What is crop modeling?
                     WHAT IS CROP MODELING?
Introduction
Crop modeling in agriculture uses quantitative 
measurements of Eco physiological processes 
to predict plant growth and development based 
on environmental conditions and crop 
management inputs. 
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Simulate a 
crop’s 
response 
These models simulate a crop’s response 
(growth or yield, for example) to the 
environment, management, water, weather, 
and soil parameters, as they interact over the 
course of a growing season.
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Growth and 
development 
of crops 
These tools mimic the growth and development 
of crops to mathematically represent the 
various components within the cropping 
system. 
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History
The concept of crop modeling dates back to the 
1960s when researchers modeled agricultural 
systems by combining both physical and 
biological principles.
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Crop models 
Crop models rely on measurable inputs (by 
sensors, machines, or hand measurement) to 
determine whatever output is of interest (plant 
growth, crop yield, soil nitrogen, crop staging, 
etc.).
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Data Needed 
for Crop 
Modeling
Crop modeling tools require a minimum set of 
data as inputs in order for the model to 
accurately complete a prediction. Some types 
and examples of input include management 
(planting date, planting density, crop variety, 
fertilization, and irrigation) etc.
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Drive 
Efficiency
Crop yield modeling can help drive efficiency in 
agricultural production systems by allowing 
farmers to manage their inputs more efficiently. 
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Increase 
Yields
One of the main goals in precision agriculture is 
to achieve the maximum crop yield while 
minimizing inputs and losses from cropping 
systems. 
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Lower 
Environmental 
Footprint
Crop yield prediction can help lower the 
environmental footprint by demonstrating the 
benefit of specific cropping practices (such as 
crop rotation or organic fertilizer application) to 
positively increase yield and lower carbon 
footprint. 
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