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SOME SPECIFIC MICROBIOLOGICAL PARAMETERS AND PREVALENCE OF SALMONELLA spp. IN RETAIL CHICKEN MEAT FROM ERZURUM PROVINCE, TURKEY AND CHARACTERIZATION OF ANTIBIOTIC RESISTANCE OF ISOLATES ALGUNS PARÂMETROS MICROBIOLÓGICOS ESPECÍFICOS E PREVALÊNCIA DE SalmonellA spp. CORTES DE CARNE DE FRANGO DA PROVÍNCIA DE ERZURUM, TURQUIA E CARACTERIZAÇÃO DA RESISTÊNCIA AOS ANTIBIÓTICOS DE ISOLADOS

Specific microbiological parameters and the presence of Salmonella spp. were investigated in 72 chicken meat samples (36 wings and 36 drumsticks) collected from markets and butcher shops. The specific microbiological parameters were determined using a conventional cultural method and the presence of Salmonella spp. in chicken samples was determined using conventional and immunomagnetic separation (IMS)-polymerase chain reaction (PCR) methods. In addition, antimicrobial susceptibility of the isolates was revealed using the Kirby-Bauer disc diffusion method. The results indicated that 30 of the 72 samples were positive for Salmonella spp. by the conventional method, and 42 of the 72 were positive by the IMS-PCR method. However, 30 of the 72 samples were positive for Salmonella spp. by both methods. The Salmonella spp. isolates were confirmed by the VITEK2 Compact System and PCR. The susceptibilities of the isolates against 10 antibiotics were determined. The results indicated that isolates (27/30) showed the highest susceptibility to gentamycin (90.00%), while the highest resistance was to nalidixic acid and tetracycline at the 100 and 93.34% levels, respectively. These results indicate a high prevalence of Salmonella spp. in poultry meat from Erzurum city, Turkey, and the antimicrobial resistance profile of these isolates should be considered for public health. The results also show that the IMS-PCR technique was superior to the conventional method for detecting Salmonella in poultry meat.

INTRODUCTION
Chicken is one of the most popular food products worldwide, because of nutritional, sensorial and economic factors. Chicken is widely consumed in homes and fast-food establishments, but can become contaminated during processing. The contamination of poultry products with Salmonella and other microorganisms is due to unhygienic conditions during the production, processing, distribution, marketing and preparation stages (DOOKERAN et al., 2012).
The genus Salmonella includes short rodshaped, facultative anaerobe, Gram-negative bacteria. Warm-blooded animals and humans are natural hosts for Salmonella spp. Detecting Salmonella spp. during production and before consumption is important to prevent food-borne salmonellosis. A Salmonella infection in humans is usually caused by consuming undercooked meat or other cross-contaminated foods, such as vegetables, milk and eggs (HASSANEIN et al., 2011). According to a report published by the Centres for Disease Control and Prevention (CDC), it is estimated that about 1.2 million people in the US have been exposed to Salmonella infections, and that an average of 23.000 hospitalisations and 450 deaths occur from these infections. The prevalence rates of Salmonella spp. in chicken meat sold in Turkey are 34-68.75%. Not only in Turkey, but in most developing countries, the absence of an epidemiological surveillance system for salmonellosis cases makes it difficult to effectively assess prevalence (KÄFERSTEIN, 2003). However, 1.993 cases of gastroenteritis in Turkey were due to Salmonella spp. in 2008, and the number increased to 2.307 in 2011, according to unpublished data from the Department of Communicable Diseases of the Turkish Public Health Institution (THSK, 2015).
Salmonellosis is one of the most important zoonotic bacterial food-borne infections worldwide. Salmonella causes severe illness in infants, elderly humans and immunocompromised patients. Generally, Salmonella infections are related to infected animals' faeces or food products of animal origin. Chicken and other poultry meat and eggs are the most significant sources of Salmonella compared with other food products. Salmonella causes gastrointestinal illness, substantial morbidity and economic burden worldwide. The clinical symptoms of salmonellosis are characterised by fever, diarrhoea, nausea, vomiting, abdominal pain, headache and occasional constipation 12-72 h after consuming contaminated food (VOSE et al., 2011).
Increased public awareness about foodborne illness has necessitated the development of rapid, sensitive and specific techniques for detecting these food-borne pathogens. Isolation and identification of Salmonella from food samples requires approximately 7 days using traditional cultural techniques. In recent years, more rapid and sensitive methods have been developed to detect and identify Salmonella in chicken meat, including immunoassays, electrical techniques and nucleic acid analyses (BENNETT et al., 1998). Among these, immunomagnetic separation (IMS) and polymerase chain reaction (PCR) have been accepted as a potential approach for detecting these pathogens (YANG et al., 2010).
IMS has been successfully used to separate and concentrate target microorganisms from food samples using magnetic beads coated with specific antibodies to a target microorganism. This method is a quite rapid, specific and technically simple approach, as the target organism is captured by the magnetic particles and removed from the media by a applying a magnetic field (LYNCH et al., 2004). Finally, the microorganism is removed from the food debris and other competing microorganisms and is subjected to an enzyme linked immunosorbent assay or PCR analysis (HAGREN et al., 2008 ;LYNCH et al., 2004).
PCR detects pathogens from foods within a few hours and is a rapid alternative method to detect Salmonella (MOGANEDI et al., 2007). Although PCR is a sensitive, rapid technique, it can be inhibited by several factors, including food components, urine and bile salts; thus, different approaches, such as IMS, have been used to remove these substances prior to PCR (SCHEU et al., 1998). IMS isolates Salmonella from other microbes and facilitates removal of PCR inhibitors of different sizes. The combination of different rapid methods for separating and concentrating specific bacteria facilitates direct detection of pathogens in foods. The combination of IMS and PCR (IMS-PCR) is considered quite accurate and rapid for isolating pathogens (TABAN; AYTAC, 2009).
The invA gene region is located in the Salmonella pathogenicity 1B island and is necessary for Salmonella to invade epithelial cells (Lei et al., 2015). This genomic region, which is found in almost all Salmonella serovars, is a powerful target for detecting Salmonella (JEONG et al., 2011).
Antimicrobial resistance has increased among food-borne pathogenic microorganisms during recent decades (TEUBER, 2001). This increase is caused by irregular use of antimicrobials in food-producing animals and the random use of antibiotics by humans (BYWATER, 2004). Antibiotic-resistant Salmonella-related data are needed to assess the potential effect of resistant isolates (three or more antibiotic-resistant strains) isolated from raw chicken meat on public health (NAIR et al., 2018).
The aim of this study was to determine the general microbiological quality characteristics and presence of Salmonella spp. in different chicken meat samples obtained from supermarkets and butcher shops. In addition, this study compared and evaluated a conventional method with IMS-PCR analysis for detecting Salmonella spp. in chicken meat samples, and determined the antibiotic susceptibility and resistance of Salmonella spp. isolated from samples against 10 different antimicrobial agents.

Material
In total, 72 packed chicken meat samples were collected from 19 different local markets and retail stores, in Erzurum city, Turkey during the May-December 2016. All samples were transferred to the laboratory under cold chain within one hour and microbiological analyses were carried out on the same day.

Microbiological Analysis
Microbiological analysis were performed according to methods of Turkish Standard Institute number of 1069 standard (TSE, 2016). Ten g of chicken meat was weighed for each sample and transferred to sterile stomacher bag which contained

Bacterial Strain
Positive control used in PCR assay was obtained from Turkey Public Health Institution Microbiology Reference Laboratories (Salmonella typhimurium RSSK 95091).

Isolation and Identification of Salmonella spp. Conventional Method
Isolation and identification of Salmonella spp. was performed according to method of ISO (2002). Briefly, 25 g of meat sample was transferred to filtered stomacher bags containing 225 mL of sterile buffered peptone water (Merck, 107228) and homogenized with masticator (Neutec Masticator, Neutec Group, Inc., Farmingdale, NY) for 90 s. Homogenized samples were incubated for preenrichment during 24 h. Then, 1 mL was transferred to tube containing Muller-Kauffmann tetrathionate/novabiocin broth (MKTTn) (Oxoid, CM1048) supplemented with novobiocin (Oxoid, SR0181) in 10 mL volume and 0.1 mL of preenriched solution was transferred to tube containing Rappaport-Vassiliadis (RV) medium (Merck, 1.07700) in 10 mL volume. For selective enrichment, the tubes with RV were incubated at 41.5°C for 24 h and inoculated tubes with MKTTn were incubated for 37°C for 24 h. Following the incubation, the enriched samples were streaked onto Xylose Lysine Tergitol-4 (XLT-4) Agar (Oxoid, CM1061) supplemented with tergitol (Oxoid, SR0237) and Xylose Lysine Deoxycholate (XLD) Agar (Merck 1.05287) and incubated at 37°C overnight. The colonies with a black centre with pinky-reddish periphery on XLD agar and black or black-centred with a yellow periphery on XLT4 Agar were accepted as suspicious for Salmonella. The suspicious colonies of Salmonella spp. were selected and identified by Triple Sugar Iron Agar (TSIA) (Oxoid, CM0277B), Lysine Iron Agar (LIA) (Oxoid, CM0381) and urea broth (Merck, 1.08483). Following the incubation at 37ºC for 24 h, typical reaction on TSIA (alkaline slant, acid butt, positive H 2 S and positive/negative gas) and LIA (alkaline slant, alkaline butt, positive H 2 S) and urea negative cultures were evaluated as suspicious for Salmonella. The other biochemical tests were performed by using GN Cards (BioMérieux, Inc., Craponne, France) including 64 different test substrates on VITEK 2 Compact system (BioMérieux) for verifying the isolates. For this purpose, the isolates were incubated at 37°C for 24 hours on blood agar. A sufficient number of colonies from pure culture were suspended in a polystyrene tube containing 3.0 mL of sterile saline solution (0.45%, pH 4.5). The McFarland turbidity of solution was adjusted to 0.5 using a turbidity meter. Then, the suspension was loaded on the GN cards. Identification of presumptive Salmonella spp. isolates were performed on VITEK2 Compact System (BioMérieux, Marcy l'Étoile, France) within 3 h using fluorescence reading of GN cards. VITEK2 Compact System Software identified the isolates as Salmonella spp. at the level of 97-99% probability.

IMS Method
Salmonella spp. were separated from preenriched samples by Dynabeads ® anti-Salmonella (ThermoFischer Scientific, 71002) according to the manufacturer's instruction. Briefly, 20 µL of Dynabeads ® anti-Salmonella was transferred into 1.5 mL of sterile eppendorfs. After, 1 mL preenriched samples were added to eppendorfs and incubated with gentle agitation for 10 min. Then, a magnetic plate was placed on the MPC-S rack and recovery of the beads was performed for 3 minutes. The IMS beads-bacteria complex on the tube wall were washed with 1 mL wash buffer (PBS with 0.05% Tween-20) following the removal of the magnetic plate from the MPC-S rack. The washing process was repeated twice. 881 Some specific microbiological… BARAN, A. et al.

Post-IMS
DNA extraction from the Dyne beadbacterial complex was performed by the boiling method. For this purpose, 100 µL of Tris-EDTA buffer solution (pH, 8.0) containing dyne beadbacteria was boiled for 10 min. At the end of the boiling, the samples were cooled on ice and centrifuged at 10.000 xg for 15 sec. The supernatant was used template in PCR.

PCR Method
PCR primers (invAFW: 5'-ACA GTG CTC GTT TAC GAC CTG AAT-3'; invARV 5'-AGA CGA CTG GTA CTG ATC GAT AAT-3') were used to specifically amplify a 284-bp genomic fragment of the invA gen, which is highly specific for Salmonella spp. for PCR assays of the isolates. The PCR amplifications were performed in a total volume of 15 µL solution containing 2 µL of template DNA, 1×PCR buffer (Sigma), 0.25 mM MgCl 2 (Sigma), 200 µM (each) dNTP (Sigma), 10 pmol of each primer, 1.25 U of Taq polymerase (Sigma). The PCR cycle condition was an initial denaturation at 95°C for 10 min; 30 cycles of 95°C 30 s, 55°C 30 s and 72°C 30 s; and a final extension at 72°C for 5 min. The amplified products were detected by electrophoresis in a 1% agarose gel in Tris/Borate/EDTA Buffer (TBE, pH 8.3) pre-stained with ethidium bromide under UV light using Gel Doc™ XR+ Gel Documentation System (BioRad, USA).

Antibiotic Susceptibility Test of Obtained Isolates
The antimicrobial susceptibilities of the obtained isolates were determined by agar disk diffusion method proposed by Clinical & Laboratory Standards Institute (CLSI, 2012). Then, isolates were spread onto petri dishes containing Nutrient agar and incubated for 18 hours at 35±1ºC. The colonies were diluted in 0.85% physiological saline and the turbidity of the inoculum was adjusted to 0.5 McFarland standard. The suspension was then streaked onto surface of Müeller Hinton Agar in petri dish with a sterile swap. The following antibiotic discs: ampicillin (AMP, Oxoid CT003B), chloramphenicol (C, Oxoid CT013B), ciprofloxacin (CIP, Oxoid CT425B), gentamicin (CN, Oxoid CT024B), kanamycin (K, Oxoid CT026B), nalidixic acid (NA, Oxoid CT031B), streptomycin (S, Oxoid CT047B), sulfamethox/trimethoprim (SXT, Oxoid CT052B), tetracycline (TE, Oxoid CT054B) trimethoprim (W, Oxoid CT076B) were dispensed onto the inoculated surface within 15 minutes. Then the petri dishes were incubated in ambient air at 35°C for 18 h. The nearest millimeter was measured with a ruler following the incubation. The results were interpreted according to the standards of Clinical & Laboratory Standards Institute (CLSI Guidelines). Antibiotic susceptibility of isolates was evaluated as resistance, intermediate and sensitive (CLSI, 2012).

Statistical Analysis
Statistical analysis was performed using SPSS Software Programme (SPSS software, version 20). P value of 0.05 or less was considered to indicate a statistically significant difference.

RESULTS
The general microbial counts (log cfu/g) in the chicken meat samples collected from 19 retail markets are presented in Table 1. The IMS-PCR technique was applied to the chicken meat samples collected from different markets and retail stores. The invA (284 bp) gene was prepared using the IMS-PCR technique. The invAFW: 5′-ACA GTG CTC GTT TAC GAC CTG AAT-3′ and invARV 5′-AGA CGA CTG GTA CTG ATC GAT AAT-3′ primers were used to specifically amplify a 284-bp genomic fragment of the invA gene. The PCR image is shown in Fig. 1. As shown in Fig. 1, lane 9 was the negative control and lane 8 was the positive control, whereas lanes 2-7 were the Salmonella spp. positive samples. Lane 1 was verified to be a Salmonella spp.-negative sample. All Salmonella spp. determined by the conventional method carried the invA gene region. The antibiotic resistance and susceptibility of 30 Salmonella spp. isolates were determined using 10 different antibiotics (Table 3). As shown in Table 3, 27 of the 30 isolates showed the highest susceptibility to CN, while all and 28 of the 30 isolates had the highest resistance to NA and TE, respectively. The highest susceptibility occurred in response to CN, followed by C, AMP, K, SXT, W, S, CIP, TE and NA, respectively. In contrast, the highest resistance was to NA and TE, followed by W, S, SXT, K, AMP, C, CIP and CN, respectively Isolates that are resistant to three or more antibiotics are classified as multidrug resistant (MDR). It was determined that all [30 (100%)] isolates were resistant to at least one antimicrobial agent, whereas 25 (83.33%) were MDR. About 47% of the isolates showed resistance to five antibiotics, while 13.33% were resistant to six antibiotics. The distribution of antimicrobial resistance characteristics of the Salmonella spp. isolates is presented in Table 4.

DISCUSSION
The general microbiological quality parameters of the samples were determined with respect to TAMB, Pseudomonas, yeast and mould, coliform bacteria, Enterococcus and Staphylococcus/Micrococcus counts. According to the Food Standard, the presence of Salmonella spp., coliform and other bacteria are accepted as the hygiene index (ISO 2001(ISO , 2002. However, the results obtained here show that the samples did not meet the standards with respect to the presence of microorganisms. High numbers of bacteria shorten shelf life by deteriorating meat quality, resulting in an economic loss. Our results seem to be due to processing and storage conditions as well as crosscontamination after processing in the markets and homes. The increase in food contamination factors and gastrointestinal diseases is associated with an increase in the risk of non-specific salmonellosis (CRUM-CIANFLONE, 2008). In Turkey and most developing countries, the absence of epidemiological studies of salmonellosis cases is an 885 Some specific microbiological… BARAN, A. et al. obstacle to effectively assess prevalence (KAFERSTEIN, 2003).
Our data corroborate previous studies revealing that chicken meat is an important food source contaminated by Salmonella spp. The difference in prevalence data for Salmonella spp. between previous studies and the present study might be due to sanitation conditions, methodological differences used to isolate the bacteria or transportation and storage conditions (LI et al., 2013). In this study, Salmonella spp. was detected in 41.67% of the chicken meat samples using the conventional method and 58.33% of the samples using the IMS-PCR technique. According to the Turkish Food Codex Communiqué on Microbiological Criteria, Salmonella spp. should not be found in any meat (TFC, 2011). Our results were lower than those reported previously (150/64, 42.66%) by Siriken et al. (2015) in Ankara, Turkey. However, Yildirim et al. (2011) reported a 34% (200/68) contamination rate by Salmonella in poultry meat in Turkey. The contamination rate of Salmonella in poultry meat varies among countries (ALVAREZ-FERNANDEZ et al., 2012). In the present study, the high prevalence rate of Salmonella in poultry meat was similar to the 60% reported in Portugal (ANTUNES et al., 2003) and 67.5% in Thailand (LERTWORAPREECHA et al., 2012), and was close to the contamination rates of 55% in Spain and 52.2% in China (YANG et al., 2011). In contrast, the incidence rates of Salmonella in poultry meat in South Korea and Pakistan were 3.7% (RAN HEE et al., 2014) and 5.26% (AKBAR; ANAL, 2013), respectively. These differences might be due to geographical location, bacteriological analytical methods, the sampling pattern or factors, such as hygiene and sanitation conditions during poultry meat production, crosscontamination or market conditions. Although the prevalence of Salmonella determined by the conventional method was close to or lower than that reported by many studies, it was possible to detect Salmonella using the IMS-PCR technique. All of these results indicate that poultry meat is an important source for Salmonella spp. infections.
The results show that the IMS-PCR technique was superior to the conventional method. The superiority of IMS-PCR over the conventional method might be due to the concentrations of the target microorganism in the samples, removal of inhibitor components or elimination of other microorganisms. Similar results were reported by Siriken et al. (2015) who detected Salmonella spp. in beef and poultry meat by both conventional and IMS methods. The IMS-PCR technique allows rapid detection of Salmonella spp. after IMS. Zheng et al. (2016) reported that IMS-PCR has an accuracy of 98.3%.
The present study is significant because it is the first report that has detected the invA gene region of Salmonella in chicken meat. The PCR products of the isolates contained a PCR-positive control, which resulted in detection of a 284 bp amplified fragment. The ability of Salmonellaspecific primers to detect Salmonella spp. rapidly and accurately was primarily due to the primer sequences selected from the invA gene. The invA protein in the inner membrane of Salmonella spp. is required for invasion of the bacterium into epithelial cells (SHARMA; DAS, 2016). All Salmonella spp. identified by conventional methods carry the invA gene region. Furthermore, this genus invades intestinal epithelial cells and the gene is found in pathogenic Salmonella spp. Therefore, it is important to identify the gene responsible for invasion. The invA gene is thought to trigger the internalisation necessary for invasion to deeper tissues, which is necessary for complete virulence of Salmonella spp. (LEI et al., 2015).
The resistance of bacteria to antibiotics has global importance in terms of failed zoonotic disease treatment. Antibiotic-resistant bacteria are important to public health, and their resistance genes are consumed with contaminated food or water (HONG et al., 2013). The most important source of antibiotic-resistant Salmonella spp. is animaloriginating foods. The data obtained from this study and other studies support these claims.
In this study, 10 different antibiotics were tested to determine antibiotic resistance of the isolates. Among the antibiotics, CN had the greatest effect on the isolates, followed by C, AMP, K, SXT, W, S, CIP, TE and NA. Cetinkaya et al. (2008) reported that Salmonella spp. was detectable in only one poultry meat sample in a study on the presence of Salmonella in different food samples (chicken parts, minced meat, ready-to-eat salad, raw vegetables and raw milk) sold in Bursa, Turkey. They identified the Salmonella spp. isolated as S. infantis and reported resistance to streptomycin, tetracycline, sulphonamides, trimethoprim, trimethoprim-sulphamethoxazole and NA. In another study, Kasimoglu Dogru et al. (2010) reported that 22 (68.75%) of 32 strains isolated from poultry meat were resistant to multiple antibiotics. According to the same study, Salmonella spp. was most resistant to NA (62.5%). Siriken et al. (2015) reported that Salmonella strains isolated from different meat samples are most resistant to vancomycin, tetracycline, streptomycin and NA. In 886 Some specific microbiological… BARAN, A. et al. the present study, the highest resistance of Salmonella spp. was to tetracycline and NA (100 and 93.34%, respectively). In contrast, Arslan and Eyi (2010) reported that 50 of 225 meat samples were positive for Salmonella spp. These isolates had the highest resistance to ampicillin and cephazoline. It was also suggested that 62% of Salmonella strains have multiple resistance to tetracycline, carbenicillin, ampicillin and sulfamethoxazoletrimethoprim.
The high resistance to trimethoprime and sulfamethoxazole-trimethoprime was not surprising due to their continued use in human and veterinary clinics in Turkey. In addition, resistance to streptomycin was significantly higher than that reported in previous studies (LESTARI et al., 2009;MOLLA et al., 2003). Other studies have shown that resistance to streptomycin is due to its high prevalence and frequent use in veterinary medicine (MIHAIU et al., 2014). A higher resistance to streptomycin (95%) was also reported by White et al. (2001) for Salmonella spp. isolated from chicken meat.
In the present study, Salmonella spp. isolated from chicken meat parts was highly resistant to NA (100%). Our findings are consistent with studies conducted in different countries (CUI et al., 2016;SODAGARI et al., 2015) and in Turkey (KASIMOGLU DOGRU et al., 2010;SIRIKEN et al., 2015). In the past, ampicillin, chloramphenicol and co-trimoxazole were used to treat salmonellosis, but they have been replaced by fluoroquinolone antibiotics, such as ciprofloxacin and cephalosporin. After tetracycline, fluoroquinolones are the most widely used antibiotic group in veterinary medicine and are expected to become more resistant. NA resistance plays a role in the first steps of the development of ciprofloxacin resistance, although low resistance (6.67%) of ciprofloxacin resistance is observed in the isolates. It should be emphasised that third-generation cephalosporins have recently become the primary drug to treat salmonellosis cases due to an increase in fluoroquinolone resistance (MAWATARI et al., 2013). For this reason, resistance to fluoroquinolones has emerged as an important public health issue because these antibiotics, which are widely used in veterinary medicine and poultry production, can cause resistance genes to be transmitted to humans through the food chain (GONZÁLEZ; ARAQUE, 2013).
In the present study, 26.67% of the isolates were resistant to ampicillin, which was remarkably lower than the resistance reported in other countries by Thung et al. (2016) in Malaysia (72.73%), Trongjit et al. (2017) in Thailand (72.4%) and Yen et al. (2014) in Vietnam (41.6%). It is not surprising that ampicillin is still preferred in the classical treatment of salmonellosis in humans and it is not often used in animal therapy in Turkey.
According to a report published by EFSA in 2014 (EFSA, 2014), Salmonella spp. isolates of broiler origin in 22 different European Union countries were reported to have the highest resistance to NA (48.7%), sulfamethoxazole (45.1%) and tetracycline (40.4%). Although the results obtained in our study excluding resistance to trimethoprim were qualitatively similar, resistance rates were determined to be higher in our study. However, these results are similar to those obtained from countries, such as Bulgaria and Hungary, as they reflect the average of the data of 22 different member countries. The low level of resistance to gentamicin (6.6%) was close to our study results (4.76%). In the same report, resistance to chloramphenicol was lower (4%), although it was high in the present study (26.98%).

CONCLUSIONS
The results showed that 30 of the 72 samples were positive for Salmonella spp. by the conventional method, while 42 of the 72 were positive by IMS-PCR. In contrast, 30 of the 72 samples were positive by both methods. In conclusion, combining the IMS and PCR methods was used effectively to isolate Salmonella from chicken than either method alone.
The 30 identified Salmonella spp. isolates were evaluated in terms of antibiotic resistance and susceptibility. The results indicated that the highest resistance was to NA and TE, while the highest susceptibility was to GN. Taken together, it is evident that chicken meat is a serious public health risk in this region in terms of the presence of Salmonella spp. in chicken meat parts, antibiotic resistance of isolates and microbiological properties of chicken meat parts.


provenance: AgriculturalAndFoodSciences,2019-2020-Agricultural And Food Sciences-2019.jsonl.gz.gz

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Production practices of local pig farmers in Ghana

Local pig production is of economic, nutritional and socio-cultural importance to livelihoods in Ghana. Data was collected from 176 local pig farmers in four regions of Ghana using pretested structured questionnaire. Majority of the farmers interviewed were males over 30 years and kept crossbred pigs (64%) with income (95%) as their main motivation. In terms of housing of pigs, most farmers use sheds (39%), about a third had permanent structures (34%), whilst the rest (22%) use stalls with a few keeping their animals in their yards (2%) or having no housing facility (2%) at all. Growth rate of pigs was a relatively important trait (49%) for the farmers compared to aesthetic traits like coat colour or ear orientation. Majority of the farmers (90%) acquired their breeding stock from family, friends and the open market with only 10% acquiring breeding stock from government breeding stations. Local pig production in the study area was characterised as semi-intensive with significant opportunities for stakeholders to make interventions for improvement through provision of improved breeds, housing, feeding and veterinary care.

 Key words: Food security, farmer education, pig production, sustainable breeding programmes.

INTRODUCTION
As human population growth increases worldwide, there is need for continuous food supply to ensure food security (FAO, 2006). For the past three consecutive years, there has been a rise in hunger worldwide. It has been reported that about 11% of the world's population is undernourished, amongst which 23.2% are in sub-Saharan Africa and 15.1% are within western Africa (FAO, 2018). Livestock serve as a key source of protein and nutritional well-being (Komatsi and Kitanishi, 2015) and local pig production becomes an attractive option on account of the ease of management, prolificacy of the species and the many small-scale farmers keeping the animals (Osei-Amponsah et al., 2017). Pig production has a high potential to increase productivity due to its fast growth rate, shorter generational interval, good feed conversion efficiency, and high litter sizes compared to cattle (Mbuthia et al., 2015). There is however the need for more information on pig production practices of local farmers in order to make appropriate recommendations for improvement to increase productivity (Adjei et al., 2015).
Characterization of pig production systems provides useful information for their improvement and conservation. There is paucity of information on the *Corresponding author. E-mail: snda2@cam.ac.uk or aryeesethlina5@gmail.com.
Author(s) agree that this article remain permanently open access under the terms of the Creative Commons Attribution License 4.0 International License on the production practices of local pig farmers which can be useful in the establishment of breeding programmes to enhance their production potentials (Adjei et al., 2015;Ayizanga et al., 2018). In this study, primary data were collected on production practices of local Ghanaian pig farmers to identify their limitations and challenges and make appropriate recommendations for improvement. The study should provide adequate information on pig production practices across all agro-ecological zones of Ghana and enhance the livelihoods of the millions of farmers including vulnerable women and children whose livelihoods to a large extent depend on pig production.

MATERIALS AND METHODS
Using purposive sampling, with assistance from District Directors and Extension officers of the Ministry of Food and Agriculture (MoFA, Ghana), pig farmers who raised local pigs were identified from selected districts of four regions of Ghana ( Figure 1). These were the Upper east region (Sudan savanna ecological zone), the Brong-Ahafo region (Guinea savanna zone), Western region (Forest zone) and Volta region (Coastal savanna zone). A total number of 135 farmers were interviewed for the study using a pretested structured questionnaire. Following FAO guidelines (FAO, 2007), data was collected on the demographic attributes of the farmers (farmer's name, age, location, educational background) and pig production practices (breeds raised, trait preferences, housing systems, feeding, access to veterinary services and mortality rate recorded on farms). The data were analyzed using Statistical Package for Social Sciences (SPSS) version 20.0 (SPSS, 2011) and the Survey Package in R version 3.5.2 (R Core Team, 2018).

Demographics of Ghanaian local pig farmers
The results of the survey indicated a significant difference (P < 0.05) between gender, age and education background of pig farmer across the different regions (Table 1). Apart from the Western region which recorded the highest number of females (87%) engaged in pig farming, the three other regions recorded relatively high number of male farmers. Across all the regions studied, majority of the farmers interviewed were young adults between the ages of 20 and 50 years. While majority of farmers in the Brong-Ahafo, Upper East and Volta regions had no formal education, it was found out that farmers in the Western region were more likely to have at least primary education.

Pig production systems in the regions
Three breed types were identified namely, the local breed thus Ashanti Dwarf pigs (ADPs), Exotic breeds (Large whites and Landraces) and crossbreeds (crosses between local pigs and exotics). From the study, it was discovered that, the majority of farmers were keeping crossbred pigs compared to either ADPs or exotics ( Figure 2). It was also found out that, most of these farmers (95%) raise pigs to serve as a source of income and not to provide just meat for the household. In terms of trait preferences, majority of the pig farmers (49%) consider the growth rate of the pigs with about a third (30%) indicating body size and body length of the pig as important. However, these pig farmers consider less the coat or skin colour of the pigs (Figure 3). Almost all the pig farmers interviewed had some sort of housing facility. About a third of the pig farmers (35%) had permanent structures, only 2% of the farmers had no housing facility with others using sheds (39%), stalls/shade (22%) or yards (2%) ( Figure 4). As shown in Figure 5, most of the pig farmers (66%) fed their pigs with kitchen left overs  (cooked cassava, fish, yam and soup) with a small fraction (11%) of farmers providing concentrates. However, some of the farmers (23%) also provide their pigs with feedstuff from agro-industrial by-products (AIBPs). According to majority (89%) of the pig farmers, feedstuffs are easily accessible but are costly. Most respondents (64%) acquired their breeding stock from family members, friends and the open market (26%) with only 10% of the respondents acquiring their breeding stocks from breeding stations (Table 2). Majority (65%) of the local pig farmers sampled had access to veterinary services with most of them (84%) recording no mortality on their farms.

DISCUSSION
In the Volta region, Upper east and Brong-Ahafo region men were mostly the owners of local pigs but were generally managed by the women with the men concentrating on managing their crop farms. However, in the Western region, the study revealed that local pigs were owned and managed by women who engage in mixed farming, this indicates the eminent role women play in livestock production. This is in agreement with FAO reports that, women play an important role in subsistence farming including livestock production and food processing (FAO, 2006(FAO, , 2008. Across the regions, apart from the Western region, most of the farmers especially in the Brong-Ahafo and Upper East regions have not had formal education. Similarly, Aina (2007) reported that majority of African farmers have not had formal education and most of them cannot read or write in any language and this contributes to their non-adoption of improved farming practices. UNESCO (2018), also reported that, as at 2010 about 58% of the Ghanaian population under the age of fifteen have had no formal education but by 2017, 84.5% of Ghanaian children were enrolled in primary education indicating the continuous effort to improve education in Ghana. Interestingly, the Western region may have recorded the highest number of educated farmers because from the year 1984 to 2000, the basic school enrolment increased significantly by 87% in an effort to curtail teenage pregnancy and other social vices in that region (GSS, 2004). The level of education of farmers recorded in the Western region may have also contributed to the high number of women engaged in pig farming in that region, as education has been identified by Kimbi et al. (2015) as one of the factors that influence farmer's adoption to change. Results of this study also revealed that, majority of the local pig farmers were young adults aged between 20 and 50 years. This is of great significance because young people can contribute immensely to agricultural development in rural communities; policy makers and agricultural development experts worldwide have expressed worry that young people have less interest in taking up farming in rural communities (FAO, 2014).
A previous study reported that pigs are raised as homestead animals to provide just food for the household (Ganaba et al., 2011), but this study revealed that, although some pig farmers still raise pigs to provide the household with food, most pig farmers currently raise pigs mainly as a source of income. The study also revealed that exotic breeds (mainly Large White, Landrace and Duroc), Ashanti Dwarf Pigs and crossbreeds are the pig breeds raised by local pig farmers in Ghana. However, the majority of farmers were engaged in crossbreeding. This result is in agreement with a report by Osei-Amponsah et al. (2017) that most farmers resort to indiscriminate crossing of the local breeds with exotic breeds in a quest to improve the productivity of local pig breeds. This however leads to inbreeding and consequently low production, reproduction, poor health and dilution of adaptive traits needed for sustainable local pig production. Unfortunately, this practice has been encouraged over the years as the Ministry of Food and Agriculture import Grand Parent exotic breeds mainly Large White and supply to farmers for breed improvement (MOFA, 2013). We believe that future interventions must be carefully planned to maintain the adaptive germplasm needed for local pig production.
In the designing of breeding programmes for breed improvement, it is important to consider farmer's trait preferences. In determining which economic traits are preferred by farmers, both this study and previous work (Ouma et al., 2007) found that trait preferences by farmers are heterogeneous. Most pig farmers prefer pig breeds with fast growth rates; some also take into consideration the animal's body size/length, litter sizes, and good mothering ability. However, only a small fraction of farmers considers the animal's coat/skin colour. This is important information to consider in developing a pig breed for local production in Ghana. Dodua et al. 179 Unlike in commercial farming where permanent housing facilities are built to provide shelter for pigs, in smallholder pig farming systems, pigs are raised under sheds, yards, stalls or shades. In the regions studied, only few farmers had permanent housing structures while majority of the sampled local pig farmers made use of sheds, yards and stalls or shades for housing their pigs. This may be due to how cheap, how accessible materials are and how easy it is to construct sheds, yards and stalls in these areas than it is for constructing permanent housing facilities (Karnuah et al., 2018).
High cost and availability of feed is one challenge most farmers face in pig production. In this study most of the farmers reported feed stuffs were available but costly. Due to this, they resort to feeding their pigs with kitchen waste and AIBPs rather than more expensive concentrates. Although the pig farmers did not keep financial records, they said they do not earn as much as commercial pig farmers. The feedstuffs used by these local pig farmers may explain the low level of income made from these pigs by the farmers. This agrees with reports by Karnuah et al. (2018) that local feedstuffs and concentrates were available in Liberia but expensive for the local pig farmer and affects the level of income of most local pig farmers.
The proximity of the various farms to each other (Supplementary Figure 1) explains why the pig farmers get their breeding stocks from friends, family and as inheritance. Only few farmers buy their breeding stock from breeding stations which are then circulated again to family and friends by crossing with other pig breeds in their localities. Due to this, there is a high possibility of indiscriminate crossbreeding among pigs in the communities studied. Finally, with regards to litter size, disease resistance and mortality experienced on farm, although these pig farmers do not keep records on their farms, they concluded that although local pigs had smaller litter sizes, they were resistant to most endemic diseases. Most farmers said they have access to veterinary services but are not able to purchase commercial drugs. Nonetheless, they record low mortality on their farms. This result agrees with reports that, local pigs are hardy, disease resistant and is able to survive drought incidence in Ghana (Adjei et al., 2015;Osei-Amponsah et al., 2017;Ayizanga et al., 2018).

CONCLUSIONS AND RECOMMENDATIONS
Local pig production in Ghana can be characterized as a semi-intensive production system with farmers keeping mostly crossbreeds. Growth rate and body size of pigs are important traits farmers consider in the selection of their breeding stock.
This study revealed that for the adoption of improved technologies by farmers, education must be prioritized. The Ministry of Education, Ghana Education Service (GES) together with other stakeholders must put in place adult education schemes for local pig farmers.
To ensure the sustainable growth of the local pig industry in Ghana, improved housing, feeding and regular training of farmers, and adoption of community-based breeding programmes are recommended.


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Environmental Science
Medicine

text:
A new method for evaluating stony debris flow rainfall thresholds: the Backward Dynamical Approach

Debris flow rainfall thresholds aim to provide a level of rainfall duration and average intensity above which the probability of a debris flow occurrence is significant. Estimating reliable thresholds for use in early warning systems has proved to be a challenging task. In fact the methodologies currently available in the literature are not entirely satisfactory since they provide thresholds unlikely low. The goal of the present research is exploring new paths aimed at improving the reliability of rainfall thresholds. A possible weak point of the literature approaches is the way the duration and the average intensity pertaining to a debris flow is determined. Up to now, these values are evaluated using only the characteristics of the hyetograph associated to a debris-flow event. In the present paper, we propose a new methodology based on volumetric relations deriving from a simplified description of the dynamic of a stony debris flow: by using these relations, from a measure of the deposited volume it is possible to estimate backwardly the volume of rain that caused the deposition; then, from this last value and the knowledge of the relevant hyetograph, it is possible to reconstruct the duration and the average intensity. Application of this new technique to a sample study area allowed us to prove the feasibility of the method and, to some extent, its capabilities: with respect to a classical literature method, the new approach produces an higher threshold and a smaller characteristic duration scales. Finally, strengths and weakness of the method have been evaluated thoroughly.

Introduction
In alpine regions, debris flows are rather common and widespread phenomena that produce considerable damages to houses and infrastructures. In the last decades, steps forward have been made in the field of mathematical and numerical modelling of these phenomena by using either a mono-phase or a two-phase approach (see e.g. Iverson, 1997;Takahashi, 1978;Brufau et al., 2000;Pirulli and Sorbino, 2008;Armanini et al., 2009;Pudasaini, 2012;Liu et al., 2017, among others) and even tools aimed at simplifying back-analysis of past events, evaluating forward scenarios of possible events and drawing up hazard maps (Iverson, 2014;Chen et al., 2016;Rosatti et al., 2018, among others) are now available. Nevertheless, a reliable forecast concerning the possibility that a storm may produce a debris flow is still hard, even just in a probabilistic framework. Attempts aiming at this goal have led to what is known in the literature as the rainfall threshold approach (Caine, 1980;Guzzetti et al., 2007). According to this approach, indicating with * Corresponding author. the duration of a rainfall event pertaining to a debris flow and with the relevant average rainfall intensity, the rainfall threshold is a function that splits the -domain in two fields: one located above the threshold and one located below. If a storm is foreseen to have a couple ( , ) falling in the upper field, it is expected that a debris flow may occur during that storm. On the contrary, if the foreseen couple does not exceed the threshold, it is expected that the storm should not generate a debris-flow.
Two main components are necessary to set up a rainfall threshold starting from an historical series of rainfalls related to debris-flow events: • a method for evaluating the amount of rain strictly relevant to each debris-flow event in term of cumulated volume of precipitation , averaged intensity and duration (we stress here that only two of the previous variables are independent); https://doi.org/10.1016/j.heliyon.2019.e01994 • a statistical approach that allows to identify a given occurrence probability threshold starting from a series of intensity-duration couples, namely ( , ) with = 1, … , and is the total number of registered events in a given area.
In the literature, there are a few papers facing the rainfall threshold topic (see Section 2 and Section 4 for specific references). They differ (sometimes slightly) in the method used to evaluate the first or the second component of the procedure described above, but they share the same hydrological perspective, based on the assumption that the proper amount of water pertaining to a debris flow event can be estimated solely on the analysis of the rain hyetograph in a suitable time interval around the debris-flow occurrence time. Unfortunately, as stressed by some authors (Staley et al., 2013;Nikolopoulos et al., 2014), thresholds obtained so far with these approaches are unlikely low and many events with rain exceeding the threshold do not give rise to any debris-flow occurrence. Therefore, rainfall thresholds are seldom used as an effective tool in debris-flow warning systems.
The goal of the research underlying this paper is exploring new paths aimed at improving the reliability of rainfall thresholds. We started our work analyzing the possible weak point of the classical literature approach and it seemed to be the way the rain volume pertaining to a debris flow is determined. Then, we have looked for an alternative methodology for determining this volume involving, in some way, not only the forcing of the phenomenon (represented by the rain hyetograph), but also the dynamics of the debris flow. The novel approach we have developed starts from the knowledge of the volume occupied by the sediments deposited in an event (typically measured by regional agencies); then, the liquid volume (i.e. the rain) responsible of this mass movement is back reconstructed by using a simplified dynamical description of the phenomenon. Finally, the duration of the storm giving rise to the estimated volume (and the relevant average intensity as well) is identified from the analysis of the rain hyetograph. We have called this method the Backward Dynamical based Approach (BDA). A proof of concept is presented in the paper by applying the BDA and a literature method to a sample study area.
The structure of the paper is the following. In Section 2, we give a synthetic overview of the classical literature approaches used to estimate the rain relevant to a debris-flow event, with particular focus on the Critical Duration Method (CDM), and we identify a possible intrinsic limit of these approaches. Section 3 is dedicated to the detailed description of the BDA theoretical framework while, in Section 4, a literature method for evaluating a threshold relation from an -series, namely the frequentist method, is briefly presented. A sample study area and the relevant rainfall thresholds obtained by using BDA and CDM are presented in Section 5. Discussion of the result is reported in Section 6. Conclusions end the paper.

Estimate of the rain relevant to a debris-flow event: the classical literature approach
In the literature, the rain relevant to a debris-flow event is assumed to be the volume of water poured into a basin from the early beginning of the storm system, indicated with (time measured respect an arbitrary reference), up to the debris-flow triggering time (see e.g. Lazzari et al., 2013). Assuming a uniform distribution of the precipitation over the basin, the volume can be evaluated in the following way: where ( ) is the measured hyetograph and is the area of the basin. The rainfall duration is then defined as the difference between two characteristic times, namely the triggering and the initial time: Finally, the average intensity can be obtained dividing the volume by the duration and the area of the basin : Therefore, considering a given hyetograph, the procedure to work out a couple ( , ) depends on the determination of the two characteristic times. Their practical determination, despite their simple definition, is not easy at all. In fact, the triggering time is seldom a piece of data known with precision. In most cases, only the date of a debrisflow occurrence is available in an event report. In these situations, the triggering time is assumed the instant in which the storm reaches the maximum rainfall intensity, i.e.
≡ (Iadanza et al., 2016). Other approaches (see e.g. Lazzari et al., 2013) assume this time as the last rainy measurement of the day.
Even more complex is the determination of the beginning of the rainfall event. In the literature there are many methods developed for this purpose and can be divided into two categories. The first collects the methods in which is estimated based on empirical and subjective rules, valid only for the specific sites for which they have been developed. The second refers to objective methods based on a probabilistic approach. Despite several works use the first way (e.g. Zhuang et al., 2015;Bel et al., 2017), the methods belonging to the second category (e.g. Bonta, 2001Bonta, , 2003Bonta and Nayak, 2008;Iadanza et al., 2016) seem more attractive because of their generality and possibility to be applied to different study areas.

A specific approach for the determination of : the Critical Duration Method
The Critical Duration Method (CDM) (Restrepo-Posada and Eagleson, 1982;Bonta and Rao, 1988) aims at identifying independent storm systems singling out a characteristic time, the Critical Duration ( ), such that if the time between two rainy periods is longer than this value, the two rainy periods can be considered belonging to independent storm systems. A synthetic summary of the statistical procedure for the evaluation of the is reported in the following Section (for a more detailed description, we refer the reader to the original papers mentioned above). Since the statistical characteristics of the storms change with the seasons, monthly values are commonly determined.
Once the has been estimated, is determined as the first dry instant before , whose time distance respect to the previous rainy instant is equal or longer than . An example of CDM application is reported in Fig. 1, where the triggering time has been considered as the of the event.

A synthetic summary of the CDM
A time sequence of rainfall records consists of rainy periods separated by non-rainy intervals. When two rainy periods are separated by a time interval of the order of minutes (or possibly even some hours), they surely cannot be considered independent, but generated by the same meteorological system. On the contrary, when the two rainy periods are separated by a long dry interval, they can be considered caused by independent weather systems. The CDM identifies, by using a statistical approach, the characteristic time interval that allows distinguishing independent rainstorms.
is defined as the minimum dry period which separates two stochastically independent rainy periods.
The main hypothesis in the CDM, verified by Restrepo-Posada and Eagleson (1982), is that the rainfall event durations are much shorter  than the interstorm (dry) periods. Under this hypothesis, as proposed by Bonta and Rao (1988), the occurrence of storms can be assimilated to a random Poisson process, whose interstorm period distribution can be approximated with an exponential function of this type: where is the reciprocal of the mean time between storms and is the dry-period duration. It is useful to recall that the variation coefficient , given by the ratio of the standard deviation to the expected value , is unitary. This feature can be used to single out the as the time beyond which the histogram of the dry periods can be well approximated with the exponential distribution. The procedure to get the is the following: 1. Subdivide a time axis in time steps long as the gauge sampling interval . Each interval is defined by: where the central value is ( − 1∕2) . Consider the number of occurrence of dry periods whose durations fall inside each interval. Build the relevant histogram (see Fig. 2(a)). It is worth noting that some intervals may be empty. 2. Call "classes" the non-empty time intervals of the previous histogram, number them from 1 to and associate to each class the central time of the relevant interval. Build the histogram of the occurrence as a function of the classes (see Fig. 2(b)). In this case, the histogram has a value associated to each class.
3. Consider the sequence given by: where [ , ] and [ , ] are, respectively, the expected value and the standard deviation for the dataset composed by the values included in the class interval [ , ] 4. Call ̃, the first value of the sequence such that 5. The value of the critical duration is defined as the time corresponding to the ̃th class, namely

=̃(8)
An example of determination is shown in Fig. 2 where the bars represent the histogram of dry-periods while the dotted lines are the values of . According to Iadanza et al. (2013), the minimum number of years of data necessary to correctly estimate is 6. In this study we have used 10 years of data.

The possible weak point of the classical approaches
As we hinted at the beginning of the paper, the methods present in the literature produce unlikely low rainfall thresholds. The reasons of this behaviour may be multiple and connected both either to the first or to the second component of the procedure depicted in the Introduction. Nevertheless, we think that this feature is mainly due to a not entirely appropriate estimate of the rain relevant to debris-flow events or, according to eq. (1), a not entirely appropriate definition of the characteristic times.
Although the triggering time presents significant uncertainties that can affect the reliability of the threshold, we think the assumption that the initial time, defined as the early beginning of the storm system during which a debris-flow event occurs, is conceptually the main weak point of the classical approaches. In fact, this choice leads to include in the duration even all the dry periods occurring within a given storm system, with a consequent average intensity that can be non-significant or non-representative of the real intensity that caused a debris-flow (see Fig. 1). Moreover, with the given definition of , the water considered relevant to a debris-flow event is not only the water directly involved in the mass movement, but also the water that leads to the pre-condition for a debris-flow event, i.e. the saturation of the soil at least for a depth equal to the eroded layer.
The reason of this basic assumption can be due to a couple of factors. Firstly, the rainfall threshold approach and the given definition of were originally proposed for generic hydrogeological phenomena, namely landslides and debris-flows together. For landslides, the water responsible of the phenomenon is probably all the water occurred before its triggering, while the water rained after that time plays a negligible role. Therefore, the assumption seems quite reasonable. On the contrary, in debris flows, the water volume flowing after the initiation of the flow plays an important role, since it determines the actual volume of the debris flow (as it will be explained in the next Section). Therefore, because of the differences of the two phenomena, an undistinguished application of the approach to landslides and to debris flows does not appear completely appropriate. The second factor, in a pure hydrological framework, is finding some features of the measured hyetograph that can be correlated to the debris flow itself to evaluate quantitatively the relevant rain. The hydrological beginning of a storm system, independently of the actual way it is determined, is therefore not only the natural choice but also perhaps even the only practical choice that can be done to characterize the starting of the rain relevant to a debris flow.

Estimate of the rain relevant to a debris-flow event: the Backward Dynamical Approach
Our perspective for estimating the ( , ) couple relevant to a debrisflow event is quite different from the perspective used so far in the literature. Hereafter we present a detailed description of the rigorous framework leading to the BDA, along with all the assumptions necessary to define it.
Let us indicate with , the volume of rain directly embroiled in the mass movement. Assuming a uniform distribution of the precipitation over the basin, this volume can be expressed in similar way to eq. (1) as: where 1 and 2 are two time limits related to the debris-flow duration and is the area of the basin. We assume that: 1. can be back reconstructed from the knowledge of , i.e. from the volume occupied by the mixture that stops in a deposition fan during the event. The relation between the two volumes, that we call "BDA relation", can be expressed formally as and can be obtained considering a simplified dynamical description of the debris-flow phenomenon, to be specified further on.
2. The time limits 1 and 2 are conceptually connected to and to the debris flow duration but, in general, not to . Since the triggering time and the duration of a debris flow event is rarely available, following Iadanza et al. (2016), we assume ≡ max while, for the time interval, we assume that it is symmetrically distributed around max , namely: where Δ is an unknown value.
The duration and average intensity of an event can be determined by using the previous three equations in the following way: i. considering eq. (10) and (11), eq. (9) can be rewritten as where the only unknown is Δ . This equation can be solved by means of a trial and error method; ii. the rainfall duration, that we will indicate with ̂i n order to distinguish from the classical duration , is equal to the length of the time interval used in the integral: iii. considering the previous two expressions, the average intensitŷ can be rewritten as: Rainfall thresholds will be calculated using couples of values (̂, ̂) instead of ( , ). Consequences of this new point of view on threshold evaluations are analysed further in the paper (see Section 6).
In order to make the previous procedure effective, it is now necessary to specify the BDA relation. This is the subject of the following Sections.

Basic assumptions for the BDA relation
The BDA relation is based on a series of assumptions and approximations that, even if apparently rough, are quite reasonable and can give, at least, the right order of magnitude of the rain estimate. We list here all the assumed hypotheses and some motivations of their reasonableness.
• We refer only to stony debris flows, i.e. flows in which silt or clay does not affect the overall behaviour of the mixture. • The concentration of sediments in the bed is constant everywhere. This assumption is generally accepted in debris-flow dynamics (Takahashi, 2007). • When a debris flow occurs, the soil is commonly completely saturated (e.g. Hungr et al., 2001). Therefore, we assume that the basin has been saturated by the rain foregoing the event, up to the level interested to the erosive stage. This condition is reasonable even if nobody has ever verified it in real cases. From this assumption and the previous ones, it follows that along the debris-flow path the pointwise water content in the terrain is everywhere (1 − ). • Since the characteristic time scale of a debris-flow event is much smaller than the time scale of the infiltration process, we assume that during a debris-flow occurrence, all the rainfall transforms into runoff. • The volume of sediments surveyed in the field after an event is the major part of the sediment involved in the debris-flow. This can be accepted if the deposition fan is a piece of territory with average slope significantly smaller than the slope in the flowing part and the volume of small-size sediments carried away with the water is negligible. This situation commonly occurs if the basin has a welldefined deposition fan. • The reach just upstream the deposition fan is characterized by an average slope = tan , where is the angle that the bed forms with an horizontal reference direction, and by a length long enough to allow the debris flow to be in uniform flow condition, i.e. with a volumetric solid concentration that, according to Takahashi (1978), for stony debris flows can be expressed as where Δ = ( − )∕ is the sediment relative submerged density, and are, respectively, the liquid and solid density while is the internal friction angle. Considering that debris flows reach the uniform flow condition in rather short lengths, this assumption seems to be applicable in most cases.

Conceptual scheme of a stony debris-flow dynamics
Besides the previous assumptions, the BDA relation is based on a schematisation of a debris-flow dynamics. Obviously, real dynamics may be much more complex than the simple conceptual scheme herein reported. Nevertheless, we think that it is reasonably representative of a large set of real conditions.
For sake of simplicity, let us consider a long plane bed with unit width and constant slope . An upper non-erodible transept followed by a lower erodible and saturated one characterizes the plane bed. At the end of the plane bed, there is a quasi-horizontal reach (see Fig. 3(a)).
The phenomena developing in the three transepts are: Transept 1: Runoff formation. Rain gives a significant contribution only in this transept.
Transept 2: Erosion of the bed material and formation of a debris-flow characterized by a concentration given by eq. (15). Transept 3: Deposition of all the sediments with saturation water entrapment. The rest of the water flow away outside the deposition fan.
In Fig. 3(a) we have sketched the phenomena occurring in the three transepts. The picture can be considered also a superimposition of three snapshots taken at three subsequent times (Lagrangian description) in the case the three phenomena occur disjointedly. Actually, it is very likely that they occur simultaneously in different position of the flow field but, in order to make the plot clearer, we preferred to represent the disjointed case.

Quantitative volumetric description of the debris-flow dynamics
In order to obtain a quantitative volumetric description of the conceptual debris-flow dynamics, it is useful turning to the one-dimensional Eulerian description of the flow sketched in Fig. 3(b). Here we report the detailed derivation of the relations valid in each transept, along with the assumptions necessary to obtain them. More complex relations can be obtained relaxing one or more hypothesis, but this generalization is left for a future work.
The debris-flow phenomenon can be described starting from the partial differential equations expressing the mass balances in a two-phase, depth-averaged framework in which there is no lag between the phases (see e.g. Armanini et al., 2009;Rosatti and Begnudelli, 2013). More specifically, the mass-balance equation of the liquid phase and the solid one can be written as: where ℎ is the mixture depth, is the solid volumetric concentration, is the mobile-bed elevation, the depth-averaged velocity and finally is the source term for the liquid phase. Here, the constant densities of each phase has been simplified. Since no source for the solid phase is present, the relevant term is null. Momentum balance of the solid phase is also accounted for by mean of the uniform flow relation expressed by eq. (15). The relations necessary for our approach are derived from the integration of the mass balance equations, namely eq. (16a) and (16b), along each transept in space, and throughout the duration of the debris flow in time. Rearranging some terms, these equations become: where the subscript refers to the -th transept, , are the -coordinate respectively of the transept initial and ending point and , are the initial and ending times of the debris-flow. Performing some formal integrations, they can be rewritten as: in which we have used the notation that [] represents the difference of the generic quantity  evaluated in minus the same quantity evaluated in . The first term of each equation represents the time variation of respectively the liquid and the solid volume flowing inside the -th transept. These terms can be neglected under the assumption that the flowing volumes are equal at the initial and at the ending time inside each transept. This may not be true in some cases, but we think that in the framework of a conceptual scheme, this approximation is acceptable.
The second term of each equation represents the difference of the fluxes integrated in time (namely, the volumes) that leave and enter the -th transept in the given time interval. Indicating with: the outflow volumes of the liquid and the solid phase in the -th transept, these second terms can be written, respectively, as: in which the inflow volumes of the -th transept are expressed as outflow volumes of the ( − 1)-th one. The first term on the right hand side of each equation represents the volumes of liquid and solid released or stored in the bed because of the bed evolution. Since is constant in time and space, they can be written as a function of the volume of bed variation in the transept, defined as: The last term in the liquid mass-balance equation represents the source term for the liquid phase, i.e. the possible volume of rain. We indicate it as:

∫ ∫ =
Considering all the previous expressions, eq. (18a-18b) can be rewritten in the following compact way: Since the bed variation and the source term are not present in each transept, it is useful to write explicitly for each reach the relevant equations according to the assumed conceptual scheme.
Transept = : here we have no upstream input therefore  , 0 = 0; we indicate with the liquid source 1 ; finally, since no bed variation is present, namely 1 = 0, then, the equations for this transept become: Transept = : indicating with − = 2 the bed volume variation connected to the erosion (with ≥ 0), we can write: In the outflow section of this transept, according to the assumptions, the concentration is constant and given by eq. (15). Indicating with ̂this concentration, the outflow volumes can be rewritten as: We can express these volumes as a function of the outflow volume of the mixture in the following way: Finally eq. (21) can be reformulated in the following form that will be used further on: Transept = : here, denotes the bed volume variation connected to deposition, namely = 3 (with ≥ 0). Moreover, since all the sediments stop in this reach according to the assumptions,  3 must be null. Therefore, the resulting equations are: Combining the equations written for each transept, it is now possible to obtain the following useful relations.

=−̂(28)
which links the eroded volume reaching the deposition fan to the relevant volume of rain.

Amplification of the debris-flow volume respect the rain volume:
in eq. (23a) we can substitute the expression of obtained from eq. (23b) and  1 from eq. (20a), obtaining: (1 −̂) 2 − = (1 − )̂ 2 It is then possible to derive the relation that is nothing but the well-known volumetric amplification relation obtained by Takahashi (2007) starting from a quite different context: it expresses the volume of debris flow as a function of the volume of rain and of the equilibrium concentration in a reach with a given slope. It is worth noting that the present derivation allows to appreciate the assumptions underlying this expression.
The liquid volume leaving the domain: by using eq. (20a) and eq. (21a) we obtain: and using relation (25), it becomes: This expresses the somewhat (a posteriori) obvious statement that the liquid volume leaving the domain is equal to the liquid volume entering upstream.

The role of the rain volume in a debris-flow
A by-product of the approach just presented is the explanation of the role of the rain volume in the simplified dynamics of a debris flow. In a Lagrangian framework, the rain volume firstly generates the hydrological flow, then induces a volume of erosion given by eq. (28), conveys downstream the sediments as a mixture whose volume is given by eq. (30) and finally, after having deposited the eroded volume, eq. (25), leaves the domain, eq. (32).

Probability threshold for intensity-duration data: the frequentist method
It is widely accepted (see e.g. Brunetti et al., 2010;Peruccacci et al., 2012;Nikolopoulos et al., 2014) that the relation between rainfall intensity and duration is a power law of type: where and are two constant parameters which are commonly estimated by using least square method (or other statistical approaches) starting from a significant set of couples ( , ). In this work, we assume that the same type of relation is valid also for the intensities and durations defined in the previous section, i.e.
The approach we have chosen in this work to estimate the probability threshold is the frequentist method (see e.g. Peruccacci et al., 2012, among others), nevertheless other methods could be applied as well (see e.g. Berti et al., 2012;Peres and Cancelliere, 2014). For sake of completeness, we present here a summary of the methodology applied to the (̂, ̂) couples, but the procedure applies to ( , ) as well. We address the reader to the mentioned paper for more details.
According to this approach, the threshold is a curve that, in a log-log plot, is a straight line parallel to eq. (34) but with a lower value of the intercept such that the probability that the measured real event data exceeds the threshold value is a given value.
In order to obtain this threshold line, firstly the following difference set must be evaluated: where ̂i s the value of the -th registered datum associated to the -th duration ̂, ̂(̂) is computed by means of eq. (34) and is the total number of registered events. Then, the probability density function of this set is approximated by using a Kernel Density Estimation as proposed by Silverman (1986). Afterwards, this last function is sampled at regular intervals and the resulting set of values are used to estimate the parameters of a normal distribution: namely the mean and the variance . Finally, the previous distribution is integrated from −∞ up to a threshold value such that the relevant probability of non-exceedance is equal to . Commonly, this value is set equal to 5%. Therefore, the threshold curve can be written as:

The rainfall thresholds for a sample study area
In order to verify the feasibility of the proposed method and to highlight the peculiarities of BDA approach respect to the classical one considered in this work, we have calculated the rainfall thresholds according to the procedures above described. The study area is Trentino-Alto Adige/Südtirol, located in the Alps, in the north of Italy (Fig. 4).

Available data
Between 2006 and 2016, 161 debris flows were recorded by the regional agency. The average area of the catchments affected by the events is nearly 1.5 km 2 . Almost none of them has a precise indication about the triggering area while for 139 cases, a quantitative estimate of the deposited volume is available. It ranges from 100 m 3 of the smallest events up to 50, 000 m 3 of the largest ones. In Fig. 5, the spatial distribution of the debris-flow events is reported on a geological map of the study area. It must be emphasized that all the events, even if occurring in geologically different areas, were characterized by a loose stony nature, and devoid of any significant presence of cohesive mud.
A network of 195 rain gauges is available with an average spatial density of approximately 1∕70 km −2 and an average altitude of 1, 400 m asl. Nearly each rain gauge used in this study has a record frequency between 5 min and 10 min.
For the same period, records of a C-band Doppler weather radar are available. The radar is located in a central position of the region on the top of Macaion peak at 1, 866 m asl (see Fig. 6), and it is effectively used to monitor an area within the range of 120 km. Precipitation is estimated converting the reflectivity into intensity of precipitation (see e.g. Uijlenhoet, 2001). The radar output data are available over a square grid cell with resolution of 500 m, while the temporal resolution is between 5 min and 6 min.
Since generally, the cumulated rainfall depends on the altitude and rapidly decreases with the increase of the distance from the event area (Marra et al., 2016), in order to get reliable rain data relevant to a debris flow, a careful choice among the available rain gauges has been performed. For each debris flow event, the choice of the representative station was obtained by using an automatic procedure. From the knowledge of the coordinates of a debris-flow basin closure point, assumed located where the deposition starts, the horizontal distance between the closure point and each rain gauge was evaluated. Among all the available rain gauges with a distance less or equal to 5 km, the representative station was assumed the one with the smallest altitude difference respect the closure point. If no instrument matched the distance criteria, the debris-flow event has been considered unserviceable for the subsequent analysis.
Rain gauge data has been used only for the estimation, while radar data has been used to estimate the ( , ) and (̂, ̂) couples. Nevertheless, because of the complex topography of the region, mountain beam shielding causes, in certain areas, a lack of measurement associated with a reported debris flow event (see Fig. 6). Therefore we have  been preliminary filtered out all the events located in places where the radar signal is weakened more than 90%. Other sources of errors, such as signal attenuation in heavy rain or wet radome attenuation (see e.g. Marra et al., 2014), have not been taken into account.

The CDM-based threshold
For this site, 45 rain gauges, linked to the 161 debris-flow available events, have been used to determine the monthly reported in Table 1 and plotted in Fig. 7.
The CDM-based threshold has been obtained by using only 109 registered events (out of the 161 registered) matching the 5 km distance and the radar signal criteria. The resulting threshold equation is: and is plotted in Fig. 8 along with the relevant ( , ) event couples.

The BDA-based threshold
The BDA methodology requires the knowledge of the volume of deposited material (as described in Section 3). Due to this constrain, only 84 debris flows of the 109 registered events with unshielded radar data are serviceable. For each debris-flow case, the couple (̂, ̂) was obtained using the following procedure: 1. the relevant basin was extracted from a DTM, setting the closure at the beginning of the deposition zone; 2. a representative bed slope was considered as the average slope of the last 50 m of the basin network; 3. the debris-flow reference concentration ̂was evaluated by means of eq. (15), where we set Δ = 1.65 and = 35 • ; 4. the volume of rain that has caused the deposition was estimated by using eq. (27) 5. the duration ̂s hould have been evaluated by solving eq. (12); nevertheless, since intensity is actually a piece-wise constant func-  tion whose constancy interval is equal to the radar sampling interval , the equation cannot be solved exactly. Therefore, the procedure used to obtain ̂w as the following: • consider the discrete hydrograph pertaining a debris flow event; place the origin of a discrete reference system in the interval where the intensity is maximum and label this interval as 0 ; • consider the following sequence of discrete integrals of the hydrograph: is even Fig. 9. BDA-based rainfall threshold for the study area. The colour scale indicates the equilibrium concentration, evaluated according to eq. (15), for each event.
where ± is the intensity of the ± -th interval located on the right and on the left of 0 respectively; • consider the integer as the first integer such that ≤ Then, since is nothing but the approximation of the integral term of eq. (12), it follows that: 6. the intensity ̂w as obtained by using eq. (14) The resulting BDA-based threshold equation, plotted in Fig. 9 along with the relevant (̂, ̂) event couples, is: 5% = 6.2̂− 0.67 (39)

Discussion
In this Section, we discuss some aspects of the results obtained for the sample study area.

The CDM-based threshold
The threshold obtained with this method, eq. (38), is similar to the ones obtained by Marra et al. (2014) and by Iadanza et al. (2016) who considered, for areas comparable to the one considered in this paper, rain data associated not only to debris flows but also to landslides. Moreover, it can be noted that in Fig. 8 Fig. 7, the dispersion of the summer months values is smaller than the dispersion of the other months. This means that for the summer period, the median value of is representative of the whole correspondent area. This is quite important because in this period the majority of the debris-flow events occur. Moreover, the summer s are shorter than in the rest of the year. This is due to the different structure of the summer storms with respect to the rainy systems of the other months. Similar trend is reported in Iadanza et al. (2016), where an area analogous to the one considered in this paper was investigated.

The BDA-based threshold
First of all, the assumption that the intensity ̂i s a power law function of ̂( see Section 4) seems to be confirmed by results, since data in Fig. 9 shows a clear linear trend in the log-log plot.  The data is distributed essentially over one scale of magnitude, ranging from 0.1 h to 1 h. This time scale is confirmed by both eyewitness and video testimonies (some of them can be found on the web) even if, a systematic and well documented study is still not available. However, this result allows a first assessment of the BDA relation reliability. On the other hand, the BDA relation is strictly connected to the Takahashi volumetric amplification relation, whose validity is widely accepted and assessed (see e.g. Rosatti et al., 2015). Therefore, the BDA relation appears to be a sufficiently robust estimator, provided that measured data is sufficiently accurate.
An accurate analysis of the result (still Fig. 9) showed several points characterized by a duration equal to the sampling interval of the radar (leftmost points) and this can be due to multiple reasons.
One of these can be the sampling interval is too long with respect to the time necessary to feed a debris-flow.
A second one is an underestimation of , whether for measurement errors or because only part of the debris-flow sediments stopped in the fan, while another significant part is not included in the measurements because it has been carried downstream by the flow. Therefore, the relevant is smaller than the actual value and consequently, the duration is shorter than the real one.
A third possible reason is an overestimation of due to a wrong estimation of a significant slope. This produces an underestimation of , as it can be deduced from eq. (27) and plotted in Fig. 10. It follows an underestimation of the event duration. Last but not least, despite is essentially correct, the debris flow has not reached the hypothesized equilibrium condition because of lack of sediments, non-erodible zones etc. Therefore, also in this case is overestimated with respect to the actual value and the event duration is underestimated.
We are not able to quantify the errors in our data, or to verify their frequency distribution. Anyhow, we think that the last three reasons are probably the most diffuse and possibly mingled. In any case, we think that our sample is sufficiently reliable. Finally, we are not able even to predict the change in the threshold if more reliable data would be available.

Comparison between the CDM-and the BDA-based thresholds
A straightforward comparison between the two thresholds, namely eq. (38) and eq. (39), shows that the BDA-based threshold is, as expected, higher than the CDM-based one (see Fig. 11). Nevertheless, we cannot conclude that BDA is a better estimator, since we are comparing quite different approaches and quantities and therefore, some care must be paid in making the comparison.
Since it is not completely right extending the validity of each threshold outside the domain used for the interpolation, there is only a limited overlapping of the two domains. In this range, the relative difference with respect to the CDM data spans from 25% to 31%.
The characteristic time scales of the two approaches are quite different: [ 1 h, 10 h ] for CDM and [ 0.1 h, 1 h ] for BDA. Namely, BDA indicates as potential debris-flow inducing rainfalls, short or very short storm durations while CDM suggests quite longer durations. This is not surprising since, as we have already stressed, we are comparing different things, deriving from different points of view. In order to highlight this difference, it is useful to compare graphically (see Fig. 12), how the same hyetograph is considered by the two approaches. It is quite clear how the same event is characterized by different duration and average intensity, and why BDA provides shorter duration and higher intensities with respect to CDM.

Some issues regarding the forecast use of the BDA-based threshold
The forecast use of the BDA-based thresholds presents some potential issues.
Starting from a forecast hyetograph, while the peak intensity can be easily identified, the potential volume of rain that determines both the duration and the average intensity, is not known. A possible empirical choice, that can be deduced from the available data but that cannot be used in general, is to consider an average duration of 0.5 h÷1.0 h centred around the peak.
It is not so uncommon in debris-flow events that the relevant hyetograph shows multiple peaks (see e.g. Rosatti et al., 2015). In these cases, it is even more complicated estimating a reference duration because the previous criterion could be largely inaccurate. Moreover, it is quite difficult to forecast if multiple peaks generate multiple events or a single long event.
A validation of the proposed reference duration choice, the development of more sophisticate approaches and the evaluation of the frequency of false positive respect the proposed BDA-based threshold are very interesting topics, but they are beyond the scope of this work and therefore are left for a future widening. Last, but not least, BDA focuses the attention only on one mandatory ingredient for a debris flow: the amount of water necessary to carry downstream the sediments. The other key element, the saturation of the basin, is completely disregarded in this approach and therefore, a more complete methodology considering both the ingredients is desirable.

Conclusions
The conclusions that can be drawn from this study are the following: • The Backward Dynamical Approach presented in this paper appears to be a reasonable theoretical framework able to single out the amount of rain strictly pertaining a debris-flow event and, consequently, the related (̂, ̂) couple. Other less simplified relations can be obtained relaxing one or more assumptions herein considered. • Every method of extracting a threshold from a ( , ) couple set can be applied to the BDA couples as well. • Analysis of a sample study area shows that the characteristic duration of the rain generating a debris-flows evaluated with BDA is one order of magnitude shorter than the characteristic duration given by CDM. This result appears to be consistent to several field observations, but a systematic validation is still missing because of lack of reliable data. Moreover the BDA-based threshold is, as expected, higher than the CDM-based one, provided that the same methodology is used to obtain the thresholds from the intensity-duration couples. Even if this result seems to overcome a bit the limit of the traditional approaches, it is not yet possible to state that this method performs better than the literature ones because a systematic validation is still not possible, once again, for lack of measured data. • The use of the BDA-based thresholds for forecast purpose presents some potential issues. Moreover, the approach does not account for the reaching of any "triggering condition"(e.g. saturation of the basin, threshold condition for transport, abatement of possible superficial sediment cohesion, etc.) and for the effect of earthquakes on these conditions (see e.g. Shieh et al., 2009;Tang et al., 2009). A more complete, and presumably more reliable methodology for the rainfall threshold determination should consider, in some way, not only the characteristics of the rain strictly pertaining to the debris flow but also the characteristics of the rain leading to the triggering conditions and at least some features of the sediments. • To fully validate the proposed method, large samples of data from different areas should be considered. Our forthcoming work aims to achieve this goal. Unfortunately, the number of well-documented debris-flow events are rather few, not just because debris flows are infrequent, but also because so far the detailed survey of the deposited volumes was extremely difficult and not considered so important by the public agencies in charge of debris-flow data collection. Nevertheless, in the last years the application of drone technology to land surveying has gratly simplified the measurement of debris-flow deposits.
We are confident that in some years the number and quality of available data should help to validate both the reliability of the proposed method and its possible future enhancements.

Author contribution statement
Giorgio Rosatti: Conceived and designed the experiments; Analyzed and interpreted the data; Wrote the paper.
Marina Pirulli: Analyzed and interpreted the data; Wrote the paper. Daniel Zugliani, Marta Martinengo: Performed the experiments; Analyzed and interpreted the data; Wrote the paper.