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mately 3000 km2), and of collecting and processing actors. A budget simulation tool was developed with Excel 2010 (Microsoft) in order to compare profits per liter of milk between

2.2. Data collection farm and processor clusters. Calculations were performed for each type of farm/processor cluster by taking into account (i) the kind Data were collected from three complementary studies con- of market which affects the General Sale Tax (GST) recovery (yes ducted in the Mantaro Valley between 2009 and 2012. The first study, for formal; no for informal), (ii) six farm types according to their conducted in 2009, aimed to investigate the degree of involve- targeted market (formal or informal) and their labor structure (one ment of small-scale dairy farms in local dairy supply chains. It was employee or only family labor), (iii) two types of processors (formal based on surveys mixing collection of quantitative data and stake- and informal, each collecting 700 l/day), (iv) average production costs holders’ interviews, conducted at three levels over 4 months (40 based on stakeholders’ practices, labor costs, the kind of equipdairy farms, 12 dairy processors, and support institutions). Data ment they use, (v) milk prices and (vi) GST rate. Calculations were related to the characterization of dairy farmers, as well as farming made for 1 day of activity in two seasons: rainy (December–April) and processing systems were extracted from this analysis. The second and dry (May–November). The exercise considered average prostudy, carried out in 2011, intended to get an overview of the duction costs of dairy farmers for each type of farm, based on the Mantaro dairy sector and to determine how the commercializa- 2009 farm survey. Average costs of dairy processors were calcution system involving small-scale producers operates. For this lated based on the costs of the two formal and the two informal purpose, 146 farmers from 1327 dairy farms (11% of the total) and cases surveyed in 2012. Milk production at farm level was reduced 26 dairy processors out of 62 dairy processors (42% of the total) by 20% during dry season to simulate the effect of decreases in forage.

from the province of Concepción were interviewed by means of Costs and products were reduced to 1 l of milk and per day, acstructured questionnaires. The sample size was calculated using the cording to the farmers’ daily production of milk, or the processor’s finite population formula with the information available from the daily production of cheese by using an average cheese yield coefNational Agricultural Census. This study provides key infor- ficient based on measurements conducted in three dairy plants.

mation about differences in markets in which dairy processors are These indicators were chosen to allow a comparison of economic involved. efficiency between the different types of farmers/processors in the The third study, conducted in 2012, aimed to understand how area irrespective of their size.

farmers’, collectors’ and processors’ relationship affects both milk quantity and quality management. In-depth interviews were conducted with three dairy processors (one large, one medium and one 3. Results small); the milk collection center belonging to a multinational company; one independent milk collector; one farmer associa- 3.1. A diversity of supply channels dealing with two tion; and at least five small-scale dairy farmers per processor, from contrasting markets three districts of the Mantaro Valley (Concepción, Matahuasi and Apata). Data regarding interactions between actors along the supply According to the 2011 survey in the study area, 13 channels of chain, milk quality, milk prices, product quality and current state distribution were identified based on the kind of processor, the kind participation were analyzed from this study. Moreover detailed data of product marketed and the kind of market involved (Table 2). Dairy 148 E. Fuentes Navarro et al./Agricultural Systems 132 (2015) 145–156 Table 2 Description of the 13 supply channels identified at Mantaro Valley (2011 survey).

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LC, Lactating cows; SD, herd stocking density; TLU, Tropical Livestock Unit (1 TLU = 1 cattle weighing 250 kg); AI, Artificial insemination; NM, Natural mating.

a Main characteristics were analyzed qualitatively to build the six farm types and then completed with quantitative information for each type of farm.

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Fig. 3. Yearly profit per cow per farm type (2009 survey). The line shows the minimum, the maximum observed and the average value per each type (n = 40).

Collectors’ milk collection varies between 400 and 5000 l per day. location. To recover part of transportation costs farmers are paid 10– These large quantities can be obtained by prospecting production areas 20% less per liter of milk than the average price in the area. This system far from dairy industries’ own supplies (approximately 30 km). Milk is provides a good source of income to collectors based on a small profit bought from farmers who cannot find enough buyers due to their remote per liter assuming a permanent collection volume above 1000 l per day.

Fig. 4. Processors’ strategies according to the period of a year, considering average level of dairy products commercialized at national level and milk price, milk production in the area and seasonal effect (precipitation) at Mantaro Valley. Monthly curve variations are based on real data (Ministerio de Agricultura del Perú (MINAG), 2014; Servicio Nacional de Meteorología e Hidrología del Perú (SENAMHI), 2014). Y-axis scales are specific to each variable and not mentioned on the figure. (A) Complementarity between processors during rainy season; (B) competition during dry season; (C) fluctuation in commercialization of dairy products due to low level of milk production; (D) competition at the beginning of the rainy season.





E. Fuentes Navarro et al./Agricultural Systems 132 (2015) 145–156 151

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Table 5 Farmers’ production costs, profits per liter and per day ($) when they sell to formal and informal dairy processors, and General Sales Taxes (GST) balance per liter of milk at Mantaro Valley (2012 survey).a

–  –  –

GST, General Sales Taxes.

a Results obtained using a budget simulation tool developed with Excel 2010, considering average values for each type of farm.

–  –  –

GST, General Sales Taxes.

a Formal dairy buying milk from a formal farmer. For 8 l of milk at 11.4% TS (total solids) required for producing 1 kg of pressed cheese sold 5.50 $/kg and GST discount on sales and purchases including milk.

b Formal dairy buying milk from an informal farmer. For 8 l of milk at 11.4% TS required for producing 1 kg of pressed cheese sold 5.50 $/kg and GST discount excluding milk.

c Informal cheese maker. For 5 l of milk at 11.4% TS required for producing 1 kg of fresh cheese sold 3.30 $/kg and no GST discount.

d Considering an average milk collection of 700 l/day.

154 E. Fuentes Navarro et al./Agricultural Systems 132 (2015) 145–156 provide buyers with a greater degree of certainty regarding the avail- stakeholders where milk supplied is adjusted through verbal arability of supply (Gow et al., 2000), a prospect of higher milk prices rangements with no incentive for farmers to improve milk quality (Sauer et al., 2012) and of higher yields to farmers (Miyata et al., or processors to support farmers technically. Nevertheless, this sitIndeed, contract farming offers many benefits, including access uation also helps both farmers and processors to reduce their to new markets, technical assistance, specialized inputs, and finan- operational costs, to have much lower entry costs, and to offer milk cial resources. However, it should be inclusive, otherwise if and dairy products at lower prices.

smallholders are mainly excluded from contracting it may serve to Collectors also play an important role in the supply chain. They exacerbate income and asset inequalities (Key and Runsten, 1999). make possible the participation of a larger number of farmers in Secondly, the fragmentation of milk production may lead to the the Mantaro dairy supply chain. However, this interaction with dairy establishment of farmers’ organizations as a way to manage and farmers often hinders the possible direct arrangements between somehow control milk supplies of their members who deliver small farmers and processors and it usually increases by up to 10% the quantities of milk every day; to reduce logistic costs along the chain average price per liter of milk in the area.

(Vijayalakshmi et al., 1995) and to provide services close to farmers’ This combination of low capitalization on the farmers’ side, needs (Faysse et al., 2012); to increase farmers’ bargaining power market uncertainty on the processors’ side, and the loose organiSauer et al., 2012; Valentinov, 2007); and to facilitate the relation- zation at both parts of the supply chain could explain why the ships with dairy processors by limiting the intermediaries between relationship between processors, collectors and farmers seems unfarmers and processors. Nevertheless, only 16% of farmers from the stable. Fluctuating behavior is observed from many farmers who sample stated that they participated in a farmer’s association. supply several operators, and from processors who try to conThirdly, without any formal contract, other types of strategies vince farmers to supply to them rather than their colleagues. This and incentives are necessary, as was observed in the Mantaro Valley. competitive supplier–client relationship reduces incentives for impleIn this kind of context offering attractive prices to farmers (Mdoe menting stricter milk quality controls and improving milk quality, and Wiggins, 1996) or avoiding farm payment delays in order to since it means more constraints for farmers who may choose to secure suppliers (Dries et al., 2009; Fałkowski, 2012) may be very supply low demanding processors or collectors.

effective. However, incentives based on milk quality were not observed with the exception of the multinational companies. Logistic 4.3. Informal chains: an opportunity or constraint?

constraints and poor regulation controls explain why milk payment in the Mantaro Valley, as in some other cases (Espinoza-Ortega et al., The simultaneous presence of formal and informal chains shows 2007; Gorton et al., 2006) is mainly based on quantity. Then, the the dynamism of the dairy sector in the Mantaro Valley, since it reat price changes from one processor to another according to the sponds to different consumers’ demand for dairy products. It allows supply and demand balance during the year and the competition smallholder farms and artisanal cheese-makers to be included in between them. the dairy sector without making big investments i.e. avoiding barriers in the form of food safety requirements, grading criteria, bans

4.2. Unstable relationship between stakeholders within the chain on side-selling and high rejection rates (Vorley, 2013). The important presence of the informal sector in Lima is explained by poor Milk production in the Mantaro Valley is based largely on the State control and the low economic status of the majority of conparticipation of small-scale farmers, something quite common in sumers that remain exceptionally poor by any standard. This situation Peruvian Andean regions (Aubron, 2007). Although these farmers is aggravated by poor knowledge and the lack of interest exbenefit from good access to markets and to irrigation (Bartl et al., pressed by most of the consumers buying dairy products without 2009b) and by the large demand for milk in the area, their milk pro- the added value of pasteurization and packaging. Other factors duction and dairy incomes remain quite limited, in most cases, due related to consumers’ preferences to buy traditional products are to a low availability of good quality fodder, especially during the linked to dietary habits and proximity to retail outlets (Francesconi dry season, and also by the poor animal genetics (Bartl et al., 2009a). et al., 2010).

The lack of capital limits their capacity to permanently invest in The involvement of informal processors in the dairy sector is quite animals, milking machines, purchase of land, or to adopt new tech- normal in developing countries, such as in Eastern Africa (Brokken nologies (Solano et al., 2000), which finally impacts on their and Seyoum, 1990; Moll et al., 2007). Similar to Peru, the informal productivity. Moreover, the imbalance between stocking rate and sector provides dairy products to poor urban areas (Padilla et al., forage production leads to the permanent purchase of forage, which 2004) and small-scale farmers are favored, since they do not have negatively impacts production costs. Farm management, includ- the pressure to adopt control measures, which are costly and they ing attention to health problems, good reproductive indices and long are not usually compensated by a higher milk price (Valeeva et al., lactating periods can all play a role in improving performance (Novo 2007). Even in an emerging country such as Argentina, only 17% of et al., 2013). However, without the capacity to face these difficul- dairies have sufficient capacity to pay for a quality assurance system ties, improving farmers’ profits is mainly linked to their capacity (Farina et al., 2005) and most of the small processors operate in into negotiate better milk prices based on processors’ competition for formal, local markets where sanitary standards are not met nor milk. worker social security or sales taxes are paid. They can thus charge Indeed, processors have to face high competition between formal lower prices for their products. Consumers may also purchase jointly and informal ones combined with unstable offer of milk and un- from formal and informal sectors. In Southern Africa, 80% of poor stable cheese demand in Lima throughout the year. Their limited people report occasional use of supermarkets but they still use local involvement in terms of contracting farmers or more formal ar- stores or street traders for daily and weekly purchases (Vorley, 2013).



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