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Crop Management Challenges
Agricultural producers,
consultants, service providers, and industry representatives are faced with
crop management and cropping system decisions throughout the growing
season. The need of the agricultural person to manage and predict a crop's
behavior over a wide range of planting dates, geographies and crops has
become increasingly important as the need (value) for good, timely decisions
and a decision making process has greatly increased. Use of crop simulation
models incorporating local climatic conditions with management operations
may increase the agricultural person’s ability to make more timely and
educated decisions.
From the Lab to the Field
Scientific crop growth
simulation models have traditionally been used to address research problems,
answer questions and most importantly, to increase knowledge on crop growth,
development and yield. The time has finally arrived in which crop
modeling tools are increasingly being deployed in producer fields to help
address questions and problems on a larger, farm scale size. The full
potential and value of crop models have not yet been realized in production
agriculture.
In-Season Management Decisions affect Yield
The emphasis in
production agriculture has been placed on attaining the maximum yield
possible, or obtaining the most economical yield. What is often not
well understood
is that yield is determined during the growing season when
critical crop management decisions are being made on a daily basis. Final
crop yield is often dependent on the quality and timeliness of the
management decisions. Tools that gather and display crop
development progress and
weather information can aid the agricultural person in making accurate and
timely decisions and can greatly enhance production and profitability.
Modeling Phenology and Yield
The ability of a crop simulation
model to predict crop development is inherently more accurate than its
ability to predict end grain yield. This is simply due to the fact
that most plants can be effectively characterized for growth based on
accumulated growing degree days, which is easy to measure and calculate and
is less affected by other environmental influences. Furthermore, the
accuracy of the model’s output decreases as the season progresses due to the
accumulation of errors through time. Crop yield is an accumulation of
several predictions of physiological processes through time and inherently
has the most errors associated with it.
Phenology and Yield
and the Environmental
Local environment is a large, uncontrollable factor in determining crop yield and
quality in any given year. Likewise, local soil characteristics, pest
factors, crop selection and management also play a large, yet
controllable role in localized yield. But due to the amazingly large number
of variables, simulating these factors over a large geographical area is not
feasible or practical. Therefore the data presented in the following tables
generalizes or excludes local "controllable
or management" factors; and therefore the sole influencing variable as
the season progresses is environment, mainly temperature, rainfall and solar
radiation.
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