Weather and Management Impact on Crop Yield Variability in Rotations

Crop rotations are designed to increase productivity and reduce costs. These
advantages are contingent upon favorable weather and require appropriate management.
Unpredictable weather poses risks to dryland crop production. Information
on how weather affects yields in different cropping systems and how
farmers could respond with management would help minimize risk and stabilize
yield and income. We evaluated the effects of preseason and growing season
weather variability on continuous and sequential cropping of corn, sorghum,
and soybean in a 12-yr span, and suggest how management decisions could
influence cropping system performance.
Models of different levels of sophistication have been developed to link yields
of individual crops with weather factors. But there is a paucity of information
on how weather and management affect yields in whole cropping systems.
Furthermore, many models demand a large amount of input data, which is a
major limitation to routine application by potential users. This study developed
simple empirical models to relate yield and management with a combined index
of composite weather variables in whole cropping systems.
The study was conducted from 1984 to 1995 at the Agricultural Research and
Development Center near Mead, NE. Correlation and regression analyses were
used to relate system performance to weather. Yield was the dependent variable
and several combined indices of weather factors were predictor variables. The
combined indices of weather or composite weather variables were biological
windows (BW) and standardized precipitation index (SPI). Biological windows
represent the time during the entire year during which rainfall and air temperature
favor biological activities. The biological windows are derived from the
mean monthly precipitation and temperature data. The SPI is the difference of
precipitation from the long-term average (>30 yr) divided by the standard deviation,
a measure used to determine how wet or dry a period of time is compared
with average weather patterns, up to a certain date. Both BW and SPI are calculated
with simple computer programs. Standard deviation was used as a measure
of yield/income variability. Weather effects on yield and income fluctuations
of the cropping systems are discussed, along with potentials for the farmer
to influence this variability through management.
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