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Record W4229073222 · doi:10.5539/sar.v11n2p58

Sustainable Agroforestry Crop Rotation System for the Tropics: A Theoretical Exposition

2022· article· en· W4229073222 on OpenAlexvenueno aff
Sir Anthony Wakwe Lawrence

Bibliographic record

VenueSustainable Agriculture Research · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Rural Development Research
Canadian institutionsnot available
Fundersnot available
KeywordsAgroforestryArable landCrop rotationEnvironmental scienceLand degradationProductivityLand useShifting cultivationAgronomyGeographyCropAgricultureForestryEconomicsEcologyBiology

Abstract

fetched live from OpenAlex

Population pressure is the key reason that has been reducing the duration of fallow in shifting cultivation. In many places, it has changed to bush fallow and subsequently is going towards the need to use available arable lands continuously. As a result, soil productivity is declining since long fallow is required for its regeneration after land is planted for a few years. An agroforestry tree crop/arable crop rotation system was proposed to mimic the natural fallow system and improve nutrient recycling through litter drops, which will improve soil organic matter. As soil organic matter improves the soil structure in addition to the ability of the soil to retain nutrients and water, the land becomes suitable for continuous crop production with appropriate fertilization regimes. The proposed tree crop/arable crop rotation will therefore result in continuous generation of income from harvestable produce in the rotation system year in year out. The paper, equally elucidated on other benefits of rotating tree crops with arable crops on the same land towards achieving maximum land productivity and obtaining benefits from the land without subjecting the land to the traditional fallowing system. This intervention will reduce abject poverty (SDG1), reduce acute hunger (SDG2), promote sustainable economic activities and growth, increase employment and decent work (SDG8) and promote sustainable industrialization and foster innovation (SDG9). The paper also identified the challenges associated with this type of rotation system and proffered suggestions on how to ameliorate such challenges.

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 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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.812
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0080.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.274
Teacher spread0.257 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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