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Influence of seasonality, environmental and anthropic factors on crop damage by wild boar Sus scrofa.


In recent decades, an expansion and increase in ungulate populations has been observed across Europe (Apollonio et al. 2010), with several species even colonising agricultural and urban areas (Cahill et al. 2012, Duarte et al. 2015, Sonnichsen et al. 2017), thus increasing problems of cohabitation with humans. In particular, the wild boar Sus scrofa often comes into conflict with humans, the main problems being the transmission of diseases to domestic animals and humans, collisions with vehicles, disturbance or threat to citizens and damage to gardens, public parks, pasture and agriculture (Meng et al. 2009, Putman et al. 2011, Barrios-Garcia & Ballari 2012).

Wild boar damage to croplands is expressed in different ways: direct consumption of crops, rooting in search of bulbs, invertebrates or tubers, seed removal, trampling and damage to agricultural infrastructure (Barrios-Garcia & Ballari 2012, Bengsen et al. 2014). Numerous methods can be employed to mitigate the negative impact of wild boars: odour repellents, solar blinkers, diversionary feeding, fertility control, crop guarding, fencing, translocation, poisoning, trapping and shooting (see review in Massei et al. 2011). However, these methods are often inadequate or expensive, and the limited funds available to public administrations often do not allow the implementation of preventative measures in all circumstances. Therefore, to act appropriately in the most critical situations, it is important to identify which factors increase the probability of damage. Wild boar damage is mainly affected by safety and forage-related factors. Safety factors comprise human presence and the distance to the edge of the nearest forests, roads, and rivers (Saito et al. 2011, Li et al. 2013, Lombardini et al. 2017), while forage-related factors include type, availability and maturation time of agricultural crops (Herrero et al. 2006, Schley et al. 2008, Gross et al. 2018), as well as the production of seeds in deciduous forests (Genov et al. 1995).

The literature relating to the impact of wild boar on agricultural land is abundant (Barrios-Garcia & Ballari 2012, Bengsen et al. 2014), but few studies concern highly human-dominated areas (e.g. Herrero et al. 2006). The Po River Plain (northern Italy) is one of the most settled regions of Europe. After the Second World War, this region underwent substantial transformation as a consequence of major industrial development and changes in the rural economy, which today are reflected by the intensive agricultural matrix characterizing the entire area (Falcucci et al. 2007). In recent decades, the wild boar has colonized the Po River Plain, where it has established a permanent presence (Ferri et al. 2014, Bon 2017, Canova & Balestrieri 2019), mainly favoured by releases of individuals and the abundance of food sources provided by agroecosystems (Carnevali et al. 2009).

Despite the wide distribution of the wild boar in the Po River Plain, to our knowledge no studies have analysed its negative impacts on the agricultural activity of the area. In this study, we examined wild boar damage to croplands in the Special Protection Area (hereafter: SPA) "Risaie della Lomellina", an intensively cultivated area of the western Po Plain, to investigate (1) the severity of damage; (2) monthly variation in the distribution of damage; (3) whether different crops are damaged according to their availability; and (4) which environmental and anthropic factors influence the presence of damage, through the formulation of a risk prediction model. Finally, on the basis of our results, we give recommendations for the management of wild boar in the region.

Study area

This study was conducted in the SPA "Risaie della Lomellina", located in the south-western sector of Lombardy (northern Italy), in the western Po Plain (Fig. 1). This is the largest SPA in Po River Plain, covering 309 [km.sub.2] at an average altitude of 95 m above sea level (range: 75-115 m). The Sesia, Po and Ticino Rivers define its western, southern and eastern borders, respectively, while the northern limit coincides with the administrative boundary separating Lombardy from Piedmont. The climate is temperate-subcontinental, with cold winters and hot summers, and a constant, high level of humidity, which facilitates the formation of fog, especially in autumn and winter. The study area is highly settled; intensively cultivated areas represent 90.6 % of the total surface, and are dominated by paddies (74.4%), followed by poplar plantations (9.7%) and arable lands (6.5%), mainly represented by maize (Carlini et al. 2010) (Fig. 1). Residual patches of natural vegetation characterize 4.3 % of the study area, and are constituted by lowland forests (2.7%, with the main species being Alnus glutinosa, Carpinus betulus, Fraxinus spp., Populus spp., Quercus robur, Salix alba and Ulmus minor), marsh vegetation (0.8 %) and scrublands (0.8 %) (Carlini et al. 2010). Natural areas are important chiefy for the conservation of numerous heron colonies (Fasola et al. 2011, Longoni et al. 2011), but the SPA has also been recognized as an important area for the conservation of lichens, flora, invertebrates and mammals (Bogliani et al. 2007). Urban settlements represent 3.8% of the study area. The presence of wild boar in the area is due to escapes, illegal releases and natural immigration from surrounding areas (primarily riparian habitats bordering the Rivers Sesia and Po). At present, density data and estimates of population size are not available.

Material and Methods

Wild boar damage to croplands

To describe damage to croplands, we acquired data from the Wildlife Service of the Province of Pavia. These data include requests for reimbursement officially formulated by farmers to the provincial administration from 2013 to 2015, and consist of date, type of crop damaged and amount of compensation paid (in Euros) for each damage event. A chi-square goodness-of-ft test was used to evaluate the existence of monthly differences in the number of events (Schley et al. 2008, Lombardini et al. 2017), without separating different crops because of low sample size (see Results section).

As these data did not include detailed information regarding the geographic location of damage events, we integrated our dataset through direct interviews with local farmers. Interviews took place in 2015, according to the willingness of farmers who, in cases of damage, escorted us to their farmlands and allowed us to inspect damaged fields. With these surveys, we sampled a total of 61 fields with damage attributable to wild boar (Fig. 1), for which we were able to define geographic coordinates, the type of crop damaged and the spatial extent of damage. All this information was included in an electronic database and georeferenced with QGIS 2.4.0 software (QGIS Development Team 2015).

To investigate whether different types of crops were damaged in proportion to their availability, we calculated from the Agricultural Information System of Lombardy ( the area occupied by paddy, maize and other crops in the study area. Other crops (soybean, wheat, sorghum and peas) were combined into a single group as each accounted for only a small proportion of the total agricultural area. We compared the overall availability of paddy, maize and other crops with the proportion of damaged area and damage frequency using a chi-square goodness-of-ft test (Neu et al. 1974, Herrero et al. 2006). Damage frequency was extrapolated from requests for reimbursement together with interviews with farmers. In the case of significant chi-square values (P < 0.05), we calculated Bonferroni confidence intervals to determine whether the wild boar selected, avoided or used different crops according to their availability (Neu et al. 1974, Herrero et al. 2006). All analyses were performed with the statistical software R 3.2.3 (R Development Core Team 2015).

Factors influencing damage distribution

Data for the geographic location of damaged fields were used to build a risk prediction model, following a use-versus-availability approach and executing a binary logistic regression analysis (Boyce et al. 2002, Johnson et al. 2006). "Damage" was set as the binary dependent variable; damaged fields assumed a value = 1, whereas a value = 0 was assigned to an equal number of fields (i.e. pseudo-absences) randomly chosen with the "random points generator" function implemented in QGIS 2.4.0 (Barbet-Massin et al. 2012). In the model we included 12 continuous variables: area, perimeter and fractal dimension of fields, distance from rivers (m), distance from forest patches (m), distance from continuous hedgerows (m), distance from discontinuous hedgerows (m), distance from urban areas (m), distance from main roads (m), distance from secondary roads (m), distance from railways (m) and population density (no. of people [km.sub.-2]). Area, perimeter and fractal dimension of fields were calculated with QGIS 2.4.0, population density was obtained from the Italian National Institute of Statistics website (, whereas all other variables were extracted from land use maps of Lombardy and Piedmont. Collinearity among variables was verified by calculating the Variance Infation Factor (VIF), using VIF = 3 as a threshold value (Zuur et al. 2010). The variable "perimeter of fields" showed a VIF value of 9.46, and was removed from the model.

Model selection was undertaken using the Akaike Information Criterion corrected for small sample size (AICc) and Akaike weights ([omega]i) (Akaike 1973, Symonds & Moussalli 2011). The relative importance of predictor variables ([omega]) was measured by the sum of Akaike weights of the models in which each variable appeared (Symonds & Moussalli 2011), and the model containing all the variables with a [omega] value [greater than or equal to] 0.50 was considered the best fitting (Barbieri & Berger 2004).

Model performance was evaluated by the area under the ROC (receiver operating characteristics) curve (AUC), which can assume values ranging from 0.50 (random prediction) to 1.00 (perfect prediction). Model discrimination ability was categorised as excellent for AUC > 0.90, good for 0.80 < AUC < 0.90, acceptable for 0.70 < AUC < 0.80, bad for 0.60 < AUC < 0.70 and null for 0.50 < AUC < 0.60 (Swets 1988).

A binary logistic regression analysis was implemented with the statistical software R 3.2.3 (R Development Core Team 2015), using the function glm (family = binomial) and "ROCR", "car", "MuMIn" and "verification" packages (Sing et al. 2005, Fox & Weisberg 2011, Barton 2013, NCAR 2015).


Wild boar damage to croplands

Between 2013 and 2015 the Wildlife Service of the Province of Pavia recorded and ascribed to wild boar 28 cases of damage to crops (Table 1). Compensation payments amounted to 9283 Euros; on average, 332 Euros were paid for individual claims (range: 0-1400 Euros). Damage occurred in maize (Zea mays), rice (Oryza sativa), soybean (Glycine max) and sorghum (Sorghum vulgare) fields, but 71% of events and 87% of compensation payments related to maize crops (Table 1). The number of claims was similar among years, with a peak of refunds paid in 2014 (mean per event: 532 Euros, range: 0-1400 Euros), followed by 2013 (mean per event: 360 Euros, range: 0-977 Euros) and 2015 (mean per event: 142 Euros, range: 0-400 Euros) (Table 1). There were strong monthly differences in the distribution of damage events (chi-square = 32.86, df = 11, P < 0.001); damage was almost entirely concentrated between April and September, with two peaks recorded in May and in summer (July-August), respectively. From October to March, only three events were documented (Fig. 2). Damage events involving maize showed a peak in May (35% of events) and an absence of events from December to February, damage to rice fields was recorded in spring, late summer and early autumn, and damage to soybean fields occurred in June and in September. The only events concerning sorghum occurred in June (Fig. 2).

Interviews with farmers confirmed maize fields as the main target of wild boar raids (n = 58 events), with rice paddy (n = 2) and other crops (n = 1) involved marginally in damage incidents. We surveyed a total of 131.1 hectares damaged by wild boar: maize (127.7 hectares) was the most affected crop, followed by other crops (2.0 hectares) and paddy (1.4 hectares). The percentage of observed damage frequency and damage area for paddy, maize and other crops differed significantly from the percentage of their respective availability (damage frequency from requests of reimbursement: [chi square] = 75.14, df = 2, P < 0.001; damage frequency from interviews: [chi square] = 324.84, df = 2, P < 0.001; damage area: [chi square] = 739.35, df = 2, P < 0.001). Maize was damaged more than expected, whereas paddy and other crops were avoided. Other crops were damaged proportionally to their availability only when considering requests of reimbursement (Table 2).

Factors influencing damage distribution

The best fitting model that explained the occurrence of wild boar damage included six predictors with a cumulative weight ([omega]) [greater than or equal to] 0.50 (Tables 3, 4). The risk of damage to fields increased positively as a function of distance from main roads, distance from continuous hedgerows and distance from urban areas. Risk of damage was negatively related to human population density, and distance from forest patches. The risk of damage was not explained by the fractal dimension of fields (Table 4). The ROC curve analysis showed the model had excellent predictive power, the area under the curve being 0.96.


In recent years, wild boar damage in the SPA "Risaie della Lomellina" has become an important issue for the local public administration. In this area, rice is the most important crop. Maize is the second most important crop and is the main target of wild boar raids, and usually represents the most damaged type of crop, also being consumed in a higher proportion than its availability (Nores et al. 1999, Herrero et al. 2006, Schley et al. 2008). Though wild boar consume a wide variety of plant species, their diet always includes at least one energy-rich plant food: acorns, beechnuts, chestnuts, olives or cereals, depending on the area (Schley & Roper 2003). In our study area, the scarcity of woodland forces boar to feed on high-energetic agricultural products (i.e. maize), inevitably resulting in conflict with humans.

The average reimbursement paid amounted to 332 Euros per event, which is lower than elsewhere in Italy (Amici et al. 2012, Lombardini et al. 2017), but comparable with studies conducted in other European countries (Linderoth & Elliger 2002, Schley et al. 2008, Bobek et al. 2017). Damage is mainly concentrated in spring and summer. Spring is the sowing season for maize, during which time wild boars unearth and consume seeds, obliging farmers to restore maize fields and causing a consequent delay in plant growth. Meanwhile, summer coincides with the milk stage of maturity in maize, which attracts wild boar because corncobs represent an important source of food at this time of year and maize plants also provide good cover in daylight hours (Wilson 2004, Schley et al. 2008, Bleier et al. 2017).

The logistic model had high predictive power, as highlighted by ROC curve analysis, and stressed the importance of cover and human disturbance in determining the risk of damage. Fields close to woodlands are commonly exploited by wild boar throughout their range (Saito et al. 2011, Li et al. 2013, Bleier et al. 2017, Lombardini et al. 2017), because forest patches provide areas to thermoregulate, a source of food when other agricultural crops are not available, and shelter from predators and human disturbance (Choquenot & Ruscoe 2003, Theuerkauf & Rouys 2008, Merta et al. 2014). The avoidance of human activities is further confirmed by the negative relationship of damage risk with distance to urban areas, human population density and main roads. Although present in many European metropolitan areas (Cahill et al. 2012), wild boar are not well suited to the urban environment, preferring to live in woodlands or in agro-forested areas (Merli & Meriggi 2006, Hebeisen et al. 2008, Amendolia et al. 2019). The model showed an unexpected increase in risk of damage with distance from continuous hedgerows. Generally, small landscape elements (such as hedgerows) are considered important ecological corridors for wildlife, representing typical passage and movement zones for many species, especially in highly human-altered landscapes (Dondina et al. 2019). However, in our study area small landscape elements are almost exclusively represented by rows of trees, with shrub vegetation relatively rare. Thus, these landscape features cannot provide adequate protection to wild boar from human disturbance. Field characteristics did not have a significant effect on the occurrence of damage, probably because of the high homogeneity of fields; indeed, the modernization of agricultural practices, particularly notable in this area, has resulted in simplification of field shape, which is increasingly large and regular, to facilitate planting, maintenance and harvesting operations by farmers. The SPA "Risaie della Lomellina" is a highly human-impacted area, where wild boar have been able to establish a permanent presence, with a consequent increase in conflict with humans. Hitherto, wild boar management in the area has been based on conjecture rather than rigorous data collection and analysis. This study represent a baseline to define adequate management strategies, which take account of the characteristics of the area, with its high level of human influence, the presence of species of conservation concern (e.g. Emys orbicularis, Rana latastei, Lycaena dispar; Bogliani et al. 2007, Sindaco et al. 2009) and the economic costs of damage, and demonstrating the need for a compromise among the preservation of human activities, biodiversity conservation and cost-effective preventative actions. Nevertheless, a caveat to our results is that, given the data in this study were based on reported damage claims, it is possible that low-level damage by wild boar went unreported. If this is the case, our analyses may be biased towards more serious cases of crop damage than more trivial instances.

Globally a variety of methods have successfully been used to reduce wild boar damage, such as fencing (Saito et al. 2011), diversionary feeding (Calenge et al. 2004), crop guarding (Thapa 2010), hunting (Geisser & Reyer 2004) and trapping (McCann & Garcelon 2008). However, the size of fields in our study area mitigates against the use of fencing due of high set-up and maintenance costs, as well as an increasing damage risk in adjacent areas (Geisser & Reyer 2004, Massei et al. 2011). Diversionary feeding can increase wild boar reproductive output, carrying capacity and hence population size (Calenge et al. 2004, Geisser & Reyer 2004, Jezek et al. 2016, Gonzalez-Crespo et al. 2018). Crop guarding, despite the low capital investment, is an intensive and time-consuming practice (Wang et al. 2006, Thapa 2010). Hunting is difficult to implement in residential areas, may cause social objections, and could impact non-target species (Treves & Naughton-Treves 2005, Liordos et al. 2017, Stillfried et al. 2017). Considering all these approaches, we suggest the use of trapping as the main method for controlling wild boar in the SPA "Risaie della Lomellina". Trapping can be used to selectively remove large numbers of animals, particularly at high densities, with a low level of disturbance to people and other wildlife, which makes it particularly suitable in densely occupied areas, where other methods are difficult or unfeasible (Campbell & Long 2009, Massei et al. 2011). The analysis of the monthly distribution of events and the risk prediction model formulated represent important tools to focus economic investment in the areas and seasons where the risk of boar damage is greatest. Based on our results, trapping sessions should be carried out before the sowing period and the milk stage of maize maturity, giving priority to fields close to forests, and at distance from main roads and urban settlements.

More research is needed to expand our understanding of wild boar ecology and biology in this region, including population density, behaviour, feeding habits, productivity, and a thorough understanding of these features of wild boar populations in the region would further enhance the effectiveness of management strategies and preventative actions (Beasley et al. 2018).


We are grateful to the Wildlife Service of the Province of Pavia for its help in data collection. We also wish to thank Gianpasquale Chiatante for his statistical advice, Carl Smith and two anonymous reviewers for their constructive comments on earlier versions of the manuscript and Federica Cappa, who kindly revised the English language. Finally, we thank Paolo Ghiselli, Mario Pavesi and everyone living in the SPA "Risaie della Lomellina" who participated in this study.


Akaike H. 1973: Information theory as an extension of the maximum likelihood principle. In: Petrov B.N. & Csaki F. (eds.), 2nd international symposium on information theory. Akademiai Kiado, Budapest: 267-281.

Amendolia S., Lombardini M., Pierucci P. & Meriggi A. 2019: Seasonal spatial ecology of the wild boar in a peri-urban area. Mammal Res.:

Amici A., Serrani F., Rossi C.M. & Primi R. 2012: Increase in crop damage caused by wild boar (Sus scrofa L.): the "refuge effect". Agron. Sustain. Dev. 32: 683-692.

Apollonio M., Andersen R. & Putman R. 2010: European ungulates and their management in the 21st century. Cambridge University Press, Cambridge.

Barbet-Massin M., Jiguet F., Albert C.H. & Thuiller W. 2012: Selecting pseudo-absences for species distribution models: how, where and how many? Methods Ecol. Evol. 3: 327-338.

Barbieri M.M. & Berger J.O. 2004: Optimal predictive model selection. Ann. Stat. 32: 870-897.

Barrios-Garcia M.N. & Ballari S.A. 2012: Impact of wild boar Sus scrofa in its introduced and native range: a review. Biol. Invasions 14: 2283-2300.

Barton K. 2013: MuMIn: Multi-model inference. Model selection and model averaging based on information criteria (AICc and alike). R package version 1.9.13. http://CRAN.R-project. org/package=MuMIn

Beasley J.C., Ditchkoff S.S., Mayer J.J. et al. 2018: Research priorities for managing invasive wild pigs in North America. J. Wildlife Manage. 82: 674-681.

Bengsen A.J., Gentle M.N., Mitchell J.L. et al. 2014: Impacts and management of wild pigs Sus scrofa in Australia. Mammal Rev. 44: 135-147.

Bleier N., Kovacs I., Schally G. et al. 2017: Spatial and temporal characteristics of the damage caused by wild ungulates in maize (Zea mays L.) crops. Int. J. Pest Manag. 63: 92-100.

Bobek B., Furtek J., Bobek J. et al. 2017: Spatio-temporal characteristics of crop damage caused by wild boar in north-eastern Poland. Crop Prot. 93: 106-112.

Bogliani G., Ludovici A.A., Arduino S. et al. 2007: Priority areas for biodiversity in Lombard Po Plain. Isabel Litografa, Gessate (MI).

Bon M. 2017: New Atlas of the mammals of Veneto. WBA Monographs 4, Verona.

Boyce M.S., Vernier P.R., Nielsen S.E. & Schmiegelow F.K.A. 2002: Evaluating resource selection functions. Ecol. Model. 157: 281-300.

Cahill S., Llimona F., Cabaneros L. & Calomardo F. 2012: Characteristics of wild boar (Sus scrofa) habituation to urban areas in the Collserola Natural Park (Barcelona) and comparison with other locations. Anim. Biodivers. Conserv. 35: 221-233.

Calenge C., Maillard D., Fournier P. & Fouque C. 2004: Efficiency of spreading maize in the garrigues to reduce wild boar (Sus scrofa) damage to Mediterranean vineyards. Eur. J. Wildlife Res. 50: 112-120.

Campbell T.A. & Long D.B. 2009: Feral swine damage and damage management in forested ecosystems. For. Ecol. Manag. 257: 2319-2326.

Canova L. & Balestrieri A. 2019: Long-term monitoring by roadkill counts of mammal populations living in intensively cultivated landscapes. Biodivers. Conserv. 28: 97-113.

Carlini E., Chiarenzi B., Gagliardi A. et al. 2010: Management plan of the SPA IT2080501 Risaie della Lomellina. Oikos Institute, Milano, Italy.

Carnevali L., Pedrotti L., Riga F. & Toso S. 2009: Ungulates in Italy: status, distribution, abundance, management and hunting of ungulate populations in Italy. Report 2001/2005. Biol. Cons. Fauna 117: 1-168.

Choquenot D. & Ruscoe W.A. 2003: Landscape complementation and food limitation of large herbivores: habitat-related constraints on the foraging efficiency of wild pigs. J. Anim. Ecol. 72: 14-26.

Dondina O., Orioli V., Chiatante G. et al. 2019: Species specialization limits movement ability and shapes ecological networks: the case study of two forest mammals. Curr. Zool. 65: 237-249.

Duarte J., Farfan M.A., Fa J.E. & Vargas J.M. 2015: Deer populations inhabiting urban areas in the south of Spain: habitat and conflicts. Eur. J. Wildlife Res. 61: 365-377.

Falcucci A., Maiorano L. & Boitani L. 2007: Changes in land-use/land-cover patterns in Italy and their implications for biodiversity conservation. Landsc. Ecol. 22: 617-631.

Fasola M., Merli E., Boncompagni E. & Rampa A. 2011: Monitoring heron populations in Italy, 1972-2010. J. Heron Biol. Conserv. 1: 1-10.

Ferri V., Soccini C. & Battisti C. 2014: Checklist of mammals of Monticchie natural reserve 17 (Lodi; northern Italy)--1985/2012. Nat. Hist. Sci. 1: 49-54.

Fox J. & Weisberg S. 2011: An R companion to applied regression, 2nd edn. Sage Publications, Thousand Oaks (CA).

Geisser H. & Reyer H.-U. 2004: Efficacy of hunting, feeding, and fencing to reduce crop damage by wild boars. J. Wildlife Manage. 68: 939-946.

Genov P.V., Tonini L. & Massei G. 1995: Crop damage by wild ungulates in a Mediterranean area. In: Botev N. (ed.), The game and the man. Proceedings of the 22nd IUGB congress, Sofa: 214-215.

Gonzalez-Crespo C., Serrano E., Cahill S. et al. 2018: Stochastic assessment of management strategies for a Mediterranean peri-urban wild boar population. PLOS ONE 13: e0202289.

Gross E.M., Lakhar B.P., Subedi N. et al. 2018: Seasonality, crop type and crop phenology influence crop damage by wildlife herbivores in Africa and Asia. Biodivers. Conserv. 27: 2029-2050.

Hebeisen C., Fattebert J., Baubet E. & Fischer C. 2008: Estimating wild boar (Sus scrofa) abundance and density using capture-resights in Canton of Geneva, Switzerland. Eur. J. Wildlife Res. 54: 391-401.

Herrero J., Garcia-Serrano A., Couto S. et al. 2006: Diet of wild boar (Sus scrofa L.) and crop damage in an intensive agroecosystem. Eur. J. Wildlife Res. 52: 245-250.

Jezek M., Hola M., Kusta T. & Cerveny J. 2016: Creeping into a wild boar stomach to find traces of supplementary feeding. Wildlife Res. 43: 590-598.

Johnson C.J., Nielsen S.E., Merrill E.H. et al. 2006: Resource selection functions based on use-availability data: theoretical motivation and evaluation methods. J. Wildlife Manage. 70: 347-357.

Li L., Shi J., Wang J. et al. 2013: Factors influencing wild boar damage in Taohongling National Nature Reserve in China: a model approach. Eur. J. Wildlife Res. 59: 179-184.

Linderoth P. & Elliger A. 2002: Schwarzwildschaden an landwirtschaft-lichen Kulturen in Baden-Wurttemberg im Jagdjahr 2000/2001. WFS-Mitteilungen 1: 1-4.

Liordos V., Kontsiotis V.J., Georgari M. et al. 2017: Public acceptance of management methods under different human-wildlife conflict scenarios. Sci. Total Environ. 579: 685-693.

Lombardini M., Meriggi A. & Fozzi A. 2017: Factors influencing wild boar damage to agricultural crops in Sardinia (Italy). Curr. Zool. 63: 507-514.

Longoni V., Rubolini D., Ambrosini R. & Bogliani G. 2011: Habitat preferences of Eurasian bitterns Botaurus stellaris booming in ricefields: implications for management. Ibis 153: 695-706.

Massei G., Roy S. & Bunting R. 2011: Too many hogs? A review of methods to mitigate impact by wild boar and feral hogs. Hum.-Wildl. Interact. 5: 79-99.

McCann B.E. & Garcelon D.K. 2008: Eradication of feral pigs from Pinnacles National Monument. J. Wildlife Manage. 72: 1287-1295.

Meng X.J., Lindsay D.S. & Sriranganathan N. 2009: Wild boars as sources for infectious diseases in livestock and humans. Philos. Trans. R. Soc. Lond. B 364: 2697-2707.

Merli E. & Meriggi A. 2006: Using harvest data to predict habitat-population relationship of the wild boar Sus scrofa in Northern Italy. Acta Theriol. 51: 383-394.

Merta D., Mocala P., Pomykacz M. & Frackowiak W. 2014: Autumn-winter diet and fat reserves of wild boars (Sus scrofa) inhabiting forest and forest-farmland environment in south-western Poland. Folia Zool. 63: 95-102.

NCAR--Research Applications Laboratory 2015: Weather forecast verification utilities. R package version 1.42.

Neu C.W., Byers C.R. & Peek J.M. 1974: A technique for analysis of utilization-availability data. J. Wildlife Manage. 38: 541-545.

Nores C., Fernandez A. & Garcia E. 1999: Wild boar damage selection in the Cantabrian Mountains, Spain. Pirineos 153-154: 194.

Putman R., Apollonio M. & Andersen R. 2011: Ungulate management in Europe: problems and practices. Cambridge University Press, Cambridge.

QGIS Development Team 2015: QGIS Geographic Information System. Open Source Geospatial Foundation Project.

R Development Core Team 2015: R: a language and environment for statistical computing. R foundation for Statistical Computing Vienna, Austria.

Saito M., Momose H. & Mihira T. 2011: Both environmental factors and countermeasures affect wild boar damage to rice paddies in Boso Peninsula, Japan. Crop Prot. 30: 1048-1054.

Schley L., Dufrene M., Krier A. & Frantz A.C. 2008: Patterns of crop damage by wild boar (Sus scrofa) in Luxembourg over a 10-year period. Eur. J. Wildlife Res. 54: 589-599.

Schley L. & Roper T.J. 2003: Diet of wild boar Sus scrofa in Western Europe, with particular reference to consumption of agricultural crops. Mammal Rev. 33: 43-56.

Sindaco R., Romano A., Andreone F. et al. 2009: Rana latastei. The IUCN Red List of Threatened Species: e.T19156A8845034.

Sing T., Sander O., Beerenwinkel N. & Langauer T. 2005: ROCR: visualizing classifier performance in R. Bioinformatics 21: 3940-3941.

Sonnichsen L., Borowik T., Podgorski T. et al. 2017: Survival rates and causes of mortality of roe deer Capreolus capreolus in a rural landscape, Eastern Poland. Mammal Res. 62: 141-147.

Stillfried M., Fickel J., Borner K. et al. 2017: Do cities represent sources, sinks or isolated islands for urban wild boar population structure? J. Appl. Ecol. 54: 272-281.

Swets J.A. 1988: Measuring the accuracy of diagnostic systems. Science 240: 1285-1293.

Symonds M.R.E. & Moussalli A. 2011: A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike's information criterion. Behav. Ecol. Sociobiol. 65: 13-21.

Thapa S. 2010: Effectiveness of crop protection methods against wildlife damage: a case study of two villages at Bardia National Park, Nepal. Crop Prot. 29: 1297-1304.

Theuerkauf J. & Rouys S. 2008: Habitat selection by ungulates in relation to predation risk by wolves and humans in the Bialowieza Forest, Poland. For. Ecol. Manag. 256: 1325-1332.

Treves A. & Naughton-Treves L. 2005: Evaluating lethal control in the management of human-wildlife conflict. In: Woodroffe R., Thirgood S. & Rabinowitz A. (eds.), People and wildlife, conflict or co-existence? Cambridge University Press, Cambridge: 86-106.

Wang S.W., Curtis P.D. & Lassoie J.P. 2006: Farmer perceptions of crop damage by wildlife in Jigme Singye Wangchuck National Park, Bhutan. Wildl. Soc. Bull. 34: 359-365.

Wilson C.J. 2004: Rooting damage to farmland in Dorset, southern England, caused by feral wild boar Sus scrofa. Mammal Rev. 34: 331-335.

Zuur A.F., Ieno E.N. & Elphick C.S. 2010: A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1: 3-14.

Fabrizio CAPPA, Marco LOMBARDINI (*) and Alberto MERIGGI

Department of Earth and Environmental Sciences, University of Pavia, Via Ferrata 1, 27 100 Pavia, Italy; e-mail:

Received 13 March 2019; Accepted 30 August 2019

(*) Corresponding Author

DOI: 10.25225/fozo.015.2019
Table 1. Number of cases and compensation payments (in Euros) for each
type of crop damaged by wild boar in the SPA "Risaie della Lomellina"
from 2013 to 2015.

Year      Maize                  Rice            Soybean

2013        9 (2, 960 [euro])    1 (643 [euro])    -
2014        6 (4, 255 [euro])    1 (0 [euro])      -
2015        5 (895 [euro])       3 (130 [euro])    2 (400 [euro])
Total      20 (8, 110 [euro])    5 (773 [euro])    2 (400 [euro])
Mean per
([euro])  405                  155               200

Year      Sorghum       Total

2013      -              10 (3, 603 [euro])
2014      1 (0 [euro])    8 (4, 255 [euro])
2015      -              10 (1, 425 [euro])
Total     1 (0 [euro])   28 (9, 283 [euro])
Mean per
([euro])  0             332

Table 2. Crop selection by wild boar in the SPA "Risaie della
Lomellina". In brackets: Bonferroni intervals. "Others" are other crops
grouped together (see Material and Methods for explanation).
(*) Significant selection or avoidance.

Crop     Availability  Use (damage frequency--requests of

Maize    0.14          0.71 (0.49-0.96) (*)
Paddies  0.69          0.18 (0.01-0.35) (*)
Others   0.17          0.11 (0.00-0.25)

Crop     Use (damage frequency--Use (damage area)

Maize    0.95 (0.89-1.00) (*)     0.97 (0.94-1.00) (*)
Paddies  0.03 (0.00-0-09) (*)     0.01 (0.00-0.04) (*)
Others   0.02 (0.00-0.06) (*)     0.02 (0.00-0.04) (*)

Table 3. Importance of predictors describing the occurrence of wild
boar damage in the SPA "Risaie della Lomellina" measured by the sum of
Akaike weights ([omega]).

Predictors                             [omega]

Distance from main roads               1.00
Population density                     0.99
Distance from forest patches           0.98
Distance from continuous hedgerows     0.91
Distance from urban areas              0.79
Fractal dimension of fields            0.59
Distance from railways                 0.41
Distance from discontinuous hedgerows  0.31
Distance from rivers                   0.31
Area of fields                         0.30
Distance from secondary roads          0.27

Table 4. Coefficients of predictors of the model examining the
occurrence of wild boar damage in the SPA "Risaie della Lomellina".
Only predictors with [omega] [greater than or equal to] 0.50 are
included in the model. SE: standard error. CI: confidence intervals.
Predictors have a significant effect when the 95 % confidence intervals
do not include zero.

Predictors                    Coefficients  SE      95 % CI

Intercept                      20.85        12.76    -4.15,    45.86
Distance from main roads        0.002        0.001    0.001,    0.003
Population density             -0.036        0.013   -0.062,   -0.010
Distance from forest patches   -0.010        0.004   -0.018,   -0.003
Distance from
continuous hedgerows            0.003        0.001    0.001,    0.006
Distance from urban areas       0.002        0.001    0.0002,   0.003
Fractal dimension of fields   -18.23         9.96   -37.76,     1.30
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Author:Cappa, Fabrizio; Lombardini, Marco; Meriggi, Alberto
Publication:Folia Zoologica
Geographic Code:4E0WE
Date:Nov 1, 2019
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