Plant nutrient removal and soil residual chemical properties as impacted by maize planting date and density.

This study aimed to measure maize (Zea mays) plant nutrient content and nutrient removal in grain, and to evaluate the residual soil nitrogen, phosphorus, and potassium as impacted by planting date and density. Field experiments were conducted to evaluate six plant densities and seven planting dates using a split-split plot design with three replications. Besides the crop growth and yield parameters, six plants were collected at the maturity and soil was sampled from each plot for nutrient analysis. Plant N, P, and K concentrations varied with planting date and density and within the ranges of 0.6-1.024%, 0.054-0.127%, and 0.75-1.71%, respectively. Grain N, P, and K concentrations decreased with plant density and varied from 1.059 to 1.558%, 0.20 to 0.319%, and 0.29 to 0.43%, respectively. Soil residual nutrient varied with depth, planting density and date. Residual N concentration in the topsoil varied from 0.6 to 37.2 mg kg-1 in 2019 and from 1.5 to 11.2 mg kg-1 in 2020 and was high under the last two planting dates. Soil residual N concentration was higher in the second layer than in the topsoil. The N concentration in the third layer varied from 0.1 to 33.2 mg kg-1 and was impacted by plant density. Topsoil P did not vary among planting dates and densities. The second and third soil layers P concentration was not affected. There was 83% increase in topsoil K in 2020 compared to 2019, and a decrease of 65 and 23% in soil K was observed in the second and third soil layers, respectively. For maize production system sustainability, future research should use a holistic approach investigating the impact of planting date, plant density on crop growth, yield, nutrient uptake and remobilization, and soil properties under different fertilizer rates to develop the fertilizer recommendation for maize while reducing the environmental impact of the production system.

Yield prediction in a peanut breeding program using remote sensing data and machine learning algorithms.

Peanut is a critical food crop worldwide, and the development of high-throughput phenotyping techniques is essential for enhancing the crop's genetic gain rate. Given the obvious challenges of directly estimating peanut yields through remote sensing, an approach that utilizes above-ground phenotypes to estimate underground yield is necessary. To that end, this study leveraged unmanned aerial vehicles (UAVs) for high-throughput phenotyping of surface traits in peanut. Using a diverse set of peanut germplasm planted in 2021 and 2022, UAV flight missions were repeatedly conducted to capture image data that were used to construct high-resolution multitemporal sigmoidal growth curves based on apparent characteristics, such as canopy cover and canopy height. Latent phenotypes extracted from these growth curves and their first derivatives informed the development of advanced machine learning models, specifically random forest and eXtreme Gradient Boosting (XGBoost), to estimate yield in the peanut plots. The random forest model exhibited exceptional predictive accuracy (R2 = 0.93), while XGBoost was also reasonably effective (R2 = 0.88). When using confusion matrices to evaluate the classification abilities of each model, the two models proved valuable in a breeding pipeline, particularly for filtering out underperforming genotypes. In addition, the random forest model excelled in identifying top-performing material while minimizing Type I and Type II errors. Overall, these findings underscore the potential of machine learning models, especially random forests and XGBoost, in predicting peanut yield and improving the efficiency of peanut breeding programs.

Genome-Wide Mapping of Quantitative Trait Loci for Yield-Attributing Traits of Peanut.

Peanuts (Arachis hypogaea L.) are important high-protein and oil-containing legume crops adapted to arid to semi-arid regions. The yield and quality of peanuts are complex quantitative traits that show high environmental influence. In this study, a recombinant inbred line population (RIL) (Valencia-C × JUG-03) was developed and phenotyped for nine traits under two environments. A genetic map was constructed using 1323 SNP markers spanning a map distance of 2003.13 cM. Quantitative trait loci (QTL) analysis using this genetic map and phenotyping data identified seventeen QTLs for nine traits. Intriguingly, a total of four QTLs, two each for 100-seed weight (HSW) and shelling percentage (SP), showed major and consistent effects, explaining 10.98% to 14.65% phenotypic variation. The major QTLs for HSW and SP harbored genes associated with seed and pod development such as the seed maturation protein-encoding gene, serine-threonine phosphatase gene, TIR-NBS-LRR gene, protein kinase superfamily gene, bHLH transcription factor-encoding gene, isopentyl transferase gene, ethylene-responsive transcription factor-encoding gene and cytochrome P450 superfamily gene. Additionally, the identification of 76 major epistatic QTLs, with PVE ranging from 11.63% to 72.61%, highlighted their significant role in determining the yield- and quality-related traits. The significant G × E interaction revealed the existence of the major role of the environment in determining the phenotype of yield-attributing traits. Notably, the seed maturation protein-coding gene in the vicinity of major QTLs for HSW can be further investigated to develop a diagnostic marker for HSW in peanut breeding. This study provides understanding of the genetic factor governing peanut traits and valuable insights for future breeding efforts aimed at improving yield and quality.

Sustaining yield and nutritional quality of peanuts in harsh environments: Physiological and molecular basis of drought and heat stress tolerance.

Climate change is significantly impacting agricultural production worldwide. Peanuts provide food and nutritional security to millions of people across the globe because of its high nutritive values. Drought and heat stress alone or in combination cause substantial yield losses to peanut production. The stress, in addition, adversely impact nutritional quality. Peanuts exposed to drought stress at reproductive stage are prone to aflatoxin contamination, which imposes a restriction on use of peanuts as health food and also adversely impact peanut trade. A comprehensive understanding of the impact of drought and heat stress at physiological and molecular levels may accelerate the development of stress tolerant productive peanut cultivars adapted to a given production system. Significant progress has been achieved towards the characterization of germplasm for drought and heat stress tolerance, unlocking the physiological and molecular basis of stress tolerance, identifying significant marker-trait associations as well major QTLs and candidate genes associated with drought tolerance, which after validation may be deployed to initiate marker-assisted breeding for abiotic stress adaptation in peanut. The proof of concept about the use of transgenic technology to add value to peanuts has been demonstrated. Advances in phenomics and artificial intelligence to accelerate the timely and cost-effective collection of phenotyping data in large germplasm/breeding populations have also been discussed. Greater focus is needed to accelerate research on heat stress tolerance in peanut. A suits of technological innovations are now available in the breeders toolbox to enhance productivity and nutritional quality of peanuts in harsh environments. A holistic breeding approach that considers drought and heat-tolerant traits to simultaneously address both stresses could be a successful strategy to produce climate-resilient peanut genotypes with improved nutritional quality.

Omics Technologies to Enhance Plant Based Functional Foods: An Overview.

Functional foods are natural products of plants that have health benefits beyond necessary nutrition. Functional foods are abundant in fruits, vegetables, spices, beverages and some are found in cereals, millets, pulses and oilseeds. Efforts to identify functional foods in our diet and their beneficial aspects are limited to few crops. Advances in sequencing and availability of different omics technologies have given opportunity to utilize these tools to enhance the functional components of the foods, thus ensuring the nutritional security. Integrated omics approaches including genomics, transcriptomics, proteomics, metabolomics coupled with artificial intelligence and machine learning approaches can be used to improve the crops. This review provides insights into omics studies that are carried out to find the active components and crop improvement by enhancing the functional compounds in different plants including cereals, millets, pulses, oilseeds, fruits, vegetables, spices, beverages and medicinal plants. There is a need to characterize functional foods that are being used in traditional medicines, as well as utilization of this knowledge to improve the staple foods in order to tackle malnutrition and hunger more effectively.

Use of Targeted Amplicon Sequencing in Peanut to Generate Allele Information on Allotetraploid Sub-Genomes.

The use of molecular markers in plant breeding has become a routine practice, but the cost per accession can be a hindrance to the routine use of Quantitative Trait Loci (QTL) identification in breeding programs. In this study, we demonstrate the use of targeted re-sequencing as a proof of concept of a cost-effective approach to retrieve highly informative allele information, as well as develop a bioinformatics strategy to capture the genome-specific information of a polyploid species. SNPs were identified from alignment of raw transcriptome reads (2 × 50 bp) to a synthetic tetraploid genome using BWA followed by a GATK pipeline. Regions containing high polymorphic SNPs in both A genome and B genomes were selected as targets for the resequencing study. Targets were amplified using multiplex PCR followed by sequencing on an Illumina HiSeq. Eighty-one percent of the SNP calls in diploids and 68% of the SNP calls in tetraploids were confirmed. These results were also confirmed by KASP validation. Based on this study, we find that targeted resequencing technologies have potential for obtaining maximum allele information in allopolyploids at reduced cost.

Genome-wide transcriptome and physiological analyses provide new insights into peanut drought response mechanisms.

Drought is one of the main constraints in peanut production in West Texas and eastern New Mexico regions due to the depletion of groundwater. A multi-seasonal phenotypic analysis of 10 peanut genotypes revealed C76-16 (C-76) and Valencia-C (Val-C) as the best and poor performers under deficit irrigation (DI) in West Texas, respectively. In order to decipher transcriptome changes under DI, RNA-seq was performed in C-76 and Val-C. Approximately 369 million raw reads were generated from 12 different libraries of two genotypes subjected to fully irrigated (FI) and DI conditions, of which ~329 million (90.2%) filtered reads were mapped to the diploid ancestors of peanut. The transcriptome analysis detected 4,508 differentially expressed genes (DEGs), 1554 genes encoding transcription factors (TFs) and a total of 514 single nucleotide polymorphisms (SNPs) among the identified DEGs. The comparative analysis between the two genotypes revealed higher and integral tolerance in C-76 through activation of key genes involved in ABA and sucrose metabolic pathways. Interestingly, one SNP from the gene coding F-box protein (Araip.3WN1Q) and another SNP from gene coding for the lipid transfer protein (Aradu.03ENG) showed polymorphism in selected contrasting genotypes. These SNPs after further validation may be useful for performing early generation selection for selecting drought-responsive genotypes.

Peanut allergen reduction and functional property improvement by means of enzymatic hydrolysis and transglutaminase crosslinking.

Enzymatic processing could reduce the allergenicity of peanut proteins while may lose the functional properties. Transglutaminase (TGase) is an enzyme for improving the functional properties of proteins/hydrolysates. No studies have been conducted on peanut hydrolysates that are crosslinked with TGase. In this study, allergenicity and functional properties of peanut protein hydrolysate cross-linked by TGase were tested. Papain, ficin and bromelain were selected out of eight food-grade enzymes for the kinetic analysis of peanut protein hydrolysis that lead to high reduction rate (K) of the IgE-binding property. Peanuts hydrolyzed by the three selected enzymes (200 AzU/g) were used for IgE binding, TGase-crosslinking and functional property characterization. After hydrolysis, the IgE-binding properties of the peanut soluble extracts were decreased (by 85%-95%); and functional properties were also decreased as compared to intact peanut protein extracts. The TGase crosslinked hydrolysates had similar IgE-binding properties to the un-crosslinked hydrolysates, but with higher functional properties.

New tools to screen wild peanut species for aflatoxin accumulation and genetic fingerprinting.

BACKGROUND: Aflatoxin contamination in peanut seeds is still a serious problem for the industry and human health. No stable aflatoxin resistant cultivars have yet been produced, and given the narrow genetic background of cultivated peanuts, wild species became an important source of genetic diversity. Wild peanut seeds, however, are not abundant, thus, an effective method of screening for aflatoxin accumulation using minimal seeds is highly desirable. In addition, keeping record of genetic fingerprinting of each accession would be very useful for breeding programs and for the identification of accessions within germplasm collections. RESULTS: In this study, we report a method of screening for aflatoxin accumulation that is applicable to the small-size seeds of wild peanuts, increases the reliability by testing seed viability, and records the genetic fingerprinting of the samples. Aflatoxin levels observed among 20 wild peanut species varied from zero to 19000 ng.g-1 and 155 ng.g-1 of aflatoxin B1 and B2, respectively. We report the screening of 373 molecular markers, including 288 novel SSRs, tested on 20 wild peanut species. Multivariate analysis by Neighbor-Joining, Principal Component Analysis and 3D-Principal Coordinate Analysis using 134 (36 %) transferable markers, in general grouped the samples according to their reported genomes. The best 88 markers, those with high fluorescence, good scorability and transferability, are reported with BLAST results. High quality markers (total 98) that discriminated genomes are reported. A high quality marker with UPIC score 16 (16 out of 20 species discriminated) had significant hits on BLAST2GO to a pentatricopeptide-repeat protein, another marker with score 5 had hits on UDP-D-apiose synthase, and a third one with score 12 had BLASTn hits on La-RP 1B protein. Together, these three markers discriminated all 20 species tested. CONCLUSIONS: This study provides a reliable method to screen wild species of peanut for aflatoxin resistance using minimal seeds. In addition we report 288 new SSRs for peanut, and a cost-effective combination of markers sufficient to discriminate all 20 species tested. These tools can be used for the systematic search of aflatoxin resistant germplasm keeping record of the genetic fingerprinting of the accessions tested for breeding purpose.

Variability of arginine content and yield components in Valencia peanut germplasm.

Peanut seeds are rich in arginine, an amino acid that has several positive effects on human health. Establishing the genetic variability of arginine content in peanut will be useful for breeding programs that have high arginine as one of their goals. The objective of this study was to evaluate the variation of arginine content, pods/plant, seeds/pod, seed weight, and yield in Valencia peanut germplasm. One hundred and thirty peanut genotypes were grown under field condition for two years. A randomized complete block design with three replications was used for this study. Arginine content was analyzed in peanut seeds at harvest using spectrophotometry. Yield and yield components were recorded for each genotype. Significant differences in arginine content and yield components were found in the tested Valencia peanut germplasm. Arginine content ranged from 8.68-23.35 µg/g seed. Kremena was the best overall genotype of high arginine content, number of pods/plant, 100 seed weight and pod yield.

Next-generation transcriptome sequencing, SNP discovery and validation in four market classes of peanut, Arachis hypogaea L.

Single-nucleotide polymorphisms, which can be identified in the thousands or millions from comparisons of transcriptome or genome sequences, are ideally suited for making high-resolution genetic maps, investigating population evolutionary history, and discovering marker-trait linkages. Despite significant results from their use in human genetics, progress in identification and use in plants, and particularly polyploid plants, has lagged. As part of a long-term project to identify and use SNPs suitable for these purposes in cultivated peanut, which is tetraploid, we generated transcriptome sequences of four peanut cultivars, namely OLin, New Mexico Valencia C, Tamrun OL07 and Jupiter, which represent the four major market classes of peanut grown in the world, and which are important economically to the US southwest peanut growing region. CopyDNA libraries of each genotype were used to generate 2 × 54 paired-end reads using an Illumina GAIIx sequencer. Raw reads were mapped to a custom reference consisting of Tifrunner 454 sequences plus peanut ESTs in GenBank, compromising 43,108 contigs; 263,840 SNP and indel variants were identified among four genotypes compared to the reference. A subset of 6 variants was assayed across 24 genotypes representing four market types using KASP chemistry to assess the criteria for SNP selection. Results demonstrated that transcriptome sequencing can identify SNPs usable as selectable DNA-based markers in complex polyploid species such as peanut. Criteria for effective use of SNPs as markers are discussed in this context.

Plant prebiotics and human health: Biotechnology to breed prebiotic-rich nutritious food crops

Microbiota in the gut play essential roles in human health. Prebiotics are non-digestible complex carbohydrates that are fermented in the colon, yielding energy and short chain fatty acids, and selectively promote the growth of Bifidobacteria and Lactobacillae in the gastro-intestinal tract. Fructans and inulin are the best-characterized plant prebiotics. Many vegetable, root and tuber crops as well as some fruit crops are the best-known sources of prebiotic carbohydrates, while the prebiotic-rich grain crops include barley, chickpea, lentil, lupin, and wheat. Some prebiotic-rich crop germplasm have been reported in barley, chickpea, lentil, wheat, yacon, and Jerusalem artichoke. A few major quantitative trait loci and gene-based markers associated with high fructan are known in wheat. More targeted search in genebanks using reduced subsets (representing diversity in germplasm) is needed to identify accessions with prebiotic carbohydrates. Transgenic maize, potato and sugarcane with high fructan, with no adverse effects on plant development, have been bred, which suggests that it is feasible to introduce fructan biosynthesis pathways in crops to produce health-imparting prebiotics. Developing prebiotic-rich and super nutritious crops will alleviate the widespread malnutrition and promote human health. A paradigm shift in breeding program is needed to achieve this goal and to ensure that newly-bred crop cultivars are nutritious, safe and health promoting.

A first insight into population structure and linkage disequilibrium in the US peanut minicore collection.

Knowledge of genetic diversity, population structure, and degree of linkage disequilibrium (LD) in target association mapping populations is of great importance and is a prerequisite for LD-based mapping. In the present study, 96 genotypes comprising 92 accessions of the US peanut minicore collection, a component line of the tetraploid variety Florunner, diploid progenitors A. duranensis (AA) and A. ipaënsis (BB), and synthetic amphidiploid accession TxAG-6 were investigated with 392 simple sequence repeat (SSR) marker bands amplified using 32 highly-polymorphic SSR primer pairs. Both distance- and model-based (Bayesian) cluster analysis revealed the presence of structured diversity. In general, the wild-species accessions and the synthetic amphidiploid grouped separately from most minicore accessions except for COC155, and were eliminated from most subsequent analyses. UPGMA analysis divided the population into four subgroups, two major subgroups representing subspecies fastigiata and hypogaea, a third group containing individuals from each subspecies or possibly of mixed ancestry, and a fourth group, either consisting of COC155 alone if wild species were excluded, or of COC155, the diploid species, and the synthetic amphidiploid. Model-based clustering identified four subgroups- one each for fastigiata and hypogaea subspecies, a third consisting of individuals of both subspecies or of mixed ancestry predominantly from Africa or Asia, and a fourth group, consisting of individuals predominantly of var fastigiata, peruviana, and aequatoriana accessions from South America, including COC155. Analysis of molecular variance (AMOVA) revealed statistically-significant (P < 0.0001) genetic variance of 16.87% among subgroups. A total of 4.85% of SSR marker pairs revealed significant LD (at r(2) ≥ 0.1). Of the syntenic marker pairs separated by distances < 10 cM, 11-20 cM, 21-50 cM, and > 50 cM, 19.33, 5.19, 6.25 and 5.29% of marker pairs were found in strong LD (P ≤ 0.01), in accord with LD extending to great distances in self pollinated crops. A threshold value of r(2) > 0.035 was found to distinguish mean r(2) values of linkage distance groups statistically from the mean r(2) values of unlinked markers; LD was found to extend to 10 cM over the entire minicore collection by this criterion. However, there were large differences in r(2) values among marker pairs even among tightly-linked markers. The implications of these findings with regard to the possibility of using association mapping for detection of genome-wide SSR marker-phenotype association are discussed.

Genotypic variability and genotype by environment interactions in oil and fatty acids in high, intermediate, and low oleic acid peanut genotypes.

Variability of genotype and genotype x environment (G x E) interactions for fatty acids are important to develop high-oleic types in peanut varietal improvement programs. The objective of this study was to determine the variation in fatty acid composition among peanut genotypes and G x E interactions of fatty acids in three groups of genotypes with high, intermediate, and low-oleic acid. Twenty-one genotypes were tested in three environments consisting of two rainy seasons and one dry season. The results indicated that G x E interactions were significant for biomass, pod yield, and harvest index and also for oleic, linoleic acids, and O/L ratio. G x E interactions were less important than genotypic main effect. For oleic acid, significant interactions were found in the intermediate and low-oleic groups only. Therefore, selection for high-oleic trait in peanut breeding programs should be effective.

Gene expression profiling in peanut using high density oligonucleotide microarrays.

BACKGROUND: Transcriptome expression analysis in peanut to date has been limited to a relatively small set of genes and only recently has a significant number of ESTs been released into the public domain. Utilization of these ESTs for oligonucleotide microarrays provides a means to investigate large-scale transcript responses to a variety of developmental and environmental signals, ultimately improving our understanding of plant biology. RESULTS: We have developed a high-density oligonucleotide microarray for peanut using 49,205 publicly available ESTs and tested the utility of this array for expression profiling in a variety of peanut tissues. To identify putatively tissue-specific genes and demonstrate the utility of this array for expression profiling in a variety of peanut tissues, we compared transcript levels in pod, peg, leaf, stem, and root tissues. Results from this experiment showed 108 putatively pod-specific/abundant genes, as well as transcripts whose expression was low or undetected in pod compared to peg, leaf, stem, or root. The transcripts significantly over-represented in pod include genes responsible for seed storage proteins and desiccation (e.g., late-embryogenesis abundant proteins, aquaporins, legumin B), oil production, and cellular defense. Additionally, almost half of the pod-abundant genes represent unknown genes allowing for the possibility of associating putative function to these previously uncharacterized genes. CONCLUSION: The peanut oligonucleotide array represents the majority of publicly available peanut ESTs and can be used as a tool for expression profiling studies in diverse tissues.

Physiology and proteomics of the water-deficit stress response in three contrasting peanut genotypes.

Peanut genotypes from the US mini-core collection were analysed for changes in leaf proteins during reproductive stage growth under water-deficit stress. One- and two-dimensional gel electrophoresis (1- and 2-DGE) was performed on soluble protein extracts of selected tolerant and susceptible genotypes. A total of 102 protein bands/spots were analysed by matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS) and by quadrupole time-of-flight tandem mass spectrometry (Q-TOF MS/MS) analysis. Forty-nine non-redundant proteins were identified, implicating a variety of stress response mechanisms in peanut. Lipoxygenase and 1l-myo-inositol-1-phosphate synthase, which aid in inter- and intracellular stress signalling, were more abundant in tolerant genotypes under water-deficit stress. Acetyl-CoA carboxylase, a key enzyme of lipid biosynthesis, increased in relative abundance along with a corresponding increase in epicuticular wax content in the tolerant genotype, suggesting an additional mechanism for water conservation and stress tolerance. Additionally, there was a marked decrease in the abundance of several photosynthetic proteins in the tolerant genotype, along with a concomitant decrease in net photosynthesis in response to water-deficit stress. Differential regulation of leaf proteins involved in a variety of cellular functions (e.g. cell wall strengthening, signal transduction, energy metabolism, cellular detoxification and gene regulation) indicates that these molecules could affect the molecular mechanism of water-deficit stress tolerance in peanut.

Genetic diversity analysis in valencia peanut (Arachis hypogaea L.) using microsatellite markers.

Cultivated peanut or groundnut (Arachis hypogaea L) is an important source of oil and protein. Considerable variation has been recorded for morphological, physiological and agronomic traits, whereas few molecular variations have been recorded for this crop. The identification and understanding of molecular genetic diversity in cultivated peanut types will help in effective genetic conservation along with efficient breeding programs in this crop. The New Mexico breeding program has embarked upon a program of improvement of Valencia peanut (belonging to the sub species fastigiata), because efforts to improve the yield potential are lacking due to lack of identified divergent exotic types. For the first time, this study has shown molecular diversity using microsatellite markers in the cultivated Valencia peanut (sub spp. fastigiata) from around the globe. In this investigation, 48 cultivated Valencia peanut genotypes have been selected and analyzed using 18 fluorescently labeled SSR (f-SSR) primer pairs. These primer pairs amplified 120 polymorphic loci among the genotypes screened and amplified from 3 to 19 alleles with an average of 6.9 allele per primer pair. The f-SSR marker data was further analyzed using cluster algorithms and principal component analysis. The results indicated that (1) considerable genetic variations were discovered among the analyzed genotypes; (2) The f-SSR based clustering could identify the putative pedigree types of the present Valencia types of diverse origins, and (3) The f-SSR in general is sufficient to obtain estimates of genetic divergence for the material in study. The results are being utilized in our breeding program for parental selection and linkage map construction.