Download or read book Using Advanced Proximal Sensing and Genotyping Tools Combined with Bigdata Analysis Methods to Improve Soybean Yield written by Mohsen Yoosefzadeh Najafabadi and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Improving yield potential in major food-grade crops such as soybean (Glycine max L.) is the most sustainable way to address the growing global food demand and its security concerns. Selections for high-yielding cultivars have been mainly focused on the yield performance per se but not necessarily on secondary related-traits associated with yield. Recent substantial advances in proximal sensing have provided plant breeders with affordable and efficient tools for evaluating a large number of genotypes for important agronomic traits, including yield, at early growth stages. Nevertheless, the implementation of large datasets generated by proximal sensing such as hyperspectral reflectance in cultivar development programs is still challenging due to the essential need for intensive knowledge in computational and statistical analyses. Therefore, this thesis was aimed to: (1) investigate the potential use of soybean hyperspectral reflectance, hyperspectral reflectance indices (HVI), and yield components such as number of nodes (NP), number of non-reproductive nodes (NRNP), number of reproductive nodes (RNP), and number of pods (PP) per plant for predicting the final seed yield using different machine learning (ML) algorithms, (2) select the top-ranking hyperspectral reflectance and HVI in predicting soybean yield and fresh biomass (FBIO) using recursive feature elimination (RFE) strategy, (3) implement genetic optimization algorithm and the improved version of the strength Pareto evolutionary algorithm 2 (SPEA2) to optimize yield components and HVI for maximizing soybean seed yield and FBIO, and (4) study the genetics of soybean yield and its secondary related-traits in order to discover genomic regions underlying the traits by using genome-wide association study (GWAS). In this study, different ML algorithms such as ensemble stacking (E-S), ensemble bagging (EB), and deep neural network (DNN) were tested to evaluate their efficiency in predicting soybean yield and FBIO production using a panel of 250 genotypes evaluated in four environments. Also, for the first time, we implemented ML algorithms in GWAS to detect the associated QTL with soybean yield components. The results of this study may provide a perspective for geneticists and breeders regarding the use of ML algorithms in phenomics and genomics that will result in the selection of superior soybean genotypes.

Download or read book Advances in Agronomy written by Donald L. Sparks and published by Elsevier. This book was released on 2023-07-29 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in Agronomy, Volume 181, the latest release in this leading reference on agronomy, contains a variety of updates and highlights new advances in the field, with each chapter written by an international board of authors. Includes numerous, timely, state-of-the-art reviews on the latest advancements in agronomy Features distinguished, well recognized authors from around the world Builds upon this venerable and iconic review series Covers the extensive variety and breadth of subject matter in the crop and soil sciences

Download or read book Marker Assisted Selection MAS in Crop Plants volume II written by Ting Peng and published by Frontiers Media SA. This book was released on 2024-06-13 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: Global climate change, reductions in arable land, and food security demands that plant breeding will continue to play an imperative role in feeding 9 billion people sustainably by 2050. In order to face this challenge, modern plant breeding will necessitate the adoption of new technologies and practices to boost production of cultivated plants by capturing or generating more favorable genetic diversity. In crop plants, the majority of agronomically important traits are quantitatively inherited, controlled by multiple genes each with a small effect (quantitative trait loci, QTLs). The most common approach to pre-breeding is to use genetic mapping to identify QTLs for key phenotypic variation followed by introgressing those QTLs into the elite gene pool with marker-assisted selection (MAS), which can enhance the selection criteria of phenotypes comparing to conventional breeding with the selection of genes. As the cost of genotyping continues to decline, the use of genotyping-by-sequencing (GBS) technologies or whole genome re-sequencing, coupled with the release of the genome sequences of plant species have permitted the development of dense arrays of single nucleotide polymorphisms (SNPs) covering the entire genome, which have in turn paved the way to genome-wide association studies (GWAS). Meanwhile, fine mapping guided by genome sequences of many plant species have facilitated the exploration of functional genes; in addition, pan-genomes constructed from various available resources such as the reference sequence and its variants, raw reads and haplotype reference panels provide a new perspective on QTL locations and potential molecular targets for plant breeding. Similarly, new approaches to marker-trait association analyses such as quantitative trait locus sequencing (QTL-seq) and quantitative trait gene sequencing (QTG-seq) that are based on bulked-segregant analysis (BSA) and whole-genome resequencing will help accelerate QTL fine-mapping and identification of the causal genes. In conclusion, the tools and strategies for MAS in modern plant breeding have been expanding in recent years. By embracing a broad array of conventional and new molecular techniques, modern plant breeding has a bright future in delivering new crop cultivars to keep our food, fiber and biobased economy diverse and safe.

Download or read book Machine Learning and Artificial Intelligence for Smart Agriculture written by Chuanlei Zhang and published by Frontiers Media SA. This book was released on 2023-02-09 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Nutrition and Management of Animals We Keep as Companions Volume II written by Anna Katharine Shoveller and published by Frontiers Media SA. This book was released on 2024-01-11 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Development of a Rapid and In field Phenotyping Tool for Screening Protein Quality in Soybeans Glycine Max Using a Miniature Near Infrared Sensor written by Xin Rong Sia and published by . This book was released on 2019 with total page 94 pages. Available in PDF, EPUB and Kindle. Book excerpt: Soybean is an economically important crop that is a major plant-based protein source for livestock diets, with the amino acid composition of soybeans being crucial for determining the quality of livestock feed. Although protein quality monitoring is important, conventional protein and amino acid analyses typically involve laborious and lengthy processes. Unsurprisingly, soybean growers and breeders have identified time-consuming wet chemistry analytical methods as a major bottleneck in improving their breeding practices, calling for faster techniques to analyze amino acids in soybeans. For instance, classical amino acid analysis methods such as ion-exchange chromatography with ninhydrin derivatization require 60 – 120 minutes of analysis time per sample and limited selectivity due to the use of optical detectors, which cannot resolve overlapping peaks. A faster alternative is the use of portable near-infrared (NIR) spectroscopy equipment combined with chemometrics that allows for direct measurement of ground soybean and even intact soybean seeds in real-time. ¬¬Our objective was to develop and evaluate the feasibility of using a sensor-based method for in-field analysis of amino acid composition in soybeans. Twenty-two soybean samples of different cultivars and grown over a period of two years across the Midwest region were selected for analysis, in addition to nineteen soy isolates, concentrates and powders obtained via online retailers. In order to develop a reliable NIR prediction model, we first needed a reliable reference method for profiling the amino acid content of the soybeans, so propyl chloroformate derivatization (PCD) coupled to gas chromatography-mass spectrometry (GC-MS) was performed to obtain the amino acid values of soybeans. GC-MS results showed high sensitivity with a LOQ of 1.1 – 14.0 ppm depending on the type of amino acid, high selectivity, and calibration curves with good linearity (R > 0.97 for most amino acids). External validation of our method with a classical amino acid analysis that uses ion-exchange chromatography with ninhydrin derivatization showed that our method is comparable in accuracy, with a correlation of R2 = 0.98, but precision needs to be improved. The largest sources of experimental errors originated from the solid-phase extraction, derivatization, and protein hydrolysis steps. Protein hydrolysis variables that had the most influence on amino acid yield was found to be the mass of samples, hydrolysis errors, and type of oxidation inhibitor used so it is recommended that these parameters are preferentially optimized. Our method demonstrated faster run times and higher selectivity than classical methods, allowing chromatographic analysis to be completed in as little as 10 mins per sample, and co-eluting peaks were successfully resolved due to the monitoring of mass fragments. Spectral collection was done using both ground soybeans and intact soybean seeds and analyzed by partial least squares regression (PLSR) to develop calibration models for predicting total protein and critical amino acid (lysine, threonine, methionine, tryptophan, cysteine) levels in soybean. The miniature NIR device we used is the first handheld device on the market to provide a spectral scanning range of between 1350 – 2500 nm, covering most of the first overtones and combination bands. This is in contrast with other miniature devices which tend to scan at lower wavelengths and cover second overtone bands, which gives less specific chemical information about the food constituents scanned. Combining spectral information with reference amino acid values determined using the classical method allowed us to build prediction models that showed good linear correlation between spectra and amino acids (r > 0.97 for ground samples, r > 0.94 for intact seeds) with low standard error of cross-validation (1.630% for protein, 0.041 – 1.630% for amino acids). Our findings support that a miniature spectrometer combined with pattern recognition is capable of real-time monitoring of important amino acids in soybeans. We used a miniature device that employed Micro Electro Mechanical Systems (MEMS) technology, resembling the quality of a Michelson interferometer with improved band resolution. The higher sensitivity and accuracy of MEMS is superior to some other miniature NIR spectrometers on the market and allowed us to successfully characterize the amino acid profile of soybeans in as little as 15 seconds.

Download or read book Genetics Genomics and Breeding of Soybean written by Kristin Bilyeu and published by CRC Press. This book was released on 2016-04-19 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: The soybean is an economically important leguminous seed crop for feed and food products that is rich in seed protein (about 40 percent) and oil (about 20 percent); it enriches the soil by fixing nitrogen in symbiosis with bacteria. Soybean was domesticated in northeastern China about 2500 BC and subsequently spread to other countries. The enormous

Download or read book Genetics and Genomics of Soybean written by Gary Stacey and published by Springer Science & Business Media. This book was released on 2008-05-07 with total page 405 pages. Available in PDF, EPUB and Kindle. Book excerpt: Soybean genomics is of great interest as one of the most economically important crops and a major food source. This book covers recent advances in soybean genome research, including classical, RFLP, SSR, and SNP markers; genomic and cDNA libraries; functional genomics platforms; genetic and physical maps; and gene expression profiles. The book is for researchers and students in plant genetics and genomics, plant biology and pathology, agronomy, and food sciences.

Download or read book Using Remote Sensing in Soybean Breeding written by Hatice Aslan and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Remote sensing technologies might serve as indirect selection tools to improve phenotyping to differentiate genotypes for yield in soybean breeding program as well as the assessment of soybean cyst nematode (SCN), Heterodera glycines. The objective of these studies were to: i) investigate potential use of spectral reflectance indices (SRIs) and canopy temperature (CT) as screening tools for soybean grain yield in an elite, segregating population; ii) determine the most appropriate growth stage(s) to measure SRI's for predicting grain yield; and iii) estimate SCN population density among and within soybean cultivars utilizing canopy spectral reflectance and canopy temperature. Experiment 1 was conducted at four environments (three irrigated and one rain-fed) in Manhattan, KS in 2012 and 2013. Each environment evaluated 48 F4- derived lines. In experiment 2, two SCN resistant cultivars and two susceptible cultivars were grown in three SCN infested field in Northeast KS, in 2012 and 2013. Initial (Pi) and final SCN soil population (Pf) densities were obtained. Analyses of covariance (ANCOVA) revealed that the green normalized vegetation index (GNDVI) was the best predictive index for yield compared to other SRI's and differentiated genotype performance across a range of reproductive growth stages. CT did not differentiate genotypes across environments. In experiment 2, relationships between GNDVI, reflectance at single wavelengths (675 and 810 nm) and CT with Pf were not consistent across cultivars or environments. Sudden death syndrome (SDS) may have confounded the relationships between remote sensing data and Pf. Therefore, it would be difficult to assess SCN populations using remote sensing based on these results.

Download or read book Utilizing Remote Sensing to Improve Yield Maps for Corn and Soybean Fields written by Jeffrey Topel (B.) and published by . This book was released on 2006 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Soybean Improvement written by Shabir Hussain Wani and published by Springer Nature. This book was released on 2022-10-17 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: Soybean (Glycine max L. (Merr)) is one of the most important crops worldwide. Soybean seeds are vital for both protein meal and vegetable oil. Soybean was domesticated in China, and since last 4-5 decades it has become one of the most widely grown crops around the globe. The crop is grown on an anticipated 6% of the world’s arable land, and since the 1970s, the area in soybean production has the highest percentage increase compared to any other major crop. It is a major crop in the United States, Brazil, China and Argentina and important in many other countries. The cultivated soybean has one wild annual relative, G. soja, and 23 wild perennial relatives. Soybean has spread to many Asian countries two to three thousand years ago, but was not known in the West until the 18th century. Among the various constraints responsible for decrease in soybean yields are the biotic and abiotic stresses which have recently increased as a result of changing climatic scenarios at global level. A lot of work has been done for cultivar development and germplasm enhancement through conventional plant breeding. This has resulted in development of numerous high yielding and climate resilient soybean varieties. Despite of this development, plant breeding is long-term by nature, resource dependent and climate dependent. Due to the advancement in genomics and phenomics, significant insights have been gained in the identification of genes for yield improvement, tolerance to biotic and abiotic stress and increased quality parameters in soybean. Molecular breeding has become routine and with the advent of next generation sequencing technologies resulting in SNP based molecular markers, soybean improvement has taken a new dimension and resulted in mapping of genes for various traits that include disease resistance, insect resistance, high oil content and improved yield. This book includes chapters from renowned potential soybean scientists to discuss the latest updates on soybean molecular and genetic perspectives to elucidate the complex mechanisms to develop biotic and abiotic stress resilience in soybean. Recent studies on the improvement of oil quality and yield in soybean have also been incorporated.

Download or read book Assessing the Efficiency of Phenotypic and Molecular Genotype Selection Methods for Complex Traits in Soybean written by Catherine Nyaguthii Nyinyi and published by . This book was released on 2011 with total page 113 pages. Available in PDF, EPUB and Kindle. Book excerpt: Soybean [Glycine max (L.) Merrill] is an important source of protein and oil for both nutritional and industrial applications. Increasing seed yield and protein concentration is the main goal of many soybean breeders to meet market demands. Soybean breeders have occasionally succeeded in producing high yielding cultivars with increased protein content using conventional means despite the negative correlation that exists between these two traits. The efficiency of breeding for seed yield and protein concentration improvement in soybean could be increased using marker assisted selection (MAS) breeding strategies to select genotypes containing favorable alleles for faster cultivar development. The objective of this study was to identify quantitative trait loci (QTL) associated with seed yield, and separately, seed protein concentration and then compare phenotypic selection (PHE) and MAS approaches for seed yield and protein concentration improvement. Two hundred and eighty two F5 derived recombinant inbred lines (RILs) were developed from a cross of Essex [centered x, actual symbol not reproducible] Williams 82 and genotyped with 1586 single nucleotide polymorphism (SNP) markers. The population was divided by days to maturity (10 days) into three tests (early, mid and late) each with 94 genotypes, with one genotype overlapping in maturity in the mid and late tests. In 2009, the three tests, parents and checks were grown in a randomized complete block design (RCBD) in: Fayetteville, AR; Harrisburg, IL and, Knoxville, TN replicated three times, and evaluated for seed yield and protein concentration. Data were combined within each test across three locations and analyzed using the MIXED procedure of SAS to determine that there were significant genotypic differences among RILs. Composite interval mapping (CIM) detected nine seed yield and ten protein concentration QTL which may be good candidates for MAS as they were environmentally stable. Selections to compare PHE, and MAS for seed yield and protein concentration provided 8 replicated field tests in four relative maturity groups grown in a RCBD replicated three times in three locations in Tennessee, in 2010. We demonstrated that both MAS and PHE may be used to select quantitative traits; however, more studies are required to optimize MAS for quantitative trait improvement.

Download or read book The Soybean Genome written by Henry T. Nguyen and published by Springer. This book was released on 2017-09-20 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines the application of soybean genome sequences to comparative, structural, and functional genomics. Since the availability of the soybean genome sequence has revolutionized molecular research on this important crop species, the book also describes how the genome sequence has shaped research on transposon biology and applications for gene identification, tilling and positional gene cloning. Further, the book shows how the genome sequence influences research in the areas of genetic mapping, marker development, and genome-wide association mapping for identifying important trait genes and soybean breeding. In closing, the economic and botanical aspects of the soybean are also addressed.

Download or read book Investigations Into Using Vegetative Indices in Soybean Breeding written by Randi R. Clark and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Yield in soybean (Glycine max (L.) Merr) needs to dramatically increase across the world to feed the growing population. Remote sensing and high-throughput phenotyping may provide a tool to better phenotype soybean genotypes. This research was conducted to: 1) examine the relationships between NDVI and CT with seed yield, maturity, lodging, and height, 2) determine if the time of day and growth stage have an effect on the spectral readings, 3) examine the relationships between spectral reflectance and traits associated with drought tolerance, and 4) evaluate how weather variables impact the ability of vegetative indices and canopy temperature to detect differences among genotypes. Ninety genotypes from the mapping population derived from the cross between KS4895 x Jackson were evaluated in Manhattan, KS, in 2013 and in McCune, Pittsburg, and Salina, KS in 2014. Genotypes were planted in a randomized complete bloc design in four-row, 3.4m long plots spaced 76 cm apart. Plant height, lodging, maturity and seed yield was collected on the center two rows of each plot. Spectral readings used to calculate a normalized differential vegetative index (NDVI) and canopy temperature (CT) were taken during reproductive growth. Nitrogen fixation trait and drought tolerance data was collected by the University of Arkansas. This population exhibited a substantial genetic variation for all traits evaluated. Correlations of NDVI and CT entry means with the agronomic traits were small and inconsistent. Time of day and growth stage were not important in differentiating genotypes. Differences in NDVI and CT did account for some genetic variation in drought tolerance traits, however, the strength of the associations were small. None of the weather variables were consistently associated with an increase or decrease in entry or error variance across the four environments. Stronger associations need to be established to use NDVI or CT to characterize differences in genotypes in a plant breeding program.

Download or read book Characterization of Soybean Seed Yield Using Optimized Phenotyping written by Brent Scott Christenson and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Crops research moving forward faces many challenges to improve crop performance. In breeding programs, phenotyping has time and economic constraints requiring new phenotyping techniques to be developed to improve selection efficiency and increase germplasm entering the pipeline. The objectives of these studies were to examine the changes in spectral reflectance with soybean breeding from 1923 to 2010, evaluate band regions most significantly contributing to yield estimation, evaluate spectral reflectance data for yield estimation modeling across environments and growth stages and to evaluate the usefulness of spectral data as an optimized phenotyping technique in breeding programs. Twenty maturity group III (MGIII) and twenty maturity group IV (MGIV) soybeans, arranged in a randomized complete block design, were grown in Manhattan, KS in 2011 and 2012. Spectral reflectance data were collected over the growing season in a total of six irrigated and water- stressed environments. Partial least squares and multiple linear regression were used for spectral variable selection and yield estimation model building. Significant differences were found between genotypes for yield and spectral reflectance data, with the visible (VI) having greater differences between genotypes than the near-infrared (NIR). This study found significant correlations with year of release (YOR) in the VI and NIR portions of the spectra, with newer released cultivars tending to have lower reflectance in the VI and high reflectance in the NIR. Spectral reflectance data accounted for a large portion of variability for seed yield between genotypes using the red edge and NIR portions of the spectra. Irrigated environments tended to explain a larger portion of seed yield variability than water-stressed environments. Growth stages most useful for yield estimation was highly dependent upon the environment as well as maturity group. This study found that spectral reflectance data is a good candidate for exploration into optimized phenotyping techniques and with further research and validation datasets, may be a suitable indirect selection technique for breeding programs.

Download or read book Using Remote Sensing and Grid based Meteorological Datasets for Regional Soybean Crop Yield Prediction and Crop Monitoring written by Preeti Mali and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Regional crop yield estimations using crop models is a national priority due to its contributions to crop security assessment and food pricing policies. Many of these crop yield assessments are performed using time-consuming, intensive field surveys. This research was initiated to test the applicability of remote sensing and grid-based meteorological model data for providing mproved and efficient predictive capabilities for crop bio-productivity. The soybean prediction model (Sinclair model) used in this research, requires daily data inputs to simulate yield which are temperature, precipitation, solar radiation, day length initialization of certain soil moisture parameters for each model run. The traditional meteorological datasets were compared with simulated South American Land Data Assimilation System (SALDAS) meteorological datasets for Sinclair model runs and for initializing soil moisture inputs. Considering the fact that grid-based meteorological data has the resolution of 1/8th of a degree, the estimations demonstrated a reasonable accuracy level and showed promise for increase in efficiency for regional level yield predictions. The research tested daily composited Normalized Difference Vegetation Index (NDVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor (both AQUA and TERRA platform) and simulated Visible/Infrared Imager Radiometer Suite (VIIRS) sensor product (a new sensor planned to be launched in the near future) for crop growth and development based on phenological events. The AQUA and TERRA fusion based daily MODIS NDVI was utilized to developed a planting date estimation method. The results have shown that daily MODIS composited NDVI values have the capability for enhanced monitoring of the soybean crop growth and development with respect to soybean growth and development. The method was able to predict planting date within ±3.4 days. A geoprocessing framework for extracting data from the grid data sources was developed. Overall, this study was able to demonstrate the utility of MODIS and VIIRS NDVI datasets and SALDAS meteorological data for providing effective inputs to crop yield models and the ability to provide an effective remote sensing-based regional crop monitoring. The utilization of these datasets helps in eliminating the ground-based data collection, which improves cost and time efficiency and also provides capability for regional crop monitoring.

Download or read book Linking Multiple Layers of Information for Understanding Soybean Yield Variability written by Ayse Irmak and published by . This book was released on 2002 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: ABSTRACT (cont.): An ANN model explained 57% of the yield variability for independent years in the McGarvey field. When the ANN was trained with data from 5 fields, the root mean square error of prediction was less than 14% of mean actual yield for two independent fields. Standard errors of attribution were 92, 262, and 171 kg/ha for losses to soybean yields due to soil pH, SCN, and weeds, respectively. Variations in water relations in 30 sites in the Heck field showed that water stress is a leading cause of variation in soybean yield. Soil water explained about 50% of the yield variability. The variable drought stress occurred after full canopy was reached, and primarily affected pod numbers. Three different techniques for analyzing spatial yield variability of soybean yields in multiple years resulted in similar conclusions. Water stress variation over space and time is a major reason for soybean yield variation. This research showed that crop-model based analysis procedures can be used to separate effects of different stresses such as water stress, soil pH, nematodes and weeds on yield when combined with statistical regression procedures. As more data are collected in precision agriculture fields, these techniques can be further developed and evaluated; the procedures have considerable promise for practical use in site-specific management.