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sustainability
Article
Adaptive Rangeland Decision-Making and Coping
with Drought
Leslie M. Roche
Department of Plant Sciences, University of California, Davis, CA 95695, USA; lmroche@ucdavis.edu
Academic Editor: Iain Gordon
Received: 30 September 2016; Accepted: 11 December 2016; Published: 17 December 2016
Abstract:
Grazinglands support the livelihoods of millions of people around the world, as well as
supply critical ecosystem services. Communities reliant on rain-fed rangelands are potentially the
most vulnerable to increasing climate variability given their dependence on highly climate-sensitive
resources. Droughts, which are gradual natural hazards, pose substantial and recurrent economic
and ecological stresses to these systems. This study examined management decision-making based
on survey responses of 479 California ranchers to: (1) identify the types of drought strategies in-place
across California’s rangelands and the operation variables driving strategy selection; and (2) examine
how individual drought adaptation is enhanced by decision-making factors. Four types of in-place
drought strategies were identified and ordered along a gradient of increasing intensity (number) of
practices used. Significant background variables driving strategy selection were operation experience
with drought, type of livestock operation, grazing system, and land ownership types. Information
resource networks, goal setting for sustainable natural resources, and management capacity all acted
to enhance individual drought adaptation—defined here by active drought planning and the number
of both reactive and proactive drought practices used. Overall, analyses revealed that flexibility in
management is a key component of adapting to and coping with drought. Climate policy planning
should take into account the diversity of strategies that have been developed by ranchers for multiple
generations and within the context of their unique operations, as well as support these working
landscapes via a range of adaptation and mitigation options to reduce vulnerability across all types
of operations.
Keywords:
adaptive capacity; climate variability and change; livestock production; ranching;
working landscapes; sustainability science; drought policy
1. Introduction
Around the world, grazinglands support the livelihoods of millions of people and provide
millions more with protein, as well as supply critical ecosystem services like water resource protection,
biodiversity conservation, and wildlife habitat [
1
,
2
]. These working landscapes include grazed
rangelands and pasturelands and occupy an estimated one-quarter to two-fifths of the world’s land
surface—making them the largest and most biologically and physically diverse land resources in the
world [
2
,
3
]. A rapidly growing world population, increasing demand for “sustainable” food systems,
and changing land uses will challenge continued delivery of ecosystem services from these land
resources—particularly under the mounting pressures of uncertain climate variability [4,5].
Communities reliant on rangelands are potentially the most vulnerable to climate variability given
their dependence on highly climate-sensitive resources [
6
,
7
]. Droughts, for example, pose substantial
and recurrent economic and ecological stress—placing ranching operations and the ecosystem services
they produce at risk. Unlike other natural hazards, drought is a gradual, complex disaster with
indistinct start and end points. Severe and widespread droughts can trigger undesirable ecological
shifts, which can impact forage and livestock production capacity and directly threaten livelihoods
Sustainability 2016,8, 1334; doi:10.3390/su8121334 www.mdpi.com/journal/sustainability
Sustainability 2016,8, 1334 2 of 13
of ranching families and communities. As the impacts of an increasingly variable climate manifest,
ranchers and land managers will potentially face more frequent and largely unpredictable climate crises,
putting the economic viability and ecological sustainability of working rangelands at increasingly
greater risk.
Developing and advancing successful policies and programs will require grassroots participation
from stakeholders to first identify and evaluate the agricultural adaptation and mitigation options
that have been successful, feasible, and socially acceptable. In the western United States, there is
growing evidence that changing climate conditions could bring about more extreme weather
events—including greater severity, frequency, duration, and extent of droughts [
8
]. Ranching families
hold multi-generational knowledge of the social, economic, and ecological outcomes of their
management strategies, which they have adapted through trial-and-error learning over time [
9
–
11
].
Additionally, ranchers have extensive personal experience in coping with drought, which can serve as
a “local” example [12,13] of climate impacts in future scenario planning for climate adaptation.
Inherently droughty systems, such as the Mediterranean climate type, provide a unique
opportunity to examine agricultural adaptation to climate variability and change. Increased drought
frequency and severity markedly compound water issues for Mediterranean climates, where normally
hot, dry summers already bring the recurrent challenge of extended drought. In California, drought
played a formative role in the state’s early history [
14
], and has continued to impact the state with
five multiyear droughts between 1960 and 2010 [
15
]. Here, I examined results of a 2011 California
Rangeland Decision-Making Survey [
10
], which was completed just prior to the current severe
multi-year drought, to better understand the in-place drought strategies that have been adapted
over time in response to changing resource conditions.
Building on the adaptive rangeland decision-making framework [
16
], the goals of this paper
were to: (1) identify the types of drought strategies in-place across California’s rangelands and
the background operation variables driving strategy selection; and (2) examine how individual
drought adaptation is enhanced by decision-making factors, including information resource networks,
operator values, and management capacity. For the second question, a conceptual model (Figure 1)
of hypothesized relationships was constructed based on pathways expected from the adaptive
rangeland decision-making framework (see Figure 1 of [
16
]). In the conceptual model, the information
resource network was assumed to influence both goal setting and management capacity [
16
–
18
].
Agricultural knowledge networks, social relationships, place-based expertise, and education have been
shown to be key pathways for information sharing and goal setting [
17
,
19
,
20
], as well as influence
management capacity through increased knowledge and access to management practices, programs,
and opportunities [
17
,
20
–
25
]. In turn, goal setting (e.g., valuing future resources), management capacity,
and past experience were expected to directly influence individual drought adaptation [12,26–28].
Sustainability 2016, 8, 1334 2 of 13
livelihoods of ranching families and communities. As the impacts of an increasingly variable climate
manifest, ranchers and land managers will potentially face more frequent and largely unpredictable
climate crises, putting the economic viability and ecological sustainability of working rangelands at
increasingly greater risk.
Developing and advancing successful policies and programs will require grassroots
participation from stakeholders to first identify and evaluate the agricultural adaptation and
mitigation options that have been successful, feasible, and socially acceptable. In the western United
States, there is growing evidence that changing climate conditions could bring about more extreme
weather events—including greater severity, frequency, duration, and extent of droughts [8].
Ranching families hold multi-generational knowledge of the social, economic, and ecological
outcomes of their management strategies, which they have adapted through trial-and-error learning
over time [9–11]. Additionally, ranchers have extensive personal experience in coping with drought,
which can serve as a “local” example [12,13] of climate impacts in future scenario planning for climate
adaptation.
Inherently droughty systems, such as the Mediterranean climate type, provide a unique
opportunity to examine agricultural adaptation to climate variability and change. Increased drought
frequency and severity markedly compound water issues for Mediterranean climates, where
normally hot, dry summers already bring the recurrent challenge of extended drought. In California,
drought played a formative role in the state’s early history [14], and has continued to impact the state
with five multiyear droughts between 1960 and 2010 [15]. Here, I examined results of a 2011
California Rangeland Decision-Making Survey [10], which was completed just prior to the current
severe multi-year drought, to better understand the in-place drought strategies that have been
adapted over time in response to changing resource conditions.
Building on the adaptive rangeland decision-making framework [16], the goals of this paper
were to: (1) identify the types of drought strategies in-place across California’s rangelands and the
background operation variables driving strategy selection; and (2) examine how individual drought
adaptation is enhanced by decision-making factors, including information resource networks,
operator values, and management capacity. For the second question, a conceptual model (Figure 1)
of hypothesized relationships was constructed based on pathways expected from the adaptive
rangeland decision-making framework (see Figure 1 of [16]). In the conceptual model, the
information resource network was assumed to influence both goal setting and management capacity
[16–18]. Agricultural knowledge networks, social relationships, place-based expertise, and education
have been shown to be key pathways for information sharing and goal setting [17,19,20], as well as
influence management capacity through increased knowledge and access to management practices,
programs, and opportunities [17,20–25]. In turn, goal setting (e.g., valuing future resources),
management capacity, and past experience were expected to directly influence individual drought
adaptation [12,26–28].
Figure 1. Conceptual model of how individual drought adaptation is potentially linked to key
decision-making factors based on pathways expected from the adaptive rangeland decision-making
framework from Lubell et al. [16].
Figure 1.
Conceptual model of how individual drought adaptation is potentially linked to key
decision-making factors based on pathways expected from the adaptive rangeland decision-making
framework from Lubell et al. [16].
Sustainability 2016,8, 1334 3 of 13
Understanding the diversity of in-place drought strategies as well as how decision-making
generally influences drought adaptation can help provide management and policy guidance on
adaptation and mitigation efforts to enhance resilience, and thus sustainability, of working rangelands
to increasing climate variability.
2. Materials and Methods
2.1. Survey Design
As described in Roche et al. [
10
], a mail survey of ranchers was deployed using the membership
list of the California Cattlemen’s Association (CCA), a nonprofit trade organization serving cattle
ranchers, beef producers, and private rangeland owners across California. Between March and June of
2011, the survey was delivered via a multi contact approach, which included an initial mail survey
and return envelope, a reminder letter including option to refuse or note ineligibility, a second mail
survey, and a final reminder card [29]. The survey was delivered to 1727 addresses, with 507 surveys
returned (33% response rate; American Association of Public Opinion Research, Response Rate 4;
respondents’ base operations represented 49 of the state’s 58 counties [
10
]). Relative to the Census
of Agriculture [
30
] for California, CCA producer members represent larger operations (median total
livestock = 200) [10]).
2.2. Data Collection
The adaptive rangeland decision-making framework, established by Lubell et al. [
16
],
is used here to identify key variables, as well as guide overall analysis. This social-ecological
framework (see Figure 1 of [
16
]) draws on several existing theories of decision-making—including
theory of planned behavior [
31
,
32
], psychological distance theory [
12
,
13
], and diffusion of
innovations [20,33]—and is applicable to many agroecological systems [16].
2.2.1. Drought Management Practices and Strategies
The survey collected responses on drought management practices, including practices used to
prepare for drought (proactive practices) and practices used in response to drought (reactive practices).
Respondents were provided with a list of 17 potential proactive and reactive practices and asked to
select all practices they had previously used in managing for drought impacts. Proactive practices
included stocking conservatively, resting pastures, incorporating yearling cattle to increase flexibility,
grassbanking or stockpiling forage, using weather predictions to adjust stocking, and adding other
livestock types to increase flexibility; reactive practices included reducing herd size, purchasing feed,
applying for government assistance programs, weaning calves early, renting additional pastures,
moving livestock to other locations, earning additional off-ranch income, selling retained yearlings,
placing livestock in a feedlot, maintaining herd size; allowing livestock condition declines, and adding
alternative on-ranch enterprise. Additionally, respondents were asked if they had previously
experienced drought, and if they had a drought management plan in place during the last drought.
2.2.2. Operation Structure
Structural characteristics of the operation have been shown to be key determinants in
agricultural decision-making [
16
,
18
,
26
,
27
,
34
,
35
]. These “farm-structure” variables shape the context
for decision-making (e.g., ability to access economic resources and inputs) and, therefore, potentially
impact individual adoption of innovations and adaptive capacity [
16
,
26
,
36
]. The survey collected
responses on several variables related to operation structure, including type and class of livestock,
land base, enterprise structure, and grazing management (see Table 1). Respondents were asked
about land ownership types (privately owned, privately leased, or publicly leased) that make up their
enterprises, and survey mailing ZIP codes were used to determine U.S. Environmental Protection
Sustainability 2016,8, 1334 4 of 13
Agency Level III Ecoregions for base operations. Ecoregions are differentiated based on geographical
similarities in resource potential and capacity to respond to disturbances [37].
Table 1.
Background operation characteristics hypothesized to influence drought decision-making.
Questions were from a rangeland mail survey delivered in March–June 2011 to 1727 producer members
of the California Cattlemen’s Association.
Question Value
Livestock
Total number of cattle 0–22,000 count
Cow-calf operation Yes/No
Yearling operation Yes/No
Integrated cow-calf and yearling operation Yes/No
Land Base
USEPA Level III Ecoregion Categorical
Total number of privately owned hectares 0–16,187 count
Total number of hectares 1–2,059,852 count
Enterprise Structure
Dependence on ranch as a source of income 1–5 scale 1
Operation includes privately owned land Yes/No
Operation includes privately leased land Yes/No
Operation includes publicly leased land Yes/No
Management
Grazing system Categorical
1Likert-scale ranging from 1 = “fully disagree” to 5 = “fully agree”.
Grazing strategies were previously classified in Roche et al. [
9
] via analysis of the mail survey’s
grazing practice questions (i.e., number of pastures, number of herds, livestock density, and timing of
grazing and rest). The resulting respondent classifications among the three identified grazing strategies
(rotational strategy, season-long continuous strategy, and year-long strategy) for respondents’ largest
area of private rangeland were used in this paper to indicate grazing system preferences.
2.2.3. Information Sources
Information networks are key in the diffusion and adoption of management practices and
successful innovations [
16
–
18
]. The survey included numerous questions about respondents’
information resources, including the level of education completed (1 to 7 scale ranging from
“did not graduate high school” to “advanced degree”) and number of family generations in
ranching (1 = first-generation rancher; 2 = parents were ranchers; 3 = grandparents were ranchers;
4 = great-grandparents were ranchers; and 5 = great-great-grandparents were ranchers). Respondents
were also asked about the number of information sources they used and the perceived quality of each
source (1 to 4 scale, ranging from “never use” to “I use this and the quality is excellent”). For analysis,
the total number of good or excellent sources were summed for each respondent.
2.2.4. Operator Goals
In individual adaptive decision-making, goal setting and valuation of the natural resources base is
key to forward planning [
16
]. Respondents were provided with a list of nine potential agricultural and
natural resource management goals (livestock production, forage production, carbon sequestration,
invasive recreation, riparian/meadow health, soil health, water quality, and wildlife) and asked to rank
each goal as it related to the operation’s priorities (rank of 1 indicated the highest priority). A rank of
“10” was assigned to any goals not ranked by individual respondents. For analysis, riparian/meadow
Sustainability 2016,8, 1334 5 of 13
health, soil health, water quality, and wildlife rankings were similarly ranked among respondents [
10
]
and therefore averaged into a single variable labeled “supporting goals”.
2.2.5. Management Capacity
Operations with more management options (i.e., flexibility) potentially have greater capacity to
cope with and adapt to changing conditions [
26
,
38
,
39
]. In addition to questions on diversity of land
ownership types, the survey included questions on respondents’ experience with common rangeland
and ranch practices—following general themes of infrastructure, vegetation management, landscape
enhancements, and herd management [
20
]. Respondents were asked if they had used each of 20 listed
practices in the past 5 years and, for the practices used, whether each practice was key, helpful,
or not effective in moving toward their goals. Respondents were also asked about their participation
in 18 different conservation programs. Conservation programs have been argued to enhance the
management portfolio for achieving goals and managing risk [16].
2.3. Data Analysis
The analysis approach was to first identify distinct classes of drought strategies based on survey
responses to drought management questions (i.e., reactive and proactive practices) using latent class
analysis (LCA). Conditional inference regression models were then used to determine operation
structure characteristics driving those class membership probabilities (from LCA). That is, the aim
of this second analysis was to identify structural characteristics of operations more likely to have a
particular class of drought strategy. Lastly, structural equation modeling (SEM) was used to examine
how decision-making factors generally influence individual drought adaptation.
2.3.1. Latent Class Analysis
LCA of drought management responses was implemented using the poLCA package in R [
40
].
LCA is a statistical method used to classify subjects into unobserved subgroups (i.e., latent classes)
based on their response patterns. Latent class models estimate the proportion of subjects in each latent
class, the probabilities of observing each response for each latent class, and each subject’s predicted
latent class membership (i.e., strength of class membership for each respondent). Individuals were
assigned to a latent class based on predicted probabilities of membership. LCA fit was determined
using Akaike Information and Criteria (AIC) and the likelihood ratio chi-square statistic [40].
2.3.2. Conditional Inference Regression Trees
Conditional inference regression tree analysis was implemented using the party package in
R [
41
] to determine the operation structure variables (Table 1)—as well as operation background
experience with drought—associated with respondent preferences for each drought strategy class
identified in LCA. This method uses tree structured regression analysis to identify variables likely to
predict membership probabilities for each previously identified latent class. This analysis provides a
non-parametric class of regression trees that accommodates multiple data types (including categorical
and numeric data) and large numbers of candidate predictor variables, as well as allows examination
of potential interactions [41–44].
2.3.3. Structural Equation Modelling
SEM was used to examine how adaptive decision-making factors influence individual drought
management based on hypothesized pathways (Figure 1). SEM is a multivariate statistical technique
that combines path and factor analyses and can therefore be used to examine complex relationships
between multiple predictor and response variables (e.g., [
45
]). Analysis was performed using Stata
13.1 (Stata Corp., College Station, TX, USA) generalized structural equation model estimation
command [
46
]. Poisson regressions were used to model count responses, logistic regressions were used
Sustainability 2016,8, 1334 6 of 13
to model binary responses, and linear regressions were used to model normally distributed variables.
Model comparisons and goodness of fit were conducted using AIC.
3. Results
3.1. Four Classes of Drought Strategies
LCA of 479 eligible respondents resulted in a final model of 4 classes of drought strategies (Table 2).
Loadings (i.e., conditional probabilities of observing each response to each question) for each drought
practice question indicate the strategies differentiate on a number of proactive and reactive practices
used (see bolded response probabilities in Table 2). The average class assignment probability ranged
from 80%–87%, indicating good to high quality classification.
Table 2.
Results of latent class analysis of drought management practice questions from a rangeland
decision-making mail survey delivered in March–June 2011 to 1727 producer members of the California
Cattlemen’s Association.
Class of Drought Strategy 2
Proportion of
Respondents 1Class 1 Class 2 Class 3 Class 4
Drought response (reactive practices)
Reduce Herd size 71% 0.08 0.69 30.86 0.91
Purchase feed 70% 0.13 0.88 0.64 0.75
Wean early 40% 0.00 0.40 0.41 0.72
Apply for government assistance 40% 0.01 0.45 0.35 0.67
Rent additional pasture 27% 0.02 0.35 0.00 1.00
Move livestock to another location 24% 0.06 0.20 0.18 0.75
Add off-farm income 23% 0.00 0.22 0.30 0.28
Sell retained yearlings 22% 0.00 0.14 0.34 0.35
Place livestock in a feedlot 8% 0.00 0.06 0.07 0.26
Maintain herd size, and allow declines in livestock condition
7% 0.00 0.11 0.04 0.04
Add an alternative on-farm enterprise 4% 0.04 0.03 0.06 0.06
Drought preparation (proactive practices)
Stock conservatively 35% 0.03 0.22 0.53 0.53
Rest pastures 24% 0.00 0.11 0.41 0.36
Incorporate cow-calf and yearling cattle in operation 22% 0.03 0.03 0.40 0.49
Grass bank 13% 0.00 0.08 0.18 0.23
Use weather predictions to adjust stocking rates 11% 0.00 0.05 0.19 0.18
Add other livestock types 3% 0.00 0.00 0.06 0.05
1
Proportion of respondents selecting response to each practice question;
2
Conditional probabilities of observing
each response under each grazing practice;
3
Response probabilities bolded to highlight primary differences
among classes.
Respondents assigned to drought strategy class 1 (10% of respondents) exhibited low adoption
of both practices in response to drought (reactive practices) and practices in preparation for drought
(proactive practices) (Table 2). Respondents assigned to class 2 (42% of respondents) used a range of
practices, with greater emphasis on common reactive practices (reduce herd size, purchase feed, wean
early, and apply for government assistance) (Table 2). Class 3 (36% of respondents) emphasized a
mixture of reactive and proactive strategies. Specifically, class 3 emphasized reactive practices such as
reduce herd size, purchase feed, and wean early; and proactive practices such as stock conservatively,
rest pastures, and incorporate cow-calf and yearling cattle in operations. Class 4 (12% of respondents)
also used both reactive and proactive strategies, but emphasized a greater number of total practices.
3.2. Background Operation Variables Linked to Drought Strategy Class Selection
Four conditional inference regression trees (Figure 2A–D)—one tree for each drought strategy
class identified in LCA—resulted from the analysis of the 479 eligible responses. In general, class 2 and
3 operations were smaller in size (Table 3). The conditional inference tree for class 1 contained only
Sustainability 2016,8, 1334 7 of 13
one significant split (p< 0.05), resulting in two terminal nodes (Figure 2A). The first split partitioned
respondents based on operation experience with drought. Operations that had previously experienced
drought were least likely (average membership probability of 0.04) to adopt drought strategy class
1; operations that had not previously experienced drought were most likely (0.87) to adopt drought
strategy class 1.
Sustainability 2016, 8, 1334 7 of 13
strategy class 1; operations that had not previously experienced drought were most likely (0.87) to
adopt drought strategy class 1.
Figure 2. Conditional inference tree models for Classes 1–4 (A–D) drought management strategies
identified in latent class analysis (LCA) of rangeland decision-making mail survey data. The
conditional inference regression models explain variation in respondents’ (n = 479) membership
probabilities acquired from LCA. Bolded values are predicted mean probabilities of respondents with
the preceding characteristics adopting each strategy. Respondents were surveyed between March–
June 2011, and were producer members of the California Cattlemen’s Association. All splits are
statistically significant at p < 0.05 level.
Table 3. Characteristics of ranching operations assigned to four emergent classes of drought strategies
based on latent class analysis of drought management questions in a rangeland decision-making mail
survey delivered March–June 2011 to 1727 producer members of the California Cattlemen’s
Association.
Drought
Strategy Class
Respondents
Assigned (%)
Mean Total
Land Area (ha)
Mean Number
of Cow/Calf
Mean Number of
Yearlings
Mean Total
Number of Cattle
1 10 1332 141 194 343
2 42 2887 220 188 412
3 36 6421 323 321 644
4 12 55,184 645 833 1482
The conditional inference tree for class 2 contained four significant splits (p < 0.05), resulting in
five terminal nodes (Figure 2B). The first split partitioned respondents based on operation experience
with drought. Operations that had not previously experienced drought had a lower probability (0.08)
of adopting drought strategy class 2. Among operations that had experienced drought, those that
integrated cow-calf and yearling animals only had a 0.31 probability of adopting the drought strategy
class 2 (Figure 2B). Operations that had experienced drought, did not integrate cow-calf and yearling
animals, and used a year-long continuous grazing system were most likely (0.62) to adopt drought
Figure 2.
Conditional inference tree models for Classes 1–4 (
A
–
D
) drought management strategies
identified in latent class analysis (LCA) of rangeland decision-making mail survey data. The conditional
inference regression models explain variation in respondents’ (n= 479) membership probabilities
acquired from LCA. Bolded values are predicted mean probabilities of respondents with the preceding
characteristics adopting each strategy. Respondents were surveyed between March–June 2011, and were
producer members of the California Cattlemen’s Association. All splits are statistically significant at
p< 0.05 level.
Table 3.
Characteristics of ranching operations assigned to four emergent classes of drought strategies
based on latent class analysis of drought management questions in a rangeland decision-making mail
survey delivered March–June 2011 to 1727 producer members of the California Cattlemen’s Association.
Drought
Strategy Class
Respondents
Assigned (%)
Mean Total
Land Area (ha)
Mean Number
of Cow/Calf
Mean Number
of Yearlings
Mean Total
Number of Cattle
1 10 1332 141 194 343
2 42 2887 220 188 412
3 36 6421 323 321 644
4 12 55,184 645 833 1482
The conditional inference tree for class 2 contained four significant splits (p< 0.05), resulting in
five terminal nodes (Figure 2B). The first split partitioned respondents based on operation experience
with drought. Operations that had not previously experienced drought had a lower probability (0.08)
of adopting drought strategy class 2. Among operations that had experienced drought, those that
integrated cow-calf and yearling animals only had a 0.31 probability of adopting the drought strategy
class 2 (Figure 2B). Operations that had experienced drought, did not integrate cow-calf and yearling
animals, and used a year-long continuous grazing system were most likely (0.62) to adopt drought
Sustainability 2016,8, 1334 8 of 13
strategy class 2. Operations that had experienced drought, did not integrate cow-calf and yearling
animals, used a rotational or season-long grazing system, and had privately leased land were the
second most likely (0.56) to adopt drought strategy class 2 (Figure 2B).
The conditional inference tree for class 3 also produced four significant splits (p< 0.05), resulting in
five terminal nodes (Figure 2C). Similar to classes 1 and 2, the first split partitioned respondents based
on operation experience with drought. Operations that had not previously experienced drought had a
lower probability (0.01) of adopting drought strategy class 3. Among operations that had experienced
drought, those that included privately leased land only had a 0.31 probability of adopting the drought
strategy class 3 (Figure 2C). Operations that had experienced drought, did not include privately leased
land, and had greater than 1376 ha of privately owned land were most likely (0.64) to adopt drought
strategy class 3. Operations that had experienced drought, did not include privately leased land, had
less than 1376 ha of privately owned land, and used a rotational grazing system were the second most
likely (0.52) to adopt drought strategy class 3 (Figure 2C).
The conditional inference tree for class 4 produced three significant splits (p< 0.05), resulting in
four terminal nodes (Figure 2D). The first split partitioned respondents based on whether the operation
included privately leased land. Operations that included privately leased land and also had access to
publicly leased land were the most likely (0.32) to adopt drought strategy class 4. The lowest adoption
probabilities for drought strategy class 4 were operations that either (1) did not include privately leased
(average membership probabilities <0.1) or (2) included privately leased land but did not have access
to public land (0.14) (Figure 2D).
3.3. Decision-Making Factors Driving Individual Drought Adaptation
Structural equation modelling results revealed past experience with drought was positively
associated with in-place drought adaptation, which was indicated by the observed variables of
active drought plan in place, number of proactive practices used, and number of reactive practices
used. Drought adaptation was significantly influenced by goal setting (Figure 3)—lower prioritization
(i.e., higher rankings) of future natural resources (e.g., forage production) negatively impacted adaptive
capacity. Drought adaptation was positively influenced by management capacity, which was indicated
by number of conservation programs used, number of key management practices used, and number of
land ownership types. Goal setting and management capacity were not significantly correlated in the
final model. Finally, the information resource network—indicated by number of generations ranching,
number of good or excellent information sources used, and education level—significantly influenced
both goal setting and management capacity.
4. Discussion
I examined drought management decision-making based on survey responses of 479 ranchers
from across California. California’s rangelands are a notable example of a system vulnerable to
increasing climate variability. Because ranchers are largely dependent on rain-fed rangelands,
this community is the most sensitive to changing climate patterns and is, therefore, commonly
the first to experience the impacts of climate extremes. Climate extremes, like long-term drought,
pose cumulative challenges to sustaining rangeland ranching operations and the ecosystem services
they provide. With much of the western U.S. predicted to experience increasingly warmer temperatures
and greater fluctuations between wet and dry conditions [
8
], it is imperative we find ways to enhance
our capacity to respond to climate stresses and, therefore, reduce system vulnerability.
While individual ranchers develop, implement, and adapt unique strategies for their operations,
revealing the identifiable patterns of management types is useful to understanding the range
of differences across a community—as well as targeting outreach and policy and planning
options [7,27,47–49]
. I identified four types (classes 1 through 4; Table 2) of drought strategies in-place
across California’s diverse rangelands. The resulting classes were ordered along a gradient of increasing
intensity (number) of drought practices used. Classes 3 and 4 exhibited high adoption rates across
Sustainability 2016,8, 1334 9 of 13
the largest number of practices—as well as greater adoption of proactive practices—suggesting these
strategies enabled the greatest flexibility in coping with and adapting to drought [
17
,
26
]. In fact,
the most used proactive practices (Table 2) emphasized maintaining flexibility and minimizing potential
vulnerability to reduced forage availability. As found by others, background operation variables
shaped the classes of strategies and practices available, affecting management capacity to adapt to
and cope with changing conditions (e.g., [
9
,
16
,
26
,
34
,
35
,
50
,
51
]). Previous operation experience with
drought was a significant primary predictor for most of the classes of drought strategies (Figure 2A–C).
In fact, compared to those with previous drought experience, responding operations with no previous
drought experience were more likely to adopt the class 1 strategy (Figure 2A)—which had the lowest
response probabilities across all practices (Table 2). Many other recent rancher surveys across the
western U.S. have also found that drought experience significantly influences drought management
planning—as much as doubling self-reported preparedness levels [
26
–
28
,
52
]. In my analysis of the
variables driving strategy selection, other significant structuring variables included type of livestock
operation (i.e., whether operation integrates cow-calf and yearling animals), grazing system (rotational
strategy, season-long continuous strategy, and year-long strategy), and land ownership (private owned,
private leased, public leased) (Figure 2B–D).
In terms of individual adaptive decision-making, information resource networks, goal
setting for sustainable natural resources, and management capacity all acted to enhance drought
adaptation—defined here by active drought planning and the number of both reactive and proactive
drought practices used (Figure 3). These results agree with others who have found that information
sharing, valuing and sustaining assets, and flexibility are key components in adapting to and coping
with change [
16
,
18
,
25
,
26
,
53
]. For example, flexibility in management capacity via diverse resource
options such as multiple land ownership types (Figures 2D and 3) increases decision-making power in
balancing operation-level forage supply and demand. These results also highlight the key roles that
trusted, boundary spanning organizations—like Cooperative Extension and USDA Natural Resources
Conservation Service (NRCS)—can play in translating science to management and continuing to
support and build individual adaptive capacity [10,17,27].
Sustainability 2016, 8, 1334 9 of 13
most used proactive practices (Table 2) emphasized maintaining flexibility and minimizing potential
vulnerability to reduced forage availability. As found by others, background operation variables
shaped the classes of strategies and practices available, affecting management capacity to adapt to
and cope with changing conditions (e.g., [9,16,26,34,35,50,51]). Previous operation experience with
drought was a significant primary predictor for most of the classes of drought strategies (Figure 2A–
C). In fact, compared to those with previous drought experience, responding operations with no
previous drought experience were more likely to adopt the class 1 strategy (Figure 2A)—which had
the lowest response probabilities across all practices (Table 2). Many other recent rancher surveys
across the western U.S. have also found that drought experience significantly influences drought
management planning—as much as doubling self-reported preparedness levels [26–28,52]. In my
analysis of the variables driving strategy selection, other significant structuring variables included
type of livestock operation (i.e., whether operation integrates cow-calf and yearling animals), grazing
system (rotational strategy, season-long continuous strategy, and year-long strategy), and land
ownership (private owned, private leased, public leased) (Figure 2B–D).
In terms of individual adaptive decision-making, information resource networks, goal setting
for sustainable natural resources, and management capacity all acted to enhance drought
adaptation—defined here by active drought planning and the number of both reactive and proactive
drought practices used (Figure 3). These results agree with others who have found that information
sharing, valuing and sustaining assets, and flexibility are key components in adapting to and coping
with change [16,18,25,26,53]. For example, flexibility in management capacity via diverse resource
options such as multiple land ownership types (Figures 2D and 3) increases decision-making power
in balancing operation-level forage supply and demand. These results also highlight the key roles
that trusted, boundary spanning organizations—like Cooperative Extension and USDA Natural
Resources Conservation Service (NRCS)—can play in translating science to management and
continuing to support and build individual adaptive capacity [10,17,27].
Figure 3. Results of structural equation modelling (SEM) analysis of rancher responses (n = 479) to
drought management questions in the rangeland decision-making mail survey, delivered March–
June 2011. Latent variables are represented by circles and measured variables are represented by
boxes; dashed arrows represent the measurement models (relationships between the measured and
latent variables) and the solid arrows represent the process model (i.e., structural relationships
between the latent variables). For the education level, scale ranged from 1 = “did not graduate high
school” to 1 = “advanced degree”; for goal rankings, scales ranged from 1 = top priority to 10 = lowest
priority; drought experience was a binomial yes/no response; and all other indicator variables were
counts (no. = number). * = p < 0.05, ** = p ≤ 0.01, *** = p < 0.001, NS = Not significant.
Figure 3.
Results of structural equation modelling (SEM) analysis of rancher responses (n= 479) to
drought management questions in the rangeland decision-making mail survey, delivered March–June
2011. Latent variables are represented by circles and measured variables are represented by boxes;
dashed arrows represent the measurement models (relationships between the measured and latent
variables) and the solid arrows represent the process model (i.e., structural relationships between the
latent variables). For the education level, scale ranged from 1 = “did not graduate high school” to
1 = “advanced degree”; for goal rankings, scales ranged from 1 = top priority to 10 = lowest priority;
drought experience was a binomial yes/no response; and all other indicator variables were counts
(no. = number). * = p< 0.05, ** = p≤0.01, *** = p< 0.001, NS = Not significant.
Sustainability 2016,8, 1334 10 of 13
5. Conclusions
Analysis of the California Rangeland Decision-Making Survey revealed that flexibility in
management is a key component of adaptation (Table 2and Figure 3). Climate policy planning
should take into account the diversity of strategies that have been developed and implemented
by ranchers for multiple generations and within the context of their unique operations, as well
as support these working landscapes via a range of adaptation and mitigation options to reduce
vulnerability across all types of operations. The existing diversity of response types suggests that not
all operations will be able to cope with and adapt to climate variability in the same ways, and this
would be particularly true for those that would need to completely transform their operations
(e.g., via climate-independent income diversification [
26
,
54
] to meet one-size-fits-all adaptation
and mitigation recommendations. Therefore, policy and planning strategies that support a mix
of incremental changes [
55
] as well as transformational changes [
56
] will provide a feasible and
flexible path to individual long-term adaptation. Ranchers and other agriculturalists in California
and elsewhere have identified organizations like Cooperative Extension and USDA NRCS as trusted
information sources [
10
,
57
,
58
]; these and other support organizations have both continued and new
roles in leading novel research and delivering technical support in building adaptation and mitigation
strategies to reduce system vulnerability to future environmental changes. Advancing agricultural
adaptation science, management, and policy will require management-scale, participatory research
approaches; collaborative and translational science partnerships; and local, state, and national policy
and program support for proactive management solutions.
Acknowledgments:
This research was funded by the US Department of Agriculture’s National Institute of Food
and Agriculture, Rangeland Research Program, Grant 2009-38415-20265. The project was made possible by the
California Cattlemen’s Association, University of California Cooperative Extension, California Farm Bureau
Federation, USDA Natural Resources Conservation Service, and the California Rangeland Conservation Coalition.
I also thank Tracy Schohr for her heroic efforts in coordinating the California Rangeland Decision-Making Survey,
as well as D. J. Eastburn and two anonymous reviewers for valuable and constructive comments on the manuscript.
Conflicts of Interest:
The author declares no conflict of interest. The funder had no role in data collection,
data analysis, or decision to publish.
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