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Record W1994311064 · doi:10.5539/jas.v7n2p26

Simulating Soil Moisture under Different Tillage Practices, Cropping Systems and Organic Fertilizers Using CropSyst Model, in Matuu Division, Kenya

2015· article· en· W1994311064 on OpenAlexvenueno aff
Mirriam Muli, Richard N. Onwonga, George N. Karuku, V. M. Kathumo, M. O. Nandukule

Bibliographic record

VenueJournal of Agricultural Science · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsnot available
FundersMcKnight Foundation
KeywordsAgronomyTillageEnvironmental sciencePloughPermanent wilting pointMonocroppingIntercroppingWater contentRandomized block designMathematicsMoistureIrrigationField capacityCroppingAgricultureBiologyGeography

Abstract

fetched live from OpenAlex

Soil moisture stress is a limiting factor in crop production particularly in arid and semi-arid lands (ASALs) as it affects many physiological and biochemical processes of plants. Research on moisture conservation measures is thus imperative. The current study used CropSyst model to simulate soil moisture under different tillage practices (oxen plough, tied ridges and furrows and ridges), cropping systems (monocropping, intercropping and crop-rotation) and organic fertilizers; farm yard manure, rock phosphate (RP) and Farmyard manure (FYM) combined with rock phosphate (RP+FYM). The study was conducted in Matuu Division, Kenya for two seasons; October 2012 to February 2013 short rain season (SRS) and March to August 2013 long rain season (LRS). The experiment was laid out in a Randomized Complete Block design with a split-split plot arrangement and replicated three times. The main plots were tillage practices whereas the split plots were cropping systems and split-split plots were organic fertilizers and a control (nothing applied). The test crops were sorghum (Sorghum bicolor L.) and sweet potato (Ipomea batatas L. lam) rotated and/or intercropped with dolichos (Lablab purpureus) and chickpea (Cicer arietinum). The CropSyst model was calibrated using measured soil texture, permanent wilting point, bulk density and initial soil moisture at the experimental site. Model validation was done using Root Mean Square Error (RMSE), percentage differences (PD) and willmott index (WI) of agreement. CropSyst model was reasonably validated as indicated by the low RMSE (0.5 to 1.3), PD (less than ±15) and WI index (close to 1). In the first season and second season, simulated soil moisture (101.91 and 108.3 mm) was significantly (P < 0.05) high in sorghum/dolichos intercrop with RP+FYM application under tied ridges and least (13.52 and 15.4 mm) in control treatment of sorghum mono crop under oxen plough. In sweet potato plots, both individual treatment and treatment interaction significantly influenced simulated soil moisture. Sweet potato-dolichos rotation (75.32 and 79.63 mm), with application of RP+FYM (75.03 and 79.39 mm) under tield ridges (95 and 100.24 mm) had highest simulated soil moisture levels under oxen plough (32.49 and 34.36 mm), sweet potato monocrop (53.46 and 55.26 mm) and control (52.52 and 55.39 mm) having the least during the first and second season, respectively. In both sorghum and sweet potato based cropping systems, soil moisture was correspondingly highest in tied ridges, intercropping and rotation systems involving dolichos and application of FYM+RP and least in control of monocropping under oxen plough. Information on effects of tillage practices, cropping systems and organic inputs could be very useful for soil water conservation purposes. Thus, using simulation models to attain the same could be the ultimate solution. A good agreement between observed and simulated soil moisture implied that CropSyst model is capable of investigating sustainable alternatives of increasing soil moisture in the ASALs.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
Research integrity0.0010.000
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 head0.050
GPT teacher head0.274
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations4
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Agricultural Science→Same topicSoil Carbon and Nitrogen Dynamics→French-language works237,207→