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Record W284751331

Gray Hair, Good-Times, and Guns: The Changing Landscape of Farm Land Values and Uses Promises Continuing Adjustments for Ag Lenders. Also: NAFTA's Continuing Effects and a Look Ahead to the 2007 Farm Bill

2006· article· en· W284751331 on OpenAlexaboutno aff
Steve Cocheo

Bibliographic record

VenueABA banking journal · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsLeaseLand tenureRentingValue (mathematics)Agricultural economicsLand ValuesAcreBusinessAgricultureEconomicsFinanceLand useGeographyLawPolitical scienceAgricultural scienceEcology
DOInot available

Abstract

fetched live from OpenAlex

In much of Ag America, the price of an acre of land hasn't got to do so much with the land itself, but the potential value of that land as part of the mechanism we call a that produces marketable commodities. At least, that was traditionally the case. But speakers on land values at the 2005 North American Agricultural Lenders Conference spoke of changing dynamics in land pricing that are part of the shifting conditions that ag lenders and their customers face in the years ahead. only goes so far Nearly half of Iowa's farmland is owned by people over 65, Prof. Michael Duffy of Iowa State University told lenders at the ABA/Canadian Bankers Association-sponsored conference. This graying of land ownership has led to the increasing transference by gift or inheritance of farmland to people who haven't lived anywhere near the family farm for years. As a result, the share of Iowa land farmed by owner-operators has fallen significantly, according to Duffy, and the significance of absentee landlords has risen. This has contributed to a fall in sales, as the younger generation takes advantage of lease income from non-owner farmers. But prices change, and, over time, this will influence who holds the land, Duffy predicted. Land values have been increasing in Iowa. Sentimentality will only go so far, he said. There comes a point for each absentee landowner when they will willingly hack off their family's farm roots in exchange for a healthy payday. The money for that payday will likely come from investors, who, Duffy said, have been playing an increasing role in land purchases while farmers have been buying land less often than in the past. Addressing the Midwest in general, speaker Jim Farrell, president, Farmers National Co., Omaha, noted that the graying trend involved not only the generation passing its land onto the next, but also the next generation itself. Many of the kids receiving land as gifts or inheritances are in their 50s or 60s, as their parents' generation has been living longer. About half of midwestern farmland is owned by absentees, Farrell said. Equally significant is another statistic Farrell cited: 60% of the farmland sold in recent years in the Midwest went to farmers, with 40% going to buyers with other uses in mind for the land. Some of the alternative uses for the land are hunting and fishing, a trend that also affects Texas farmland, which Charles Gilliland, research economist at Texas A&M University's Real Estate Center, addressed. Gilliland noted that Texas farmland is sought not only by farmers, but also by consumers looking for recreational acres and by retirees hoping to realize dreams of ending their days on a farm or ranch. Investors--many of them bored with lackluster stock markets and hoping to profit from rising prices--also compete with farmers for the land. Many have snapped up sites with potential to be turned from farming to retail use. While farmers dominated the land buying process in Texas in the middle of the 1990s, consumers and investors now dominate the farmland market, according to Gilliland. Indeed, the economist said that Texas land prices show more correlation with the state's personal income statistics than with its farm income numbers. Happily ever NAFTA? One of the early ads for America Online showed a farmer arguing in an AOL chat-room with a NAFTA supporter. Finally the frustrated farmer stopped typing, picked up his shotgun, and terminated the dialup connection, with extreme prejudice. The formation of the North American Free Trade Agreement was an emotional time. Several conference sessions touched on how it all turned out. The cigar-chomping Prof. Barry Flinchbaugh, of Kansas State University, who frequently exhibits his own talent for verbal blasts, didn't disappoint fans waiting to hear his take on what NAFTA has meant. …

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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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.078
Threshold uncertainty score0.155

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.006
Scholarly communication0.0130.013
Open science0.0010.004
Research integrity0.0110.011
Insufficient payload (model declined to judge)0.0280.005

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 head0.009
GPT teacher head0.217
Teacher spread0.208 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2006
Admission routes1
Has abstractyes

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Same venueABA banking journalSame topicAgricultural Economics and PolicyFrench-language works237,207