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

text:
Prevalence of Subclinical Mastitis among Small Ruminants and Isolation of Some Etiological Bacterial Pathogens in Jimma Town, Ethiopia

Subclinical Mastitis in small ruminants is of concern due to the animal welfare, economic, public health, productivity, and livelihood impacts that it may pose. A cross-sectional study was conducted from October 2017 to April 2018 in and around Jimma town, Southwest, Ethiopia; to estimate the prevalence of subclinical mastitis, to isolate bacterial pathogens responsible for the occurrence of subclinical mastitis, and to identify risk factors associated with the development of subclinical mastitis in small ruminants. Milk samples were collected from 186 apparently healthy lactating small ruminants, tested on the field with the California Mastitis Test, and samples tested positive were subjected to bacteriological examinations. Out of 372 milk samples collected, 92(24.8%) samples from 70 animals were positive by the California Mastitis Test and these were cultured on different media. Through bacteriological examinations, six isolates of bacteria were identified which include; S. aureus (8.1%), S. epidermidis (10.2%), S. intermidus (2.2%), S. hyicus (1.6%), S. agalactiae (3.2%), and E. coli (12.4%). The highest prevalence of subclinical mastitis recorded was due to Staphylococcus species (22%) followed by E. coli (12.4%) and the least prevalence was due to S. agalactiae (3.2%). The overall prevalence of subclinical mastitis among small ruminants was 37.6%. Age and parity number variations among small ruminants were found important in influencing the prevalence in statistically significant (P = 0.00) extents. The highest prevalence of subclinical mastitis was recorded in old (24.7%) and in small ruminants having >5 parity numbers (24.7%). The high prevalence of small ruminant subclinical mastitis in the study area suggests that intervention strategies their-goal-being prevention and control of subclinical mastitis should be designed; so as to improve the welfare of animals, to save people consuming the milk of small ruminants from zoonotic risks, and to harvest the diverse benefits that may be generated from small ruminant production.

production and productivity and producers' benefits remain far below to the expectation due to continuously occurring production constraints such as disease, malnutrition, poor animal production system, reproductive inefficiency, management constraints, and general lack of veterinary care. Among these factors, disease takes great portions and could be caused by infectious and non-infectious agents (Tibbo et al., 2003). In dairy industry, udder health problem is one of the major diseases posing negative impacts on milk production, milk quality and animal welfare (Seegers et al., 2003).Among udder health problems, mastitis constitutes one of the major problems and is defined as an inflammation of the mammary glands characterized by changes in the physical and chemical features of milk and pathological changes in the glandular tissue often with non-specific and complex factors responsible for its occurrence (Radostits et al., 2007). It occurs worldwide in dairy animals and it has been described to have an extreme zoonotic and economic impact ( The prevalence of clinical mastitis is less than 5% in small ruminants, while the subclinical mastitis is much more common with a prevalence that ranges from 18 to 34% (Chingwen et al., 2002). Mastitis in small ruminants is mainly subclinical and results in decreased milk yield, may lead to growth retardation and higher mortality rate among suckling kid and lambs (McDougall et al., 2002).The most common causes of subclinical mastitis in most herds or flocks are bacterial in origin. The bacterial agents isolated from mastitic milk in small ruminants are mainly; Staphylococcus species, Streptococcus species, Pasteurella species, Pseudomonas species, Proteus species, Salmonella species, Corynebacterium species, Bacillus species and E. coli (Contreras et al., 2007). Among the bacterial agents, Staphylococcus species have been implicated as the leading causes of subclinical mastitis with higher prevalence in goat and sheep. Especially, Coagulase-Negative Staphylococcus species (CNS) has been the most common reported cause of subclinical mastitis (Bergonier et al., 2003b).
Early diagnosis of subclinical mastitis is vital because changes in udder tissue take place much earlier than they become apparent. Various approaches, based on physical and chemical changes of milk and cultural isolation of organisms (gold standard) are practiced for the diagnosis of subclinical mastitis in small ruminants (Contreras et al., 2007). To diagnose the SCM, indirect tests such as CMT, WST and SCC are commonly used both in cows and small ruminants. White Side Test (WST) is also an indirect, easily applicable screening test for sub clinical mastitis in which 4% sodium hydroxide solution are used as reagent (Contreras et al., 2007).
Characterizing the etiology of bacterial infection is critical for understanding the grounds for establishing of infection and devising appropriate treatments to control the contagion. However, very little research has been done to explore the patterns of intramammary infections acquiring in small ruminants (Leitner et al., 2007). Therefore, this study was gird with the objectives of: To estimate the prevalence of subclinical mastitis among small ruminants in Jimma town, Southwestern, Ethiopia; to isolate bacterial pathogens responsible for the occurrence of subclinical mastitis in small ruminants; and to identify risk factors associated with the development of subclinical mastitis in small ruminants.

Mastitis in Small Ruminants -
The term mastitis was derived from the two Greek words "Mastos-"(for breast) and "-itis" (for inflammation) (Doğruer et al., 2010). It is one of the main udder problems and defined as an inflammation of the mammary glands characterized by changes in the physico-chemical features of milk and pathological changes in the glandular tissue often with non-specific and complex factors responsible for its occurrence (Radostits et al., 2007). In small ruminants, mastitis is a very significant disease that affects not only the meat and milk production, but also the well-being of shoats and human health as well (Abdullah, 2016; Tolone et al., 2016).

Types of Mastitis in Small Ruminants -
Mastitis are a disease that occurs in several different forms. Generally in animal, mastitis is divided into clinical and sub-clinical forms (Doğruer et al., 2010).

Clinical Mastitis -
It occurs by having the evidence of both milk and udder changes with cardinal signs of inflammation upon physical examination, which includes swelling, pain, hotness (heat), redness, or necrosis of one or more halve udders and abnormal discharge of milk (presence of flakes or blood), as well as other systemic symptoms such as anorexia, fever or agalactia (Radostits et al., 2007). Usually the consequence of clinical mastitis is toxemia and gangrenous necrosis of the udder (Omaleki et al., 2011). The clinical syndrome can follow variable courses, ranging from hyper-acute to chronic. The occurrence of chronic disease may produce clinical pictures, which become gradually less clinically evident .

Subclinical Mastitis in Small Ruminants -
Subclinical mastitis in small ruminants of Africa causes a significant impact on the production and productivity of dairy goat and sheep's due to its association with the absence of clinical signs to be detected by the clinicians or farmers. Lactating ewes/ does with subclinical mastitis does not show any clinical symptoms and giving apparently normal milk for long period which only known after indirect tests done and cultured with bacteriological examinations which makes them positive to mastitis (Kiossis et al., 2007). Identification of animal affected with subclinical mastitis is much more difficult than recognition of those with clinical mastitis. Detection of subclinical mastitis may require some additional indirect tests such as with CMT test, SCC and microbiological tests (Plummer and Plummer, 2011).

Incidence and Etiology of Mastitis in Small
Ruminants -Incidence of the clinical mastitis in small ruminant is usually below 5% and can lead to mortality or culling up to 90% of infected animal in the flock. However, in certain herds and flocks having of poor management system it may exceed 30% (Goodridge et al., 2004). Mostly, sporadic cases of clinical mastitis are caused by Staphylococcus aureus, coagulase-negative Staphylococci (CNS), Arcanobacterium pyogenes, Corynebacterium, Pasteurella species, and Pseudomonas species. Outbreak of clinical mastitis most frequently involves Staphylococcus species, Streptococcus species and other opportunistic agents such as Aspergillus species, Pseudomonas species, Burkholderia species, and Serratia species (Croft et al., 2000;Plummer and Plummer, 2011).
The most common cause of subclinical mastitis in most herds or flocks is bacterial in origin. Among them, Coagulase-Negative Staphylococcus species (CNS) have been implicated as the leading cause of subclinical mastitis with prevalence rates of 71% and 78%, respectively, in goats and sheep. The second most common reported cause of subclinical mastitis is S. aureus, with reported prevalence rates of 8% in goats and 4% in sheep (Bergonier et al., 2003b). Streptococcus species also have been isolated in cases of subclinical mastitis with prevalence rate ranges between 1.1% and 6.8% of subclinical mastitis cases (Mork et al., 2007;Hall et al., 2007;Ndegwa et al., 2001).

Epidemiology of Small Ruminants Mastitis -
In dairy animals, mastitis causing pathogens are classified depending on their epidemiological behavior. However, such description is not as clear for small ruminants as it is for dairy cattle. During time of milking of the small ruminants, the likely source of Coagulase-Negative Staphylococci (CNS) are the skin on the teats along skin around the inner side of legs; thus, many CNS bacteria become persisted and long-term chronic infections (Piessens et al., 2011).CNS may leak out to the milk from the infected udder during milking and then spread via the milking equipment to other animals. Thus, the source of mastitis pathogens in small ruminants should not be assumed based simply on the behavior of these pathogens in dairy cows (Olechnowicz and Jaśkowski, 2014). The main reservoir of the contagious pathogens is the mammary gland. S. aureus and Str. Species are considered among the contagious pathogens. Consequently, these bacteria are spread from animal to other or between udders of the same animal during the milking process (Contreras et al., 2003). Apparently this refers to that S. aureusis germ capable of contaminating different surfaces and easily isolated from the skin and mucosa of the healthy animals. The role of environmental sources of S. aureus is less important than its presence in the mammary gland habitat, but may help in the spreading of infection from milking of the infected animal and milking machines or dairy workers can transmit it, but are not the origin of these infections. Generally, bacteria causing mastitis are often classified as "contagious"; if it is believed that the source of infection of milk is infected mammary glands with subclinical mastitis, or "environmental"; if the bacteria are considered to be opportunistic pathogens that normally reside in the environment around the animals (Ruegg, 2011).

Gram-positive agents
Staphylococcus species -These species are Grampositive cocci, non-motile, non-spore forming, aerobic and facultative anaerobic bacteria. They are found in clusters like bunches of grapes and appear violet color when stained, catalase positive and usually oxidasenegative. Also, they could hydrolyse arginine and produce acetone and attack sugar by fermentation ( Barrow and Feltham, 2004). Considering the fact that, S. aureus, Coagulase-negative Staphylococcus species (CNS) such as S. epidermidis, S. saprophyticus and S. intermidus are the important causative agents of subclinical mastitis in small ruminants (Windria et al., 2016).
Streptococcus species -These species are also Grampositive cocci in pairs or chains, non-motile, non-spore forming, aerobic and facultative anaerobic organisms. They are both catalase and oxidase negative, and ferment carbohydrate ( Barrow and Feltham, 2004). Despite the pathological importance of Streptococci species in mastitis, they do not represent a group of especially prevalent pathogens on small ruminant flocks and are usually reported in somewhat less than 5-10% of cases of sheep and goats mastitis. However, they majorly cause clinical mastitis, and a number of epidemiological studies on subclinical mastitis even report the absence of these pathogens. This situation seems clearly different in certain areas of dairy small ruminant production, in which major problems on farms are diagnosed due to Str. agalactiae (Contreras et al., 2003).

Enterococcus species -Enterococcus species are
Gram-positive cocci in pairs or short chains. They are non-motile (except few species), non-spore forming, aerobic and facultative anaerobic, catalase and oxidase-negative. They ferment carbohydrates (Barrow and Feltham, 2004). Those Gram positive bacteria are associated with mastitis in sheep, goat and cattle; include Enterococcus faecium and Enterococcus faecalis (Contreras and Rodríguez, 2011).

Gram-negative agents
E. coli -E. coli is Gram-negative rods, motile, aerobic and facultative anaerobic. Catalase-positive, Oxidasenegative, ferment sugars and produce gas ( Barrow and Feltham, 2004). They are organisms commonly found in the environment in order to cause the coliform diseases and considered as opportunistic pathogen.
Pasteurella species -Pasteurella species are Gramnegative rods, non-motile, aerobic and facultative anaerobic. Usually, they are Catalase-positive, Oxidase-positive and can ferment sugar ( Barrow and Feltham, 2004). The main source of infection from P. haemolytica for the teat of the ewe is the mouth of the lamb. In dairy sheep, where ewes are milked by hand or machine after lambs are removed, the incidence of P. haemolytica mastitis is dramatically reduced after lambs are weaned because there is no longer presence of organism on the teat of the ewe.
Mycoplasma species -Mycoplasma is a genus of bacteria that lack a cell wall around their cell membrane. They do not possess rigid cell walls, they are resistant to antibiotics such as penicillin, which interfere with the synthesis of bacterial cell walls. Mycoplasma species are the smallest bacterial cells yet discovered typically, about 0.1 µm in diameter. It can be present in the milk of sheep and goats and can cause mastitis in the animal (Quinn and Markey, 2003). Contagious agalactia (CA) is a serious disease of small dairy ruminants that has a substantial economic impact on the goat and sheep milk industries. The main etiological agent of the disease is Mycoplasma agalactiae, although other species, such as Mycoplasma mycoides subsppcapri, Mycoplasma capricolum Sub spp. Capricolum and Mycoplasma putrefaciens, are pathogenic in goats (Contreras and Rodríguez, 2011).

Viral Agents
The mammary gland is more susceptible to injury by ovine progressive pneumonia virus (OPPV), nine of 15 experimentally infected sheep and all six naturally infected sheep had lympho-plasmacytic mastitis. Caprine arthritis-encephalitis virus (CAEV), like other Lentiviruses, causes a persistent lifelong infection. The CAEV induced subclinical infection in most goats. Most of infected does are likely to have a viral infection of the mammary gland, which is a target organ for the virus.

Fungal Agents
Cryptococcus neoformans which is yeast causes mastitis in cattle, buffaloes and experimentally in goats. Crytptococcus species inoculation into the glands of 10 goats resulted in mastitis in all of them but, there was no spread to the un-inoculated udder half. Mastitis may be acute with severe swelling of the gland and the supramammary lymphnodes, severe fall in milk yield and appearance of viscid mucoid gray white secretion. It is of public health importance especially where milk is taken without pasteurization since it causes human Cryptococcoses.

Physical Examination
Visual examination and Udder palpation -Clinical examination of the small ruminant udder should be performed in order to palpate possible lesions on the skin of the udder and note visually color changes, injuries and nodules as well as changes in the general shape of the udder for example, increase in size and atrophy. Pain is one of clinical sign of the mastitic udder; therefore, any pain reaction during palpation should be noted. Furthermore the presence of nodules or hardness within the gland should be recorded. Each teat was held between the thumb and the index finger, and palpated throughout its length to evaluate its shape, size and consistency; teats of the same udder were compared with each other (Mavrogianni et al., 2004). Finally, the supra-mammary lymph nodes of the examined animal should be palpated, in order to detect possible alterations in size or consistency (Mavrogianni et al., 2005).
Visual examination of the mammary gland secretion -The first few streams of milk are to be drawn from the teat onto a paddle or on the palm of the gloved hand of the investigator. Expression of milk can be evaluated; for example, expression in drops can be indicative of teats enosis. Then, presence of abnormal features in mammary secretion (e.g., clots, flakes, tints) should also be recorded (Mavrogianni et al., 2004).

Chemical Examinations
Milk tests -There are various tests used to determine type of the causative organisms and the infection level of the herd or even individual animal. Wherefore making recommendations about a diagnostic test, it is important to consider the consequences of falsenegative and false-positive results. In the case of mastitis, the costs of treatment or culling of positive animals are important factors, and the aim is generally to reduce the overall prevalence of infection without necessarily eliminating the disease; therefore, specificity takes priority. However, to eliminate a disease, the sensitivity would have a higher priority than specificity (

Californian mastitis test (CMT) -
The California mastitis test (CMT) is the quick and easy method used to detect subclinical mastitis. Agents in the reagent react with the DNA present in the epithelial and inflammatory cells to form a gel. The more DNA in the milk, the more gel is formed and the higher a count of the bacteria. The results are essentially subjected to assessment of the amount of gel, the degree of precipitation and gel formations are graded. The grades were: 0) no precipitate; 1) trace precipitate; 2) distinct precipitate/weak gel formation; 3) distinct gel formation; and 4) strong gel formation.
Somatic cell count (SCC) -The most common method for the detection of high bacteria counts in milk is the measurement of the somatic cell count (SCC), which gives an indication of whether an immune response to mastitis has begun in an animal. The numbers of epithelial and inflammatory cells (per ml) are counted in a milk sample, usually taken on the 'test' days at regular intervals during lactation. The numbers of somatic cells indicate the degree to which an immune response has been mounted against organisms that cause intra mammary infections (IMI). The SCC can be regarded as a surrogate measure of mastitis (Barillet et al., 2001). It is important to know the threshold level of SCC because it has impact on the milk production and health of sheep and goats in dairy animals, current estimates in the range of 600,000 to 800,000 cells per ml indicate presence of infection and animal with less than 500,000 cells per ml are considered to be 'healthy' (Barillet et al., 2001).

Modified White side test (WST) -California Mastitis
Test and Whiteside Test are recommended as threshold values for subclinical mastitis offering at least 93% accuracy of the diagnostic methods. In the WST, nucleic acids of the leucocytes of milk form a sodium salt with 0.4% NaOH producing a gelatinous mass to which serum solids and fat globules become absorbed to produce the characteristic precipitate of the reaction.

Bacteriological Examination of Milk Sample
Bacteriological examination of milk samples remains the 'gold standard' for etiological diagnosis of the mastitis in small ruminants. Isolation of bacteria in pure culture and sufficient growth from only one milk sample from a ewe/doe with mastitis suffices to demonstrate the etiological role of these organisms in the disease (Contreras et al., 2007). Repeat samplings and bacteriological examinations are required only in following up efficacy of treatment regimes or monitoring control programs. The greatest advantage of bacteriological examination of milk samples is the possibility to demonstrate the etiological role of the isolated organisms in mastitis. Nevertheless, the method requires some time for definitive results and cannot be performed onsite (Fragkou et al., 2014). Specificity and positive predictive value were higher for post-milking samples than for pre-milking samples and false positive diagnoses were more frequent for pre-milking samples. Post-milking samples should be used to diagnosis of small ruminant intra-mammary infection .

Factors that Affects Occurrence and Timing of Onset of the Mastitis
Various factors have been associated with the occurrence of mastitis in small ruminants such as parity, breed, udder halves, region and the number of lambs/ kids born. Notable suckling two or three lambs is associated with a greater mastitis risk than suckling only one lamb per ewe ( Moroni et al., 2005b). In addition, production forms and management practices may give rise to differences both in the epidemiology, bacteriology and clinical manifestations of mastitis. The timing of the onset of mastitis is important, in previous review of five studies by ; they reported that the majority of cases of mastitis occurred from the beginning of machine milking and during the first third of lactation.

Economic Impacts of Mastitis in Small Ruminants
Mastitis is of importance from three perspectives : hygienic, legal, and economic, that represent in reduced growth of lambs and their mortality, as well as mortality of ewes, treatment costs, reduced milk production and milk prices dependent on cellular quality in certain areas. The worldwide losses due to the disease are approximately $35 billion annually (Bilal et al., 2004;Abdullah, 2016). Mastitis has a major impact on both economy and animal welfare in small ruminant production and associated with a reduction of milk yield and quality in ewe due both clinical and subclinical ewe's udder infection (Albenzio et al., 2002). While, mastitis caused by microbial infections in dairy goats reduces milk yield, modifies milk composition, and potentially contributes to morbidity in herds (Gutierrez-Chavez et al., 2016). In this context, manufacture of products from the meat and milk of small ruminants presents an opportunity to improve the economics of farming worldwide, thus, economic losses due to mastitis are of major concern to the dairy industry. Loss of milk quality caused by subclinical infection in dairy sheep has a negative effect on cheese manufacture (Abdelgawad et al., 2016).

Treatment of Mastitis in Small Ruminants
Treatment techniques of mastitis in small ruminant had been described by many hypotheses . Theoretically, the aim of 'in-lactation' treatment is the clinical and bacteriological cure of infected halves of clinically affected ewes with mastitis and functional recovery. In fact, for acute and per-acute mastitis, the aim is to avoid death and allow culling. In any case, affected animal must be removed from the dairy flock until culling or complete recovery. Antimicrobials are used in treatment of mastitis predominantly during lactation and, less frequently, during the dry period (Onni et al., 2011).

Antibiotherapy of Small Ruminants Mastitis
During Lactation Period -The major antibacterial used as treatments for mastitis have been directed against Gram-positive organism are particularly, Staphylococci and Streptococci. In addition, application of management practices that decrease the prevalence of contagious pathogens such as Strepto-coccus species and S. aureus has shifted the focus of mastitis control and economic losses to environmental pathogens that are associated with more frequent episodes of subclinical mastitis. That way, antibiotherapy should start immediately after detection of the first signs of the disease on mastiticudder and should be performed using effective antimicrobial agents (Erskine et al., 2003).
2.9.3. Dry-off Treatment of Mastitis -Indiscriminate use of antimicrobial agents can result in bacterial resistance and super infection, prophylactic use of antibiotics is restricted to clinical situations in which the benefits outweigh the potential risks. The use of antibiotics for the prevention is better than the treatment of infections and the duration of prophylaxis should be closely observed to prevent unnecessary antibiotic exposure (Richard et al., 2012). This prophylaxis program was applied in previous study by Kiossis et al., (2007) to test the effectiveness of a standard treatment for the control of subclinical mastitis during lactating period in a modern, commercial dairy sheep farm, resulted in limitation of subclinical mastitis during lactation and a better health status for udders entering the dry period. While the intra-mammary ampicillin dicloxacillin treatment had the best treatment rates, the combination of intramuscular amoxicillin clavulinic acid was also successful. Intramuscular amoxicillin-clavulinic acid as the sole treatment was not as effective as intra-mammary therapy (Doğruer et al., 2010).

Control Techniques for Small Ruminants Mastitis
The fight against mastitis should not be limited to treating isolated clinical episodes, but also requires the surveillance and control of both, clinical and subclinical mastitis (Paterna et al., 2014). The know-ledge of risk factors and etiological agents involved are also important in order to recommend specific and efficient control measures for both clinical and subclinical mastitis (Arsenault et al., 2008). Prevention of mastitis in small ruminants can be described upon programs that include vaccination, culling, application of good husbandry control and proper maintenance of milking machines, better milking routine without over milking and removal of clusters without impacts, and hygiene after milking. Improved techniques depend on a better understanding of the nature of predisposing factors and breeding for resistance are the approach that is considered as a sustainable methods for mastitis control (Conington et al., 2008).

MATERIAL AND METHODS:
3.1. Study Area -The current study was conducted from October, 2017 to April, 2018 in the Southwestern Ethiopia, in and around Jimma town, the capital of the Jimma zone. The town is about 346 KM away from Addis Ababa, capital city of Ethiopia. It has a latitude and longitude of 7°40′N 36°50'E/7.667°N 36.833'E respectively. The altitude of Jimma is 1780 meters above sea level (masl). It has a tropical rain forest climate with bimodal rainfall of the long annual wet season from March to October. The temperature at Jimma is in a comfortable range, with the daily mean staying between 20℃ and 25℃ year-round. The production system observed around the area was croplivestock mixed production system (CSA, 2010).

Study Population -Animals included in this
study were apparently healthy lactating local breeds and extensively managed small ruminants (sheep and goats) from selected four different districts in and around Jimma town, Jimma zone, Southwest, Ethiopia. A total of 186 small ruminants (75 sheep and 111 goats) from different districts and animals found at different age, stage of lactation, and parity numbers were considered. Small ruminant populations were classified in to three groups of age depending on their parity number which includes; adult groups for one to two parity, medium groups for three to four parity and older groups for five and above numbers of parity by asking the owners of animals.

Study Design -
A cross sectional study was conducted from October, 2017 to April, 2018 in Southwestern Ethiopia, in and around Jimma town to estimate the prevalence of subclinical mastitis in small ruminants at the study area. In the current study, the districts found in Jimma zone were selected and grouped in a cluster, and within selected districts (Tiro Afeta, Kersa, Gumay and Jimma town) certain villages were selected according to simple random for milk samples collection.

Sample Size Determination -
To determine the prevalence of small ruminant subclinical mastitis in and around Jimma town, an expected prevalence of 50% was taken into consideration. The sample size for the current study was determined by the epidemiological formula with 95% confidence interval and 5% absolute precision: n = 1.96 2* Pexp*(1-Pexp)/d² Where: n = required sample size, Pexp = expected prevalence, 1.96² = 95% confidence interval d² = desired absolute precision. Based on the formula, the minimum sample size required was 384 from 192 target animals. However, in present study, since it is a dry season of the year from December to February months at the study area, it was impossible to collect 384 samples from the target population because of weaning of lambs and kids in the season the study was conducted. Therefore, maximum effort was exerted to get samples during the study period and a total of 372 milk samples were collected from 186 small ruminants.

Sampling Procedures/Methods -
In the current study, apparently healthy lactating small ruminants were sampled to find the target disease by excluding the animals with physically injured udders. From a total of 186 apparently healthy lactating sheep and goats sampled in the study area, 372 milk samples were collected and tested with CMT reagent. During the time of sample collection from the animals, all necessary hygienic protocols were maintained in order to avoid false-positive and false-negative results.

Milk Sample Collection
Procedures -Milk samples were collected according to the National Mastitis Council (NMC). After the udder halves have been washed with tap water, soap and dried with clean dry cloth, the teat ends were swabbed with cotton soaked in 70% ethyl alcohol for purpose of disinfection. Then after, teat ends dried, the first three milking streams were discarded and approximately 10ml of milk was collected aseptically from each halves in to sterile test tube. Each sample was labeled legibly and accompanied by necessary identification information which includes date of sampling, species of the animals, type of sample, halves of udder, age of the animal, parity number, stages of lactation, and area from which the samples were taken.

Californian Mastitis Test (CMT) of Milk
Samples -Milk samples taken from each udder halves were examined by CMT test and this screening test was conducted according to the standard procedure (Quinn et. al., 2002). In current study, all samples were examined on the field by using CMT reagent and CMT negative samples were decided by absence of gel form-ation and taking grade 0 and trace; then, they were recorded and discarded. However, CMT positive milk samples were whose moderately and highly form precipitation during mixed with CMT reagent and transported at +4℃ by packing within the icebox to laboratory for bacteriological tests and preservation purpose until procedures were completed according to the standard protocols with NMC.  (Quinn et. al., 2002).

Methods of Data Management and Analysis -
The collected data was checked, coded and entered in to Microsoft Excel Work Sheet and analyzed by using SPSS 20 software. Descriptive statistics like frequency and percentage were also calculated to describe small ruminant subclinical mastitis prevalence and compareison of the degree of association between risk factors was done by using chi-square (χ2) test. In all the cases, 95% confidence interval and 5% absolute precision errors were considered.
A P-value < 0.05 was used to verify statistically significance difference and when the P-value was found to be > 0.05, the difference in prevalence was considered insignificant. The prevalence of subclinical mastitis was calculated according to both the number of the collected samples and total number of the sampled animals described separately.

Prevalence of Small Ruminant Subclinical Mastitis and its Association With Risk Factors -
From a total of 186 apparently healthy lactating sheep and goats considered, 70(37.6%) of them were found to be infected with subclinical mastitis based on CMT. And out of a total of 372 milk samples collected, 92 (24.8%) of the samples from 70 small ruminants were positive for CMT and bacteriological tests. During the study period, potential risk factors such as species of animals, age, parity numbers and lactation stage were taken in to consideration to estimate the association with the occurrence of the subclinical mastitis in small ruminants.

Occurrence of Subclinical Mastitis between
Species of Animals -Among 75 and 111 sheep and goats species respectively tested with CMT and bacteriological tests, some of them tested positive and others negative with (P=0.107). Observed results indicated that, species as a risk factor is statistically not significant with occurrence of subclinical mastitis in small ruminant populations at study area with a total prevalence of 37.6% (Table 1).

Occurrence of the Subclinical Mastitis among Different Age Categories of Animals -
In the study, the difference in age among small ruminants was found to be greatly associated with the occurrence of subclinical mastitis in small ruminants (P=0.00).Out of the 186 animals, 49 (26.3%) were adult and 9 (4.8%) of them were showed positive result for subclinical mastitis of the small ruminant at study area. While among sampled population, 61 (32.8%) were medium and 15 (8.1%) animal numbers showed positive result for SCM of sheep and goats. Also, from 186 sample small ruminants, 76 (40.9%) were in older group and out of them 46 (24.7%) were found positive for subclinical mastitis.

Occurrence of Small Ruminant Subclinical
Mastitis among Parity Numbers -In present study, parity number of the target population were grouped according to their number of lambs/kids gave by asking the owners. Animals were classified in to 3 classes of groups like age and described. Parity number of animals was found to influence prevalence of subclinical mastitis in statistically significant state (P = 0.00). For this study, parity numbers and ages of animal were more related to each other based on information gathered from animal owners about number of lambs/kids they gave to them and decision to the age of ewe/doe was made based on their parity numbers ( Table 2).

Occurrence of Small Ruminant Subclinical
Mastitis among the Stages of Lactation -Out of 186 healthy lactating does and ewes, 63 (33.9%), 69 (37.1%) and 54 (29%) at their early, mid and late lactation stages respectively and showed differences in subclinical mastitis (Fig 1). The prevalence of subclinical mastitis among lactation stages of small ruminants was different but was not statistically significant (P = 0.09).

Fig 2:
Graphical presentation of SCM prevalence in small ruminants among lactation stages.

Prevalence of Small Ruminant Subclinical
Mastitis based on the CMT Test -In current study, 186 healthy lactating sheep and goats were subjected to CMT, out which 70 (37.6%) animals were positive for CMT, based on degree of gel formation. From 186 animals, 372 milk samples were collected and examined, out of which 92 (24.8%) were positive with CMT (Fig 2). However, these 92 samples were taken from right halve positive and left halve negative or vice versa and/or both halves positive of the 70 sheep and goats. Due to this reason, the overall prevalence calculation was used rather than individual udder halves' prevalence calculation. And the overall prevalence of the subclinical mastitis based on CMT result was estimated to be 37.6%.

DISCUSSION:
The overall 37.6% prevalence recorded of subclinical mastitis among small ruminants in the study area was found to closely agree with prevalence reported by Chingwen et al., (2002), who reported the prevalence of SCM in sheep and goats to be between 18% and 34%. Out of75 lactating ewes, 23 (30.6%) of them shown positive result for the subclinical mastitis. The total prevalence of target disease in small ruminant population at study area was recorded as 12.4%. This infection prevalence is relatively lower when compared with reports in other countries like Jordan (24.8%) by Almajali et al., (2003), Iran (39%) by Batavani et al., (2003), and Portugal (70%-92%) by Quiroga et al., (2003). The prevalence difference observed might be due to geographical variation, breed differences, management system they follow and there was no machine milking system in present study. For this reason, present finding is not agreed with above reports. However, the current result was more related with finding of the Gebrewahid et al., But, overall recorded prevalence of the present study in goat was slightly higher than those reported in Ethiopia 18.0% (Gebrewahid et al., 2012) and in South Ethiopia 15.5% (Megersa et al., 2009). All mentioned above differences might be due to many reasons such as; variation in number of sampled animals, difference in host breed and management system risk factors and in present study, there was no machine milking of target populations which might expose the animals to intra-mammary infection.
In present study, age of the animals is considered as one of the important forceful risk factors and clearly significant difference (P=0.00) was found between the three groups animal age which were grouped according to their parity number. The highest prevalence of SCM infections were recorded in does and ewes found at older age with prevalence of 24.7%. While, medium class of age was infected at second level with rate of 8.1% and lastly, adult age groups were affected at lower level of SCM with population prevalence of 4.8%.
In this study, the total target animal prevalence of SCM at study area was 37 In current study, stages of lactation were also considered as one of the important risk factors and three lactation stages were used, i.e early, mid and late lactation stage. Though, animals at the early lactation stage were less infected with total population prevalence of 9.7% and both animals at mid and late lactation stages were found at equal prevalence of infection in target population of 14%. Present report showed that, the rate of SCM occurrence in small ruminants was increased with prolonged lactation. This result was found to be in agreement with previous reports of Leungo et al., (2004) as he was reported the prevalence of E. coli in small ruminant 25%. This difference might be due to geographical variation, tests used in the methodology, exposure rate of hosts to the bacteria, management system of the flocks and there was no machine milking in present study.

CONCLUSION AND RECOMMENDATIONS:
The current study estimated a high prevalence of small ruminant subclinical mastitis, occurred due to some bacterial pathogens such as S. aureus, S. epidermidis, S. intermidus, S. hyicus, Str. agalactiae and E. coli with considerable prevalence. The highest prevalence of subclinical mastitis in target population was caused by Staphyloccocus species followed by E. coli and the lowest prevalence was estimated in Streptococcus agalactiae. The high prevalence of small ruminant mastitis in the study area may be associated with the lack of community awareness about the disease and the less attention given by professionals. Due to this reason, reported prevalence of small ruminant subclinical mastitis will be very important with regards to animal welfare, economic loss, and public health aspects. Among the risk factors considered (i.e. species, age, stage of lactation and parity numbers) in target population, age and parity number of the animals were indicated to influence the prevalence of subclinical mastitis in statistically significant condition. Based on the above conclusion, the following recommendations are forwarded:- Since milk was observed to be consumed only by elders and sometimes by diseased children; community awareness should be performed about the risks associated with consumption of milk of small ruminants, and how to maintain proper udder hygiene before and after milking.  Subclinical mastitis can cause growth retardation in small ruminants which may lead to poor meat and carcass quality, hide and skin, and wool production; and thus, sheep and goats should be given attention and proper diagnoses and treatment practices be practiced.  As the older age groups and higher parity numbers are mostly affected classes, owners should remove such animals on time from their flocks, so as to minimize the risks of pathogen distribution.  Further research should be conducted with emphasis given to the epidemiology of the disease and molecular characterization of infectious pathogens responsible for its occurrence.

ACKNOWLEDGEMENTS:
First of all, I would like to provide my great gratitude for the almighty God. I am highly impressed to express my heartfelt thanks and gratitude to University of Gondar, College Veterinary Medicine and Animal Sciences for its effort in designing externship programs. I would like to extend my thanks to Jimma University, School of Veterinary Medicine, microbiology laboratory staff workers for their patience, permission and delivery full information during my research works. Last but not least, I would forward my heartfelt thanks to my advisor, Dr. Abebe Belete who helped me by devoting his time to read and correct this study manuscript and to enable me finish all my works by solving the many challenges I encountered.

CONFLICTS OF INTEREST:
The Authors declare there is no competing interest to publish the present research study.


extfieldsofstudy: Economics Medicine

text:
An empirical study on spatial–temporal dynamics and influencing factors of apple production in China

In the context of supply-side structural reform, revealing the characteristics of spatial–temporal dynamics and influencing factors of China’s apple production layout is of great significance to ensure apple supply and demand balance and timely adjustment of industrial policies and regional layout strategies. Based on national and provincial apple production data from 1978 to 2016, this study used the apple production concentration index to analyse the evolution characteristics of regional apple production patterns in China. A theoretical analysis framework was established and a spatial econometric model was used to quantitatively explore the influencing factors of China’s apple production layout. The results showed that, first, since the reform and opening-up policy, a general trend of fluctuating growth was found for apple production in China. The centre of apple production layout moved in the southwest direction, with the shift from the Bohai Bay region to the Loess Plateau region. Second, apple production had a significant spatial correlation, while the degree of spatial agglomeration gradually decreased. Third, these changes were significantly influenced by apple comparative income, infrastructure, policies, and climatic conditions. Therefore, it is necessary to continue optimizing and adjusting the apple spatial layout to enhance the technological progress and economic effect of the apple industry and to ensure the stability and balance of regional supply and demand.

Introduction
Since the reform and opening up policy, the apple has taken the lead in entering market-oriented reform. It was also among the first batch of agricultural products that China planned to fully liberalize in the 1980s [1]. According to the statistics data from the National Bureau of Statistics of China (NBSC) [2,3], in the era of rising fruit consumption by both urban and rural residents, the area of apple production increased from 0.68 million ha in 1978 to 2.32 million ha in 2016, with an average annual growth rate of 3.29 percent. Meanwhile, the output from apple production has increased from 2.28 million tons in 1978 to 43.88 million tons in a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 2016, with an 8.10 percent average annual growth rate. In 2016, the areas under apple production and apple output in China accounted for 46.09 percent and 47.41 percent of the global area and output of apple production, respectively. This showed that the area and output in China had increased sharply from 17.30 percent and 7.06 percent in 1978. Thus, China has become the world's largest apple producer [4,5]. However, with an increase in income and the improved standard of living among urban and rural residents, consumers are paying more attention to the quality and safety of agricultural products as well as the nutritional value of foods [6][7][8][9], thereby accelerating the upgrade of apple market demand structure [10][11][12][13][14]. The spill-over effects of high relative prices of apple, policy support for industrial poverty alleviation, and the heterogeneity of farmers' individual rationality induced the blind production and expansion of apple production areas in unsuitable and sub-suitable areas. As a consequence, the proportion of apple production areas in unsuitable and sub-suitable areas is too high, the quality and price of apple are gradually declining, and the spatial development of apple production layout is uneven and uncoordinated. The existing apple production and supply model is struggling to adapt to changes in market demand in the new era. In addition, the development of the apple industry faced an imbalance structure between supply and quality structure [15]. Domestic labour costs have risen rapidly, driving up production costs of labour-intensive agricultural products, such as apple. Moreover, the increasing apple import volume of China has produced a crowding-out effect on the domestic apple supply market. The continued growth of domestic apple production has further strengthened the internal and external pressures of the domestic apple industry and forced apple production to improve in quality and efficiency. Driven by high output, high import, and high cost of production, the pressure of structural reform on the supply side of the apple industry is highlighting the necessity and urgency to optimize the spatial layout of China's apple production.
The issue of the spatial layout of China's apple production has attracted widespread attention from the Chinese government and scholars. To adjust the production structure of apple and to optimize the regional industrial layout, the Ministry of Agriculture and Rural Affairs (MARA) of the People's Republic of China has successively formulated a series of policy plans, such as The Regional Layout Planning of Dominant Agricultural Products (2003)(2004)(2005)(2006)(2007), The Advantageous Regional Development Plan for Apple (2003)(2004)(2005)(2006)(2007) The Regional Layout Planning of National Advantage Agricultural Products (2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015), and Apple Advantage Regional Planning (2008Planning ( -2015. From the academic point of view, studies on apple production layout mainly focus on the following aspects. First, some studies analysed changes in the apple production layout of China, e.g. Liu and Fan [16] and Zhang et al. [17], who found that the apple production layout of China has gradually changed from the four main producing areas of the Bohai Bay, the Loess Plateau, the Yellow River Old Road, and the Southwest Cold and Highland to the two advantageous areas of the Bohai Bay and the Loess Plateau, and the dominant appleproducing areas transferred to the northwest. Second, the literatures addressing the change mechanism of apple production layout in China used the panel data models to reveal that the endogenous competition effect of inter-regional apple production [18], changes in labour and land cost [19][20][21][22], the ecological factors of apple growth [18,20], climate change [23,24], and policy intervention [20] are the main motivations for promoting change in the apple production layout of China. Third, some researchers, such as Zhang et al. [25] and Feng and Huo [26], analysed the spatial agglomeration characteristics of the apple environment total factor productivity (ETFP) and found that the development of the apple ETFP in the main apple production areas in China had obvious spatial correlation. Wang and Wei [27] believed that the trend of changes in the spatial layout of apple production and seasonal changes are the main reasons for apple price fluctuations.
In summary, first, most studies focused on the time series changes, geographical aggregation characteristics, and influencing factors of crops, including grain [28,29], vegetables [30], tea [18,20], and peanut [31]. However, the understanding of the spatial pattern of China's apple production is still very limited. Although some researchers have used different research methods to analyse the characteristics of regional pattern changes in China's apple production over a certain period from different spatial scales, the research conclusions are quite different, due to the different research time intervals, methodological approaches, and measurement indicators, and it is difficult to fully reflect the evolution of the production pattern and the characteristics of agglomeration since the marketization reform of China's apple industry in 1978. Second, due to the wide and diverse nature of apple production areas in China, the significant differences in regional resource endowments, technical equipment levels, market development, supply-demand relationship, and government industrial support policies, the production layout, and the spatial development of apples are uneven and uncoordinated. The geographical pattern of production fluctuates greatly. Since the reform and opening up, certain questions have arisen, such as, what kind of spatial-temporal dynamics characteristics exist in China's apple-producing areas? What are the factors that affect the apple production layout? The existing literatures cannot provide an in-depth analysis of these issues and ignore the impact of spatial effects on the apple production layout. Third, under the realistic background of the significant change in the relationship between production supply and market demand, it is important to ensure the supply stability and regional balance of apple supply and demand and to adjust apple industrial policies and regional layout in the process of promoting agricultural supply-side structural reform and implementing a rural revitalization strategy. Therefore, to fill the existing gap, and to provide a scientific basis for optimizing and adjusting apple production layout and institutional arrangements, stabilizing and improving China's apple production level, this study revealed the dynamic evolution trend and the spatial-temporal characteristics of China's apple production layout from the national and provincial perspectives. Moreover, this study used geographic analysis methods and spatial econometric models to incorporate spatial interaction effects into empirical analysis to explore the driving factors affecting the apple production layout.
Thus, the rest of this study is structured as follows: Section 2 theoretically discusses the influencing factors of apple production layout. Section 3 introduces the data and primary methods used in the analysis. Section 4 provides a descriptive analysis of spatial-temporal dynamics of apple production in China. Section 5 uses a spatial econometric model to empirically analyse the factors affecting the apple production layout. Section 6 concludes with a summary of the major findings and their policy implications.

Theoretical analysis
According to the theory of agricultural production economics, apple production is an organic combination of natural and economic reproduction. In the process of natural reproduction, apple production is bound by natural resources, such as climate and land resources [32]. In the process of economic reproduction, apple production is subject to levels of economic and social development, the comparative benefit of crops, scientific and technological progress, infrastructure development, and policy interventions. The combined effects of the above factors have motivated the apple production layout. Therefore, based on the six factors of resource environment, opportunity cost, infrastructure, technological progress, consumer demand, and policy environment, this study combined the actual situation of China's apple production development and data accessibility and empirically analysed the influencing factors of China's apple production layout. Referring to existing research, this study used the apple area concentration index (i.e. the proportion of apple area in each province to the total apple area nationwide) to characterize the agglomeration degree of apple production layout.

Resource environment
Natural disasters caused by changes in climate factors, such as temperature and rainfall, directly affect apple yield and quality [18,28], which in turn affect apple production efficiency and farmers' enthusiasm. China's apple production areas are widely distributed, and the climatic conditions vary greatly among provinces [33]. The natural disasters and the degree of disasters caused by climate change affect the layout of apple production [29,32]. This study used the area of apple production affected by disasters to characterize the impact of natural disasters on apple production in each province. In addition, according to the resource endowment theory, regions should produce and export products based on their relatively abundant factors. As one of the most basic agricultural production resources, cultivated land resources are the main resource constraint affecting apple production. Generally, there is a positive relationship between the area of apple production and cultivated land resources, but with the advancement of industrialization and urbanization, the reduction of cultivated land resources and population growth have led to a decrease in per capita arable land area [34]. The changes in per capita arable land area in different provinces and regions also vary due to differences in industrialization and urbanization development levels and population changes. This study used the per capita arable land area of each province to characterize their status of cultivated land resources. Based on the above analysis, the following hypotheses were made: H1: The disaster area of apples has a negative impact on the agglomeration of apple production layout.
H2: Per capita arable land area has a positive impact on the agglomeration of apple production layout.

Opportunity costs
According to the theory of comparative advantage and the assumption of the "rational economic man", farmers choose whether to engage in agricultural production based on the principle of maximizing income [35]. Due to the uncertainty of output caused by the natural and market risks of agricultural production, the lack of comparative advantages of agriculture over non-agricultural industries, coupled with the rise in labour wages of non-agricultural industries, has led to an increase in the opportunity cost of apple cultivation. Therefore, the thrust formed by the inherent weakness of agriculture and the pull of the high remuneration in nonagricultural industries had jointly promoted the shift of agricultural labour to non-agricultural industries [36]. The differences in the development level of non-agricultural industries in different provinces and regions led to the creation of different non-agricultural employment opportunities for apple farmers, thus, provinces and regions with high non-agricultural opportunities may have less apple production areas and vice versa. This study used the proportion of rural non-agricultural labour to rural labour in each province to characterize non-agricultural employment opportunities in each province. Moreover, besides the choice between agricultural and non-agricultural industries, farmers also need to choose a planting structure within the agricultural system. If income from apple production is higher than that of other crops, farmers are more inclined to grow apples. Therefore, the comparative benefit of apple production will also have an impact on the area of apple production [30]. Due to the lack of data on the net profit per acre of apples and other crops in some provinces, this study employed the methods used by Zhang and Wang [31], where the proportion of area of the apple production to a total area of crop production in each province was used to characterize the comparative benefits of apple production in the corresponding province. Based on the above analysis, the following hypotheses were made: H3: Non-agricultural employment opportunities have a negative impact on the agglomeration of apple production layout.
H4: Comparative apple income has a positive impact on the agglomeration of apple production layout.

Infrastructure
Apple cultivation is a natural reproduction process, and its growth requires sufficient water. However, most of China's apple production areas are distributed in the northern mountainous and hilly areas, where the climate is mainly dry and less rainy [5]. Therefore, the conditions of irrigation facilities are important factors affecting apple cultivation [28]. This study used the effective irrigated area of apples in each province to characterize the conditions of their apple irrigation facilities. Moreover, according to the agricultural location theory, the geographic distance of agricultural production and sales determines the land use structure. As a highvalue agricultural product (HVP), apple is highly commoditized and marketized. The transportation conditions are the link connecting apple production to consumption and are also an important condition for realizing apple market value [18]. Therefore, the transportation conditions in each province affect its apple production area. The land transport density of each province is used to characterize its transportation conditions. Based on the above analysis, the following hypotheses were made: H5: Apple irrigation facilities have a positive impact on the agglomeration of apple production layout.
H6: Traffic conditions have a positive impact on the agglomeration of apple production layout.

Technological progress
The impact of technological progress on apple production layout is mainly reflected in the apple yield level. The progress of apple production technology is conducive to the improvement of apple production efficiency and economic benefits [18]. However, due to the differences in the levels of production technology innovation and promotion in various provinces, the apple's comparative income with other crops has changed, which in turn affects the apple production layout. Most scholars used time trends to express technological advancement variables, but for apple production, technological progress is more manifested in yield increase. Therefore, this study used apple yield rather than a time trend to indicate technological advancement. Considering 1978 as a base year, the apple yield was set to be 1 in 1978, and the standardized yield from 1979-2016 is used to determine the technological advancement of apples in each province. Based on the above analysis, the following hypothesis was made: H7: The apple's technological advancement has a positive impact on the agglomeration of apple production layout.

Consumption demand
According to the agricultural location theory, the high transportation costs and product losses of long-distance transportation have limited agricultural production and circulation to a certain area. Due to the high degree of commercialization, the demand for apple consumption stimulates the growth of apple production in the region. In general, apple consumption demand is positively related to the size and density of the population [17]. This study used the proportion of the total population at the end of the year in each province to the total national population at the end of the year to characterize the potential apple consumption demand in each province. Based on the above analysis, the following hypothesis was made: H8: Apple consumption demand has a positive impact on the agglomeration of apple production layout.

Policy environment
Changes in national policies have an impact on the enthusiasm of apple farmers and apple production area of the region [37]. For example, to ensure national food security, the government imposed the "Provincial Governor Responsibility System" (PGRS) policy from 1995 to 1997 [38,39]. However, due to limited arable land resources, this policy has a certain crowding-out effect on apple production. In 2003 and 2009, the government released the Apple Advantage Regional Development Plan (2003)(2004)(2005)(2006)(2007) and Apple Advantage Regional Planning (2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015) (AARP), and the implementation of the advantageous regional layout plans has a certain impact on the agglomeration of apple production layout [18,40]. Based on the above analysis, the following hypotheses were made: H9: The PGRS has a negative impact on the agglomeration of apple production layout. H10: The AARP has a positive impact on the agglomeration of apple production layout.

Data sources and description
In the absence of farm-level panel data, aggregate data are often used for analysing the factors influencing China's apple production layout. Evaluations relying on aggregate data can reveal the characteristics of China's apple production layout agglomeration at the regional or national levels. The data set used in the present study covered 23 apple-producing provinces in China's mainland for thirty-nine consecutive years (1978-2016), with a total of 858 observations. To maintain data consistency and comparability, this study incorporated Chongqing data into Sichuan province. In this way, there are 22 provinces. Data used in this study were derived from the China Rural Statistical Yearbook , Statistics of Agriculture in New China for 60 Years, China Statistical Yearbook , and statistical yearbooks of various provinces. According to the Apple Regionalization Plan of China Agriculture Research System (CARS), China's apple production area is divided into five regions, including the Bohai Bay production area (including Shandong, Hebei, Liaoning, Tianjin, and Beijing), the Loess Plateau production area (including Shaanxi, Gansu, Shanxi, Ningxia, and Qinghai), the Yellow River Old Road production area (including Henan, Anhui, and Jiangsu), the Southwest Cold and Highland production area (including Sichuan, Chongqing, Yunnan, Guizhou, and Tibet), and the Special production area (including Inner Mongolia, Heilongjiang, Jilin, Hubei, and Xinjiang). Among them, the Bohai Bay and the Loess Plateau production areas are the advantageous apple-producing areas in China [5].

Empirical methodology
Apple production concentration index (APCI). This study used the apple production concentration index to analyse the evolution characteristics of regional apple production patterns in China. This indicator not only comprehensively examines the contribution of apple output in each province to the total national apple output in a certain period of time but also compares differences in the growth rate of apple output in each province. The calculation formula is as follows: where C i and P i indicate the apple production concentration index (APCI) and apple output of the provinces i, respectively. Exploratory spatial data analysis (ESDA). Spatial autocorrelation is a crucial method of ESDA [41,42]. It reveals the interaction mechanism between spatial agglomeration and spatial heterogeneity between research objects through the description and visualization of certain geographical phenomena [43] and is a measure of the agglomeration degree of a certain geographical phenomenon in the spatial domain [44,45]. In this study, spatial autocorrelation is used to test whether the area of apple production in a certain province is significantly correlated with the areas of apple production in neighbouring provinces. According to the spatial autocorrelation coefficient, it is divided into positive and negative correlations. A positive correlation indicates that the area of apple production in a certain province has the same trend as the areas of apple production in its neighbouring provinces, while a negative correlation indicates just the opposite. According to the size of the spatial analysis range, spatial autocorrelation can be divided into global and local spatial autocorrelation.
Global spatial autocorrelation is used to describe the spatial characteristics of apple production nationwide and to determine whether there are agglomeration characteristics in apple production in space. Spatial statistics use the global spatial autocorrelation statistics, such as Global Moral's I, Global Geary's, and Gettis' G, to measure the spatial correlation and spatial difference of the overall region. Compared with other global spatial autocorrelation statistics, Global Moral's I index is less susceptible to deviation from normal distribution [46,47]. Therefore, this study chooses Global Moral's I index to measure the global spatial autocorrelation of China's apple production. The specific calculation method is as follows: where x i and x j indicate the apple areas of the provinces i and j, respectively, i, j = 1, 2, � � �, n (n is the total number of provinces, n = 22), � x is the average area of apple production in all provinces, and w ij indicates the elements of the spatial weight matrix W. The value of Global Moral's I index is between -1 and 1. A positive value of Global Moral's I indicates a positive spatial autocorrelation in the spatial distribution of apple area in neighbouring provinces and a negative value indicates a negative spatial autocorrelation. The closer the value of Global Moral's I is to ±1, the stronger the space autocorrelation of apple production is; the value of 0 indicates that there is no spatial autocorrelation in apple production and it is spatially random.
Global spatial autocorrelation focuses on analysing the spatial distribution of a certain geographical phenomenon in the overall regional space, ignoring the spatial correlation of this geographical phenomenon in some local areas, and thus local spatial autocorrelation is used to analyse whether there is high-or low-value aggregation in apple production between each province and its neighbouring provinces. This study used the local spatial autocorrelation index (Local Moral's I index) and the spatial association local indicator Local Indicators of Spatial Association (LISA) to measure the spatial agglomeration, heterogeneity, or random distribution characteristics of apple production in various local areas [48]. The specific calculation method is as follows: where n, x i , x j , � x, w ij have the same meaning as in Eq 2. The Local Moral's I index has a similar meaning to the Global Moral's I index. A positive Local Moral's I indicates that the high (low) value of a province is surrounded by high (low) values, and a negative Local Moral's I indicates that the high (low) value of a province is surrounded by low (high) values.
Local spatial autocorrelation can visualize the spatial pattern of the local differences in apple production through Moran scatter plots and local spatial autocorrelation clustering maps. Moran scatter plots and local spatial autocorrelation cluster plots are based on the numerical magnitude of Local Moral's I and its significance, and the local spatial units are divided into HH (high-high), LH (low-high), LL (low-low), HL(high-low), and other types [49], where the HH indicates that the apple area of a certain province and its neighbouring provinces is relatively large; the LH suggests that the apple area of a certain province is small, but the area of its neighbouring provinces is larger; the LL indicates that the apple area of a province and its neighbouring provinces are relatively small; and the HL indicates that the apple area in a certain province is larger, but that of its neighbouring provinces is smaller. The left spatially randomly distributed regions indicate that Local Moral's I did not pass the significance test and that the spatial agglomeration was not strong.
Spatial econometric model. In some areas, the relatively high price spill-over effect of apple induced the expansion of the production area in adjacent areas, which led to the spatial correlation of apple production layout. However, the existing research on the layout of apple production mostly used panel data models in the empirical analysis, which masks the role of spatial differences and ignores the influence of spatial effects on the agglomeration of apple production layout. At present, the spatial econometric analysis method is an effective means to quantitatively study the layout of agricultural production. The spatial econometric analysis method abandons the assumption of the traditional econometric that space is homogeneous and adds spatial interaction effects to the econometric model, which can provide more accurate analysis. The spatial correlation of the subject is the premise and basis for constructing the spatial econometric model. Therefore, based on the spatial correlation analysis, this study further used the spatial panel data model to quantitatively analyse the influencing factors of China's apple production layout agglomeration. The spatial panel data models mainly include the Spatial Lag Regression (SLR) model, the Spatial Error Model (SEM) and the Spatial Dubin Model (SDM). Among them, the SDM is the general form of the SLR and SEM models and is also a standard framework for capturing various types of spatial spill-over effects [44]. Therefore, this study used the SDM for empirical analysis. The matrix form of the model is as follows: where R indicates the concentration index of the apple area, W indicates the adjacent spatial weight matrix or the geographic distance spatial weight matrix, WR indicates the spatial lag term of the apple area concentration index, and X indicates the influencing factors of China's apple production layout agglomeration. ε reports the error term.

Temporal dynamics of apple production
From the analysis of changes in apple production in China (Figs 1 and 2), its volatility is obvious. The area of apple plantation, total output, per capita output, and yield have shown a volatile growth trend. Among them, the fluctuation of plantation areas is larger. From its changes, apple production in China has gone through the following five stages: 1. Phase I (Stable period (1978)(1979)(1980)(1981)(1982)(1983)(1984)): apple production areas remained essentially unchanged, maintained at approximately 0.70 million ha, and the total output, yield and per capita output of apples were also stable. The reason is that although the rural economic system reform began in 1978 and the rural commodity economy was active, it was not compatible with production development and social needs due to the single distribution channels of fruits, the restrictions on purchase and sale policies, price management, and the weak construction of circulation infrastructure. The contradiction was prominent, which led to the slow development of China's apple production and sales. From 1978 to 1984, China's fruit business implemented a planning management model with the supply and marketing cooperative as the main body, which restricted the development of apple production. A (1985-1991)): the area under apple production expanded rapidly, from 0.87 million ha in 1985 to 1.66 million ha in 1991, with an average annual growth rate of 11.48 percent. The total apple output and per capita output remained basically unchanged, while apple yield declined. The findings revealed that, although the area of apple production rapidly expanded during this period, the apple production system still used the traditional cultivation methods and techniques, which might be the main reason for the declined apple yield. Therefore, the effect of increasing the area of apple production was not obvious.

Phase II (Rapid growth period
3. Phase III (Rapid growth period B (1992-1996)): the area of apple production increased sharply from 1.91 million ha in 1992 to 2.99 million ha in 1996, with an average annual growth rate of 11.76 percent. In response, the total output, yield, and per capita output increased rapidly. In particular, the national apple area reached a record high level in 1996, with the total apple output close to 0.14 million tons, and the apple yield increased from 3,424.39 kg/ha in 1992 to 5,707.45 kg/ha in 1996, with an average annual growth rate of 13.62 percent. The results revealed that, although the apple yield has increased yearly due to the introduction of new varieties and the improvement of production technology, the contribution of the increased area of apple production was still greater than the corresponding yield. It can be seen that the increase in apple production during this period still depended on the traditional method of increasing production area.

Phase IV (Rapid decline period (1997-2003))
: with an average annual growth rate of -6.47 percent, the apple production area rapidly decreased from 2.84 million ha in 1997 to 1.90 million ha in 2003. The apple yield increased from 6,066.66 kg/ha to 11,103.98 kg/ha, with an average annual growth rate of 10.60 percent, which offset the impact of production area reduction on total apple output. The reason is that since 1997, national apple production has entered a stage of adjustment, which has greatly reduced the low-effect apple orchards, such as unsuitable varieties, old orchards, and poorly managed orchards in both the unsuitable and suitable areas for apples, while the apple area in the apple eugenic and high-economy areas developed steadily. At the same time, the apple yield level has increased rapidly, and its effect on increasing output has become increasingly visible due to several factors such as the use of improved varieties, technology innovation, upgrading of orchard equipment and infrastructure, and improving the skills of fruit farmers.

Phase V (Slow growth period (2004-2016))
: though the apple area increased gradually from 1.88 million ha in 2004 to 2.32 million ha in 2016, with an average annual growth rate of 1.80 percent, the apple yield increased from 12,615.90 kg/ha to 18,883.72 kg/ha, resulting in a continuous increase in total apple output and per capita output. The reason is that since 2001, China's apple production has gradually changed from quantitative to quality-effective, the apple production area has become more reasonable, and production has entered into a new stage of industrialization. Meanwhile, with the continuous improvements in apple production technology and management level since 2003, the contribution of yield in total apple output has continued to be higher than the increase in output due to the increase of the apple production area.
In general, China's apple area, total output, per capita output, and yield all increased from 1978 to 2016 to different degrees. Among them, the area of production increased from 0.68 million ha to 2.32 million ha, with an average annual growth rate of 3.29 percent. The total apple output increased from 2.28 million tons to 43.88 million tons, with an average annual growth rate of 8.10 percent. Likewise, per capita apple output increased from 2.36 kg to 31.74 kg, with an average annual growth rate of 7.08 percent. Similarly, with an average annual growth rate of 4.66 percent, the apple yield increased from 3,351.17 kg/ha to 18,883.72 kg/ha.  3. Apple production in the Yellow River Old Road production area continued to be stable. The concentration of apple production in the Yellow River Old Road production area increased slightly, from 8.55 percent in 1978 to 12.13 percent in 2016, with an increase of 3.58 percent. In this area, the concentration of apple production in Henan, which is the main producing area of the Yellow River Old Road production area, increased by 3.09 percent. Moreover, the concentrations of apple production in Anhui and Jiangsu increased by 0.36 percent and 0.13 percent, respectively.

Spatial concentration characteristics of apple production
4. The status of apple production in the Southwest Cold and Highland production area remained essentially unchanged, with its apple production concentration merely increasing by 0.20 percent from 1978 to 2016. In this area, the concentration of apple production in Yunnan and Guizhou increased by 0.41 percent and 0.07 percent, respectively, while the concentration of apple production in Tibet decreased by 0.10 percent.
5. The concentration of apple production in the Special production area increased slightly. The concentration of apple production in the Special production area increased from 2.88 percent in 1978 to 4.19 percent in 2016, with an increase of 1.31 percent. Xinjiang is a typical representative in the Special production area, and its apple production concentration increased from 1.78 percent in 1978 to 3.11 percent in 2016, showing an increase of 1.33 percent. Additionally, the concentration of apple production in Heilongjiang and Inner Mongolia remained mostly unchanged, while the concentration of apple production in Jilin and Hubei decreased by 0.32 percent and 0.09 percent, respectively (Table 1).
In summary, the concentration of apple production in the dominant area of the Loess Plateau has continued to increase and has become China's largest apple production area. The Bohai Bay area has gradually declined to become the second-largest apple production area in China. From the perspective of the distribution pattern of apple production provinces, the top 6 provinces in apple production concentration were transformed from Shandong, Liaoning, Hebei, Henan, Shaanxi, and Shanxi in 1978 to Shaanxi, Shandong, Henan, Shanxi, Hebei and Gansu in 2016. The concentrations of apple production in some provinces in the central and western regions (such as Shaanxi, Shanxi, and Gansu) continued to rise, and these regions have gradually become China's main apple production provinces. The reason is that China's apple production technology is relatively backward overall at present, as most apple production links, such as fertilization, pesticide application, bagging, pruning and fruit picking, were mainly completed by laborers. With the rapid progress of industrialization and urbanization in the Bohai Bay area, a large number of high-quality labourers were off-farm, resulting in the rising labour costs of apple production, which in turn prompted the transfer of apple production from the expensive labour areas in the east to the inexpensive labour areas in the central and western regions. It can be seen from the production area and the provincial level that the centre of China's apple production is moving southwest, shifting from the dominant area of the Bohai Bay to the dominant area of the Loess Plateau.

Spatial autocorrelation of apple production
GeoDa v.1.12 and ArcGIS10.3.1 software were used to carry out the spatial autocorrelation test of apple areas in various provinces of China from 1978 to 2016. The results are shown in Table 2.
The Global Moral's I index of the apple area in each province from 1978 to 2016 is calculated using Eq 2. The results are shown in Table 2. It can be seen that the Global Moral's I index of apple area has been positive over the years, and most of the Global Moral's I index has passed at least the 10 percent significance level test, indicating that apple production in China at the provincial level has not been randomly distributed in space since 1978 and has significant spatial autocorrelation. The areas with relatively close apple production levels showed spatial agglomeration characteristics. That is, if a province has a large apple area, so do their neighbouring provinces. Similarly, if a province has a smaller apple area, so do their neighbouring provinces. China's apple production presented a significant spatial spill-over effect.
Over time, the Global Moral's I index of apple acreage showed an "M-type" change trend, and the Z value of the test statistic showed the same trend. Overall, the Global Moral's I index and Z statistic of the apple area showed a downward trend, which decreased from 0.2079 and 2.3847 in 1978 to 0.1194 and 1.6416 in 2016, indicating that the spatial agglomeration effect of China's apple production has been wave-like since 1978. As time progresses, the spatial agglomeration degree of the apple area is gradually decreasing.
Using Eq 3, the Local Moral's I index value of the apple area in each province of China is calculated using the GeoDa0.95i and ArcGIS10.2 software, and the areas where the Local Moral's I index passes the 5 percent significance level test were divided into HH (high-high), LH (low-high), LL (low-low) and HL (high-low) types. The result showed that the number of HH and LL types decreased from 7 provinces in 1978 to 6 provinces in 2016, indicating that the spatial agglomeration characteristics of apple area were weakened, and this result is consistent with the result of the global space autocorrelation analysis of the apple area. Specifically, the regions with smaller apple areas are mainly distributed in the east, central, and southern China regions, which are south of the Yangtze River. The regions with large apple areas are mainly distributed in the central part of the north. The areas of production in HH-type provinces (Hebei and Henan) in 1978 were transformed to Shanxi and Henan provinces in 2016. The high agglomeration apple area moved to the southwest. In 2016, the spatial correlations of the six provinces (including Shanxi, Henan, Hunan, Jiangxi, Fujian, and Guangdong) were all tested by the 5 percent significance level. Among them, Shanxi and Henan provinces and their neighbouring provinces had more areas of apple production, and they belonged to the hot spot area. The areas of apple production in Hunan, Jiangxi, Fujian, and Guangdong provinces and their neighbouring provinces were relatively small, belonging to the cold spot area. This shows that China's apple production is mainly concentrated in the central part of the north, centred on Shanxi and Henan.

The influencing factors of spatial-temporal dynamics of apple production in China
Explanatory variables. According to the theoretical analysis, from the six aspects of resource environment, opportunity costs, infrastructure, technological progress, consumption demand, and policy environment, we chose variables such as apple disaster areas, per capita arable land, non-agricultural employment opportunities, apple comparative income, apple effective irrigation area, transportation density, apple yield, population proportion, food policy and industry planning to explain the spatial-temporal dynamics of China's apple production. Table 3 presents the meaning and summary statistics of the variables.

Estimation results and discussion
Based on Moral's I index, there was significant spatial autocorrelation in apple production. If spatial autocorrelation is ignored, the regression analysis may exhibit a "pseudo-regression" phenomenon. To select the most suitable model, this study used the Stata version 15.0 software package for the likelihood ratio (LR) and Hausman tests. The p-value of the LR test is 0.0000, so we can reject the null hypothesis that the SDM can degenerate into the SLR or the SEM. The Hausman statistic is -1,039.36, which is negative, so the null hypothesis of the random effect can be accepted. Therefore, it is most appropriate to finally determine the SDM with random effects using Stata version 15.0 software. The SDM model estimation results are shown in Table 4. As illustrated in Table 4, the SDM using two spatial weight matrices has consistent estimation results, indicating that the model estimation results were relatively robust. Since the SDM using the geographic weight matrix had a better goodness-of-fit (R 2 = 0.9273), the interpretation of the estimation results is based on the estimation results of the model. From the significance of each coefficient, comparative apple income, apple effective irrigated area, transportation density, the PGRS, the AARP, and apple disaster area have significant effects on the agglomeration of the apple production layout with decreasing influence. The remaining variables are not significant but are consistent with the expected direction.
1. Comparative apple income has the most significant positive impact on the agglomeration of apple production layout, which is consistent with the expected direction of H4, indicating that farmers will compare the economic benefits between different local crops when investing in different agricultural production projects and determine the optimal production plan according to the principle of maximizing income [30]. When the apple income in the region is higher than that of other crops, rational farmers will choose to increase the apple area, which is conducive to increasing the agglomeration degree of regional apple production.
2. Infrastructure, such as water conservancy systems and transportation, has a significant positive impact on the agglomeration of apple production layout, which is consistent with the expected direction of H5 and H6, indicating that irrigation facilities and other facilities are conducive to improving apple production conditions and ensuring the stability of apple production [29,32]. The accessibility degree of transportation is conducive to increasing the efficiency of apple circulation, facilitating the docking of apple production and sales, and contributing to the realization and enhancement of the market value of apples [30]. 3. The policy environment has a significant impact on the agglomeration of apple production layout. Among them, the PGRS has a significant negative impact on the agglomeration of apple production layout, which is consistent with the expected direction of H9, indicating that the PGRS and other food security policies have promoted the continuous expansion of grain area and produced an extrusion effect on apple production [38,39]. Apple advantageous regional planning policy has had a significant positive impact on the agglomeration of apple production layout, which is consistent with the expected direction of H10, indicating that apple advantageous regional planning guided apple production to the Loess Plateau and Bohai Bay dominant areas and was conducive to the agglomeration of apple production layout [18].
4. The areas of production affected by disasters have a significant positive impact on the agglomeration of apple production layout, which is inconsistent with the expected direction of H1. On the one hand, as a result of upgrading and promotion of orchard equipment and infrastructure, the prevention and mitigation ability of apple disaster has increased, and the negative impact of natural disasters on apple production has been minimized [18,50], which is not a key factor affecting farmers' apple cultivation [30]. On the other hand, although the disaster probability of apple production in non-appropriate and sub-suitable areas is high, the spill-over effect of the higher relative price of apples in the region, the policy support of industrial poverty alleviation, and the heterogeneity of farmers' individual rationality induced an increase in the proportions of apple area in the unsuitable and subsuitable areas, which in turn caused the apple disaster area to have a positive impact on the agglomeration of apple production layout.

Conclusions and policy implications
This study used descriptive statistical analysis and spatial econometric analysis to quantitatively reveal the evolution trend, agglomeration characteristics, and influencing factors of China's apple production spatial layout from 1978 to 2016. The results showed the following.
(1) Since 1978, China's apple production has shown a volatile growth trend, which has experienced five stages: stable period, rapid increase period I, rapid increase period II, rapid decline period, and slow increase period. (2) The centre of apple production in China has moved to the southwest part of the country (the advantage area of the Loess Plateau surpassed the advantage area of the Bohai Bay to become the largest apple production region in China). The concentrations of apple production have increased in 13 provinces and decreased in 9 provinces. (3) Apple production showed significant spatial autocorrelation, but its spatial agglomeration decreased. The hotspot regions of the apple area evolved from Hebei and Henan in 1978 to Shanxi and Henan in 2016, and the main apple-producing areas have gradually shifted from the eastern region to the central region. (4) Comparative apple income, apple effective irrigation area, transportation density, "provincial governors assuming responsibility for the rice bag", apple advantage regional planning, and apple disaster area are the main factors affecting the agglomeration of apple production layout. Apple industry agglomeration is the key to stabilizing the supply in apple products and ensuring the safety of the apple industry. It is also an inevitable trend in the development of the apple industry. Timely grasping the evolution trend of the centralization production level and agglomeration characteristics of the apple industry is an important premise to scientifically optimize the production layout of the apple industry and is also an important content to steadily advance the regional division of labour and specialization and to stabilize and ensure the adequate supply of apple products.
Based on the above conclusions, the following policy implications were drawn. First, based on the supply-side structural reform fundamentals, it is necessary to improve the quality and profit of apples. The comparative income of apples is the basis for decision-making by farmers to grow apples and is also the scientific principle for promoting the concentration of apple production in the region. Under the realistic background of the transformation of the apple industry from quantitative to qualitative and upgrading the apple's consumer market demand, the government should improve the quality and market value of apples based on the regional comparative advantages, strengthen spatial agglomeration in advantageous regions (such as the Loess Plateau production area), and build some modern apple production bases to increase economic benefits. Second, as infrastructure development is the basis for ensuring the production and value of apples, it is necessary to pay more attention to the construction of farmland water conservancy facilities, agricultural product transportation, and supporting facilities to improve the production efficiency and product circulation. Third, under the policy background of supply-side structural reform and rural revitalization strategy, it is imperative to accurately grasp the evolution patterns of apple production layout and agglomeration characteristics and adhere to the principle of adaptability of resource endowment and production reality. Furthermore, combined with the development need of the apple industry in the new era, it is necessary to formulate a reasonable layout plan for the apple-producing area, regulate the implementation of regional apple industry planning, enhance the apple production in advantageous production regions and strengthen the technological advancement of apple industry agglomeration. Fourth, under the climate change background of frequent natural disasters in apple, construction of new apple orchards must be according to scientific evaluation of the suitability for apple cultivation. At the same time, disaster prevention and mitigation strategies (such as anti-hail net, antifreeze, artificial pollination, and artificial grass) should be implemented to reduce disaster losses and stabilize the apple supply.
Compared with the existing literature, this study more clearly revealed the spatial-temporal dynamics of apple production. However, there are some shortcomings. First, the growth of apple trees is significantly affected by geographical microclimate. In the future, research needs to be conducted on smaller spatial scales, such as county-level and grid units. Resource endowments and polices, especially low-grade units, may affect the apple production of small-scale spatial units. Second, due to data limitations of national statistics, this study did not address the monopoly in the submarkets, which could be defined according to geometric boundaries or type of apple, and failed to use spatial panel data for a more comprehensive analysis. These issues need to be further explored in future research.