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Enregistrement 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 sur OpenAlexaboutno aff
Steve Cocheo

Notice bibliographique

RevueABA banking journal · 2006
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueAgricultural Economics and Policy
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésLeaseLand tenureRentingValue (mathematics)Agricultural economicsLand ValuesAcreBusinessAgricultureEconomicsFinanceLand useGeographyLawPolitical scienceAgricultural scienceEcology
DOInon disponible

Résumé

récupéré en direct d'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. …

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,005
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,078
Score d'incertitude au seuil0,155

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,005
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0080,006
Communication savante0,0130,013
Science ouverte0,0010,004
Intégrité de la recherche0,0110,011
Charge utile insuffisante (le modèle a refusé de juger)0,0280,005

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,009
Tête enseignante GPT0,217
Écart entre enseignants0,208 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2006
Routes d'admission1
Résumé présentoui

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