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Nicholas J Turland - One of the best experts on this subject based on the ideXlab platform.
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Regnum Vegetabile Volume 159
'International Association for Plant Taxonomy', 2019Co-Authors: Nicholas J Turland, David L Hawksworth, Patrick S Herendeen, Jh Wiersema, Barrie Fr, Greuter W, Knapp S, Kusber W-h, Li D-z, Marhold KAbstract:The rules that govern the scientific naming of algae, fungi, and Plants are revised at the Nomenclature Section of an International Botanical Congress (IBC). This edition of the International Code of Nomenclature for algae, fungi, and Plants embodies the decisions of the XIX IBC, which took place in Shenzhen, China in July, 2017. This Shenzhen Code supersedes the Melbourne Code (McNeill & al. in Regnum Veg. 154. 2012), published six years ago after the XVIII IBC in Melbourne, Australia, and like its five predecessors, it is written entirely in (British) English. The Melbourne Code was translated into Chinese, French, Italian, Japanese, Korean, Portuguese, Spanish, and Turkish; it is anticipated that the Shenzhen Code, too, will become available in several languages. In questions about the meaning of provisions in translated editions of this Code, the English edition is definitive.© 2018, International Association for Plant Taxonomy. All rights reserved. No part of this book may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage and retrieval system, or be translated into any other language, without written permission from the copyright holder. https://www.iapt-taxon.org/nomen/main.ph
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international code of nomenclature for algae fungi and Plants shenzhen code adopted by the nineteenth international botanical congress shenzhen china july 2017
2018Co-Authors: Nicholas J Turland, John H Wiersema, Fred R Barrie, Werner Greuter, David L Hawksworth, Patrick S Herendeen, Sandra Knapp, Wolfhenning Kusber, Dezhu Li, Karol MarholdAbstract:© 2018, International Association for Plant Taxonomy. All rights reserved. No part of this book may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage and retrieval system, or be translated into any other language, without written permission from the copyright holder.
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international code of nomenclature for algae fungi and Plants shenzhen code adopted by the nineteenth international botanical congress shenzhen china july 2017
2018Co-Authors: Nicholas J Turland, John H Wiersema, Fred R Barrie, Werner Greuter, David L Hawksworth, Patrick S Herendeen, Sandra Knapp, Wolfhenning Kusber, Dezhu Li, Karol MarholdAbstract:© 2018, International Association for Plant Taxonomy. All rights reserved. No part of this book may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage and retrieval system, or be translated into any other language, without written permission from the copyright holder. https://www.iapt-taxon.org/nomen/main.php
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xvii international botanical congress preliminary mail vote and report of congress action on nomenclature proposals
Taxon, 2005Co-Authors: John Mcneill, Nicholas J Turland, Tod F Stuessy, Elvira HorandlAbstract:A preliminary guiding mail vote on nomenclature proposals is required by Provision 4(a) of Division III (Provisions for the Governance of the Code) of the International Code of Botanical Nomenclature (McNeill & al. in Regnum Veg. 146. 2006). A “Synopsis of Proposals” was published in Taxon 60: 243–286. 2011. Ballot forms were distributed with the February 2011 issue of Taxon to all individual members of the International Association for Plant Taxonomy (IAPT) and mailed from Vienna in early March to other persons who were either members of a permanent nomenclature committee or authors of proposals. The deadline for return of ballots was 31 May 2011. All ballots received by that date were included in the vote count. Out of approximately 1400 ballots distributed, 140 valid ballots (10%) were returned. There were no ballots returned unsigned so none had to be disregarded. Submission was by mail (112), by fax (12), or as scanned attachments to e-mails (16). Although electronic submission was not confined to fax (as for the Vienna Congress) there was no change in the proportion using electronic means and the majority of ballots (80% of those returned) continued to be submitted in hard copy by regular mail. In all, 338 proposals to amend the Code at the Melbourne Congress were published in advance, summarized in the “Synopsis of Proposals” and included in the preliminary mail vote, by a small margin the largest number at any Congress since the Paris Congress in 1954 (see Table 1). Apart from a block of 26 proposals for editorial modification of the Glossary (App. VII), there were no special circumstances surrounding the proposals so it would seem that the general trend toward fewer proposals suggested in the report on the decisions in Vienna (McNeill & al. in Taxon 54: 1057–1064. 2005) has not been maintained. Of the 338 proposals, 190 were single-authored, 79 had two authors, 58 had three or more authors, and 11 came from The Special Committee on Electronic Publication. The tabulation below (Table 4 on p. 5 ff.) gives the result of the preliminary mail vote for each proposal, in the XVIII International Botanical Congress: Preliminary mail vote and report of Congress action on nomenclature proposals
Elizabeth C Moylan - One of the best experts on this subject based on the ideXlab platform.
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Electronic revolution in Plant Taxonomy
BMC evolutionary biology, 2011Co-Authors: Elizabeth C MoylanAbstract:This Editorial highlights recent changes to the International Code of Botanical Nomenclature and the implications these changes have for electronic journals.
Michael J Balick - One of the best experts on this subject based on the ideXlab platform.
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does the name really matter the importance of botanical nomenclature and Plant Taxonomy in biomedical research
Journal of Ethnopharmacology, 2014Co-Authors: Bradley C Bennett, Michael J BalickAbstract:Abstract Ethnopharmacological relevance Medical research on Plant-derived compounds requires a breadth of expertise from field to laboratory and clinical skills. Too often basic botanical skills are evidently lacking, especially with respect to Plant Taxonomy and botanical nomenclature. Binomial and familial names, synonyms and author citations are often misconstrued. The correct botanical name, linked to a vouchered specimen, is the sine qua non of phytomedical research. Without the unique identifier of a proper binomial, research cannot accurately be linked to the existing literature. Perhaps more significant, is the ambiguity of species determinations that ensues of from poor taxonomic practices. This uncertainty, not surprisingly, obstructs reproducibility of results—the cornerstone of science. Materials and methods Based on our combined six decades of experience with medicinal Plants, we discuss the problems of inaccurate Taxonomy and botanical nomenclature in biomedical research. This problems appear all too frequently in manuscripts and grant applications that we review and they extend to the published literature. We also review the literature on the importance of Taxonomy in other disciplines that relate to medicinal Plant research. Results and discussion In most cases, questions regarding orthography, synonymy, author citations, and current family designations of most Plant binomials can be resolved using widely-available online databases and other electronic resources. Some complex problems require consultation with a professional Plant taxonomist, which also is important for accurate identification of voucher specimens. Researchers should provide the currently accepted binomial and complete author citation, provide relevant synonyms, and employ the Angiosperm Phylogeny Group III family name. Taxonomy is a vital adjunct not only to Plant-medicine research but to virtually every field of science. Conclusions Medicinal Plant researchers can increase the precision and utility of their investigations by following sound practices with respect to botanical nomenclature. Correct spellings, accepted binomials, author citations, synonyms, and current family designations can readily be found on reliable online databases. When questions arise, researcher should consult Plant taxonomists.
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phytomedicine 101 Plant Taxonomy for preclinical and clinical medicinal Plant researchers
Journal of The Society for Integrative Oncology, 2008Co-Authors: Bradley C Bennett, Michael J BalickAbstract:Plants are the primary source of medicine for most of the world. The most fundamental step in the scientific study of medicinal Plants is establishing their botanical identity. Many studies lack voucher specimens, which serve as permanent records of scientific investigations. This omission makes positive identification impossible and hinders reproducibility. Even when vouchers are cited, scientific names are often mishandled. A random survey of titles and abstracts of 100 publications revealed 20 with taxonomic errors. Mistakes included a lack of author citations, misspellings, and use of older synonyms instead of currently accepted names. A seemingly minor orthographic error makes it impossible to search electronic databases for information about a species. Medicinal Plant manuscripts and National Institutes of Health proposals commonly lack scientific rigor in dealing with botanical names and documentation. This article examines common taxonomic problems relevant to medicinal Plant research and provides a basic guide to Plant Taxonomy for medicinal Plant researchers. Voucher specimens and their preparation, Plant identification, and botanical nomenclature are discussed. References and other resources to assist investigators are cited.
Martin Pierre - One of the best experts on this subject based on the ideXlab platform.
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Identifying explicit and tacit knowledge in a life science knowledge base
Société Française de Bio-Informatique, 2021Co-Authors: Saoud Johanna, Gutierrez Alain, Huchard Marianne, Silvie Pierre, Martin PierreAbstract:An alternative to the use of synthetic pesticides and antibiotics in agriculture is to spray local Plants extracts, in aqueous or essential oil form. To this end, the Knomana knowledge base [1] compiles various knowledge sets on Plant use such as the 42000 descriptions of pesticidal Plant uses for Plant, animal, and public health presented in the literature. As the One Health approach dictates to be aware of the additional uses of these pesticidal Plants to prevent their unintended effects on the animal, the human, and their environment, the challenge for the domain experts (e.g. entomologist, pathologist) is thus to identify the pesticidal Plants in Knomana considering the One Health approach. With the aim to present knowledge to the expert using a compact and comprehensive formalism, in [2], we computed the Duquenne-Guigues basis (DGB) of implications on an excerpt of Knomana, in which each Plant is described using its Taxonomy (i.e. species, genus, and family), its consumption as food, and its use in medical care. The DGB method is based on Formal Concept Analysis (FCA) and provides a cardinality-minimal set of non-redundant implications. By considering a reduced knowledge set, this work identified 3 types of knowledge elements in the implications: knowledge on Plant use at diverse Taxonomy levels (e.g. Plants from Meliaceae family are not consumed as food), Plant Taxonomy (e.g. A Plant from Salvia genus is from Lamiaceae family), and side effect of the knowledge set (e.g. A Plant from the Piperaceae family is from the genus Piper). This latter illustration is not in accordance with taxonomic referential and thus informs on the extent of knowledge inserted in Knomana. Moreover, as Plant Taxonomy is known by the experts, removing it from the implications eases their reading but makes it tacit knowledge. Implementing this method to select pesticidal Plants requires to consider Knomana as a multidimensional (ternary) dataset, and thus to use the extension of FCA devoted to this kind of dataset, i.e. Relational Concept Analysis (RCA). As computing the DGB of implications based on RCA provides linked set of implications which includes the existential quantifier, converting this formulation as practical expression is a need for the domain experts. This poster describes the implemented workflow that formulates Knomana knowledge on pesticidal Plants as implications, from which the implicit knowledge elements were removed and the side effects are highlighted to alert the expert. This workflow was developed using the library fca4j from Cogui software (http://www.lirmm.fr/cogui/), that provides the RCA based DGB of implications, and using a post-process which differentiates the 3 types of knowledge elements within the implications. As an illustration, this poster presents the implications on Spodoptera frugiperda, a highly polyphagous insect that is close to invade South of Europe. The perspective of this work is to identify pesticidal European Plants species that share chemical components similarities with Plants used to control this pest in its native area
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Explicit versus tacit knowledge in duquenne-guigues basis of implications: Preliminary results
s.n., 2021Co-Authors: Saoud Johanna, Gutierrez Alain, Huchard Marianne, Silvie Pierre, Marnotte Pascal, Martin PierreAbstract:Formal Concept Analysis (FCA) comes with a range of rel- evant techniques for knowledge analysis, such as conceptual structures or implications. The Duquenne-Guigues basis of implications provides a cardinality minimal set of non-redundant implications. The concern of a domain expert is to discover new knowledge within this implication set. The objective of this paper is to collect and discuss the di_erent pat- terns of implications extracted from a dataset on Plants used in medical care or consumed as food. We identify 16 patterns combining 3 types of knowledge elements (KE). The patterns highlight redundant KEs, in particular, those corresponding to Plant Taxonomy, as it is familiar knowl- edge for the experts. Removing these KEs from the implications makes them tacit. We suggest a post-process for cleaning up the implications before reporting them to the experts
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Explicit versus Tacit Knowledge in Duquenne-Guigues Basis of Implications: Preliminary Results
HAL CCSD, 2021Co-Authors: Saoud Johanna, Gutierrez Alain, Huchard Marianne, Silvie Pierre, Marnotte Pascal, Martin PierreAbstract:Formal Concept Analysis (FCA) comes with a range of relevant techniques for knowledge analysis, such as conceptual structures or implications. The Duquenne-Guigues basis of implications provides a cardinality minimal set of non-redundant implications. The concern of a domain expert is to discover new knowledge within this implication set. The objective of this prospective paper is to collect and discuss the different patterns of implications extracted from a dataset on Plants used in medical care or consumed as food. We identify 16 patterns combining 3 types of knowledge elements (KE). The patterns highlight redundant KEs, or KEs of little interest, in particular, those corresponding to Plant Taxonomy, as it is familiar knowledge for the experts. Removing these KEs from the implications would make them tacit. We suggest a postprocess for cleaning up the implications before reporting them to the experts. In addition, we discuss the different patterns and how an implication classification based on patterns could help the experts
D L Schuiling - One of the best experts on this subject based on the ideXlab platform.
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growth and development of true sago palm metroxylon sagu rottboll with special reference to accumulation of starch in the trunk a study on morphology genetic variation and ecophysiology and their implications for cultivation
2009Co-Authors: D L SchuilingAbstract:Keywords: Metroxylon sagu, Arecaceae, starch crops, Plant growth and development, Plant morphology, inflorescence structure, electron microscopy, phenological scale, genetic variation, Plant Taxonomy, folk Taxonomy, ethnobotany, leaf area, leaf area index, starch accumulation, starch distribution, Plant ecophysiology, tropical lowlands, wetlands, traditional processing, estate cultivation, agronomy, Moluccas, Maluku. True sago palm (Metroxylon sagu Rottboll) is a stout, clustering palm adapted to swampy tropical lowland conditions. Each axis in a sago palm clump flowers once at the end of its life after having amassed a large amount of starch in its trunk. Man can harvest this starch by felling the trunk, pulverizing the pith and leaching the starch out with water, and use it like other starches for food or non-food purposes. It is a staple food mainly in eastern Indonesia and in Papua New Guinea where it is harvested mostly from semi-managed stands. For establishing sago palm as a full-fledged Plantation crop, desirable because of its envisaged large yield potential as a perennial, its niche habitat, and its potential as a raw material provider for bio-ethanol production, the scientific base for establishing the right felling time to harvest the starch needed strengthening. Between October 1988 and November 1990, 27 sago trunks in the Adult Vegetative (AV) or Generative (G) phase belonging to six varieties were selected from semi wild sago stands in the Moluccas, eastern Indonesia: 23 trunks (4 varieties) on the alluvial coastal plain near Hatusua village, Seram Island, and 4 trunks (2 varieties) on hilly terrain near Siri Sori Serani village, Saparua Island. These trunks were felled, dissected, morphologically described and sampled for the amount and distribution of starch they contained. The leafless parts of the trunks were 4.45 to 19.65 m long, had a mean starch density of 4.6 to 254 kg/m3 and contained five to 777 kg of starch (maximum found in a whole trunk: 819 kg). To link starch content to age, the ages of the sampled trunks had to be estimated. To enable age estimation by counting leaf scars on the trunk, the leaf unfolding rate of 36 AV-phase palms around Hatusua (31 palms) and Siri-Sori Serani (5 palms) was monitored for varying periods between 1989 and 1992. Probably due to large variation in habitat and genetic make up, this rate varied from 2 to 14 leaves per year (mean 7.85), rendering number of leaf scars unfit as accurate age estimator. Also trunk height proved unfit for this purpose. From monitoring 5 G-phase palms, the G-phase could be subdivided into 3 sub-phases (G1, G2, G3), recognizable from the ground by the phased development of the successive orders of inflorescence branches. By combining gathered morphological and monitoring data, a phenological scale of a model palm was composed consisting of two parallel timelines of hidden and outwardly visible events: two years after the start of the Establishment (E) phase, the first AV-phase leaf is initiated in the apical growing point, to unfold only 2.5 years later; the initiation of the first AV-phase tissues is followed 12.5 to 14.5 years later by the initiation of the first G-phase tissues, followed 4 to 5.5 years later by the shedding of fruits, and finally by a 2- to 5-year Recycling phase (name proposed here) in which the axis decays and collapses. This scale, which accounts for the large time gap between initiation of trunk parts and their becoming visible, may help to correctly time cultural measures. The 27 sampled trunks could tentatively be ranked according to physiological age into 4 AV phase classes and 9 G phase classes. Since the examined palms belonged to 6 different local varieties, their relative rareness or commonness had to be established to assess the validity of the findings. Based on literature and on interviews with informants, an overview of locally recognised sago palm varieties is presented. The number of unique variety names in 32 localities in Indonesia and Papua New Guinea totalled 325, ranging from 2 (spined vs unspined only) to 34 per locality. On the basis of this survey, the Hatusua varieties were considered average. The nomenclatural category folk variety (fovar, fv.) is proposed to unambiguously name local varieties by adding to the variety name an indication of the location where, and (if known) the ethnic/linguistic group by which that name is used. Leaf area estimation methods were devised to enable investigation of the relationship between leaf area and starch content. In the AV-phase the Total leaf area (TLA) of a sago palm axis ranged from 200 m2 to 325 m2, one axis having an exceptional TLA of 388 m2. The TLA in the G-phase before fruiting mostly remained within the same range, possibly exceeding it for a short period early in that stage. The Leaf area index (LAI) of an individual axis showed an upward trend from 1 - 1.5 in the E-phase to 1.25 - 1.75 in the AV-phase, to more than 2 in the early G-phase, followed by a decrease to about 1.5 again in the late G-phase before fruiting. No fruiting palms were available for analysis. The TLA and LAI of a single trunk could not be linked to the mean starch density of its pith, nor to the total amount of starch the pith contained. Generally, starch density in the trunk first increased with height above ground level, reached a maximum about half-way to two-thirds up the leafless part of the trunk, and then sharply dropped towards the top of the trunk. From the late AV phase onward the maximum starch density ranged from 238 to 284 kg/m3. The four trunks with the highest maximum starch densities, all closely around 280 kg/m3, belonged to three different varieties, suggesting that 280 kg/m3 may be considered the maximum starch storage capacity in the pith of any variety. The starch distribution pattern in the leafless part of the trunk showed a tendency to evolve with age from two tailed (density gradually increasing from base, gradually decreasing towards top) to one tailed (density gradually increasing from base, sharply decreasing towards top). The differences in distribution pattern found strongly suggested that there must be other factors besides age and development phase affecting starch accumulation. Attempts to determine the effect of palm variety and of the environment mostly failed. Potential yield of a model palm based on the maximum starch density of 280 kg/m3 was estimated at 840 kg of dry starch. That this amount is much higher than generally found may partly be due to poor recovery ratios, as the results of a traditionally processed trunk demonstrated: only 47% of the starch in the processed trunk part was recovered, and if the unharvested starch present in the traditionally discarded basal and top part of the trunk is taken into account, recovery drops to 44%. In an attempt to establish the point in time at which a sago palm starts to be a nett consumer of its own starch, the course of the energy producing and consuming capacity of an axis during its life time was modelled based on the assumption that by the end of the AV-phase the existing TLA of the axis produces just the amount of energy needed to maintain existing biomass, to keep up the normal regular growth, and to fill new trunk with starch. Using this model, assimilate requirements for building and maintaining the inflorescence and the fruits could not be met by the production capacity of the leaves plus the starch reserves in the trunk. For this modelling approach to succeed in predicting the turning point from nett production to nett consumption of starch by a sago palm axis, additional data on chemical composition of its parts and on assimilation rate are needed. Lack of precise data on the age of the sampled trunks and lack of uniformity of their genetic make up and growing conditions made it impossible to arrive at the sought-after detailed timetable of the evolution of trunk starch accumulation and depletion to base the right felling time of a sago palm on. The high starch density found in the trunk of a palm with half-grown fruits indicated that depletion of starch reserves by the palm itself may set in much later than generally assumed. Once the course of starch accumulation in time in a single axis is unravelled, the next research question should be how this adds up in a clump - the actual production unit in a Plantation - with axes of different age. Timing felling in such a situation should be aimed at maintaining a maximum starch accumulation rate for the Plantation as a whole rather than at harvesting a maximum amount of starch per trunk. Data sheets of each palm examined containing all primary and some secondary data, and including photographs, are appended in digital form.
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growth and development of true sago palm metroxylon sagu rottboll with special reference to accumulation of starch in the trunk a study on morphology genetic variation and ecophysiology and their implications for cultivation
2009Co-Authors: D L SchuilingAbstract:Keywords: Metroxylon sagu, Arecaceae, starch crops, Plant growth and development, Plant morphology, inflorescence structure, electron microscopy, phenological scale, genetic variation, Plant Taxonomy, folk Taxonomy, ethnobotany, leaf area, leaf area index, starch accumulation, starch distribution, Plant ecophysiology, tropical lowlands, wetlands, traditional processing, estate cultivation, agronomy, Moluccas, Maluku. True sago palm (Metroxylon sagu Rottboll) is a stout, clustering palm adapted to swampy tropical lowland conditions. Each axis in a sago palm clump flowers once at the end of its life after having amassed a large amount of starch in its trunk. Man can harvest this starch by felling the trunk, pulverizing the pith and leaching the starch out with water, and use it like other starches for food or non-food purposes. It is a staple food mainly in eastern Indonesia and in Papua New Guinea where it is harvested mostly from semi-managed stands. For establishing sago palm as a full-fledged Plantation crop, desirable because of its envisaged large yield potential as a perennial, its niche habitat, and its potential as a raw material provider for bio-ethanol production, the scientific base for establishing the right felling time to harvest the starch needed strengthening. Between October 1988 and November 1990, 27 sago trunks in the Adult Vegetative (AV) or Generative (G) phase belonging to six varieties were selected from semi wild sago stands in the Moluccas, eastern Indonesia: 23 trunks (4 varieties) on the alluvial coastal plain near Hatusua village, Seram Island, and 4 trunks (2 varieties) on hilly terrain near Siri Sori Serani village, Saparua Island. These trunks were felled, dissected, morphologically described and sampled for the amount and distribution of starch they contained. The leafless parts of the trunks were 4.45 to 19.65 m long, had a mean starch density of 4.6 to 254 kg/m3 and contained five to 777 kg of starch (maximum found in a whole trunk: 819 kg). To link starch content to age, the ages of the sampled trunks had to be estimated. To enable age estimation by counting leaf scars on the trunk, the leaf unfolding rate of 36 AV-phase palms around Hatusua (31 palms) and Siri-Sori Serani (5 palms) was monitored for varying periods between 1989 and 1992. Probably due to large variation in habitat and genetic make up, this rate varied from 2 to 14 leaves per year (mean 7.85), rendering number of leaf scars unfit as accurate age estimator. Also trunk height proved unfit for this purpose. From monitoring 5 G-phase palms, the G-phase could be subdivided into 3 sub-phases (G1, G2, G3), recognizable from the ground by the phased development of the successive orders of inflorescence branches. By combining gathered morphological and monitoring data, a phenological scale of a model palm was composed consisting of two parallel timelines of hidden and outwardly visible events: two years after the start of the Establishment (E) phase, the first AV-phase leaf is initiated in the apical growing point, to unfold only 2.5 years later; the initiation of the first AV-phase tissues is followed 12.5 to 14.5 years later by the initiation of the first G-phase tissues, followed 4 to 5.5 years later by the shedding of fruits, and finally by a 2- to 5-year Recycling phase (name proposed here) in which the axis decays and collapses. This scale, which accounts for the large time gap between initiation of trunk parts and their becoming visible, may help to correctly time cultural measures. The 27 sampled trunks could tentatively be ranked according to physiological age into 4 AV phase classes and 9 G phase classes. Since the examined palms belonged to 6 different local varieties, their relative rareness or commonness had to be established to assess the validity of the findings. Based on literature and on interviews with informants, an overview of locally recognised sago palm varieties is presented. The number of unique variety names in 32 localities in Indonesia and Papua New Guinea totalled 325, ranging from 2 (spined vs unspined only) to 34 per locality. On the basis of this survey, the Hatusua varieties were considered average. The nomenclatural category folk variety (fovar, fv.) is proposed to unambiguously name local varieties by adding to the variety name an indication of the location where, and (if known) the ethnic/linguistic group by which that name is used. Leaf area estimation methods were devised to enable investigation of the relationship between leaf area and starch content. In the AV-phase the Total leaf area (TLA) of a sago palm axis ranged from 200 m2 to 325 m2, one axis having an exceptional TLA of 388 m2. The TLA in the G-phase before fruiting mostly remained within the same range, possibly exceeding it for a short period early in that stage. The Leaf area index (LAI) of an individual axis showed an upward trend from 1 - 1.5 in the E-phase to 1.25 - 1.75 in the AV-phase, to more than 2 in the early G-phase, followed by a decrease to about 1.5 again in the late G-phase before fruiting. No fruiting palms were available for analysis. The TLA and LAI of a single trunk could not be linked to the mean starch density of its pith, nor to the total amount of starch the pith contained. Generally, starch density in the trunk first increased with height above ground level, reached a maximum about half-way to two-thirds up the leafless part of the trunk, and then sharply dropped towards the top of the trunk. From the late AV phase onward the maximum starch density ranged from 238 to 284 kg/m3. The four trunks with the highest maximum starch densities, all closely around 280 kg/m3, belonged to three different varieties, suggesting that 280 kg/m3 may be considered the maximum starch storage capacity in the pith of any variety. The starch distribution pattern in the leafless part of the trunk showed a tendency to evolve with age from two tailed (density gradually increasing from base, gradually decreasing towards top) to one tailed (density gradually increasing from base, sharply decreasing towards top). The differences in distribution pattern found strongly suggested that there must be other factors besides age and development phase affecting starch accumulation. Attempts to determine the effect of palm variety and of the environment mostly failed. Potential yield of a model palm based on the maximum starch density of 280 kg/m3 was estimated at 840 kg of dry starch. That this amount is much higher than generally found may partly be due to poor recovery ratios, as the results of a traditionally processed trunk demonstrated: only 47% of the starch in the processed trunk part was recovered, and if the unharvested starch present in the traditionally discarded basal and top part of the trunk is taken into account, recovery drops to 44%. In an attempt to establish the point in time at which a sago palm starts to be a nett consumer of its own starch, the course of the energy producing and consuming capacity of an axis during its life time was modelled based on the assumption that by the end of the AV-phase the existing TLA of the axis produces just the amount of energy needed to maintain existing biomass, to keep up the normal regular growth, and to fill new trunk with starch. Using this model, assimilate requirements for building and maintaining the inflorescence and the fruits could not be met by the production capacity of the leaves plus the starch reserves in the trunk. For this modelling approach to succeed in predicting the turning point from nett production to nett consumption of starch by a sago palm axis, additional data on chemical composition of its parts and on assimilation rate are needed. Lack of precise data on the age of the sampled trunks and lack of uniformity of their genetic make up and growing conditions made it impossible to arrive at the sought-after detailed timetable of the evolution of trunk starch accumulation and depletion to base the right felling time of a sago palm on. The high starch density found in the trunk of a palm with half-grown fruits indicated that depletion of starch reserves by the palm itself may set in much later than generally assumed. Once the course of starch accumulation in time in a single axis is unravelled, the next research question should be how this adds up in a clump - the actual production unit in a Plantation - with axes of different age. Timing felling in such a situation should be aimed at maintaining a maximum starch accumulation rate for the Plantation as a whole rather than at harvesting a maximum amount of starch per trunk. Data sheets of each palm examined containing all primary and some secondary data, and including photographs, are appended in digital form.
