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Author: Byron J Evers Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Globally wheat is one of the three most important cereal crops globally providing 20% of protein and total calories consumed. In the world as well as the state of Kansas, wheat is planted on more acres than any other crop. Additionally, wheat sales generated $1.27 billion in revenue in 2021 making wheat an economic driver for the entire state. However, the annual genetic gain in wheat is 0.8-1.2% and is not sufficient to support the increasing global population. Therefore, the adoption of new technology and computational methods are critical to increase genetic gain and increase wheat adaptability both globally and in the Central Plains. Proper temporal resolution is critical for quality HTP sensor data collection, as collection at key physiological growing points can increase yield prediction and assist with phenotypic selection. However, growth stages are dependent on weather and fluctuate both across locations and years. This makes day of year or day after sowing a poor phenology metric, particularly with winter wheat where the vernalization requirement compounds phenology prediction challenges and significantly shifts developmental stages relative to calendar days. This study was designed to assess the performance of various phenology models to predict heading time of both historically adapted and experimental genotypes of wheat genotypes in Kansas. The results suggest that full season models with multi-phase coefficients can increase phenology prediction over traditional thermal indices. However, using cumulative thermal times after the vernalization requirements also provided phenology predictions that were statistically similar to the full season phase change models. Genotype by environment interactions is a prominent issue for breeding programs, particularly when performance testing elite lines across multiple locations and years. In addition to macroenvironments, variations in soil properties have shown to develop microenvironments within location years. These soil microenvironments can potentially be quantified through both traditional and precision agriculture tools. Whereas, traditional soil sampling density is limited by cost and time, precision agriculture on-the-go soil sensors have the potential to gather large quantities of data. However, these measurements are often giving only relative measurements. Through this experiment two sensor platforms were evaluated as potential tools to quantify spatial variability within breeding programs. This study showed that soil spatial variability does impact genotype yield performance and that indirect measurements from both sensor platforms can quantify this impact. The continued development of high quality, cost effective multi-spectral imaging devices has led to numerous studies to evaluate this technologies ability to predict traits and grain yield. Despite these advancements the widespread implementation of these tools for selection has been slow and most breeders still rely on harvested grain yield and visual selection for cultivar advancement. The intention of this experiment was to evaluate high spatial resolution data from, multi-spectral sensors at multi-temporal collection points to make yield group rank order selections. Additionally, a random forest algorithm was used to evaluate the potential of incorporating machine learning with HTP data as a selection tool. Although the rank order correlations were higher than the correlation to grain yield, the selection accuracies of random forest were not statistically better than the no-information rate. However, this study does lay the groundwork for future similar studies using alternative sensor aided metrics and machine learning algorithms. Overall, the combined results of these studies show that these precision agriculture tools have to potential to increase genetic gain in plant breeding. However, these studies also show that both sensor and computational limitations still exist. Moving forward it is pivotal that future studies focus on technology combinations that have the potential to easily be implemented within a breeding program.
Author: Byron J Evers Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Globally wheat is one of the three most important cereal crops globally providing 20% of protein and total calories consumed. In the world as well as the state of Kansas, wheat is planted on more acres than any other crop. Additionally, wheat sales generated $1.27 billion in revenue in 2021 making wheat an economic driver for the entire state. However, the annual genetic gain in wheat is 0.8-1.2% and is not sufficient to support the increasing global population. Therefore, the adoption of new technology and computational methods are critical to increase genetic gain and increase wheat adaptability both globally and in the Central Plains. Proper temporal resolution is critical for quality HTP sensor data collection, as collection at key physiological growing points can increase yield prediction and assist with phenotypic selection. However, growth stages are dependent on weather and fluctuate both across locations and years. This makes day of year or day after sowing a poor phenology metric, particularly with winter wheat where the vernalization requirement compounds phenology prediction challenges and significantly shifts developmental stages relative to calendar days. This study was designed to assess the performance of various phenology models to predict heading time of both historically adapted and experimental genotypes of wheat genotypes in Kansas. The results suggest that full season models with multi-phase coefficients can increase phenology prediction over traditional thermal indices. However, using cumulative thermal times after the vernalization requirements also provided phenology predictions that were statistically similar to the full season phase change models. Genotype by environment interactions is a prominent issue for breeding programs, particularly when performance testing elite lines across multiple locations and years. In addition to macroenvironments, variations in soil properties have shown to develop microenvironments within location years. These soil microenvironments can potentially be quantified through both traditional and precision agriculture tools. Whereas, traditional soil sampling density is limited by cost and time, precision agriculture on-the-go soil sensors have the potential to gather large quantities of data. However, these measurements are often giving only relative measurements. Through this experiment two sensor platforms were evaluated as potential tools to quantify spatial variability within breeding programs. This study showed that soil spatial variability does impact genotype yield performance and that indirect measurements from both sensor platforms can quantify this impact. The continued development of high quality, cost effective multi-spectral imaging devices has led to numerous studies to evaluate this technologies ability to predict traits and grain yield. Despite these advancements the widespread implementation of these tools for selection has been slow and most breeders still rely on harvested grain yield and visual selection for cultivar advancement. The intention of this experiment was to evaluate high spatial resolution data from, multi-spectral sensors at multi-temporal collection points to make yield group rank order selections. Additionally, a random forest algorithm was used to evaluate the potential of incorporating machine learning with HTP data as a selection tool. Although the rank order correlations were higher than the correlation to grain yield, the selection accuracies of random forest were not statistically better than the no-information rate. However, this study does lay the groundwork for future similar studies using alternative sensor aided metrics and machine learning algorithms. Overall, the combined results of these studies show that these precision agriculture tools have to potential to increase genetic gain in plant breeding. However, these studies also show that both sensor and computational limitations still exist. Moving forward it is pivotal that future studies focus on technology combinations that have the potential to easily be implemented within a breeding program.
Author: Megan Calvert Publisher: ISBN: Category : Languages : en Pages :
Book Description
Growing populations and shifting climatic conditions are placing constraints on global food security that have not previously been experienced. Novel technologies are being developed to combat this challenge at many levels including crop germplasm improvement. It is unlikely that all of these technologies will provide a significant benefit to crop breeding programs whilst still being economically viable. The use of high-throughput phenotyping (HTP) in crop breeding programs is becoming more commonplace due in part to the relatively low establishment costs for several of the technologies. Genomic prediction models are also becoming more common in crop breeding programs due to their success in animal breeding schemes and decreases in sequencing costs for genotyping. Here we examine the utility of several HTP technologies and genomic prediction in wheat breeding in Kansas. An uncrewed aerial system (UAS) measuring reflectance values at different bandwidths was used to formulate vegetation indices (VIs) which are known to correlate with economically valuable phenotypes. The genetic architecture of these VIs was examined in an association mapping population of winter wheat (Triticum aestivum) and their use for genomic prediction was examined in the Kansas State University (KSU) winter wheat breeding program. Several economic and population parameters were determined under which genomic prediction would be favored in the breeding program. Based on simulated and empirical observations of model accuracy the KSU breeding program could potentially make larger genetic gains using genomic prediction than traditional phenotypic selection methods.
Author: Robert C. Gaynor Publisher: ISBN: Category : Languages : en Pages :
Book Description
Wheat breeders are constantly working to develop new wheat varieties with improved performance for agronomically important traits such as yield and disease resistance. Identifying better ways of phenotyping germplasm, developing methods for predicting performance based on genetic information, and identifying novel sources of genetic disease resistance can all improve the efficiency of breeding efforts. Three studies relating to these research interests were conducted. Synthetic hexaploid wheat lines were screened for resistance to root-lesion nematodes, an economically important pest of wheat. This resulted in the identification of three lines resistant to the root-lesion nematode species Pratylenchus thornei. Grain yield data from multi-location yield trials and average yields for counties in Kansas were used to identify wheat production areas in Kansas. Knowledge obtained from this study is useful for both interpreting data from yield trials and deciding where to place them in order to identify new higher yielding varieties. These data also aided the final research study, developing a genomic selection (GS) model for yield in the Kansas State University wheat breeding program. This model was used to assess the accuracy of GS in conditions experienced in a breeding project. Available measurements of GS have been constructed using simulations or using conditions not typical of those experienced in a wheat breeding program. The estimate of accuracy determined in this study was less than many of the reported measurements. This measure of accuracy will aid in determining if GS is a cost efficient tool for use in wheat breeding.
Author: Atena Haghighattalab Publisher: ISBN: Category : Languages : en Pages :
Book Description
Wheat breeders are in a race for genetic gain to secure the future nutritional needs of a growing population. Multiple barriers exist in the acceleration of crop improvement. Emerging technologies are reducing these obstacles. Advances in genotyping technologies have significantly decreased the cost of characterizing the genetic make-up of candidate breeding lines. However, this is just part of the equation. Field-based phenotyping informs a breeder's decision as to which lines move forward in the breeding cycle. This has long been the most expensive and time-consuming, though most critical, aspect of breeding. The grand challenge remains in connecting genetic variants to observed phenotypes followed by predicting phenotypes based on the genetic composition of lines or cultivars. In this context, the current study was undertaken to investigate the utility of UAS in assessment field trials in wheat breeding programs. The major objective was to integrate remotely sensed data with geospatial analysis for high throughput phenotyping of large wheat breeding nurseries. The initial step was to develop and validate a semi-automated high-throughput phenotyping pipeline using a low-cost UAS and NIR camera, image processing, and radiometric calibration to build orthomosaic imagery and 3D models. The relationship between plot-level data (vegetation indices and height) extracted from UAS imagery and manual measurements were examined and found to have a high correlation. Data derived from UAS imagery performed as well as manual measurements while exponentially increasing the amount of data available. The high-resolution, high-temporal HTP data extracted from this pipeline offered the opportunity to develop a within season grain yield prediction model. Due to the variety in genotypes and environmental conditions, breeding trials are inherently spatial in nature and vary non-randomly across the field. This makes geographically weighted regression models a good choice as a geospatial prediction model. Finally, with the addition of georeferenced and spatial data integral in HTP and imagery, we were able to reduce the environmental effect from the data and increase the accuracy of UAS plot-level data. The models developed through this research, when combined with genotyping technologies, increase the volume, accuracy, and reliability of phenotypic data to better inform breeder selections. This increased accuracy with evaluating and predicting grain yield will help breeders to rapidly identify and advance the most promising candidate wheat varieties.
Author: Ashley Abigail Lorence Publisher: ISBN: Category : Languages : en Pages :
Book Description
Sensor technology has become more important in precision agriculture, by real time sensing for site specific management to monitor crops during the season especially nitrogen (N). In Kansas N available in the soils can vary year to year or over a course of a year. The objective of this study was to compare current available passive (PS) and active optical sensor technologies (AOS) performance in regards to sky conditions effects and derive the NDVI (normalized difference vegetation index) relationship to wheat yield, as well as evaluate KSU optical sensor-based N recommendations against KSU soil test N recommendation system and sUAS (small unmanned aircraft systems) based recommendation algorithms with the PS and AOS platforms. Each year (2015-2016 & 2016-2017) five field trails across Kansas were conducted during the winter wheat crop year in cooperation with county ag agents, farmers, and KSU Agronomy Experiment Fields. Treatments consisted of N response curve, 1st and 2nd generation KSU N recommendation algorithms, sUAS based recommendation algorithms, and KSU soil test based N recommendations applied in the spring using N rates ranging from 0 to 140 kg ha−1. Results indicate the Holland Scientific Rapid Scan and MicaSense RedEdge NDVI data was strongly correlated and generated strong relationships with grain yield at 0.60 and 0.57 R2 respectively. DJI X3 lacks an NIR band producing uncalibrated false NDVI and no relationship to grain yield at 0.03 R2. Calibrated NDVI from both sensors are effective for assessing yield potential and could be utilized for developing N recommendation algorithms. However, sensor based treatments preformed equal to higher yields compared the KSU soil test recommendations, as well as reduced the amount of fertilizer applied compared to the soil test recommendation. The intensive management algorithm was the most effective in determining appropriate N recommendations across locations. This allows farmers to take advantage of potential N mineralization that can occur in the spring. Further research is needed considering on setting the NUE (nitrogen use efficiency) in KSU N rec. algorithms for effects of management practice, weather, and grain protein for continued refinement.
Author: Huan Wang Publisher: ISBN: Category : Languages : en Pages :
Book Description
Crop assessment and monitoring is important to crop management both at crop production level and research plot level, such as high-throughput phenotyping in breeding programs. Optical sensors based agricultural applications have been around for decades and have soared over the past ten years because of the potential of some new technologies to be low-cost, accessible, and high resolution for crop remote sensing which can help to improve crop management to maintain producers' income and diminish environmental degradation. The overall objective of this study was to develop methods and compare the different optical sensors in crop assessment and monitoring at different scales and perspectives. At crop production level, we reviewed the current status of different optical sensors used in precision crop production including satellite-based, manned aerial vehicle (MAV)-based, unmanned aircraft system (UAS)-based, and vehicle-based active or passive optical sensors. These types of sensors were compared thoroughly on their specification, data collection efficiency, data availability, applications and limitation, economics, and adoption. At research plot level, four winter wheat experiments were conducted to compare three optical sensors (a Canon T4i® modified color infrared (CIR) camera, a MicaSense RedEdge® multispectral imager and a Holland Scientific® RapidScan CS-45® hand-held active optical sensor (AOS)) based high-throughput phenotyping for in-season biomass estimation, canopy estimation, and grain yield prediction in winter wheat across eleven Feekes stages from 3 through 11.3. The results showed that the vegetation indices (VIs) derived from the Canon T4i CIR camera and the RedEdge multispectral camera were highly correlated and can equally estimate winter wheat in-season biomass between Feekes 3 and 11.1 with the optimum point at booting stage and can predict grain yield as early as Feekes 7. Compared to passive sensors, the RapidScan AOS was less powerful and less temporally stable for biomass estimation and yield prediction. Precise canopy height maps were generated from a CMOS sensor camera and a multispectral imager although the accuracy could still be improved. Besides, an image processing workflow and a radiometric calibration method were developed for UAS based imagery data as bi-products in this project. At temporal dimension, a wheat phenology model based on weather data and field contextual information was developed to predict the starting date of three key growth stages (Feekes 4, 7, and 9), which are critical for N management. The model could be applied to new data within the state of Kansas to optimize the date for optical sensor (such as UAS) data collection and save random or unnecessary field trips. Sensor data collected at these stages could then be plugged into pre-built biomass estimation models (mentioned in the last paragraph) to estimate the productivity variability within 20% relative error.
Author: Karansher Singh Sandhu Publisher: ISBN: Category : Wheat Languages : en Pages : 292
Book Description
Classical plant breeding has evolved considerably during the last century. However, the rate of genetic gain is insufficient to cope with a 2% annual increase in the human population, which is expected to reach 9.8 billion by 2050. Plant breeders and scientists are under pressure to develop new varieties and crops having higher yield, higher nutritional value, climate resilience, and disease and insect resistance. The solution requires the merging of new techniques like next-generation sequencing, genome-wide association studies, genomic selection, high throughput phenotyping, speed breeding, machine and deep learning, and CRISPR mediating gene editing with previously used tools and breeder's skills. The main goal of this research was to explore the potential of genomics, phenomics, machine and deep learning tools in a wheat (Triticum aestivum L.) breeding program. Grain yield and grain protein content (GPC) are two traits very important in hard red spring wheat breeding, yet difficult to select for due to their well-known negative correlation. A nested association mapping population was used to map the regions controlling the stability of grain protein content. This study also demonstrated that genome-wide prediction of GPC with ridge regression best linear unbiased (rrBLUP) estimates reached up to r = 0.69. Genomic selection (GS) is transforming the field of plant breeding and implementing models that improve prediction accuracy for complex traits is needed. Analytical methods for complex datasets traditionally used in other disciplines represent an opportunity for improving prediction accuracy. We predicted five different quantitative traits with varying genetic architecture using cross-validations, independent validations, and different sets of SNP markers. Deep learning models gave 0 to 5% higher prediction accuracy than rrBLUP model under both cross and independent validations for all five traits used in this study. Screening for end-use quality traits is usually secondary to grain yield due to high labor needs, cost of testing, and large seed requirements for phenotyping. Genomic selection provides an alternative to predict performance using genome-wide markers under forward and across location predictions, where previous years dataset can be used to build the models. Nine different models, including two machine learning and two deep learning models, were explored for cross-validation, forward, and across locations predictions. The prediction accuracies for different traits varied from 0.45 - 0.81, 0.29 - 0.55, and 0.27 - 0.50 under cross-validation, forward, and across location predictions. Genomics and phenomics have the potential to revolutionize the field of plant breeding. Incorporation of secondary correlated traits in GS models has been demonstrated to improve accuracy. In another study, ability to predict GPC and grain yield was assessed using secondary traits, univariate, covariate, and multivariate GS models for within and across cycle predictions. Our results indicate that GS accuracy increased by an average of 12 for GPC and 20% for grain yield by including secondary traits in the models. An increased prediction ability for GPC and grain yield with the inclusion of secondary traits demonstrates the potential to improve the genetic gain per unit time and cost in wheat breeding.
Author: Daljit Singh Publisher: ISBN: Category : Languages : en Pages :
Book Description
A central question in modern biology is to understand the genotype-to-phenotype (G2P) link, that is, how the genetics of an organism results in specific characteristics. However, prediction of phenotypes from genotypes is a difficult problem due to the complex nature of genomes, the environment, and their interactions. While the recent advancements in genome sequencing technologies have provided almost unlimited access to high-density genetic markers, large-scale rapid and accurate phenotyping of complex plant traits remains a major bottleneck. Here, we demonstrate field-based complex trait assessment approaches using a commercially available light-weight Unmanned Aerial Systems (UAS). By deploying novel data acquisition and processing pipelines, we quantified lodging, ground cover, and crop growth rate of 1745 advanced spring wheat lines at multiple time-points over the course of three field seasons at three field sites in South Asia. High correlations of digital measures to visual estimates and superior broad-sense heritability demonstrate these approaches are amenable for reproducible assessment of complex plant traits in large breeding nurseries. Using these validated high-throughput measurements, we applied genome-wide association and prediction models to assess the underlying genetic architecture and genetic control. Our results suggest a diffuse genetic architecture for lodging and ground cover in wheat, but heritable genetic variation for prediction and selection in breeding programs. The logistic regression-derived parameters of dynamic plant height exhibited strong physiological linkages with several developmental and agronomic traits, suggesting the potential targets of selection and the associated tradeoffs. Taken together, our highly reproducible approaches provide a proof-of-concept application of UAS-based phenomics that is scalable to tens-of-thousands of plots in breeding and genetic studies as will be needed to understand the G2P and increase the rate of gain for complex traits in crop breeding.
Author: Minzan Li Publisher: Springer ISBN: 9783030704346 Category : Technology & Engineering Languages : en Pages : 0
Book Description
Soil and crop sensing is a fundamental component and the first important step in precision agriculture. Unless the level of soil and crop variability is known, appropriate management decisions cannot be made and implemented. In the last few decades, various ground-based sensors have been developed to measure spatial variability in soil properties and nutrients, crop growth and yield, and pest conditions. Remote sensing as an important data collection tool has been increasingly used to map soil and crop growth variability as spatial, spectral and temporal resolutions of image data have improved significantly in recent years. While identifying spatial variability of soil and crop growth within fields is an important first step towards precision management, using that variability to formulate variable rate application plans of farming inputs such as fertilizers and pesticides is another essential step in precision agriculture.The purpose of this book is to present the historical, current and future developments of soil and crop sensing technologies with fundamentals and practical examples. The first chapter gives an overview of soil and crop sensing technologies for precision crop production. The next six chapters provide details on theories, methods, practical applications, as well as challenges and future research needs for all aspects of soil and crop sensing. The last two chapters show how soil and crop sensing technologies can be used for plant phenotyping and precision fertilization. The chapters are written by some of the world’s leading experts who have contributed significantly to the developments of precision agriculture technologies, especially in the area of soil and crop sensing. They use their knowledge, experiences, and successful stories to present informative and up-to-date information on relevant topics. Therefore, this book is an invaluable addition to the literature and can be used as a reference by scientists, engineers, practitioners, and college students for the dissemination and advancement of precision agriculture technologies for practical applications.
Author: Byron J Evers
Author: Byron J Evers
Author: Megan Calvert
Author: Robert C. Gaynor
Author: Atena Haghighattalab
Author: Ashley Abigail Lorence
Author: Huan Wang
Author: Karansher Singh Sandhu
Author: Daljit Singh
Author: Edwin Lee Eisele
Author: Minzan Li