«Embargo information No embargo required Item downloaded from Downloaded on 2016-05-23T13:09:50Z Thesis to obtain the ...»
Stakeholders’ preference to interact without formal agreements is explained by the high demand of milk in the area and the benefits that dairy farmers and processors, especially from the informal sector, receive for trading informally, i.e. reduction in their operational costs, reduction in entry costs, and the ability to offer milk and dairy products at lower prices. Indeed, it allows smallholder farms and artisanal cheese-makers to be included in the dairy sector without making big investments. This situation generates a type of relationship between stakeholders where milk supplied is adjusted through verbal arrangements with no incentive for farmers to improve milk quality or for processors to support farmers technically. Although no formal contracts are applied, other types of strategies are used in Mantaro Valley to ensure a constant milk supply. Strategies applied worldwide such as offering attractive prices to farmers (Mdoe and Wiggins, 1996) or avoiding farm payment delays in order to secure suppliers (Fałkowski, 2012; Dries et al., 2009) seems to be also very effective in this particular context.
According to our results, organizational strategies like formal contracts are difficult to implement in Mantaro Valley. Contract farming can provide benefits to dairy stakeholders. Nevertheless, the coexistence of a dual market with the presence of one sector which is less demanding in terms of quality, and the constant risk that farmers would prefer to sell to parallel spot markets if the milk price offered rises above the contract price, compound the main difficulties for such implementation. Some disincentives for dairy processors to contract with smallholders are associated with the transaction costs related with providing inputs, credit, extension services, and product collection and grading (Key and Runsten, 1999). Dairies which sell to the Qali Warma national program are the only ones involved in a formal agreement between its milk suppliers. However, a constraint of this program is that it creates uncertainty between farmers and processors during the time of school holidays, as the
establishment of farmers’ associations as a way to manage and control milk quality supplies of farmers’ members who deliver small quantities of milk every day. Nevertheless, it means building trust among farmers, which is still problematic in the Mantaro Valley for historical reasons.
2.3.2. Milk quality as key point of differentiation between formal and informal sector:
The informal sector in developing countries provides dairy products to poor urban areas (Padilla et al.,
2004) and small-scale farmers are favored, since they do not have the pressure to adopt costly quality control measures that are not usually compensated by a higher milk price (Valeeva et al., 2007). In the Peruvian case, the poor State control (Delgado and Maurtua, 2003) and the low economic status of the majority of consumers who remain exceptionally poor by any standard seems to explain the important presence of the informal sector in Lima. This situation disfavors the formal sector, which needs to fulfill market requirements and supply a constant quality of dairy products while competing with the informal sector for a constant milk supply in the area. Thus, the co-existence of dual markets with a differentiation in consumers’ demands and without any type of regulation provides fragility to the whole sector: The formal sector faces difficulties in achieving high milk quality standards and the informal sector earns more profits but remains sensitive to State controls.
In this context, high levels of milk quality can be difficult to achieve by formal dairy processors. Indeed, pushing milk suppliers too hard to fulfill milk quality standards without rewarding their performance could make them move to processors with less interest in quality aspects of milk, affecting at the end processors’ level of milk quantities collected. Milk payment in the Mantaro Valley, as in some other cases (Espinoza-Ortega et al., 2007; Gorton et al., 2006) is still based on a flat price changing from one processor to another according to the supply and demand balance during the year and the competition between them. Thus, an innovative way to ensure milk supply and high levels of milk quality in Mantaro Valley can be achieved by the implementation of payment systems that guarantee a win-win scenario for small-scale farmers and dairy processors; it means satisfying both their economic expectations for providing or buying milk. In this respect, alternative payment systems based on quality should be explored jointly with the stakeholders.
new dairy technology on the general status of raw milk quality This chapter analyzes the current milk quality status, the effect of stakeholders’ practices on milk quality and the changes in the perception of milk quality after the introduction of ultrasound milk analyzer equipment (UMA) in Mantaro Valley. Milk chemical and hygienic quality in Mantaro Valley is highly variable at farm level and processor gate. Few husbandry practices are frequently applied and low concern about the effects of these practices on the final product explains the lack of an established milk quality management program in the study area. Stakeholders showed interest and desire to learn more about milk quality after the introduction of UMA. Nevertheless, some constraints to interpreting the outputs from UMA created conflicts between farmers and processors. Results showed that permanent use of some husbandry practices can help dairy farmers improve their current milk quality status in the study area, but the time and cost of the implementation of these practices need to be somehow rewarded by dairy processors. Moreover, the successful implementation of new technologies like UMA requires a better establishment of coordination process between both stakeholders. More detail information is available in volume 2 of the present document.
3.1 Materials and methods Data was obtained from a sample of 20 smallholder dairy producers, 3 dairy processors and a collection center, in order to get a detailed evaluation of milk quality in the area. Dairy processors varied in terms of volume of milk processed, technological level and market orientation. Dairy farmers were selected taking into account the large diversity of herd size, average milk production and type of milking system. The farm sample structure included 60% of small-scale farmers (production of 20-50 l/farm/day and 8.5 l/cow/day), 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 last two categories were regrouped for analytical purposes.
Data were collected over 12 months from April 2012 to March 2013. Every month milk samples were taken from bulk tanks of every selected farm and dairy processor. In both cases, chemical milk
Average milk chemical composition (percentage of milk fat, milk protein, total solids, lactose and minerals) and physical milk properties (density, water added) of each milk sample was measured with Master Eco® ultrasound milk analyzer (http://www.milkotester.com/data/Master%20Eco.pdf). The analyzer was calibrated every month using reference AOAC methods (Helrich, 1990). Somatic cell count (SCC) was obtained with the indirect on-farm test Porta SCC®, which converts results of an enzymatic reaction into an estimated SCC (Rodrigues et al., 2009). Hygienic status, measured with the Methylene blue reduction test (MBRT), was performed according to the IDF (1990) protocol. MBRT is based on the time milk takes to decolorize methylene blue by the activity of the reducing bacteria present in the milk. The more contaminated the milk, the faster color changes from blue to white.
Bulk milk samples (50ml) were taken once per month from each of the 20 farmers. For each farmer, samples (morning and afternoon) were collected and analyzed from bulk churns at the end of each milking, and then pooled to obtain an average daily value. A similar sampling protocol was performed during a period of one week every month to the milk from the processors’ truck and the independent collector of each of the 3 processors evaluated. Evaluation of milk hygienic deterioration from farm gate to plant gate was performed on one dairy farm per processor. Samples from the same milk were taken at three different times: the first one in the afternoon on day-1; the second one the next morning when the milk was collected at farm gate; and the last one when the milk arrived at plant gate. Along with milk analyses, the farmer’s husbandry practices were recorded (Annex 3) once a month and compared to a set of recommended management practices based on the literature (Table 8) A complementary evaluation was done to determine changes in the perception of milk quality, based on the introduction of the ultrasound milk analyzer (UMA) in the production system. Farmers were split in small-scale and large-scale farmers to evaluate differences in understanding milk quality issues. As this analysis was conducted in situ, it was possible to record stakeholders’ reactions about the UMA and obtain measurements that could be immediately discussed with farmers and processors.
Concurrently, 10 dairies who bought ultrasound milk analyzer equipment (UMA) after our intervention were interviewed to know what they are doing with it. Information about the use of the equipment, frequency of the use and the main constraints they are facing using the UMA were obtained (Annex 4).
composition and its relationship with farmers’ practices were carried out considering the 12-month dataset, which included 238 milk samples at farm level and 480 at plant gate. The XLStat™2012.6.01 software (Addinsoft, Paris, France) was used for that purpose. For the analysis of the introduction of UMA, average and percentages were carried out using Microsoft Excel™ 2010.
3.2. Synthesis of the Results 3.2.1. Milk quality status in Mantaro Valley Milk quality in Mantaro Valley varied largely within the sample, highlighting the fact that processors had to face a large diversity of batch quality among farmers and from one day to the next. Average milk chemical quality parameters were found to be acceptable compared to the values generally recommended for this product by Peruvian legislation (Table 7). However, milk fat content, MBRT and average somatic cells counts showed the highest variability.
respectively 4.7 and 5.7 mean MBRT value (p value 0.0001), and the average SCC exceeded the upper limit of international standards, since only one third of milk samples were below 200,000 cells / ml. Milk Total Solids (TS) and MBRT were affected by the season due to rainfall and temperature variations. During the dry season, when rainfall and temperature are low, TS decreases due to the limited availability of good quality forage, while hygiene improves because of lower bacterial contamination (Figure 7).
Figure 7 Methylene blue reduction test (hours) and total solids at farm level (%). Dry season = no rain and low temperature; rainy season: rainfall and high temperature 3.2.2. Effects of stakeholders’ practices on raw milk quality One feeding practice out of five evaluated and eight milking and sanitary practices out of twenty one were identified as frequently applied, i.e. exceeding half of monthly observations during the 12-month
and grazing on plots, with an average of 15.9 and 13.6 kg/cow/day of dry matter intake per diet for large-scale and small-scale farmers respectively.
were linked to the availability of green forage with some diversity between farms in terms of forage quality and quantity (Table 9). Mineral salt was frequently applied, while the daily supply of concentrates was generally limited to between 1.5 and 2 kg of wheat bran per cow. However, largescale farmers were more concerned with providing feed by-products and a constant source of maize stems than smallholders were, which was reflected in higher levels of net energy for lactation provided (23.7 vs. 20.4 Mcal/cow/day in average respectively).
Milking and sanitary practices frequently adopted by farmers included the use of a milking parlor, access to a potable source of water, cleaning animal housing at least once per day and cleaning churns before milking. Nevertheless, milk churns were generally cleaned without detergent and milking parlors consisted of small spaces of concrete flooring in the housing barn. Other basic practices were applied such as washing the cow’s udder before milking and drying it after washing, filtering the milk before depositing in churns and keeping an updated notebook of treatment records. Some practices varied according to herd size. Large farmers showed more interest in applying practices that demand
cases at least once per year, and half of them milked by mechanical means, had a milking parlor, had a cold system after milking and utilized a waiting yard where cows rested before milking. Small-scale farmers usually cleaned the barn more often than large-scale farms, since they milked their cows in the same small space where the animals also rest.
Milk chemical composition was not significantly correlated to feed energy and protein available in the diet on the whole farm sample (Table 10). Indeed, linking net energy provided by the diet, milk fat content and dominant farm breed leads to a large range of results with Brown Swiss farms showing a much better efficiency in transforming energy in milk fat. This result highlights the difficulty in establishing clear feeding strategies to achieve higher values of milk chemical components in the feeding conditions prevailing in the area studied.