代做data编程、代做Java，Python编程语言、Python程序代写代写

日期：2020-12-02 11:16

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 soybean

farmers 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.

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:

1. Perform exploratory 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 Ag Project

2. Evaluation Dataset for Ag Project

Key:

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 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 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

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

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)