In this challenge, you will be tasked with predicting the arabinoxylan level using data from different sources. The dataset provides information about fertilizer treatment, crop system, and seasonal temperature from 2014 to 2021.

The dataset is based on the following columns:
YEAR: Year of the crop.
CROPPING_SYSTEM: farming system used for the crop.
TREATMENT: fertilizer treatment for the crop.
AVR_APRIL_TEMP: weather temperature in April in C degrees.
AVR_MAY_TEMP: weather temperature in May in C degrees.
AVR_JUNE_TEMP: weather temperature in June in C degrees.
AVR_JULY_TEMP: weather temperature in July in C degrees.
AVR_AUGUST_TEMP: weather temperature in August in C degrees.
TOTAL_PRECIPITATION:  total rainfall between April and August in mm.
PRECIPITATION_APRIL: rainfall in April in mm.
PRECIPITATION_MAY: rainfall in May in mm.
PRECIPITATION_JUNE: rainfall in June in mm.
PRECIPITATION_JULY: rainfall in July in mm.
PRECIPITATION_AUGUST: rainfall in August in mm.
BARLEY_YIELD: total amount of barley produced in tons/ha.
AX_CONTENT: Arabinoxylan content in g/100g.

The primary goal is to implement a robust machine learning regressor model to accurately predict the quantity of arabinoxylan (AX_CONTENT) out of a diverse dataset of selected variables listed above.