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# Agricultural Rodent Control Using Barn Owls: Is It Profitable?

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We develop a model to evaluate the profitability of controlling rodent damage by placing barn owl nesting boxes in agricultural areas. The model incorporates the spatial patterns of barn owl predation pressure on rodents, and the impact of this predation pressure on nesting choices and agricultural output. We apply the model to data collected in Israel and find the installation of nesting boxes profitable. While this finding indicates that economic policy instruments to enhance the adoption of this biological control method are redundant, it does support stricter regulations on rodent control using rodenticides.
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AGRICULTURAL RODENT CONTROL USING BARN OWLS:
ISITPROFITABLE?
IDDO KAN,YOAV MOTRO,NIR HORVITZ,AYAL KIMHI,YOSSI LESHEM,YORAM YOM-TOV,
AND RAN NATHAN
We develop a model to evaluate the proﬁtability of controlling rodent damage by placing barn
owl nesting boxes in agricultural areas. The model incorporates the spatial patterns of barn owl
predation pressure on rodents, and the impact of this predation pressure on nesting choices and
agricultural output. We apply the model to data collected in Israel and ﬁnd the installation of
nesting boxes proﬁtable. While this ﬁnding indicates that economic policy instruments to enhance
the adoption of this biological control method are redundant, it does support stricter regulations
on rodent control using rodenticides.
Key words: agricultural damage control, environmental regulation, barn owl, rodent.
JEL codes: Q15, Q18, Q57.
Rodent damage to agriculture results in
double-digit percentages of yield reduction
across the globe (Singleton 2003;Leirs 2003).
This considerable damage suggests that the
effectiveness of conventional rodent-control
methods such as tillage, sanitation, trap-
ping, and rodenticide applications is limited
(Stenseth et al. 2003). The application of
rodenticides is frequently ineffective due
to the rapid immigration of rodents from
adjacent untreated areas, and because rodent
population outbreaks are unpredictable.
Moreover, rodenticides are often consid-
ered by farmers to be too costly (Skonhoft
et al. 2006;Davis et al. 2004;Stenseth et al.
2003). Risks of mortality by self-poisoning
Iddo Kan and Ayal Kimhi are afﬁliated with the Department
of Agricultural Economics and Management, The Hebrew
University of Jerusalem, Israel, and with the Center for
Agricultural Economic Research, Israel. Kimhi is also the
Deputy Director of the Taub Center for Social Policy Stud-
ies in Israel. Yoav Motro, Nir Horvitz, and Ran Nathan are
afﬁliated with the Department of Ecology, Evolution and
Behavior, The Hebrew University of Jerusalem, Israel. Yossi
Leshem and Yoram Yom-Tov are afﬁliated with the Depart-
ment of Zoology, The George S. Wise School of Life Sciences,
Tel Aviv University, Israel. We thank Shaul Aviel, Uria Sha-
hak, Myriam Freund, Michael Heiman, Shaul Ginzberg, Eyal
Lev, Yoav Cohen, Ruth Aviel, Neria Lifshitz, Dan Alon, Yael
Chassin, Michal Azaz, Ricky Ketner, Motti Charter, Uriel
Safriel, Rivka Rabinowitz, Gila Kahila Bar-Gal, Eitan Tcher-
nov, Moshe Coll, the editor, and two anonymous reviewers.
This study was partly funded by the USAid MERC, grant
TA-MOU-06-M25-078, and by the Center for Agricultural
Economic Research, Israel. Correspondence may be sent to:
iddo.kan@mail.huji.ac.il.
(Eddleston 2000) and detrimental impacts
on non-target animals (Cox and Smith 1990)
The use of barn owls (Tyto alba) as a bio-
logical control method could be a more
cost-effective alternative, which might also
reduce the negative externalities associated
with rodenticides.
Barn owls, nocturnal raptors having a
nearly worldwide distribution, prey on a
variety of rodent species, many of which are
agricultural pests. Barn owls use pre-existing
cavities for nesting (Taylor 1994), and there-
fore face a scarcity in nesting sites. This trait
enables harnessing the barn owl’s hunting
abilities to control rodents in agricultural
ﬁelds by introducing artiﬁcial nesting boxes.
Nesting boxes are already used in various
parts of the world to help control a wide
range of crop-damaging rodents. For exam-
ple, such boxes are used to protect oil palms
in Malaysia (Duckett 1976) and rice in India
(Parshad 1999). However, there is insufﬁcient
scientiﬁc information on how the technique
can be most effectively applied (Leshem
et al. 2010) and there is inconsistent evidence
1990;Wood and Fee 2003). Moreover, to the
best of our knowledge, the proﬁtability of
rodent control using barn owls has never
been assessed. Consequently, there is a lack
of scientiﬁc knowledge that could prove
Amer. J. Agr. Econ. 96(3): 733–752; doi: 10.1093/ajae/aat097
Published online December 18, 2013
© The Author (2013). Published by Oxford University Press on behalf of the Agricultural and Applied Economics
734 April 2014 Amer. J. Agr. Econ.
useful to farmers who are planning rodent-
control activities and to regulators hoping to
literature on bioeconomics and agricultural
damage-control economics by conducting
a rigorous evaluation of the method’s prof-
itability. To this end, we develop a spatial
bioeconomic model and apply it using unique
agronomic and zoological data collected in
the agricultural ﬁelds of Kibbutz Sde Eliyahu,
Israel.
Economic analyses of agroecological sys-
tems face conceptual and empirical chal-
lenges (Zhang et al. 2007), particularly due
to the presence of complex spatiotempo-
ral processes. Applications depend heavily
on the availability of biological and agro-
nomic data; examples include Brown, Lynch,
and Zilberman (2002),Nordblom et al.
(2002),Polasky et al. (2005),Grifﬁths et al.
(2008), and Polasky et al. (2011). Our data
enabled us to develop an economic model
that incorporates three functions associated
with spatial processes; the development of
these functions itself contributes to the scien-
tiﬁc disciplines of agronomy, biogeography,
and animal movement behavior. The ﬁrst
function describes barn owls’ spatial preda-
tion patterns. During the breeding season
(March–October), the nesting place con-
stitutes a point source for the barn owl’s
predation activity.1Hence, the predation
pressure exercised by barn owls on their
surroundings is expected to diminish with
distance from the nesting place. Behavioral
examination of other raptors has revealed
variability in predation attractiveness across
land uses (Thirgood, Redpath, and Graham
2003). Using radiotelemetry records of barn
owl locations, this study is, to our knowledge,
the ﬁrst to estimate a function of spatial dis-
tribution of barn owl predation pressure that
accounts for the impact of both distance and
land-use appeal. The second function models
the selection of nesting boxes by barn owls
for breeding. Previous estimates indicate
that the probability of nest-box occupancy
depends on box attributes (e.g., the entrance
aspect) and on its location in relation to
various land uses (Frey et al. 2011;Charter
et al. 2012). Here, we estimate a nest-box
1A barn owl’s predatory act starts and ends at its nesting place,
and the bird catches a single prey in each hunting act (Lessells
and Stephens 1983). These habits make the nesting place the
point source of the barn owl’s spatial impact on rodents.
occupancy probability function that explic-
itly incorporates the impact of a wide range
of crops, the distance between one nesting
box and others, and the occupancy status
of the box in the previous season. Taking
advantage of the spatial variability of barn
owl predation pressures on ﬁelds, our third
(crop-production) function treats the barn
owl predation pressure as damage-control
input.
An additional contribution of this study
stems from the recursive estimation pro-
cess we use, which ensures consistency of
the biospatial processes associated with the
three functions: the estimated predation-
pressure function is used for computing
predation-pressure variables that serve as
explanatory variables in the estimation of
both the box-occupancy probability function
and the crop-production function. As a result,
we obtain an integrative functional system
that enables us to evaluate the contribution
of barn owls to agricultural outputs through
simulations of nesting-box locations. The
locations of the boxes in relation to land
uses impact ﬁeld outputs by determining the
expected predation pressures applied on the
ﬁelds, where these expected pressures are
the products of the predation pressures exer-
cised on the ﬁelds from occupied boxes, and
the probability of the boxes being occupied.
Speciﬁcally, we use the model to compute
alfalfa (Medicago sativa) outputs under three
scenarios of nesting-box distribution: (a)
under the observed locations of the 58 boxes
currently placed in the Kibbutz’s ﬁelds, (b)
in the absence of these boxes, and (c) under
a simulated distribution of the 58 boxes that
maximizes alfalfa-production proﬁts. These
three simulations enable us to evaluate both
the contribution of the boxes to proﬁt in their
current locations and the extent to which
proﬁts could potentially be increased.
Our dataset is a panel of detailed zoo-
logical and agronomic information that is
unique in its suitability to our study. First,
barn owl nesting boxes have been placed in
the Kibbutz’s ﬁelds since 1983 (Motro et al.
2010), so that during the period covered by
our data (1999–2008), the barn owl popula-
tion was already familiar with the presence
of the boxes.2Second, the area covered is
2According to Wood and Fee (2003), the presence of nesting
boxes may also affect the barn owl population, and thus the
occupancy probability of boxes and rodent-damage control. We
controlled for this potential dynamic effect in the estimation of
Kan et al. Agricultural Rodent Control Using Barn Owls: Is it Proﬁtable? 735
sufﬁciently large to encompass the sizable
spatial range inﬂuenced by barn owls. Third,
the agricultural lands are heterogeneous, so
that our analysis can account for the impact
of different land uses on the spatial patterns
of barn owl predation pressure, and on the
selection of nesting places. Finally, agricul-
tural production is centrally managed by
the Kibbutz, so that variability in skills, con-
straints, and other management factors is
minimized.
Barn owls affect agricultural outputs only
indirectly through their predation impact on
rodents, which in turn affect crops through
herbivory. The impact of rodent herbivory is
spatially distributed in relation to rodents’
breeding places. Thus, a comprehensive
analysis should account for the spatial dis-
tributions of both the raptors and their prey.
Unfortunately, due to high monitoring costs
and methodological difﬁculties, reliable
and continuous estimates of rodent spatial
distribution and population size were not
obtainable. Therefore, our analysis over-
looks the explicit process by which rodents
channel the impact of barn owls on yield.
To separate the indirect damage-control
effect of the barn owls from the direct impact
of production inputs, we adopt the formu-
lation approach used by Lichtenberg and
Zilberman (1986),Babcock, Lichtenberg, and
Zilberman (1992),Blackwell and Pagoulatos
(1992), and others, wherein a damage-
abatement function is integrated into the
production function. Speciﬁcally, we apply
the model developed by Saha, Shumway,
and Havenner (1997), which also allows
controlling for the interactive effects of pro-
duction inputs, damage-abatement inputs, and
external factors.
Due to data availability, we focus on
alfalfa production, which is a perennial,
multiharvested legume grown mainly for
fodder. Alfalfa is highly prone to rodent
damage because rodents accumulate over
the multiannual crop growth period, while
almost no agromechanical measures can
be implemented in the ﬁelds (Moran and
Keidar 1993). Poisoning rodents in alfalfa
exhibits low performance due to the con-
stant presence of the fresh, nutritious, green
foliage favored by the rodents (Proulx
1998). This makes alfalfa a good case study
the box-occupancy probability function, but found no signiﬁcant
impact.
for examining the economic efﬁciency of
agricultural rodent control by using barn
owls.
Our results indicate that barn owls’ con-
tribution to the Kibbutz’s alfalfa outputs,
under the observed locations of the nest-
ing boxes, amounts to nearly 10%, yielding
a net-proﬁt increase of more than $200/ha per year. Given that we overlook potential contributions to the outputs of other crops, our evaluation probably underestimates the overall proﬁt contribution of barn owls. Note that our evaluation refers to the proﬁt contribution of the barn owls nesting in the Kibbutz’s nesting boxes only, and it measures the proﬁt contribution above and beyond the rodent-control effects of agronomic activ- ities, sanitation, and farmer-independent factors within and outside of the studied area (e.g., barn owls and other raptors nesting in unmonitored places). However, as farmers at Sde Eliyahu completely avoid rodenti- cide application for ideological reasons, our dataset cannot be used to directly compare biological and conventional rodent-control methods. The Model Our model describes the management of a nesting box system subdivided into Iﬁelds in a farm area, where all other agrobiological impacts are considered exogenous. Since our analysis relies on the spatial distribution of predation pressure, intra-ﬁeld variations need to be captured; therefore, we consider an artiﬁcial division of ﬁeld i,i=1, ...,I, into Miuniform-sized land units (e.g., hectares). Let mi,mi=1, ...,Mi, denote a speciﬁc land unit in ﬁeld i. The unit miis geographically represented by its central point, the coor- dinates of which are incorporated in the two-dimensional column vector umi. The two- row matrix ui=um1,...,uMiincorporates the coordinates of all land units in ﬁeld i, and u=(u1,...,uI)is the coordinate matrix of all land units on the farm. The vector vi contains all other speciﬁc attributes of ﬁeld i, such as soil type, microclimate, and installed infrastructures, and v=(v1,...,vI)is deﬁned accordingly. Spread over the farm are Knesting boxes, where xkis the two-dimensional coordi- nate vector of the location of nesting box k;k=1, ...,K; and x=(x1,...,xK)is the matrix of the coordinates of all Kboxes. The by guest on May 22, 2014http://ajae.oxfordjournals.org/Downloaded from 736 April 2014 Amer. J. Agr. Econ. suitability of box kas a nest is affected by a vector of exogenous attributes, ak, such as shading conditions, and by a set of features of the box, denoted hk, including the box’s height, color, and entrance direction. The matrices a=(a1,...,aK)and h=(h1,...,hK) are deﬁned accordingly. Various crops, j, are routinely grown on the farm. Let δij be an indicator variable that has a value of 1 if crop j,j=1, ...,J, is assigned to a speciﬁc ﬁeld, i.TheI×Jmatrix of crop attributions to ﬁelds, δ, is deﬁned accordingly. The function rij (u,x,δ,a,h)= K k=1 likj (dik (ui,xk)) (1) Sk(u,x,δ,ak,hk) represents the cumulative predation pres- sure applied by barn owls nesting in the K boxes on the rodents within Miland units of ﬁeld i, when this ﬁeld is allotted to crop j. Cumulative pressure is deﬁned as the sum of the products of two functions. The ﬁrst function in equation (1), likj (dik (ui,x)),is called the predation-pressure function, and represents the intensity of the predation activity applied by a barn owl nesting in box kin the area of ﬁeld iif the ﬁeld is devoted to crop j. This intensity is measured by the probability that the barn owl will hunt in ﬁeld i, which in turn is a function of dik (ui,xk), the vector of distances from box kto all Mi area units of ﬁeld i. The predation-pressure function is crop-speciﬁc, and therefore differ- entiates the hunting preferences of barn owls toward different crops. The second function in equation (1), Sk(u,x,δ,ak,hk), is termed the box-selection function, and expresses the probability of box kbeing selected as a nesting place by a pair of barn owls. This probability depends on the box’s attributes akand hk; it also depends on the features of the box’s surrounding environment, includ- ing the type of crops allotted to the farm’s ﬁelds, represented by δ, and the distances to all other boxes and land units in the farm, which in turn depend on the locations of all the boxes (x) and land units (u). The land use around the box may affect its occupancy rate if barn owls prefer nesting in boxes close to land uses that are favored for predation. The relevance of the distance to other boxes stems from the potential territorial behavior of barn owls. In our empirical estimation of the box-selection function, we control for these spatial effects using variables, which, for consistency, were calculated based on the estimated predation-pressure function. Let qij be the vector of production inputs applied to ﬁeld iwhen it is assigned to crop j, and let matrix qincorporate the I×Jsets of qij production inputs. The vectors b1and b2incorporate exogenous factors associ- ated with production and costs, respectively, including managerial skills, climate condi- tions, input constraints, and prices of inputs and outputs. Given u,a,v,b1, and b2, a proﬁt- maximizing farmer will decide on the optimal assignment of crops to the ﬁelds, δ, the appli- cation of inputs to the ﬁelds, q ij,i=1, ...,I, j=1, ...,J, the features of the boxes, h k, k=1, ...,K, and the location of the boxes, x. The maximal proﬁt is: w= I i=1 J j=1 δ ijpjHij (rij(u,x,δ,a,h),(2) q ij,vi,b1)c(q,h,x,δ,b2) where pjis crop j’s output price, Hij(rij (u,x,δ,a,h),q ij,vi,b1)is a production function speciﬁc to ﬁeld iand crop j, and c(·) is a farm-level cost function. Equation (2) integrates the manage- ment tools and the spatial agrobiological relationships into one comprehensive function, which constitutes the basis for our empirical analysis. Note that our estimation of the production function Hij(rij (u,x,δ,a,h),q ij,vi,b1)uses the model developed by Saha, Shumway, and Havenner (1997), wherein the ﬁeld-level inputs q, ﬁeld characteristics v, and farm-level attributes b1are all associated with direct production impact, as well as indirect effects through damage control. In addition, while decid- ing on the assignment of crops to ﬁelds, δ, farmers may account for the box locations, x. However, in our empirical analysis we found that the observed assignment δis indepen- dent of x; that is, δis dictated primarily by other considerations, such as crop rotation, and is therefore exogenous. Empirical Speciﬁcations and Estimation Results Our empirical application uses data from 1999–2008, covering the lands of Kibbutz by guest on May 22, 2014http://ajae.oxfordjournals.org/Downloaded from Kan et al. Agricultural Rodent Control Using Barn Owls: Is it Proﬁtable? 737 Sde Eliyahu in the Beit She’an Valley, Israel (3230N, 3530E). This is a semi-arid region with average annual rainfall of about 250 mm, mild winters, and dry, hot summers. Fifty- eight barn owl nesting boxes were placed in various ﬁelds between 1983 and 1996. The 12.5 km2study area comprises heterogeneous land uses—altogether 11(J=11)—including residential zones, ﬁeld crops, and fruit plan- tations. There were 54 ﬁelds (I=54), which we divided into land units (mi)of56m 2 each. The spatial distribution of boxes across diverse land uses enabled us to investigate how the variation in land use affects the predation pressure exerted on rodents by barn owls, as well as the occupancy rates of nesting boxes. In the following sub-section, we apply a survival analysis to estimate a function describing the spatial distribution of the predation pressure exercised by the barn owls on the various land uses around the nesting boxes. This function generates explanatory variables that we used to esti- mate both the box-selection function and the alfalfa-production function. The Predation-Pressure Function The predation-pressure function likj(dik (ui, xk)) is deﬁned as the probability that a barn owl nesting in box kwill search for prey in ﬁeld iif the ﬁeld is devoted to crop j,asa function of the distance between the box and the ﬁeld. A ﬁeld located further from the nest is expected to be less appealing to the owls because longer ﬂights are required. The appeal of the ﬁeld also depends on the crop grown there, through the size of the rodent population it attracts, and the preying condi- tions it offers. For instance, the presence of perching points and the heights of plants may affect the barn owl’s chances of pinpointing its prey, as well as its hunting success. Let dmik(umi,xk)be the distance between box kand the centroid of land unit miwithin ﬁeld i. The predation-pressure function for ﬁeld i, when assigned to crop j, is: (3) likj(dik (ui,xk)) = Mi mi=1 Prj(dmik(umi,xk)) where Prj(dmik(umi,xk)) is a probability density function speciﬁc to crop j, which represents the probability of the barn owl nesting in box kto hunt at point mi. Estimating Prj(dmik(umi,xk)) requires two datasets. The ﬁrst represents the barn owl’s “actual predation behavior,” and includes records of the locations of barn owls while hunting, from which the crop at each hunting location can be identiﬁed and each bird’s dis- tance from its nesting box can be computed. The second dataset reﬂects the “predation opportunities” of barn owls, and is speciﬁcally required to estimate differences in the hunt- ing appeal of different crops. We employed a recursive estimation procedure that involves survival analyses based on radiotelemetry records of barn owls in Sde Eliyahu’s ﬁelds, the selection of a functional form based on the Akaike Information Criterion (AIC), and estimation of crop-preference parameters. In the online appendix A, we detail the data and the estimation procedure. Here we report the estimation result of the selected gamma probability density function: Prj(dmik)=(4) aj 0.11 exp 0.16 ln πd2 mik3.25 0.025 exp 6.35 ln πd2 mik3.25 2π2d3 mik where ajis a crop-preference parameter whose value is presented in table 1for the 11 land uses. Except for the case of legumes, barn owls seem to prefer trees, probably owing to the advantage provided by perches (Kay et al. 1994). The estimated predation-pressure func- tion has a few noteworthy implications. First, our evaluation of the contribution of barn owls to agricultural proﬁts is based on alfalfa production. Given that the estimated crop-preference parameters for alfalfa are among the lowest, this crop is not attractive to barn owls, and therefore our focus on alfalfa may considerably underestimate the overall contribution of barn owls to proﬁt. Second, as our evaluations rely heavily on the predation-pressure function, validation is required. In the on-line appendix B, we pro- vide empirical evidence showing that (a) the calculated predation pressures can explain the hunting patterns of barn owls in alfalfa ﬁelds as found in the radiotelemetry survey, and (b) endogeneity due to reverse causality is unlikely, meaning that larger alfalfa yields do not stimulate predation pressure. Finally, the predation-pressure function is also used by guest on May 22, 2014http://ajae.oxfordjournals.org/Downloaded from 738 April 2014 Amer. J. Agr. Econ. Table 1. Estimates of Crop-Preference Parameters Land Use αj(tvalue) R2F-statistic Fallow 0.69 (3.16)∗∗∗ 0.40 9.96 Alfalfa, year 1 0.39 (2.54)∗∗ 0.30 6.46 Alfalfa, year 2+0.29 (2.51)∗∗ 0.30 6.29 Corn 0.12 (1.95)∗∗∗ 0.20 3.80 Legumes 2.08 (4.66)∗∗∗ 0.59 21.74 Wheat 0.79 (3.12)∗∗∗ 0.39 9.75 Vegetables 0.42 (2.49)∗∗ 0.29 6.22 Citrus 1.26 (2.76)∗∗ 0.34 7.63 Dates 2.64 (5.71)∗∗∗ 0.69 32.65 Olives 1.89 (1.90)0.19 3.61 Residential areas 0.88 (4.91)∗∗∗ 0.62 24.11 Note:denotes signiﬁcance at a 10% level, ∗∗ denotes signiﬁcance at a 5% level, and ∗∗∗ denotes signiﬁcance at a 1% level. to validate our assumption that the box loca- tions (x) do not affect the optimal assignment of ﬁelds to crops (δ).3 The Box-Selection Function From the farmer’s point of view, the prof- itability associated with installing and maintaining a nesting box depends primarily on the probability of the box being in use. The proximity of the box to attractive hunt- ing areas has a potentially important impact on the probability of the box being occupied. Hence, the location of each box is likely to be a key determinant of its occupancy rate, and in turn a key determinant of the efﬁcacy of rodent control by barn owls overall. Barn owls reselect their nesting places once a year, at the onset of the breeding season. Previous studies have indicated the dependence of nesting rate in boxes on their physical features and geographical attributes (Charter et al. 2010). In this study, we esti- mate an occupancy probability function in which the impacts of the boxes’ prox- imity to certain crops and to other boxes are incorporated based on the estimated predation-pressure function. The average occupancy rate for the 58 boxes over the 10-year period is 43%, ranging from 20% to 62%. For comparison, Wood and Fee (2003) report occupancy rates of 70% in oil palm estates in Malaysia. Our explanatory variables can be classiﬁed into 3We estimated a multinomial logit model of the probability that ﬁelds are assigned to non-perennial crops, in which the calculated cumulative predation pressure exercised by all of the boxes on the ﬁelds constitutes the explanatory variable; the corresponding coefﬁcients were found to be statistically insigniﬁcant for all crops. three groups. The ﬁrst group includes time- invariant features of the boxes themselves, including dummy variables for three entrance directions and a dummy for boxes located in the shade. The second group of variables represents the environment of each box. We hypothesize that boxes located closer to land uses that provide better hunting conditions are more attractive for nesting. Variables in this group represent the predation pres- sure exerted by barn owls nesting in a box on rodents in the aforementioned 11 land uses (see table 1), conditional on its being occupied. The predation pressure was com- puted for each box k, land-use j, and year t, t=1, ..., 10, by: (5) Lkjt = I i=1 δijt likj(dik (ui,xk)) using the parameters estimated for the predation-pressure function likj(dik (ui,xk)). The predation pressure represents the prob- ability (or fraction of time) that a barn owl nesting in box kwill hunt in the ﬁelds assigned to crop j. Note that the pressures computed for Sde Eliyahu’s residential area and for perennial plantation ﬁelds are time- invariant, since δijt =δij for all t=1, ...,10. In addition, to control for potential terri- torial effects in nest selection, we include an “engagement probability” variable that measures the probability of interac- tion between a barn owl nesting in box k and those nesting in all other boxes. The engagement-probability variable is calcu- lated by applying equation (4) to the distance from box kto every other box, and averaging across boxes. For all of the variables in this by guest on May 22, 2014http://ajae.oxfordjournals.org/Downloaded from Kan et al. Agricultural Rodent Control Using Barn Owls: Is it Proﬁtable? 739 group (i.e., the predation pressures on the 11 land uses and the engagement probability), second-degree polynomials are included to allow for non-linear effects. It is worth noting that yields in the ﬁelds surrounding the nest might affect the nest- ing rate, and controlling for this effect could introduce endogeneity into our analysis. However, this is not relevant for the spe- ciﬁc case of alfalfa, since box occupancy is generally established before the ﬁrst alfalfa harvest. The third group of variables includes time-speciﬁc variables—annual rainfall at Sde Eliyahu, the number of years since the box was installed, year ﬁxed effects, and the total number of nesting boxes throughout Israel to control for potential impacts on the barn owl population. In addition, since boxes occupied in previous years may signal favorable nesting conditions, we included a lagged dependent variable that indicates whether the box was occupied in the previous year. This entails estimating a dynamic probit model, with unobserved box heterogeneity. Following Wooldridge (2005), we internalize into the model the correlation between the initial dependent variable (denoted gk0) and the unobserved heterogeneity (ηk)using a linear function: ηk=θ0+θ1gk0+zkθ2+φk, where zk=(zk1,...,zk10)is the row vec- tor of all explanatory variables in all time periods, θ0,θ1, and θ2are coefﬁcients, and φk|(gk0,zk)N(0, σ2 φ). This yields a dynamic probit model with response probability: Pr(gkr =1)=(zkt ψ+ζgk,t1+θ0 (6) +θ1gk0+zkθ2+φk) where gkt is the dichotomous dependent vari- able, zkt is a vector of exogenous variables, and ψand ζare the coefﬁcients of interest. This equation can be estimated by standard random-effect probit software (e.g., by the xtprobit command in Stata). Due to sample size limitations, the zk vector in our application incorporates, for each year t, the sum of the predation pres- sures over all non-perennial crops, which are the time-variant variables. The stepAIC procedure (Venables and Ripley 2002) was employed to select the set of variables to be retained in the model based on the AIC. Table 2reports the estimation results. Only one variable from the group of time-invariant features of the boxes was retained by the stepAIC procedure: the shade conditions.4As could be expected in hot environments, shaded boxes are sig- niﬁcantly more attractive. The agricultural environment also appears to play an impor- tant role in nesting box occupancy. The coefﬁcients of alfalfa and wheat, which are known to be favored by rodents, are pos- itive. That is, larger predation pressure on these crops (i.e., a reduction in the distance between the box and the ﬁelds where these crops are grown, which in turn increases the probability that the barn owl nesting in the box will hunt in these ﬁelds) increases the probability of the box being occupied. However, this ﬁnding is inconsistent with the predation habits (table 1), implying that hunt- ing patterns might not always be accounted for in the nest-selection stage. Note that the variable measuring the predation pres- sure on alfalfa ﬁelds in their ﬁrst production year was eliminated by the stepAIC pro- cedure; this indicates a possible learning process in which barn owls gradually recog- nize the alfalfa ﬁelds, or come to understand their appeal. Proximity to date palms also increases nesting probability,5possibly due to the preference for perches as prowl points and the abundance of rodents in date plan- tations, particularly rats.6On the other hand, barn owls tend to avoid nesting in boxes located close to residential areas. This may be attributed to territorial effects, as some barn owls routinely nest in Sde Eliyahu’s residential areas, or to aversion to human presence, light, and noise. We hypothesize that the distance between neighboring nests will reﬂect an attraction-repulsion balance, converging to some favorable intermediate distance.7This hypothesis is reinforced by the opposite signs of the coefﬁcients of the engagement-probability and engagement- probability-squared variables, which indicate 4The dummy variable indicating the east-facing side of the box entrance was retained in the second-best model of the stepAIC procedure, which has a relative likelihood that is 7% lower than the ﬁrst best (Burnham and Anderson 2002). 5The marginal effects of the crops’ predation-pressure variables with statistically signiﬁcant non-linear effects (pressures on dates and residential areas) are found to be monotonic throughout the whole sample range of these variables. 6Shaul Aviel, personal communication, Sde Eliyahu, March 2011. 7Barn owls exhibit type B territorial behavior patterns (sensu Taylor 1994), where the area of breeding activity (the nest) is defended, but the hunting area is not. This implies that the territorial effect will be limited, and even reversed, if distances between boxes become large enough, and barn owls may tend to avoid nesting in boxes that are too isolated. by guest on May 22, 2014http://ajae.oxfordjournals.org/Downloaded from 740 April 2014 Amer. J. Agr. Econ. Table 2. Estimation Results for the Dynamic Probit Nesting Function Sample Mean Coefﬁcient Marginal Probability Variable (St. Dev.) (Zvalue) Effect (tvalue)a Occupancy (gkt, dependent variable) 0.4293 (0.4954) Shaded conditions (dummy) 0.2414 (0.4283) 0.582 (2.74)∗∗∗ 0.239 (2.47)∗∗ Pressure on alfalfa, year 2+ (Probability) 0.0302 (0.0499) 2.452 (1.84)1.009 (1.84) Pressure on wheat (Probability) 0.1014 (0.0933) 1.696 (1.83)0.698 (1.67) Pressure on dates (Probability) 0.0407 (0.0651) 11.42 (2.20)∗∗ 3.074 (1.70) Pressure on dates squared 0.0059 (0.0127) 48.61 (2.09)∗∗ Pressure on residential areas (Probability) 0.0105 (0.0150) 87.48 (3.16)∗∗∗ 24.95 (1.93) Pressure on residential areas squared 0.0003 (0.0008) 1,278 (3.04)∗∗∗ Engagement probability (Probability) 0.1232 (0.0683) 7.759 (2.04)∗∗ 0.211 (0.38) Engagement probability squared 0.0198 (0.0246) 29.40 (2.62)∗∗∗ Annual rainfall (cm/year) 24.780 (7.973) 0.047 (3.48)∗∗∗ 0.019 (4.11)∗∗∗ 2003 (dummy) 0.1000 (0.3003) 0.501 (1.57) 0.206 (1.83) 2004 (dummy) 0.1000 (0.3003) 0.653 (3.26)∗∗∗ 0.269 (2.81)∗∗∗ Occupied in previous year (gk,t1) (dummy) 0.3931 (0.4889) 0.793 (6.31)∗∗∗ 0.327 (5.36)∗∗∗ Occupied in 1998 (gk0) (dummy) 0.1379 (0.3451) 0.535 (2.77)∗∗∗ 0.220 (3.02)∗∗∗ Sum of pressures on variant crops in 2002 (Probability) 0.3148 (0.1879) 8.496 (1.42) 3.497 (1.04) Sum of pressures on variant crops in 2004 (Probability) 0.3223 (0.1873) 11.55 (1.60) 4.755 (1.25) Sum of pressures on variant crops in 2007 (Probability) 0.3100 (0.1906) 4.987 (1.67)2.053 (1.37) Constant 1.725 (4.24)∗∗∗ σφ3.49 ×104 σ2 φ(1+σ2 φ)11.22 ×107 Observations 580 Log likelihood 306.5 AIC 649.1 Pseudo R20.23 Notes: denotes signiﬁcance at a 10% level, ∗∗ denotes signiﬁcance at a 5% level, and ∗∗∗ denotes signiﬁcance at a 1% level. aThe marginal effects were evaluated for the mean values of the explanatory variables. Standard errors of the marginal effect statistics were calcu- lated by bootstrap procedure. the existence of a distance between boxes at which occupancy rate is maximized. By employing the estimated probability den- sity function (equation 4), we found that, ceteris paribus, an average distance of 410m between a box and all other boxes maximizes the box’s occupancy probability. For compar- ison, the actual average distance between a box and all other boxes in the study area was approximately 1 km. In other words, increas- ing the density of the 58 boxes might increase their occupancy rates. The coefﬁcient of the annual rainfall vari- able is positive. This can be explained by the associated higher availability of vegetation as food in the ﬁelds and waterways, which in turn stimulates the population growth of rodents and possibly other prey. by guest on May 22, 2014http://ajae.oxfordjournals.org/Downloaded from Kan et al. Agricultural Rodent Control Using Barn Owls: Is it Proﬁtable? 741 Occupancy in the previous year (the lagged dependent variable gk,t1) has a signiﬁcant positive effect, indicating the potential impor- tance of signals that might be maintained in the boxes between years.8There is also a strong correlation between the unob- served heterogeneity (ηk) and the initial value of the dependent variable (occupied in 1998, gk0). On the other hand, ηkis weakly correlated with the sums of the predation pressures on time-variant crops that were retained by the stepAIC procedure. Employ- ing a log-likelihood test, the hypothesis σ2 φ1+σ2 φ1=0 was not rejected, imply- ing that the panel probit estimator does not signiﬁcantly differ from the pooled probit estimator. The Alfalfa-Production Function Alfalfa is routinely grown at Sde Eliyahu. Each year, on average, 42 of the total 540ha of agricultural land at Kibbutz Sde Eliyahu are allocated to alfalfa. Our data encompass a panel of 429 alfalfa harvests in 21 ﬁelds from 1999–2008. An alfalfa ﬁeld is cultivated and sown during the autumn of the ﬁrst pro- duction year, remains untreated during the rainy winter season, and is then harvested up to 11 times during the dry seasons from April to September for a few years—usually not more than 4 sequential ones due to yield reduction. The ﬁelds are fertilized once every autumn, and irrigated twice following each harvest. The irrigation dose per harvest is determined according to the growing period only, and is therefore exogenous.9Most ﬁelds are irrigated by sprinklers, and some by a moving platform, which enables treatment against rodents by ﬂood irrigation. Rodent control in all ﬁelds is based on barn owls and other factors, including other predators, natural ﬂooding of canals during the winter, and agronomic activities such as plowing, control of other pests, and routine sanitation of ﬁeld margins and waterways. Nevertheless, as reﬂected by the high proportion of rodents in the barn owls’ diet (Charter et al. 2009), a large rodent population is present in Sde Eliyahu’s ﬁelds. 8While mature barn owls tend to stay year-round within a certain region, in our sample they rarely returned to the same nesting box in a subsequent year. 9Rainfall events during the harvest seasons are rare, and the irrigation dose of each harvest corresponds to the months of its growing period: 140 mm/harvest in April, May and September, 160 mm/harvest in June, and 180mm/harvest in July and August. To estimate the alfalfa-production func- tion, we adopt the model developed by Saha, Shumway, and Havenner (1997): (7) y=H[W,G(X,Q)] where yis the quantity produced, Wis a vec- tor of direct production inputs, Xdenotes a vector of damage-control agents, G(·)is the abatement function, and QWis a subset of the inputs in Wthat, in addition to their direct impact, also indirectly affect the yield through interactions with damage-control agents. For example, irrigation may directly increase yields and at the same time change the rodent population, thereby altering the effectiveness of rodent control by barn owls. Saha, Shumway, and Havenner (1997) suggested the empirical speciﬁcation: (8) y=Y(W,β)·G(X,Q,ω,e)·exp(ε) wherein (9) G(X,Q,ω,e)=exp [A(X,Q,ω)e] is the abatement function, Y(·)and A(·)are continuous and differentiable functions, β and ωare vectors of parameters, and eand εare error terms. By assuming eN(μ,1 ), εN(0, 1)and cov(e,ε)ρ, one obtains the heteroscedastic error term εA(·)e. Hence: (10) lnyN[ln Y(·)μA(·),B(·)] where B(·)1+A(·)22A(·)ρ. These tractable assumptions allow an exact for- mulation of the expectation of output and its variance: E(y)¯ y=Y(·)·exp B(·) 2μA(·) (11) V(y)=¯ y2·{exp[B(·)]−1}.(12) Following Saha, Shumway, and Havenner (1997), we assume a linear speciﬁcation for A(·): (13) A(X,Q,ω)=ω0+ωXX+ωQQ and a Cobb-Douglas functional form for Y(·): (14) Y(W,β)= ς Wβς ς·exp(βDWD) by guest on May 22, 2014http://ajae.oxfordjournals.org/Downloaded from 742 April 2014 Amer. J. Agr. Econ. where βςis the coefﬁcient of the continuous variable Wς,WDis a set of dummy vari- ables, and βDis their corresponding vector of coefﬁcients. Our dependent variable is yi1th, the weight (in ton/ha) of alfalfa harvested from ﬁeld iduring harvest number hin year t, where j=1 denotes alfalfa production. The set of damage-control variables in Xincludes only the effect of the barn owls, represented by the cumulative predation pressure exer- cised by the occupied nesting boxes in the alfalfa ﬁelds. For some ﬁeld ithat is assigned to alfalfa (j=1) in year t, this cumulative pressure represents the probability that a barn owl nesting in some box kwill hunt in this ﬁeld, summed across all of the occupied boxes in Sde Eliyahu in that year. In view of equation (1), the cumulative probability is calculated for each ﬁeld i, which is assigned to alfalfa in year tby: (15) ri1t= K k=1 ξkt lik1(dik (ui,xk)) where ξkt is an indicator variable receiving a value of 1 if box kis occupied in year t, and 0 otherwise. The per-hectare cumulative pre- dation pressure of the ﬁeld is included in X, as well as its square, to control for a potential non-linear effect. The vector Qincorporates all of the other explanatory variables, encompassing the vectors q,v, and b1in equation (2). These variables include rainfall (mm/year), irriga- tion (mm/harvest), ﬁeld size (ha), time since the previous harvest (days), average temper- ature during the period from the preceding harvest, and a set of dummy variables indicat- ing the availability of ﬂood irrigation in the ﬁeld, the assignment of the ﬁeld to organic production, the production year (1999 to 2008), the ﬁeld (21 ﬁelds), the serial year of production in the ﬁeld (ranging from 1 to 4), and the serial harvest number (ranging from 1 to 11). We assume that all of these explana- tory variables are included in Q(i.e., Q=W) for two reasons: ﬁrst, ﬁelds under conven- tional production obtain similar treatments against pests and herbs before the harvesting period. That is, other than barn owl predation pressure, there is no variability across these alfalfa ﬁelds with respect to damage-control variables. Organic production entails the avoidance of not only pesticides, but also fertilization; therefore, the dummy variable indicating organic production cannot be excluded from W. The second reason for our assumption stems from the size of our set of explanatory variables, which is too large relative to the sample size to detach variables from Winto a Qsubgroup based on a sep- arability pretest (see Saha, Shumway, and Havenner 1997). The parameters ω,β,μ, and ρwere esti- mated by maximizing the log-likelihood function (LLF): LLF(ω,β,μ,ρ) =−1 2 i t hlnBi1th(·) +[lnyi1th(·)ln Yi1th (·)+μAi1th(·)]2 Bi1th(·) (16) using a non-linear maximization technique. The hypothesis that the error term εA(·)e is normally distributed was not rejected (P-value =0.22). Table 3reports the sam- ple means and standard deviations of the variables, the estimated coefﬁcients of the functions Y(·)and A(·), and the marginal effects of the variables on production mean and variance. The formulation in equation (9) implies that the effect of each variable in A(·)on damage abetment is opposite in sign to its estimated coefﬁcient. The marginal effect of the predation pressure is positive, implying that barn owls abate damage (i.e., the pro- duction of an alfalfa ﬁeld increases with the probability that barn owls will hunt in that ﬁeld, where the increase is channeled by the damage-abatement element of the produc- tion function). This ﬁnding provides evidence for a real contribution of the barn owls to agricultural productivity. The contribution of the predation pressure exhibits a diminishing return to scale, as the coefﬁcients of preda- tion pressure and predation-pressure squared have opposite signs. However, since the coefﬁcient of the squared variable is not sta- tistically signiﬁcant, we cannot reject convex responses of abatement to predation-pressure increases.10 10 Nevertheless, even a concave response of the output to a change in the level of damage control can stem from various characteristics of the damage-abatement process; therefore, as shown by Foxand Weersink (1995),we cannot make any deductions from these ﬁndings on the nature of the impact of barn owls on rodents, or on the relations between rodent populations and yields. by guest on May 22, 2014http://ajae.oxfordjournals.org/Downloaded from Kan et al. Agricultural Rodent Control Using Barn Owls: Is it Proﬁtable? 743 Table 3. Estimation Results for the Production and Damage-Control Functions Coefﬁcient (Zvalue) Marginal Effect (tvalue)a VariablebSample Mean (St. Dev.) Production, Y(·)Damage Control, A(·)Mean E(y) Variance V(y) Production (dependent variable, ton/harvest-ha) 1.910 (0.643) Predation pressure (Prob./ha) 0.028 (0.038) 0.735 (2.66)∗∗∗ 123.4 (5.64)∗∗∗ 42.266 (3.15)∗∗∗ Predation pressure squared 2.24 ×104(4.55 ×104) 23.49 (1.54) Flood irrigation (dummy) 0.443 (0.497) 1.022 (1.95)5.873 ×103(1.96)0.472 (0.43) 0.051 (0.05) Irrigation (mm/harvest) 139.1 (52.31) 0.017 (3.17)∗∗∗ 2.602×106(0.36) 0.003 (1.16) 9.314 ×104(2.60)∗∗ Organic (dummy) 0.387 (0.488) 0.591 (3.00)∗∗∗ 4.183 ×103(2.89)∗∗∗ 0.062 (0.08) 0.143 (0.31) Growing period (day/harvest) 22.26 (10.51) 0.026 (3.65)∗∗∗ 1.815 ×104(4.70)∗∗∗ 0.011 (0.50) 3.75 ×103(0.76) Temperature (C) 31.52 (4.522) 0.105 (0.68) 1.959 ×105(0.16) 0.014 (0.68) 4.87 ×103(1.08) Precipitation (mm/year) 248.9 (65.60) 0.059 (0.90) 4.585 ×105(0.08) 0.01 (0.58) 3.31 ×103(1.87) Field size (ha) 10.37 (4.305) 0.257 (2.09)∗∗ 3.082 ×104(1.89)3.01 ×104(0.59) 1.02 ×105(0.98) Year no. 2 (dummy) 0.445 (0.498) 0.630 (6.82)∗∗∗ 4.591 ×103(8.93)∗∗∗ 0.103 (0.10) 0.110 (0.41) Year no. 3 (dummy) 0.138 (0.345) 0.267 (2.75)∗∗∗ 7.861 ×104(1.12) 0.284 (0.39) 0.022 (0.09) Year no. 4 (dummy) 0.019 (0.135) 0.206 (0.98) 2.482 ×103(1.55) 0.146 (0.16) 0.157 (0.13) Harvest no. 2 (dummy) 0.131 (0.337) 0.940 (2.79)∗∗∗ 4.009 ×103(2.28)∗∗ 1.084 (0.45) 1.204 (0.29) Harvest no. 3 (dummy) 0.131 (0.337) 0.782 (2.31)∗∗ 2.484 ×103(1.34) 1.080 (0.45) 0.549 (0.26) Harvest no. 4 (dummy) 0.126 (0.332) 0.563 (1.55) 1.116 ×103(0.49) 0.919 (0.42) 0.210 (0.15) Harvest no. 5 (dummy) 0.119 (0.324) 0.640 (1.63) 4.019 ×103(1.78)0.372 (0.21) 0.690 (0.15) Harvest no. 6 (dummy) 0.110 (0.313) 0.606 (1.45) 4.442 ×103(1.87)0.219 (0.13) 0.739 (0.10) Harvest no. 7 (dummy) 0.100 (0.301) 0.433 (1.07) 3.510 ×103(1.45) 0.058 (0.04) 0.385 (0.16) Harvest no. 8 (dummy) 0.086 (0.281) 0.188 (0.49) 2.458 ×103(1.11) 0.182 (0.17) 0.121 (0.13) Harvest no. 9 (dummy) 0.054 (0.226) 0.217 (0.56) 2.472 ×103(1.14) 0.133 (0.12) 0.148 (0.07) Harvest no. 10 (dummy) 0.012 (0.107) 0.359 (0.76) 5.636 ×103(2.31)∗∗ 0.346 (0.40) 0.785 (0.20) Constant (ton/harvest-ha) 0.024 (0.38) μ1.276 (6.40)∗∗∗ ρ0.956 (134.8)∗∗∗ Observations 429 Log likelihood 25.75 Pseudo R20.26 Notes: denotes signiﬁcance at a 10% level, ∗∗ denotes signiﬁcance at a 5% level, and ∗∗∗ denotes signiﬁcance at a 1% level. aThe marginal effects were evaluated for the mean values of the explanatory variables. Standard errors of the marginal effect statistics were calculated by bootstrap procedure. The effects of dummy variables are the difference in the mean and variance under substitution of 0 and 1 in all observations at all bootstrap replications. bDummies for years and ﬁelds are not shown. by guest on May 22, 2014http://ajae.oxfordjournals.org/Downloaded from 744 April 2014 Amer. J. Agr. Econ. Note that our estimation of the effect of barn owls on alfalfa output is based on the position of the occupied nesting boxes in relation to the alfalfa ﬁelds. The box loca- tions, however, might be non-random. That is, Sde Eliyahu farmers may have intention- ally located the boxes closer to ﬁelds with higher alfalfa yields. To examine this possibil- ity, we estimated the model again, this time with the predation pressure in equation (15) calculated as if all boxes are occupied (i.e., ξkt =1 for all k=1, ...,K). This resulted in a lower value for the log-likelihood function (LLF =12.38), and statistically insigniﬁcant coefﬁcients for the predation pressure and its square. We therefore reject the hypothesis that the positive impact of barn owls on the output of alfalfa ﬁelds can be attributed to the locations of the boxes rather than to the locations of the occupied boxes only. Returning to table 3, the effects of all other explanatory variables that appear in both Y(·)and A(·)on production and damage abatement are opposite in sign. Thus, these effects seem to offset each other such that the marginal effects on mean output are all statistically insigniﬁcant. With respect to the variance of output, only the marginal effects of irrigation and rainfall are statistically signiﬁcant, and both reduce yield volatility. Flood irrigation has a direct negative effect on alfalfa yield. This irrigation method is less efﬁcient than sprinkling, since much ﬂood- water is lost through deep-percolation ﬂows. However, ﬂood irrigation affects abatement positively. While we cannot reject the hypoth- esis of zero marginal effect of irrigation on mean alfalfa output, the coefﬁcient of irri- gation in Y(·)indicates a negative marginal product. This can be explained by the irriga- tion doses (see endnote 9), which, according to the evaporation in the Beit She’an Valley during the months of alfalfa growth (IMS 2013), are more than 25% higher than the recommended doses (Tzukerman 2004). Over-irrigation can result in reduced alfalfa yield (Donovan and Meek 1983;Mueller, Frate, and Campbell-Mathews 2007), and is more likely to occur under conditions of non-uniform inﬁltration (Feinerman, Letey, and Vaux 1983), saline irrigation water that requires excess irrigation for salt leaching and low water prices, as is the case in the Beit She’an Valley. As expected, organic production tends to yield lower output than conventional production. However, it also stimulates abatement, possibly owing to the avoidance of fertilization, which in turn discourages weed growth. A longer period between harvests increases output, but decreases abatement, probably due to the longer time afforded for the establishment of populations of damaging agents. This may also explain the lower abatement level in the second year of production compared to the ﬁrst year (the effects are opposite in sign to the coefﬁcients). Productivity is higher in the second and third harvests compared to all other harvests. Larger ﬁelds are more productive, but abatement is lower, indicating a negative interaction between ﬁeld size and the barn owls’ effect.11 That is, as boxes are usually located at the margins of the ﬁeld, the effect of barn owls on rodents in the inte- rior areas of the ﬁeld is lower, and further decreases with increasing ﬁeld size. To test the sensitivity of our results to the functional form speciﬁcation adopted from Saha, Shumway, and Havenner (1997),we estimated a linear production function using the same set of explanatory variables, in which all variables were treated as produc- tive inputs. The estimation results (online appendix C), with respect to the signs and signiﬁcance levels of the coefﬁcients of barn owl predation pressure and its square, are almost the same as those reported in table 3. The two speciﬁcations yield almost iden- tical estimates of the predation pressure’s marginal effect. This ﬁnding reinforces previous results by Carrasco-Tauber and Mofﬁtt (1992) and Lansink and Carpentier (2001), which contradict Lichtenberg and Zilberman’s (1986) hypothesis that damage- abatement speciﬁcations lead to lower estimates of the marginal effect of abate- ment inputs. On the other hand, the linear speciﬁcation yields marginal effects with higher signiﬁcance levels for almost all of the other explanatory variables. Simulations With the estimated functions of predation pressure, box selection and alfalfa produc- tion, and additional price and cost data,12 11 The nature of the interaction between some two variables Xand Qin the abatement process, calculated by 2G/∂XQ, can be qualitatively represented by the sign of the product of their coefﬁcients, ωXωQ. 12 Barn owls affect revenues by changing per-hectare pro- ductivity, and they entail ﬁxed per-hectare costs associated with by guest on May 22, 2014http://ajae.oxfordjournals.org/Downloaded from Kan et al. Agricultural Rodent Control Using Barn Owls: Is it Proﬁtable? 745 we are in a position to plug these elements into equation (2) to evaluate the proﬁtability of biological rodent control by barn owls. Four scenarios are compared. Scenario 1 represents the observed situation, in which the 58 nesting boxes are in their current loca- tions throughout the ﬁelds of Sde Eliyahu. In scenario 2, we simulate alfalfa produc- tion in the absence of all nesting boxes, such that rodents are controlled only by the aforementioned agronomic and natural fac- tors. In scenario 3, we run an algorithm that searches for the vector of optimal locations of the 58 nesting boxes, x, which maximizes the expected proﬁts of the alfalfa ﬁelds. The alfalfa revenues in this scenario are calculated by: (17) R=p1 I i=1 δi1E[y(Wi,Xi)] where δi1stands for the probability of ﬁeld ibeing assigned to alfalfa production, as computed based on our sample, and E[y(Wi,Xi)]is the ﬁeld’s output expecta- tion as in equation (11). Scenario 4 is similar to scenario 3, but instead of expected rev- enues, the function to be maximized under xincorporates the certainty-equivalent revenues: CE =p1 I i=1 δi1E[y(Wi,Xi)](18) V[y(Wi,Xi)] 2E[y(Wi,Xi)] where V[y(Wi,Xi)]is the output’s variance expressed by equation (12), and is the Arrow-Pratt measure of relative risk aver- sion, which was evaluated by Bar-Shira, Just, and Zilberman (1997) to be 0.611 for farm- ers in Israel. As barn owls increase both the production expectation and the variance the installation and maintenance of nesting boxes. The output price is$264/ton, as reported by the Israel Field Crops Growers
Association (2010), for alfalfa under conventional production.
Variable costs associated with harvesting and hauling amount to
$38/ton (IMARD 2010). The per-box costs were estimated at$50/year, based on an installation cost of $250, a 10-year lifetime, with one renovation at a cost of$60 and 0.1 working days per
year for monitoring and cleaning. Attributing the costs of all 58
nesting boxes to the 42 ha allocated to alfalfa in Sde Eliyahu in
an average year, we get a cost of $69/ha per year. (see table 3), the certainty-equivalent rev- enue captures their counter effects on the production and risk premium. The objective of scenarios 3 and 4 is to get an idea of how much higher the proﬁts of Sde Eliyahu’s alfalfa ﬁelds could be if the 58 boxes were originally located so as to maxi- mize those proﬁts. These scenarios, however, are associated with the challenge of solving a complicated non-linear spatial optimization problem, and are based on extrapolations of our estimated functions. Therefore, simpliﬁ- cations and constraints are needed to obtain practical and computable results. Our estimation of the alfalfa production function Hi1(ri1(u,x,δ,a,h),vi,b1)implies that alfalfa outputs would considerably increase with cumulative predation pressure (table 3). The estimated predation-pressure function likj(dik (ui,xk)) tells us that the cumu- lative pressure on alfalfa ﬁelds will increase convexly as the distance between those ﬁelds and occupied nesting boxes decreases. The proximity of nesting boxes to alfalfa ﬁelds may also increase their occupancy rate, as can be learned from the estimated nesting selection function sk(u,x,δ,ak,hk)in table 2. Integrating these three effects implies that proﬁts would be maximized if as many nest- ing boxes as possible were to be located as close as feasible to the alfalfa ﬁelds. A coun- teracting factor is the occupancy rate, which decreases when boxes become too close to each other. Nesting rates may also be restricted by the impact of the boxes’ dis- tances from other land uses, such as date trees. However, while our model captures these opposing forces, the reliability of our predictions is expected to diminish as we extrapolate further. For instance, nesting rates may be limited by unobserved vari- ables such as overall barn owl population in the relevant area, or competition with other predators such as jackals, kestrels, and wild- cats, which may affect the barn owls’ hunting success. In addition, the location of the boxes should account for operational farming prac- tices, such as the movement of cultivation machinery. These considerations can be taken into account in the model by introducing constraints. The two optimization scenar- ios incorporate two additional constraints: (a) all of the boxes are restricted to being located at the borders of the ﬁelds, at least 100 m apart, and (b) the per-hectare alfalfa production in every ﬁeld is restricted to no more than 25 ton/year, which is 25% higher by guest on May 22, 2014http://ajae.oxfordjournals.org/Downloaded from 746 April 2014 Amer. J. Agr. Econ. Figure 1. Optimal (scenarios 3 and 4) versus current (scenario 1) distribution of nesting boxes in relation to alfalfa crop-rotation ﬁelds than the typical alfalfa productivity reported by IMARD (2010). In addition, to facilitate computation, our optimization algorithm considers 1,500 predeﬁned potential points (located at the borders of all of Sde Eliyahu’s ﬁelds, 100 m apart), and searches among them for the optimal location of one box at a time. This is a stepwise procedure in which the ﬁrst box is located at the optimal point given that it is the only one in the area; the second box is optimally located given the location of the ﬁrst box, and so on.13 Apparently, scenarios 3 and 4 both yield a similar solution for x. Figure 1shows current versus optimal nesting box distributions rel- ative to the ﬁelds with positive probabilities of being assigned to alfalfa throughout crop rotations. The simulation results are summarized in table 4. Scenario 2 constitutes a benchmark for the calculation of the contribution of barn 13 Finding the optimal solution would require computing the value of the objective functions for each of the 2.3 ×10105[= 1500!/(581442!)]combinations of 58 boxes placed in 1,500 loca- tions. While our algorithm applies only 85,347 [= (1501 ×1500 1443 ×1442)/2]computations, and therefore may not hit upon the optimal location, it is computationally feasible, and believed to be satisfactory for evaluating the extent to which proﬁts of alfalfa ﬁelds could be increased. owls’ rodent control to production and proﬁt under the other three scenarios. The yield expectation under scenario 2 is computed by the use of equation (11), while substituting ri1=0 for all i=1, ...,Iinto equation (15) and holding all other variables at their time- average levels. The alfalfa output attributable to the presence of the 58 nesting boxes in their current locations equals the difference between the expected productions under scenarios 1 and 2. This calculation results in a contribution of 1.35 ton/ha per year, which constitutes 9.4% of the observed production expectation of 14.38 ton/ha per year. The associated proﬁt contribution amounts to$235.8/ha per year. The calculated proﬁt con-
tribution based on the certainty-equivalent
proﬁts is slightly higher ($245.0/ha per year); this is because the increase in the production expectation exceeds that of the variance. Thus, rodent control by barn owls is found to be proﬁtable.14 As noted, these are likely 14 The proﬁtability of alfalfa production is rather small, and may even be negative in certain years, so that an increase of 9.4% in production can make a signiﬁcant difference in terms of proﬁts. Based on production studies published by extension specialists at UC Davis (2008),Texas AgriLife Extension Service (2011), and the University of Wisconsin (2011), an output increase of 9.4% implies proﬁt increases of 31%, 67%, and 107%, respectively. A similar study published by Iowa State University (2011) found by guest on May 22, 2014http://ajae.oxfordjournals.org/Downloaded from Kan et al. Agricultural Rodent Control Using Barn Owls: Is it Proﬁtable? 747 Table 4. Rodent-Control Scenarios Scenario 1 Scenario 2 Scenarios3&4 The 58 Nesting The 58 The 58 Nesting Boxes Are Boxes Are in Nesting Located So as to Maximize Their Current Boxes Are Alfalfa Expected Proﬁts and Description Locations Eliminated Certainty-Equivalent Proﬁts Average distance between box and all other boxes (km) 1.00 – 1.61 Average occupancy rate of boxes 0.46 – 0.61 Average pressure on alfalfa ﬁelds (Probability/ha) 4.54 ×1050.00 1.35 ×104 Average alfalfa production expectation (ton/ha per year) 14.38 13.03 17.07 Average standard deviation of production (ton/ha per year) 8.11 7.63 9.29 Average certainty equivalent production (ton/ha per year) 13.17 11.78 15.68 Proﬁt increase compared to Scenario 2 ($/ha per
year):
Expected proﬁts 235.8 0.00 845.2
Certainty-equivalent
proﬁts
245.0 0.00 812.9
to be underestimates of the contribution of
barn owls to overall proﬁtability, since we
completely ignore the potential contribution
of the nesting boxes to the yields of other
crops, most of which are more attractive for
barn owl hunting than alfalfa (see table 1).
Compared with the current box locations
(scenario 1), boxes in scenarios 3 and 4 are
located around the ﬁelds with high proba-
bilities of being assigned to alfalfa (ﬁgure 1),
and the average nesting rate is consider-
ably higher than the observed nesting rate
(table 4). Consequently, the average per-
hectare predation pressure on alfalfa ﬁelds
is an order of magnitude higher; thus, the
portion of the production associated with
the presence of barn owls increases from
9.4% to more than 23%, and the computed
contribution of the barn owls to alfalfa prof-
its is 3.3 times of that under the observed
situation. These results highlight the con-
siderable impact of the locations of nesting
net losses in alfalfa production, yet an output increase of 9.4%
could have reduced the losses by 22%. Similarly,a study provided
by IMARD (2010) also found net losses in alfalfa production
in Israel, and here a 9.4% yield increase could reduce losses by
50%.
boxes on alfalfa proﬁts. In a broader per-
spective, our case study of rodent control
using barn owls illustrates the importance of
the spatial distribution of sources of agro-
biological agents (e.g., honeybee apiaries) as
a farming-management tool.
The question arises of how robust these
evaluations are to functional form speciﬁca-
tions. We examine this question with respect
to the production function by simulating
scenarios 1, 2, and 3 using the aforemen-
tioned linear speciﬁcation. Apparently, unlike
the marginal effect, the linear production
function yields evaluations of barn owls’
contributions to outputs and proﬁts that are
higher under both the observed and optimal
locations of the boxes (online appendix C).
This ﬁnding indicates that the evaluation
obtained using the functional form of damage
abatement is rather conservative.
As indicated by scenarios 3 and 4, if alfalfa
is assumed to be the only crop whose proﬁt
can be increased by barn owl activity, the
current spatial distribution of nesting boxes
at Sde Eliyahu is not optimal; the returns
on some of the boxes may not even cover
their installation and maintenance costs.
To examine this issue further, we applied
748 April 2014 Amer. J. Agr. Econ.
-4
0
4
8
12
16
20
24
28
32
36
40
44
0 102030405060
$/box/ha per year Boxes Current box locations Optimal box locations Figure 2. Marginal proﬁts of the number of boxes under current and optimized locations our stepwise optimization algorithm to the 58 observed locations of the boxes. This enabled us to compute the marginal proﬁt of each additional nesting box, as presented in ﬁgure 2. A similar curve is presented for the optimal box locations, as selected by the optimization algorithm under scenarios 3 and 4.15 As suspected, in their current loca- tions, 10 of the nesting boxes do not cover their costs. Nevertheless, as already noted, the array of boxes as a whole is still prof- itable. While the marginal proﬁt curve under scenarios 3 and 4 ﬂuctuates noticeably and exhibits a decreasing trend, all of the boxes are proﬁtable. Conclusions Our empirical application has two policy implications. First, the results indicate that rodent control by barn owls is proﬁtable from a farming point of view. Thus, under a hypothetical case in which rodenticides are absolutely prohibited, aside from informing farmers about the potential proﬁtability of the method and developing guidance and training programs, additional governmen- tal intervention to promote adoption of the method (e.g., by policy instruments such as 15 The curves exhibit non-monotonic patterns due to the spatial interrelations among the boxes; each additional box can affect the occupancy probability of the boxes located earlier by the stepwise optimization algorithm, and thus yield a larger marginal proﬁtability compared to the previous box. subsidies) is unnecessary.16 However, more active governmental intervention may be warranted if rodenticides are allowed, or in areas where farmers own small agricultural plots where spillover effects of the barn owl damage-abatement services may lead to free riding and thus to the placement of a suboptimal number of nesting boxes. Sec- ond, despite the fact that we cannot directly compare the proﬁtability of rodent control by barn owls to that of rodent control by rodenticides, our analysis evaluates proﬁt contributions that are both signiﬁcant and can be considered underestimates of the overall returns stemming from barn owl dam- age control. Thus, if policy-makers such as those in Israel (IMARD 2011) are looking to reduce the considerable environmental dam- age caused by rodenticides (e.g., Yom-Tov and Mendelssohn 1988;Zurita et al. 2007), our ﬁndings provide strong arguments for more severely restricting the regulatory conditions under which rodenticides are permitted. This study leaves a good deal of room for future research. For example, data on the various ecological, zoological, and economical components of the barn owl system can be collected at a ﬁner resolution to elucidate the costs and beneﬁts of control by barn owls versus alternative actions. The barn owls’ contribution to proﬁt may be assessed with respect to more crops, which would enable the computation of a more realistic opti- mal spatial distribution of boxes, including a determination of the optimal number of boxes. The proﬁtability of the method should also be compared to that of rodenticides. Finally, our application evaluates contri- butions to farmers’ proﬁts only; designing rodent-control policies based on a wider social perspective would require valuations of the environmental damage abated through the avoidance of rodenticide use, the beneﬁts associated with preserving barn owls, and the impacts of barn owls on other endangered species. The spatial economic model developed in this study is applicable to other agrobio- logical systems, particularly those associated with point-source spatial impacts. Examples 16 In recent years, the method has been rapidly adopted by Israeli farmers, partly owing to the governmental training activ- ities and ﬁnancial support for the installation, maintenance, and monitoring of nesting boxes (IMEP 2009;Motro et al. 2010). by guest on May 22, 2014http://ajae.oxfordjournals.org/Downloaded from Kan et al. Agricultural Rodent Control Using Barn Owls: Is it Proﬁtable? 749 include the selection of locations for hon- eybee apiaries, which inﬂuence pollination services and honey production (Manning and Wallis 2005), the management of patches of non-crop habitats to enhance natural pest control (Bianchi, Booij, and Tscharntke 2006), and the positions of cattle watering points, which affect the spatial variation of vegetation in rangelands and thus meat pro- duction (Ludwig et al. 1999). 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Ecotoxicological Eval- uation of Sodium Fluoroacetate on Aquatic Organisms and Investigation of the Effects on Two Fish Cell Lines. Chemosphere 67:1–12. by guest on May 22, 2014http://ajae.oxfordjournals.org/Downloaded from ... Labuschagne et al. (2016) carried out a literature review to assess the effectiveness of biological control by raptors for rodent pest management in agriculture, and found that most of the literature focused on barn owls but also that there are too few studies, and of those the minority showed scientific rigor. Many studies reported the food habits of barn owls in agricultural landscapes (e.g., Charter et al., 2009;Kross et al., 2016) but few reported actual impact on pest populations, crop damage and economic implications (but see Motro, 2011;Kan et al., 2013;Browning et al., 2016). Kan et al. (2013) found that in Beit-She'an Valley, a semi-arid agricultural environment in the north of Israel, alfalfa yields could increase from 13.03 (if no nesting boxes were in place) to 17.07 tons per hectare if the existing 58 barn owl nesting boxes would be placed optimally (compared to 14.38 tons per hectare under the current locations of the nesting boxes). ... ... Many studies reported the food habits of barn owls in agricultural landscapes (e.g., Charter et al., 2009;Kross et al., 2016) but few reported actual impact on pest populations, crop damage and economic implications (but see Motro, 2011;Kan et al., 2013;Browning et al., 2016). Kan et al. (2013) found that in Beit-She'an Valley, a semi-arid agricultural environment in the north of Israel, alfalfa yields could increase from 13.03 (if no nesting boxes were in place) to 17.07 tons per hectare if the existing 58 barn owl nesting boxes would be placed optimally (compared to 14.38 tons per hectare under the current locations of the nesting boxes). ... Article Rodent agricultural pests cause significant food loss every year. Attempts at mitigation via chemical pest control may cause secondary poisoning and harm non‐target species. Biological pest control by bolstering barn owl Tyto alba populations through the provision of artificial nest boxes is in use in several countries. The national biological pest control project of Israel began in the early 1980s in the Mediterranean zone and was subsequently expanded to the northwestern Negev desert, including areas adjacent to nature reserves and natural sand dunes, a threatened habitat in Israel. We analyzed prey of barn owls in the northwestern Negev to determine whether owls preyed on non‐target endemic, threatened rodents. A total of 14 632 prey items were collected from 95 nesting boxes between 2013 and 2016. We found that barn owls feed on protected and locally endangered species such as Gerbillus andersoni allenbyi (vulnerable − VU), Gerbillus pyramidum (VU), the endemic Meriones sacramenti (endangered) and Gerbillus gerbillus (critically endangered). These species constitute a significant proportion of barn owl diets (sometimes more than half of the prey items in a single nest box), especially in areas under 5 km from sand dunes, suggesting that bolstering the barn owl population may threaten locally endangered species. It could be hypothesized that agricultural crops serve as a resource for endangered species, allowing their numbers to increase, in which case their conservation status should be reassessed. However, extensive rodent trappings in agricultural fields in the region revealed <1% Gerbillus individuals, while in nearby dunes only gerbils were trapped. Insufficient data are available to assess whether or not M. sacramenti could have become a local pest. Our study highlights the risks that may be associated with the use of barn owls as rodent control agents in areas where natural open landscapes and nature reserves are interspersed with agricultural landscapes. ... The need to control rodent pests has led to an interest in integrated pest management using biological control agents, primarily barn owls (Labuschagne et al. 2016). By installing nest boxes in vineyards, farmers can attract barn owls, which may be able to act as natural predators and reduce populations of rodent pests (Meyrom et al. 2009, Paz et al. 2012, Kan et al. 2013, Browning et al. 2017. However, barn owls are mobile predators and they show a preference for open natural habitats (Taylor 1994), so the effectiveness of nest box installation on pest control delivery may depend on the composition of landscapes surrounding wine grape vineyards (Chart er et al. 2012). ... Article Full-text available Abstract Landscape composition can strongly affect the delivery of ecosystem services in agroecosystems. Conserving uncultivated habitats can support ecosystem services, but in Mediterranean biomes, these lands can also increase the area susceptible to wildfires. In the world‐renowned wine‐producing region of Napa Valley, California, wine grape growers install nest boxes to attract American barn owls (Tyto furcata), which may reduce rodent crop damage. Annual monitoring of 273 nest boxes began in 2015, and devastating wildfires burned approximately 60,000 ha in the region in 2017, including homes and businesses, as well as some vineyards and uncultivated land. The goal of this study was to determine whether changes in nest box occupancy were attributed to wildfires, nest box design, land cover type, or some combination of these variables. Occupancy surveys before and after these wildfires revealed changes in habitat selection at the nest scale. Occupancy increased during the study, reaching its highest point after the fires. Owls were found breeding in recently burned areas that were previously unoccupied and modeling results showed that nest box occupancy had a positive relationship with burned areas, particularly with edges of the fire perimeter. Barn owls also consistently showed a strong preference for taller nest boxes that were surrounded by more grassland than other land cover types and a moderate selection for wooden over plastic boxes. These results illustrate an incentive for the conservation of uncultivated habitat, particularly grassland, in vineyard ecosystems, and they provide an example of a mobile pest predator’s response to wildfire disturbance. In this case, results suggest an agroecosystem service made resilient to wildfire by the owls’ selection of burned and uncultivated habitats. ... Another experiment in the same settings revealed high variation in capture duration (from first attack to a successful capture) of spiny mice (Acomys cahirinus) and Günther's voles (Microtus guentheri), ranging from 0.5 sec to 43 min (63). The longest prey captures occurred while hunting voles, the preferred prey of owls in Israel (65,66) and in the Hula Valley in particular (67). Thus, as it is hard to catch a moving target, adopting irreproducible (and thus unpredictable) movement tactics may prove beneficial for a predator. ... Preprint Movement tracks of wild animals frequently fit models of anomalous rather than simple diffusion, mostly reported as ergodic superdiffusive motion combining area-restricted search within a local patch and larger-scale commuting between patches, as highlighted by the L\'evy walk paradigm. Since L\'evy walks are scale invariant, superdiffusive motion is also expected within patches, yet investigation of such local movements has been precluded by the lack of accurate high-resolution data at this scale. Here, using rich high-resolution movement datasets ($>\! 7 \times 10^7\$ localizations) from 70 individuals and continuous-time random walk modeling, we found subdiffusive behavior and ergodicity breaking in the localized movement of three species of avian predators. Small-scale, within-patch movement was qualitatively different, not inferrable and separated from large-scale inter-patch movement via a clear phase transition. Local search is characterized by long power-law-distributed waiting times with diverging mean, giving rise to ergodicity breaking in the form of considerable variability uniquely observed at this scale. This implies that wild animal movement is scale specific rather than scale free, with no typical waiting time at the local scale. Placing these findings in the context of the static-ambush to mobile-cruise foraging continuum, we verify predictions based on the hunting behavior of the study species and the constraints imposed by their prey.
... There is a wealth of literature on pesticide use and its determinants (Fan et al., 2020, Liu and Huang, 2013, Möhring et al., 2020b, Möhring et al., 2020c, Serra et al., 2005, Sunding and Zivin, 2000. Earlier literature has also addressed pest prevention measures, their adoption and interdependencies with pesticide use (Brown, 2018, Kan et al., 2013, Olson and Roy, 2005. However, the link between extension source, adoption of preventive measures and pesticide applications has not been empirically established in the literature so far. ...
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Does it matter whether farmers receive advice on pest management strategies from public or from private (pesticide company affiliated) extension services? We use survey data from 733 Swiss fruit growers who are currently contending with an infestation by an invasive pest, the fruit fly Drosophila Suzukii. We find that farmers who are advised by public extension services are more likely (+9-10%) to use preventive measures (e.g. nets) while farmers who are advised by private extension services are more likely (+8-9%) to use synthetic insecticides. These results are robust to the inclusion of various covariates, ways to cluster standard errors, and inverse probability weighting. We also show that our results are unlikely to be driven by omitted variable bias. Our findings have implications for the current debates on both the ongoing privatization of agricultural extension and concerns regarding negative environmental and health externalities of pesticide use.
... The study area reported nine raptors, including T. alba. This raptor has an extensive global distribution and is a well-known predator of rodents, providing an economic service to farmers by controlling the rodent population in farmlands (Marti et al., 2005;Kan et al., 2013;Kross et al., 2016). Tyto alba is nocturnal (Taylor, 1994;Abramsky et al., 1996) and can hunt nocturnal rodents in the farmlands within the study area. ...
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In this paper, we consider a fractional-order eco-epidemic model based on the Rosenzweig– MacArthur predator–prey model. The model is derived by assuming that the prey may be infected by a disease. In order to take the memory effect into account, we apply two fractional differential operators, namely the Caputo fractional derivative (operator with power-law kernel) and the Atangana–Baleanu fractional derivative in the Caputo (ABC) sense (operator with Mittag–Leffler kernel). We take the same order of the fractional derivative in all equations for both senses to maintain the symmetry aspect. The existence and uniqueness of solutions of both eco-epidemic models (i.e., in the Caputo sense and in ABC sense) are established. Both models have the same equilibrium points, namely the trivial (origin) equilibrium point, the extinction of infected prey and predator point, the infected prey free point, the predator-free point and the co-existence point. For a model in the Caputo sense, we also show the non-negativity and boundedness of solution, perform the local and global stability analysis and establish the conditions for the existence of Hopf bifurcation. It is found that the trivial equilibrium point is a saddle point while other equilibrium points are conditionally asymptotically stable. The numerical simulations show that the solutions of the model in the Caputo sense strongly agree with analytical results. Furthermore, it is indicated numerically that the model in the ABC sense has quite similar dynamics as the model in the Caputo sense. The essential difference between the two models is the convergence rate to reach the stable equilibrium point. When a Hopf bifurcation occurs, the bifurcation points and the diameter of the limit cycles of both models are different. Moreover, we also observe a bistability phenomenon which disappears via Hopf bifurcation.
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Large-scale conversion of uncultivated land to agriculture threatens wildlife and can diminish ecosystem services provided by nature. Understanding how wildlife provision ecosystem services may incentivize wildlife conservation in agricultural landscapes. Attracting Barn Owls (Tyto furcata) to nest on farms for pest management has been implemented worldwide but has not been evaluated in vineyard agroecosystems. Napa Valley, California, is a renowned winegrape growing region, and viticulturists encourage Barn Owl occupancy to help minimize damage from Botta’s pocket gophers (Thomomys bottae) and voles (Microtus spp.). This study modeled the use of habitats in space and time by hunting Barn Owls, providing information about their potential to provide the critical ecosystem service of pest consumption. We used global positioning system tags to track hunting owls and used a resource selection function to compare used and available habitats. We constructed the intensity of use and home range-movement maps using a time local convex hull analysis from location data. We found that Barn Owls selected uncultivated habitats when hunting, some of which were relatively rare on the landscape. Approximately, one-third of Barn Owl hunting locations occurred in vineyards, but this use was out of proportion to the availability of vineyards, which comprised 50% of the area around nest boxes. The owls’ use of vineyards increased with decreasing amount of selected uncultivated habitat in the landscape. However, as reported by a previous study, the occupancy of nest boxes in vineyards increases with uncultivated habitats nearby. Future research should model landscape composition to determine the amount of preferred habitat necessary to support occupancy as well as hunting in vineyards. A true test of pest management by Barn Owls awaits experimentation coupled with monitoring rodent populations.
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To date, nest orientation and location in hole-nesting birds have been studied mainly in temperate regions and in diurnal cavity breeders. Here we studied the effect of exposure, orientation, and habitat on nest box occupation and breeding success of Barn Owls in a semi-arid environment. The occupation of nest boxes varied with exposure and orientation. A higher percentage of occupation and more Barn Owl nestlings per breeding attempt were found in nest boxes located in the shade than in the sun, and in those facing east/north rather than other directions. The temperature in the nest boxes varied, being lowest in those located in the shade and in those facing east. Nest boxes located in crop fields fledged more young per breeding attempt than those located in date plantations. We suggest that the higher nest box occupation and number of nestlings fledged was probably due to the lower temperatures in those boxes, an important factor in a hot/arid environment, although alternative explanations are also considered.
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A guide to using S environments to perform statistical analyses providing both an introduction to the use of S and a course in modern statistical methods. The emphasis is on presenting practical problems and full analyses of real data sets.
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