Comparative analysis of competitiveness of exporting agroindustrial beef chains in Goiás state
Bibliographic record
Abstract
Brazil stands as the largest exporter of beef, and Goiás stands out as the second largest exporter state, third in beef production and quarter in effective herd, according to 2008 data (SEPIN, 2009a). Because of the importance of this activity to the State economy and the currency and relevance of the theme, we chose to comparatively analyze the competitiveness of agro-industrial exporting chains of beef in the State of Goiás. We started from the assumption that export food chains (formed from the slaughtering units) of beef in Goiás present settings, actors, resources and different transactions, which facilitates the design of differentiated chains. In addition, we started with the assumption that the slaughterhouses are the dynamizing actors and that the beef exporting chains in Goiás realize competitive advantages. We analyzed the areas of production (beef cattle), slaughter and processing (refrigerators), and distribution and consumption. Competitive analysis has been developed by drivers and sub-factors of competitiveness, which included 20 drivers and 146 sub-factors, considering the three segments. Their assessment was conducted from the perspective of slaughter and processing segment (refrigerators). In addition, we used secondary data and interviews with key actors. Through the data collected, it was found that the chains, even inserted a single geographical clipping, have different characteristics, such as: physical infrastructure, economic structure, management, market share, size of transactions, types of distribution channels, structure of distribution in foreign markets, types of transport, types of slaughtering, among others. Regarding coordination, the opinions of respondents disagreed, demonstrating clearly that there is no coordination defined in chain, but that the slaughters and processors are the strongest candidates do coordinate the whole chain. Even with differences in the designs of the two chains, the marks awarded to the drivers (three segments) by two refrigeration units, in most cases followed a similar trend, although not identical assignments of notes. It can be stated from the data that the two reviewed exporter chains have a competitive advantage. However, the livestock production sector had the lowest scores, demonstrating that the State of Goiás, even with comparative advantages, requires changes in management, use of more advanced technologies, and creating incentives for government actions that may be implemented. The other segments, even with the competitive advantages, require joint action in order to maintain their competitive position, and try to it. Some suggestions for joint activities involving the private and public sector were drawn up, namely: support and encourage the technological advancement of the production process and management of farms in the State of Goiás; coordination of production by market demand, adding value beef; improved system of regulation and supervision, cooperation between actors in the chain, diversification of markets for meat products, tax incentives, and improving distribution logistics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".