Possible roles, positions, factors and components of dairying in organic farming – a rewiev, mapping, survey and comparison in the Czech Republic

The full-value experiment is questionable in evaluation organic dairying. It is problem to do a trial under comparable conditions for comparison of organic and conventional farming because of legislative reasons and necessity of long period of such event. Most of comparisons are carried out as practice descriptive observations and any of them has been carried out about milk production. That is main reason, why the aim of this work is to carry out a opening of monitoring of some production conditions and results of bio-dairying in the Czech Republic (CR). The quality aspects of sources, procedures and products are main topics of solution of projects about organic farming philosophy, in particular in solution of organic dairy foodstuff chain. There were choosen twelve organic dairy farms (survey II, 2006) for more detail research of production conditions according to results of exploratory questionnaire (2006, survey I, n = 85 pieces of questionnaire and 58 organic farms, which practicise dairying) in the CR. The climatology characteristics of selected organic dairy farms were as follows: (I) 562±149 m above sea level on the average (from 270 to 970 m a. s. l.); (II) 571.0±69.9 m above sea level, mean year temperature 6.0±1.1 ºC and average year rainfall sum 843.0±184.3 mm. It is clear according to previously mentioned figures that the organic (ecology) dairy farming is realized mostly in the mountain or sub-mountain areas (less favourable areas, LFAs) as compared to climatic conditions of CR mean profile. The results of investigation of organic farm (E) and breeder conditions and dairy cow health state, reproduction performance and milk quality in organic farms (I data file) as compared to conventional dairy cow herds (K) were: milk yield (E) was 14.2±3.4 kg of milk/cow/day on average and 5165±1112 kg/cow/year; E farms have 50 % free stables, some of them as different untraditional modifications (mostly in herds with low number of dairy cows); it is necessary to increase this amount for welfare improvement in the future; there are 52 % of binding stables in K herds; there (E) is high occurrence frequency of can milking equipments (46.4 %); there are 5.4 % cases of hand milking, 21.4 % of pipeline milking equipments and 26.8 % of milking parlours; there (K) are 3 % of can milking equipments, 50 % of pipeline milking equipments and 47 % of milking parlours; the average organic herd has 60±91 heads it means about 1/3 of K herd in the CR; geometrical average (xg) of organic herd size is 17 heads; daily milk deliveries were 1318±1475 kg in summer and 976±1368 kg in winter season (there is too high variability in the mentioned indicators); breed structure of E herds is 59.8 % of Bohemian Spotted cattle, 18.8 % of Holstein (H), 12.5 % of Jersey breed; H breed is dominating 47.5 % in K herds; average ratio of excluded milk (for secretion disorders or treatment) is 2.99 % in E herds and 4.6 % in K herds (P<0.01); also there (E) is lower occurrence of clinical mastitis 0.53±1.97 %; service period is 124.3 days in K and 98.7±46,1 days in E herds on average (P<0.01); there (E) is better insemination index 1.66±0.45 in comparison to K herds 2.07 (P<0.01); there is longer longevity as duration of production life of dairy cows in E herds (6.02 lactations, „about 141 % better”) in comparison to K herds (2.50 lactations, P<0.01); milk quality showed the average total mesophilic bacteria count (CPM) 36.0±26.8 ths. CFU/ml in organic farms (E), which is comparable to the conventional farms (K); somatic cell count (PSB) was 192±87 ths./ml in E herds and 256 ths./ml in K herds, which is in connection with the lower ratio of milk exclusion from delivery in E herds; an occurrence of residues of inhibitory substances (RIL) was not reported in E herds, which is more advantageous in comparison to the K herds (0.16 %) and it could be an impact of lowered antibiotica drug use; the average fat and lactose contents (T; 4.05±0.19 %) and (L; 4.83±0.15 %) are well comparable with K farms and the results show on higher energy deficiency in E herd nutrition. The water quality (II) is necessary in dairying as well. Drinking water is necessary for health of animals (their watering) and for milk quality (milking equipment sanitation) as well. Drinking water is asked in dairy farms by legislation. The E farm water quality: the nitrate level varied in the range from 1.63 to 28 mg/l with average 10.5 mg/l in ecological farms and standard limit 50 mg/l was not exceeded; the levels of nitrite and ammonia ions were mostly under detection limit of method; legislative limit <0.5 mg/l was not exceeded by nitrite and once by ammonia ions 0.81 mg/l. The microbiological indicators are more sensitive of course. In total the limits were exceeded 7× u in coliform bacteria, 3× in streptococci and Escherichia coli was confirmed 3× (in comparison to demand 0). Therefore it is necessary to take care of incidental water source sanitation. The effect of origin of water source (communal water pipes or own well in the organic farm area) which was used in the organic farming (II) was: the more marked result differences were not observed between own wells (S) and communal water supply (V) in E farms; an exception was stated in insignificantly better results of hygienic indicators of communal supply; therefore it is necessary to put the higher importance on sanitation of own water sources. There were identified eight own wells and four communal supply. E. g. nitrate levels were a little higher for wells 11.7 > 8.2 mg/l. The nitrites were not different. Chemical oxygen consumption was 0.45 and 0.52 mg/l. The more expressive differences were identified in chlorides, sulphates and Mg: 8.33 and 3.02 mg/l; 27.9 and 16.8 mg/l; 18.9 and 3.5 mg/l

A COMPARISON OF SELECTED MILK INDICATORS IN ORGANIC HERDS WITH CONVENTIONAL HERD AS REFERENCE

In a historical sense, current organic farming is an old-new alternative under changed world conditions. Organic dairying (O) is an alternative of friendly use of the environment in time of presupposed global climate changes. Potential impact of organic farming on raw cow-milk quality, composition and properties, as conpared to conventional milk production (C), were evaluatedin this paper on the basis of selectedm ilk indicators (MIs). Total solids, whey volume, pH of milk fermentation ability (FAM-pH), FAM streptococci, FAM noble lactic acid bacteria, I and Cu were higher in C milk (P0.05) were observed in pH, rennet coagulation time, curd quality, FAM lactobacilli and streptococci/lactobacilli, Na, Mn and Zn. In general, the differences were a little more advantageous for O milk from both technological and nutritional point of view, particularly because of AS (0.461 .81m m), FAM-T (27.3 4.6 ) , Ca (1172 < l257 mg.kg-1)P, ( 950 < l004 mg. kg-1) and Mg 107.4<ll2.0mg.kg{) results. Organic milk can also produce better environment for yoghurt fermentation. Nevertheless, the results obtained should not be overestimated as both sources produced milk of good quality. Additional results are needed to prove organic milk benefits

Development and validation of a weather-based model for predicting infection of loquat fruit by Fusicladium eriobotryae

A mechanistic, dynamic model was developed to predict infection of loquat fruit by conidia of Fusicladium eriobotryae, the causal agent of loquat scab. The model simulates scab infection periods and their severity through the sub-processes of spore dispersal, infection, and latency (i.e., the state variables); change from one state to the following one depends on environmental conditions and on processes described by mathematical equations. Equations were developed using published data on F. eriobotryae mycelium growth, conidial germination, infection, and conidial dispersion pattern. The model was then validated by comparing model output with three independent data sets. The model accurately predicts the occurrence and severity of infection periods as well as the progress of loquat scab incidence on fruit (with concordance correlation coefficients .0.95). Model output agreed with expert assessment of the disease severity in seven loquatgrowing seasons. Use of the model for scheduling fungicide applications in loquat orchards may help optimise scab management and reduce fungicide applications.This work was funded by Cooperativa Agricola de Callosa d'En Sarria (Alicante, Spain). Three months' stay of E. Gonzalez-Dominguez at the Universita Cattolica del Sacro Cuore (Piacenza, Italy) was supported by the Programa de Apoyo a la Investigacion y Desarrollo (PAID-00-12) de la Universidad Politecnica de Valencia. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.González Domínguez, E.; Armengol Fortí, J.; Rossi, V. (2014). Development and validation of a weather-based model for predicting infection of loquat fruit by Fusicladium eriobotryae. PLoS ONE. 9(9):1-12. https://doi.org/10.1371/journal.pone.0107547S11299Sánchez-Torres, P., Hinarejos, R., & Tuset, J. J. (2009). Characterization and Pathogenicity ofFusicladium eriobotryae, the Fungal Pathogen Responsible for Loquat Scab. Plant Disease, 93(11), 1151-1157. doi:10.1094/pdis-93-11-1151Gladieux, P., Caffier, V., Devaux, M., & Le Cam, B. (2010). Host-specific differentiation among populations of Venturia inaequalis causing scab on apple, pyracantha and loquat. Fungal Genetics and Biology, 47(6), 511-521. doi:10.1016/j.fgb.2009.12.007González-Domínguez, E., Rossi, V., Armengol, J., & García-Jiménez, J. (2013). Effect of Environmental Factors on Mycelial Growth and Conidial Germination ofFusicladium eriobotryae, and the Infection of Loquat Leaves. Plant Disease, 97(10), 1331-1338. doi:10.1094/pdis-02-13-0131-reGonzález-Domínguez, E., Rossi, V., Michereff, S. J., García-Jiménez, J., & Armengol, J. (2014). Dispersal of conidia of Fusicladium eriobotryae and spatial patterns of scab in loquat orchards in Spain. European Journal of Plant Pathology, 139(4), 849-861. doi:10.1007/s10658-014-0439-0Becker, C. M. (1994). Discontinuous Wetting and Survival of Conidia ofVenturia inaequalison Apple Leaves. Phytopathology, 84(4), 372. doi:10.1094/phyto-84-372Hartman, J. R., Parisi, L., & Bautrais, P. (1999). Effect of Leaf Wetness Duration, Temperature, and Conidial Inoculum Dose on Apple Scab Infections. Plant Disease, 83(6), 531-534. doi:10.1094/pdis.1999.83.6.531Holb, I. J., Heijne, B., Withagen, J. C. M., & Jeger, M. J. (2004). Dispersal of Venturia inaequalis Ascospores and Disease Gradients from a Defined Inoculum Source. Journal of Phytopathology, 152(11-12), 639-646. doi:10.1111/j.1439-0434.2004.00910.xRossi, V., Giosue, S., & Bugiani, R. (2003). Influence of Air Temperature on the Release of Ascospores of Venturia inaequalis. Journal of Phytopathology, 151(1), 50-58. doi:10.1046/j.1439-0434.2003.00680.xStensvand, A., Gadoury, D. M., Amundsen, T., Semb, L., & Seem, R. C. (1997). Ascospore Release and Infection of Apple Leaves by Conidia and Ascospores ofVenturia inaequalisat Low Temperatures. Phytopathology, 87(10), 1046-1053. doi:10.1094/phyto.1997.87.10.1046Machardy WE (1996) Apple scab. Biology, epidemiology and management. St. Paul: APS Press. 545.James, J. R. (1982). Environmental Factors Influencing Pseudothecial Development and Ascospore Maturation ofVenturia inaequalis. Phytopathology, 72(8), 1073. doi:10.1094/phyto-72-1073Li, B., Zhao, H., Li, B., & Xu, X.-M. (2003). Effects of temperature, relative humidity and duration of wetness period on germination and infection by conidia of the pear scab pathogen (Venturia nashicola). Plant Pathology, 52(5), 546-552. doi:10.1046/j.1365-3059.2003.00887.xLi, B.-H., Xu, X.-M., Li, J.-T., & Li, B.-D. (2005). Effects of temperature and continuous and interrupted wetness on the infection of pear leaves by conidia of Venturia nashicola. Plant Pathology, 54(3), 357-363. doi:10.1111/j.1365-3059.2005.01207.xUMEMOTO, S. (1990). Dispersion of ascospores and conidia of causal fungus of Japanese pear scab, Venturia nashicola. Japanese Journal of Phytopathology, 56(4), 468-473. doi:10.3186/jjphytopath.56.468Rossi, V., Salinari, F., Pattori, E., Giosuè,, S., & Bugiani, R. (2009). Predicting the Dynamics of Ascospore Maturation ofVenturia pirinaBased on Environmental Factors. Phytopathology, 99(4), 453-461. doi:10.1094/phyto-99-4-0453Spotts, R. A. (1991). Effect of Temperature and Wetness on Infection of Pear byVenturia pirinaand the Relationship Between Preharvest Inoculation and Storage Scab. Plant Disease, 75(12), 1204. doi:10.1094/pd-75-1204Spotts, R. A. (1994). Factors Affecting Maturation and Release of Ascospores ofVenturia pirinain Oregon. Phytopathology, 84(3), 260. doi:10.1094/phyto-84-260Villalta, O., Washington, W. S., Rimmington, G. M., & Taylor, P. A. (2000). Australasian Plant Pathology, 29(4), 255. doi:10.1071/ap00048Villalta, O. N., Washington, W. S., Rimmington, G. M., & Taylor, P. A. (2000). Effects of temperature and leaf wetness duration on infection of pear leaves by Venturia pirina. Australian Journal of Agricultural Research, 51(1), 97. doi:10.1071/ar99068Lan, Z., & Scherm, H. (2003). Moisture Sources in Relation to Conidial Dissemination and Infection byCladosporium carpophilumWithin Peach Canopies. Phytopathology, 93(12), 1581-1586. doi:10.1094/phyto.2003.93.12.1581Lawrence, Jr., E. G. (1982). Environmental Effects on the Development and Dissemination ofCladosporium carpophilumon Peach. Phytopathology, 72(7), 773. doi:10.1094/phyto-72-773Gottwald, T. R. (1985). Influence of Temperature, Leaf Wetness Period, Leaf Age, and Spore Concentration on Infection of Pecan Leaves by Conidia ofCladosporium caryigenum. Phytopathology, 75(2), 190. doi:10.1094/phyto-75-190Latham, A. J. (1982). Effects of Some Weather Factors andFusicladium effusumConidium Dispersal on Pecan Scab Occurrence. Phytopathology, 72(10), 1339. doi:10.1094/phyto-72-1339MARZO, L., FRISULLO, S., LOPS, F., & ROSSI, V. (1993). Possible dissemination of Spilocaea oleagina conidia by insects (Ectopsocus briggsi). EPPO Bulletin, 23(3), 389-391. doi:10.1111/j.1365-2338.1993.tb01341.xLOPS, F., FRISULLO, S., & ROSSI, V. (1993). Studies on the spread of the olive scab pathogen, Spilocaea oleagina. EPPO Bulletin, 23(3), 385-387. doi:10.1111/j.1365-2338.1993.tb01340.xObanor, F. O., Walter, M., Jones, E. E., & Jaspers, M. V. (2007). Effect of temperature, relative humidity, leaf wetness and leaf age on Spilocaea oleagina conidium germination on olive leaves. European Journal of Plant Pathology, 120(3), 211-222. doi:10.1007/s10658-007-9209-6Obanor, F. O., Walter, M., Jones, E. E., & Jaspers, M. V. (2010). Effects of temperature, inoculum concentration, leaf age, and continuous and interrupted wetness on infection of olive plants by Spilocaea oleagina. Plant Pathology, 60(2), 190-199. doi:10.1111/j.1365-3059.2010.02370.xViruega, J. R., Moral, J., Roca, L. F., Navarro, N., & Trapero, A. (2013). Spilocaea oleaginain Olive Groves of Southern Spain: Survival, Inoculum Production, and Dispersal. Plant Disease, 97(12), 1549-1556. doi:10.1094/pdis-12-12-1206-reViruega, J. R., Roca, L. F., Moral, J., & Trapero, A. (2011). Factors Affecting Infection and Disease Development on Olive Leaves Inoculated withFusicladium oleagineum. Plant Disease, 95(9), 1139-1146. doi:10.1094/pdis-02-11-0126Eikemo, H., Gadoury, D. M., Spotts, R. A., Villalta, O., Creemers, P., Seem, R. C., & Stensvand, A. (2011). Evaluation of Six Models to Estimate Ascospore Maturation in Venturia pyrina. Plant Disease, 95(3), 279-284. doi:10.1094/pdis-02-10-0125Li, B.-H., Yang, J.-R., Dong, X.-L., Li, B.-D., & Xu, X.-M. (2007). A dynamic model forecasting infection of pear leaves by conidia of Venturia nashicola and its evaluation in unsprayed orchards. European Journal of Plant Pathology, 118(3), 227-238. doi:10.1007/s10658-007-9138-4Rossi, V., Giosuè, S., & Bugiani, R. (2007). A-scab (Apple-scab), a simulation model for estimating risk of Venturia inaequalis primary infections. EPPO Bulletin, 37(2), 300-308. doi:10.1111/j.1365-2338.2007.01125.xXU, X.-M., BUTT, D. J., & SANTEN, G. (1995). A dynamic model simulating infection of apple leaves by Venturia inaequalis. Plant Pathology, 44(5), 865-876. doi:10.1111/j.1365-3059.1995.tb02746.xRoubal, C., Regis, S., & Nicot, P. C. (2012). Field models for the prediction of leaf infection and latent period ofFusicladium oleagineumon olive based on rain, temperature and relative humidity. Plant Pathology, 62(3), 657-666. doi:10.1111/j.1365-3059.2012.02666.xPayne, A. F., & Smith, D. L. (2012). Development and Evaluation of Two Pecan Scab Prediction Models. Plant Disease, 96(9), 1358-1364. doi:10.1094/pdis-03-11-0202-reTrapman M, Jansonius PJ (2008) Disease management in organic apple orchards is more than applying the right product at the correct time. Ecofruit-13th International Conference on Cultivation Technique and Phytopathological Problems in Organic Fruit-Growing: Proceedings to the Conference from 18th February to 20th February 2008 at Weinsberg/Germany. 16–22.HOLB, I. J., JONG, P. F., & HEIJNE, B. (2003). Efficacy and phytotoxicity of lime sulphur in organic apple production. Annals of Applied Biology, 142(2), 225-233. doi:10.1111/j.1744-7348.2003.tb00245.xGent, D. H., Mahaffee, W. F., McRoberts, N., & Pfender, W. F. (2013). The Use and Role of Predictive Systems in Disease Management. Annual Review of Phytopathology, 51(1), 267-289. doi:10.1146/annurev-phyto-082712-102356Alavanja, M. C. R., Hoppin, J. A., & Kamel, F. (2004). Health Effects of Chronic Pesticide Exposure: Cancer and Neurotoxicity. Annual Review of Public Health, 25(1), 155-197. doi:10.1146/annurev.publhealth.25.101802.123020Brent KJ, Hollomon DW (2007) Fungicide resistance in crop pathogens: How can it be managed? FRAC Monog 2. Fungicide Resistance Action Committee.Shtienberg, D. (2013). Will Decision-Support Systems Be Widely Used for the Management of Plant Diseases? Annual Review of Phytopathology, 51(1), 1-16. doi:10.1146/annurev-phyto-082712-102244Leffelaar P (1993) On Systems Analysis and Simulation of Ecological Processes. Kluwer. London.Rossi V, Giosuè S, Caffi T (2010) Modelling plant diseases for decision making in crop protection. In: Oerke E-C, Gerhards R, Menz G, Sikora RA, editors. Precision Crop Protection-the Challenge and Use of Heterogeneity.Hui, C. (2006). Carrying capacity, population equilibrium, and environment’s maximal load. Ecological Modelling, 192(1-2), 317-320. doi:10.1016/j.ecolmodel.2005.07.001Townsend C, Begon M, Harper J (2008) Essentials of ecology. John Wiley and Sons. New York. 510.Zadoks J, Schein R (1979) Epidemiology and plant disease management. Oxford University Press, New York. 427.Bennett, J. C., Diggle, A., Evans, F., & Renton, M. (2013). Assessing eradication strategies for rain-splashed and wind-dispersed crop diseases. Pest Management Science, 69(8), 955-963. doi:10.1002/ps.3459Ghanbarnia, K., Dilantha Fernando, W. G., & Crow, G. (2009). Developing Rainfall- and Temperature-Based Models to Describe Infection of Canola Under Field Conditions Caused by Pycnidiospores of Leptosphaeria maculans. Phytopathology, 99(7), 879-886. doi:10.1094/phyto-99-7-0879Gilligan, C. A., & van den Bosch, F. (2008). Epidemiological Models for Invasion and Persistence of Pathogens. Annual Review of Phytopathology, 46(1), 385-418. doi:10.1146/annurev.phyto.45.062806.094357Buck, A. L. (1981). New Equations for Computing Vapor Pressure and Enhancement Factor. Journal of Applied Meteorology, 20(12), 1527-1532. doi:10.1175/1520-0450(1981)0202.0.co;2Madden L V, Hughes G, van den Bosch F (2007) The study of plant disease epidemics. APS press. St. Paul. 421.González-Domínguez E, Rodríguez-Reina J, García-Jiménez J, Armengol J (2014) Evaluation of fungicides to control loquat scab caused by Fusicladium eriobotryae. Plant Heal Prog Accepted.De Wolf, E. D., & Isard, S. A. (2007). Disease Cycle Approach to Plant Disease Prediction. Annual Review of Phytopathology, 45(1), 203-220. doi:10.1146/annurev.phyto.44.070505.143329Krause, R. A., & Massie, L. B. (1975). Predictive Systems: Modern Approaches to Disease Control. Annual Review of Phytopathology, 13(1), 31-47. doi:10.1146/annurev.py.13.090175.000335Fourie, P., Schutte, T., Serfontein, S., & Swart, F. (2013). Modeling the Effect of Temperature and Wetness on Guignardia Pseudothecium Maturation and Ascospore Release in Citrus Orchards. Phytopathology, 103(3), 281-292. doi:10.1094/phyto-07-11-0194Gadoury, D. M. (1982). A Model to Estimate the Maturity of Ascospores ofVenturia inaequalis. Phytopathology, 72(7), 901. doi:10.1094/phyto-72-901Holtslag, Q. A., Remphrey, W. R., Fernando, W. G. D., St-Pierre, R. G., & Ash, G. H. B. (2004). The development of a dynamic diseaseforecasting model to controlEntomosporium mespilionAmelanchier alnifolia. Canadian Journal of Plant Pathology, 26(3), 304-313. doi:10.1080/07060660409507148Legler SEE, Caffi T, Rossi V (2013) A Model for the development of Erysiphe necator chasmothecia in vineyards. Plant Pathol. DOI:10.1111/ppa.12145.Luo, Y., & Michailides, T. J. (2001). Risk Analysis for Latent Infection of Prune by Monilinia fructicola in California. Phytopathology, 91(12), 1197-1208. doi:10.1094/phyto.2001.91.12.1197Gadoury, D. M. (1986). Forecasting Ascospore Dose of Venturia inaequalis in Commercial Apple Orchards. Phytopathology, 76(1), 112. doi:10.1094/phyto-76-112Gent, D. H., De Wolf, E., & Pethybridge, S. J. (2011). Perceptions of Risk, Risk Aversion, and Barriers to Adoption of Decision Support Systems and Integrated Pest Management: An Introduction. Phytopathology, 101(6), 640-643. doi:10.1094/phyto-04-10-0124Schut, M., Rodenburg, J., Klerkx, L., van Ast, A., & Bastiaans, L. (2014). Systems approaches to innovation in crop protection. A systematic literature review. Crop Protection, 56, 98-108. doi:10.1016/j.cropro.2013.11.017Mills W, Laplante A (1954) Diseases and insect in the orchard. Cornell Ext Bull 711.GVA (2013) Octubre-Noviembre 2013. Butlletí d’avisos 13.MacHardy, W. E. (1989). A Revision of Mills’s Criteria for Predicting Apple Scab Infection Periods. Phytopathology, 79(3), 304. doi:10.1094/phyto-79-30

Population dynamics of the monogenean Anoplodiscus cirrusspiralis on the snapper, Pagrus auratus

Populations of Anoplodiscus cirrusspiralis were monitored for 1 year on tagged individual snapper in experimental cages kept in a large on-shore pond with flow-through filtered sea water. The cages were stocked with small and large fish at either low or high initial density. Irrespective of size and density, snapper with light initial infections maintained light infections, whereas fish with heavy initial infections showed fluctuations in parasite population size throughout the year. These data indicate that some snapper have an innate resistance to infection by A. cirrusspiralis, with little evidence for acquired immunity induced by heavy infection. Parasite longevity was greater on the pectoral fin than caudal fin, and greater on large than small fish irrespective of fish density; longevity was greater on susceptible fish than on resistant fish Recruitment and mortality rates were greater on the pectoral fin and in low density cages, but were not influenced by fork length