Seeds are the vehicle for delivering the improvements in a crop to the farmer’s field. They are therefore a critical input in agricultural production. Seeds are also unique in that they must remain alive and healthy when they are used and that they are also the input that farmers can produce by themselves. Module 4 Seed Sector Regulatory Framework provides information on the elements of the regulations that govern the seed value chain – from variety registration through quality seed production to distribution and marketing. The materials covered include information about national seed policy, seed law and regulations, their definitions, purpose and interactions.
Farmers and gardeners have long appreciated a wide variety of plants and have nurtured them for meals, healing, and exchange. But diversity too often has been surrendered to monocultures of fields and spirits, predisposing much of modern agriculture to uniformity and, consequently, vulnerability. Today it is primarily at the individual level—such as growing and saving a strange old bean variety or a curious-looking gourd—that any lasting conservation actually takes place. As scientists grapple with the erosion of genetic diversity of crops and their wild relatives, old-timey farmers and gardeners continue to save, propagate, and pass on folk varieties and heirloom seeds. Virginia Nazarea focuses on the role of these seedsavers in the perpetuation of diversity. She thoughtfully examines the framework of scientific conservation and argues for the merits of everyday conservation—one that is beyond programmatic design. Whether considering small-scale rice and sweet potato farmers in the Philippines or participants in the Southern Seed Legacy and Introduced Germplasm from Vietnam in the American South, she explores roads not necessarily less traveled but certainly less recognized in the conservation of biodiversity. Through characters and stories that offer a wealth of insights about human nature and society, Heirloom Seeds and Their Keepers helps readers more fully understand why biodiversity persists when there are so many pressures for it not to. The key, Nazarea explains, is in the sovereign spaces seedsavers inhabit and create, where memories counter a culture of forgetting and abandonment engendered by modernity. A book about theory as much as practice, it profiles these individuals, who march to their own beat in a world where diversity is increasingly devalued as the predictability of mass production becomes the norm. Heirloom Seeds and Their Keepers offers a much-needed, scientifically researched perspective on the contribution of seedsaving that illustrates its critical significance to the preservation of both cultural knowledge and crop diversity around the world. It opens new conversations between anthropology and biology, and between researchers and practitioners, as it honors conservation as a way of life.
This book mainly deals with grassland digitalization and recognition through computer vision, which will make contributions to implement of grass auto recognition and data acquisition. Taking advantage of computer vision, it focuses on intrinsic feature extraction to realize the functions such as auto recognition of forage seeds and microscope images mosaic. The book presents a new approach for identification of grass seeds, with clear figures and detailed tables. It enlightens reader by solving the traditional problems of pratacultural science through the aid of computer science.
This is the first scholarly reference work to cover all the major scientific themes and facets of the subject of seeds. It outlines the latest fundamental biological knowledge about seeds, together with the principles of agricultural seed processing, storage and sowing, the food and industrial uses of seeds, and the roles of seeds in history, economies and cultures. With contributions from 110 expert authors worldwide, the editors have created 560 authoritative articles, illustrated with plentiful tables, figures, black-and-white and color photographs, suggested further reading matter and 670 supplementary definitions. The contents are alphabetically arranged and cross-referenced to connect related entries.
This text is intended for plant physiologists, molecular biologists, biochemists, biotechnologists, geneticists, horticulturalists, agromnomists and botanists, and upper-level undergraduate and graduate students in these disciplines. It integrates advances in the diverse and rapidly-expanding field of seed science, from ecological and demographic aspects of seed production, dispersal and germination, to the molecular biology of seed development. The book offers a broad, multidisciplinary approach that covers both theoretical and applied knowledge.
We are proud to present the DAGM 2002 proceedings, which are the result of the e?orts of many people. First, there are the many authors, who have submitted so many excellent cont- butions. We received more than 140 papers, of which we could only accept about half in order not to overload the program. Only about one in seven submitted papers could be delivered as an oral presentation, for the same reason. But it needs to be said that almost all submissions were of a really high quality. This strong program could not have been put together without the support of the Program Committee. They took their responsibility most seriously and we are very grateful for their reviewing work, which certainly took more time than anticipated, given the larger than usual number of submissions. Our three invited speakers added a strong multidisciplinary component to the conference. Dr. Antonio Criminisi of Microsoft Research (Redmond, USA) dem- strated how computer vision can literally bring a new dimension to the app- ciation of art. Prof. Philippe Schyns (Dept. of Psychology, Univ. of Glasgow, UK) presented intriguing insights into the human perception of patterns, e.g., the role of scale. Complementary to this presentation, Prof. Manabu Tanifuji of the Brain Science Institute in Japan (Riken) discussed novel neurophysiological ?ndings on how the brain deals with the recognition of objects and their parts.
Ending malnutrition in all forms is a global development priority. Investment in nutrition can yield high returns in terms of reduced health costs, increased productivity and improved human resources capacity and economic growth (Covic & and Hendriks 2016; Shekar et al. 2017). Nutrition policy-making and program interventions in developing countries fail to bring together several sectors that contribute to nutrition improvement. Since food systems influence the type of food produced, understanding relevant drivers of a country’s food system with an emphasis on nutrition can help to end malnutrition (Per Pinstrup-Andersen 2012a; HLPE 2017; Babu and Kataki 2003). In this paper, we adopt a food systems perspective to review Myanmar’s current food system. With the help of a review of the literature and two national consultative stakeholder workshops, we examine Myanmar’s current food system. This is a crucial step since it identifies gaps existing in the current policies/ strategies being implemented. After the review, we developed an AIT (analyze gaps, identify priority investment areas, and track progress) operational framework that can be used to increase the nutrition-sensitivity of a food system. Applying this framework to Agriculture Development Strategy (ADS), this paper presents an analysis of the gaps that need to be addressed to make ADS nutrition-sensitive, provide priority investment areas, and a tracking system which monitors the progress of these investments.
The chapters of this book on seed dispersal are divided into four parts: (1) frugivores and frugivory (8 chapters); (2) seed and seedling shadows (7 chapters); (3) seed fate and establishment (eight chapters); and (4) management implications and conservation (six chapters). The book presents both recent advances and reviews of current knowledge.
Artificial Intelligence (AI) is an extensive concept that can be interpreted as a concentration on designing computer programs to train machines to accomplish functions like or better than hu-mans. An important subset of AI is Machine Learning (ML), in which a computer is provided with the capacity to learn its own patterns instead of the patterns and restrictions set by a human programmer, thus improving from experience. Deep Learning (DL), as a class of ML techniques, employs multilayered neural networks. The application of AI to plant science research is new and has grown significantly in recent years due to developments in calculation power, proficien-cies of hardware, and software progress. AI algorithms try to provide classifications and predic-tions. As applied to plant breeding, particularly omics data, ML as a given AI algorithm tries to translate omics data, which are intricate and include nonlinear interactions, into precise plant breeding. The applications of AI are extending rapidly and enhancing intensely in sophistication owing to the capability of rapid processing of huge and heterogeneous data. The conversion of AI techniques into accurate plant breeding is of great importance and will play a key role in the new era of plant breeding techniques in the coming years, particularly multi-omics data analysis. Advancements in plant breeding mainly depend upon developing statistical methods that harness the complicated data provided by analytical technologies identifying and quantifying genes, transcripts, proteins, metabolites, etc. The systems biology approach used in plant breeding, which integrates genomics, transcriptomics, proteomics, metabolomics, and other omics data, provides a massive amount of information. It is essential to perform accurate statistical analyses and AI methods such as ML and DL as well as optimization techniques to not only achieve an understanding of networks regulation and plant cell functions but develop high-precision models to predict the reaction of new Genetically Modified (GM) plants in special conditions. The constructed models will be of great economic importance, significantly reducing the time, labor, and instrument costs when finding optimized conditions for the bio-exploitation of plants. This Research Topic covers a wide range of studies on artificial intelligence-assisted plant breeding techniques, which contribute to plant biology and plant omics research. The relevant sub-topics include, but are not restricted to, the following: • AI-assisted plant breeding using omics and multi-omics approaches • Applying AI techniques along with multi-omics to recognize novel biomarkers associated with plant biological activities • Constructing up-to-date ML modeling and analyzing methods for dealing with omics data related to different plant growth processes • AI-assisted omics techniques in the plant defense process • Combining AI-assisted omics and multi-omics techniques using plant system biology approaches • Combining bioinformatics tools with AI approaches to analyze plant omics data • Designing cutting-edge workflow and developing innovative AI biology methods for omics data analysis