Land Suitability Study in Ultisols for Soybean Based on Soil Fauna
Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".