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Record W2055925003 · doi:10.5539/sar.v4n2p31

A Stochastic Frontier Analysis of Technical Efficiency of Maize Production Under Minimum Tillage in Zambia

2015· article· en· W2055925003 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.

venuePublished in a venue whose home country is Canada.
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

VenueSustainable Agriculture Research · 2015
Typearticle
Languageen
FieldDecision Sciences
TopicEfficiency Analysis Using DEA
Canadian institutionsnot available
FundersCollege of Engineering, Michigan State UniversityMichigan State University
KeywordsTillageAgricultural economicsProduction–possibility frontierEconomicsProductivityProduction (economics)AgricultureAgricultural productivityAgricultural scienceMathematicsEnvironmental scienceGeographyAgronomyEconomic growth

Abstract

fetched live from OpenAlex

<p>Minimum tillage and other conservation agriculture practices are not only associated with income gains but are also claimed to be the panacea to the declining agricultural productivity and soil degradation problems in Africa and across the world. The few studies on technical efficiency related to the agricultural sector performance in Zambia have not attempted to determine how technically efficient smallholder farmers that produce maize under minimum tillage are. This study used stochastic frontier analysis based on both the half-normal and exponential model distributions on 2008 cross-sectional nationally representative data of 160 smallholder maize farm households that adopted minimum tillage in Zambia. Results indicate that maize farmers face increasing returns to scale (1.074) implying that there were opportunities for them to improve their technical efficiency as they were operating in stage I of their production functions. The half-normal and exponential model distributions indicate average technical efficiency scores of 60 and 71.7 percent, respectively. Their respective lowest efficiency scores were 9.3 and 8.5 percent. The highest efficiency scores for the half-normal and exponential model distributions were 89.3 and 90.9 percent. Maximum likelihood estimation results show that marital status, level of education of household head, square of household size, off farm income, agro-ecological region III, distance to vehicular road and access to loans are statistically significant factors that affect technical efficiency of smallholder maize farmers that practice minimum tillage in Zambia. The study calls for increased infrastructural development through construction of improved road network, schools and colleges in remote areas as a means to increasing accesss to knowledge and other agricultural services in order to enhance their technical efficiency levels. It also recommends promotion of minimum tillage practices in recommended agro-ecological regions to improve their technical efficiency. The study further acclaims for increased access to loans by smallholder maize farmers that practice minimum tillage as this would in one way induce them to invest in improved varieties and equipment that would help enhance their technical efficiency in Zambia.</p>

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)0.021
metaresearch head score (Gemma)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.784
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.039
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.103
GPT teacher head0.423
Teacher spread0.320 · 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