Agriculture Reference
In-Depth Information
Table 15.2. Annual average crop grain yield and energy balances for KBS LTER
MCSE annual cropping systems over the period 1989-2007.
System
Crop Yield
Crop Rotation Energy Balance
a
Energy
Efficiency
Output:Input
Ratio
Corn
Wheat
Soybean
System
Farming
Energy
Inputs
Food
Energy
Output
Net
Energy
Gain
(Mg ha
−1
yr
−1
)
(GJ ha
−1
yr
−1
)
Conventional
5.90
3.54
2.33
3.92
7.1
72.7
65.6
10
No-till
6.25
3.74
2.65
4.21
4.9
78.5
73.6
16
Reduced Input
5.23
3.09
2.57
3.63
5.2
66.9
61.7
13
Biologically
Based
4.08
2.05
2.48
2.87
4.8
53.1
48.3
11
Alfalfa
6.85
5.5
26.1
20.6
5
a
Energy balance of systems is based on actual farm management operations and inputs (from Gelfand et al. 2010).
Food energy output is for direct human consumption except in the case of alfalfa, which is fed to livestock.
Year-to-year yield variability has been high in all MCSE systems (Smith et al.
2007). This is not surprising, given that annual precipitation has ranged from 60
to 110 cm per year over this period. Precipitation is historically evenly distributed
throughout the growing season at KBS, but over the last two decades dry spells
have commonly occurred during critical crop development stages, and well-drained
KBS loam soils have a limited ability to buffer midseason drought because of their
relatively low moisture holding capacity (150 mm to 1 m; Crum and Collins 1995).
Climatic predictions for Michigan as for the Midwest call for a lower frequency
but increasing severity of precipitation events (Schoof et al. 2010; Gage et al. 2015,
Chapter 4 in this volume).
The MCSE experimental design for annual crops—with one crop rotation phase
present per year—allows management effects on interannual yield variability to
be tested for each crop for a different set of years. Smith et al. (2007) analyzed
temporal variability by calculating the coefficient of variation for interannual grain
yield to show that corn yield variability was not influenced by management system.
In wheat, however, variability followed the ranking No-till (coefficient of variation,
CV = 0.22) < Conventional (0.35) < Reduced Input (0.40) < Biologically Based
(0.50). Soybean yield variability overall was lower, and the response to manage-
ment was similar to that of wheat: No-till (CV = 0.18) = Conventional (0.18) <
Reduced Input (0.25) < Biologically Based (0.34). The lower temporal variabil-
ity in No-till and Conventional systems suggests that intensive use of agricultural
chemicals can mitigate the impacts of weather variability—whether due to weeds,
nutrient supply, timely access to fields, or some other less obvious factor. This find-
ing stands in contrast to a long-term field experiment in Pennsylvania showing that
organic production systems maintained yields in low rainfall years (Lotter et al.
2003). Improvements in soil organic matter and water storage have been proposed
