Averaging and additive approaches are the most popular way to combine indicators, due to their simplicity and transparency. However, raw data values present in commensur ability in measurement units and need to be transformed to a consistent unitless scale. The standardization technique chosen should respect both the data properties and the theoretical framework. After input indicators are standardized, one must decide how to assign weights for their combination, starting with whether they should be equal or some form of unequal. Equal weighting is the most common approach in measuring social vulnerability; given a lack of specific knowledge about the relative importance of variables, equal weighting is seen as the simplest logical assumption. Unequal weights can be determined by various approaches, including those that are normative, data-driven, or hybrid . In many circumstances, unequal weights are assigned using expert knowledge to rank and assign the weights to each indicator. Besides the simple approaches of averaging and adding to combine indicators, Reckien presented other approaches,plant bench indoor including inductive, hierarchical, and fuzzy boundary. An inductive approach focusses on variable reduction using methodologies such as PCA and factor analysis.The fuzzy boundaries approach joins important variables using fuzzy reasoning . This allows one to handle uncertain and vague data using a series of decision-making steps.
The uncertainty and simplification involved in selecting, weighting, and aggregating indicators has been criticized for assessing vulnerability using indicator-based approaches. But in the end, aggregating indicators in these ways is the best option for quantifying and assessing vulnerability in most cases, because the creation of a complete and accurate mechanistic model for the causes of vulnerability is beyond our scope at this time in most situations .High mountainous regions are some of the most fragile and sensitive ecosystems in the world . These areas, in many cases, are also home to the poorest populations and those most vulnerable to natural disasters. Under ‘normal’ circumstances, those living in South America’s mountain range—the Andes—experience a wide range of environmental variability and high levels of uncertainty . Andean communities, over the past centuries, have evolved practices that enable them to survive large environmental changes such as those involving climate variability. However, South America’s mountain systems are not well understood in terms of climate change and the vulnerability and adaptive capacities of their people . Peru ranks among the top thirty countries in the world in terms of risk from climate related hazards . The Peruvian Andes, in the last quarter century, have experienced considerable environmental change; current assessments identify it as particularly vulnerable to climate change . According to the World Bank , the country’s GDP has increased, but poverty and inequality persist, especially among farmers. Peru possesses a rich and relatively recent household-level dataset about farmers and about impacts from weather-related events. This richness of data is uncommon in developing countries, especially about farmers and at high resolutions. During the past century, the Andean temperatures have increased, helping diseases and pests to reach crops at higher elevations and causing crop failure at lower elevations . In addition, factors such as the timing, type, and number of crops grown are also changing .
These changes have had salient impacts on Andean livelihoods and landscapes alike. But the resources and capabilities of decision-makers are stretched thin, making it difficult to give proper attention to the effects of a changing climate. The Peruvian Andes, bordering with Bolivia, ends with a high plateau named the Altiplano; it is home to the Departamentoof Puno. This region has 68% of its territory within a zone characterized by pronounced daily temperature oscillations . Puno is frequently exposed to frost, droughts, and heavy precipitation; its climate is highly variable and semi-arid . The Altiplano has a minimum altitude of 3,500 meters, which is close to what is considered the limit for most viable agriculture . The region is occupied by three linguistic groups: Quechua, Aymara, and Spanish speaking mestizos. The present-day Quechuas are descendants of the Incas, and the present day Aymaras are descendants of the various semi-independent groups which lived around Lake Titicaca during the Inca’s expansion to the South . According to the latest census, 39% of Puno’s population identifies Quechua as their native language, 34% Spanish, and 28% the Aymara language. However, rural populations are mainly divided into Quechuas and Aymaras. Three distinctive sub-regions can be observed in the region: Quechua areas ; Aymara areas ; and the rest of the provinces with a combination of both groups . The mestizos are heavily concentrated in the city of Puno and in few of the province capitals. Puno was part of Bolivia until 1824, when it became part of Peru. Despite the relatively recent change, Aymara and Quechua groups in Peru identify themselves strongly as Peruvians and consider BolivianAymaras and Bolivian-Quechuas to be Bolivians and not fellow Aymaras and Quechuas . The topography of the region affects the division of Quechuas from the Cuzco region with those from the Puno region.
Kollas is the name given to both Aymaras and Quechuas living in the Altiplano by Cuzco’s Quechua groups. These groups have seen Kollas to be ‘genotypically and phenotypically different,’ mainly because of the effect on the skin of exposure to the intense sun and cold temperatures . In terms of social indicators, the Puno region is in a generally dire situation. A total of 42% of residents have at least one unsatisfied basic need. Potable water is accessible to only 48% of the region’s population; 87% have access to electricity. The region has an infant mortality rate of 28 per thousand live births; 59% of the infant morbidity is related to acute respiratory infections. Furthermore, the region has the highest percentage of childhood anemia with an occurrence of 76% in children under 3 years-old and 62% in children under 5 years old. Some municipalities in the region have above 90% of their children experiencing anemia. In Puno,greenhouse rolling racks primary school enrollment is slightly higher than the national average, and illiteracy characterizes 12% of the population. In 2012, second-grade students in Peru took a learning achievement test for text comprehension and mathematics. At the national level, 30% passed the text comprehension and 13% the mathematics portion; however, in Puno, 19% and 8% passed, respectively. Rural areas in Puno obtained passing levels of 2% in both aspects of the test, while urban areas obtained 25% and 10% . Students in public schools passed at the rate of 14% in text comprehension and 6% in mathematics; students in private schools passed at rates of 42% and 14%, respectively.Puno has an average income of approximately 300 USD per month. However, poverty has been reduced from 78% in 2004 to 34% in 2014. Income levels vary at the province level: Moho has the highest poverty level with 93%, most provinces are close to 80%, and Puno and San Roman Provinces are the lowest at close to 60% . Peru considers people to be under the poverty line if their income is lower than the cost of the minimum “basket of goods” . However, the government also identifies those poor populations whose income is less than the value of the food ingredients in the basket. These populations are under extreme poverty conditions. Extreme poverty was reduced from 44% in 2004 to 9% in 2014; however, extreme poverty in the provinces of San Roman and Puno is still 15% and 30%, respectively. Peru created “macro regions” to promote decentralization by combining efforts from various departamentos that bridge political and economic strengths. Puno, one of seven departamentos that form the South Macro Region, has the highest growth rate in the area but has a Gross Domestic Product that is inferior to the macro regional average. In terms of regional competitiveness, Puno is in the 23rd position out of 24 departamentos. Furthermore, the Gross Value Added —an important component of the GDP—for Puno’s region represents 3% of Peru’s GVA. The most substantial economic sector is agriculture with a contribution of 16%, followed by mining with 13%, and manufacturing with 10% of the GVA. Puno has a population that is 63% in the working ages group, followed by 31% in the 14 years-old and younger group. With a clear majority of the population in the working age group, employment conditions in the region are an important component of the economy.One of the biggest problems with employment, however, is that 89% of the jobs are in the informal economy. This is a set of economic activities that do not comply with regulation provided by the state for that specific activity . Therefore, informal employment jobs do not have the benefits stipulated by law , such as insurance or retirement.
Micro-businesses—businesses with one to ten employees—are 99% of the economic units in Puno. All of these conditions present difficulties for the development of a stronger formal economy sector. Farmers in the Andes and specially in Puno have experienced a history of social conflicts and uprisings. During colonial times, indigenous group rebelled against mistreatment from Spanish officials; neither side wanted to give quarter and much blood was shed . Peasant rebels demanding better treatment managed to control vast areas of the Puno for two years . Another widespread revolt happened between 1910 and 1925 due to economic changes that led rural populations to attack landlords and merchants. In 1969, the Velasco government introduced an agrarian reform that led to massive out-migration and more unequal land distribution . The reform was intended to destroy the landlord class; two million hectares were expropriated . However, the reform’s poor implementation led to nine out of every ten hectares expropriated to be managed by co-operative managers from the Ministry of Agriculture. Membership and benefit from those co-operatives were not reaching peasants that worked on them. Furthermore, only 20% of rural families received land; those lands averaged 3.2 hectares per family . Constraints stemming from these changes are still felt in today’s agriculture, and they are exacerbated by the challenges that mountainous regions bring to agriculture. Peru can be divided into eight agroecological zones that are differentiated by variables that directly affect land use. Puno possesses six of these zones: Janca , Puna , Suni , Quechua , Yunga , and Forest . The most common agroecological zone for Puno is Suni, which encompasses 75% of the region’s farms. It is considered a land of frosts with a mild cold climate and a rocky and steep terrain. Suni land allows farmers to grow quinoa, potato, ollucos, bean, and barley, among other crops. Agriculture productivity in mountain regions depends on factors such as slope, altitude, aspect, soil, moisture, and receipt of solar radiation . Unpredictability in mountainous environments increases the probability of production shortfalls, which lead to almost yearly unmet subsistence needs . Despite a clear majority of the departamento having limitations due to its altitude, 51% of Puno’s population was engaged in agriculture in 2012 . Of these farmers, 83% are mainly dedicated to subsistence farming. The region’s productivity suffers from a lack of irrigation . According to GAV, the most common crops are potatoes , oats to feed livestock , alfalfa , barley to feed livestock , quinoa , others , and coffee . However, the most important crops according to the number of hectares planted are potatoes and quinoa . The agricultural calendar for Peru starts in August and goes until July of the following year. In the region of Puno, most of the planting occurs from September to November. Much of the harvesting occurs from April to June. The potential agricultural landscape of the region covers 4,384,905 hectares, with grassland covering most of the land. Despite agriculture’s poor potential for commercialization, this economic sector absorbs 44% of the employed population. The productivity per worker ranks 17 out of 24 departamentos. The conditions of the farms in this poor region are challenging for increasing productivity. Only 7% of the farms have irrigation, and 40% of the farming households are led by a woman, which create challenges in social networking since participation in the same networks as men is not always possible. Farmers that are above 60 years of age comprise 33% of the household heads; only 1% of farmers are teenagers. In terms of the household structure, 35% are parents with kids, 28% are singleperson households, and 17% are married couples without children.