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> Phenomena > Biologic Function > Drought Tolerance

Drought Tolerance

Drought tolerance refers to the ability of plants to withstand periods of low water availability and maintain growth and productivity.
This trait is crucial for crop resilience and food security, especially in regions impacted by climate change.
Researchers can leverage PubCompare.ai to enhance their drought tolerance studies by identifying the most effective research protocols across scientific literature, preprints, and patents.
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Most cited protocols related to «Drought Tolerance»

1
Comprehensive Plant Trait Database (TRY)
The TRY data compilation focuses on 52 groups of traits characterizing the vegetative and regeneration stages of plant life cycle, including growth, reproduction, dispersal, establishment and persistence (Table 2). These groups of traits were collectively agreed to be the most relevant for plant life-history strategies, vegetation modelling and global change responses on the basis of existing shortlists (Grime et al., 1997 ; Weiher et al., 1999 ; Lavorel & Garnier, 2002 ; Cornelissen et al., 2003b; Díaz et al., 2004 ; Kleyer et al., 2008 ) and wide consultation with vegetation modellers and plant ecologists. They include plant traits sensu stricto, but also ‘performances’ (sensuViolle et al., 2007 ), such as drought tolerance or phenology.
Quantitative traits vary within species as a consequence of genetic variation (among genotypes within a population/species) and phenotypic plasticity. Ancillary information is necessary to understand and quantify this variation. The TRY dataset contains information about the location (e.g. geographical coordinates, soil characteristics), environmental conditions during plant growth (e.g. climate of natural environment or experimental treatment), and information about measurement methods and conditions (e.g. temperature during respiration or photosynthesis measurements). Ancillary data also include primary references.
By preference individual measurements are compiled in the database, like single respiration measurements or the wood density of a specific individual tree. The dataset therefore includes multiple measurements for the same trait, species and site. For some traits, e.g. leaf longevity, such data are only rarely available on single individuals (e.g. Reich et al., 2004 ), and data are expressed per species per site instead. Different measurements on the same plant (resp. organ) are linked to form observations that are hierarchically nested. The database structure ensures that (1) the direct relationship between traits and ancillary data and between different traits that have been measured on the same plant (resp. organ) is maintained and (2) conditions (e.g. at the stand level) can be associated with the individual measurements (Kattge et al., 2010 ). The structure is consistent with the Extensible Observation Ontology (OBOE; Madin et al., 2008 (link)), which has been proposed as a general basis for the integration of different data streams in ecology.
The TRY dataset combines several preexisting databases based on a wide range of primary data sources, which include trait data from plants grown in natural environments and under experimental conditions, obtained by a range of scientists with different methods. Trait variation in the TRY dataset therefore reflects natural and potential variation on the basis of individual measurements at the level of single organs, and variation due to different measurement methods and measurement error (random and bias).
Kattge J., Díaz S., Lavorel S., Prentice I.C., Leadley P., Bönisch G., Garnier E., Westoby M., Reich P.B., Wright I.J., Cornelissen J.H., Violle C., Harrison S.P., Van Bodegom P.M., Reichstein M., Enquist B.J., Soudzilovskaia N.A., Ackerly D.D., Anand M., Atkin O., Bahn M., Baker T.R., Baldocchi D., Bekker R., Blanco C.C., Blonder B., Bond W.J., Bradstock R., Bunker D.E., Casanoves F., Cavender-Bares J., Chambers J.Q., Chapin FS I.I.I., Chave J., Coomes D., Cornwell W.K., Craine J.M., Dobrin B.H., Duarte L., Durka W., Elser J., Esser G., Estiarte M., Fagan W.F., Fang J., Fernández-Méndez F., Fidelis A., Finegan B., Flores O., Ford H., Frank D., Freschet G.T., Fyllas N.M., Gallagher R.V., Green W.A., Gutierrez A.G., Hickler T., Higgins S.I., Hodgson J.G., Jalili A., Jansen S., Joly C.A., Kerkhoff A.J., Kirkup D., Kitajima K., Kleyer M., Klotz S., Knops J.M., Kramer K., Kühn I., Kurokawa H., Laughlin D., Lee T.D., Leishman M., Lens F., Lenz T., Lewis S.L., Lloyd J., Llusià J., Louault F., Ma S., Mahecha M.D., Manning P., Massad T., Medlyn B.E., Messier J., Moles A.T., Müller S.C., Nadrowski K., Naeem S., Niinemets Ü., Nöllert S., Nüske A., Ogaya R., Oleksyn J., Onipchenko V.G., Onoda Y., Ordoñez J., Overbeck G., Ozinga W.A., Patiño S., Paula S., Pausas J.G., Peñuelas J., Phillips O.L., Pillar V., Poorter H., Poorter L., Poschlod P., Prinzing A., Proulx R., Rammig A., Reinsch S., Reu B., Sack L., Salgado-Negret B., Sardans J., Shiodera S., Shipley B., Siefert A., Sosinski E., Soussana J.F., Swaine E., Swenson N., Thompson K., Thornton P., Waldram M., Weiher E., White M., White S., Wright S.J., Yguel B., Zaehle S., Zanne A.E, & Wirth C. (2011). TRY – a global database of plant traits. Global Change Biology, 17(9), 2905-2935.
Full text: Click here
Publication 2011
Cell Respiration Climate Drought Tolerance Genetic Diversity Genotype Growth Disorders Life History Strategies Phenotypic Plasticity Photosynthesis Plant Diseases Plant Leaves Plants Regeneration Reproduction Respiratory Rate Therapies, Investigational Trees
2
Evaluating Stress Tolerance in Arabidopsis Seedlings
One-week-old young seedlings aseptically grown on 1/2 MS agar medium were transferred into pots filled with the same amount of compost soil and grown for another 1 or 2 weeks before stress treatments were applied. For drought tolerance assay, soil-grown plants were fully watered, and then withheld irrigation for 4 weeks, followed by rewatering plants. Survival rates were scored 1 week after rewatering. For salt tolerance assay, soil-grown plants were treated with progressively applied high salt stress by irrigating plants with NaCl solutions of stepwisely increasing concentrations (50, 100, and 200 mM) every 4 days and lasting at the concentration of 200 mM for 12 days when chlorophyll contents were measured according to the method (Lichtenthaler 1987 (link)). Cold treatment was performed by transferring the plants into a low temperature incubator for desired durations (−4°C, Sanyo, MIR-253). After 7 days-recovery under normal growth conditions, the survival rates were recorded. For oxidative stress treatment, leaves of similar developmental stages (7th and 8th rosette leaves) were detached from 3 or 4 week-old plants aseptically grown on 1/2 MS agar medium and floated abaxial side up in 2 μM methyl viologen (MV, Sigma–Adrich) solution under controlled growth conditions. Chlorophyll contents of the detached leaves were measured as described (Lichtenthaler 1987 (link)).
Zhang X., Wang L., Meng H., Wen H., Fan Y, & Zhao J. (2011). Maize ABP9 enhances tolerance to multiple stresses in transgenic Arabidopsis by modulating ABA signaling and cellular levels of reactive oxygen species. Plant Molecular Biology, 75(4), 365-378.
Publication 2011
Agar Biological Assay Chlorophyll Cold Temperature Drought Tolerance Growth Disorders Marijuana Abuse MS 1-2 Oxidative Stress Paraquat Plants Salt Stress Salt Tolerance Seedlings Sodium Chloride
3
Detecting Drought Tolerance QTLs
Candidate QTL regions for drought tolerance were identified using two trait mapping approaches: (1) interval mapping for identifying M-QTLs and (2) epistatic interaction analysis for detecting QTL interactions. Composite interval mapping (CIM) was employed for detection of M-QTLs using Windows QTL Cartographer version 2.5 (Wang et al. 2010 ). In parallel, for detection of E-QTLs, a two-locus QTL analysis or two-dimensional (2D) genome scanning was conducted using software QTLNetwork version 2.0 (http://ibi.zju.edu.cn/software/qtlnetwork/) allowing simultaneous detection of M-QTLs, E-QTLs, and the QTLs involved in epistatic (Q × Q) and QTL by environment (Q × E) interactions as described in Gautami et al. (2012 (link)). The threshold for declaring QTL is set to P value of 0.05 by permutation method (1,000 permutations).
Varshney R.K., Thudi M., Nayak S.N., Gaur P.M., Kashiwagi J., Krishnamurthy L., Jaganathan D., Koppolu J., Bohra A., Tripathi S., Rathore A., Jukanti A.K., Jayalakshmi V., Vemula A., Singh S.J., Yasin M., Sheshshayee M.S, & Viswanatha K.P. (2013). Genetic dissection of drought tolerance in chickpea (Cicer arietinum L.). TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik, 127(2), 445-462.
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Publication 2013
Drought Tolerance Genome
4
Evaluating Drought Tolerance in Crop Genotypes
For the germination test, only ten seeds per genotype were randomly selected for each of three of the replications. The seeds were placed on wetted filter paper in 9-cm-diameter Petri dishes in order to evaluate the growth performance and phenotypic variation in seedling related traits among genotypes. Drought stress was induced by adding PEG-6000 at a concentration of 20% (w/v), while distilled water without PEG was used as a control (unstressed seeds). The Petri dishes were placed in an incubator at 20°C in the darkness. The experiment was conceived as a randomized complete block design (RCBD). The seeds were considered as germinated when the radicle reached at least 2 mm in length. Germination was scored at 24 hours intervals for 12 consecutive days. In total, twenty-two germination and growth performance-related traits were scored as explain in Table 1.
Germination parameters were assessed according to International Seed Testing Association rules (ISTA) as follows:
Germination percentage is expressed as (G%) G%=nN×100
Where n is the number of germinated seeds at the end of the experiment and N is the total number of total sown seeds.
Germination pace is expressed as (GP)
GP=N∑(n×g)×100,
Where N is the total number of germinated seeds at the end of the experiment and n is the number of germinated seeds on day g (1, 2, 3,..).
The drought tolerance index (DTI) was calculated for G% and GP, according to the equations:
DTI(G%)=G%underdroughtG%undercontrol×100
DTI(GP)=GPunderdroughtGPundercontrol×100
The reduction of G% and GP were also calculated as follows:
ReductionofG%=G%undercontrol–G%underdrought
ReductionofGP=GPundercontrol–GPunderdrought
For Shoot length (SL) and root length (RL) measurement, 10 seeds were grown in rolling paper [28 (link)], the paper rolls were placed in 1L beakers half filled with water for control and 20% PEG for drought. After 12 days, SL and RL (in cm) were manually measured using a scaled ruler. The fresh weight (FW) of seedlings was recorded (g) using a sensitive balance (Sartorius AC 1215, Germany). The shoot-root ratio (SRR) was calculated as the ratio of the SL to the RL. Reduction of SL, RL, and FW were calculated as follows:
ReductionofSL=SLundercontrol–SLunderdrought
ReductionofRL=RLundercontrol–RLunderdrought
Also, the drought tolerance index (DTI) was calculated for SL and RL:
SL_DTI=SLunderdroughtSLundercontrol×100
RL_DTI=RLunderdroughtRLundercontrol×100
Thabet S.G., Moursi Y.S., Karam M.A., Graner A, & Alqudah A.M. (2018). Genetic basis of drought tolerance during seed germination in barley. PLoS ONE, 13(11), e0206682.
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Publication 2018
Darkness DNA Replication Droughts Drought Tolerance Genotype Germination Hyperostosis, Diffuse Idiopathic Skeletal Plant Embryos Plant Roots Polyethylene Glycol 6000 Seedlings
5
Genome-Wide Association Study of Maize Drought Tolerance
GWAS of maize drought-tolerant genes was performed by analysing a maize natural variation panel consisting of 368 inbred lines collected from TST and temperate regions19 (link). Plant drought tolerance of different inbred lines was phenotypied as previously described24 (link). Briefly, the natural variation panel of maize consisting of 368 maize inbred lines was planted in a cultivation pool (6 × 1.4 × 0.22 m, length × width × depth) in which 5 ton of loam were mixed with 0.25 ton of chicken manure. To phenotype the drought tolerance of each genotype, watering was withheld when the seedlings developed three true leaves. Re-watering was applied to recover the surviving plants when clear wilting difference was observed. After rehydration for 6 days, the SR of each genotype was scored. The phenotypic data were obtained from six replicated experiments. The 56,110 genomic and 525,105 transcriptomic SNPs, with minor allele frequency (MAF) ≥0.05 were used for GWAS. The standard mixed linear model was applied (TASSEL 3.1.0)59 (link), in which the population structure (Q) and kinship (K) were estimated as previously described24 (link). Briefly, principal components of the association panel were calculated by EIGENSTRAT60 (link) using the high-quality 525,105 SNP data with MAF≥0.05 (ref. 61 (link)). The first two dimensions were used in the principal component analysis to estimate the population structure, which could explain the 11.01% of the phenotypic variation. These results were comparable to those that were calculated by STRUCTURE. The analysis was completed by the lm function in an R program. Single-marker association analysis was initially performed to filter out markers that had no relationship with the trait (P≥0.995). Subsequently, 1,822 SNP markers on each chromosome were chosen to estimate the kinship coefficient (K) by SPAGeDi. Markers that were in approximate linkage equilibrium with each other were determined from PLINK62 (link) based on SNP pruning (window size 50, step size 50 and the LD R2 threshold is 0.2), and the number of the subset markers was 85,806. The suggestive P value threshold to control the genome-wide type 1 error rate was 1.17 × 10−5, which was considered as the significance cutoff for the association. ZmNAC111-based association mapping was performed within 146 temperate and 116 TST maize inbred lines, which were representative of the whole population. The ZmNAC111 promoter (∼0.7 kb), coding regions (including introns) and 5′- and 3′-UTR sequences were amplified and sequenced. These sequences were assembled using ContigExpress in Vector NTI Advance 10 (Invitrogen) and aligned using MEGA version 5 (http://megasoftware.net/). Polymorphisms (SNPs and InDels) were identified and their association to drought tolerance was calculated again by TASSEL 3.1.0, under the standard MLM, with MAF≥0.05.
Mao H., Wang H., Liu S., Li Z., Yang X., Yan J., Li J., Tran L.S, & Qin F. (2015). A transposable element in a NAC gene is associated with drought tolerance in maize seedlings. Nature Communications, 6, 8326.
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Publication 2015
3' Untranslated Regions Chickens Chromosome Markers Cloning Vectors Droughts Drought Tolerance Gene Expression Profiling Genes Genetic Polymorphism Genome Genome-Wide Association Study Genotype INDEL Mutation Introns Maize Phenotype Plants Rehydration Seedlings Single Nucleotide Polymorphism Tassel

Most recents protocols related to «Drought Tolerance»

1
Overexpression of Ceres cDNA 12723147 Confers Drought Tolerance
Not available on PMC !

Example 6

Ceres cDNA 12723147 encodes an Arabidopsis putative aldo/keto reductase. Ectopic expression of Ceres cDNA 12723147 under the control of the CaMV35S promoter induces the following phenotypes:

    • Germination on high concentrations of polyethylene glycol (PEG), mannitol and abscissic acid (ABA).
    • Continued growth on high concentration of PEG, mannitol and ABA.
      Generation and Phenotypic Evaluation of T1 Lines Containing 35S::cDNA 12723147.

Wild-type Arabidopsis Wassilewskija (WS) plants were transformed with a Ti plasmid containing cDNA 12723147 in the sense orientation relative to the CaMV35S constitutive promoter. The Ti plasmid vector used for this construct, CRS338, contains PAT and confers herbicide resistance to transformed plants. Ten independently transformed events were selected and evaluated for their qualitative phenotype in the T1 generation. No positive or negative phenotypes were observed in the T1 plants.

Screens of Superpools on High PEG, Mannitol, and ABA as Surrogate Screens for Drought Tolerance.

Seeds from 13 superpools (1,200 T2 seeds from each superpool) from the CaMV35S or 32449 over-expression lines were tested on 3 drought surrogate screens (high concentrations of PEG, mannitol, and ABA) as described above. T3 seeds were collected from the resistant plants and analyzed for resistance on all three surrogate drought screens.

Once cDNA 12723147 was identified in resistant plants from each of the three surrogate drought screens, the five individual T2 events containing this cDNA (SR01013) were screened on high PEG, mannitol, and ABA to identify events with the resistance phenotype.

Superpools (SP) are referred to as SP1, SP2 and so on. The letter following the hyphen refers to the screen (P=PEG, M=mannitol, and A=ABA) and the number following the letter refers to a number assigned to each plant obtained from that screen on that superpool. For example, SP1-M18 is the 18th plant isolated from a mannitol screen of Superpool 1.

Qualitative and Quantitative Analysis of 2 Independent Events Representing 35S::cDNA 12659859 (SR01010) on PEG, Mannitol and ABA

To identify two independent events of 35S::cDNA 12659859 showing PEG, mannitol, and ABA resistance, 36 seedlings from each of two events, SR01013-01 and -02 were screened as previously described. BastaR segregation was assessed to verify that the lines contained a single insert segregating in a 3:1 (R:S) ratio as calculated by a chi-square test (Table 6-1). Both lines (01 and 02) segregated for a single insert in the T2 generation (Table 1)

TABLE 6-1
BastaR segregation for SR01013 individual events
Probability
EventResistantSensitiveTotalof Chi-test*
SR01013-01305350.14323
SR01013-02306360.24821
SR01013-01-3341360.00248**
SR01013-02-2320320.00109**
*Chi-test to determine whether actual ratio of resistant to sensitive differs form the expected 3:1 ratio.
**Significantly different than a 3:1 (R:S) ratio

Lines SR01013-01 and -02 were chosen as the two events because they had a strong and consistent resistance to PEG, mannitol and ABA. The controls were sown the same day and in the same plate as the individual lines. The PEG (Tables 6-2 and 6-3), mannitol (Tables 6-4 and 6-5) and ABA (Tables 6-6 and 6-7) segregation ratios observed for SR01013-01 and -02 are consistent with the presence of single insert as demonstrated by chi-square, similar to what we observed for BastaR resistance (Table 6-1).

The progeny from one resistant T2 plant from each of these two events were tested in the same manner as the T2. Resistance to PEG, mannitol and ABA was also observed in the T3 generation. Taken together, the segregation of resistant seedlings containing cDNA 12723147 from two events on all three drought surrogate screens and the inheritance of this resistance in a subsequent generation, provide strong evidence that cDNA 12723147 when over-expressed can provide tolerance to drought.

TABLE 6-2
Chi-square analysis assuming a 3:1 (R:S) ratio for progeny of
SR01013-01T2 containing 35S::cDNA 12723147 on PEG.
Probability
EventObservedExpectedχ2of Chi-Test
PEG Resistant22270.9260.054
PEG Sensitive1492.778
36363.704

TABLE 6-3
Chi-square analysis assuming a 3:1 (R:S) ratio for progeny of
SR01013-02 T2 containing 35S::cDNA 12723147 on PEG.
Probability
EventObservedExpectedχ2of Chi-Test
PEG Resistant26270.037.700
PEG Sensitive109.111
3636.148

TABLE 6-4
Chi-square analysis assuming a 3:1 (R:S) ratio for progeny of
SR01013-01 T2 containing 35S::cDNA 12723147 on mannitol.
Probability
EventObservedExpectedχ2of Chi-Test
Mannitol Resistant2827.037.700
Mannitol Sensitive89.111
3636.148

TABLE 6-5
Chi-square analysis assuming a 3:1 (R:S) ratio for progeny of
SR01013-02 T2 containing 35S::cDNA 12723147 on mannitol.
Probability
EventObservedExpectedχ2of Chi-Test
Mannitol Resistant18273.0005
Mannitol Sensitive1899
363612

TABLE 6-6
Chi-square analysis assuming a 3:1 (R:S) ratio for progeny of
SR01013-02 T2 containing 35S::cDNA 12723147 on ABA.
EventObservedExpectedχ2Probability
ABA Resistant1324 5.0427.098
ABA Sensitive19 815.125
323220.167

TABLE 6-7
Chi-square analysis assuming a 3:1 (R:S) ratio for progeny of
SR01013-02 T2 containing 35S::cDNA 12723147 on ABA.
EventObservedExpectedχ2Probability
ABA Resistant1324 5.0427.098
ABA Sensitive19 815.125
323220.167
FIG. 5 provides the results of the consensus sequence (SEQ ID NOs: 178-200) analysis based on Ceres cDNA 12723147.

US11859195B2. Nucleotide sequences and polypeptides encoded thereby useful for modifying plant characteristics (2024-01-02). CERES, INC. [US]. Inventors: Cory Christensen [US], Nestor Apuya [US], Kenneth A. Feldmann [US].
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Patent 2024
14-3-3 Proteins Abscisic Acid Aldo-Keto Reductase Arabidopsis CERE Cloning Vectors Consensus Sequence DNA, Complementary Droughts Drought Tolerance Ectopic Gene Expression Germination Herbicide Resistance Mannitol Pattern, Inheritance Phenotype Plant Embryos Plants Plant Tumor-Inducing Plasmids Polyethylene Glycols Seedlings
2
Microbial Seed Treatments for Enhanced Crop Performance
Not available on PMC !

Example 2

A. Seed Treatment with Isolated Microbe

In this example, an isolated microbe from Tables 1-3 will be applied as a seed coating to seeds of corn (Zea mays). Upon applying the isolated microbe as a seed coating, the corn will be planted and cultivated in the standard manner.

A control plot of corn seeds, which did not have the isolated microbe applied as a seed coating, will also be planted.

It is expected that the corn plants grown from the seeds treated with the seed coating will exhibit a quantifiably higher biomass than the control corn plants.

The biomass from the treated plants may be about 1-10% higher, 10-20% higher, 20-30% higher, 30-40% higher, 40-50% higher, 50-60% higher, 60-70% higher, 70-80% higher, 80-90% higher, or more.

The biomass from the treated plants may equate to about a 1 bushel per acre increase over the controls, or a 2 bushel per acre increase, or a 3 bushel per acre increase, or a 4 bushel per acre increase, or a 5 bushel per acre increase, or more.

In some aspects, the biomass increase is statistically significant. In other aspects, the biomass increase is not statistically significant, but is still quantifiable.

B. Seed Treatment with Microbial Consortia

In this example, a microbial consortium, comprising at least two microbes from Tables 1-3 will be applied as a seed coating to seeds of corn (Zea mays). Upon applying the microbial consortium as a seed coating, the corn will be planted and cultivated in the standard manner.

A control plot of corn seeds, which did not have the microbial consortium applied as a seed coating, will also be planted.

It is expected that the corn plants grown from the seeds treated with the seed coating will exhibit a quantifiably higher biomass than the control corn plants.

The biomass from the treated plants may be about 1-10% higher, 10-20% higher, 20-30% higher, 30-40% higher, 40-50% higher, 50-60% higher, 60-70% higher, 70-80% higher, 80-90% higher, or more.

The biomass from the treated plants may equate to about a 1 bushel per acre increase over the controls, or a 2 bushel per acre increase, or a 3 bushel per acre increase, or a 4 bushel per acre increase, or a 5 bushel per acre increase, or more.

In some aspects, the biomass increase is statistically significant. In other aspects, the biomass increase is not statistically significant, but is still quantifiable.

C. Treatment with Agricultural Composition Comprising Isolated Microbe

In this example, an isolated microbe from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.

For example, it is anticipated that a farmer will apply the agricultural composition to the corn seeds simultaneously upon planting the seeds into the field. This can be accomplished, for example, by applying the agricultural composition to a hopper/bulk tank on a standard 16 row planter, which contains the corn seeds and which is configured to plant the same into rows. Alternatively, the agricultural composition can be contained in a separate bulk tank on the planter and sprayed into the rows upon planting the corn seed.

A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.

It is expected that the corn plants grown from the seeds treated with the agricultural composition will exhibit a quantifiably higher biomass than the control corn plants.

The biomass from the treated plants may be about 1-10% higher, 10-20% higher, 20-30% higher, 30-40% higher, 40-50% higher, 50-60% higher, 60-70% higher, 70-80% higher, 80-90% higher, or more.

The biomass from the treated plants may equate to about a 1 bushel per acre increase over the controls, or a 2 bushel per acre increase, or a 3 bushel per acre increase, or a 4 bushel per acre increase, or a 5 bushel per acre increase, or more.

In some aspects, the biomass increase is statistically significant. In other aspects, the biomass increase is not statistically significant, but is still quantifiable.

D. Treatment with Agricultural Composition Comprising Microbial Consortia

In this example, a microbial consortium, comprising at least two microbes from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.

For example, it is anticipated that a farmer will apply the agricultural composition to the corn seeds simultaneously upon planting the seeds into the field. This can be accomplished, for example, by applying the agricultural composition to a hopper/bulk tank on a standard 16 row planter, which contains the corn seeds and which is configured to plant the same into rows. Alternatively, the agricultural composition can be contained in a separate bulk tank on the planter and sprayed into the rows upon planting the corn seed.

A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.

It is expected that the corn plants grown from the seeds treated with the agricultural composition will exhibit a quantifiably higher biomass than the control corn plants.

The biomass from the treated plants may be about 1-10% higher, 10-20% higher, 20-30% higher, 30-40% higher, 40-50% higher, 50-60% higher, 60-70% higher, 70-80% higher, 80-90% higher, or more.

The biomass from the treated plants may equate to about a 1 bushel per acre increase over the controls, or a 2 bushel per acre increase, or a 3 bushel per acre increase, or a 4 bushel per acre increase, or a 5 bushel per acre increase, or more.

In some aspects, the biomass increase is statistically significant. In other aspects, the biomass increase is not statistically significant, but is still quantifiable.

A. Seed Treatment with Isolated Microbe

In this example, an isolated microbe from Tables 1-3 will be applied as a seed coating to seeds of corn (Zea mays). Upon applying the isolated microbe as a seed coating, the corn will be planted and cultivated in the standard manner.

A control plot of corn seeds, which did not have the isolated microbe applied as a seed coating, will also be planted.

It is expected that the corn plants grown from the seeds treated with the seed coating will exhibit a quantifiable and superior ability to tolerate drought conditions and/or exhibit superior water use efficiency, as compared to the control corn plants.

The drought tolerance and/or water use efficiency can be based on any number of standard tests from the art, e.g leaf water retention, turgor loss point, rate of photosynthesis, leaf color and other phenotypic indications of drought stress, yield performance, and various root morphological and growth patterns.

B. Seed Treatment with Microbial Consortia

In this example, a microbial consortium, comprising at least two microbes from Tables 1-3 will be applied as a seed coating to seeds of corn (Zea mays). Upon applying the microbial consortium as a seed coating, the corn will be planted and cultivated in the standard manner.

A control plot of corn seeds, which did not have the microbial consortium applied as a seed coating, will also be planted.

It is expected that the corn plants grown from the seeds treated with the seed coating will exhibit a quantifiable and superior ability to tolerate drought conditions and/or exhibit superior water use efficiency, as compared to the control corn plants.

The drought tolerance and/or water use efficiency can be based on any number of standard tests from the art, e.g leaf water retention, turgor loss point, rate of photosynthesis, leaf color and other phenotypic indications of drought stress, yield performance, and various root morphological and growth patterns.

C. Treatment with Agricultural Composition Comprising Isolated Microbe

In this example, an isolated microbe from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.

For example, it is anticipated that a farmer will apply the agricultural composition to the corn seeds simultaneously upon planting the seeds into the field. This can be accomplished, for example, by applying the agricultural composition to a hopper/bulk tank on a standard 16 row planter, which contains the corn seeds and which is configured to plant the same into rows. Alternatively, the agricultural composition can be contained in a separate bulk tank on the planter and sprayed into the rows upon planting the corn seed.

A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.

It is expected that the corn plants grown from the seeds treated with the with the agricultural composition will exhibit a quantifiable and superior ability to tolerate drought conditions and/or exhibit superior water use efficiency, as compared to the control corn plants.

The drought tolerance and/or water use efficiency can be based on any number of standard tests from the art, e.g leaf water retention, turgor loss point, rate of photosynthesis, leaf color and other phenotypic indications of drought stress, yield performance, and various root morphological and growth patterns.

D. Treatment with Agricultural Composition Comprising Microbial Consortia

In this example, a microbial consortium, comprising at least two microbes from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.

For example, it is anticipated that a farmer will apply the agricultural composition to the corn seeds simultaneously upon planting the seeds into the field. This can be accomplished, for example, by applying the agricultural composition to a hopper/bulk tank on a standard 16 row planter, which contains the corn seeds and which is configured to plant the same into rows. Alternatively, the agricultural composition can be contained in a separate bulk tank on the planter and sprayed into the rows upon planting the corn seed.

A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.

It is expected that the corn plants grown from the seeds treated with the with the agricultural composition will exhibit a quantifiable and superior ability to tolerate drought conditions and/or exhibit superior water use efficiency, as compared to the control corn plants.

The drought tolerance and/or water use efficiency can be based on any number of standard tests from the art, e.g leaf water retention, turgor loss point, rate of photosynthesis, leaf color and other phenotypic indications of drought stress, yield performance, and various root morphological and growth patterns.

A. Seed Treatment with Isolated Microbe

In this example, an isolated microbe from Tables 1-3 will be applied as a seed coating to seeds of corn (Zea mays). Upon applying the isolated microbe as a seed coating, the corn will be planted and cultivated in the standard manner.

A control plot of corn seeds, which did not have the isolated microbe applied as a seed coating, will also be planted.

It is expected that the corn plants grown from the seeds treated with the seed coating will exhibit a quantifiable and superior ability to utilize nitrogen, as compared to the control corn plants.

The nitrogen use efficiency can be quantified by recording a measurable change in any of the main nitrogen metabolic pool sizes in the assimilation pathways (e.g., a measurable change in one or more of the following: nitrate, nitrite, ammonia, glutamic acid, aspartic acid, glutamine, asparagine, lysine, leucine, threonine, methionine, glycine, tryptophan, tyrosine, total protein content of a plant part, total nitrogen content of a plant part, and/or chlorophyll content), or where the treated plant is shown to provide the same or elevated biomass or harvestable yield at lower nitrogen fertilization levels compared to the control plant, or where the treated plant is shown to provide elevated biomass or harvestable yields at the same nitrogen fertilization levels compared to a control plant.

B. Seed Treatment with Microbial Consortia

In this example, a microbial consortium, comprising at least two microbes from Tables 1-3 will be applied as a seed coating to seeds of corn (Zea mays). Upon applying the microbial consortium as a seed coating, the corn will be planted and cultivated in the standard manner.

A control plot of corn seeds, which did not have the microbial consortium applied as a seed coating, will also be planted.

It is expected that the corn plants grown from the seeds treated with the seed coating will exhibit a quantifiable and superior ability to utilize nitrogen, as compared to the control corn plants.

The nitrogen use efficiency can be quantified by recording a measurable change in any of the main nitrogen metabolic pool sizes in the assimilation pathways (e.g., a measurable change in one or more of the following: nitrate, nitrite, ammonia, glutamic acid, aspartic acid, glutamine, asparagine, lysine, leucine, threonine, methionine, glycine, tryptophan, tyrosine, total protein content of a plant part, total nitrogen content of a plant part, and/or chlorophyll content), or where the treated plant is shown to provide the same or elevated biomass or harvestable yield at lower nitrogen fertilization levels compared to the control plant, or where the treated plant is shown to provide elevated biomass or harvestable yields at the same nitrogen fertilization levels compared to a control plant.

C. Treatment with Agricultural Composition Comprising Isolated Microbe

In this example, an isolated microbe from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.

For example, it is anticipated that a farmer will apply the agricultural composition to the corn seeds simultaneously upon planting the seeds into the field. This can be accomplished, for example, by applying the agricultural composition to a hopper/bulk tank on a standard 16 row planter, which contains the corn seeds and which is configured to plant the same into rows. Alternatively, the agricultural composition can be contained in a separate bulk tank on the planter and sprayed into the rows upon planting the corn seed.

A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.

It is expected that the corn plants grown from the seeds treated with the agricultural composition will exhibit a quantifiable and superior ability to utilize nitrogen, as compared to the control corn plants.

The nitrogen use efficiency can be quantified by recording a measurable change in any of the main nitrogen metabolic pool sizes in the assimilation pathways (e.g., a measurable change in one or more of the following: nitrate, nitrite, ammonia, glutamic acid, aspartic acid, glutamine, asparagine, lysine, leucine, threonine, methionine, glycine, tryptophan, tyrosine, total protein content of a plant part, total nitrogen content of a plant part, and/or chlorophyll content), or where the treated plant is shown to provide the same or elevated biomass or harvestable yield at lower nitrogen fertilization levels compared to the control plant, or where the treated plant is shown to provide elevated biomass or harvestable yields at the same nitrogen fertilization levels compared to a control plant.

D. Treatment with Agricultural Composition Comprising Microbial Consortia

In this example, a microbial consortium, comprising at least two microbes from Tables 1-3 will be applied as an agricultural composition, administered to the corn seed at the time of sowing.

For example, it is anticipated that a farmer will apply the agricultural composition to the corn seeds simultaneously upon planting the seeds into the field. This can be accomplished, for example, by applying the agricultural composition to a hopper/bulk tank on a standard 16 row planter, which contains the corn seeds and which is configured to plant the same into rows. Alternatively, the agricultural composition can be contained in a separate bulk tank on the planter and sprayed into the rows upon planting the corn seed.

A control plot of corn seeds, which are not administered the agricultural composition, will also be planted.

It is expected that the corn plants grown from the seeds treated with the agricultural composition will exhibit a quantifiable and superior ability to utilize nitrogen, as compared to the control corn plants.

The nitrogen use efficiency can be quantified by recording a measurable change in any of the main nitrogen metabolic pool sizes in the assimilation pathways (e.g., a measurable change in one or more of the following: nitrate, nitrite, ammonia, glutamic acid, aspartic acid, glutamine, asparagine, lysine, leucine, threonine, methionine, glycine, tryptophan, tyrosine, total protein content of a plant part, total nitrogen content of a plant part, and/or chlorophyll content), or where the treated plant is shown to provide the same or elevated biomass or harvestable yield at lower nitrogen fertilization levels compared to the control plant, or where the treated plant is shown to provide elevated biomass or harvestable yields at the same nitrogen fertilization levels compared to a control plant.

The inoculants were prepared from isolates grown as spread plates on R2A incubated at 25° C. for 48 to 72 hours. Colonies were harvested by blending with sterile distilled water (SDW) which was then transferred into sterile containers. Serial dilutions of the harvested cells were plated and incubated at 25° C. for 24 hours to estimate the number of colony forming units (CFU) in each suspension. Dilutions were prepared using individual isolates or blends of isolates (consortia) to deliver 1×105 cfu/microbe/seed and seeds inoculated by either imbibition in the liquid suspension or by overtreatment with 5% vegetable gum and oil.

Seeds corresponding to the plants of table 15 were planted within 24 to 48 hours of treatment in agricultural soil, potting media or inert growing media. Plants were grown in small pots (28 mL to 200 mL) in either a controlled environment or in a greenhouse. Chamber photoperiod was set to 16 hours for all experiments on all species. Air temperature was typically maintained between 22-24° C.

Unless otherwise stated, all plants were watered with tap water 2 to 3 times weekly. Growth conditions were varied according to the trait of interest and included manipulation of applied fertilizer, watering regime and salt stress as follows:

    • Low N—seeds planted in soil potting media or inert growing media with no applied N fertilizer
    • Moderate N—seeds planted in soil or growing media supplemented with commercial N fertilizer to equivalent of 135 kg/ha applied N
    • Insol P—seeds planted in potting media or inert growth substrate and watered with quarter strength Pikovskaya's liquid medium containing tri-calcium phosphate as the only form phosphate fertilizer.
    • Cold Stress—seeds planted in soil, potting media or inert growing media and incubated at 10° C. for one week before being transferred to the plant growth room.
    • Salt stress—seeds planted in soil, potting media or inert growing media and watered with a solution containing between 100 to 200 mg/L NaCl.

Untreated (no applied microbe) controls were prepared for each experiment. Plants were randomized on trays throughout the growth environment. Between 10 and 30 replicate plants were prepared for each treatment in each experiment. Phenotypes were measured during early vegetative growth, typically before the V3 developmental stage and between 3 and 6 weeks after sowing. Foliage was cut and weighed. Roots were washed, blotted dry and weighed. Results indicate performance of treatments against the untreated control.

TABLE 15
StrainShootRoot
Microbe sp.IDCropAssayIOC (%)IOC (%)
Bosea thiooxidans123EfficacyEfficacy
overall100%100%
Bosea thiooxidans54522WheatEarly vigor - insol P30-40 —
Bosea thiooxidans54522RyegrassEarly vigor50-60 50-60 
Bosea thiooxidans54522RyegrassEarly vigor - moderate P0-100-10
Duganella violaceinigra111EfficacyEfficacy
overall100%100%
Duganella violaceinigra66361TomatoEarly vigor0-100-10
Duganella violaceinigra66361TomatoEarly vigor30-40 40-50 
Duganella violaceinigra66361TomatoEarly vigor20-30 20-30 
Herbaspirillum huttiense222Efficacy—
overall100%
Herbaspirillum huttiense54487WheatEarly vigor - insol P30-40 —
Herbaspirillum huttiense60507MaizeEarly vigor - salt stress0-100-10
Janthinobacterium sp.222Efficacy—
Overall100%
Janthinobacterium sp.54456WheatEarly vigor - insol P30-40 —
Janthinobacterium sp.54456WheatEarly vigor - insol P0-10—
Janthinobacterium sp.63491RyegrassEarly vigor - drought0-100-10
stress
Massilia niastensis112EfficacyEfficacy
overall80%80%
Massilia niastensis55184WheatEarly vigor - salt stress0-1020-30 
Massilia niastensis55184WinterEarly vigor - cold stress0-1010-20 
wheat
Massilia niastensis55184WinterEarly vigor - cold stress20-30 20-30 
wheat
Massilia niastensis55184WinterEarly vigor - cold stress10-20 10-20 
wheat
Massilia niastensis55184WinterEarly vigor - cold stress<0<0
wheat
Novosphingobium rosa211EfficacyEfficacy
overall100%100%
Novosphingobium rosa65589MaizeEarly vigor - cold stress0-100-10
Novosphingobium rosa65619MaizeEarly vigor - cold stress0-100-10
Paenibacillus amylolyticus111EfficacyEfficacy
overall100%100%
Paenibacillus amylolyticus66316TomatoEarly vigor0-100-10
Paenibacillus amylolyticus66316TomatoEarly vigor10-20 10-20 
Paenibacillus amylolyticus66316TomatoEarly vigor0-100-10
Pantoea agglomerans323EfficacyEfficacy
33%50%
Pantoea agglomerans54499WheatEarly vigor - insol P40-50 —
Pantoea agglomerans57547MaizeEarly vigor - low N<00-10
Pantoea vagans55529MaizeEarly vigor<0<0
(formerly P. agglomerans)
Polaromonas ginsengisoli111EfficacyEfficacy
66%100%
Polaromonas ginsengisoli66373TomatoEarly vigor0-100-10
Polaromonas ginsengisoli66373TomatoEarly vigor20-30 30-40 
Polaromonas ginsengisoli66373TomatoEarly vigor<010-20 
Pseudomonas fluorescens122Efficacy—
100%
Pseudomonas fluorescens54480WheatEarly vigor - insol P>100 —
Pseudomonas fluorescens56530MaizeEarly vigor - moderate N0-10—
Rahnella aquatilis334EfficacyEfficacy
80%63%
Rahnella aquatilis56532MaizeEarly vigor - moderate N10-20 —
Rahnella aquatilis56532MaizeEarly vigor - moderate N0-100-10
Rahnella aquatilis56532WheatEarly vigor - cold stress0-1010-20 
Rahnella aquatilis56532WheatEarly vigor - cold stress<00-10
Rahnella aquatilis56532WheatEarly vigor - cold stress10-20 <0
Rahnella aquatilis57157RyegrassEarly vigor<0—
Rahnella aquatilis57157MaizeEarly vigor - low N0-100-10
Rahnella aquatilis57157MaizeEarly vigor - low N0-10<0
Rahnella aquatilis58013MaizeEarly vigor0-1010-20 
Rahnella aquatilis58013MaizeEarly vigor - low N0-10<0
Rhodococcus erythropolis313Efficacy—
66%
Rhodococcus erythropolis54093MaizeEarly vigor - low N40-50 —
Rhodococcus erythropolis54299MaizeEarly vigor - insol P>100 —
Rhodococcus erythropolis54299MaizeEarly vigor<0<0
Stenotrophomonas chelatiphaga611EfficacyEfficacy
60%60%
Stenotrophomonas chelatiphaga54952MaizeEarly vigor0-100-10
Stenotrophomonas chelatiphaga47207MaizeEarly vigor<0 0
Stenotrophomonas chelatiphaga64212MaizeEarly vigor0-1010-20 
Stenotrophomonas chelatiphaga64208MaizeEarly vigor0-100-10
Stenotrophomonas chelatiphaga58264MaizeEarly vigor<0<0
Stenotrophomonas maltophilia612EfficacyEfficacy
43%66%
Stenotrophomonas maltophilia54073MaizeEarly vigor - low N50-60 —
Stenotrophomonas maltophilia54073MaizeEarly vigor<00-10
Stenotrophomonas maltophilia56181MaizeEarly vigor0-10<0
Stenotrophomonas maltophilia54999MaizeEarly vigor0-100-10
Stenotrophomonas maltophilia54850MaizeEarly vigor 00-10
Stenotrophomonas maltophilia54841MaizeEarly vigor<00-10
Stenotrophomonas maltophilia46856MaizeEarly vigor<0<0
Stenotrophomonas rhizophila811EfficacyEfficacy
12.5%37.5%
Stenotrophomonas rhizophila50839MaizeEarly vigor<0<0
Stenotrophomonas rhizophila48183MaizeEarly vigor<0<0
Stenotrophomonas rhizophila45125MaizeEarly vigor<0<0
Stenotrophomonas rhizophila46120MaizeEarly vigor<00-10
Stenotrophomonas rhizophila46012MaizeEarly vigor<0<0
Stenotrophomonas rhizophila51718MaizeEarly vigor0-100-10
Stenotrophomonas rhizophila66478MaizeEarly vigor<0<0
Stenotrophomonas rhizophila65303MaizeEarly vigor<00-10
Stenotrophomonas terrae221EfficacyEfficacy
50%50%
Stenotrophomonas terrae68741MaizeEarly vigor<0<0
Stenotrophomonas terrae68599MaizeEarly vigor<00-10
Stenotrophomonas terrae68599Capsicum *Early vigor20-30 20-30 
Stenotrophomonas terrae68741Capsicum *Early vigor10-20 20-30 

The data presented in table 15 describes the efficacy with which a microbial species or strain can change a phenotype of interest relative to a control run in the same experiment. Phenotypes measured were shoot fresh weight and root fresh weight for plants growing either in the absence of presence of a stress (assay). For each microbe species, an overall efficacy score indicates the percentage of times a strain of that species increased a both shoot and root fresh weight in independent evaluations. For each species, the specifics of each independent assay is given, providing a strain ID (strain) and the crop species the assay was performed on (crop). For each independent assay the percentage increase in shoot and root fresh weight over the controls is given.

US11871752B2. Agriculturally beneficial microbes, microbial compositions, and consortia (2024-01-16). BIOCONSORTIA, INC. [US]. Inventors: Peter Wigley [NZ], Susan Turner [US], Caroline George [NZ], Thomas Williams [US], Kelly Roberts [US], Graham Hymus [US], Kelvin Lau [NZ].
Full text: Click here
Patent 2024
Ammonia Asparagine Aspartic Acid Biological Assay Bosea thiooxidans Calcium Phosphates Capsicum Cells Chlorophyll Cold Shock Stress Cold Temperature Crop, Avian Dietary Fiber DNA Replication Droughts Drought Tolerance Embryophyta Environment, Controlled Farmers Fertilization Glutamic Acid Glutamine Glycine Growth Disorders Herbaspirillum Herbaspirillum huttiense Leucine Lolium Lycopersicon esculentum Lysine Maize Massilia niastensis Methionine Microbial Consortia Nitrates Nitrites Nitrogen Novosphingobium rosa Paenibacillus Paenibacillus amylolyticus Pantoea agglomerans Pantoea vagans Phenotype Phosphates Photosynthesis Plant Development Plant Embryos Plant Leaves Plant Proteins Plant Roots Plants Polaromonas ginsengisoli Pseudoduganella violaceinigra Pseudomonas Pseudomonas fluorescens Rahnella Rahnella aquatilis Retention (Psychology) Rhodococcus erythropolis Rosa Salt Stress Sodium Chloride Sodium Chloride, Dietary Stenotrophomonas chelatiphaga Stenotrophomonas maltophilia Stenotrophomonas rhizophila Stenotrophomonas terrae Sterility, Reproductive Strains Technique, Dilution Threonine Triticum aestivum Tryptophan Tyrosine Vegetables Zea mays
3
Marker-Assisted Backcross Breeding for Drought Tolerance
Initially, a cross was made between HD2733 and C306 to transfer drought stress tolerance QTLs into HD2733. The MABB procedure followed here is represented in Figure 1. The true F1s were identified using foreground SSR markers and backcrossed to the recurrent parent. The BC1F1s were subjected to foreground and initial background selection with a set of 64 polymorphic markers. Twenty-five lines positive for target QTLs with maximum recurrent parent genome (RPG) recovery coupled with phenotypic similarity to the recipient parent were selected. The 21 selected lines were backcrossed to the recurrent parent and selfed to generate BC2F1 and BC1F2 seeds. BC2F1 and BC1F2 lines were repeated for the MABB process involving foreground and background selection with 120 polymorphic SSR markers.
The polymorphic SSR markers were used to construct a schematic map illustrating the genomic contributions of donor and recurrent parents with Graphical GenoType (GGT) v2.045 software to identify backcross-derived lines possessing the maximum recurrent parent genome. The positive foreground-selected plants genotyped for polymorphic markers at each backcross/selfing generation and recurrent parent genome recovery (G) were estimated using the following formula: G = [(X + ½Y) × 100]/N; here, N is the total number of parental polymorphic markers screened, X is the number of markers showing homozygosity for recurrent parental alleles, and Y is the number of markers showing heterozygosity for parental alleles. Based on the recovery of the recurrent parental genome and the presence of targeted donor genomic regions, 50 lines were selected from BC2F1 and BC1F2 plants and advanced through selfing.
Thirteen plants were selected from advanced BC2F2 lines based on maximum recovery for RPG through background and foreground selection and visible phenotypic similarity with the recurrent parent strain, HD2733, while BC1F3 lines were selfed and advanced to BC1F4 generations. A total of 10 BC1F4 plants were again selected based on foreground selection and maximum background recovery of the recurrent parental genome. The selected 13 BC2F3 and 10 BC1F4 plants were evaluated for morphological and physiological traits and yield performance and further advanced through selfing for evaluation under a national testing trial.
Kumar P.K., Bellundagi A., Krishna H., Mallikarjuna M.G., Thimmappa R.K., Rai N., Shashikumara P., Sinha N., Jain N., Singh P.K., Singh G.P, & Prabhu K.V. (2023). Development of bread wheat (Triticum aestivum L) variety HD3411 following marker-assisted backcross breeding for drought tolerance. Frontiers in Genetics, 14, 1046624.
Full text: Click here
Publication 2023
Alleles Drought Tolerance Genome Heterozygote Homozygote Neutrophil Parent Phenotype physiology Plant Embryos Plants Quantitative Trait Loci Strains Tissue Donors
4
Drought Tolerance Evaluation of Wheat
The BC1F4 and BC2F3 families were raised under restricted irrigation conditions (two irrigations were carried out at 21 and 40 days after sowing), following an augmented design protocol, where parents were replicated as checks. The observations on introgressed progeny lines in the field for various traits contributing to drought tolerance and yield parameters were recorded as per CIMMYT guidelines published in “Physiological Breeding II: A field guide to wheat phenotyping” (Pask et al., 2012 ). The data were recorded for various morphological traits viz., 50% days to heading (DH), days to anthesis (DA), days to maturity (DM), plant height (PH), number of tillers (NT), spike length (SL), peduncle length (PL), 1,000 kernel weight (TKW), biomass, harvest index (HI), yield per plot (Y/P), and physiological traits like chlorophyll content, canopy temperature, and the normalized difference vegetation index were scored at three different stages of wheat development: the vegetative stage (late boot stage, Z49), the grain filling stage (early milk stage, Z73), and the grain maturity stage (late milk stage, Z85) according to Zadok’s scale (Zadoks et al., 1974 (link)).
Kumar P.K., Bellundagi A., Krishna H., Mallikarjuna M.G., Thimmappa R.K., Rai N., Shashikumara P., Sinha N., Jain N., Singh P.K., Singh G.P, & Prabhu K.V. (2023). Development of bread wheat (Triticum aestivum L) variety HD3411 following marker-assisted backcross breeding for drought tolerance. Frontiers in Genetics, 14, 1046624.
Full text: Click here
Publication 2023
Cereals Chlorophyll Drought Tolerance Milk Parent physiology Plants Triticum aestivum
5
Developing Drought-Tolerant Wheat
The experiment material consisted of the HD2733 cultivar, one of India’s most popular wheat varieties for the northeastern plain zone (NEPZ). However, HD2733 is susceptible to drought stress, resulting in substantial yield losses under field conditions of limited irrigation. The donor parent for the drought tolerance QTL was C306. This cultivar is suitable for drought-prone and rain-fed conditions and has been widely adopted in the NEPZ and NWPZ (northwestern plain zone).
Kumar P.K., Bellundagi A., Krishna H., Mallikarjuna M.G., Thimmappa R.K., Rai N., Shashikumara P., Sinha N., Jain N., Singh P.K., Singh G.P, & Prabhu K.V. (2023). Development of bread wheat (Triticum aestivum L) variety HD3411 following marker-assisted backcross breeding for drought tolerance. Frontiers in Genetics, 14, 1046624.
Full text: Click here
Publication 2023
Droughts Drought Tolerance Parent Rain Tissue Donors Triticum aestivum

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More about "Drought Tolerance"

drought resilience, water stress, abiotic stress, plant adaptation, climate change, qPCR, gene expression, microscopy, sample preparation