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Author's personal copy Trop Anim Health Prod (2014) 46:1419–1426 DOI 10.1007/s11250-014-0658-6


Effects of dairy husbandry practices and farm types on raw milk quality collected by different categories of dairy processors in the Peruvian Andes Eduardo Fuentes & Joe Bogue & Carlos Gómez & Jorge Vargas & Pierre-Yves Le Gal Received: 11 May 2014 / Accepted: 11 August 2014 / Published online: 30 August 2014 # Springer Science+Business Media Dordrecht 2014

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765 mm/year but most dairy production benefits from irrigat- an estimated SCC (Rodrigues et al. 2009). Hygienic status, ed forages, such as ray grass and clover. The genetic compo- measured with the methylene blue reduction test (MBRT), sition of herds is dominated by Holstein and Brown Swiss was performed according to the IDF protocol (IDF 1990).

breeds and their crosses with the local breed “Criollo.” Dairy Bulk milk samples (50 ml) were taken once per month supply chains in this region involve a wide diversity of from each of the 20 farmers (Table 1). For each farmer, farmers and processors. Processors can collect milk by them- samples (morning and afternoon) were collected and analyzed selves at farm gate, or by buying milk collected by indepen- from bulk churns at the end of each milking and then pooled to dent collectors. These stakeholders interact in formal and obtain an average daily value. A similar sampling protocol informal chains according to their own regulation, control was performed during a period of 1 week every month to the processes, and type of targeted markets (Fig. 1). processors’ truck and the independent collector of each of the three processors evaluated. A complementary evaluation was Sampling selection performed to one dairy farm per processor to determine milk hygienic deterioration from farm gate to plant gate (Table 1).

Along with milk analyses, the farmer’s husbandry practices Based on a preliminary survey assessing the diversity of processors, a milk collection center belonging to a multina- were recorded once a month and compared to a set of recomtional company, one informal and two formal dairy processors mended management practices based on the literature (Table 2).

and three independent milk collectors, were purposively sampled. They varied in terms of volume of milk collected, Descriptive statistics (average, coefficient of variation, and technological level, and market orientation. Twenty dairy correlations) for analyzing milk quality composition and its relationship with farmers’ practices were carried out from the farmers were then selected among the suppliers of these milk buyers. The sample farms included 60 % of small-scale 12-month dataset. The XLStat™2012.6.01 software farmers (production of 20–50 l/farm/day and 8.5 l/cow/day); (Addinsoft, Paris, France) as an add-on to Microsoft Excel™ 2010 was used for that purpose.

30 % of medium-scale farmers (production of 50–100 l/farm/ day and 10.5 l/cow/day); and 10 % of large-scale farmers (production exceeding 100 l/farm/day and 12.0 l/cow/day).

The two last categories were pooled for analytical purposes.

Results Milk quality analysis Milk quality management at farm level Data were collected over 12 months from April 2012 to March

2013. Average total solids, fat, and protein content of each Results of milk chemical composition were found to be acmilk sample were measured with Master Eco® ultrasound ceptable, compared to the values recommended by Peruvian milk analyzer (http://www.milkotester.com/data/Master% legislation, with an average of 37 g/kg for fat content, 34 g/kg 20Eco.pdf). The analyzer was calibrated every month using for protein content, and 119 g/kg for total solids (TS). Eightyreference AOAC methods (Helrich 1990). Somatic cell count eight percent of milk samples were able to simultaneously (SCC) was obtained with the indirect on-farm test Porta fulfill minimum requirements of protein and fat content SCC®, which converts results of an enzymatic reaction into demanded by formal dairy processors without any significant

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TS and SCC values above the minimum recommended variation (Fig. 4). This result highlights the difficulty in esFig. 2). TS and MBRT tend to present a seasonal variation tablishing clear feeding strategies to achieve higher values of (Fig. 3). During the dry season, when rainfall and temperature milk chemical components in the feeding conditions prevailare low, TS decreases due to the limited availability of good ing in the study area.

quality forage, while hygiene improves because of lower In contrast to expected results, gathering cows in a waiting bacterial contamination. yard, use of a milking parlor, or milking by mechanical means Nine husbandry practices out of 26 evaluated were identi- were negatively correlated with values of MBRT (Table 3).

fied as frequently applied, i.e., exceeding half of monthly The poor cleaning regime of these buildings (cleaning less observations during the 12-month monitoring (Table 2). than once a day) or a lack of a deep cleaning of milking Feeding practices were mainly based on cut green forage equipment could explain this result. Values of milk somatic distributed at stable and grazing plots, with an average dry cells showed positive association with low cow dirtiness matter intake of 15.9 and 13.6 kg/cow/day for large-scale and score. Moreover, a decreased level of SCC was also obtained small-scale farmers, respectively. Farmers did not calculate when the animal house was cleaned at least once per day and diets based on targeted production levels of milk. The when dairy farms had permanent access to a potable source of amounts distributed were linked to the availability of green water.

forage. Large-scale farmers were more concerned in providing by-product feeds and corn stover than smallholders, which From farm to plant gate was reflected in higher net energy for lactation provided (23.7 vs. 20.4 Mcal/cow/day on average, respectively). Some Chemical quality was not affected during collection by indemilking practices varied according to herd size (Table 2). pendent collectors (average milk TS of 119 g/kg at farm level Large-scale farmers showed more interest in applying prac- vs. 116 g/kg at plant gate), since processors generally control tices that demand higher economic investments. Small-scale milk density at plant gate to avoid adulteration. But hygienic farmers usually cleaned the animal house more often than quality was considerably reduced, in relation to the way large-scale ones, since they milked their cows in the same collectors operate (average MBRT value of 324 min at farm small space where the animals also rested. level vs. 114 min at plant gate). Indeed, independent collectors Milk chemical composition was not significantly correlated use big blue plastic containers because of their larger capacity, with feed energy and protein availability in the diet for the and they do not always clean them with detergent. They use a overall farm sample. Farms with Brown Swiss cattle show cloth filter on the top of the containers but without replacing it more efficiency in transforming energy into milk fat, while regularly. In order to maximize milk quantities per daily farms with Holstein or crossbreed show a positive link be- transport, they collect only once per day, they mix milk from tween energy and fat content, but with a large range of several farmers in the same container, and they spend several

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Fig. 3 Methylene blue reduction test (hours) and total solids at farm level (%). Dry season, no rain and low temperature; rainy season, rainfall and high temperature hours collecting milk before arriving at the plant gate. These variability (coefficient of variation 68 %) and lower SCC practices reflect the fact that their income depends on the milk levels for milk collected by processors themselves (average quantity they collect each day. Milk chemical quality from SCC of 283,505 cells/ml vs. 425,683 cells/ml, respectively).

processors themselves was similar to the collectors’ one (av- Indeed, dairy processors had more control of the milk collecterage milk TS of 117 g/kg vs. milk TS of 116 g/kg, respec- ed, since they dealt with less farmers compared with indepentively). Same result was obtained for the hygienic quality dent collectors.

(average MBRT of 132 min vs. MBRT of 114 min, respec- In both cases, the absence of a cool chain from farm to plant tively). However, SCC parameter showed the highest gate affects hygiene quality, based on MBRT, when milk from

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1 Cirad, UMR Innovation, 73 Avenue Jean-François Breton, 34398 Montpellier, France 2 Department of Food Business and Development, University College Cork, Cork City, Ireland 3 Universidad Nacional Agraria La Molina, Apartado 12-056, Lima, Peru.

Abstract Simulation tools can be helpful for supporting stakeholders in better planning and managing dairy supply chains and exploring alternative ways of organizing chains. This paper presents a support approach dealing with strategic issues that dairy processors face in interaction with their suppliers and buyers, i.e. selecting their product portfolio according to markets opportunities and designing milk payment systems encouraging dairy farmers to supply good quantity and quality milk throughout the year. This approach is based on the design of a simulation tool called DairyPlant developed with Excel. DairyPlant calculates the daily profit obtained by a dairy processing unit and the daily gross products obtained by each of its suppliers according to its product portfolio, its milk payment system and its suppliers’ individual milk quantity and quality profile. Calculations take into account the processing yield defined by the software user for each marketed and intermediate product. Payment systems may include a base price and up to three quality components. The approach was tested with two small-scale dairy plants in the Mantaro Valley (Peru). It showed the processors that (i) they could increase their total profits by modifying their current portfolio towards higher value products, assuming milk delivered to the plant attains a given quality; (ii) they do not pay correctly farmers who deliver good quality milk and overpay some bad quality milk; (iii) their profits would not be affected by adopting a payment system based on milk quality. Advantages and limits of DairyPlant are discussed in the light of an extended use of the support approach in other locations.

Key words: Computer model; processing; dairy products; incentives; payment scheme

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