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Land Suitability Study in Ultisols for Soybean Based on Soil Fauna

2014· article· en· W1663810233 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Tropical Soils · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Land Suitability Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsUltisolEnvironmental scienceAgroforestryLand useFaunaSoil waterForestryGeographySoil scienceEcologyBiology

Abstract

fetched live from OpenAlex

Evaluation of land suitability for soybean by involving the presence and biodiversity of soil fauna has been conducted. The research was done on thecenter of soybean plantations in Ultisols soils in Banten, Lampung, and Lahat (south Sumatera) Provinces.  The objective of research was to determine the interaction between soil fauna  diversity in Ultisols soil and productivity of soybean. The research used a Survey Method.  Every location was divided into three categories of vegetation performance, such as, less vegetation, average vegetation, and very fertile vegetation with two replicates.  The chemical, physical, and biological properties of soils from every unit sampling were analyzed. The results showed that nutrient and chemical properties of soil which directly influenced the growth and production of soybean was P-potential, P-available, K-available, B (Boron), Ca and pH; the physical properties were pores drainage, pores rapid drainage, soil water content, and soil permeability. The presence of earthworm did not have direct effect to soybean, except as  the 3th between variables, meaning that the presence of earthworms affected soil physical properties, soil physical properties affected nutrient availability, nutrient availability affected the biomass and yield of soybean. Keywords: Earthworm, land suitability, soil fauna,  soybeans, Ultisol Normal 0 false false false IN X-NONE X-NONE MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:Table Normal; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:Calibri,sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:Times New Roman; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;} [ How to Cite : Anwar EK, R Nurlaily, Sarmah and J Purwani. 2013. Land Suitability Study in Ultisols for Soybean Based on Soil Fauna. J Trop Soils 18 (3) : 231-239 . Doi: 10.5400/jts.2013.18. 3 . 231] [ Permalink/DOI: www.dx.doi.org/10.5400/jts.2013.18.3.93 ] REFERENCES Alexander M. 1977. Introduction of Soil Microbiology. John Wiley and Sons, New York-Chichester-Brisbane-Toronto-Singapore, 467 p. Anwar EK.  2007. Pengaruh Inokulan Cacing Tanah dan Pemberian Bahan Organik terhadap Kesuburan dan Produktivitas Tanah Ultisols. J Trop Soils 12: 121-130 (in Indonesian). Anwar EK, RDM Simanungkalit, E Santoso and Sukristiyobubowo. 2010. Population density and distribution in wetland earthworm organic farming  systems, semi organic and conventional. Biota, J Biol Sci 15: 113-117. Ayuke FO,L Brussard, BVanlauwe, J Six, DK Lelei, CN Kibunja and MM Pulleman. 2011. Soil fertility management: Impacts on soil macrofauna, soil aggregation and soil organic matter allocation. Appl Soil Ecol 48: 53-62. Balai Penelitian Tanah.  2005.  Petunjuk Tekniks Analisis Kimia Tanah, Tanaman, Air, dan Pupuk.  Badan Penelitian dan Pengembangan Pertanian Departemen Pertanian.  Bogor, 136 p. (in Indonesian). Dayan A, 1979. Introduction Methods Statistik. Jilid I, LP3ES, Jakarta (in Indonesian). Djaenudin D, H Marwan, H Subagjo and A Hidayat. 2003. Technical Guidelines for Agricultural Land Evaluation. Research Institute for Soil, Puslitbangtanak, Agricultural Research Agency,  154p. Djaenudin D, H Marwan, H Subagyo, A Mulyani and N Suharta. 2003a. Kriteria Kesesuaian Lahan untuk Komoditas Pertanian. Versi 3. Pusat Penelitian Tanah dan Agroklimat, Bogor (in Indonesian) Drapper N and H Smith 1976. Applied Regression Analysis, Second Edition. WileyIntersciencea division of John Wiley & Sons. Inc. 605 Third Avenue, New York N.10158 Edwards CA and JR Lofty. 1977. Biology of Earthworms. A Boo Halsted Press, John Wiley & Sons, New York. 333 p. Giller KE, MH Beare, P Lavelle, AMB Izac and MJ Swift. 1997. Agricultural Intensification, Soil Biodiversity, and agroecosystem function. Appl Soil Ecol 6: 3-16. ICALRRD [Center for Agricultural Land Resources Research and Development]. 2006. Soil Physical Properties and Methods of analysis. Agency for Agricultural Research and Development Department of Agriculture. 282p. ICALRRD [Center for Agricultural Land Resources Research and Development].  2007. Soil Biology Analysi Methods. Agency for Agricultural Research and Development Department of Agriculture. Kilowasid MLH, TS Syamsudin, FX Susilo and E Sulistyawati. 2012. Ecological Diversity of Soil Fauna as Ecosystem Engineers in Small-Holder Cocoa Plantation in South Konawe. J Trop Soils 17: 173-180. Lal R. 1995. Sustainable Management of Soil Resources in the humic Tropics. United Nations University Press, Tokio-New York-Paris, pp. 25-29. Rao S. 1994. Soil microorganisms and plant growth. Publisher University of Indonesia, 354 p. Soil Survey Staff. 1998. Keys to Soil Taxonomy. 8th Edition. USDA Natural Resources Conservation Service. Washington DC Subowo G,  I Anas, G Djajakirana, A Abdurachman and S Hardjowigeno. 2002. Pemanfaatan cacing tanah untuk meningkatkan produktivitas Ultisols lahan kering. J Tanah Iklim 20: 35-46 (in Indonesian). Subowo G. 2010. Peranan biologi tanah dalam evaluasi kesesuaian lahan pertanian kawasan mega diversity tropika basah. Balai Besar Litbang Sumberdaya Lahan Pertanian. Badan Litbang Pertanian. J Sumberdaya Lahan 4: 93-102 (in Indonesian). Subowo G. 2011. Penambangan Sistem Terbuka Ramah Lingkungan dan Upaya Reklamasi Pasca Tambang untuk Memperbaiki Kualitas Sumberdaya Lahan dan Hayati Tanah. J Sumberdaya Lahan 5: 83-94 (in Indonesian). Zangarle A,  A Pando and P Lavelle. 2011. Do earthworms and roots cooperate to build soil macroaggregates? Geoderma 167-168: 303 -309.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.395

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.257
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it