FARMLAND VALUES AND CREDIT CONDITIONS
Bibliographic record
Abstract
There was an annual increase of 12 percent in the Seventh Federal Reserve District’s agricultural land values in 2022— which helped them reach a new peak, even though the yearly gain was smaller than that of 2021. Values for “good” farmland in the District were unchanged in the fourth quarter of 2022 from the third quarter, according to 147 agricultural bankers who responded to the January survey. Sixteen percent of the survey respondents expected farmland values to rise during the January through March period of 2023, 10 percent expected them to fall, and 74 percent expected them to be stable. District agricultural credit conditions during the fourth quarter of 2022 remained healthy. In the final quarter of 2022, repayment rates for non-real-estate farm loans were again higher than a year ago, plus loan renewals and extensions were lower than a year ago once more. Less than 1 percent of agricultural borrowers were not likely to qualify for operating credit at the survey respondents’ banks in 2023 after qualifying in the previous year. That said, non-real-estate farm loan demand relative to a year ago was lower for the tenth consecutive quarter. There were again more funds available for lending than in the same quarter of the prior year at survey respondents’ banks in the final quarter of 2022, after the streak of 12 quarters with more funds available had been interrupted in the third quarter of 2022. The average loan-to-deposit ratio for the District rose to 70.6 percent in the fourth quarter of 2022—its highest reading since the fourth quarter of 2020. At the end of 2022, the District’s average nominal interest rates on farm operating, feeder cattle, and farm real estate loans were at their highest levels in 15 years, whereas average real rates for all three were last higher at the end of the first quarter of 2021.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 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".