Zucc. (0.5% of the ∼1,100 G. soja accessions in the USDA collection). The α-SNAP encoded by rhg1-ds is unique at an important site of amino acid variation and shares with the rhg1-a and rhg1-b α-SNAP proteins the traits of cytotoxicity and altered N-ethylmaleimide sensitive factor (NSF) protein interaction. Copy number assays indicate three repeats of rhg1-ds. G. soja PI 507613 and PI 507623 exhibit resistance to HG type 2.5.7 SCN populations, in part because of contributions from other loci. In a segregating F2 population, rhg1-b and rhg1-ds made statistically indistinguishable contributions to resistance to a partially virulent HG type 2.5.7 SCN population. Hence, the unusual multigene copy number variation Rhg1 haplotype was present but rare in ancestral G. soja and was present in accessions that offer multiple traits for SCN resistance breeding. The accessions were initially identified for study based on an unlinked SNP.

Genome Wide Association Study Uncovers the QTLome for Osmotic Adjustment and Related Drought Adaptive Traits in Durum Wheat

Osmotic adjustment (OA) is a major component of drought resistance in crops. The genetic basis of OA in wheat and other crops remains largely unknown. In this study, 248 field-grown durum wheat elite accessions grown under well-watered conditions, underwent a progressively severe drought treatment started at heading. Leaf samples were collected at heading and 17 days later. The following traits were considered: flowering time (FT), leaf relative water content (RWC), osmotic potential (ψs), OA, chlorophyll content (SPAD), and leaf rolling (LR). The high variability (3.89-fold) in OA among drought-stressed accessions resulted in high repeatability of the trait (h² = 72.3%). Notably, a high positive correlation (r = 0.78) between OA and RWC was found under severe drought conditions. A genome-wide association study (GWAS) revealed 15 significant QTLs (Quantitative Trait Loci) for OA (global R² = 63.6%), as well as eight major QTL hotspots/clusters on chromosome arms 1BL, 2BL, 4AL, 5AL, 6AL, 6BL, and 7BS, where a higher OA capacity was positively associated with RWC and/or SPAD, and negatively with LR, indicating a beneficial effect of OA on the water status of the plant. The comparative analysis with the results of 15 previous field trials conducted under varying water regimes showed concurrent effects of five OA QTL cluster hotspots on normalized difference vegetation index (NDVI), thousand-kernel weight (TKW), and/or grain yield (GY). Gene content analysis of the cluster regions revealed the presence of several candidate genes, including bidirectional sugar transporter SWEET, rhomboid-like protein, and S-adenosyl-L-methionine-dependent methyltransferases superfamily protein, as well as DREB1. Our results support OA as a valuable proxy for marker-assisted selection (MAS) aimed at enhancing drought resistance in wheat.

Drought Tolerant Near Isogenic Lines of Pusa 44 Pyramided With qDTY2.1 and qDTY3.1, Show Accelerated Recovery Response in a High Throughput Phenomics Based Phenotyping

Reproductive stage drought stress (RSDS) is a major challenge in rice production worldwide. Cultivar development with drought tolerance has been slow due to the lack of precise high throughput phenotyping tools to quantify drought stress-induced effects. Most of the available techniques are based on destructive sampling and do not assess the progress of the plant's response to drought. In this study, we have used state-of-the-art image-based phenotyping in a phenomics platform that offers a controlled environment, non-invasive phenotyping, high accuracy, speed, and continuity. In rice, several quantitative trait loci (QTLs) which govern grain yield under drought determine RSDS tolerance. Among these, qDTY2.1 and qDTY3.1 were used for marker-assisted breeding. A set of 35 near-isogenic lines (NILs), introgressed with these QTLs in the popular variety, Pusa 44 were used to assess the efficiency of image-based phenotyping for RSDS tolerance. NILs offered the most reliable contrast since they differed from Pusa 44 only for the QTLs. Four traits, namely, the projected shoot area (PSA), water use (WU), transpiration rate (TR), and red-green-blue (RGB) and near-infrared (NIR) values were used. Differential temporal responses could be seen under drought, but not under unstressed conditions. NILs showed significant level of RSDS tolerance as compared to Pusa 44. Among the traits, PSA showed strong association with yield (80%) as well as with two drought tolerances indices, stress susceptibility index (SSI) and tolerance index (TOL), establishing its ability in identifying the best drought tolerant NILs. The results revealed that the introgression of QTLs helped minimize the mean WU per unit of biomass per day, suggesting the potential role of these QTLs in improving WU-efficiency (WUE). We identified 11 NILs based on phenomics traits as well as performance under imposed drought in the field. The study emphasizes the use of phenomics traits as selection criteria for RSDS tolerance at an early stage, and is the first report of using phenomics parameters in RSDS selection in rice.

Determination of morpho-physiological and yield traits of maize inbred lines (Zea mays L.) under optimal and drought stress conditions

Globally, climate change could hinder future food security that concurrently implies the importance of investigating drought stress and genotype screening under stressed environments. Hence, the current study was performed to screen 45 diverse maize inbred lines for 18 studied traits comprising phenological, physiological, morphological, and yield characters under optimum and water stress conditions for two successive growing seasons (2018 and 2019). The results showed that growing seasons and water regimes significantly influenced (p < 0.01) most of the studied traits, while inbred lines had a significant effect (p < 0.01) on all of the studied traits. The findings also showed a significant increase in all studied characters under normal conditions compared to drought conditions, except chlorophyll content, transpiration rate, and proline content which exhibited higher levels under water stress conditions. Furthermore, the results of the principal component analysis indicated a notable distinction between the performance of the 45 maize inbred lines under normal and drought conditions. In terms of grain yield, the drought tolerance index (DTI) showed that Nub60 (1.56), followed by Nub32 (1.46), Nub66 (1.45), and GZ603 (1.44) were the highest drought-tolerant inbred lines, whereas Nub46 (0.38) was the lowest drought-tolerant inbred line. These drought-tolerant inbred lines were able to maintain a relatively high grain yield under normal and stress conditions, whereas those drought-sensitive inbred lines showed a decline in grain yield when exposed to drought conditions. The hierarchical clustering analysis based on DTI classified the forty-five maize inbred lines and eighteen measured traits into three column- and row-clusters, as inbred lines in cluster-3 followed by those in cluster-2 exhibited greater drought tolerance in most of the studied traits. Utilizing the multi-trait stability index (MTSI) criterion in this study identified nine inbred lines, including GZ603, as stable genotypes in terms of the eighteen studied traits across four environments. The findings of the current investigation motivate plant breeders to explore the genetic potential of the current maize germplasm, especially in water-stressed environments.

Optical Imaging Resources for Crop Phenotyping and Stress Detection

With a rapidly increasing population, diminishing resource availability, and variation in environment, there is a need to change agricultural production to deliver long-term food security. To deliver such change, we need crops that are productive and tolerant to different stress factors. The traditional methods of obtaining data for phenotyping under field conditions, e.g., for morphological traits such as canopy structure or physiological traits such as plant stress-related traits, are laborious and time-consuming. A variety of imaging tools in the visible, spectral, and thermal infrared ranges allow data collection for quantitative studies of complex traits and crop monitoring. These tools can be used on crop phenotyping and monitoring platforms for high-throughput assessment of traits in order to better understand plant stress responses and the physiological pathways underlying yield. The applications and brief review of these imaging techniques are described and discussed in this chapter.

Recent Advances for Drought Stress Tolerance in Maize (Zea mays L.): Present Status and Future Prospects

Drought stress has severely hampered maize production, affecting the livelihood and economics of millions of people worldwide. In the future, as a result of climate change, unpredictable weather events will become more frequent hence the implementation of adaptive strategies will be inevitable. Through utilizing different genetic and breeding approaches, efforts are in progress to develop the drought tolerance in maize. The recent approaches of genomics-assisted breeding, transcriptomics, proteomics, transgenics, and genome editing have fast-tracked enhancement for drought stress tolerance under laboratory and field conditions. Drought stress tolerance in maize could be considerably improved by combining omics technologies with novel breeding methods and high-throughput phenotyping (HTP). This review focuses on maize responses against drought, as well as novel breeding and system biology approaches applied to better understand drought tolerance mechanisms and the development of drought-tolerant maize cultivars. Researchers must disentangle the molecular and physiological bases of drought tolerance features in order to increase maize yield. Therefore, the integrated investments in field-based HTP, system biology, and sophisticated breeding methodologies are expected to help increase and stabilize maize production in the face of climate change.

High-Throughput Profiling of Metabolic Phenotypes Using High-Resolution GC-MS

Metabolite profiling provides insights into the metabolic signatures, which themselves are considered as phonotypes closely related to the agronomic and phenotypic traits such as yield, nutritional values, stress resistance, and nutrient use efficiency. GC-MS is a sensitive and high-throughput analytical platform and has been proved to be a vital tool for the analysis of primary metabolism to provide an overview of cellular and organismal metabolic status. The potential of GC-MS metabolite profiling as a tool for detecting metabolic changes in plants grown in a high-throughput plant phenotyping platform was explored. In this chapter, we describe an integrated workflow of semi-targeted GC-high-resolution (HR)-time-of-flight (TOF)-MS metabolomics with both the analytical and computational steps, focusing mainly on the sample preparation, GC-HR-TOF-MS analysis part, and data analysis for plant phenotyping efforts.

A New Deep Learning Calibration Method Enhances Genome-Based Prediction of Continuous Crop Traits

Genomic selection (GS) has the potential to revolutionize predictive plant breeding. A reference population is phenotyped and genotyped to train a statistical model that is used to perform genome-enabled predictions of new individuals that were only genotyped. In this vein, deep neural networks, are a type of machine learning model and have been widely adopted for use in GS studies, as they are not parametric methods, making them more adept at capturing nonlinear patterns. However, the training process for deep neural networks is very challenging due to the numerous hyper-parameters that need to be tuned, especially when imperfect tuning can result in biased predictions. In this paper we propose a simple method for calibrating (adjusting) the prediction of continuous response variables resulting from deep learning applications. We evaluated the proposed deep learning calibration method (DL_M2) using four crop breeding data sets and its performance was compared with the standard deep learning method (DL_M1), as well as the standard genomic Best Linear Unbiased Predictor (GBLUP). While the GBLUP was the most accurate model overall, the proposed deep learning calibration method (DL_M2) helped increase the genome-enabled prediction performance in all data sets when compared with the traditional DL method (DL_M1). Taken together, we provide evidence for extending the use of the proposed calibration method to evaluate its potential and consistency for predicting performance in the context of GS applied to plant breeding.

Formation and Development of Taproots in Deciduous Tree Species

Trees are generally long-lived and are therefore exposed to numerous episodes of external stimuli and adverse environmental conditions. In certain trees e.g., oaks, taproots evolved to increase the tree's ability to acquire water from deeper soil layers. Despite the significant role of taproots, little is known about the growth regulation through internal factors (genes, phytohormones, and micro-RNAs), regulating taproot formation and growth, or the effect of external factors, e.g., drought. The interaction of internal and external stimuli, involving complex signaling pathways, regulates taproot growth during tip formation and the regulation of cell division in the root apical meristem (RAM). Assuming that the RAM is the primary regulatory center responsible for taproot growth, factors affecting the RAM function provide fundamental information on the mechanisms affecting taproot development.

Assay system for mesocotyl elongation and hydrotropism of maize primary root in response to low moisture gradient

We designed and validated a test system that simulates a growth environment for Zea mays L. maize seedlings under conditions of low moisture gradient in darkness. This system allowed us to simultaneously measure mesocotyl elongation and the primary root hydrotropic response in seedlings before the emergence phase in a collection of maize hybrids. We found great variation in these two traits with statistically significant reduction of their elongations under the low moisture gradient condition that indicate the richness of maize genetic diversity. Hence, the objective of designing a new test system that evaluates the association between these underground traits with the potential use to measure other traits in maize seedlings related to early vigor was achieved.

Genomic prediction and QTL mapping of root system architecture and above-ground agronomic traits in rice (Oryza sativa L.) with a multitrait index and Bayesian networks

Root system architecture (RSA) is a crucial factor in resource acquisition and plant productivity. Roots are difficult to phenotype in the field, thus new tools for predicting phenotype from genotype are particularly valuable for plant breeders aiming to improve RSA. This study identifies quantitative trait loci (QTLs) for RSA and agronomic traits in a rice (Oryza sativa) recombinant inbred line (RIL) population derived from parents with contrasting RSA traits (PI312777 × Katy). The lines were phenotyped for agronomic traits in the field, and separately grown as seedlings on agar plates which were imaged to extract RSA trait measurements. QTLs were discovered from conventional linkage analysis and from a machine learning approach using a Bayesian network (BN) consisting of genome-wide SNP data and phenotypic data. The genomic prediction abilities (GPAs) of multi-QTL models and the BN analysis were compared with the several standard genomic prediction (GP) methods. We found GPAs were improved using multitrait (BN) compared to single trait GP in traits with low to moderate heritability. Two groups of individuals were selected based on GPs and a modified rank sum index (GSRI) indicating their divergence across multiple RSA traits. Selections made on GPs did result in differences between the group means for numerous RSA. The ranking accuracy across RSA traits among the individual selected RILs ranged from 0.14 for root volume to 0.59 for lateral root tips. We conclude that the multitrait GP model using BN can in some cases improve the GPA of RSA and agronomic traits, and the GSRI approach is useful to simultaneously select for a desired set of RSA traits in a segregating population.

High-throughput field phenotyping reveals genetic variation in photosynthetic traits in durum wheat under drought

Chlorophyll fluorescence (ChlF) is a powerful non-invasive technique for probing photosynthesis. Although proposed as a method for drought tolerance screening, ChlF has not yet been fully adopted in physiological breeding, mainly due to limitations in high-throughput field phenotyping capabilities. The light-induced fluorescence transient (LIFT) sensor has recently been shown to reliably provide active ChlF data for rapid and remote characterisation of plant photosynthetic performance. We used the LIFT sensor to quantify photosynthesis traits across time in a large panel of durum wheat genotypes subjected to a progressive drought in replicated field trials over two growing seasons. The photosynthetic performance was measured at the canopy level by means of the operating efficiency of Photosystem II ( Fq'/Fm' ) and the kinetics of electron transport measured by reoxidation rates ( Fr1' and Fr2' ). Short- and long-term changes in ChlF traits were found in response to soil water availability and due to interactions with weather fluctuations. In mild drought, Fq'/Fm' and Fr2' were little affected, while Fr1' was consistently accelerated in water-limited compared to well-watered plants, increasingly so with rising vapour pressure deficit. This high-throughput approach allowed assessment of the native genetic diversity in ChlF traits while considering the diurnal dynamics of photosynthesis.

Automated stomata detection in oil palm with convolutional neural network

Stomatal density is an important trait for breeding selection of drought tolerant oil palms; however, its measurement is extremely tedious. To accelerate this process, we developed an automated system. Leaf samples from 128 palms ranging from nursery (1 years old), juvenile (2–3 years old) and mature (> 10 years old) were collected to build an oil palm specific stomata detection model. Micrographs were split into tiles, then used to train a stomata object detection convolutional neural network model through transfer learning. The detection model was then tested on leaf samples acquired from three independent oil palm populations of young seedlings (A), juveniles (B) and productive adults (C). The detection accuracy, measured in precision and recall, was 98.00% and 99.50% for set A, 99.70% and 97.65% for set B, and 99.55% and 99.62% for set C, respectively. The detection model was cross-applied to another set of adult palms using stomata images taken with a different microscope and under different conditions (D), resulting in precision and recall accuracy of 99.72% and 96.88%, respectively. This indicates that the model built generalized well, in addition has high transferability. With the completion of this detection model, stomatal density measurement can be accelerated. This in turn will accelerate the breeding selection for drought tolerance.