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北美商科代写 Final Project代写

2022-06-02 10:59 星期四 所属: 作业代写 浏览:77

北美商科代写

Final Project

北美商科代写 The world population has grown six hundred percentage – from one billion to about six billion – in the last two hundred years.

Problem Description:

Sustainability of the human race in different parts of the world is challenged by the shortage of food. The world population has grown six hundred percentage – from one billion to about six billion – in the last two hundred years. According to the Population Institute, roughly, 230 thousand more babies are born every day. The World Food Programme estimates that about 795 million people do not have adequate food to lead a healthy life. About 3.1 million children die every year because of poor nutrition. On the other hand, land used for farming has been decreasing which makes the burden of food shortage acute. Regardless, simply attempting to increase the land available for farming is unlikely to sustain the needed food supply. To address this great problem, this project expects you to develop an analytics framework to aid soybeanfarmers select up to a given number of varieties of soybeans from a large set of available varieties to maximize the yield at a target farm.  北美商科代写

Every year soybean farmers make decisions about the varieties to be grown at their farm.While making this decision, they consider uncertainty due to weather, soil conditions, and yield studies of different varieties. They could choose just one variety or a mix of few varieties to hedge against uncertainties. You are expected to utilize the dataset provided to propose a framework which integrates descriptive, predictive, and prescriptive analytics to optimally select up to five varieties of soybeans.

Deliverables:  北美商科代写

  1. Performexploratory data analytics to unearth patterns in the given data and utilize those patterns in making predictions and prescriptions.
  2. Construct one or more prediction models to predict yield of different experimental varieties.
  3. Optimize the portfolio of (experimental) varieties to be grown at the target farm. The optimal portfolio can have at most 5 varieties of soybean. It is not necessary but you are welcome to use the methods you learn in prescriptive analytics class to construct the optimal portfolio.

Data Sets:

  1. Training Data for AgProject
  2. Evaluation Dataset for AgProject
北美商科代写
北美商科代写

Key:  北美商科代写

GrowingSeason Year Date
Location trial location code Id number
Genetics breeding group Group ID
Experiment Experiment number Experiment ID
Latitude Latitude Decimal degrees
Longitude Longitude Decimal degrees
Variety Variety code Variety ID
Variety_Yield Variety yield

北美商科代写

Bushels per acre adjusted by

moisture

Commercial_Yield Commercial yield for the trial Bushels per acre adjusted by

moisture

Yield_Difference yield difference between experiment and commercial

varieties in a trial

Bushels per acre adjusted by moisture
Location_Yield Average site yield (approximately,

checks across experiments)

Bushels per acre adjusted by

moisture

RelativeMaturity Relative Maturity Interval Relative maturity interval

(region) based on the location

Weather1 Climate type based on temperature, precipitation and

solar radiation

Climate class
Weather2 Season type Season class
Probability Probability of growing soybean Probability of growing

soybeans in the nearby area of the site

RelativeMaturity25
Probability of growing soybean of RM 2.5 to 3 Probability of growing

soybeans in the nearby area of the site

Prob_IRR Probability of irrigation

北美商科代写

Probability of field irrgation nearby the area of the

site

Soil_Type Soil type based on texture,

available water holding capacity, and soil drainage

Soil Class
TEMP_03 Sum of the temperatures for the season 2003 Daily degree Celsius sum

between April 1st and October 31st

TEMP_04 Sum of the temperatures for the season 2004 Daily degree Celsius sum between April 1st and October

31st

TEMP_05 Sum of the temperatures for the season 2005 Daily degree Celsius sum

between April 1st and October 31st

TEMP_06
Sum of the temperatures for the season 2006 Daily degree Celsius sum between April 1st and October

31st

TEMP_07 Sum of the temperatures for the season 2007 Daily degree Celsius sum between April 1st and October

31st

TEMP_08 Sum of the temperatures for the season 2008 Daily degree Celsius sum

between April 1st and October 31st

TEMP_09 Sum of the temperatures for the season 2009 Daily degree Celsius sum between April 1st and October

31st

Median_Temp Median Sum of temperatures for season between 1994 and 2007 Daily degree Celsius sum

between April 1st and October 31st

PREC_03
Sum of the precipitation for the season 2003 Daily degree Celsius sum between April 1st and October

31st

PREC_04 Sum of the precipitation for the

season 2004

Precipitation sum between

April 1st and October 31st

PREC_05 Sum of the precipitation for the

season 2005

Precipitation sum between

April 1st and October 31st

PREC_06 Sum of the precipitation for the

season 2006   北美商科代写

Precipitation sum between

April 1st and October 31st

PREC_07 Sum of the precipitation for the

season 2007

Precipitation sum between

April 1st and October 31st

PREC_08 Sum of the precipitation for the

season 2008

Precipitation sum between

April 1st and October 31st

PREC_09 Sum of the precipitation for the

season 2009

Precipitation sum between

April 1st and October 31st

Median_Prec
Median Sum of precipitation for

season between 1994 and 2007

Precipitation sum between

April 1st and October 31st

RAD_03 Sum of the solar radiation for the season 2003 Daily Watts per sq. meter solar

radiation sum between April 1st and October 31st

RAD_04 Sum of the solar radiation for the season 2004 Daily Watts per sq. meter solar radiation sum between April 1st

and October 31st

RAD_05 Sum of the solar radiation for the season 2005 Daily Watts per sq. meter solar radiation sum between April 1st

and October 31st

RAD_06 Sum of the solar radiation for the season 2006 Daily Watts per sq. meter solar

radiation sum between April 1st and October 31st

RAD_07 Sum of the solar radiation for the season 2007 Daily Watts per sq. meter solar radiation sum between April 1st

and October 31st

RAD_08
Sum of the solar radiation for the season 2008 Daily Watts per sq. meter solar

radiation sum between April 1st and October 31st

RAD_09 Sum of the solar radiation for the season 2009 北美商科代写 Daily Watts per sq. meter solar

radiation sum between April 1st and October 31st

RAD_MED Median Sum of solar radiation for season between 1994 and 2007 Daily Watts per sq. meter solar radiation sum between April 1st

and October 31st

PH1 Topsoil ( 10 to 20 cm depth ) pH pH units
AWC1 Topsoil ( 10 to 20 cm depth ) Available water capacity in 150 cm

soil profile

cm
Clay1 Topsoil clay content ( 10 to 20 cm

depth )

Percentage
Silt1 Topsoil silt content ( 10 to 20 cm

depth )

Percentage
Sand1
Topsoil sand content ( 10 to 20 cm

depth )

Percentage
Sand2 Soil sand content from another soil

source

Percentage (5-30 cm)
Silt2 Soil silt content from another soil

source

Percentage (5-30 cm)
Clay2 Soil clay content from another soil

source

Percentage (5-30 cm)
PH2 Soil ph from another soil source pH (5-30 cm)
CEC Soil cation exchange from another

soil source

cmol per kilo (5-30 cm)
CE Soil cation exchange from another

soil source

cmol per kilo (5-30 cm)
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北美商科代写

 

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