Re: Breeding Field Crops Pdf Free Download
Nelson Suggs
Plant phenotyping forms the core of crop breeding, allowing breeders to build on physiological traits and mechanistic science to inform their selection of material for crossing and genetic gain. Recent rapid progress in high-throughput techniques based on machine vision, robotics, and computing (plant phenomics) enables crop physiologists and breeders to quantitatively measure complex and previously intractable traits. By combining these techniques with affordable genomic sequencing and genotyping, machine learning, and genome selection approaches, breeders have an opportunity to make rapid genetic progress. This review focuses on how field-based plant phenomics can enable next-generation physiological breeding in cereal crops for traits related to radiation use efficiency, photosynthesis, and crop biomass. These traits have previously been regarded as difficult and laborious to measure but have recently become a focus as cereal breeders find genetic progress from 'Green Revolution' traits such as harvest index become exhausted. Application of LiDAR, thermal imaging, leaf and canopy spectral reflectance, Chl fluorescence, and machine learning are discussed using wheat and sorghum phenotyping as case studies. A vision of how crop genomics and high-throughput phenotyping could enable the next generation of crop research and breeding is presented.
Crop breeding is the art and science of improving important agricultural plants for the benefit of humankind. Crop breeders work to make our food, fiber, forage, and industrial crops more productive and nutritious. Crops provide for an expanding global population with increasing dietary expectations. Environmental protection is also improved by the work of crop breeders.
Plant breeding has been practiced by farmers since the dawn of agriculture, as they selected plants for larger seeds, more tasty fruits, and other valuable traits. Today, both farmers and scientists work to breed plants.
In addition, crop breeders gather a lot of information about the unique qualities of each plant. This means plant breeders have to be savvy in the art of working with vast amounts of data. Developing methods to store, share, and quickly analyze these data will produce significant advances in plant breeding.
Now is an exciting time to study plant breeding and genetics. People worldwide are more interested than ever in their food, its textures, nutritional quality, flavors, and medicinal applications, the stability and diversity of both supply and demand, and the impact of food production, consumption and distribution on human health and on the environment. Low-cost DNA and RNA sequencing methods, high throughput chemical analyses, remote sensing, gene editing, imaging, and robotics have increased the type and the volume of data that we can bring to bear on these questions. Computing and statistical methods have developed apace to bring it all together and enable us to make predictions about future varieties.
The Field of Plant Breeding welcomes applicants from around the world who are eager to be leaders in discovery and innovation and who seek to develop solutions to pressing problems and grand global challenges. We provide training that links current science with well-defined problems and methods to tackle them. Our alumni work at the cutting edge of their fields and are sought after in academia, government, and industry. We encourage prospective applicants to correspond directly with faculty members whose interests match their own. The list below outlines general topics of research and/or crops studied for each field member. Additional information, including more detailed descriptions of faculty research and courses, is available on the plant breeding website.
Mark Tester, Professor of Bioscience at KAUST
King Abdullah University of Science and Technology (KAUST) is located on the Red Sea coast in Saudi Arabia. To address global challenges related to food, water, energy and the environment, KAUST conducts curiosity-driven and goal-oriented research. A new speed breeding program was started in late 2018 in their research greenhouse.
Problem:
In Saudi Arabia, growers must daily tackle challenging environmental conditions in the desert region. The KAUST plant science department works to develop plants with enhanced tolerance towards environmental stress while establishing sustainable agricultural systems for growers and field farmers. The KAUST speed breeding program will enable accelerated crop research and the breeding of new more resistant crop varieties with seed selection and improvement of genetics.
Bohra, A., Kilian, B., Sivasankar, S., Caccamo, M., Mba, C., McCouch, S.R., Varshney, R.K. 2021. Reap the crop wild relatives for breeding future crops. Trends in Biotechnology. doi.org/10.1016/j.tibtech.2021.08.009
Gepts P. 2018. The domestication of our food crops. In: Chrispeels MJ, Gepts P (editors). Plants, Genes & Agriculture: Sustainability through Biotechnology. Oxford University Press, Cary, North Carolina.
Native Seeds/SEARCH ( ) offers a wide variety of seeds of Three Sisters crops for sale. NS/S is a nonprofit seed conservation organization in Tucson, Arizona specializing in seeds of indigenous communities of the Southwestern U.S. and Northern Mexico.
Feature papers represent the most advanced research with significant potential for high impact in the field. A FeaturePaper should be a substantial original Article that involves several techniques or approaches, provides an outlook forfuture research directions and describes possible research applications.
The general objectives of Breeding Field Crops are: to review essential features in plant reproduction, Mendelian genetic principles, and related genetic phenomena that contribute to plant breeding practices; to describe and explain basic plant breeding methods and techniques; to emphasize the importance of selecting the breeding objectives whose improvement will contribute the greatest economic benefit to the farmer growing the new cultivars; and to describe procedures for the increase, maintenance, and distribution of seeds or vegetative propagules of new crop cultivars.
This STTR Phase II Award has been critical in enabling EarthSense, Inc. to mature our field autonomy and machine-vision technologies for agriculture. These technologies form the core of our "Terra" Autonomy and AI platform that powers our commercial products:
The TerraSentia field phenotyping platform was the primary focus of this award. The TerraSentia platform was designed with initial support from NSF SBIR Phase I (Award # 1820332, July 2018-June 2019). Some of the underlying technologies of TerraSentia were created at the University of Illinois at Urbana Champaign through funding from the ARPA-e TERRA program for creating high-throughput field phenotyping technologies.
Since 2018 EarthSense's public and private sector customers have deployed the increasingly autonomous and capable TerraSentia field robots around the world, resulting in the breaking of the "field phenotyping bottleneck".
Phenotyping is the mesurement of physical characgeristic of plants. Measuring the physical properties of a large variety of plants is critical for identifying the genetic characteristics of plants that will result in the best performance under the enivironmental conditions (such as soil conditions, drought, heat, cold, excess rainfaill, diseases and pests, etc.) and management practices (fertilizing, irrigation, etc.) that the plants will experience. In other words, phenotyping plants at high volume and high frequency is critical for enabling crop scientsts to create the next generation of productive, sustainable, climate resilient, and profitable crops.
However, before the TerraSentia system, filed phenotyping was extremely labor intensive and inefficient, since many of the important traits of crops exist under the plant canopy. These traits, such as stem width, fruit count and dminesions, leaf and canopy architecture, etc. have to be meausred by hand, one plant at a time. This causes the field phenotyping bottleneck, resulting in a crop improvement process that is much slower than it can be.
To break the field phenotyping bottleneck, EarthSense had to solve three key problems: reliable autonomy, machine-learning algorithms capable extracting accurate plant traits from complex real-world field data, and capacity to analyze this data in a computationally efficient manner - including on the low-cost, compact TerraSentia robots with limited onboard compute capacity.
The TerraSentia field phenotyping system is now being used by a variety of field research scientists inculding at major resarch universities inclusing Cornell, UC Davis, Michigan State, Iowa State, Purdue, University of Illinois, Texas A&M, Washington State, University of Missouri, and others. It is also being used by United States Department of Agriculture - Agricultural Research Service, for crops including corn, field beans, sugarcane, and others. Finally, it is being widely used by major global agricultural companies to customize their products for regions including in the US, South America, Europe, and South & South-East Asia.
Objective:
1. Deployment of root-knot nematode resistance in cotton and peanut.1.A. Determine the economic value of growing nematode-resistant vs. a susceptible cultivar in continuous and rotated peanut. 1.B. Evaluate the economic effect of growing M. incognita-resistant cotton in fields with damaging levels of the nematode.2. Identify and screen nematode resistant crops that can be grown in rotation with cotton and peanut. 2.A. Identify sources of resistance to Meloidogyne incognita in sorghum that differ from known sources of resistance. 2.B. Identify sorghum cultivars that are poor hosts for Pratylenchus brachyurus and Meloidogyne arenaria.3. Increased understanding of the interactions between plant-parasitic nematodes and the soil microbiology community and how that contributes to disease. 3.A. Evaluate the interactions of nematode parasitism, the salicylic acid (SA) and jasmonic acid (JA) plant defense pathways, and Fusarium oxysporum f.sp. vasinfectum in the Fusarium wilt disease complex in cotton. 3.B. Investigate the contribution of predatory nematodes in suppressing root-knot nematodes.3.C. Identify the host and environmental factors that influence the attachment of Pasteuria penetrans spores to Meloidogyne arenaria.