Comparison of alternative sources of farmland values
Bibliographic record
Abstract
Purpose Consistent and reliable data on farmland values is critical to assessing the overall financial health of agricultural producers. However, little is known about the idiosyncrasies and similarities of standard land value data sources – US Department of Agriculture (USDA), Federal Reserve Bank land value surveys, and transaction prices. The purpose of this paper is to determine the differences and similarities of land value movements from three land value data sources. Design/methodology/approach In addition to Oklahoma transaction prices, two survey sources are considered: the USDA annual report and the quarterly Tenth District Survey of Agricultural Credit Conditions administered by the Federal Reserve Bank of Kansas City. The paper describes each data set and identifies differences in data sampling, collection, and reporting. Average values of Oklahoma farmland across data sources are examined. USDA estimates are regressed against quarterly Federal Reserve values across multiple states to determine the point in time represented by USDA estimates. Granger causality tests determine if Federal Reserve land value estimates anticipate movements in USDA land value estimates. Findings It is found that all three data sources are highly correlated, but transaction prices tend to be higher, especially for irrigated cropland and ranchland. USDA land values are reported as representing land values on January first, but instead they more closely represent first and second quarter land values according to a multi‐state comparison to changes in quarterly Federal Reserve land values. Given the finding that first quarter Federal Reserve Bank land values lead USDA land values and that they are published before the USDA release, Federal Reserve land values are a timely indicator of agricultural producers' financial position. Originality/value No previous research has addressed the topic of how various sources of agricultural land values compare.
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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".