document_title: Ving Rhames

question_text: who does the voice of the arby's commercials

question_tokens: who does the voice of the arby 's commercials

text: Ving Rhames

text: Early life and education[edit]

text: Rhames was born in Harlem, New York City, the son of Reather, a homemaker, and Ernest Rhames, an auto mechanic.[1][2] His parents were raised as sharecroppers in South Carolina.[2][3] He was named after the late NBC journalist, Irving R. Levine.[4]

text: He entered New York's High School of Performing Arts, where he discovered his love of acting. After high school, he studied drama at SUNY Purchase, where fellow acting student Stanley Tucci gave him his nickname "Ving". Rhames later transferred to the Juilliard School's Drama Division (Group 12: 1979–1983)[5] where he graduated with a Bachelor of Fine Arts degree in 1983.[6]

text: Career[edit]

text: Rhames first appeared on Broadway in the play The Boys of Winter in 1984. He started out in film in Wes Craven's The People Under the Stairs (1991) as Leroy; he watched over Kevin Kline as Secret Service agent Duane Stevensen in Dave (1993); and he played Marsellus Wallace in Pulp Fiction (1994).

text: Rhames played Dr. Peter Benton's brother-in-law on the TV medical drama ER, a recurring role he filled for three seasons. He played ace computer hacker Luther Stickell opposite Tom Cruise in Brian De Palma's Mission: Impossible (1996). In 1997 Rhames portrayed the character of Nathan 'Diamond Dog' Jones in the popular film Con Air. and Muki in the Ice Cube film Dangerous Ground.

text: Rhames won a Golden Globe in 1998 for Best Actor – Miniseries or Television Film in HBO's Don King: Only in America. At the ceremony he gave his award to fellow nominee Jack Lemmon, saying, "I feel that being an artist is about giving, and I'd like to give this to you." Lemmon was clearly touched by the gesture as was the celebrity audience who gave Lemmon a standing ovation. Lemmon, who tried unsuccessfully to give the award back to Rhames, said it was "one of the nicest, sweetest moments I've ever known in my life." The Hollywood Foreign Press Association announced later that they would have a duplicate award prepared for Rhames. That moment was #98 on E!'s 101 Awesome Moments in Entertainment.[7][8][9] The New York Times lauded Rhames for the act, writing that in doing so he "demonstrated his capacity for abundant generosity."[9]

text: Rhames appeared in Striptease (1996) as the wisecracking bodyguard Shad, Bringing Out the Dead (1999), then reprised his Luther Stickell role for Mission: Impossible 2 (2000).  He played Johnnie Cochran in American Tragedy (2000), the ex-con boyfriend of Jodie's mother in the John Singleton film Baby Boy, portrayed a gay drag queen in the television movie Holiday Heart, contributed his voice for the character of Cobra Bubbles in Lilo & Stitch (2002) and the subsequent TV series, and played a stoic cop fighting zombie hordes in Dawn of the Dead (2004) and Day of the Dead (2008) remakes. Rhames has also appeared in a series of television commercials for RadioShack, usually performing with Vanessa L. Williams.

text: In March 2005, Rhames played the lead role on a new Kojak series, on the USA Network cable channel (and on ITV4 in the UK). The bald head, lollipops, and "Who loves ya, baby?" catchphrase remained intact, but little else remained from the Savalas original.

text: Rhames voiced the part of Tobias Jones in the computer game Driver 3.

text: Reprising his Luther role, Rhames co-starred in Mission: Impossible III (2006), had a cameo appearance in Mission: Impossible – Ghost Protocol (2011), and played a major role in Mission: Impossible – Rogue Nation (2015) and Mission: Impossible – Fallout (2018), the fifth and sixth films, respectively.[10] He is the only actor besides Tom Cruise to appear in all six Mission: Impossible films. It was announced that he would have a role in the Aquaman-based show Mercy Reef, however due to the integration of The WB and UPN for the new network, CW, Mercy Reef was not picked up. Rhames played a homosexual, possibly also homicidal, firefighter who comes out of the closet in I Now Pronounce You Chuck and Larry. He narrates the BET television series American Gangster.

text: In the 2008 film Saving God, he played an ex-con who is released from prison a changed man looking to take over his father's former church congregation in a deteriorating neighborhood. Rhames stars in Phantom Punch, a biopic of boxer Sonny Liston released directly to DVD as well as The Tournament portraying a fighter out to win a no-rules tournament.

text: Rhames makes an appearance in Ludacris's song "Southern Gangstas" on his album Theater of the Mind. Rappers Playaz Circle and Rick Ross are also featured on the track.

text: In 2010, he filed a lawsuit against the producer of a film titled Red Canvas,[11] claiming that he had only been paid $175,000 of a $200,000 contract.

text: He filmed the movie Submission with Ernie Reyes, Jr. and UFC lightweight contender Gray Maynard and Randy Couture.

text: In 2015, he filmed a series of commercials for The ADT Corporation.

text: Rhames is one of the narrators for UFC.

text: Rhames introduced the New England Patriots and the Atlanta Falcons in the 2017 Super Bowl LI (51).

text: Rhames's deep voice is the center of many recent (2015–present) Arby's commercials, with the catchline 'Arby's, we have the meats!'

text: Filmography[edit]

text:
Film

Year

Title

Role

Notes

1984
Go Tell It on the Mountain
Young Gabriel Grimes

1986
Native Son
Jack

1988
Patty Hearst
Cinque Mtume

1989
Casualties of War
Lt. Reilly

1990
The Long Walk Home
Herbert Cotter

1990
Jacob's Ladder
George

1991
Flight of the Intruder
CPO Frank McRae

1991
Homicide
Robert Randolph

1991
The People Under the Stairs
Leroy

1992
Stop! Or My Mom Will Shoot
Mr. Stereo

1993
Blood in Blood Out
Ivan

1993
Dave
Duane Stevenson

1993
The Saint of Fort Washington
Little Leroy

1994
Pulp Fiction
Marsellus Wallace

1994
Drop Squad
Garvey

1995
Kiss of Death
Omar

1996
Mission: Impossible
Luther Stickell

1996
Striptease
Shad

1997
Dangerous Ground
Muki

1997
Rosewood
Mann

1997
Con Air
Nathan 'Diamond Dog' Jones

1998
Body Count
Pike

1998
Out of Sight
Buddy Bragg

1999
Entrapment
Aaron Thibadeaux
Nominated—Blockbuster Entertainment Award for Best Supporting Actor – Action

1999
Bringing Out the Dead
Marcus
Nominated—Satellite Award for Best Supporting Actor

2000
Mission: Impossible 2
Luther Stickell

2001
Baby Boy
Melvin
Nominated—Black Reel Award for Best Supporting Actor

2001
Final Fantasy: The Spirits Within
Ryan Whittaker

2002
Undisputed
George "Iceman" Chambers

2002
Lilo & Stitch
Cobra Bubbles
Voice only

2002
Dark Blue
Arthur Holland
Nominated—Black Reel Award for Best Supporting Actor

2003
Stitch! The Movie
Cobra Bubbles
Voice only

2003
Sin
Eddie Burns

2004
Dawn of the Dead
Sgt. Kenneth Hall

2005
Back in the Day
Joseph "J-Bone" Brown

2005
Animal
James "Animal" Allen

2005
Shooting Gallery
Cue Ball Carl Bridgers

2006
Mission: Impossible III
Luther Stickell

2006
Idlewild
Spats

2006
Leroy & Stitch
Cobra Bubbles
Voice only

2007
I Now Pronounce You Chuck and Larry
Fred G. Duncan

2008
Animal 2
James "Animal" Allen

2008
A Broken Life
Vet

2008
Day of the Dead
Capt. Rhodes

2008
Saving God
Armstrong Cane

2008
Phantom Punch
Sonny Liston

2009
Echelon Conspiracy
Agent Dave Grant

2009
The Bridge to Nowhere
Nate

2009
Evil Angel
Carruthers

2009
The Tournament
Joshua Harlow

2009
The Goods: Live Hard, Sell Hard
Jibby Newsome

2009
Surrogates
The Prophet

2009
Give 'Em Hell, Malone
Boulder

2010
Operation: Endgame
Judgement

2010
Caged Animal
Miles "Cain" Skinner

2010
Master Harold...and the Boys
Sam

2010
Piranha 3D
Deputy Fallon

2010
Red Canvas
Himself

2010
King of the Avenue
Norman De'Sha

2010
Death Race 2
R.H. Weyland

2011
Julia X
The Man

2011
Pimp Bullies
Miguel

2011
Soldiers of Fortune
Grimaud Torneur

2011
The River Murders
Captain Langley

2011
Mission: Impossible – Ghost Protocol
Luther Stickell
Cameo

2011
Zombie Apocalypse
Henry

2012
Mafia
Renzo Wes

2012
Seven Below
Jack

2012
Piranha 3DD
Deputy Fallon

2012
Won't Back Down
Principal Thompson

2013
Death Race 3: Inferno
R.H. Weyland

2013
Force of Execution
Ice Man

2014
Jamesy Boy
Conrad

2015
Mission: Impossible – Rogue Nation
Luther Stickell

2017
Guardians of the Galaxy Vol. 2
Charlie-27
Cameo

2017
The Star
Thaddeus
Voice only

2017
Father Figures
Rod Hamilton

2018
Mission: Impossible – Fallout
Luther Stickell

text:
Television

Year

Title

Role

Notes

1985
Miami Vice
Georges
Episode: "The Maze"

1986
Crime Story
Hector Lincoln
Episode: "Abrams for the Defense"

1987
Miami Vice
Walker Monroe
Episode: "Child's Play"

1987
Tour of Duty
SP4 Tucker
Episode: "Burn Baby, Burn"

1988
Spenser: For Hire
Henry Brown
Episode: "McAllister"

1989
Men
Charlie Hazard
6 episodes

1989
The Equalizer
Luther Paxton
Episode: "Suicide Squad"

1990
When You Remember Me
Leon
TV film

1994–1996
ER
Walter Robbins
8 episodes

1995
Ed McBain's 87th Precinct: Lightning
Detective Artie Brown
TV film

1995
New York Undercover
Max Villareal
Episode: "Olde Thyme Religion"

1995
Deadly Whispers
Det. Jackson
TV film

1997
Don King: Only in America
Don King
TV filmGolden Globe Award for Best Actor – Miniseries or Television FilmNominated—Primetime Emmy Award for Outstanding Lead Actor in a Miniseries or a MovieNominated—Satellite Award for Best Actor – Miniseries or Television FilmNominated—Screen Actors Guild Award for Outstanding Performance by a Male Actor in a Miniseries or Television Movie

2000
American Tragedy
Johnnie Cochran
TV film

2000
Holiday Heart
Holiday Heart
TV filmNominated—Black Reel Award for Best Actor on Television

2001
UC: Undercover
Quito Real
3 episodes

2002
Sins of the Father
Garrick Jones
TV filmNominated—Black Reel Award for Best Actor on Television

2002
Little John
John Morgan
TV film

2002
RFK
Judge Jones
TV film

2002
The Proud Family
Garrett Krebs
Episode: "A Hero for Halloween"

2002–2003
The District
Attorney General Troy Hatcher
5 episodes

2003
Lilo & Stitch: The Series
Cobra Bubbles
Voice only

2003
Jimmy Neutron
Chief
Episode: "Operation: Rescue Jet Fusion"

2005
Kojak
Theo Kojak
9 episodesNominated—Black Reel Award for Best Actor on Television

2006
Aquaman
McCaffery
TV pilot

2007
Football Wives
Frank Wallingford
TV pilot

2010
Gravity
Dogg McFee
10 episodes

2011
Black Jack
Black Jack
TV film

2013
Monday Mornings
Dr. Jorge Villanueva
10 episodes

2014
A Day Late and a Dollar Short
Cecil Price
TV film

text:
Video games

Year

Title

Role

Notes

2003
Mission: Impossible – Operation Surma
Luther Stickell
Voice only

2004
Driver 3
Tobias Jones
Voice only

2017
Call of Duty: WWII
Jefferson Potts
Voice only

text: Awards and nominations[edit]

text:
Year

Award

Category

Work

Result

Ref.

1998

Golden Globe Awards

Best Actor in Mini-Series or Motion Picture

Don King: Only in America

Won

[12]

Primetime Emmy Awards

Best Actor in Mini-Series or Movie

Don King: Only in America

Nominated

Acalpulco Black Film Festival

Best Actor

Rosewood

Nominated

Image Awards

Outstanding Actor in Motion Picture

Rosewood

Nominated

Outstanding Actor in Television or Mini-Series

Don King: Only in America

Nominated

Satellite Awards

Best Actor - Mini-Series or TV Film

Don King: Only in America

Nominated

Screen Actors Guild Awards

Best Actor - Mini-Series or TV Film

Don King: Only in America

Nominated

2000

Blockbuster Entertainment Awards

Best Supporting Actor - Action

Entrapment

Nominated

Satellite Awards

Best Supporting Actor - Comedy or Music

Bringing out the Dead

Nominated

ShoWest Convention

Best Supporting Actor

Won

[13]

2001

Black Reel Awards

Best Actor - Network

Holiday Heart

Nominated

Image Awards

Best Supporting Actor - Motion Picture

Mission: Impossible 2

Nominated

Locarno International Film Festival

Special Mention

Baby Boy

Won

2002

Black Reel Awards

Best Supporting Actor

Baby Boy

Nominated

Image Awards

Outstanding Actor in a Drama Series

UC: Undercover

Nominated

Supporting Actor in a Motion Picture

Baby Boy

Nominated

2003

Black Reel Awards

Best Actor - Mini-Series

Sins of the Father

Nominated

Image Awards

Best Supporting Actor - Comedy

The Proud Family

Nominated

Best Actor - Mini-Series or TV Film

Sins of the Father

Nominated

2004

Black Reel Awards

Best Supporting Actor

Dark Blue

Nominated

2006

Black Reel Awards

Best Actor - Television

Kojak

Nominated

2015

Black Reel Awards

Best Actor - Television

A Day Late and a Dollar Short

Nominated

Image Awards

Best Actor - Television

Nominated


document_title: Manchester by the Sea (film)

question_text: who plays joe in manchester by the sea

question_tokens: who plays joe in manchester by the sea

root

text: Manchester by the Sea (film)

text: Plot[edit]

text: The story takes place in the present while injecting the narrative with a series of flashbacks that inform the viewer about significant events from the past and the characters' motivations. The protagonist is Lee Chandler, a janitor and handyman in Quincy, Massachusetts, who lives a solitary life in a basement apartment.

tag: p

text: The movie opens to various scenes of him performing a variety of tasks for the tenants of the apartment complex where he works. Even so, his interaction with the tenants is minimal. After being reprimanded by his boss for swearing at an irritated tenant, he gets into a drunken bar fight with two businessmen because he believes they were "looking" at him.

text: While shoveling snow, Lee receives word that his brother Joe has suffered cardiac arrest. Joe dies before Lee can get to the hospital, and Lee insists on being the one to tell Joe's teenage son, Patrick, about Joe's death. While making funeral arrangements, they learn that Joe's body cannot be buried until spring when the ground thaws, and it needs to be kept refrigerated until then, so Lee remains in Manchester-by-the-Sea, Massachusetts until the delayed burial. While reading Joe's will with Joe's lawyer, Lee is shocked to discover that Joe named him Patrick's guardian.

text: During the scene, the viewer is shown — through flashbacks — that Lee once lived in Manchester with his then-wife Randi and their three small children, until his negligence while intoxicated led to a house fire that killed the children. No criminal charges were filed against him; however, after being questioned at the police station, Lee pulled a gun from a police officer's holster and attempted suicide. In light of these events, Lee remains reluctant to commit to the guardianship and unwilling to move back to Manchester, where the locals treat him as an outcast.

text: Even so, he is opposed to Patrick's reconnecting with his mother, Elise (who through flash-backs is revealed to have substance abuse problems and to have abandoned the family), and so Lee begins planning for Patrick to move to Boston with him. Patrick, who is deeply rooted in the Manchester community, strongly objects to the idea. Lee only commits to staying through the end of the school year.

text: Over time, Patrick and Lee reestablish their bonds, although conflict continues to arise between them when it comes to making decisions about Joe's boat, Patrick's girlfriends, and their future living arrangements. Over Lee's objections, Patrick notifies Elise of Joe's death by email, and she invites him to have lunch with her. She has found Christianity and sobriety with her fiancé, Jeffrey, but during an awkward meal with them, Patrick finds himself unable to connect with her. Patrick is further unsettled when Jeffrey emails him, insisting on being an intermediary in any future communication between Patrick and his mother. Additionally, Lee's positive comments about Elise's sobriety lead Patrick to believe that his uncle is trying to get rid of him, straining their relationship even more. As a result, Lee takes some steps to possibly extend his stay in Manchester, and he begins to seek ways to spend more time with Patrick.

text: One day, however, Lee runs into Randi, her newborn Dylan, and her friend Rachel. After Rachel leaves to get the car, a sobbing Randi expresses great remorse for her treatment of Lee during the divorce and asks him to have lunch with her. Despite being remarried, she confesses that she still loves him. He thanks her for her comments, but explains that "there's nothing there." When she insists that they reconnect and pleads with him not to "just die," he leaves before he can become emotional. Distraught, a drunk Lee picks a fight with strangers at a bar and gets knocked out, only to be rescued by George, a family friend. Lee awakens in George's home and breaks down in tears. At home, Patrick shows his uncle deference after observing Lee's battered state, as well as the pictures of his deceased children that he keeps in the bedroom.

text: Soon after, Lee arranges for George and his wife to adopt Patrick so the teen can stay in Manchester while Lee gets a new job in Boston. When Patrick asks Lee why staying is not an option, Lee admits that he "can't beat it." At this, Patrick cries and Lee comforts him. During a walk after Joe's burial service, Lee tells Patrick that he is searching for a residence in Boston with an extra room so that Patrick can visit whenever he wants. In the final scene, Lee and Patrick go fishing on Joe's refurbished boat that Patrick has inherited.

text: Cast[edit]

text:
Casey Affleck as Lee Chandler, a grief-stricken loner
Michelle Williams as Randi, Lee's ex-wife
Kyle Chandler as Joseph "Joe" Chandler, Lee's brother
Lucas Hedges as Patrick Chandler, 16-year-old son of Joe and Elise

Ben O'Brien as young Patrick

Gretchen Mol as Elise Chandler, Joe's former wife
C.J. Wilson as George, the Chandlers' family friend
Tate Donovan as Patrick's hockey coach
Kara Hayward as Silvie McGann, one of Patrick's girlfriends
Anna Baryshnikov as Sandy, one of Patrick's girlfriends
Heather Burns as Jill, Sandy's mother
Erica McDermott as Sue, boat yard boss
Matthew Broderick as Jeffrey, Elise's fiancé
Oscar Wahlberg as Joel, Patrick's friend
Stephen Henderson as Mr. Emery, Lee's boss
Josh Hamilton as Wes, Joe's Lawyer

tag: ul

text: Director Kenneth Lonergan appears briefly as a pedestrian.

text: Themes[edit]

text: The film is a treatment of profound grief from which it is difficult or impossible to recover. In an essay by Colin Fleming for Cineaste magazine, he says that "the question Lonergan invites us to ask ourselves is how on earth would we be able to carry on after an event so tragically full of loss and guilt."[5] Speaking to the persistence of grief, Film Comment magazine says that "Lonergan is telling us that Lee's grief cannot be contained or subdued because his past lives on wherever he goes."[6] Remarking on the way flash-backs appear suddenly in the movie, critic Anthony Lane says that Lonergan "proceeds on the assumption that things are hard, some irreparably so, and that it's the job of a film not to smooth them over."[7] However, one critic noted that juxtaposed to the tragedy is "the harsh comedy that colors much of the dialogue, and the near-farcical frequency with which things go wrong."[8] Along those same lines, critic Steven Mears called the film "a study of grief and reticence that finds droll humor in those very sources,"[9] and Richard Alleva says the loving but tense relationship between Lee and Patrick "keeps the story nicely balanced between rough hewn comedy and delicate pathos."[10] Explaining his objective, Lonergan said, "I don't like the fact that, nowadays, it feels like it's not permissible to leave something unresolved... Some people live with their trauma for years. I'm not interested in rubbing people's faces in suffering... But I don't like this lie that everybody gets over things that easily. Some people can't get over something major that's happened to them at all; why can’t they have a movie too?"[11]

text: The film's events takes place through the cultural filter of a blue-collar, New England community. John Krasinski and Matt Damon initially approached Lonergan about developing the story in New England.[12] As Lonergan researched the areas surrounding Manchester-by-the-Sea, he sought to include details specific to the area, for example its distance from Quincy, the delayed burial because of the frozen ground in a historical cemetery, and the realities of fishing life.[12] Critic Sam Lansky remarked that his New England roots make the lead character "disinclined to emote,"[13] and Tom Shone said that Lonergan's dialog forces "the story’s heartbreak to peep from behind these tough, flinty New England exteriors."[14]

text: Development and production[edit]

text: Matt Damon and John Krasinski had been brainstorming a film about an "emotionally crippled" handyman, and brought the idea to Lonergan for his input, with the idea that Krasinski would star and Damon would direct.[11] Damon had previously worked with Lonergan in a play and on the 2011 film Margaret.[15] Both actors became occupied with other projects while Lonergan worked on the screenplay for three years.[11] After Damon read a rough draft of the film, he insisted that Lonergan should direct it and that he himself would star in it.[11][16] They announced they would collaborate on the project on September 6, 2014,[citation needed] and pre-production began on September 8, 2014.[15] However, Damon would not have a break in his schedule for another year,[11] and in early December 2014, while filming The Finest Hours, Casey Affleck revealed to The Boston Globe that he would replace Damon in the role of Lee Chandler.[17] Damon said he "wouldn't give this role up to anybody but Casey Affleck."[11]

text: Affleck officially replaced Damon on January 5, 2015.[18] Days later, Michelle Williams joined the cast (on January 9) as Lee Chandler's wife.[19] Kyle Chandler signed onto the film that February 24, cast as Affleck's character's older brother.[20] In April 2015, Lucas Hedges joined the cast of the film.[21] Lonergan has said that Hedges' audition was "special", but that he was unsure of how the young actor would do "because his background is so different [from that of the character]. Ultimately he did a beautiful job."[12] It was later revealed Erica McDermott had also joined the cast.[22]

text: Gigi Pritzker was set to produce and finance the film through her company Odd Lot Entertainment.[15] However, in March 2015, it was reported that Sierra / Affinity had come aboard to finance the film, while Kimberly Steward would produce and finance through her company K Period Media, Kevin J. Walsh through his company B Story, Chris Moore through CMP, and Damon through his company Pearl Street Films. The film was executive produced by Josh Godfrey, John Krasinski, Declan Baldwin, and Bill Migliore; Pritzker was no longer attached to produce.[23]

text: Coming from a theater background, Lonergan had the actors do table read-style rehearsals for two weeks prior to filming.[24]

text: Filming[edit]

text: Principal photography began on March 23, 2015, in the namesake town of Manchester-by-the-Sea, Massachusetts.[25] On the second day, filming took place elsewhere on the North Shore, at locations in Beverly, Gloucester, Swampscott, Lynn, and Salem.[26]

text: Release[edit]

text: The film had its world premiere on January 23, 2016, at the Sundance Film Festival.[27] Shortly after, Amazon Studios acquired U.S. distribution rights to the film for $10 million, beating out Sony Pictures Classics, Universal Pictures, Lionsgate, and Fox Searchlight.[28][29] This was the second largest disclosed purchase, behind The Birth of a Nation at $17.5 million.[30] Roadside Attractions, of which Lionsgate owns 45%, was later announced to be co-distributing the film with Amazon.[31]

text: The film was screened at the Telluride Film Festival on September 2, 2016,[32] the Toronto International Film Festival on September 8, 2016,[33][34] the New York Film Festival on October 1, 2016,[35][36] and the BFI London Film Festival on October 8, 2016.[37] The film was theatrically released on November 18, 2016.[38] The film was made available to stream on Amazon in the US on February 7, 2017,[39] and on Amazon Prime on May 5 of that year.[citation needed]

text: Reception[edit]

text: Box office[edit]

text: Manchester by the Sea grossed $47.7 million in the United States and Canada and $30 million in other territories for a worldwide total of $77.7 million, against a production budget of $8.5 million.[3]

text: The film began a limited release on November 18, 2016, and grossed $256,498 from four theaters for the weekend, making for a per-theater average of $64,125. It began a wide release on December 16, 2016, opening against Rogue One and Collateral Beauty, and grossed $4.2 million, finishing 6th at the box office.[40]

text: Critical response[edit]

text: Manchester by the Sea received critical praise for Lonergan's direction and screenplay and for the performances of Affleck, Williams and Hedges, with Rolling Stone calling it the "must-see film" of the 2016 Sundance Festival.[11] On Rotten Tomatoes, the film has an approval rating of 95% based on 285 reviews, with an average rating of 8.9/10. The site's critical consensus reads, "Manchester by the Sea delivers affecting drama populated by full-bodied characters, marking another strong step forward for writer-director Kenneth Lonergan."[41] On Metacritic, the film has a weighted average score of 96 out of 100, based on 52 critics, indicating "universal acclaim".[42]

text: Giving the film five-out-of-five stars, Phil de Semlyen of Empire called the film "masterfully told and beautifully acted" and favorably compared Affleck's performance to that of Marlon Brando in On the Waterfront, saying "there’s not much higher praise than that."[43] Justin Chang of Variety said it was a "beautifully textured, richly enveloping drama."[44] Critic Peter Travers said the movie "ranks with the year’s very best films," and that "it takes a piece out of you."[45] Matt Zoller Seitz of RogerEbert.com gave the film four-out-of-four stars, stating that it is "the kind of movie you'll want to see a second time with someone who hasn't seen it yet, to remember what it was like to watch it for the first time."[46] Anthony Lane with The New Yorker called the film "carefully constructed, compellingly acted, and often hard to watch."[8] Leah Greenblatt of Entertainment Weekly said that "the exquisitely crafted, emotionally ragged Manchester doesn’t just ask for time and effort; it earns it."[47] The film was included on many end-of-year lists, including those of the American Film Institute,[48] Rolling Stone,[49] the BBC,[50] The New York Times,[51] Los Angeles Times,[52] and The Washington Post.[53]

text: The scene which drew the most critical praise featured the characters Randi and Lee speaking again years after their divorce. Anthony Lane called it the "highlight" of the movie.[7] Speaking of William's performance, Tom Shone said that "if this actress were put on earth to do one thing only, it would be this scene."[14] Zoller called the scene "a duet of mortification and mercy that stacks up with the best of Mike Leigh (Secrets and Lies)...The effect is cathartic."[46] Affleck says he strove to show restraint while acting the scene, explaining, "The challenge was to have all of those feelings and hold it without weeping and wailing and gnashing your teeth. To be there, and not be there."[13]

text: Accolades[edit]

text: Manchester by the Sea received six nominations at the 89th Academy Awards: Best Picture, Best Director, Best Actor (Affleck), Best Supporting Actor (Hedges), Best Supporting Actress (Williams) and Best Original Screenplay,[54] winning two for Best Original Screenplay for Lonergan and Best Actor for Affleck. It was the first-ever film released by a digital streaming service to be nominated for Best Picture.[55]

text: Affleck won the Golden Globe Award for Best Actor – Motion Picture Drama; the film was nominated for four additional awards at the 74th ceremony: Best Picture, Best Director, Best Supporting Actress (for Williams) and Best Screenplay.[56]

text: Manchester by the Sea received six nominations at the 70th British Academy Film Awards, including Best Film, Best Direction, Best Actor in a Leading Role, Best Actress in a Supporting Role and Best Original Screenplay.[57] Affleck won Best Actor in a Leading Role and Lonergan won Best Original Screenplay.[58]

text: Music[edit]

text: Lesley Barber was selected to score the film, as she had previously worked on Lonergan's film You Can Count On Me.[59] She developed the first musical themes after reading the script.[60] Once she established the themes, she worked with the orchestra to create a specific sound that emoted the sound of the ocean with "underlying tension"—but with lightness—for exterior shots.[59] Barber took early inspiration from New England church music from the 1700s, including Calvinist hymns and other music of the Pilgrims and Colonial era, with its emphasis on a cappella vocals.[61] Barber also developed the music to complement her understanding of the essence of the film's scenes based on emails and conversations with Lonergan.[59] For example, to score scenes that reflected Lee's "interior landscape", she sent the music to her daughter Jacoba Barber Rozema (an opera major),[60] and then recorded her singing in her dorm room via Skype.[60][61] Barber said the more confined space made for a "perfect sound."[61] Orchestration was added to the track later.[60] For the opening flash-back of Lee in happier times, Barber recorded her daughter's vocals in a large, spacious auditorium.[59] Instead of giving Lonergan demos, Barber provided him with more finished pieces earlier into the editing process to allow the director to have a better sense of the music while editing.[59] The film was cut down after the Sundance screening, requiring Barber to rework and simplify some of the music.[59]

text: Critic Steven Mears says the film's score "gives form" to the feelings of blue-collar Americans.[9] Bobby Finger with Jezabel called it "an elegant white noise...a hypnotic soundtrack to focused thought."[62] Caitlin Warren of Spindle Magazine said the score perfectly adds "to the raw emotion of the film without ever overwhelming it to the point of feeling contrived or cheesy."[63] Film score critic Jonathan Broxton said the music "dwells in the bitterness of regret" and that it has "a captivating, dream-like quality that is...dramatically appropriate for the film it accompanies."[64]

text: The use of "Adagio in G minor" to accompany the reveal of Lee's tragedy got mixed reviews. The New Yorker said that "any piece of music that has been used for Rollerball, Gallipoli, and Flashdance has, by definition, been squeezed dry."[7] Mark Kermode of the BBC said that the piece "has been overused so much in pop entertainment that when it turns up, it's distracting."[65] On the other hand, Koresky noted that in the montage, "Lonergan thickly lays on the Handel, Bach, and Albinoni, making this sequence almost surreally operatic in its horror."[6] In an interview, Lonergan said he originally used the piece as a stand-in track while editing, but ultimately decided to keep it in.[65] The Academy of Motion Picture Arts and Sciences ruled the film's score ineligible for Oscar consideration due to the volume of music from classical composers on the soundtrack. In a statement Barber released to Variety, she said, "While I accept the Academy’s decision, I also support my director's decision to use these pieces and I'm also very proud of the substantial contribution that the original score made to the film as well."[61]

text: Soundtrack[edit]

text: All music is written by Lesley Barber unless otherwise noted. The orchestra was conducted by James Shearman.[64] Jacoba Barber-Rozema, Barber's daughter, provided special vocals.[64] The album was recorded and mixed by XXX, and edited by Mick Gormley.[64] The album was produced by Barber and Stefan Karrer.[64]

text:
No.
Title
Writer(s)
Performer
Length

1.
"Manchester by the Sea Chorale"

Jacoba Barber Rozema
2:20

2.
"Manchester Minimalist Piano and Strings"

2:19

3.
"Plymouth Chorale"

Rozema, Musica Sacra Chorus and Orchestra[66]
1:25

4.
"Pifa (Pastoral Symphony)" (from Messiah)
George Frederick Handel
Musica Sacra Chorus and Orchestra
3:00

5.
"Smoke"

1:53

6.
"Floating 149" (A Cappella)

Rozema
1:53

7.
"Floating 149" (Strings Reprise)

2:20

8.
"Sonata for Oboe & Piano, 1st Movement"
Camille Saint-Saëns
Gerhard Kanzian and Ed Lewis
2:25

9.
"Manchester Minimalist Piano and Strings" (Strings Reprise)

1:04

10.
"He Shall Feed His Flock Like a Shepherd; Come Unto Him" (from Messiah)
Handel
Musica Sacra Chorus and Orchestra
5:11

11.
"Manchester Minimalist Piano and Strings" (Variation)

2:19

12.
"Adagio per Archi E Organo in Sol Minore"
Tomaso Albinoni and Remo Giazotto
The London Philharmonic Orchestra
8:34

13.
"Smoke Reprise With Bass And Strings"

1:37

14.
"I'm Beginning to See the Light"
Duke Ellington, Don George, Johnny Hodges, and Harry James
Ella Fitzgerald and The Ink Spots
2:44

15.
"Chérubin"
Jules Massenet
Munich Radio Orchestra with the choir chorus of the Bavarian State Opera
3:01

16.
"Manchester By The Sea Strings Reprise"

2:19

Total length:
44:07

tag: table


document_title: Least squares

question_text: who is credited with developing the method of least squares for regression

text: Least squares

text: History[edit]

text: Context[edit]

text: The method of least squares grew out of the fields of astronomy and geodesy, as scientists and mathematicians sought to provide solutions to the challenges of navigating the Earth's oceans during the Age of Exploration. The accurate description of the behavior of celestial bodies was the key to enabling ships to sail in open seas, where sailors could no longer rely on land sightings for navigation.

text: The method was the culmination of several advances that took place during the course of the eighteenth century:[4]

text:
The combination of different observations as being the best estimate of the true value; errors decrease with aggregation rather than increase, perhaps first expressed by Roger Cotes in 1722.
The combination of different observations taken under the same conditions contrary to simply trying one's best to observe and record a single observation accurately. The approach was known as the method of averages. This approach was notably used by Tobias Mayer while studying the librations of the moon in 1750, and by Pierre-Simon Laplace in his work in explaining the differences in motion of Jupiter and Saturn in 1788.
The combination of different observations taken under different conditions. The method came to be known as the method of least absolute deviation. It was notably performed by Roger Joseph Boscovich in his work on the shape of the earth in 1757 and by Pierre-Simon Laplace for the same problem in 1799.
The development of a criterion that can be evaluated to determine when the solution with the minimum error has been achieved. Laplace tried to specify a mathematical form of the probability density for the errors and define a method of estimation that minimizes the error of estimation. For this purpose, Laplace used a symmetric two-sided exponential distribution we now call Laplace distribution to model the error distribution, and used the sum of absolute deviation as error of estimation. He felt these to be the simplest assumptions he could make, and he had hoped to obtain the arithmetic mean as the best estimate. Instead, his estimator was the posterior median.

text: The method[edit]

text: The first clear and concise exposition of the method of least squares was published by Legendre in 1805.[5] The technique is described as an algebraic procedure for fitting linear equations to data and Legendre demonstrates the new method by analyzing the same data as Laplace for the shape of the earth. The value of Legendre's method of least squares was immediately recognized by leading astronomers and geodesists of the time.

text: In 1809 Carl Friedrich Gauss published his method of calculating the orbits of celestial bodies. In that work he claimed to have been in possession of the method of least squares since 1795. This naturally led to a priority dispute with Legendre. However, to Gauss's credit, he went beyond Legendre and succeeded in connecting the method of least squares with the principles of probability and to the normal distribution. He had managed to complete Laplace's program of specifying a mathematical form of the probability density for the observations, depending on a finite number of unknown parameters, and define a method of estimation that minimizes the error of estimation. Gauss showed that arithmetic mean is indeed the best estimate of the location parameter by changing both the probability density and the method of estimation. He then turned the problem around by asking what form the density should have and what method of estimation should be used to get the arithmetic mean as estimate of the location parameter. In this attempt, he invented the normal distribution.

text: An early demonstration of the strength of Gauss' method came when it was used to predict the future location of the newly discovered asteroid Ceres. On 1 January 1801, the Italian astronomer Giuseppe Piazzi discovered Ceres and was able to track its path for 40 days before it was lost in the glare of the sun. Based on these data, astronomers desired to determine the location of Ceres after it emerged from behind the sun without solving Kepler's complicated nonlinear equations of planetary motion. The only predictions that successfully allowed Hungarian astronomer Franz Xaver von Zach to relocate Ceres were those performed by the 24-year-old Gauss using least-squares analysis.

text: In 1810, after reading Gauss's work, Laplace, after proving the central limit theorem, used it to give a large sample justification for the method of least square and the normal distribution. In 1822, Gauss was able to state that the least-squares approach to regression analysis is optimal in the sense that in a linear model where the errors have a mean of zero, are uncorrelated, and have equal variances, the best linear unbiased estimator of the coefficients is the least-squares estimator. This result is known as the Gauss–Markov theorem.

text: The idea of least-squares analysis was also independently formulated by the American Robert Adrain in 1808. In the next two centuries workers in the theory of errors and in statistics found many different ways of implementing least squares.[6]

text: Problem statement[edit]

text: This section does not cite any sources. Please help improve this section by adding citations to reliable sources. Unsourced material may be challenged and removed. (February 2012) (Learn how and when to remove this template message)

text:
The objective consists of adjusting the parameters of a model function to best fit a data set. A simple data set consists of n points (data pairs)

(

x

i

,

y

i

)

{\displaystyle (x_{i},y_{i})\!}

, i = 1, ..., n, where

x

i

{\displaystyle x_{i}\!}

 is an independent variable and

y

i

{\displaystyle y_{i}\!}

 is a dependent variable whose value is found by observation. The model function has the form

f
(
x
,
β
)

{\displaystyle f(x,\beta )}

, where m adjustable parameters are held in the vector

β

{\displaystyle {\boldsymbol {\beta }}}

. The goal is to find the parameter values for the model that "best" fits the data. The least-squares method finds its optimum when the sum, S, of squared residuals

text: is a minimum. A residual is defined as the difference between the actual value of the dependent variable and the value predicted by the model. Each data point has one residual. Both the sum and the mean of the residuals are equal to zero.

text:
An example of a model is that of the straight line in two dimensions. Denoting the y-intercept as

β

0

{\displaystyle \beta _{0}}

 and the slope as

β

1

{\displaystyle \beta _{1}}

, the model function is given by

f
(
x
,

β

)
=

β

0

+

β

1

x

{\displaystyle f(x,{\boldsymbol {\beta }})=\beta _{0}+\beta _{1}x}

. See linear least squares for a fully worked out example of this model.

text: A data point may consist of more than one independent variable. For example, when fitting a plane to a set of height measurements, the plane is a function of two independent variables, x and z, say. In the most general case there may be one or more independent variables and one or more dependent variables at each data point.

text: Limitations[edit]

text: This regression formulation considers only residuals in the dependent variable. There are two rather different contexts in which different implications apply:

text:
Regression for prediction. Here a model is fitted to provide a prediction rule for application in a similar situation to which the data used for fitting apply. Here the dependent variables corresponding to such future application would be subject to the same types of observation error as those in the data used for fitting. It is therefore logically consistent to use the least-squares prediction rule for such data.
Regression for fitting a "true relationship". In standard regression analysis, that leads to fitting by least squares, there is an implicit assumption that errors in the independent variable are zero or strictly controlled so as to be negligible. When errors in the independent variable are non-negligible, models of measurement error can be used; such methods can lead to parameter estimates, hypothesis testing and confidence intervals that take into account the presence of observation errors in the independent variables.[7] An alternative approach is to fit a model by total least squares; this can be viewed as taking a pragmatic approach to balancing the effects of the different sources of error in formulating an objective function for use in model-fitting.

text: Solving the least squares problem[edit]

text: The minimum of the sum of squares is found by setting the gradient to zero. Since the model contains m parameters, there are m gradient equations:

text:
and since

r

i

=

y

i

−
f
(

x

i

,

β

)

{\displaystyle r_{i}=y_{i}-f(x_{i},{\boldsymbol {\beta }})}

, the gradient equations become

text: The gradient equations apply to all least squares problems. Each particular problem requires particular expressions for the model and its partial derivatives.

text: Linear least squares[edit]

text: A regression model is a linear one when the model comprises a linear combination of the parameters, i.e.,

text:
where the function

ϕ

j

{\displaystyle \phi _{j}}

 is a function of

x

{\displaystyle x}

.

text: Letting

text:
we can then see that in that case the least square estimate (or estimator, in the context of a random sample),

β

{\displaystyle {\boldsymbol {\beta }}}

 is given by

text: For a derivation of this estimate see Linear least squares (mathematics).

text: Non-linear least squares[edit]

text:
There is, in some cases, a closed-form solution to a non-linear least squares problem – but in general there is not. In the case of no closed-form solution, numerical algorithms are used to find the value of the parameters

β

{\displaystyle \beta }

 that minimizes the objective. Most algorithms involve choosing initial values for the parameters. Then, the parameters are refined iteratively, that is, the values are obtained by successive approximation:

text:
where a superscript k is an iteration number, and the vector of increments

Δ

β

j

{\displaystyle \Delta \beta _{j}}

 is called the shift vector. In some commonly used algorithms, at each iteration the model may be linearized by approximation to a first-order Taylor series expansion about

β

k

{\displaystyle {\boldsymbol {\beta }}^{k}}

:

text: The Jacobian J is a function of constants, the independent variable and the parameters, so it changes from one iteration to the next. The residuals are given by

text:
To minimize the sum of squares of

r

i

{\displaystyle r_{i}}

, the gradient equation is set to zero and solved for

Δ

β

j

{\displaystyle \Delta \beta _{j}}

:

text: which, on rearrangement, become m simultaneous linear equations, the normal equations:

text: The normal equations are written in matrix notation as

text: These are the defining equations of the Gauss–Newton algorithm.

text: Differences between linear and nonlinear least squares[edit]

text:
The model function, f, in LLSQ (linear least squares) is a linear combination of parameters of the form

f
=

X

i
1

β

1

+

X

i
2

β

2

+
⋯

{\displaystyle f=X_{i1}\beta _{1}+X_{i2}\beta _{2}+\cdots }

 The model may represent a straight line, a parabola or any other linear combination of functions. In NLLSQ (nonlinear least squares) the parameters appear as functions, such as

β

2

,

e

β
x

{\displaystyle \beta ^{2},e^{\beta x}}

 and so forth. If the derivatives

∂
f

/

∂

β

j

{\displaystyle \partial f/\partial \beta _{j}}

 are either constant or depend only on the values of the independent variable, the model is linear in the parameters. Otherwise the model is nonlinear.
Algorithms for finding the solution to a NLLSQ problem require initial values for the parameters, LLSQ does not.
Like LLSQ, solution algorithms for NLLSQ often require that the Jacobian can be calculated. Analytical expressions for the partial derivatives can be complicated. If analytical expressions are impossible to obtain either the partial derivatives must be calculated by numerical approximation or an estimate must be made of the Jacobian.
In NLLSQ non-convergence (failure of the algorithm to find a minimum) is a common phenomenon whereas the LLSQ is globally concave so non-convergence is not an issue.
NLLSQ is usually an iterative process. The iterative process has to be terminated when a convergence criterion is satisfied. LLSQ solutions can be computed using direct methods, although problems with large numbers of parameters are typically solved with iterative methods, such as the Gauss–Seidel method.
In LLSQ the solution is unique, but in NLLSQ there may be multiple minima in the sum of squares.
Under the condition that the errors are uncorrelated with the predictor variables, LLSQ yields unbiased estimates, but even under that condition NLLSQ estimates are generally biased.

text: These differences must be considered whenever the solution to a nonlinear least squares problem is being sought.

text: Least squares, regression analysis and statistics[edit]

text: The method of least squares is often used to generate estimators and other statistics in regression analysis.

text: Consider a simple example drawn from physics. A spring should obey Hooke's law which states that the extension of a spring y is proportional to the force, F, applied to it.

text:
constitutes the model, where F is the independent variable. To estimate the force constant, k, a series of n measurements with different forces will produce a set of data,

(

F

i

,

y

i

)
,

i
=
1
,
…
,
n

{\displaystyle (F_{i},y_{i}),\ i=1,\dots ,n\!}

, where yi is a measured spring extension. Each experimental observation will contain some error. If we denote this error

ε

{\displaystyle \varepsilon }

, we may specify an empirical model for our observations,

text: There are many methods we might use to estimate the unknown parameter k. Noting that the n equations in the m variables in our data comprise an overdetermined system with one unknown and n equations, we may choose to estimate k using least squares. The sum of squares to be minimized is

text: The least squares estimate of the force constant, k, is given by

text: Here it is assumed that application of the force causes the spring to expand and, having derived the force constant by least squares fitting, the extension can be predicted from Hooke's law.

text: In regression analysis the researcher specifies an empirical model. For example, a very common model is the straight line model which is used to test if there is a linear relationship between dependent and independent variable. If a linear relationship is found to exist, the variables are said to be correlated. However, correlation does not prove causation, as both variables may be correlated with other, hidden, variables, or the dependent variable may "reverse" cause the independent variables, or the variables may be otherwise spuriously correlated. For example, suppose there is a correlation between deaths by drowning and the volume of ice cream sales at a particular beach. Yet, both the number of people going swimming and the volume of ice cream sales increase as the weather gets hotter, and presumably the number of deaths by drowning is correlated with the number of people going swimming. Perhaps an increase in swimmers causes both the other variables to increase.

text: In order to make statistical tests on the results it is necessary to make assumptions about the nature of the experimental errors. A common (but not necessary) assumption is that the errors belong to a normal distribution. The central limit theorem supports the idea that this is a good approximation in many cases.

text:
The Gauss–Markov theorem. In a linear model in which the errors have expectation zero conditional on the independent variables, are uncorrelated and have equal variances, the best linear unbiased estimator of any linear combination of the observations, is its least-squares estimator. "Best" means that the least squares estimators of the parameters have minimum variance. The assumption of equal variance is valid when the errors all belong to the same distribution.
In a linear model, if the errors belong to a normal distribution the least squares estimators are also the maximum likelihood estimators.

text: However, if the errors are not normally distributed, a central limit theorem often nonetheless implies that the parameter estimates will be approximately normally distributed so long as the sample is reasonably large. For this reason, given the important property that the error mean is independent of the independent variables, the distribution of the error term is not an important issue in regression analysis. Specifically, it is not typically important whether the error term follows a normal distribution.

text:
In a least squares calculation with unit weights, or in linear regression, the variance on the jth parameter, denoted

var

(

β
^

j

)

{\displaystyle \operatorname {var} ({\hat {\beta }}_{j})}

, is usually estimated with

text: where the true error variance σ2 is replaced by an estimate based on the minimised value of the sum of squares objective function S. The denominator, n − m, is the statistical degrees of freedom; see effective degrees of freedom for generalizations.

text: Confidence limits can be found if the probability distribution of the parameters is known, or an asymptotic approximation is made, or assumed. Likewise statistical tests on the residuals can be made if the probability distribution of the residuals is known or assumed. The probability distribution of any linear combination of the dependent variables can be derived if the probability distribution of experimental errors is known or assumed. Inference is particularly straightforward if the errors are assumed to follow a normal distribution, which implies that the parameter estimates and residuals will also be normally distributed conditional on the values of the independent variables.

text: Weighted least squares[edit]

text: A special case of generalized least squares called weighted least squares occurs when all the off-diagonal entries of Ω (the correlation matrix of the residuals) are null; the variances of the observations (along the covariance matrix diagonal) may still be unequal (heteroscedasticity).

text:
The expressions given above are based on the implicit assumption that the errors are uncorrelated with each other and with the independent variables and have equal variance. The Gauss–Markov theorem shows that, when this is so,

β
^

{\displaystyle {\hat {\boldsymbol {\beta }}}}

 is a best linear unbiased estimator (BLUE). If, however, the measurements are uncorrelated but have different uncertainties, a modified approach might be adopted. Aitken showed that when a weighted sum of squared residuals is minimized,

β
^

{\displaystyle {\hat {\boldsymbol {\beta }}}}

 is the BLUE if each weight is equal to the reciprocal of the variance of the measurement

text: The gradient equations for this sum of squares are

text: which, in a linear least squares system give the modified normal equations,

text: When the observational errors are uncorrelated and the weight matrix, W, is diagonal, these may be written as

text: If the errors are correlated, the resulting estimator is the BLUE if the weight matrix is equal to the inverse of the variance-covariance matrix of the observations.

text:
When the errors are uncorrelated, it is convenient to simplify the calculations to factor the weight matrix as

w

i
i

=

W

i
i

{\displaystyle w_{ii}={\sqrt {W_{ii}}}}

. The normal equations can then be written in the same form as ordinary least squares:

text: where we define the following scaled matrix and vector:

text: This is a type of whitening transformation; the last expression involves an entrywise division.

text: For non-linear least squares systems a similar argument shows that the normal equations should be modified as follows.

text: Note that for empirical tests, the appropriate W is not known for sure and must be estimated. For this feasible generalized least squares (FGLS) techniques may be used.

text: If the uncertainty of the observations is not known from external sources, then the weights could be estimated from the given observations. This can be useful, for example, to identify outliers. After the outliers have been removed from the data set, the weights should be reset to one.[8]

text: Relationship to principal components[edit]

text:
The first principal component about the mean of a set of points can be represented by that line which most closely approaches the data points (as measured by squared distance of closest approach, i.e. perpendicular to the line). In contrast, linear least squares tries to minimize the distance in the

y

{\displaystyle y}

 direction only. Thus, although the two use a similar error metric, linear least squares is a method that treats one dimension of the data preferentially, while PCA treats all dimensions equally.

text: Regularized versions[edit]

text: This section may be too technical for most readers to understand. Please help improve it to make it understandable to non-experts, without removing the technical details. The talk page may contain suggestions. (February 2016) (Learn how and when to remove this template message)

text: Tikhonov regularization[edit]

text:
In some contexts a regularized version of the least squares solution may be preferable. Tikhonov regularization (or ridge regression) adds a constraint that

∥
β

∥

2

{\displaystyle \|\beta \|^{2}}

, the L2-norm of the parameter vector, is not greater than a given value. Equivalently, it may solve an unconstrained minimization of the least-squares penalty with

α
∥
β

∥

2

{\displaystyle \alpha \|\beta \|^{2}}

 added, where

α

{\displaystyle \alpha }

 is a constant (this is the Lagrangian form of the constrained problem). In a Bayesian context, this is equivalent to placing a zero-mean normally distributed prior on the parameter vector.

text: Lasso method[edit]

text:
An alternative regularized version of least squares is Lasso (least absolute shrinkage and selection operator), which uses the constraint that

∥
β
∥

{\displaystyle \|\beta \|}

, the L1-norm of the parameter vector, is no greater than a given value.[9][10][11] (As above, this is equivalent to an unconstrained minimization of the least-squares penalty with

α
∥
β
∥

{\displaystyle \alpha \|\beta \|}

 added.) In a Bayesian context, this is equivalent to placing a zero-mean Laplace prior distribution on the parameter vector.[12] The optimization problem may be solved using quadratic programming or more general convex optimization methods, as well as by specific algorithms such as the least angle regression algorithm.

text:
One of the prime differences between Lasso and ridge regression is that in ridge regression, as the penalty is increased, all parameters are reduced while still remaining non-zero, while in Lasso, increasing the penalty will cause more and more of the parameters to be driven to zero. This is an advantage of Lasso over ridge regression, as driving parameters to zero deselects the features from the regression. Thus, Lasso automatically selects more relevant features and discards the others, whereas Ridge regression never fully discards any features. Some feature selection techniques are developed based on the LASSO including Bolasso which bootstraps samples,[13] and FeaLect which analyzes the regression coefficients corresponding to different values of

α

{\displaystyle \alpha }

 to score all the features.[14]

text: The L1-regularized formulation is useful in some contexts due to its tendency to prefer solutions where more parameters are zero, which gives solutions that depend on fewer variables.[9] For this reason, the Lasso and its variants are fundamental to the field of compressed sensing. An extension of this approach is elastic net regularization.


document_title: Malchus

question_text: who cut off the high priest's ear

question_tokens: who cut off the high priest 's ear

text: Malchus

text: This article needs additional citations for verification. Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. (June 2011) (Learn how and when to remove this template message)

text: Malchus /ˈmælkəs/ is the servant of the Jewish High Priest Caiaphas who participated in the arrest of Jesus as written in the four gospels. According to the Bible, one of the disciples, Simon Peter, being armed with a sword, cut off the servant's ear in an attempt to prevent the arrest of Jesus.

text: Simon cutting off Malchus' ear is related in all four canonical gospels, in Matthew 26:51, Mark 14:47, Luke 22:50-51, and John 18:10–11, but Simon and Malchus are named only in the Gospel of John. Also, Luke is the only gospel that says Jesus healed the servant. This was Jesus' last recorded miracle prior to his resurrection.

text: The relevant passage in the Gospel of John, KJV, reads:

text:
10 Then Simon Peter having a sword drew it, and smote the high priest's servant, and cut off his right ear. The servant's name was Malchus.
11 Then said Jesus unto Peter, Put up thy sword into the sheath: the cup which my Father hath given me, shall I not drink it?

text: Although rejecting biblical inerrancy, James F. McGrath suggests that this account reflects an event that did happen, in that early Christians would hardly have invented a story portraying themselves as violent.[2]

text: Later in Chapter 18, John records that a relative of Malchus witnessed Peter's assault in the Garden of Gethsemane, and identified Peter as a follower of Christ. Peter denied this.

text: Thornton Wilder wrote a short play entitled, "The Servant's Name Was Malchus". It appeared in the collection The Angel That Troubled the Waters and Other Plays.

text: Malchus was portrayed by Paul Brightwell in the 2013 TV miniseries The Bible.

text: "Malchus" is a book by W. G. Griffiths published in 2002 by RiverOaks Publishing.


document_title: History of agriculture

question_text: where do scholars agree agriculture most likely emerged in asia

text: History of agriculture

text: Origins[edit]

text: Origin hypotheses[edit]

text: Scholars have developed a number of hypotheses to explain the historical origins of agriculture. Studies of the transition from hunter-gatherer to agricultural societies indicate an antecedent period of intensification and increasing sedentism; examples are the Natufian culture in southwest Asia, and the Early Chinese Neolithic in China. Current models indicate that wild stands that had been harvested previously started to be planted, but were not immediately domesticated.[1][2]

text: Localised climate change is the favoured explanation for the origins of agriculture in the Levant.[3] When major climate change took place after the last ice age (c. 11,000 BC), much of the earth became subject to long dry seasons.[4] These conditions favoured annual plants which die off in the long dry season, leaving a dormant seed or tuber. An abundance of readily storable wild grains and pulses enabled hunter-gatherers in some areas to form the first settled villages at this time.[5]

text: Early development[edit]

text: Early people began altering communities of flora and fauna for their own benefit through means such as fire-stick farming and forest gardening very early.[6][7][8] Exact dates are hard to determine, as people collected and ate seeds before domesticating them, and plant characteristics may have changed during this period without human selection. An example is the semi-tough rachis and larger seeds of cereals from just after the Younger Dryas (about 9,500 BC) in the early Holocene in the Levant region of the Fertile Crescent. Monophyletic characteristics were attained without any human intervention, implying that apparent domestication of the cereal rachis could have occurred quite naturally.[9]

text: Agriculture began independently in different parts of the globe, and included a diverse range of taxa. At least 11 separate regions of the Old and New World were involved as independent centers of origin.[10] Some of the earliest known domestications were of animals. Pigs were domesticated in Mesopotamia around 11,000 BC.[11] Sheep were domesticated in Mesopotamia between 11,000 and 9,000 BC.[12] Cattle were domesticated from the wild aurochs in the areas of modern Turkey and Pakistan around 8,500 BC.[13] Camels were domesticated late, perhaps around 3,000 BC.[14]

text: It was not until after 9,500 BC that the eight so-called founder crops of agriculture appear: first emmer and einkorn wheat, then hulled barley, peas, lentils, bitter vetch, chick peas and flax. These eight crops occur more or less simultaneously on Pre-Pottery Neolithic B (PPNB) sites in the Levant, although wheat was the first to be grown and harvested on a significant scale. At around the same time (9400 BC), parthenocarpic fig trees were domesticated.[16][17]

text: By 7,000 BC, sowing and harvesting reached the fertile soil of Mesopotamia, where Sumerians systematized it and scaled it up. By 8,000 BC, farming was entrenched on the banks of the River Nile. About this time, agriculture was developed independently in the Far East, probably in China, with rice rather than wheat as the primary crop. Maize was domesticated from the wild grass teosinte in West Mexico by 6,700 BC.[18] The potato (8,000 BC), tomato,[19] pepper (4,000 BC), squash (8,000 BC) and several varieties of bean (8,000 BC onwards) were domesticated in the New World.[20] Agriculture was independently developed on the island of New Guinea.[21] In Greece from c. 11,000 BC lentils, vetch, pistachios, and almonds were cultivated, while wild oats and wild barley appear in quantity from c. 7,000 BC alongside einkorn wheat, barley, sheep, goats and pigs,[22][23] while emmer was used on Cyprus between 9,100 and 8,600 BC.[24][25] Banana cultivation of Musa acuminata, including hybridization, dates back to 5,000 BC, and possibly to 8,000 BC, in Papua New Guinea.[26][27] Bees were kept for honey in the Middle East around 7,000 BC.[28] Archaeological evidence from various sites on the Iberian peninsula suggest the domestication of plants and animals between 6,000 and 4,500 BC.[25] Céide Fields in Ireland, consisting of extensive tracts of land enclosed by stone walls, date to 3,500 BC and are the oldest known field systems in the world.[29][30] The horse was domesticated in the Pontic steppe around 4,000 BC.[31] In Siberia, Cannabis was in use in China in Neolithic times and may have been domesticated there; it was in use both as a fibre for ropemaking and as a medicine in Ancient Egypt by about 2,350 BC.[32]

text: In China, rice and millet were domesticated by 8,000 BC, followed by mung, soy and azuki beans. In the Sahel region of Africa, local rice and sorghum were domesticated by 5,000 BC. Kola nut and coffee were domesticated in Africa.[33] In New Guinea, ancient Papuan peoples began practicing agriculture around 7,000 BC, domesticating sugarcane and taro.[34] In the Indus Valley from the eighth millennium BC onwards at Mehrgarh, 2-row and 6-row barley were cultivated, along with einkorn, emmer, and durum wheats, and dates. In the earliest levels of Merhgarh, wild game such as gazelle, swamp deer, blackbuck, chital, wild ass, wild goat, wild sheep, boar, and nilgai were all hunted for food. These are successively replaced by domesticated sheep, goats, and humped zebu cattle by the fifth millennium BC, indicating the gradual transition from hunting and gathering to agriculture.[35] Maize and squash were domesticated in Mesoamerica; potato in South America, and sunflower in the Eastern Woodlands of North America.[36]

text: Civilizations[edit]

text: Sumer[edit]

text: Sumerian farmers grew the cereals barley and wheat, starting to live in villages from about 8,000 BC. Given the low rainfall of the region, agriculture relied on the Tigris and Euphrates rivers. Irrigation canals leading from the rivers permitted the growth of cereals in large enough quantities to support cities. The first ploughs appear in pictographs from Uruk around 3,000 BC; seed-ploughs that funneled seed into the ploughed furrow appear on seals around 2300 BC. Vegetable crops included chickpeas, lentils, peas, beans, onions, garlic, lettuce, leeks and mustard. They grew fruits including dates, grapes, apples, melons, and figs. Alongside their farming, Sumerians also caught fish and hunted fowl and gazelle. The meat of sheep, goats, cows and poultry was eaten, mainly by the elite. Fish was preserved by drying, salting and smoking.[37][38]

text: Ancient Egypt[edit]

text: The civilization of Ancient Egypt was indebted to the Nile River and its dependable seasonal flooding. The river's predictability and the fertile soil allowed the Egyptians to build an empire on the basis of great agricultural wealth. Egyptians were among the first peoples to practice agriculture on a large scale, starting in the pre-dynastic period from the end of the Paleolithic into the Neolithic, between around 10,000 and 4,000 BC.[39] This was made possible with the development of basin irrigation.[40] Their staple food crops were grains such as wheat and barley, alongside industrial crops such as flax and papyrus.[39]

text: Indus valley[edit]

text: Cotton was cultivated by the 5th-4th millennium BC.[41]

text: Wheat, barley, and jujube were domesticated in the Indian subcontinent by 9,000 BC, soon followed by sheep and goats.[42] Barley and wheat cultivation—along with the domestication of cattle, primarily sheep and goats—followed in Mehrgarh culture by 8,000–6,000 BC.[43][44] This period also saw the first domestication of the elephant.[42] Pastoral farming in India included threshing, planting crops in rows—either of two or of six—and storing grain in granaries.[44][45] By the 5th millennium BC, agricultural communities became widespread in Kashmir.[44] Irrigation was developed in the Indus Valley Civilization by around 4,500 BC.[46] The size and prosperity of the Indus civilization grew as a result of this innovation, leading to more thoroughly planned settlements which used drainage and sewers.[46] Archeological evidence of an animal-drawn plough dates back to 2,500 BC in the Indus Valley Civilization.[47]

text: Ancient China[edit]

text: Records from the Warring States, Qin Dynasty, and Han Dynasty provide a picture of early Chinese agriculture from the 5th century BC to 2nd century AD which included a nationwide granary system and widespread use of sericulture. An important early Chinese book on agriculture is the Chimin Yaoshu of AD 535, written by Jia Sixia.[48] Jia's writing style was straightforward and lucid relative to the elaborate and allusive writing typical of the time. Jia's book was also very long, with over one hundred thousand written Chinese characters, and it quoted many other Chinese books that were written previously, but no longer survive.[49] The contents of Jia's 6th century book include sections on land preparation, seeding, cultivation, orchard management, forestry, and animal husbandry. The book also includes peripherally related content covering trade and culinary uses for crops.[50] The work and the style in which it was written proved influential on later Chinese agronomists, such as Wang Zhen and his groundbreaking Nong Shu of 1313.[49]

text: For agricultural purposes, the Chinese had innovated the hydraulic-powered trip hammer by the 1st century BC.[51] Although it found other purposes, its main function to pound, decorticate, and polish grain that otherwise would have been done manually. The Chinese also began using the square-pallet chain pump by the 1st century AD, powered by a waterwheel or oxen pulling an on a system of mechanical wheels.[52] Although the chain pump found use in public works of providing water for urban and palatial pipe systems,[53] it was used largely to lift water from a lower to higher elevation in filling irrigation canals and channels for farmland.[54] By the end of the Han dynasty in the late 2nd century, heavy ploughs had been developed with iron ploughshares and mouldboards.[55][56] These would slowly spread west, revolutionizing farming in Northern Europe by the 10th century. (Glick, however, argues for a development of the Chinese plough as late as the 9th century, implying its spread east from similar designs known in Italy by the 7th century.)[57]

text: Asian rice was domesticated 8,200–13,500 years ago in China, with a single genetic origin from the wild rice Oryza rufipogon,[58] in the Pearl River valley region of China. Rice cultivation then spread to South and Southeast Asia.[59]

text: Ancient Greece and Hellenistic world[edit]

text: The major cereal crops of the ancient Mediterranean region were wheat, emmer, and barley, while common vegetables included peas, beans, fava, and olives, dairy products came mostly from sheep and goats, and meat, which was consumed on rare occasion for most people, usually consisted of pork, beef, and lamb.[60] Agriculture in ancient Greece was hindered by the topography of mainland Greece that only allowed for roughly 10% of the land to be cultivated properly, necessitating the specialized exportation of oil and wine and importation of grains from Thrace (centered in what is now Bulgaria) and the Greek colonies of southern Russia.[61] During the Hellenistic period, the Ptolemaic Empire controlled Egypt, Cyprus, Phoenicia, and Cyrenaica, major grain-producing regions that mainland Greeks depended on for subsistence, while the Ptolemaic grain market also played a critical role in the rise of the Roman Republic.[61] In the Seleucid Empire, Mesopotamia was a crucial area for the production of wheat, while nomadic animal husbandry was also practiced in other parts.[61]

text: Roman Empire[edit]

text: In the Greco-Roman world of Classical antiquity, Roman agriculture was built on techniques originally pioneered by the Sumerians, transmitted to them by subsequent cultures, with a specific emphasis on the cultivation of crops for trade and export. The Romans laid the groundwork for the manorial economic system, involving serfdom, which flourished in the Middle Ages. The farm sizes in Rome can be divided into three categories. Small farms were from 18-88 iugera (one iugerum is equal to about 0.65 acre). Medium-sized farms were from 80-500 iugera (singular iugerum). Large estates (called latifundia) were over 500 iugera.[62] The Romans had four systems of farm management: direct work by owner and his family; slaves doing work under supervision of slave managers; tenant farming or sharecropping in which the owner and a tenant divide up a farm’s produce; and situations in which a farm was leased to a tenant.[62]

text: Mesoamerica[edit]

text: In Mesoamerica, wild teosinte was transformed through human selection into the ancestor of modern maize, more than 6,000 years ago. It gradually spread across North America and was the major crop of Native Americans at the time of European exploration.[63] Other Mesoamerican crops include hundreds of varieties of locally domesticated squash and beans, while cocoa, also domesticated in the region, was a major crop.[34] The turkey, one of the most important meat birds, was probably domesticated in Mexico or the U.S. Southwest.[64]

text: In Mesoamerica, the Aztecs were active farmers and had an agriculturally focused economy. The land around Lake Texcoco was fertile, but not large enough to produce the amount of food needed for the population of their expanding empire. The Aztecs developed irrigation systems, formed terraced hillsides, fertilized their soil, and developed chinampas or artificial islands, also known as "floating gardens". The Mayas between 400 BC to 900 AD used extensive canal and raised field systems to farm swampland on the Yucatán Peninsula.[65][66]

text: South America[edit]

text: In the Andes region of South America, with civilizations including the Inca, the major crop was the potato, domesticated approximately 7,000–10,000 years ago.[67][68][69] Coca, still a major crop to this day, was domesticated in the Andes, as were the peanut, tomato, tobacco, and pineapple.[34] Cotton was domesticated in Peru by 3,600 BC.[70] Animals were also domesticated, including llamas, alpacas, and guinea pigs.[71]

text: North America[edit]

text: The indigenous people of the Eastern U.S.A. domesticated numerous crops. Sunflowers, tobacco,[72] varieties of squash and Chenopodium, as well as crops no longer grown, including marsh elder and little barley, were domesticated.[73][74] Wild foods including wild rice and maple sugar were harvested.[75] The most common varieties of strawberry were domesticated from Eastern North America.[76] Two major crops, pecans and Concord grapes, were utilized extensively in prehistoric times but do not appear to have been domesticated until the 19th century.[77][78]

text: The indigenous people in what is now California and the Pacific Northwest practiced various forms of forest gardening and fire-stick farming in the forests, grasslands, mixed woodlands, and wetlands, ensuring that desired food and medicine plants continued to be available. The natives controlled fire on a regional scale to create a low-intensity fire ecology which prevented larger, catastrophic fires and sustained a low-density agriculture in loose rotation; a sort of "wild" permaculture.[79][80][81][82]

text: A system of companion planting called the Three Sisters was developed in North America. Three crops that complemented each other were planted together: winter squash, maize (corn), and climbing beans (typically tepary beans or common beans). The maize provides a structure for the beans to climb, eliminating the need for poles. The beans provide the nitrogen to the soil that the other plants use, and the squash spreads along the ground, blocking the sunlight, helping prevent the establishment of weeds. The squash leaves also act as a "living mulch".[83][84]

text: Australia[edit]

text: From the time of British colonization of Australia in 1788, Indigenous Australians were characterised as nomadic hunter-gatherers who did not engage in agriculture, despite evidence to the contrary. In 1969, the archaeologist Rhys Jones proposed that Indigenous Australians engaged in systematic burning as a way of enhancing natural productivity, what has been termed fire-stick farming.[85] In the 1970s and 1980s archaeological research in south west Victoria established that the Gunditjmara and other groups had developed sophisticated eel farming and fish trapping systems over a period of nearly 5,000 years.[86] The archaeologist Harry Lourandos suggested in the 1980s that there was evidence of 'intensification' in progress across Australia,[87] a process that appeared to have continued through the preceding 5,000 years. These concepts led the historian Bill Gammage to argue that in effect the whole continent was a managed landscape.[6]

text: In two regions of Australia, the central west coast and eastern central Australia, forms of early agriculture may have been practiced. People living in permanent settlements of over 200 residents sowed or planted on a large scale and stored the harvested food. The Nhanda and Amangu of the central west coast grew yams (Dioscorea hastifolia), while various groups in eastern central Australia (the Corners Region) planted and harvested bush onions (yaua - Cyperus bulbosus), native millet (cooly, tindil – Panicum decompositum) and a sporocarp, ngardu (Marsillea drumondii).[6]:281–304[88]

text: Middle Ages and Early Modern[edit]

text: From 100 BC to 1600 AD, world population continued to grow along with land use, as evidenced by the rapid increase in methane emissions from cattle and the cultivation of rice.[89]

text: Arab world[edit]

text: From the 8th century, the medieval Islamic world underwent a transformation in agricultural practice, described by the historian Andrew Watson as the Arab Agricultural Revolution.[90] This transformation was driven by a number of factors including the diffusion of many crops and plants along Muslim trade routes, the spread of more advanced farming techniques, and an agricultural-economic system which promoted increased yields and efficiency. The shift in agricultural practice changed the economy, population distribution, vegetation cover, agricultural production, population levels, urban growth, the distribution of the labour force, cooking, diet, and clothing across the Islamic world. Muslim traders covered much of the Old World, and trade enabled the diffusion of many crops, plants and farming techniques across the region, as well as the adaptation of crops, plants and techniques from beyond the Islamic world.[90] This diffusion introduced major crops to Europe by way of Al-Andalus, along with the techniques for their cultivation and cuisine. Sugar cane, rice, and cotton were among the major crops transferred, along with citrus and other fruit trees, nut trees, vegetables such as aubergine, spinach and chard, and the use of spices such as cumin, coriander, nutmeg and cinnamon. Intensive irrigation, crop rotation, and agricultural manuals were widely adopted. Irrigation, partly based on Roman technology, made use of noria water wheels, water mills, dams and reservoirs.[90][91][92]

text: Europe[edit]

text: The Middle Ages saw further improvements in agriculture. Monasteries spread throughout Europe and became important centers for the collection of knowledge related to agriculture and forestry. The manorial system allowed large landowners to control their land and its laborers, in the form of peasants or serfs.[93] During the medieval period, the Arab world was critical in the exchange of crops and technology between the European, Asia and African continents. Besides transporting numerous crops, they introduced the concept of summer irrigation to Europe and developed the beginnings of the plantation system of sugarcane growing through the use of slaves for intensive cultivation.[94]

text: By AD 900, developments in iron smelting allowed for increased production in Europe, leading to developments in the production of agricultural implements such as ploughs, hand tools and horse shoes. The carruca heavy plough improved on the earlier scratch plough, with the adoption of the Chinese mouldboard plough to turn over the heavy, wet soils of northern Europe. This led to the clearing of northern European forests and an increase in agricultural production, which in turn led to an increase in population.[95][96] At the same time, some farmers in Europe moved from a two field crop rotation to a three field crop rotation in which one field of three was left fallow every year. This resulted in increased productivity and nutrition, as the change in rotations permitted nitrogen-fixing legumes such as peas, lentils and beans.[97] Improved horse harnesses and the whippletree further improved cultivation.[98]

text: Watermills were introduced by the Romans, but were improved throughout the Middle Ages, along with windmills, and used to grind grains into flour, to cut wood and to process flax and wool.[99]

text: Crops included wheat, rye, barley and oats. Peas, beans, and vetches became common from the 13th century onward as a fodder crop for animals and also for their nitrogen-fixation fertilizing properties. Crop yields peaked in the 13th century, and stayed more or less steady until the 18th century.[100] Though the limitations of medieval farming were once thought to have provided a ceiling for the population growth in the Middle Ages, recent studies[101][102] have shown that the technology of medieval agriculture was always sufficient for the needs of the people under normal circumstances, and that it was only during exceptionally harsh times, such as the terrible weather of 1315–17, that the needs of the population could not be met.[103][104]

text: Columbian exchange[edit]

text: After 1492, a global exchange of previously local crops and livestock breeds occurred. Maize, potatoes, sweet potatoes and manioc were the key crops that spread from the New World to the Old, while varieties of wheat, barley, rice and turnips traveled from the Old World to the New. There had been few livestock species in the New World, with horses, cattle, sheep and goats being completely unknown before their arrival with Old World settlers. Crops moving in both directions across the Atlantic Ocean caused population growth around the world and a lasting effect on many cultures.[105] Maize and cassava were introduced from Brazil into Africa by Portuguese traders in the 16th century,[106] becoming staple foods, replacing native African crops.[107]

text: After its introduction from South America to Spain in the late 1500s, the potato became a staple crop throughout Europe by the late 1700s. The potato allowed farmers to produce more food, and initially added variety to the European diet. The increased supply of food reduced disease, increased births and reduced mortality, causing a population boom throughout the British Empire, the US and Europe.[108] The introduction of the potato also brought about the first intensive use of fertilizer, in the form of guano imported to Europe from Peru, and the first artificial pesticide, in the form of an arsenic compound used to fight Colorado potato beetles. Before the adoption of the potato as a major crop, the dependence on grain had caused repetitive regional and national famines when the crops failed, including 17 major famines in England between 1523 and 1623. The resulting dependence on the potato however caused the European Potato Failure, a disastrous crop failure from disease that resulted in widespread famine and the death of over one million people in Ireland alone.[109]

text: Modern agriculture[edit]

text: British agricultural revolution[edit]

text: Between the 16th century and the mid-19th century, Britain saw a large increase in agricultural productivity and net output. New agricultural practices like enclosure, mechanization, four-field crop rotation to maintain soil nutrients, and selective breeding enabled an unprecedented population growth to 5.7 million in 1750, freeing up a significant percentage of the workforce, and thereby helped drive the Industrial Revolution. The productivity of wheat went up from about 19 bushels per acre in 1720 to around 30 bushels by 1840, marking a major turning point in history.[110]

text: Advice on more productive techniques for farming began to appear in England in the mid-17th century, from writers such as Samuel Hartlib, Walter Blith and others.[111] The main problem in sustaining agriculture in one place for a long time was the depletion of nutrients, most importantly nitrogen levels, in the soil. To allow the soil to regenerate, productive land was often let fallow and in some places crop rotation was used. The Dutch four-field rotation system was popularised by the British agriculturist Charles Townshend in the 18th century. The system (wheat, turnips, barley and clover), opened up a fodder crop and grazing crop allowing livestock to be bred year-round. The use of clover was especially important as the legume roots replenished soil nitrates.[112]

text: The mechanisation and rationalisation of agriculture was another important factor. Robert Bakewell and Thomas Coke introduced selective breeding, and initiated a process of inbreeding to maximise desirable traits from the mid 18th century, such as the New Leicester sheep. Machines were invented to improve the efficiency of various agricultural operation, such as Jethro Tull's seed drill of 1701 that mechanised seeding at the correct depth and spacing and Andrew Meikle's threshing machine of 1784. Ploughs were steadily improved, from Joseph Foljambe's Rotherham iron plough in 1730[113] to James Small's improved "Scots Plough" metal in 1763. In 1789 Ransomes, Sims & Jefferies was producing 86 plough models for different soils.[114] Powered farm machinery began with Richard Trevithick's stationary steam engine, used to drive a threshing machine, in 1812.[115] Mechanisation spread to other farm uses through the 19th century. The first petrol-driven tractor was built in America by John Froelich in 1892.[116]

text: The scientific investigation of fertilization began at the Rothamsted Experimental Station in 1843 by John Bennet Lawes. He investigated the impact of inorganic and organic fertilizers on crop yield and founded one of the first artificial fertilizer manufacturing factories in 1842. Fertilizer, in the shape of sodium nitrate deposits in Chile, was imported to Britain by John Thomas North as well as guano (birds droppings). The first commercial process for fertilizer production was the obtaining of phosphate from the dissolution of coprolites in sulphuric acid.[117]

text: 20th century[edit]

text: Dan Albone constructed the first commercially successful gasoline-powered general purpose tractor in 1901, and the 1923 International Harvester Farmall tractor marked a major point in the replacement of draft animals (particularly horses) with machines. Since that time, self-propelled mechanical harvesters (combines), planters, transplanters and other equipment have been developed, further revolutionizing agriculture.[118] These inventions allowed farming tasks to be done with a speed and on a scale previously impossible, leading modern farms to output much greater volumes of high-quality produce per land unit.[119]

text: The Haber-Bosch method for synthesizing ammonium nitrate represented a major breakthrough and allowed crop yields to overcome previous constraints. It was first patented by German chemist Fritz Haber. In 1910 Carl Bosch, while working for German chemical company BASF, successfully commercialized the process and secured further patents. In the years after World War II, the use of synthetic fertilizer increased rapidly, in sync with the increasing world population.[121]

text: In the past century agriculture has been characterized by increased productivity, the substitution of synthetic fertilizers and pesticides for labor, water pollution,[122] and farm subsidies.[123] Other applications of scientific research since 1950 in agriculture include gene manipulation,[124][125] hydroponics,[126] and the development of economically viable biofuels such as ethanol.[127]

text: In recent years there has been a backlash against the external environmental effects of conventional agriculture, resulting in the organic movement.[128] Famines continued to sweep the globe through the 20th century. Through the effects of climactic events, government policy, war and crop failure, millions of people died in each of at least ten famines between the 1920s and the 1990s.[129]

text: The historical processes that have allowed agricultural crops to be cultivated and eaten well beyond their centers of origin continues in the present through globalization. On average, 68.7% of a nation's food supplies and 69.3% of its agricultural production are of crops with foreign origins.[130]

text: Green Revolution[edit]

text: The Green Revolution was a series of research, development, and technology transfer initiatives, between the 1940s and the late 1970s. It increased agriculture production around the world, especially from the late 1960s. The initiatives, led by Norman Borlaug and credited with saving over a billion people from starvation, involved the development of high-yielding varieties of cereal grains, expansion of irrigation infrastructure, modernization of management techniques, distribution of hybridized seeds, synthetic fertilizers, and pesticides to farmers.[131]

text: Synthetic nitrogen, along with mined rock phosphate, pesticides and mechanization, have greatly increased crop yields in the early 20th century. Increased supply of grains has led to cheaper livestock as well. Further, global yield increases were experienced later in the 20th century when high-yield varieties of common staple grains such as rice, wheat, and corn were introduced as a part of the Green Revolution. The Green Revolution exported the technologies (including pesticides and synthetic nitrogen) of the developed world to the developing world. Thomas Malthus famously predicted that the Earth would not be able to support its growing population, but technologies such as the Green Revolution have allowed the world to produce a surplus of food.[132]

text: Although the Green Revolution significantly increased rice yields in Asia, yield increases have not occurred in the past 15–20 years.[133] The genetic "yield potential" has increased for wheat, but the yield potential for rice has not increased since 1966, and the yield potential for maize has "barely increased in 35 years".[133] It takes only a decade or two for herbicide-resistant weeds to emerge, and insects become resistant to insecticides within about a decade, delayed somewhat by crop rotation.[133]

text: Organic agriculture[edit]

text: For most of its history, agriculture has been organic, without synthetic fertilisers or pesticides, and without GMOs. With the advent of chemical agriculture, Rudolf Steiner called for farming without synthetic pesticides, and his Agriculture Course of 1924 laid the foundation for biodynamic agriculture.[134] Lord Northbourne developed these ideas and presented his manifesto of organic farming in 1940. This became a worldwide movement, and organic farming is now practiced in most countries.[135]