Adaption to Climate Change through Fallow Rotation in the U.S. Pacific Northwest

Abstract

In this paper, we study the use of wheat land fallow production systems as a climate change adaptation strategy. Using data from the U.S. Census of Agriculture, we find that fallow is an important adaption strategy for wheat farms in the U.S. Pacific Northwest region. In particular, we find that a warmer and wetter climate increases the share of fallow in total cropland and thus reduces cropland in production. Our simulations project that, on average by 2050, the share of fallow (1.5 million acres in 2012) in the U.S. Pacific Northwest region will increase by 1.3% (0.12 million acres) under a medium climate change scenario and by 1.8% (0.16 million acres) under a high climate change scenario.

Full text

climate
Article
Adaption to Climate Change through Fallow Rotation
in the U.S. Pacific Northwest
Hongliang Zhang 1, *, Jianhong E. Mu 2and Bruce A. McCarl 3
1Department of Applied Economics, Oregon State University, Corvallis, OR 97330, USA
2
Department of Geography and Environmental Sustainability, University of Oklahoma, Norman, OK 73019,
USA; mujh1024@gmail.com
3Department of Agricultural Economics, Texas A&M University, College Station, TX 77843, USA;
mccarl@tamu.edu
*Correspondence: zhangh@oregonstate.edu
Academic Editor: Yang Zhang
Received: 11 July 2017; Accepted: 8 August 2017; Published: 15 August 2017
Abstract:
In this paper, we study the use of wheat land fallow production systems as a climate
change adaptation strategy. Using data from the U.S. Census of Agriculture, we find that fallow is
an important adaption strategy for wheat farms in the U.S. Pacific Northwest region. In particular,
we find that a warmer and wetter climate increases the share of fallow in total cropland and thus
reduces cropland in production. Our simulations project that, on average by 2050, the share of fallow
(1.5 million acres in 2012) in the U.S. Pacific Northwest region will increase by 1.3% (0.12 million
acres) under a medium climate change scenario and by 1.8% (0.16 million acres) under a high climate
change scenario.
Keywords: agriculture; fallow rotation; climate change; adaptation
1. Introduction
Wheat is the most widely grown cereal grain, occupying 16% of global arable land [
1
]. Wheat also
provides about 19% of global human calories and 21% of the protein [
2
]. Climate change may disrupt
wheat yields with the Intergovernmental Panel on Climate Change (IPCC) showing estimates as large
as a 5% reduction in the absence of adaptation [3].
A growing body of literature has examined climate change adaptation. Adaptation strategies
include alterations in planting dates, irrigation technologies, agricultural land use, crop mix and
cropping systems, and the use of crop insurance [
4
–
12
]. Another possible adaptation strategy is
the use of fallow, where land is left idle to accumulate moisture as a means of adapting to dry
conditions [13–15].
In this paper, we investigate the extent to which fallow is an observed adaptation strategy to drier
climates and the extent to which it might change under climate change. We will examine the observed
relationship of fallow share to climate using farm level census data for wheat farms in the U.S. Pacific
Northwest (PNW) region. We will also project the consequences of climate change for fallow share
using climate projections from 20 global climate models in the Coupled Model Intercomparison Project
Phase 5 (CMIP5).
In the PNW 4.23 million acres of wheat were planted in 2016 and 75% of the planted wheat was
winter wheat [
16
]. Most of that wheat was rainfed and grown between the Cascades and the Northern
Rocky Mountains. There are four major cereal cropping systems in the region: (1) the rotation of winter
wheat and spring crops; (2) winter wheat-fallow rotation; (3) transitional wheat that combines spring
crop rotation and fallow; and (4) irrigated wheat [
5
]. The spring crop rotation system predominates
in the wetter regions. As rainfall diminishes, the transitional system that has a three-year rotation
Climate 2017,5, 64; doi:10.3390/cli5030064 www.mdpi.com/journal/climate

Climate 2017,5, 64 2 of 11
with fallow every third year appears, then a fallow system is used with winter wheat grown every
other year.
2. Fallow Response Estimation Strategy
In order to investigate the effects on fallow share, we will estimate an equation that predicts
the proportional share of fallow wheat lands as influenced by climate, soil characteristics, irrigation
incidence, land retirement programs, farm size, farmer experience, land tenure and farmer off-farm
employment. A linear probability model is used in this estimation.
2.1. Data
The study area is Oregon, Washington and Idaho in the U.S. Pacific Northwest. Our main data
source is the Census of Agriculture for the years 2002, 2007 and 2012, which covers almost all farms
and provides information on farm operation [
17
]. For this study, we are able to access data at the
individual farm level. Since we have a large sample, and extremely small farms may behave differently,
we only use data for wheat farms with more than 50 acres (1 acre = 0.4 hectares).
The census data used include farm level variables of fallow share in total cropland, whether the
farm sales exceed $250,000 per year, the percent of wheat acres that are irrigated and the share of land
enrolled in the Conservation Reserve and Wetland Reserve Programs (CRP and WRP). We also include
farmer characteristics, such as years of farming experience, whether or not they own the land and their
major job occupation. The resultant data set covers 17,773 wheat farms over the three census years.
In our sample, 38% are classified as wheat farms using fallow practices. Panel A in Table 1presents
summary statistics on economic variables and farmer characteristics.
To account for systematic differences among wheat farms across the study region, we include
data on soil characteristics. These data come from the Gridded Soil Survey Geographic (gSSURGO)
database [
18
]. ZIP Code level soil variables are generated by taking the acreage-weighted average
across all gSSURGO polygons within that ZIP Code. These data include land slope, amount of soil
organic matter, sand, silt and clay contents, the soil loss tolerance (T) factor and the soil erodibility
factor. Panel B in Table 1presents summary statistics for soil variables.
With respect to climate variables, daily weather data are drawn from a gridded, 4-km resolution,
surface meteorological dataset [
19
,
20
]. With that data set we compute the annual precipitation and
average temperature over the September–June winter wheat growing season. The climate variables
we used are the 22-year averaged growing season precipitation and temperature, the number of
growing degree-days and the number of freezing degree-days. We also create standard deviations of
precipitation and average temperature as well as growing and freezing degree-days. Panel C in Table 1
presents summary statistics on climate variables.

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Table 1. Summary of statistics of wheat farms in the Pacific Northwest region from the U.S. Census of Agriculture.
All Farms Farms That Did
Not Fallow
Farms That
Did Fallow Variable Description
Mean Std. Dev. Mean Std. Dev. Mean Std. Dev.
Panel A
Fallow proportion 11.20 17.87 0.00 0.00 29.53 17.34 Share of fallowed cropland in percent
Irrigation proportion 0.39 0.48 0.55 0.49 0.12 0.30 Percent of irrigated wheat acreage
CRP and WRP programs 0.06 0.21 0.04 0.22 0.09 0.18 Share of cropland under CRP and WRP programs
Classified as a large farm 0.61 0.49 0.61 0.49 0.60 0.49 Annual farm revenue of over $250,000 (1 = yes, 0 = no)
Years of farming experience 25.86 13.68 25.50 13.65 26.44 13.73 Farming experience (years)
Land tenure 0.82 0.38 0.85 0.36 0.78 0.41 Farmland fully or partially owned by an operator (1 = yes, 0 = no)
Farming occupation 0.90 0.30 0.89 0.31 0.91 0.29 Operator occupation (1 = farming, 0 = employed off-farm)
Panel B
Slope 14.00 8.62 12.63 8.79 16.23 7.84 Average land slope in percent
Soil organic content 7.88 4.41 7.90 4.79 7.85 3.69 Soil organic matter in 1 meter depth (kg C/m2)
Sand content 27.27 12.18 28.37 12.69 25.47 11.06 Percent of particles with 0.05–2 mm in diameter
Silt content 45.32 11.46 44.08 11.45 47.35 11.20 Percent of particles with 0.002–0.05 mm in diameter
Clay content 15.27 5.85 15.78 6.05 14.44 5.42 Percent of particles with <0.002 mm in diameter
Soil loss tolerance (T) factor 3.65 0.72 3.63 0.72 3.69 0.72 Soil loss tolerance factor (tons/acre/year)
Erodibility factor 0.37 0.09 0.36 0.09 0.37 0.09 Soil erodibility factor (value range from 0.02–0.68)
Panel C
Precipitation 16.22 9.75 16.75 11.05 15.35 7.05 22-year average of growing season total precipitation (inch)
Average temperature 7.09 1.74 7.09 1.87 7.09 1.49 22-year average of growing season average temperature (◦C)
Std. dev. precipitation 3.61 2.11 3.83 2.38 3.24 1.49 Standard deviation of growing season total precipitation (inch)
Std. dev. average temp. 0.77 0.11 0.77 0.12 0.77 0.10 Standard deviation of growing season average temperature (◦C)
Maximum temperature 13.16 1.54 13.27 1.64 12.99 1.36 22-year average of growing season maximum temperature (◦C)
Std. dev. maximum temp. 0.94 0.12 0.95 0.13 0.92 0.09
Standard deviation of growing season maximum temperature (
◦
C)
Growing degree-days 23.82 3.79 23.92 4.04 23.65 3.34 22-year average of growing degree-days (100 degree-days)
Freezing degree-days 2.38 1.67 2.50 1.84 2.20 1.34 22-year average of freezing degree-days (100 degree-days)
Std. dev. GDD 1.52 0.15 1.52 0.17 1.52 0.13 Standard deviation of growing degree-days (100 degree-days)
Std. dev. FDD 1.12 0.47 1.13 0.52 1.10 0.37 Standard deviation of freezing degree-days (100 degree-days)
Sample size 17,773 11,033 6740
Notes: All climate variables in Panel C are computed over the winter wheat growing season from September to June (inclusive).

Climate 2017,5, 64 4 of 11
2.2. Estimation Equation
We now turn to the estimation procedure. We estimate the observed proportion of fallow in total
cropland as a function of climate, soil, and demographic factors in a panel data setting as commonly
done in spatial analogue studies [21]. The estimation model is written as:
sit =α0+θt+f(cit ,β)+γXit +δei+εit (1)
where
sit
gives the percentage that fallow is of total cropland in wheat farm
i
in year
t
.
cit
gives climate
conditions facing farmer iin year t.
Xit
is a vector of socio-economic variables that characterize farmer
i(including both time-varying and time-invariant variables).
ei
is a vector of soil variables for the
region where farmer iis located. θtis a year-state fixed effect, and εit is a disturbance term.
The justification for using a spatial analogue approach is that the temporal variation in climate
conditions is much smaller than the range of expected climate changes, but when including variations
over space, we have sufficient variation and thus integrate both into our analysis. This has been
applied repeatedly in climate change and agricultural literature [
22
–
25
]. Additionally, the use of fallow
is a multiple-year commitment, which precludes short-run adjustments. Thus, the spatial analogue
approach is appropriate to capture non-marginal changes in cropping systems.
We estimate three versions of the model, each with different combinations of climate variables.
These include: (1) one with only linear terms for precipitation and temperature—the simple climate
model; (2) one where we add squared terms for precipitation and temperature—the climate squared
model and (3) one where we add the squared terms and precipitation and temperature standard
deviations—the climate squared and variability model.
3. Results
We estimate the fallow share equation using the Ordinary Least Squares (OLS) method. Table A1
lists the coefficient estimates for the three specifications described above. In presenting these results,
we focus on marginal effects as reported in Table 2. Standard errors in all models are clustered by
ZIP Code to mitigate farm-level spatial autocorrelation because the Moran’s I statistic rejects the zero
spatial autocorrelation hypothesis.
Table 2. Estimated marginal effects on fallow share for three model specifications (Unit: %).
Variables Simple Climate Climate Squared Climate Squared
and Variability
Precipitation −0.37*** −0.88*** −1.18***
(0.07) (0.11) (0.17)
Average temperature 0.51** 0.60** 0.50
(0.25) (0.30) (0.31)
Std. dev. precipitation 1.64***
(0.63)
Std. dev. average temperature 1.47
(0.063)
Irrigation proportion −0.19*** −0.20*** −0.20***
(0.01) (0.01) (0.01)
CRP and WRP programs −2.82*** −3.25*** −3.43***
(1.03) (1.10) (1.14)
Classified as a large farm −1.02*** −1.21*** −1.14***
(0.34) (0.33) (0.32)
Years of farming experience 0.03** 0.03*** 0.03**
(0.01) (0.01) (0.01)

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Table 2. Cont.
Variables Simple Climate Climate Squared Climate Squared
and Variability
Land tenure −2.16*** −2.19*** −2.23***
(0.38) (0.37) (0.37)
Farming occupation 0.93* 0.94* 0.92*
(0.49) (0.49) (0.49)
Slope −0.10 −0.03 0.03
(0.06) (0.06) (0.06)
Soil organic content −0.66*** −0.43** −0.37*
(0.21) (0.21) (0.21)
Sand content −0.44*** −0.43*** −0.37***
(0.10) (0.10) (0.10)
Silt content -0.02 0.07 0.12
(0.11) (0.11) (0.11)
Clay content −0.94*** −0.91*** −0.90***
(0.17) (0.16) (0.16)
Soil loss tolerance (T) factor 2.10* 1.75 1.23
(1.17) (1.14) (1.16)
Erodibility factor −37.41*** −43.18*** −41.00***
(11.70) (11.26) (11.18)
Intercept Yes Yes Yes
State-year dummy variables Yes Yes Yes
R-squared 0.350 0.359 0.361
Observations 17,773 17,773 17,773
Notes: We estimate three versions of the linear probability model using different combinations of climate variables.
The three sets of climate variables are: (1) the simple climate model with the 22-year averaged growing season
precipitation and average temperature; (2) the same variables in the simple climate model and squared terms of
the climate variables and (3) the linear and squared climate variables and standard deviations of precipitation
and temperature. Standard errors are given in parentheses. Coefficient significance is marked with *** p < 0.01,
** p < 0.05, * p < 0.1.
3.1. Impacts of Climate Factors
The simple climate model in Table 2shows a negative effect of precipitation and a positive effect
of temperature. This likely occurs because most of the wheat farms in this region are dryland farms.
An increase in precipitation or wetter climates increases soil moisture, which lessens the need to fallow.
Hotter conditions increase evaporation from soil and plant evapotranspiration and the need for soil
moisture from fallow.
In the climate squared specification, we find again a negative precipitation effect and a positive
temperature effect but with larger magnitudes compared to the linear specification. This implies
non-linear relationships between precipitation and temperature with the share of fallowed cropland
(in Table A1).
When we add climate variability variables we find larger effects for precipitation and essentially
the same effect of temperature. We also find an additional significant positive effect related to
precipitation variation, meaning more variation increases the use of fallow. This finding suggests that
fallow is an adaptation strategy that can be used for managing precipitation variability.
3.2. Impacts of Non-Climate Variables
Across all model specifications, irrigation has a negative effect on the share of fallowed cropland.
This is understandable because irrigation obviates the need for water management through fallow.
Years of farming experience and farming occupation are found to have positive effects on the share
of fallow but the effect of farming occupation is statistically insignificant. These results show that
experienced farmers and full-time farmers see the need to better manage soil moisture.

Climate 2017,5, 64 6 of 11
Percentage of cropland under CRP and WRP programs has a significant negative effect on the
share of fallow, reflecting that wheat farms are more likely to enroll less productive cropland and thus
reduce the amount of cropland in fallow. Soil variables, including sand and clay contents and soil
erodibility factors, affect the share of fallow negatively due to differences in the soil water retaining
and restoration capacities. This also reflects a larger opportunity cost of fallowing cropland with fertile
soils. For example, soils with higher sand contents likely produce lower crop yields [
26
,
27
], and thus
farmers are more likely to fallow cropland with soils of higher sand contents.
3.3. Robustness Checks
We conduct two robustness checks related to our econometric model specifications (Table 3).
The robustness checks involve re-estimating the climate squared and variability model using alternative
temperature variables. In particular, following studies in the literature, we use daily maximum
temperature [10,12] as well as growing and freezing degree-days [24,28].
Table 3. Estimated marginal effects of climate on fallow share for robustness checks (Unit: %).
Variables Using Maximum
Temperature
Using Growing and Freezing
Degree-days
Precipitation −1.29*** −1.08***
(0.17) (0.19)
Std. dev. precipitation 2.04*** 1.38*
(0.61) (0.71)
Maximum temperature 0.10
(0.33)
Std. dev. maximum temperature −15.77***
(4.82)
Growing degree-days 0.56*
(0.31)
Freezing degree-days 1.47
(1.60)
Std. dev. growing degree-days −3.71
(3.66)
Std. dev. freezing degree-days −2.14
(2.88)
Notes: We re-estimate two versions of the climate squared and variability model in Table 2by using different
temperature variables for robustness check: (1) 22-year average and standard deviation of growing season maximum
temperature –using maximum temperature; (2) 22-year averages and standard deviations of growing degree-days
and freezing degree-days –using growing and freezing degree-days. Precipitation, soil and socioeconomic variables
are the same as in the third model in Table 2. Standard errors are in parentheses with significance levels marked as
*** p < 0.01, ** p < 0.05, * p < 0.1.
We find the use of alternative temperature variables has a minimal effect on the effects of
precipitation. We also find growing degree-days have a similar effect on the share of fallow as average
temperature does in the climate squared and variability model in Table 2, while maximum temperature
and freezing degree-days have insignificant effects on the fallow share. These results suggest that the
effects of precipitation and temperature on the fallow share are robust to these alternative temperature
specifications. Thus, we conclude that fallow is an adaptation option to a changing climate.
4. Cropland Fallow Implications of the Projected Future Climate
Now we examine the effects of projected 2050 climate change on fallow. The simulation uses
estimates from the climate squared and variability model in Table 2.
We use projections drawn from the 20 global climate models in CMIP5. The specific global
climate models included in this paper are: (1) CCSM4, (2) CSIRO-Mk3-6-0, (3) inmcm4, (4) IPSL-
CM5A-LR, (5) IPSL-CM5A-MR, (6) IPSL-CM5B-LR, (7) MRI-CGCM3, (8) NorESM1-M, (9) bcc-csm1-1,
(10) bcc-csm1-1-m, (11) BNU-ESM, (12) CanESM2, (13) CNRM-CM5, (14) GFDL-ESM2G, (15)

Climate 2017,5, 64 7 of 11
GFDL-ESM2M, (16) HadGEM2-CC365, (17) HadGEM2-ES365, (18) MIROC5, (19) MIRC-ESM and (20)
MIROC-ESM-CHEM [
29
]. For each projection, daily weather data are drawn from those downscaled
by Abatzoglou [
19
,
20
] for both historical (1950–2012) and future (2015–2050) periods. These data are
available at the University of Idaho (http://maca.northwestknowledge.net).
Our projections use two emission scenarios, Representative Concentration Pathways (RCP) 4.5
and 8.5, which represent medium and high greenhouse gas emission levels under moderate and no
climate policy. Figure 1summarizes the projected PNW climate changes arising across the CMIP5
models under RCPs 4.5 and 8.5. Growing season temperature increases across all climate models by
2050 with an average warming of +1.2
◦
C under RCP 4.5 (intermodel range +0.5
◦
C to +2.1
◦
C) and of
+1.5
◦
C under RCP 8.5 (intermodel range +0.8
◦
C to +2.2
◦
C). Most climate models project increases in
growing season precipitation by 2050, with a multi-model mean increase of +16 mm under RCP 4.5
(intermodel range
−
38 mm to 57 mm) and of +14 mm under RCP 8.5 (intermodel range –65 mm to
84 mm).
Climate 2017, 5, x FOR PEER REVIEW 7 of 11
4. Fallow Cropland Implications of the Projected Future Climate
Now we examine the effects of projected 2050 climate change on fallow. The simulation uses
estimates from the climate squared and variability model in Table 2.
We use projections drawn from the 20 global climate models in CMIP5. The specific global
climate models included in this paper are: (1) CCSM4, (2) CSIRO-Mk3-6-0, (3) inmcm4, (4) IPSL-
CM5A-LR, (5) IPSL-CM5A-MR, (6) IPSL-CM5B-LR, (7) MRI-CGCM3, (8) NorESM1-M, (9) bcc-csm1-
1, (10) bcc-csm1-1-m, (11) BNU-ESM, (12) CanESM2, (13) CNRM-CM5, (14) GFDL-ESM2G, (15)
GFDL-ESM2M, (16) HadGEM2-CC365, (17) HadGEM2-ES365, (18) MIROC5, (19) MIRC-ESM and
(20) MIROC-ESM-CHEM [29]. For each projection, daily weather data are drawn from those
downscaled by Abatzoglou [19,20] for both historical (1950–2012) and future (2015–2050) periods.
These data are available at the University of Idaho (http://maca.northwestknowledge.net).
Our projections use two emission scenarios, Representative Concentration Pathways (RCP) 4.5
and 8.5, which represent medium and high greenhouse gas emission levels under moderate and no
climate policy. Figure 1 summarizes the projected PNW climate changes arising across the CMIP5
models under RCPs 4.5 and 8.5. Growing season temperature increases across all climate models by
2050 with an average warming of +1.2 °C under RCP 4.5 (intermodel range +0.5 °C to +2.1 °C) and of
+1.5 ° C under RCP 8.5 (intermodel range +0.8 °C to +2.2 °C). Most climate models project increases
in growing season precipitation by 2050, with a multi-model mean increase of +16 mm under RCP
4.5 (intermodel range −38 mm to 57 mm) and of +14 mm under RCP 8.5 (intermodel range –65 mm to
84 mm).
Figure 1. Projected changes in mean growing season total precipitation and average temperature by
2050 with a baseline period from 1982–2011. Each dot represents a projection from a particular CMIP5
climate model.
We simulate the share of fallow in total cropland under two climate change scenarios (RCPs 4.5
and 8.5), holding all non-climate variables constant in 2012. The results are summarized in Figure 2.
The ensemble projection across all climate models indicates that in wheat farms more land will be
fallowed due to a warmer and wetter climate, again showing fallow as an adaptation method.
Specifically, on average by 2050, the share of fallowed cropland (1.5 million acres in 2012) will be
increased by 1.3% (0.12 million acres) under a medium climate change scenario (RCP 4.5) and by 1.8%
Figure 1.
Projected changes in mean growing season total precipitation and average temperature by
2050 with a baseline period from 1982–2011. Each dot represents a projection from a particular CMIP5
climate model.
We simulate the share of fallow in total cropland under two climate change scenarios (RCPs 4.5
and 8.5), holding all non-climate variables constant in 2012. The results are summarized in Figure 2.
The ensemble projection across all climate models indicates that in wheat farms more land will
be fallowed due to a warmer and wetter climate, again showing fallow as an adaptation method.
Specifically, on average by 2050, the share of fallowed cropland (1.5 million acres in 2012) will be
increased by 1.3% (0.12 million acres) under a medium climate change scenario (RCP 4.5) and by 1.8%
(0.16 million acres) under a high climate change scenario (RCP 8.5). Overall, future climate change
projections by 2050 are shown to have a small positive effect on fallow acreage in the PNW region,
with a large uncertainty arising from global climate models.

Climate 2017,5, 64 8 of 11
Climate 2017, 5, x FOR PEER REVIEW 8 of 11
(0.16 million acres) under a high climate change scenario (RCP 8.5). Overall, future climate change
projections by 2050 are shown to have a small positive effect on fallow acreage in the PNW region,
with a large uncertainty arising from global climate models.
Figure 2. Projected change in fallow share for wheat farms in the PNW region by 2050 (Unit: %).
5. Conclusions
In this paper, we investigate the relationship between climate and the use of fallow for PNW
wheat farms. We find that decreases in growing season precipitation increase the share of fallowed
cropland, as do increases in growing season temperature. Using future 2050 climate projections, our
simulation results indicate that the share of fallowed cropland will increase by 1.3% and 1.8% under
medium and high emission scenarios, respectively, but with substantial uncertainty. These findings
suggest that climate causes PNW farmers to put more cropland in fallow, indicating as the climate
evolves fallowing is an adaptation strategy.
There are several shortcomings and extensions to this work. First, we only consider the use of
fallow, not the changes in types of agricultural land use. With a changing climate, it is likely that
some dryland farmers will convert their land to an irrigated use or shift cropland to rangeland or
Figure 2. Projected change in fallow share for wheat farms in the PNW region by 2050 (Unit: %).
5. Conclusions
In this paper, we investigate the relationship between climate and the use of fallow for PNW
wheat farms. We find that decreases in growing season precipitation increase the share of fallowed
cropland, as do increases in growing season temperature. Using future 2050 climate projections,
our simulation results indicate that the share of fallowed cropland will increase by 1.3% and 1.8%
under medium and high emission scenarios, respectively, but with substantial uncertainty. These
findings suggest that climate causes PNW farmers to put more cropland in fallow, indicating as the
climate evolves fallowing is an adaptation strategy.
There are several shortcomings and extensions to this work. First, we only consider the use of
fallow, not the changes in types of agricultural land use. With a changing climate, it is likely that
some dryland farmers will convert their land to an irrigated use or shift cropland to rangeland or
pastureland, while the reverse may also occur. Expanding the study to consider this would be valuable,
particularly since land use change is also an observed adaptation strategy [
25
]. Second, our estimates
are a reduced form in the sense that we capture the net effect of the climate on changing cropping
systems. Estimating a structural model of a specific crop system is left for future research. Third,

Climate 2017,5, 64 9 of 11
our predictions on the share of fallowed cropland are based on current socioeconomic conditions and
non-climate biophysical conditions. Future research needs to design scenarios with consistent climate,
biophysical and socioeconomic conditions, technologies and policies for projecting changes in fallow
acreage [
30
]. Lastly, our model does not capture the CO
2
fertilization effect which has been shown to
strongly affect wheat [31] and incorporating this would be desirable.
Acknowledgments: This research was supported in part by USDA-NIFA award #2011-68002-30191.
Author Contributions:
Hongliang Zhang and Jianhong E. Mu constructed the initial paper draft, and
Bruce A. McCarl
improved the organization and presentation to generate the final draft.
Conflicts of Interest:
The authors declare no conflict of interest. The funding sponsor had no role in the design of
the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision
to publish the results.
Appendix A
Table A1. Estimated coefficients on fallow share for three model specifications.
Variables Simple Climate Add Climate
Squared
Add Climate Squared
and Variability
Precipitation −0.37*** −1.40*** −1.65***
(0.07) (0.20) (0.23)
Average temperature 0.51** −1.23 −0.40
(0.25) (0.97) (1.05)
Precipitation square 0.02*** 0.01***
(0.00) (0.00)
Average temperature square 0.13 0.06
(0.08) (0.09)
Std. dev. precipitation 1.64***
(0.63)
Std. dev. average temperature 1.47
(6.29)
Irrigation proportion −0.19*** −0.20*** −0.20***
(0.01) (0.01) (0.01)
CRP and WRP programs −2.82*** −3.25*** −3.43***
(1.03) (1.10) (1.14)
Classified as a large farm −1.02*** −1.21*** −1.14***
(0.34) (0.33) (0.32)
Years of farming experience 0.03** 0.03*** 0.03**
(0.01) (0.01) (0.01)
Land tenure −2.16*** −2.19*** −2.23***
(0.38) (0.37) (0.37)
Farming occupation 0.93* 0.94* 0.92*
(0.49) (0.49) (0.49)
Slope −0.10 −0.03 0.03
(0.06) (0.06) (0.06)
Soil organic content −0.66*** −0.43** −0.37*
(0.21) (0.21) (0.21)
Sand content −0.44*** −0.43*** −0.37***
(0.10) (0.10) (0.10)
Silt content −0.02 0.07 0.12
(0.11) (0.11) (0.11)
Clay content −0.94*** −0.91*** −0.90***
(0.17) (0.16) (0.16)
Soil loss tolerance (T) factor 2.10* 1.75 1.23
(1.17) (1.14) (1.16)
Erodibility factor −37.41*** −43.18*** −41.00***
(11.70) (11.26) (11.18)
Constant 59.64*** 72.79*** 63.48***
(7.70) (8.17) (9.95)
State-year dummy variables Yes Yes Yes
Observations 17,773 17,773 17,773
R-squared 0.350 0.359 0.361
Notes: Standard errors are given in parentheses. Coefficient significance is marked with *** p < 0.01, ** p < 0.05,
* p < 0.1
.

Climate 2017,5, 64 10 of 11
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