Onion (Allium cepa L.) is a very important vegetable grown all over the world and consumed in various forms. The damage caused by diseases is difficult to measure, however depending on meteorological conditions economic losses can exceed 50% [1-5]. Several Allium crop diseases are caused by Botrytis spp., which results in yield losses in different parts of the world. Botrytis cinerea infects more than 200 host plants worldwide. In onions it causes leaf fleck, blight and is the causal agent of neck rot [3,4,6]. The fungus overwinters in the form of sclerotia and may survive more than 2 years in the soil. During the onion growth season B. cinerea causes oval and small spots on the leaves [4,9].
Postharvest diseases cause major losses during onion storage [7,8]. Disease severity is influenced by the weather and storage conditions, crop rotations, harvesting periods and disease control methods. The physiological maturity of onions at harvest can also affect the incidence and severity of rot in stored onion bulbs. If the control of onion diseases in the field is not efficient, the storage losses will also increase . Neck rot is primarily a storage disease, but the origin of infection is associated with the field. The fungus usually infects onions through the neck tissues or wounds in the bulbs . The major pathogens responsible for postharvest diseases are Botrytis, Penicillium, Mucor, Fusarium and Aspergillus, while B. cinerea is among the prevailing fungi [7,8,10].
It is difficult to control Botrytis spp. because of the capability of the fungus to attack vegetables at almost any stage of growth, and infect all plant parts, including leaves, stems, flowers and fruits [4,9,11]. The appropriate agro-technical handling, pesticide application time, crop rotation and proper varieties have very important effects on the control of these diseases, and also influence crop storage time and the onion’s quality characteristics [3-5,8,9,12,13].
The primary strategy for controlling Botrytis spp. and other pathogens are pesticides. Conventionally onion diseases are controlled by routine applications of fungicides once a week. The consumption of pesticides in the world is about two million tonnes per year. Chemical pesticides have negative effects on the environment and to human nutrition. The usage of pesticides is becoming more restricted, based on public consideration about the negative influences of pesticides on human health and the environment. The inadequate usage of pesticides also leads to pesticide resistance [3,4,7,14,15]. These kind of applications are becoming unacceptable, because they are being used - whether necessary or not [3,4,11].
Development of fungal infections is associated with meteorological conditions. Forecasting models help to minimize the usage of fungicides so that they are applied only when needed. The B. cinerea forecasting model calculates how favourable the period is for the risk of infection. Forecasting models predict occurrence and intensity of a disease according to weather, host or pathogen data. The primary factors for Botrytis cinerea occurrence are leaf wetness, high humidity or rain periods together with optimal temperature. Various types of forecasting models have been developed, but they all are based on the calculation of a disease index predicting the optimal conditions for disease development [3,4,7,8,12,14-17].
Bulger et al.  found correlations for leaf wetness periods and temperature. If leaf wetness periods last more than 32 hours at 20°C, the probability of grey mould infection is 60% [2-4]. The iMETOS®sm onion leaf fleck risk forecasting model indicates the possibility of developing B. cinerea based on the interaction between air temperature and duration of leaf wetness [5,13].
Influence of the B. cinerea forecasting model on onion disease and the timing and efficacy of fungicide application were investigated at the LAMMC Institute of Horticulture in 2012-2014. The aim was to evaluate the iMETOS®sm B. cinerea forecasting model and determine an effective onion leaf fleck management system to estimate the variability of onion bulb pathogens during storage.
2.1 Field experiments
Three–year field experiments were carried out at the Institute of Horticulture, Lithuanian Research Centre for Agriculture and Forestry in the Central part of Lithuania (55°04’58.1”N 23°48’30.0”E) in May-September of 2012– 2014. We investigated the iMETOS®sm (Pessl Instruments, Austria) onion leaf fleck risk forecasting model as an effective onion B. cinerea management system.
The experimental trials were set up according to EPPO standards PP1/152(4) and PP1/120(2) [18,19]. Onion cv. ‘Stuttgarter Riesen’ was grown from sets, on 10.0 m long and 1.0 m wide beds of four rows. The experiment was initiated every year at the beginning of May.
During experimental years the onions were harvested at the end of August (2013-08-22) or in the beginning of September (2012-09-11 and 2014-09-08). The trials were arranged in random complete blocks. The experiments were replicated four times. Onion experimental plots were not irrigated.
The fungicide applications against onion leaf fleck were made according to the conventional plant protection system and the iMETOS®sm forecasting model. The fungicide conventional system was applied when the first symptoms of leaf fleck appeared and a second time 10 days later. According to the iMETOS®sm forecasting model applications were made when the model indicated disease risk. Applications in both systems were made with Signum 1.8 kg ha-1 (6.7% pyraclostrobin + 26.7% boscalid), twice every 10–14 days, and the last spray was conducted no later than 14 days before harvesting. The experimental design of application times are provided in Table 1.
Yield-loss potential for B. cinerea was calculated using formula: Yield-loss potential (%) = 100 x ((Sprayed¬untreated)/sprayed), in this formula sprayed = means bulbs yielded from experimental plots were sprayed according to the iMETOS®sm or conventional treatment, untreated stands for a bulbs yield from control plots that were not treated with fungicide .
2.1.1 Forecasting model
The iMETOS®sm forecasting model is an automatic meteorological database station, recording air temperature, leaf wetness, relative humidity, precipitation amount and other parameters which are needed for the disease calculations. The station data are received on the internet http://metos.at/pikernel_dev/index_new.php/, where the model records and calculates the risk of infection on an hourly basis. The model calculates accurate disease infection risk at the range of 10 km around the meteorological station [5,12,13].
The onion leaf fleck forecasting model calculates favourable periods for the risk of infection. The figure indicates a leaf wetness period leading to 30% B. cinerea risk. Dry days reduce the risk time (Fig. 1). The infection prevalence depends on the excess of humidity on onion leaves. The model calculates the risk of onion B. cinerea infection during the leaf wetness period from 4 to 16 hours at the optimal temperature 7 – 24°C and indicates the disease. According to manufacturer guidelines, if the iMETOS®sm leaf fleck risk model shows the infection risk periods more than 60% (which lasts longer than 3 days), it is recommended to apply fungicides. The infection risk periods are classified into groups; a group is established if infection lasts longer than 3 days or more than 60%.
2.2 Neck rots and pathogen assessmentduring the onion bulbs storage
Healthy onion bulbs (Fig. 2 a) of the same size and free of physical damage were assessed and collected in a mesh bags (Fig. 2 c) from each replication of field trials. Onion bulbs were stored at the temperature of 2-40C. One replication comprised of 50 onion bulbs. Incidence of neck rot was evaluated according to the EPPO standards PP1/054(3) and PP1/120(2) [18, 19, 21] after 2 and 4 months of storage. At the same time, the occurrence of B. cinerea (Fig. 2 b) was assessed for other pathogens on onion bulbs. We first identified the pathogens visually, and then they were cultured on PDA and identified according to the morphological traits typical of the colonies microscopically [3,6,20]. The relative density (RD% – percent ratio of species number to a total number of species) – was calculated according Gonzalez et al. .
2.3 Data analysis
All experiments were carried out in four replications. The data were analysed with Anova module of STATISTICA 7.0 software. We used two-way ANOVA to determine yield differences between treatments, factor A - different harvest year, factor B - different plant protection system. A standard error (SE) was estimated for every experimental point and marked in a figure as an error bar. The means were compared with the least significant difference (R05) and evaluated at the probability level of 95% (P = 0.05).
3.1 Influence of management method on the onion yield
Comparison of the three year’s data showed that the yields for the iMETOS®sm based leaf fleck management were higher compared to the control and conventional treatments. Two-way analysis of variance (ANOVA) was carried out according to the following scheme: factor A different harvest year, factor B different plant protection system were used to reveal the significance of factors for onion yield (Table 2). Two-way ANOVA showed that experimental treatments were significant at P < 0.05 probability for onion yield. The experimental data revealed that investigation year had significant influence on onion yield. In 2012-2014, there were significant differences between treatments (F = 0.51, df = 2, P < 0.6087), with the highest yield was in the iMETOS®sm treatment. In the iMETOS®sm 2012 the treatment yield amounted up to 48.00 t ha-1, an increase by 3.51 t ha-1higher than the control. In 2013 the iMETOS®sm yield was 32.02 t ha-1 and in 2014 - 32.38 t ha-1. These year yield increases were 3.87 t ha-1 and 3.40 t ha-1 higher than the controls. These results demonstrate that all experimental year fungicide applications based on the iMETOS®sm model were more precise and obtained higher yields than conventional treatments.
The iMETOS®sm forecasting model was helpful in evaluating the favourable periods for the risk of Botrytis cinerea infection in onions and led to specify the fungicide application timing and increase the onion bulbs yield on average by 3.59 t ha-1 and 3.16 t ha-1 during the years of 2012-2014 compared to untreated and conventionally treated fields, respectively. In 2012-2014 the iMETOS®sm treatment yield increase was 3.51 t ha-1, 3.87 t ha-1 and 3.40 t ha-1 higher than the control (Table 2).
The yield-loss potential of B. cinerea in the control treatment was highest in 2013 compared to other years. The yield-loss potential averaged 8.81% compared with the conventional, 10.85% with iMETOS®sm treatments.
The iMETOS®sm based leaf fleck managements system yield-loss potential was 2.05% lower than the conventional. This demonstrates that the model was more precise.
According to the 2012-2014 data, it was determined that the conditions varied from year to year and plant protection products were used at various times (Table 1, 3, Fig. 3). The applications according to the iMETOS®sm model occurred before symptoms of leaf fleck were detected.
Analysis of the forecasting model records showed that conditions for leaf fleck in 2012 were favourable both in June and July (Fig. 3). When infection risk periods are more than 60%, and last longer than 3 days, it is recommended to use fungicides. The infection risk in 2012 lasted for 21 days greater than 60% and 8 days in June, 13 days in July (Table 3). Onion leaf fleck risk development in 2012 formed four periods (one period in June and three in July). Favourable conditions for leaf fleck occurred on the 23rd day (one risk period lasted more than 60% for 7 days) of June and in July – 3rd (for 4 d.), 8th (for 3 d.) and 12th (for 5 d.). Risk periods for infection were determined by the following factors: the air temperature was within 8.0 – 25.9°C, and the leaf wetness period lasted within 0-1440 minutes. In 2012 first spraying according to the model was made 19 days earlier than the conventional application (Table 1).
Analysis of the 2013 forecasting model records showed that the conditions for leaf fleck were unfavourable in June, but favourable in July (Fig. 3). The 2013 model data showed that the risk of infection lasted for 15 days, 1 day in June and 14 in July (Table 3). In July only two risk periods formed. Favourable conditions for leaf fleck formed on the 2nd day (risk period lasted more than 60% for 4 days) and 12th day (for 9 d) of July (Fig. 3). Infection risk periods were determined by air temperatures within 13.3 – 26.4°C and the leaf wetness periods lasted within 0-1410 minutes. In 2013 the first spraying according to the iMETOS®sm forecasting model was applied 6 days earlier than the conventional applications (Table 1).
According to the forecasting model, conditions for the leaf fleck in 2014 were similar to the year 2012 (Fig. 3). The risk of infection in 2014 was greater than 60% and lasted for 29 days, 19 days in June and 10 in July (Table 3). In 2014, six risk periods formed: 4 in June and 2 in July. Favourable conditions for onion leaf fleck formed on the 1st day (risk period lasted more than 60% for 6 days), 13th (5), 19th (4) 24th day (4) of June and on 1st (7) and 9th day (3) of July. Risk periods for infection factors like air temperature was within 11.5-25.1°C and leaf wetness period lasted within 0-1350 minutes. In 2014 the first spraying according to the forecasting model was applied 23 days earlier than conventional applications (Table 1).
According to the conventional plant protection system, the first sprays were made in the first and second weeks of July. The application dates, according to the iMETOS®sm forecasting model, vary in different years (Table 1). In 2012-2013 applications according to the model were made in proximate dates in the third week of June and in the first week of July. In 2014 the application dates were different, taking place during the third week of June.
The iMETOS®sm showed that in different years the conditions for leaf fleck varied. The most favourable conditions for onion leaf fleck development in Lithuania formed in July, but this mostly depended on yearly weather conditions. One of the most efficient tools to reduce consumption of plant protection products is disease forecasting, which allows optimizing the usage of fungicides.
In all of our 2012-2014 experimental years the iMETOS®sm forecasting model showed favourable conditions for B. cinerea infection. The results of the iMETOS®sm 2014 data showed that favourable conditions for leaf fleck started earlier, on the 1st of June, compared with other years. In 2012 and 2013 the risk of disease was recorded on the 21st and 30th of June (Table 3).
3.2 Influence of management method on pathogen occurrence
During the 2012-2014 storage experiments we detected several types of pathogens that infected and damaged onions (Fig. 4). Through all experimental years fungal (44%) and bacterial (41%) injuries were dominant, but followed by insects (7%) and physiological (9%) injuries. The most common pathogen in all three experimental years was B. cinerea. During storage, onion bulbs were infected with four fungi genera: B. cinerea, Aspergillus spp ., Penicillium spp. and Fusarium spp. In 2012 – 2014 in all plant protection systems bacterial injuries manifested at 41%, 36% and 49% respectively (Fig. 4 a, b, c). In the control treatment B. cinerea (27%) injuries were significantly higher compared with conventional and iMETOS®sm treatments, followed by Aspergillus spp. (10%), Penicillium spp. (5%) and Fusarium spp. (1%) fungal injuries (Fig. 4 a). In the conventional and iMETOS®sm treatments, B. cinerea injuries during storage were 14% and 15% respectively (Fig. 4 b, c). The onions in Comparison of the relative density of infections after the iMETOS®sm treatment were infected by B. cinerea at 2 and 4 months of storage showed dissimilar results in a rate of 15%, 14% for Penicillium spp., 11% for Aspergillus all experimental years (Fig. 5 a, b). The relative density spp. and 7% for Fusarium spp. (RD, %) of bacterial damage ranged from 20.69% to 58.62% in all treatments. The relative density of B. cinerea, Aspergillus spp. and Penicillium spp. in 2012 averaged to up to 1.96%, 22.43% and 5.66% respectively after 2 months storage.
Our findings indicate that in 2012 the relative density of B. cinerea, Aspergillus spp. , Fusarium spp. and Penicillium spp. averaged 0.95%, 4.6%, 26.81% and 5.75% respectively after 4 months. In 2013 the relative density of B. cinerea, Aspergillus spp. , Fusarium spp. and Penicillium spp. averaged 17.87%, 1.33%, 9.03% and 2.92% respectively after 2 months of storage. Using different data obtained after 4 months of storage, relative density of B. cinerea and Fusarium spp. averaged up to 13.89 and 1.55%. B. cinerea relative density in 2014 after 2 and 4 months of storage increased up to 52.56 and 50.37%.
Favourable weather conditions in 2014 (according to the forecasting model there were six B. cinerea risk periods, Table 3) led to an increase up to 73% of B. cinerea bulb injuries (Fig. 4). High B. cinerea infection rates revealed the importance of disease control methods on the open field to disease prevalence in stored bulbs. The relative density from untreated plots was about 22% higher than in conventionally or iMETOS®sm treatments (Fig. 5). According to the overall data it seems that the highest relative density of injuries was recorded after 2 months of storage.
In the 2012-2014 experimental years different types of infections appeared (Table 4). It was concluded that most of the onion bulbs were infected by mixed types of infections. The predominant infections were B. cinerea and bacterial rot, although individual onion bulbs were often infected by more than three pathogens. In 2012 the bulbs were mostly injured by physiological (24.69%) and Aspergillus spp. (24.56%) infections after 2 months of storage and Penicillium spp. (22.52%) and Fusarium spp. (20.83%) infections after 4 months of storage. Bacterial infections were found throughout all three experimental years; it should be noted that bacteria accompanied all rots and the higher prevalence was 13.59% in 2013 after 2 months of storage. Analysis of 2013 data showed that bulb injuries were from insects (14.29%), bacteria (13.59%), Fusarium spp. (12.50%) and B. cinerea (8.84%) after 2 months of storage. In 2014 there were only three types of infections: bacteria, Aspergillus spp. and Penicillium spp. The leading infections were made by B. cinerea after 2 and 4 months of storage (10.20% and 11.56%). Highest prevalence was detected after 2 months of storage. Fungal (44%) and bacterial (41%) injuries dominated among postharvest diseases of onion bulbs, which much lower detection rates of physiological (9%) and insect (7%) associated injuries.
4.1 Botrytis spp. management with forecas¬ting models
The necrotrophic fungus B. cinerea causes numerous types of diseases on many plants. B. cinerea is difficult to control because of its different modes of infecting and various hosts of inoculum sources [3,4,6]. During the vegetable growing season significant amounts of pesticides are used to control fungal diseases. Conventionally growers use several different chemical classed pesticides in mixtures or rotation during the growing season for resistance management. Disease forecasting models could help to reduce pesticide usage by providing better predictions of the environmental conditions that facilitate development of diseases. Such models allow reduced consumption of fungicides and applications are made only when the conditions for the disease are favourable [3-5,7,8,11,13]. The use of forecasting models without adaptation to local conditions is inadequate and inconsistent, because meteorological conditions vary [3,4].
Shtienberg  found that by using B. squamosa Blight-alert model it is possible to apply fungicides only when needed and calculated that using the model resulted in an average savings of 29%. Use of the forecasting model system enabled fungicide reduction up to 60% . Miličevič et al.  used the Botman forecasting model and developed an integrated and effective open-field strawberry control strategy from B. cinerea . Botman forecasting model based B. cinerea control in the greenhouse was statistically equally effective as conventional control methods . Raudonis et al.  found that using the iMETOS®sm Venturia inaequalis forecasting model optimizes application time from 30% up to 41% compared with the conventional. Using a forecasting model Botrytis incidence was 50% lower than the non-treated controls. Reduction in the number of fungicide applications using both Broome and Bulger models were similar. All treatments using the forecasting model had significantly higher yields . After reviewing the literature it was found that only the iMETOS®sm forecasting model is suitable to control onion B. cinerea, based on the results of our study. Survilienė-Radzevičė et al.  found that forecasting allows fungicide application precisely when it is needed, which leads us to environmentally friendly plant protection. It is the first time that the study was done in order to investigate the onion iMETOS®sm B. cinerea forecasting model. Our investigations confirm the results of other researchers demonstrating that that forecasting models are important predictors of disease and allow more precise use of fungicides. We discovered that the most favourable conditions for leaf fleck development occur in the July (Fig. 3, Tab. 3).
4.2 Pathogen occurrence during storage
Onion storage is affected by the environment, pathogens and crop genetic factors. It is important to consider the losses that occur during the storage of onion bulbs because it affects the continuous supply to the industry. Diseases caused by Botrytis are considered to be one of the most important onion fungal diseases which reduce yield, and also can devastate bulb yield during storage. Besides yield reduction, disease poses effects during post-harvest [4,10,23]. The bulb rot symptoms usually begin in the field and become severe during the storage time. Aspergillus spp., Botrytis spp. and Fusarium spp. are known as the onion seed-borne fungi and they are the agents that cause the rot diseases. The number of rotten bulbs varies depending on year . Lorbeer et al.  found that primary infection of Botrytis rot depends on plant protection systems. Jonathan et al.  found that Aspergillus spp., Fusarium spp. and Penicillium spp. are the predominant pathogens that are associated with onion spoilage. The pathogens infect bulbs through wounds, obsolescent or dead plant tissue or improperly dried necks. The major problem of onion storage is neck rot, caused by B. cinerea and prevailing in cool and moist climate areas [3,4,6]. Our findings confirmed other researchers and in turn allowed us to demonstrate that the predominant infections were caused by B. cinerea, Aspergillus spp. and Penicillium spp. We also discovered that if meteorological conditions are favourable for leaf fleck, this causes more injuries from B. cinerea during storage in that year (Tab. 3, Fig. 4). Our storage experiments confirm that appropriate and optimal timing of controlling diseases reduces B. cinerea (Fig. 4, Tab. 2). Our findings suggest that it is very important to use disease forecasting models for effective disease control, only applying fungicides when the conditions for the disease are favourable and to reduce overall usage.
The paper presents research findings obtained through the long-term research program “Harmful Organisms in Agro and Forest Ecosystems (KOMAS)” implemented by the Lithuanian Research Centre for Agriculture and Forestry.
Bulger M.A., Ellis M.A., Madden L.V., Influence of temperature and wetness duration on infection of flowers by Botrytis cinerea and disease incidence of fruit originating from infected flowers, Phytopathology, 1987, 77 (8), 1225-1230. Google Scholar
Elad Y., Williamson B., Ttudzynski P., Delen N., Botrytis: Biology, Pathology and Control, 2nd ed., Springer, Dordrecht, The Netherlands, 2007 Google Scholar
Raudonis L., Valiuškaitė A., Survilienė E., Investigations of efficiency of disease and pest warning models of horticultural plants using iMETOS internet based warning system, Sodininkyste ir Darzininkyste, scientific articles, 2008, 27 (3), 277-287 (in Lithuanian) Google Scholar
Agrios G. E., Plant pathology, 5th ed., Elsevier Academic Press, Amsterdam, 2005 Google Scholar
Shtienberg, D., Rational Management of Botrytis-Incited Diseases: Integration of Control Measures and Use of Warning Systems, Elad Y. et al. (ed.) Botrytis: Biology, Pathology and Control, 2nd ed., Springer, Dordrecht, The Netherlands, 2007, 235-347 Google Scholar
Wright P.J., Grant D.G., Triggs C. M., Effects of onion (Allium cepa) plant maturity at harvest and method of topping on bulb quality and incidence of rots in storage, N. Z. J. Crop Hortic. Sci., 2001, 29:2, 85-91, CrossrefGoogle Scholar
Droby S., Lichter A., Post-harvest Botrytis infection: etiology, development and management, Elad Y. et al. (ed.) Botrytis: Biology, Pathology and Control, 2nd ed., Springer, Dordrecht, The Netherlands, 2007, 349-367 Google Scholar
Mishra R.K., Jaiswal R.K., Kumar D., Saabale P.R., Singh A., Management of major diseases and ninsect pests of onion and garlic: A comprehensive review, J. Plant Breed. Crop Sci., 2014, 6 (11), 160-170 Google Scholar
Rasiukevičiūtė N., Valiuškaitė A., Survilienė-Radzevičė E., Supronienė S., Investigation of Botrytis cinerea risk forecasting model in strawberries, Proc. Latv. Acad. Sci. B Nat. Exact. Appl. Sci., 2013, 67 (2), 195-198, CrossrefGoogle Scholar
Survilienė-Radzevičė E., Rasiukevičiūtė N., Valiuškaitė A., Kazlauskaitė S., Duchovskienė L., Evaluation of iMETOS®sm Botrytis cinerea and Botrytis squamosa forecasting models in different agro-ecological regions, Proceedings of the 6th International Scientific conference Rural Development-2013 (28–29 November, 2013, Akademija, Kaunas distr.), Akademija, Kaunas dists., Lithuania, 2013, 6 (2), 254-257. Google Scholar
Miličevič T., Ivič, D., Cvjetkovič B., Duralija B., Possibilities of Strawberry Integrated Disease Management in Different Cultivation Systems, Agric. conspec. Sci., 2006, 71 (4), 129-134 Google Scholar
Valiuškaitė A., Raudonis L., Survilienė E., Control of grey mould and white leaf spot in strawberry, Zemdirbyste-Agriculture, 2008, 95 (3), 221–226. Google Scholar
Legard D.E., MacKenzie S.J., Mertely J.C., Chandler C.K., Peres N.A., Development of a reduced use fungicide program for control of Botrytis fruit rot on annual winter strawberry, Plant Dis., 2005, 89, 1353–1358Google Scholar
MacKenzie S. J., Peres N. A., Use of leaf wetness and temperature to time fungicide applications to control Botrytis fruit rot of strawberry in Florida, Plant Dis., 2012, 96, 529-536, dx.Doi.org/10.1094/PDIS-03-11-0182 Google Scholar
Vidhyasekaran P., Concise encyclopedia of plant pathology. Binghamton, (New York): Haworth Press, 2004 Google Scholar
Gonzalez H.H.L., Martinez E.J., Pacin A., Resnik S.L., Relationship between Fusarium graminearum and Aspergillus alternata contamination and deoxynivalenol occurrence of Argentinian durum wheat, Mycopathologia, 1999, 144, 97-102, Doi:10.1023/A:1007020822134 CrossrefGoogle Scholar
Lorbeer J. W., Seyb A. M., Boer A., Ende J. A., Botrytis species on bulb crops,. Elad Y. et al. (ed.) In Botrytis: Biology, Pathology and Control, 2nd ed., Springer, Dordrecht, The Netherlands, 2007, 273-294 Google Scholar
Jonathan S.G., Olawuyi O.J., Aina D.A., Dada H.G., Oyelakin A.O., Influence of storage time on bio deterioration, aflatoxin contamination and food values of onion (Allium cepa), Nat. Sci., 2012, 10 (11), 26-32 Google Scholar
About the article
Published Online: 2016-10-21
Published in Print: 2016-01-01
Conflict of interest: Authors declare nothing to disclose.
Citation Information: Open Life Sciences, ISSN (Online) 2391-5412, DOI: https://doi.org/10.1515/biol-2016-0036.
© 2016 Neringa Rasiukevièiute et al., published by De Gruyter Open. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0