Agriculture Reference
In-Depth Information
yield to be converted to standard moisture content. A capacitance-based sensor is
often used for measuring instantaneous moisture and the sensor measures the con-
ductivity of the grain as it moves past the sensor plates. This conductivity is directly
related to the moisture in the grain. Ground speed can be measured by a magnetic
wheel/shaft sensor, a radar speed sensor, or a GPS receiver. The cutting width can be
easily determined for row crops, but a cutting width sensor may be needed for non-
row crops for accurate yield measurement. Instantaneous yield can be calculated
using the following formula:
36
f
sw
Y
=
(4.1)
where
Y
denotes yield (t/ha),
f
is the flow rate (kg/s),
s
is the ground speed (km/h),
and
w
is the effective cutting width (m). To convert grain yield to standard moisture
content, yield can be multiplied by a factor of (1 − actual moisture)/(1 − standard
moisture). Generally, when actual moisture is less than the standard moisture, no
conversion is necessary.
Yield mapping is easy in principle, but presents a challenge if accurate and reli-
able maps are to be obtained, because so many sensors are involved in a yield moni-
toring system. Therefore, it is important to understand the errors associated with a
yield data set and to keep them to a minimum. Blackmore and Marshall (1996) and
Blackmore and Moore (1999) identified a number of errors associated with yield
monitor data. Some of the significant errors include unknown cutting width, time
lag of grain through the threshing mechanism, sensor accuracy and calibration, and
GPS errors. Numerous techniques have been developed to address these problems.
Time lag, also known as time delay and throughput lag, refers to the time it takes for
the crop to pass through the mechanisms of a harvester before reaching the point of
measurement by a yield monitor. Time lag will result in mismatches between yield
measurements and their positions. Obviously, time lag varies with harvester equip-
ment, sensor location, crop being harvested, and other factors.
Different methods have been used to determine accurate estimates of time lag.
Searcy et al. (1989) used a first-order time delay function with a step input to model
grain flow for the combine. Wagner and Schrock (1989) obtained crop entry and exit
time lags based on the times the combine header entered and left the plot as well as
the times grain flow measurement began and ended. Stott et al. (1993) determined
time lag by comparing grain flow rates collected in alternating directions across
a known zero-yielding portion of the field. Birrell et al. (1996) used both a simple
time delay model and a first-order model to calculate instantaneous yield response.
Several other studies have been conducted to estimate time lag and examine its sta-
bility (Murphy et al., 1995; Nolan et al., 1996; Whelan and McBratney, 1997; Chosa
et al., 2001).
Although different models and transfer functions have been used to characterize
flow dynamics in a harvester, they are technically difficult for adoption by practition-
ers. Therefore, applying a constant time lag to match flow data to positions is the
most widely used method. Most commercial yield mapping software packages such
